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Rebrand to FastRTC (#60)

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---------

Co-authored-by: Freddy Boulton <freddyboulton@hf-freddy.local>

* remove docs from dist

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---------

Co-authored-by: Freddy Boulton <freddyboulton@hf-freddy.local>
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Freddy Boulton
2025-02-24 01:13:42 -05:00
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parent 36190066ec
commit 853d6a06b5
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push: push:
branches: branches:
- main - main
pull_request:
branches:
- main
permissions: permissions:
contents: write contents: write
pull-requests: write
deployments: write
pages: write
jobs: jobs:
deploy: deploy:
runs-on: ubuntu-latest runs-on: ubuntu-latest
@@ -25,4 +33,18 @@ jobs:
restore-keys: | restore-keys: |
mkdocs-material- mkdocs-material-
- run: pip install mkdocs-material - run: pip install mkdocs-material
- run: mkdocs gh-deploy --force - name: Build docs
run: mkdocs build
- name: Deploy to GH Pages (main)
if: github.event_name == 'push'
run: mkdocs gh-deploy --force
- name: Deploy PR Preview
if: github.event_name == 'pull_request'
uses: rossjrw/pr-preview-action@v1
with:
source-dir: ./site
preview-branch: gh-pages
umbrella-dir: pr-preview
action: auto

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backend/**/templates/ backend/**/templates/
demo/MobileNetSSD_deploy.caffemodel demo/MobileNetSSD_deploy.caffemodel
demo/MobileNetSSD_deploy.prototxt.txt demo/MobileNetSSD_deploy.prototxt.txt
demo/scratch
.gradio
.vscode
.DS_Store .DS_Store
test/ test/
.env .env

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<h1 style='text-align: center; margin-bottom: 1rem'> Gradio WebRTC ⚡️ </h1> <div style='text-align: center; margin-bottom: 1rem; display: flex; justify-content: center; align-items: center;'>
<h1 style='color: white; margin: 0;'>FastRTC</h1>
<img src='https://huggingface.co/datasets/freddyaboulton/bucket/resolve/main/fastrtc_logo_small.png'
alt="FastRTC Logo"
style="margin-right: 10px;">
</div>
<div style="display: flex; flex-direction: row; justify-content: center"> <div style="display: flex; flex-direction: row; justify-content: center">
<img style="display: block; padding-right: 5px; height: 20px;" alt="Static Badge" src="https://img.shields.io/pypi/v/gradio_webrtc"> <img style="display: block; padding-right: 5px; height: 20px;" alt="Static Badge" src="https://img.shields.io/pypi/v/fastrtc">
<a href="https://github.com/freddyaboulton/gradio-webrtc" target="_blank"><img alt="Static Badge" style="display: block; padding-right: 5px; height: 20px;" src="https://img.shields.io/badge/github-white?logo=github&logoColor=black"></a> <a href="https://github.com/freddyaboulton/fastrtc" target="_blank"><img alt="Static Badge" src="https://img.shields.io/badge/github-white?logo=github&logoColor=black"></a>
<a href="https://freddyaboulton.github.io/gradio-webrtc/" target="_blank"><img alt="Static Badge" src="https://img.shields.io/badge/Docs-ffcf40"></a>
</div> </div>
<h3 style='text-align: center'> <h3 style='text-align: center'>
Stream video and audio in real time with Gradio using WebRTC. The Real-Time Communication Library for Python.
</h3> </h3>
Turn any python function into a real-time audio and video stream over WebRTC or WebSockets.
## Installation ## Installation
```bash ```bash
pip install gradio_webrtc pip install fastrtc
``` ```
to use built-in pause detection (see [ReplyOnPause](https://freddyaboulton.github.io/gradio-webrtc//user-guide/#reply-on-pause)), install the `vad` extra: to use built-in pause detection (see [ReplyOnPause](https://fastrtc.org/)), and text to speech (see [Text To Speech](https://fastrtc.org/userguide/audio/#text-to-speech)), install the `vad` and `tts` extras:
```bash ```bash
pip install gradio_webrtc[vad] pip install fastrtc[vad, tts]
``` ```
For stop word detection (see [ReplyOnStopWords](https://freddyaboulton.github.io/gradio-webrtc//user-guide/#reply-on-stopwords)), install the `stopword` extra: ## Key Features
```bash - 🗣️ Automatic Voice Detection and Turn Taking built-in, only worry about the logic for responding to the user.
pip install gradio_webrtc[stopword] - 💻 Automatic UI - Use the `.ui.launch()` method to launch the webRTC-enabled built-in Gradio UI.
``` - 🔌 Automatic WebRTC Support - Use the `.mount(app)` method to mount the stream on a FastAPI app and get a webRTC endpoint for your own frontend!
- ⚡️ Websocket Support - Use the `.mount(app)` method to mount the stream on a FastAPI app and get a websocket endpoint for your own frontend!
- 📞 Automatic Telephone Support - Use the `fastphone()` method of the stream to launch the application and get a free temporary phone number!
- 🤖 Completely customizable backend - A `Stream` can easily be mounted on a FastAPI app so you can easily extend it to fit your production application. See the [Talk To Claude](https://huggingface.co/spaces/fastrtc/talk-to-claude) demo for an example on how to serve a custom JS frontend.
## Docs ## Docs
https://freddyaboulton.github.io/gradio-webrtc/ [https://fastrtc.org](https://fastrtc.org)
## Examples ## Examples
See the [Cookbook](https://fastrtc.org/pr-preview/pr-60/cookbook/) for examples of how to use the library.
<table> <table>
<tr> <tr>
<td width="50%"> <td width="50%">
<h3>🗣️ Audio Input/Output with mini-omni2</h3> <h3>🗣️👀 Gemini Audio Video Chat</h3>
<p>Build a GPT-4o like experience with mini-omni2, an audio-native LLM.</p> <p>Stream BOTH your webcam video and audio feeds to Google Gemini. You can also upload images to augment your conversation!</p>
<video width="100%" src="https://github.com/user-attachments/assets/58c06523-fc38-4f5f-a4ba-a02a28e7fa9e" controls></video> <video width="100%" src="https://github.com/user-attachments/assets/9636dc97-4fee-46bb-abb8-b92e69c08c71" controls></video>
<p> <p>
<a href="https://huggingface.co/spaces/freddyaboulton/mini-omni2-webrtc">Demo</a> | <a href="https://huggingface.co/spaces/freddyaboulton/gemini-audio-video-chat">Demo</a> |
<a href="https://huggingface.co/spaces/freddyaboulton/mini-omni2-webrtc/blob/main/app.py">Code</a> <a href="https://huggingface.co/spaces/freddyaboulton/gemini-audio-video-chat/blob/main/app.py">Code</a>
</p>
</td>
<td width="50%">
<h3>🗣️ Google Gemini Real Time Voice API</h3>
<p>Talk to Gemini in real time using Google's voice API.</p>
<video width="100%" src="https://github.com/user-attachments/assets/ea6d18cb-8589-422b-9bba-56332d9f61de" controls></video>
<p>
<a href="https://huggingface.co/spaces/fastrtc/talk-to-gemini">Demo</a> |
<a href="https://huggingface.co/spaces/fastrtc/talk-to-gemini/blob/main/app.py">Code</a>
</p>
</td>
</tr>
<tr>
<td width="50%">
<h3>🗣️ OpenAI Real Time Voice API</h3>
<p>Talk to ChatGPT in real time using OpenAI's voice API.</p>
<video width="100%" src="https://github.com/user-attachments/assets/178bdadc-f17b-461a-8d26-e915c632ff80" controls></video>
<p>
<a href="https://huggingface.co/spaces/fastrtc/talk-to-openai">Demo</a> |
<a href="https://huggingface.co/spaces/fastrtc/talk-to-openai/blob/main/app.py">Code</a>
</p>
</td>
<td width="50%">
<h3>🤖 Hello Computer</h3>
<p>Say computer before asking your question!</p>
<video width="100%" src="https://github.com/user-attachments/assets/afb2a3ef-c1ab-4cfb-872d-578f895a10d5" controls></video>
<p>
<a href="https://huggingface.co/spaces/fastrtc/hello-computer">Demo</a> |
<a href="https://huggingface.co/spaces/fastrtc/hello-computer/blob/main/app.py">Code</a>
</p>
</td>
</tr>
<tr>
<td width="50%">
<h3>🤖 Llama Code Editor</h3>
<p>Create and edit HTML pages with just your voice! Powered by SambaNova systems.</p>
<video width="100%" src="https://github.com/user-attachments/assets/98523cf3-dac8-4127-9649-d91a997e3ef5" controls></video>
<p>
<a href="https://huggingface.co/spaces/fastrtc/llama-code-editor">Demo</a> |
<a href="https://huggingface.co/spaces/fastrtc/llama-code-editor/blob/main/app.py">Code</a>
</p> </p>
</td> </td>
<td width="50%"> <td width="50%">
<h3>🗣️ Talk to Claude</h3> <h3>🗣️ Talk to Claude</h3>
<p>Use the Anthropic and Play.Ht APIs to have an audio conversation with Claude.</p> <p>Use the Anthropic and Play.Ht APIs to have an audio conversation with Claude.</p>
<video width="100%" src="https://github.com/user-attachments/assets/650bc492-798e-4995-8cef-159e1cfc2185" controls></video> <video width="100%" src="https://github.com/user-attachments/assets/fb6ef07f-3ccd-444a-997b-9bc9bdc035d3" controls></video>
<p> <p>
<a href="https://huggingface.co/spaces/freddyaboulton/talk-to-claude">Demo</a> | <a href="https://huggingface.co/spaces/fastrtc/talk-to-claude">Demo</a> |
<a href="https://huggingface.co/spaces/freddyaboulton/talk-to-claude/blob/main/app.py">Code</a> <a href="https://huggingface.co/spaces/fastrtc/talk-to-claude/blob/main/app.py">Code</a>
</p>
</td>
</tr>
<tr>
<td width="50%">
<h3>🎵 Whisper Transcription</h3>
<p>Have whisper transcribe your speech in real time!</p>
<video width="100%" src="https://github.com/user-attachments/assets/87603053-acdc-4c8a-810f-f618c49caafb" controls></video>
<p>
<a href="https://huggingface.co/spaces/fastrtc/whisper-realtime">Demo</a> |
<a href="https://huggingface.co/spaces/fastrtc/whisper-realtime/blob/main/app.py">Code</a>
</p>
</td>
<td width="50%">
<h3>📷 Yolov10 Object Detection</h3>
<p>Run the Yolov10 model on a user webcam stream in real time!</p>
<video width="100%" src="https://github.com/user-attachments/assets/f82feb74-a071-4e81-9110-a01989447ceb" controls></video>
<p>
<a href="https://huggingface.co/spaces/fastrtc/object-detection">Demo</a> |
<a href="https://huggingface.co/spaces/fastrtc/object-detection/blob/main/app.py">Code</a>
</p> </p>
</td> </td>
</tr> </tr>
@@ -76,366 +149,169 @@ https://freddyaboulton.github.io/gradio-webrtc/
</p> </p>
</td> </td>
</tr> </tr>
<tr>
<td width="50%">
<h3>🤖 Llama Code Editor</h3>
<p>Create and edit HTML pages with just your voice! Powered by SambaNova systems.</p>
<video width="100%" src="https://github.com/user-attachments/assets/a09647f1-33e1-4154-a5a3-ffefda8a736a" controls></video>
<p>
<a href="https://huggingface.co/spaces/freddyaboulton/llama-code-editor">Demo</a> |
<a href="https://huggingface.co/spaces/freddyaboulton/llama-code-editor/blob/main/app.py">Code</a>
</p>
</td>
<td width="50%">
<h3>🗣️ Talk to Ultravox</h3>
<p>Talk to Fixie.AI's audio-native Ultravox LLM with the transformers library.</p>
<video width="100%" src="https://github.com/user-attachments/assets/e6e62482-518c-4021-9047-9da14cd82be1" controls></video>
<p>
<a href="https://huggingface.co/spaces/freddyaboulton/talk-to-ultravox">Demo</a> |
<a href="https://huggingface.co/spaces/freddyaboulton/talk-to-ultravox/blob/main/app.py">Code</a>
</p>
</td>
</tr>
<tr>
<td width="50%">
<h3>🗣️ Talk to Llama 3.2 3b</h3>
<p>Use the Lepton API to make Llama 3.2 talk back to you!</p>
<video width="100%" src="https://github.com/user-attachments/assets/3ee37a6b-0892-45f5-b801-73188fdfad9a" controls></video>
<p>
<a href="https://huggingface.co/spaces/freddyaboulton/llama-3.2-3b-voice-webrtc">Demo</a> |
<a href="https://huggingface.co/spaces/freddyaboulton/llama-3.2-3b-voice-webrtc/blob/main/app.py">Code</a>
</p>
</td>
<td width="50%">
<h3>🤖 Talk to Qwen2-Audio</h3>
<p>Qwen2-Audio is a SOTA audio-to-text LLM developed by Alibaba.</p>
<video width="100%" src="https://github.com/user-attachments/assets/c821ad86-44cc-4d0c-8dc4-8c02ad1e5dc8" controls></video>
<p>
<a href="https://huggingface.co/spaces/freddyaboulton/talk-to-qwen-webrtc">Demo</a> |
<a href="https://huggingface.co/spaces/freddyaboulton/talk-to-qwen-webrtc/blob/main/app.py">Code</a>
</p>
</td>
</tr>
<tr>
<td width="50%">
<h3>📷 Yolov10 Object Detection</h3>
<p>Run the Yolov10 model on a user webcam stream in real time!</p>
<video width="100%" src="https://github.com/user-attachments/assets/c90d8c9d-d2d5-462e-9e9b-af969f2ea73c" controls></video>
<p>
<a href="https://huggingface.co/spaces/freddyaboulton/webrtc-yolov10n">Demo</a> |
<a href="https://huggingface.co/spaces/freddyaboulton/webrtc-yolov10n/blob/main/app.py">Code</a>
</p>
</td>
<td width="50%">
<h3>📷 Video Object Detection with RT-DETR</h3>
<p>Upload a video and stream out frames with detected objects (powered by RT-DETR) model.</p>
<p>
<a href="https://huggingface.co/spaces/freddyaboulton/rt-detr-object-detection-webrtc">Demo</a> |
<a href="https://huggingface.co/spaces/freddyaboulton/rt-detr-object-detection-webrtc/blob/main/app.py">Code</a>
</p>
</td>
</tr>
<tr>
<td width="50%">
<h3>🔊 Text-to-Speech with Parler</h3>
<p>Stream out audio generated by Parler TTS!</p>
<p>
<a href="https://huggingface.co/spaces/freddyaboulton/parler-tts-streaming-webrtc">Demo</a> |
<a href="https://huggingface.co/spaces/freddyaboulton/parler-tts-streaming-webrtc/blob/main/app.py">Code</a>
</p>
</td>
<td width="50%">
</td>
</tr>
</table> </table>
## Usage ## Usage
This is an shortened version of the official [usage guide](https://freddyaboulton.github.io/gradio-webrtc/user-guide/). This is an shortened version of the official [usage guide](https://freddyaboulton.github.io/gradio-webrtc/user-guide/).
To get started with WebRTC streams, all that's needed is to import the `WebRTC` component from this package and implement its `stream` event. - `.ui.launch()`: Launch a built-in UI for easily testing and sharing your stream. Built with [Gradio](https://www.gradio.app/).
- `.fastphone()`: Get a free temporary phone number to call into your stream. Hugging Face token required.
### Reply on Pause - `.mount(app)`: Mount the stream on a [FastAPI](https://fastapi.tiangolo.com/) app. Perfect for integrating with your already existing production system.
Typically, you want to run an AI model that generates audio when the user has stopped speaking. This can be done by wrapping a python generator with the `ReplyOnPause` class
and passing it to the `stream` event of the `WebRTC` component.
```py
import gradio as gr
from gradio_webrtc import WebRTC, ReplyOnPause
def response(audio: tuple[int, np.ndarray]): # (1)
"""This function must yield audio frames"""
...
for numpy_array in generated_audio:
yield (sampling_rate, numpy_array, "mono") # (2)
with gr.Blocks() as demo: ## Quickstart
gr.HTML(
"""
<h1 style='text-align: center'>
Chat (Powered by WebRTC ⚡️)
</h1>
"""
)
with gr.Column():
with gr.Group():
audio = WebRTC(
mode="send-receive", # (3)
modality="audio",
)
audio.stream(fn=ReplyOnPause(response),
inputs=[audio], outputs=[audio], # (4)
time_limit=60) # (5)
demo.launch() ### Echo Audio
```
1. The python generator will receive the **entire** audio up until the user stopped. It will be a tuple of the form (sampling_rate, numpy array of audio). The array will have a shape of (1, num_samples). You can also pass in additional input components.
2. The generator must yield audio chunks as a tuple of (sampling_rate, numpy audio array). Each numpy audio array must have a shape of (1, num_samples).
3. The `mode` and `modality` arguments must be set to `"send-receive"` and `"audio"`.
4. The `WebRTC` component must be the first input and output component.
5. Set a `time_limit` to control how long a conversation will last. If the `concurrency_count` is 1 (default), only one conversation will be handled at a time.
### Reply On Stopwords
You can configure your AI model to run whenever a set of "stop words" are detected, like "Hey Siri" or "computer", with the `ReplyOnStopWords` class.
The API is similar to `ReplyOnPause` with the addition of a `stop_words` parameter.
```py
import gradio as gr
from gradio_webrtc import WebRTC, ReplyOnPause
def response(audio: tuple[int, np.ndarray]):
"""This function must yield audio frames"""
...
for numpy_array in generated_audio:
yield (sampling_rate, numpy_array, "mono")
with gr.Blocks() as demo:
gr.HTML(
"""
<h1 style='text-align: center'>
Chat (Powered by WebRTC ⚡️)
</h1>
"""
)
with gr.Column():
with gr.Group():
audio = WebRTC(
mode="send",
modality="audio",
)
webrtc.stream(ReplyOnStopWords(generate,
input_sample_rate=16000,
stop_words=["computer"]), # (1)
inputs=[webrtc, history, code],
outputs=[webrtc], time_limit=90,
concurrency_limit=10)
demo.launch()
```
1. The `stop_words` can be single words or pairs of words. Be sure to include common misspellings of your word for more robust detection, e.g. "llama", "lamma". In my experience, it's best to use two very distinct words like "ok computer" or "hello iris".
### Audio Server-To-Clien
To stream only from the server to the client, implement a python generator and pass it to the component's `stream` event. The stream event must also specify a `trigger` corresponding to a UI interaction that starts the stream. In this case, it's a button click.
```py
import gradio as gr
from gradio_webrtc import WebRTC
from pydub import AudioSegment
def generation(num_steps):
for _ in range(num_steps):
segment = AudioSegment.from_file("audio_file.wav")
array = np.array(segment.get_array_of_samples()).reshape(1, -1)
yield (segment.frame_rate, array)
with gr.Blocks() as demo:
audio = WebRTC(label="Stream", mode="receive", # (1)
modality="audio")
num_steps = gr.Slider(label="Number of Steps", minimum=1,
maximum=10, step=1, value=5)
button = gr.Button("Generate")
audio.stream(
fn=generation, inputs=[num_steps], outputs=[audio],
trigger=button.click # (2)
)
```
1. Set `mode="receive"` to only receive audio from the server.
2. The `stream` event must take a `trigger` that corresponds to the gradio event that starts the stream. In this case, it's the button click.
### Video Input/Output Streaming
Set up a video Input/Output stream to continuosly receive webcam frames from the user and run an arbitrary python function to return a modified frame.
```py
import gradio as gr
from gradio_webrtc import WebRTC
def detection(image, conf_threshold=0.3): # (1)
... your detection code here ...
return modified_frame # (2)
with gr.Blocks() as demo:
image = WebRTC(label="Stream", mode="send-receive", modality="video") # (3)
conf_threshold = gr.Slider(
label="Confidence Threshold",
minimum=0.0,
maximum=1.0,
step=0.05,
value=0.30,
)
image.stream(
fn=detection,
inputs=[image, conf_threshold], # (4)
outputs=[image], time_limit=10
)
if __name__ == "__main__":
demo.launch()
```
1. The webcam frame will be represented as a numpy array of shape (height, width, RGB).
2. The function must return a numpy array. It can take arbitrary values from other components.
3. Set the `modality="video"` and `mode="send-receive"`
4. The `inputs` parameter should be a list where the first element is the WebRTC component. The only output allowed is the WebRTC component.
### Server-to-Client Only
Set up a server-to-client stream to stream video from an arbitrary user interaction.
```py
import gradio as gr
from gradio_webrtc import WebRTC
import cv2
def generation():
url = "https://download.tsi.telecom-paristech.fr/gpac/dataset/dash/uhd/mux_sources/hevcds_720p30_2M.mp4"
cap = cv2.VideoCapture(url)
iterating = True
while iterating:
iterating, frame = cap.read()
yield frame # (1)
with gr.Blocks() as demo:
output_video = WebRTC(label="Video Stream", mode="receive", # (2)
modality="video")
button = gr.Button("Start", variant="primary")
output_video.stream(
fn=generation, inputs=None, outputs=[output_video],
trigger=button.click # (3)
)
demo.launch()
```
1. The `stream` event's `fn` parameter is a generator function that yields the next frame from the video as a **numpy array**.
2. Set `mode="receive"` to only receive audio from the server.
3. The `trigger` parameter the gradio event that will trigger the stream. In this case, the button click event.
### Additional Outputs
In order to modify other components from within the WebRTC stream, you must yield an instance of `AdditionalOutputs` and add an `on_additional_outputs` event to the `WebRTC` component.
This is common for displaying a multimodal text/audio conversation in a Chatbot UI.
``` py title="Additional Outputs"
from gradio_webrtc import AdditionalOutputs, WebRTC
def transcribe(audio: tuple[int, np.ndarray],
transformers_convo: list[dict],
gradio_convo: list[dict]):
response = model.generate(**inputs, max_length=256)
transformers_convo.append({"role": "assistant", "content": response})
gradio_convo.append({"role": "assistant", "content": response})
yield AdditionalOutputs(transformers_convo, gradio_convo) # (1)
with gr.Blocks() as demo:
gr.HTML(
"""
<h1 style='text-align: center'>
Talk to Qwen2Audio (Powered by WebRTC ⚡️)
</h1>
"""
)
transformers_convo = gr.State(value=[])
with gr.Row():
with gr.Column():
audio = WebRTC(
label="Stream",
mode="send", # (2)
modality="audio",
)
with gr.Column():
transcript = gr.Chatbot(label="transcript", type="messages")
audio.stream(ReplyOnPause(transcribe),
inputs=[audio, transformers_convo, transcript],
outputs=[audio], time_limit=90)
audio.on_additional_outputs(lambda s,a: (s,a), # (3)
outputs=[transformers_convo, transcript],
queue=False, show_progress="hidden")
demo.launch()
```
1. Pass your data to `AdditionalOutputs` and yield it.
2. In this case, no audio is being returned, so we set `mode="send"`. However, if we set `mode="send-receive"`, we could also yield generated audio and `AdditionalOutputs`.
3. The `on_additional_outputs` event does not take `inputs`. It's common practice to not run this event on the queue since it is just a quick UI update.
=== "Notes"
1. Pass your data to `AdditionalOutputs` and yield it.
2. In this case, no audio is being returned, so we set `mode="send"`. However, if we set `mode="send-receive"`, we could also yield generated audio and `AdditionalOutputs`.
3. The `on_additional_outputs` event does not take `inputs`. It's common practice to not run this event on the queue since it is just a quick UI update.
## Deployment
When deploying in a cloud environment (like Hugging Face Spaces, EC2, etc), you need to set up a TURN server to relay the WebRTC traffic.
The easiest way to do this is to use a service like Twilio.
```python ```python
from twilio.rest import Client from fastrtc import Stream, ReplyOnPause
import os import numpy as np
account_sid = os.environ.get("TWILIO_ACCOUNT_SID") def echo(audio: tuple[int, np.ndarray]):
auth_token = os.environ.get("TWILIO_AUTH_TOKEN") # The function will be passed the audio until the user pauses
# Implement any iterator that yields audio
# See "LLM Voice Chat" for a more complete example
yield audio
client = Client(account_sid, auth_token) stream = Stream(
handler=ReplyOnPause(detection),
token = client.tokens.create() modality="audio",
mode="send-receive",
rtc_configuration = { )
"iceServers": token.ice_servers, ```
"iceTransportPolicy": "relay",
} ### LLM Voice Chat
with gr.Blocks() as demo: ```py
... from fastrtc import (
rtc = WebRTC(rtc_configuration=rtc_configuration, ...) ReplyOnPause, AdditionalOutputs, Stream,
... audio_to_bytes, aggregate_bytes_to_16bit
)
import gradio as gr
from groq import Groq
import anthropic
from elevenlabs import ElevenLabs
groq_client = Groq()
claude_client = anthropic.Anthropic()
tts_client = ElevenLabs()
# See "Talk to Claude" in Cookbook for an example of how to keep
# track of the chat history.
def response(
audio: tuple[int, np.ndarray],
):
prompt = groq_client.audio.transcriptions.create(
file=("audio-file.mp3", audio_to_bytes(audio)),
model="whisper-large-v3-turbo",
response_format="verbose_json",
).text
response = claude_client.messages.create(
model="claude-3-5-haiku-20241022",
max_tokens=512,
messages=[{"role": "user", "content": prompt}],
)
response_text = " ".join(
block.text
for block in response.content
if getattr(block, "type", None) == "text"
)
iterator = tts_client.text_to_speech.convert_as_stream(
text=response_text,
voice_id="JBFqnCBsd6RMkjVDRZzb",
model_id="eleven_multilingual_v2",
output_format="pcm_24000"
)
for chunk in aggregate_bytes_to_16bit(iterator):
audio_array = np.frombuffer(chunk, dtype=np.int16).reshape(1, -1)
yield (24000, audio_array)
stream = Stream(
modality="audio",
mode="send-receive",
handler=ReplyOnPause(response),
)
```
### Webcam Stream
```python
from fastrtc import Stream
import numpy as np
def flip_vertically(image):
return np.flip(image, axis=0)
stream = Stream(
handler=flip_vertically,
modality="video",
mode="send-receive",
)
```
### Object Detection
```python
from fastrtc import Stream
import gradio as gr
import cv2
from huggingface_hub import hf_hub_download
from .inference import YOLOv10
model_file = hf_hub_download(
repo_id="onnx-community/yolov10n", filename="onnx/model.onnx"
)
# git clone https://huggingface.co/spaces/fastrtc/object-detection
# for YOLOv10 implementation
model = YOLOv10(model_file)
def detection(image, conf_threshold=0.3):
image = cv2.resize(image, (model.input_width, model.input_height))
new_image = model.detect_objects(image, conf_threshold)
return cv2.resize(new_image, (500, 500))
stream = Stream(
handler=detection,
modality="video",
mode="send-receive",
additional_inputs=[
gr.Slider(minimum=0, maximum=1, step=0.01, value=0.3)
]
)
```
## Running the Stream
Run:
### Gradio
```py
stream.ui.launch()
```
### Telephone (Audio Only)
```py
stream.fastphone()
```
### FastAPI
```py
app = FastAPI()
stream.mount(app)
# Optional: Add routes
@app.get("/")
async def _():
return HTMLResponse(content=open("index.html").read())
# uvicorn app:app --host 0.0.0.0 --port 8000
``` ```

25
backend/gradio_webrtc/__init__.py → backend/fastrtc/__init__.py
View File

@@ -5,7 +5,17 @@ from .credentials import (
) )
from .reply_on_pause import AlgoOptions, ReplyOnPause, SileroVadOptions from .reply_on_pause import AlgoOptions, ReplyOnPause, SileroVadOptions
from .reply_on_stopwords import ReplyOnStopWords from .reply_on_stopwords import ReplyOnStopWords
from .speech_to_text import stt, stt_for_chunks from .speech_to_text import MoonshineSTT, get_stt_model
from .stream import Stream
from .text_to_speech import KokoroTTSOptions, get_tts_model
from .tracks import (
AsyncAudioVideoStreamHandler,
AsyncStreamHandler,
AudioEmitType,
AudioVideoStreamHandler,
StreamHandler,
VideoEmitType,
)
from .utils import ( from .utils import (
AdditionalOutputs, AdditionalOutputs,
Warning, Warning,
@@ -17,13 +27,7 @@ from .utils import (
audio_to_float32, audio_to_float32,
) )
from .webrtc import ( from .webrtc import (
AsyncAudioVideoStreamHandler,
AsyncStreamHandler,
AudioVideoStreamHandler,
StreamHandler,
WebRTC, WebRTC,
VideoEmitType,
AudioEmitType,
) )
__all__ = [ __all__ = [
@@ -44,11 +48,14 @@ __all__ = [
"ReplyOnPause", "ReplyOnPause",
"ReplyOnStopWords", "ReplyOnStopWords",
"SileroVadOptions", "SileroVadOptions",
"stt", "get_stt_model",
"stt_for_chunks", "MoonshineSTT",
"StreamHandler", "StreamHandler",
"Stream",
"VideoEmitType", "VideoEmitType",
"WebRTC", "WebRTC",
"WebRTCError", "WebRTCError",
"Warning", "Warning",
"get_tts_model",
"KokoroTTSOptions",
] ]

0
backend/gradio_webrtc/credentials.py → backend/fastrtc/credentials.py
View File

0
backend/gradio_webrtc/pause_detection/__init__.py → backend/fastrtc/pause_detection/__init__.py
View File

0
backend/gradio_webrtc/pause_detection/vad.py → backend/fastrtc/pause_detection/vad.py
View File

85
backend/gradio_webrtc/reply_on_pause.py → backend/fastrtc/reply_on_pause.py
View File

@@ -4,12 +4,15 @@ from dataclasses import dataclass
from functools import lru_cache from functools import lru_cache
from logging import getLogger from logging import getLogger
from threading import Event from threading import Event
from typing import Any, Callable, Generator, Literal, Union, cast from typing import Any, AsyncGenerator, Callable, Generator, Literal, cast
import click
import numpy as np import numpy as np
from numpy.typing import NDArray
from gradio_webrtc.pause_detection import SileroVADModel, SileroVadOptions from .pause_detection import SileroVADModel, SileroVadOptions
from gradio_webrtc.webrtc import EmitType, StreamHandler from .tracks import EmitType, StreamHandler
from .utils import create_message, split_output
logger = getLogger(__name__) logger = getLogger(__name__)
@@ -18,8 +21,23 @@ counter = 0
@lru_cache @lru_cache
def get_vad_model() -> SileroVADModel: def get_vad_model() -> SileroVADModel:
"""Returns the VAD model instance.""" """Returns the VAD model instance and warms it up with dummy data."""
return SileroVADModel() try:
import importlib.util
mod = importlib.util.find_spec("onnxruntime")
if mod is None:
raise RuntimeError("Install fastrtc[vad] to use ReplyOnPause")
except (ValueError, ModuleNotFoundError):
raise RuntimeError("Install fastrtc[vad] to use ReplyOnPause")
model = SileroVADModel()
# Warm up the model with dummy data
print(click.style("INFO", fg="green") + ":\t Warming up VAD model.")
for _ in range(10):
dummy_audio = np.zeros(102400, dtype=np.float32)
model.vad((24000, dummy_audio), None)
print(click.style("INFO", fg="green") + ":\t VAD model warmed up.")
return model
@dataclass @dataclass
@@ -40,19 +58,27 @@ class AppState:
responding: bool = False responding: bool = False
stopped: bool = False stopped: bool = False
buffer: np.ndarray | None = None buffer: np.ndarray | None = None
responded_audio: bool = False
ReplyFnGenerator = Union[ ReplyFnGenerator = (
# For two arguments
Callable[ Callable[
[tuple[int, np.ndarray], list[dict[Any, Any]]], [tuple[int, NDArray[np.int16]], list[dict[Any, Any]]],
Generator[EmitType, None, None], Generator[EmitType, None, None],
], ]
Callable[ | Callable[
[tuple[int, np.ndarray]], [tuple[int, NDArray[np.int16]]],
Generator[EmitType, None, None], Generator[EmitType, None, None],
], ]
] | Callable[
[tuple[int, NDArray[np.int16]]],
AsyncGenerator[EmitType, None],
]
| Callable[
[tuple[int, NDArray[np.int16]], list[dict[Any, Any]]],
AsyncGenerator[EmitType, None],
]
)
async def iterate(generator: Generator) -> Any: async def iterate(generator: Generator) -> Any:
@@ -152,6 +178,8 @@ class ReplyOnPause(StreamHandler):
def reset(self): def reset(self):
super().reset() super().reset()
if self.phone_mode:
self.args_set.set()
self.generator = None self.generator = None
self.event.clear() self.event.clear()
self.state = AppState() self.state = AppState()
@@ -164,25 +192,44 @@ class ReplyOnPause(StreamHandler):
return None return None
else: else:
if not self.generator: if not self.generator:
self.send_message_sync(create_message("log", "pause_detected"))
if self._needs_additional_inputs and not self.args_set.is_set(): if self._needs_additional_inputs and not self.args_set.is_set():
asyncio.run_coroutine_threadsafe( if not self.phone_mode:
self.wait_for_args(), self.loop self.wait_for_args_sync()
).result() else:
self.latest_args = [None]
self.args_set.set()
logger.debug("Creating generator") logger.debug("Creating generator")
audio = cast(np.ndarray, self.state.stream).reshape(1, -1) audio = cast(np.ndarray, self.state.stream).reshape(1, -1)
if self._needs_additional_inputs: if self._needs_additional_inputs:
self.latest_args[0] = (self.state.sampling_rate, audio) self.latest_args[0] = (self.state.sampling_rate, audio)
self.generator = self.fn(*self.latest_args) self.generator = self.fn(*self.latest_args) # type: ignore
else: else:
self.generator = self.fn((self.state.sampling_rate, audio)) # type: ignore self.generator = self.fn((self.state.sampling_rate, audio)) # type: ignore
logger.debug("Latest args: %s", self.latest_args) logger.debug("Latest args: %s", self.latest_args)
self.state.responding = True self.state.responding = True
try: try:
if self.is_async: if self.is_async:
return asyncio.run_coroutine_threadsafe( output = asyncio.run_coroutine_threadsafe(
self.async_iterate(self.generator), self.loop self.async_iterate(self.generator), self.loop
).result() ).result()
else: else:
return next(self.generator) output = next(self.generator) # type: ignore
audio, additional_outputs = split_output(output)
if audio is not None:
self.send_message_sync(create_message("log", "response_starting"))
self.state.responded_audio = True
if self.phone_mode:
if additional_outputs:
self.latest_args = [None] + list(additional_outputs.args)
return output
except (StopIteration, StopAsyncIteration): except (StopIteration, StopAsyncIteration):
if not self.state.responded_audio:
self.send_message_sync(create_message("log", "response_starting"))
self.reset()
except Exception as e:
import traceback
traceback.print_exc()
logger.debug("Error in ReplyOnPause: %s", e)
self.reset() self.reset()

18
backend/gradio_webrtc/reply_on_stopwords.py → backend/fastrtc/reply_on_stopwords.py
View File

@@ -12,8 +12,8 @@ from .reply_on_pause import (
ReplyOnPause, ReplyOnPause,
SileroVadOptions, SileroVadOptions,
) )
from .speech_to_text import get_stt_model, stt_for_chunks from .speech_to_text import get_stt_model
from .utils import audio_to_float32 from .utils import audio_to_float32, create_message
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
@@ -47,14 +47,16 @@ class ReplyOnStopWords(ReplyOnPause):
) )
self.stop_words = stop_words self.stop_words = stop_words
self.state = ReplyOnStopWordsState() self.state = ReplyOnStopWordsState()
# Download Model self.stt_model = get_stt_model("moonshine/base")
get_stt_model()
def stop_word_detected(self, text: str) -> bool: def stop_word_detected(self, text: str) -> bool:
for stop_word in self.stop_words: for stop_word in self.stop_words:
stop_word = stop_word.lower().strip().split(" ") stop_word = stop_word.lower().strip().split(" ")
if bool( if bool(
re.search(r"\b" + r"\s+".join(map(re.escape, stop_word)) + r"\b", text) re.search(
r"\b" + r"\s+".join(map(re.escape, stop_word)) + r"[.,!?]*\b",
text.lower(),
)
): ):
logger.debug("Stop word detected: %s", stop_word) logger.debug("Stop word detected: %s", stop_word)
return True return True
@@ -64,7 +66,7 @@ class ReplyOnStopWords(ReplyOnPause):
self, self,
): ):
if self.channel: if self.channel:
self.channel.send("stopword") self.channel.send(create_message("stopword", ""))
logger.debug("Sent stopword") logger.debug("Sent stopword")
def send_stopword(self): def send_stopword(self):
@@ -97,7 +99,9 @@ class ReplyOnStopWords(ReplyOnPause):
self.model_options, self.model_options,
return_chunks=True, return_chunks=True,
) )
text = stt_for_chunks((16000, state.post_stop_word_buffer), chunks) text = self.stt_model.stt_for_chunks(
(16000, state.post_stop_word_buffer), chunks
)
logger.debug(f"STT: {text}") logger.debug(f"STT: {text}")
state.stop_word_detected = self.stop_word_detected(text) state.stop_word_detected = self.stop_word_detected(text)
if state.stop_word_detected: if state.stop_word_detected:

3
backend/fastrtc/speech_to_text/__init__.py Normal file
View File

@@ -0,0 +1,3 @@
from .stt_ import MoonshineSTT, get_stt_model
__all__ = ["get_stt_model", "MoonshineSTT", "get_stt_model"]

81
backend/fastrtc/speech_to_text/stt_.py Normal file
View File

@@ -0,0 +1,81 @@
from functools import lru_cache
from pathlib import Path
from typing import Literal, Protocol
import click
import librosa
import numpy as np
from numpy.typing import NDArray
from ..utils import AudioChunk, audio_to_float32
curr_dir = Path(__file__).parent
class STTModel(Protocol):
def stt(self, audio: tuple[int, NDArray[np.int16 | np.float32]]) -> str: ...
def stt_for_chunks(
self,
audio: tuple[int, NDArray[np.int16 | np.float32]],
chunks: list[AudioChunk],
) -> str: ...
class MoonshineSTT(STTModel):
def __init__(
self, model: Literal["moonshine/base", "moonshine/tiny"] = "moonshine/base"
):
try:
from moonshine_onnx import MoonshineOnnxModel, load_tokenizer
except (ImportError, ModuleNotFoundError):
raise ImportError(
"Install fastrtc[stt] for speech-to-text and stopword detection support."
)
self.model = MoonshineOnnxModel(model_name=model)
self.tokenizer = load_tokenizer()
def stt(self, audio: tuple[int, NDArray[np.int16 | np.float32]]) -> str:
sr, audio_np = audio # type: ignore
if audio_np.dtype == np.int16:
audio_np = audio_to_float32(audio)
if sr != 16000:
audio_np: NDArray[np.float32] = librosa.resample(
audio_np, orig_sr=sr, target_sr=16000
)
if audio_np.ndim == 1:
audio_np = audio_np.reshape(1, -1)
tokens = self.model.generate(audio_np)
return self.tokenizer.decode_batch(tokens)[0]
def stt_for_chunks(
self,
audio: tuple[int, NDArray[np.int16 | np.float32]],
chunks: list[AudioChunk],
) -> str:
sr, audio_np = audio
return " ".join(
[
self.stt((sr, audio_np[chunk["start"] : chunk["end"]]))
for chunk in chunks
]
)
@lru_cache
def get_stt_model(
model: Literal["moonshine/base", "moonshine/tiny"] = "moonshine/base",
) -> STTModel:
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
m = MoonshineSTT(model)
from moonshine_onnx import load_audio
audio = load_audio(str(curr_dir / "test_file.wav"))
print(click.style("INFO", fg="green") + ":\t Warming up STT model.")
m.stt((16000, audio))
print(click.style("INFO", fg="green") + ":\t STT model warmed up.")
return m

BIN
backend/fastrtc/speech_to_text/test_file.wav Normal file
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Binary file not shown.

634
backend/fastrtc/stream.py Normal file
View File

@@ -0,0 +1,634 @@
import logging
from pathlib import Path
from typing import (
Any,
AsyncContextManager,
Callable,
Literal,
TypedDict,
cast,
)
import gradio as gr
from fastapi import FastAPI, Request, WebSocket
from fastapi.responses import HTMLResponse
from gradio import Blocks
from gradio.components.base import Component
from pydantic import BaseModel
from typing_extensions import NotRequired
from .tracks import HandlerType, StreamHandlerImpl
from .webrtc import WebRTC
from .webrtc_connection_mixin import WebRTCConnectionMixin
from .websocket import WebSocketHandler
logger = logging.getLogger(__name__)
curr_dir = Path(__file__).parent
class Body(BaseModel):
sdp: str
type: str
webrtc_id: str
class UIArgs(TypedDict):
title: NotRequired[str]
"""Title of the demo"""
icon: NotRequired[str]
"""Icon to display on the button instead of the wave animation. The icon should be a path/url to a .svg/.png/.jpeg file."""
icon_button_color: NotRequired[str]
"""Color of the icon button. Default is var(--color-accent) of the demo theme."""
pulse_color: NotRequired[str]
"""Color of the pulse animation. Default is var(--color-accent) of the demo theme."""
class Stream(WebRTCConnectionMixin):
def __init__(
self,
handler: HandlerType,
*,
additional_outputs_handler: Callable | None = None,
mode: Literal["send-receive", "receive", "send"] = "send-receive",
modality: Literal["video", "audio", "audio-video"] = "video",
concurrency_limit: int | None | Literal["default"] = "default",
time_limit: float | None = None,
rtp_params: dict[str, Any] | None = None,
rtc_configuration: dict[str, Any] | None = None,
additional_inputs: list[Component] | None = None,
additional_outputs: list[Component] | None = None,
ui_args: UIArgs | None = None,
):
self.mode = mode
self.modality = modality
self.rtp_params = rtp_params
self.event_handler = handler
self.concurrency_limit = cast(
(int | float),
1 if concurrency_limit in ["default", None] else concurrency_limit,
)
self.time_limit = time_limit
self.additional_output_components = additional_outputs
self.additional_input_components = additional_inputs
self.additional_outputs_handler = additional_outputs_handler
self.rtc_configuration = rtc_configuration
self._ui = self._generate_default_ui(ui_args)
self._ui.launch = self._wrap_gradio_launch(self._ui.launch)
def mount(self, app: FastAPI):
app.router.post("/webrtc/offer")(self.offer)
app.router.websocket("/telephone/handler")(self.telephone_handler)
app.router.post("/telephone/incoming")(self.handle_incoming_call)
app.router.websocket("/websocket/offer")(self.websocket_offer)
lifespan = self._inject_startup_message(app.router.lifespan_context)
app.router.lifespan_context = lifespan
@staticmethod
def print_error(env: Literal["colab", "spaces"]):
import click
print(
click.style("ERROR", fg="red")
+ f":\t Running in {env} is not possible without providing a valid rtc_configuration. "
+ "See "
+ click.style("https://fastrtc.org/deployment/", fg="cyan")
+ " for more information."
)
raise RuntimeError(
f"Running in {env} is not possible without providing a valid rtc_configuration. "
+ "See https://fastrtc.org/deployment/ for more information."
)
def _check_colab_or_spaces(self):
from gradio.utils import colab_check, get_space
if colab_check() and not self.rtc_configuration:
self.print_error("colab")
if get_space() and not self.rtc_configuration:
self.print_error("spaces")
def _wrap_gradio_launch(self, callable):
import contextlib
def wrapper(*args, **kwargs):
lifespan = kwargs.get("app_kwargs", {}).get("lifespan", None)
@contextlib.asynccontextmanager
async def new_lifespan(app: FastAPI):
if lifespan is None:
self._check_colab_or_spaces()
yield
else:
async with lifespan(app):
self._check_colab_or_spaces()
yield
if "app_kwargs" not in kwargs:
kwargs["app_kwargs"] = {}
kwargs["app_kwargs"]["lifespan"] = new_lifespan
return callable(*args, **kwargs)
return wrapper
def _inject_startup_message(
self, lifespan: Callable[[FastAPI], AsyncContextManager] | None = None
):
import contextlib
import click
def print_startup_message():
self._check_colab_or_spaces()
print(
click.style("INFO", fg="green")
+ ":\t Visit "
+ click.style("https://fastrtc.org/userguide/api/", fg="cyan")
+ " for WebRTC or Websocket API docs."
)
@contextlib.asynccontextmanager
async def new_lifespan(app: FastAPI):
if lifespan is None:
print_startup_message()
yield
else:
async with lifespan(app):
print_startup_message()
yield
return new_lifespan
def _generate_default_ui(
self,
ui_args: UIArgs | None = None,
):
ui_args = ui_args or {}
same_components = []
additional_input_components = self.additional_input_components or []
additional_output_components = self.additional_output_components or []
if additional_output_components and not self.additional_outputs_handler:
raise ValueError(
"additional_outputs_handler must be provided if there are additional output components."
)
if additional_input_components and additional_output_components:
same_components = [
component
for component in additional_input_components
if component in additional_output_components
]
for component in additional_output_components:
if component not in same_components:
same_components.append(component)
if self.modality == "video" and self.mode == "receive":
with gr.Blocks() as demo:
gr.HTML(
f"""
<h1 style='text-align: center'>
{ui_args.get("title", "Video Streaming (Powered by WebRTC ⚡️)")}
</h1>
"""
)
with gr.Row():
if additional_input_components:
with gr.Column():
for component in additional_input_components:
component.render()
button = gr.Button("Start Stream", variant="primary")
with gr.Column():
output_video = WebRTC(
label="Video Stream",
rtc_configuration=self.rtc_configuration,
mode="receive",
modality="video",
)
for component in additional_output_components:
if component not in same_components:
component.render()
output_video.stream(
fn=self.event_handler,
inputs=self.additional_input_components,
outputs=[output_video],
trigger=button.click,
time_limit=self.time_limit,
concurrency_limit=self.concurrency_limit, # type: ignore
)
if additional_output_components:
assert self.additional_outputs_handler
output_video.on_additional_outputs(
self.additional_outputs_handler,
outputs=additional_output_components,
)
elif self.modality == "video" and self.mode == "send":
with gr.Blocks() as demo:
gr.HTML(
f"""
<h1 style='text-align: center'>
{ui_args.get("title", "Video Streaming (Powered by WebRTC ⚡️)")}
</h1>
"""
)
with gr.Row():
if additional_input_components:
with gr.Column():
for component in additional_input_components:
component.render()
with gr.Column():
output_video = WebRTC(
label="Video Stream",
rtc_configuration=self.rtc_configuration,
mode="send",
modality="video",
)
for component in additional_output_components:
if component not in same_components:
component.render()
output_video.stream(
fn=self.event_handler,
inputs=[output_video] + additional_input_components,
outputs=[output_video],
time_limit=self.time_limit,
concurrency_limit=self.concurrency_limit, # type: ignore
)
if additional_output_components:
assert self.additional_outputs_handler
output_video.on_additional_outputs(
self.additional_outputs_handler,
outputs=additional_output_components,
)
elif self.modality == "video" and self.mode == "send-receive":
css = """.my-group {max-width: 600px !important; max-height: 600 !important;}
.my-column {display: flex !important; justify-content: center !important; align-items: center !important};"""
with gr.Blocks(css=css) as demo:
gr.HTML(
f"""
<h1 style='text-align: center'>
{ui_args.get("title", "Video Streaming (Powered by WebRTC ⚡️)")}
</h1>
"""
)
with gr.Column(elem_classes=["my-column"]):
with gr.Group(elem_classes=["my-group"]):
image = WebRTC(
label="Stream",
rtc_configuration=self.rtc_configuration,
mode="send-receive",
modality="video",
)
for component in additional_input_components:
component.render()
if additional_output_components:
with gr.Column():
for component in additional_output_components:
if component not in same_components:
component.render()
image.stream(
fn=self.event_handler,
inputs=[image] + additional_input_components,
outputs=[image],
time_limit=self.time_limit,
concurrency_limit=self.concurrency_limit, # type: ignore
)
if additional_output_components:
assert self.additional_outputs_handler
image.on_additional_outputs(
self.additional_outputs_handler,
inputs=additional_output_components,
outputs=additional_output_components,
)
elif self.modality == "audio" and self.mode == "receive":
with gr.Blocks() as demo:
gr.HTML(
"""
<h1 style='text-align: center'>
FastAPI (Powered by WebRTC ⚡️)
</h1>
"""
)
with gr.Row():
with gr.Column():
for component in additional_input_components:
component.render()
button = gr.Button("Start Stream", variant="primary")
if additional_output_components:
with gr.Column():
output_video = WebRTC(
label="Audio Stream",
rtc_configuration=self.rtc_configuration,
mode="receive",
modality="audio",
icon=ui_args.get("icon"),
icon_button_color=ui_args.get("icon_button_color"),
pulse_color=ui_args.get("pulse_color"),
)
for component in additional_output_components:
if component not in same_components:
component.render()
output_video.stream(
fn=self.event_handler,
inputs=self.additional_input_components,
outputs=[output_video],
trigger=button.click,
time_limit=self.time_limit,
concurrency_limit=self.concurrency_limit, # type: ignore
)
if additional_output_components:
assert self.additional_outputs_handler
output_video.on_additional_outputs(
self.additional_outputs_handler,
inputs=additional_output_components,
outputs=additional_output_components,
)
elif self.modality == "audio" and self.mode == "send":
with gr.Blocks() as demo:
gr.HTML(
f"""
<h1 style='text-align: center'>
{ui_args.get("title", "Audio Streaming (Powered by WebRTC ⚡️)")}
</h1>
"""
)
with gr.Row():
with gr.Column():
with gr.Group():
image = WebRTC(
label="Stream",
rtc_configuration=self.rtc_configuration,
mode="send-receive",
modality="audio",
)
for component in additional_input_components:
if component not in same_components:
component.render()
if additional_output_components:
with gr.Column():
for component in additional_output_components:
component.render()
image.stream(
fn=self.event_handler,
inputs=[image] + additional_input_components,
outputs=[image],
time_limit=self.time_limit,
concurrency_limit=self.concurrency_limit, # type: ignore
)
if additional_output_components:
assert self.additional_outputs_handler
image.on_additional_outputs(
self.additional_outputs_handler,
inputs=additional_output_components,
outputs=additional_output_components,
)
elif self.modality == "audio" and self.mode == "send-receive":
with gr.Blocks() as demo:
gr.HTML(
f"""
<h1 style='text-align: center'>
{ui_args.get("title", "Audio Streaming (Powered by WebRTC ⚡️)")}
</h1>
"""
)
with gr.Row():
with gr.Column():
with gr.Group():
image = WebRTC(
label="Stream",
rtc_configuration=self.rtc_configuration,
mode="send-receive",
modality="audio",
icon=ui_args.get("icon"),
icon_button_color=ui_args.get("icon_button_color"),
pulse_color=ui_args.get("pulse_color"),
)
for component in additional_input_components:
if component not in same_components:
component.render()
if additional_output_components:
with gr.Column():
for component in additional_output_components:
component.render()
image.stream(
fn=self.event_handler,
inputs=[image] + additional_input_components,
outputs=[image],
time_limit=self.time_limit,
concurrency_limit=self.concurrency_limit, # type: ignore
)
if additional_output_components:
assert self.additional_outputs_handler
image.on_additional_outputs(
self.additional_outputs_handler,
inputs=additional_output_components,
outputs=additional_output_components,
)
elif self.modality == "audio-video" and self.mode == "send-receive":
with gr.Blocks() as demo:
gr.HTML(
f"""
<h1 style='text-align: center'>
{ui_args.get("title", "Audio Streaming (Powered by WebRTC ⚡️)")}
</h1>
"""
)
with gr.Row():
with gr.Column():
with gr.Group():
image = WebRTC(
label="Stream",
rtc_configuration=self.rtc_configuration,
mode="send-receive",
modality="audio-video",
icon=ui_args.get("icon"),
icon_button_color=ui_args.get("icon_button_color"),
pulse_color=ui_args.get("pulse_color"),
)
for component in additional_input_components:
if component not in same_components:
component.render()
if additional_output_components:
with gr.Column():
for component in additional_output_components:
component.render()
image.stream(
fn=self.event_handler,
inputs=[image] + additional_input_components,
outputs=[image],
time_limit=self.time_limit,
concurrency_limit=self.concurrency_limit, # type: ignore
)
if additional_output_components:
assert self.additional_outputs_handler
image.on_additional_outputs(
self.additional_outputs_handler,
inputs=additional_output_components,
outputs=additional_output_components,
)
return demo
@property
def ui(self) -> Blocks:
return self._ui
@ui.setter
def ui(self, blocks: Blocks):
self._ui = blocks
async def offer(self, body: Body):
return await self.handle_offer(
body.model_dump(), set_outputs=self.set_additional_outputs(body.webrtc_id)
)
async def handle_incoming_call(self, request: Request):
from twilio.twiml.voice_response import Connect, VoiceResponse
response = VoiceResponse()
response.say("Connecting to the AI assistant.")
connect = Connect()
connect.stream(url=f"wss://{request.url.hostname}/telephone/handler")
response.append(connect)
response.say("The call has been disconnected.")
return HTMLResponse(content=str(response), media_type="application/xml")
async def telephone_handler(self, websocket: WebSocket):
handler = cast(StreamHandlerImpl, self.event_handler.copy())
handler.phone_mode = True
async def set_handler(s: str, a: WebSocketHandler):
if len(self.connections) >= self.concurrency_limit:
await cast(WebSocket, a.websocket).send_json(
{
"status": "failed",
"meta": {
"error": "concurrency_limit_reached",
"limit": self.concurrency_limit,
},
}
)
await websocket.close()
return
ws = WebSocketHandler(
handler, set_handler, lambda s: None, lambda s: lambda a: None
)
await ws.handle_websocket(websocket)
async def websocket_offer(self, websocket: WebSocket):
handler = cast(StreamHandlerImpl, self.event_handler.copy())
handler.phone_mode = False
async def set_handler(s: str, a: WebSocketHandler):
if len(self.connections) >= self.concurrency_limit:
await cast(WebSocket, a.websocket).send_json(
{
"status": "failed",
"meta": {
"error": "concurrency_limit_reached",
"limit": self.concurrency_limit,
},
}
)
await websocket.close()
return
self.connections[s] = [a] # type: ignore
def clean_up(s):
self.clean_up(s)
ws = WebSocketHandler(
handler, set_handler, clean_up, lambda s: self.set_additional_outputs(s)
)
await ws.handle_websocket(websocket)
def fastphone(
self,
token: str | None = None,
host: str = "127.0.0.1",
port: int = 8000,
**kwargs,
):
import secrets
import threading
import time
import urllib.parse
import click
import httpx
import uvicorn
from gradio.networking import setup_tunnel
from gradio.tunneling import CURRENT_TUNNELS
from huggingface_hub import get_token
app = FastAPI()
self.mount(app)
t = threading.Thread(
target=uvicorn.run,
args=(app,),
kwargs={"host": host, "port": port, **kwargs},
)
t.start()
url = setup_tunnel(
host, port, share_token=secrets.token_urlsafe(32), share_server_address=None
)
host = urllib.parse.urlparse(url).netloc
URL = "https://api.fastrtc.org"
r = httpx.post(
URL + "/register",
json={"url": host},
headers={"Authorization": token or get_token() or ""},
)
r.raise_for_status()
data = r.json()
code = f"{data['code']}"
phone_number = data["phone"]
reset_date = data["reset_date"]
print(
click.style("INFO", fg="green")
+ ":\t Your FastPhone is now live! Call "
+ click.style(phone_number, fg="cyan")
+ " and use code "
+ click.style(code, fg="cyan")
+ " to connect to your stream."
)
minutes = str(int(data["time_remaining"] // 60)).zfill(2)
seconds = str(int(data["time_remaining"] % 60)).zfill(2)
print(
click.style("INFO", fg="green")
+ ":\t You have "
+ click.style(f"{minutes}:{seconds}", fg="cyan")
+ " minutes remaining in your quota (Resetting on "
+ click.style(f"{reset_date}", fg="cyan")
+ ")"
)
print(
click.style("INFO", fg="green")
+ ":\t Visit "
+ click.style(
"https://fastrtc.org/userguide/audio/#telephone-integration",
fg="cyan",
)
+ " for information on making your handler compatible with phone usage."
)
try:
while True:
time.sleep(0.1)
except (KeyboardInterrupt, OSError):
print(
click.style("INFO", fg="green")
+ ":\t Keyboard interruption in main thread... closing server."
)
r = httpx.post(
URL + "/unregister",
json={"url": host, "code": code},
headers={"Authorization": token or get_token() or ""},
)
t.join(timeout=5)
for tunnel in CURRENT_TUNNELS:
tunnel.kill()

3
backend/fastrtc/text_to_speech/__init__.py Normal file
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@@ -0,0 +1,3 @@
from .tts import KokoroTTSOptions, get_tts_model
__all__ = ["get_tts_model", "KokoroTTSOptions"]

90
backend/fastrtc/text_to_speech/tts.py Normal file
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@@ -0,0 +1,90 @@
import asyncio
import re
from dataclasses import dataclass
from functools import lru_cache
from typing import AsyncGenerator, Generator, Literal, Protocol
import numpy as np
from huggingface_hub import hf_hub_download
from numpy.typing import NDArray
class TTSOptions:
pass
class TTSModel(Protocol):
def tts(self, text: str) -> tuple[int, NDArray[np.float32]]: ...
async def stream_tts(
self, text: str, options: TTSOptions | None = None
) -> AsyncGenerator[tuple[int, NDArray[np.float32]], None]: ...
def stream_tts_sync(
self, text: str, options: TTSOptions | None = None
) -> Generator[tuple[int, NDArray[np.float32]], None, None]: ...
@dataclass
class KokoroTTSOptions(TTSOptions):
voice: str = "af_heart"
speed: float = 1.0
lang: str = "en-us"
@lru_cache
def get_tts_model(model: Literal["kokoro"] = "kokoro") -> TTSModel:
m = KokoroTTSModel()
m.tts("Hello, world!")
return m
class KokoroTTSModel(TTSModel):
def __init__(self):
from kokoro_onnx import Kokoro
self.model = Kokoro(
model_path=hf_hub_download("fastrtc/kokoro-onnx", "kokoro-v1.0.onnx"),
voices_path=hf_hub_download("fastrtc/kokoro-onnx", "voices-v1.0.bin"),
)
def tts(
self, text: str, options: KokoroTTSOptions | None = None
) -> tuple[int, NDArray[np.float32]]:
options = options or KokoroTTSOptions()
a, b = self.model.create(
text, voice=options.voice, speed=options.speed, lang=options.lang
)
return b, a
async def stream_tts(
self, text: str, options: KokoroTTSOptions | None = None
) -> AsyncGenerator[tuple[int, NDArray[np.float32]], None]:
options = options or KokoroTTSOptions()
sentences = re.split(r"(?<=[.!?])\s+", text.strip())
for s_idx, sentence in enumerate(sentences):
if not sentence.strip():
continue
chunk_idx = 0
async for chunk in self.model.create_stream(
sentence, voice=options.voice, speed=options.speed, lang=options.lang
):
if s_idx != 0 and chunk_idx == 0:
yield chunk[1], np.zeros(chunk[1] // 7, dtype=np.float32)
yield chunk[1], chunk[0]
def stream_tts_sync(
self, text: str, options: KokoroTTSOptions | None = None
) -> Generator[tuple[int, NDArray[np.float32]], None, None]:
loop = asyncio.new_event_loop()
# Use the new loop to run the async generator
iterator = self.stream_tts(text, options).__aiter__()
while True:
try:
yield loop.run_until_complete(iterator.__anext__())
except StopAsyncIteration:
break

689
backend/fastrtc/tracks.py Normal file
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@@ -0,0 +1,689 @@
"""gr.WebRTC() component."""
from __future__ import annotations
import asyncio
import functools
import inspect
import logging
import threading
import time
import traceback
from abc import ABC, abstractmethod
from collections.abc import Callable
from typing import (
Any,
Generator,
Literal,
TypeAlias,
Union,
cast,
)
import anyio.to_thread
import av
import numpy as np
from aiortc import (
AudioStreamTrack,
MediaStreamTrack,
VideoStreamTrack,
)
from aiortc.contrib.media import AudioFrame, VideoFrame # type: ignore
from aiortc.mediastreams import MediaStreamError
from numpy import typing as npt
from fastrtc.utils import (
AdditionalOutputs,
DataChannel,
create_message,
current_channel,
player_worker_decode,
split_output,
)
logger = logging.getLogger(__name__)
VideoNDArray: TypeAlias = Union[
np.ndarray[Any, np.dtype[np.uint8]],
np.ndarray[Any, np.dtype[np.uint16]],
np.ndarray[Any, np.dtype[np.float32]],
]
VideoEmitType = (
VideoNDArray | tuple[VideoNDArray, AdditionalOutputs] | AdditionalOutputs
)
VideoEventHandler = Callable[[npt.ArrayLike], VideoEmitType]
class VideoCallback(VideoStreamTrack):
"""
This works for streaming input and output
"""
kind = "video"
def __init__(
self,
track: MediaStreamTrack,
event_handler: VideoEventHandler,
channel: DataChannel | None = None,
set_additional_outputs: Callable | None = None,
mode: Literal["send-receive", "send"] = "send-receive",
) -> None:
super().__init__() # don't forget this!
self.track = track
self.event_handler = event_handler
self.latest_args: str | list[Any] = "not_set"
self.channel = channel
self.set_additional_outputs = set_additional_outputs
self.thread_quit = asyncio.Event()
self.mode = mode
self.channel_set = asyncio.Event()
self.has_started = False
def set_channel(self, channel: DataChannel):
self.channel = channel
current_channel.set(channel)
self.channel_set.set()
def set_args(self, args: list[Any]):
self.latest_args = ["__webrtc_value__"] + list(args)
def add_frame_to_payload(
self, args: list[Any], frame: np.ndarray | None
) -> list[Any]:
new_args = []
for val in args:
if isinstance(val, str) and val == "__webrtc_value__":
new_args.append(frame)
else:
new_args.append(val)
return new_args
def array_to_frame(self, array: np.ndarray) -> VideoFrame:
return VideoFrame.from_ndarray(array, format="bgr24")
async def process_frames(self):
while not self.thread_quit.is_set():
try:
await self.recv()
except TimeoutError:
continue
def start(
self,
):
asyncio.create_task(self.process_frames())
def stop(self):
super().stop()
logger.debug("video callback stop")
self.thread_quit.set()
async def wait_for_channel(self):
if not self.channel_set.is_set():
await self.channel_set.wait()
if current_channel.get() != self.channel:
current_channel.set(self.channel)
async def recv(self): # type: ignore
try:
try:
frame = cast(VideoFrame, await self.track.recv())
except MediaStreamError:
self.stop()
return
await self.wait_for_channel()
frame_array = frame.to_ndarray(format="bgr24")
if self.latest_args == "not_set":
return frame
args = self.add_frame_to_payload(cast(list, self.latest_args), frame_array)
array, outputs = split_output(self.event_handler(*args))
if (
isinstance(outputs, AdditionalOutputs)
and self.set_additional_outputs
and self.channel
):
self.set_additional_outputs(outputs)
self.channel.send(create_message("fetch_output", []))
if array is None and self.mode == "send":
return
new_frame = self.array_to_frame(array)
if frame:
new_frame.pts = frame.pts
new_frame.time_base = frame.time_base
else:
pts, time_base = await self.next_timestamp()
new_frame.pts = pts
new_frame.time_base = time_base
return new_frame
except Exception as e:
logger.debug("exception %s", e)
exec = traceback.format_exc()
logger.debug("traceback %s", exec)
class StreamHandlerBase(ABC):
def __init__(
self,
expected_layout: Literal["mono", "stereo"] = "mono",
output_sample_rate: int = 24000,
output_frame_size: int = 960,
input_sample_rate: int = 48000,
) -> None:
self.expected_layout = expected_layout
self.output_sample_rate = output_sample_rate
self.output_frame_size = output_frame_size
self.input_sample_rate = input_sample_rate
self.latest_args: list[Any] = []
self._resampler = None
self._channel: DataChannel | None = None
self._loop: asyncio.AbstractEventLoop
self.args_set = asyncio.Event()
self.channel_set = asyncio.Event()
self._phone_mode = False
@property
def loop(self) -> asyncio.AbstractEventLoop:
return cast(asyncio.AbstractEventLoop, self._loop)
@property
def channel(self) -> DataChannel | None:
return self._channel
@property
def phone_mode(self) -> bool:
return self._phone_mode
@phone_mode.setter
def phone_mode(self, value: bool):
self._phone_mode = value
def set_channel(self, channel: DataChannel):
self._channel = channel
self.channel_set.set()
async def fetch_args(
self,
):
if self.channel:
self.channel.send(create_message("send_input", []))
logger.debug("Sent send_input")
async def wait_for_args(self):
if not self.phone_mode:
await self.fetch_args()
await self.args_set.wait()
else:
self.args_set.set()
def wait_for_args_sync(self):
try:
asyncio.run_coroutine_threadsafe(self.wait_for_args(), self.loop).result()
except Exception:
import traceback
traceback.print_exc()
async def send_message(self, msg: str):
if self.channel:
self.channel.send(msg)
logger.debug("Sent msg %s", msg)
def send_message_sync(self, msg: str):
asyncio.run_coroutine_threadsafe(self.send_message(msg), self.loop).result()
logger.debug("Sent msg %s", msg)
def set_args(self, args: list[Any]):
logger.debug("setting args in audio callback %s", args)
self.latest_args = ["__webrtc_value__"] + list(args)
self.args_set.set()
def reset(self):
self.args_set.clear()
def shutdown(self):
pass
def resample(self, frame: AudioFrame) -> Generator[AudioFrame, None, None]:
if self._resampler is None:
self._resampler = av.AudioResampler( # type: ignore
format="s16",
layout=self.expected_layout,
rate=self.input_sample_rate,
frame_size=frame.samples,
)
yield from self._resampler.resample(frame)
EmitType: TypeAlias = (
tuple[int, npt.NDArray[np.int16 | np.float32]]
| tuple[int, npt.NDArray[np.int16 | np.float32], Literal["mono", "stereo"]]
| AdditionalOutputs
| tuple[tuple[int, npt.NDArray[np.int16 | np.float32]], AdditionalOutputs]
| None
)
AudioEmitType = EmitType
class StreamHandler(StreamHandlerBase):
@abstractmethod
def receive(self, frame: tuple[int, npt.NDArray[np.int16]]) -> None:
pass
@abstractmethod
def emit(
self,
) -> EmitType:
pass
@abstractmethod
def copy(self) -> StreamHandler:
pass
def start_up(self):
pass
class AsyncStreamHandler(StreamHandlerBase):
@abstractmethod
async def receive(self, frame: tuple[int, npt.NDArray[np.int16]]) -> None:
pass
@abstractmethod
async def emit(
self,
) -> EmitType:
pass
@abstractmethod
def copy(self) -> AsyncStreamHandler:
pass
async def start_up(self):
pass
StreamHandlerImpl = StreamHandler | AsyncStreamHandler
class AudioVideoStreamHandler(StreamHandlerBase):
@abstractmethod
def video_receive(self, frame: VideoFrame) -> None:
pass
@abstractmethod
def video_emit(
self,
) -> VideoEmitType:
pass
@abstractmethod
def copy(self) -> AudioVideoStreamHandler:
pass
class AsyncAudioVideoStreamHandler(StreamHandlerBase):
@abstractmethod
async def video_receive(self, frame: npt.NDArray[np.float32]) -> None:
pass
@abstractmethod
async def video_emit(
self,
) -> VideoEmitType:
pass
@abstractmethod
def copy(self) -> AsyncAudioVideoStreamHandler:
pass
VideoStreamHandlerImpl = AudioVideoStreamHandler | AsyncAudioVideoStreamHandler
AudioVideoStreamHandlerImpl = AudioVideoStreamHandler | AsyncAudioVideoStreamHandler
AsyncHandler = AsyncStreamHandler | AsyncAudioVideoStreamHandler
HandlerType = StreamHandlerImpl | VideoStreamHandlerImpl | VideoEventHandler | Callable
class VideoStreamHandler(VideoCallback):
async def process_frames(self):
while not self.thread_quit.is_set():
try:
await self.channel_set.wait()
frame = cast(VideoFrame, await self.track.recv())
frame_array = frame.to_ndarray(format="bgr24")
handler = cast(VideoStreamHandlerImpl, self.event_handler)
if inspect.iscoroutinefunction(handler.video_receive):
await handler.video_receive(frame_array)
else:
handler.video_receive(frame_array) # type: ignore
except MediaStreamError:
self.stop()
def start(self):
if not self.has_started:
asyncio.create_task(self.process_frames())
self.has_started = True
async def recv(self): # type: ignore
self.start()
try:
handler = cast(VideoStreamHandlerImpl, self.event_handler)
if inspect.iscoroutinefunction(handler.video_emit):
outputs = await handler.video_emit()
else:
outputs = handler.video_emit()
array, outputs = split_output(outputs)
if (
isinstance(outputs, AdditionalOutputs)
and self.set_additional_outputs
and self.channel
):
self.set_additional_outputs(outputs)
self.channel.send(create_message("fetch_output", []))
if array is None and self.mode == "send":
return
new_frame = self.array_to_frame(array)
# Will probably have to give developer ability to set pts and time_base
pts, time_base = await self.next_timestamp()
new_frame.pts = pts
new_frame.time_base = time_base
return new_frame
except Exception as e:
logger.debug("exception %s", e)
exec = traceback.format_exc()
logger.debug("traceback %s", exec)
class AudioCallback(AudioStreamTrack):
kind = "audio"
def __init__(
self,
track: MediaStreamTrack,
event_handler: StreamHandlerBase,
channel: DataChannel | None = None,
set_additional_outputs: Callable | None = None,
) -> None:
super().__init__()
self.track = track
self.event_handler = cast(StreamHandlerImpl, event_handler)
self.current_timestamp = 0
self.latest_args: str | list[Any] = "not_set"
self.queue = asyncio.Queue()
self.thread_quit = asyncio.Event()
self._start: float | None = None
self.has_started = False
self.last_timestamp = 0
self.channel = channel
self.set_additional_outputs = set_additional_outputs
def set_channel(self, channel: DataChannel):
self.channel = channel
self.event_handler.set_channel(channel)
def set_args(self, args: list[Any]):
self.event_handler.set_args(args)
def event_handler_receive(self, frame: tuple[int, np.ndarray]) -> None:
current_channel.set(self.event_handler.channel)
return cast(Callable, self.event_handler.receive)(frame)
def event_handler_emit(self) -> EmitType:
current_channel.set(self.event_handler.channel)
return cast(Callable, self.event_handler.emit)()
async def process_input_frames(self) -> None:
while not self.thread_quit.is_set():
try:
frame = cast(AudioFrame, await self.track.recv())
for frame in self.event_handler.resample(frame):
numpy_array = frame.to_ndarray()
if isinstance(self.event_handler, AsyncHandler):
await self.event_handler.receive(
(frame.sample_rate, numpy_array) # type: ignore
)
else:
await anyio.to_thread.run_sync(
self.event_handler_receive, (frame.sample_rate, numpy_array)
)
except MediaStreamError:
logger.debug("MediaStreamError in process_input_frames")
break
def start(self):
if not self.has_started:
loop = asyncio.get_running_loop()
if isinstance(self.event_handler, AsyncHandler):
callable = self.event_handler.emit
start_up = self.event_handler.start_up()
else:
callable = functools.partial(
loop.run_in_executor, None, self.event_handler_emit
)
start_up = anyio.to_thread.run_sync(self.event_handler.start_up)
self.process_input_task = asyncio.create_task(self.process_input_frames())
self.process_input_task.add_done_callback(
lambda _: logger.debug("process_input_done")
)
self.start_up_task = asyncio.create_task(start_up)
self.start_up_task.add_done_callback(
lambda _: logger.debug("start_up_done")
)
self.decode_task = asyncio.create_task(
player_worker_decode(
callable,
self.queue,
self.thread_quit,
lambda: self.channel,
self.set_additional_outputs,
False,
self.event_handler.output_sample_rate,
self.event_handler.output_frame_size,
)
)
self.decode_task.add_done_callback(lambda _: logger.debug("decode_done"))
self.has_started = True
async def recv(self): # type: ignore
try:
if self.readyState != "live":
raise MediaStreamError
if not self.event_handler.channel_set.is_set():
await self.event_handler.channel_set.wait()
if current_channel.get() != self.event_handler.channel:
current_channel.set(self.event_handler.channel)
self.start()
frame = await self.queue.get()
logger.debug("frame %s", frame)
data_time = frame.time
if time.time() - self.last_timestamp > 10 * (
self.event_handler.output_frame_size
/ self.event_handler.output_sample_rate
):
self._start = None
# control playback rate
if self._start is None:
self._start = time.time() - data_time # type: ignore
else:
wait = self._start + data_time - time.time()
await asyncio.sleep(wait)
self.last_timestamp = time.time()
return frame
except Exception as e:
logger.debug("exception %s", e)
exec = traceback.format_exc()
logger.debug("traceback %s", exec)
def stop(self):
logger.debug("audio callback stop")
self.thread_quit.set()
super().stop()
class ServerToClientVideo(VideoStreamTrack):
"""
This works for streaming input and output
"""
kind = "video"
def __init__(
self,
event_handler: Callable,
channel: DataChannel | None = None,
set_additional_outputs: Callable | None = None,
) -> None:
super().__init__() # don't forget this!
self.event_handler = event_handler
self.args_set = asyncio.Event()
self.latest_args: str | list[Any] = "not_set"
self.generator: Generator[Any, None, Any] | None = None
self.channel = channel
self.set_additional_outputs = set_additional_outputs
def array_to_frame(self, array: np.ndarray) -> VideoFrame:
return VideoFrame.from_ndarray(array, format="bgr24")
def set_channel(self, channel: DataChannel):
self.channel = channel
def set_args(self, args: list[Any]):
self.latest_args = list(args)
self.args_set.set()
async def recv(self): # type: ignore
try:
pts, time_base = await self.next_timestamp()
await self.args_set.wait()
if self.generator is None:
self.generator = cast(
Generator[Any, None, Any], self.event_handler(*self.latest_args)
)
current_channel.set(self.channel)
try:
next_array, outputs = split_output(next(self.generator))
if (
isinstance(outputs, AdditionalOutputs)
and self.set_additional_outputs
and self.channel
):
self.set_additional_outputs(outputs)
self.channel.send(create_message("fetch_output", []))
except StopIteration:
self.stop()
return
next_frame = self.array_to_frame(next_array)
next_frame.pts = pts
next_frame.time_base = time_base
return next_frame
except Exception as e:
logger.debug("exception %s", e)
exec = traceback.format_exc()
logger.debug("traceback %s ", exec)
class ServerToClientAudio(AudioStreamTrack):
kind = "audio"
def __init__(
self,
event_handler: Callable,
channel: DataChannel | None = None,
set_additional_outputs: Callable | None = None,
) -> None:
self.generator: Generator[Any, None, Any] | None = None
self.event_handler = event_handler
self.current_timestamp = 0
self.latest_args: str | list[Any] = "not_set"
self.args_set = threading.Event()
self.queue = asyncio.Queue()
self.thread_quit = asyncio.Event()
self.channel = channel
self.set_additional_outputs = set_additional_outputs
self.has_started = False
self._start: float | None = None
super().__init__()
def set_channel(self, channel: DataChannel):
self.channel = channel
def set_args(self, args: list[Any]):
self.latest_args = list(args)
self.args_set.set()
def next(self) -> tuple[int, np.ndarray] | None:
self.args_set.wait()
current_channel.set(self.channel)
if self.generator is None:
self.generator = self.event_handler(*self.latest_args)
if self.generator is not None:
try:
frame = next(self.generator)
return frame
except StopIteration:
self.thread_quit.set()
def start(self):
if not self.has_started:
loop = asyncio.get_running_loop()
callable = functools.partial(loop.run_in_executor, None, self.next)
asyncio.create_task(
player_worker_decode(
callable,
self.queue,
self.thread_quit,
lambda: self.channel,
self.set_additional_outputs,
True,
)
)
self.has_started = True
async def recv(self): # type: ignore
try:
if self.readyState != "live":
raise MediaStreamError
self.start()
data = await self.queue.get()
if data is None:
self.stop()
return
data_time = data.time
# control playback rate
if data_time is not None:
if self._start is None:
self._start = time.time() - data_time # type: ignore
else:
wait = self._start + data_time - time.time()
await asyncio.sleep(wait)
return data
except Exception as e:
logger.debug("exception %s", e)
exec = traceback.format_exc()
logger.debug("traceback %s", exec)
def stop(self):
logger.debug("audio-to-client stop callback")
self.thread_quit.set()
super().stop()

84
backend/gradio_webrtc/utils.py → backend/fastrtc/utils.py
View File

@@ -5,10 +5,11 @@ import json
import logging import logging
import tempfile import tempfile
from contextvars import ContextVar from contextvars import ContextVar
from typing import Any, Callable, Protocol, TypedDict, cast from typing import Any, Callable, Literal, Protocol, TypedDict, cast
import av import av
import numpy as np import numpy as np
from numpy.typing import NDArray
from pydub import AudioSegment from pydub import AudioSegment
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
@@ -31,6 +32,20 @@ class DataChannel(Protocol):
def send(self, message: str) -> None: ... def send(self, message: str) -> None: ...
def create_message(
type: Literal[
"send_input",
"fetch_output",
"stopword",
"error",
"warning",
"log",
],
data: list[Any] | str,
) -> str:
return json.dumps({"type": type, "data": data})
current_channel: ContextVar[DataChannel | None] = ContextVar( current_channel: ContextVar[DataChannel | None] = ContextVar(
"current_channel", default=None "current_channel", default=None
) )
@@ -48,7 +63,6 @@ def _send_log(message: str, type: str) -> None:
) )
if channel := current_channel.get(): if channel := current_channel.get():
print("channel", channel)
try: try:
loop = asyncio.get_running_loop() loop = asyncio.get_running_loop()
asyncio.run_coroutine_threadsafe(_send(channel), loop) asyncio.run_coroutine_threadsafe(_send(channel), loop)
@@ -131,7 +145,7 @@ async def player_worker_decode(
and channel() and channel()
): ):
set_additional_outputs(outputs) set_additional_outputs(outputs)
cast(DataChannel, channel()).send("change") cast(DataChannel, channel()).send(create_message("fetch_output", []))
if frame is None: if frame is None:
if quit_on_none: if quit_on_none:
@@ -153,6 +167,9 @@ async def player_worker_decode(
) )
format = "s16" if audio_array.dtype == "int16" else "fltp" # type: ignore format = "s16" if audio_array.dtype == "int16" else "fltp" # type: ignore
if audio_array.ndim == 1:
audio_array = audio_array.reshape(1, -1)
# Convert to audio frame and resample # Convert to audio frame and resample
# This runs in the same timeout context # This runs in the same timeout context
frame = av.AudioFrame.from_ndarray( # type: ignore frame = av.AudioFrame.from_ndarray( # type: ignore
@@ -167,7 +184,6 @@ async def player_worker_decode(
processed_frame.time_base = audio_time_base processed_frame.time_base = audio_time_base
audio_samples += processed_frame.samples audio_samples += processed_frame.samples
await queue.put(processed_frame) await queue.put(processed_frame)
logger.debug("Queue size utils.py: %s", queue.qsize())
except (TimeoutError, asyncio.TimeoutError): except (TimeoutError, asyncio.TimeoutError):
logger.warning( logger.warning(
@@ -178,12 +194,12 @@ async def player_worker_decode(
import traceback import traceback
exec = traceback.format_exc() exec = traceback.format_exc()
logger.debug("traceback %s", exec) print("traceback %s", exec)
logger.error("Error processing frame: %s", str(e)) print("Error processing frame: %s", str(e))
continue continue
def audio_to_bytes(audio: tuple[int, np.ndarray]) -> bytes: def audio_to_bytes(audio: tuple[int, NDArray[np.int16 | np.float32]]) -> bytes:
""" """
Convert an audio tuple containing sample rate and numpy array data into bytes. Convert an audio tuple containing sample rate and numpy array data into bytes.
@@ -217,7 +233,7 @@ def audio_to_bytes(audio: tuple[int, np.ndarray]) -> bytes:
return audio_buffer.getvalue() return audio_buffer.getvalue()
def audio_to_file(audio: tuple[int, np.ndarray]) -> str: def audio_to_file(audio: tuple[int, NDArray[np.int16 | np.float32]]) -> str:
""" """
Save an audio tuple containing sample rate and numpy array data to a file. Save an audio tuple containing sample rate and numpy array data to a file.
@@ -247,7 +263,9 @@ def audio_to_file(audio: tuple[int, np.ndarray]) -> str:
return f.name return f.name
def audio_to_float32(audio: tuple[int, np.ndarray]) -> np.ndarray: def audio_to_float32(
audio: tuple[int, NDArray[np.int16 | np.float32]],
) -> NDArray[np.float32]:
""" """
Convert an audio tuple containing sample rate (int16) and numpy array data to float32. Convert an audio tuple containing sample rate (int16) and numpy array data to float32.
@@ -274,40 +292,64 @@ def audio_to_float32(audio: tuple[int, np.ndarray]) -> np.ndarray:
def aggregate_bytes_to_16bit(chunks_iterator): def aggregate_bytes_to_16bit(chunks_iterator):
leftover = b"" # Store incomplete bytes between chunks """
Aggregate bytes to 16-bit audio samples.
This function takes an iterator of chunks and aggregates them into 16-bit audio samples.
It handles incomplete samples and combines them with the next chunk.
Parameters
----------
chunks_iterator : Iterator[bytes]
An iterator of byte chunks to aggregate
Returns
-------
Iterator[NDArray[np.int16]]
"""
leftover = b""
for chunk in chunks_iterator: for chunk in chunks_iterator:
# Combine with any leftover bytes from previous chunk
current_bytes = leftover + chunk current_bytes = leftover + chunk
# Calculate complete samples n_complete_samples = len(current_bytes) // 2
n_complete_samples = len(current_bytes) // 2 # int16 = 2 bytes
bytes_to_process = n_complete_samples * 2 bytes_to_process = n_complete_samples * 2
# Split into complete samples and leftover
to_process = current_bytes[:bytes_to_process] to_process = current_bytes[:bytes_to_process]
leftover = current_bytes[bytes_to_process:] leftover = current_bytes[bytes_to_process:]
if to_process: # Only yield if we have complete samples if to_process:
audio_array = np.frombuffer(to_process, dtype=np.int16).reshape(1, -1) audio_array = np.frombuffer(to_process, dtype=np.int16).reshape(1, -1)
yield audio_array yield audio_array
async def async_aggregate_bytes_to_16bit(chunks_iterator): async def async_aggregate_bytes_to_16bit(chunks_iterator):
leftover = b"" # Store incomplete bytes between chunks """
Aggregate bytes to 16-bit audio samples.
This function takes an iterator of chunks and aggregates them into 16-bit audio samples.
It handles incomplete samples and combines them with the next chunk.
Parameters
----------
chunks_iterator : Iterator[bytes]
An iterator of byte chunks to aggregate
Returns
-------
Iterator[NDArray[np.int16]]
An iterator of 16-bit audio samples
"""
leftover = b""
async for chunk in chunks_iterator: async for chunk in chunks_iterator:
# Combine with any leftover bytes from previous chunk
current_bytes = leftover + chunk current_bytes = leftover + chunk
# Calculate complete samples n_complete_samples = len(current_bytes) // 2
n_complete_samples = len(current_bytes) // 2 # int16 = 2 bytes
bytes_to_process = n_complete_samples * 2 bytes_to_process = n_complete_samples * 2
# Split into complete samples and leftover
to_process = current_bytes[:bytes_to_process] to_process = current_bytes[:bytes_to_process]
leftover = current_bytes[bytes_to_process:] leftover = current_bytes[bytes_to_process:]
if to_process: # Only yield if we have complete samples if to_process:
audio_array = np.frombuffer(to_process, dtype=np.int16).reshape(1, -1) audio_array = np.frombuffer(to_process, dtype=np.int16).reshape(1, -1)
yield audio_array yield audio_array

369
backend/fastrtc/webrtc.py Normal file
View File

@@ -0,0 +1,369 @@
"""gr.WebRTC() component."""
from __future__ import annotations
import logging
from collections.abc import Callable
from typing import (
TYPE_CHECKING,
Any,
Concatenate,
Iterable,
Literal,
ParamSpec,
Sequence,
TypeVar,
cast,
)
from gradio import wasm_utils
from gradio.components.base import Component, server
from gradio_client import handle_file
from .tracks import (
AudioVideoStreamHandlerImpl,
StreamHandler,
StreamHandlerBase,
StreamHandlerImpl,
VideoEventHandler,
)
from .webrtc_connection_mixin import WebRTCConnectionMixin
if TYPE_CHECKING:
from gradio.blocks import Block
from gradio.components import Timer
if wasm_utils.IS_WASM:
raise ValueError("Not supported in gradio-lite!")
logger = logging.getLogger(__name__)
# For the return type
R = TypeVar("R")
# For the parameter specification
P = ParamSpec("P")
class WebRTC(Component, WebRTCConnectionMixin):
"""
Creates a video component that can be used to upload/record videos (as an input) or display videos (as an output).
For the video to be playable in the browser it must have a compatible container and codec combination. Allowed
combinations are .mp4 with h264 codec, .ogg with theora codec, and .webm with vp9 codec. If the component detects
that the output video would not be playable in the browser it will attempt to convert it to a playable mp4 video.
If the conversion fails, the original video is returned.
Demos: video_identity_2
"""
EVENTS = ["tick", "state_change"]
def __init__(
self,
value: None = None,
height: int | str | None = None,
width: int | str | None = None,
label: str | None = None,
every: Timer | float | None = None,
inputs: Component | Sequence[Component] | set[Component] | None = None,
show_label: bool | None = None,
container: bool = True,
scale: int | None = None,
min_width: int = 160,
interactive: bool | None = None,
visible: bool = True,
elem_id: str | None = None,
elem_classes: list[str] | str | None = None,
render: bool = True,
key: int | str | None = None,
mirror_webcam: bool = True,
rtc_configuration: dict[str, Any] | None = None,
track_constraints: dict[str, Any] | None = None,
time_limit: float | None = None,
mode: Literal["send-receive", "receive", "send"] = "send-receive",
modality: Literal["video", "audio", "audio-video"] = "video",
rtp_params: dict[str, Any] | None = None,
icon: str | None = None,
icon_button_color: str | None = None,
pulse_color: str | None = None,
button_labels: dict | None = None,
):
"""
Parameters:
value: path or URL for the default value that WebRTC component is going to take. Can also be a tuple consisting of (video filepath, subtitle filepath). If a subtitle file is provided, it should be of type .srt or .vtt. Or can be callable, in which case the function will be called whenever the app loads to set the initial value of the component.
format: the file extension with which to save video, such as 'avi' or 'mp4'. This parameter applies both when this component is used as an input to determine which file format to convert user-provided video to, and when this component is used as an output to determine the format of video returned to the user. If None, no file format conversion is done and the video is kept as is. Use 'mp4' to ensure browser playability.
height: The height of the component, specified in pixels if a number is passed, or in CSS units if a string is passed. This has no effect on the preprocessed video file, but will affect the displayed video.
width: The width of the component, specified in pixels if a number is passed, or in CSS units if a string is passed. This has no effect on the preprocessed video file, but will affect the displayed video.
label: the label for this component. Appears above the component and is also used as the header if there are a table of examples for this component. If None and used in a `gr.Interface`, the label will be the name of the parameter this component is assigned to.
every: continously calls `value` to recalculate it if `value` is a function (has no effect otherwise). Can provide a Timer whose tick resets `value`, or a float that provides the regular interval for the reset Timer.
inputs: components that are used as inputs to calculate `value` if `value` is a function (has no effect otherwise). `value` is recalculated any time the inputs change.
show_label: if True, will display label.
container: if True, will place the component in a container - providing some extra padding around the border.
scale: relative size compared to adjacent Components. For example if Components A and B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide as B. Should be an integer. scale applies in Rows, and to top-level Components in Blocks where fill_height=True.
min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.
interactive: if True, will allow users to upload a video; if False, can only be used to display videos. If not provided, this is inferred based on whether the component is used as an input or output.
visible: if False, component will be hidden.
elem_id: an optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
elem_classes: an optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
render: if False, component will not render be rendered in the Blocks context. Should be used if the intention is to assign event listeners now but render the component later.
key: if assigned, will be used to assume identity across a re-render. Components that have the same key across a re-render will have their value preserved.
mirror_webcam: if True webcam will be mirrored. Default is True.
rtc_configuration: WebRTC configuration options. See https://developer.mozilla.org/en-US/docs/Web/API/RTCPeerConnection/RTCPeerConnection . If running the demo on a remote server, you will need to specify a rtc_configuration. See https://freddyaboulton.github.io/gradio-webrtc/deployment/
track_constraints: Media track constraints for WebRTC. For example, to set video height, width use {"width": {"exact": 800}, "height": {"exact": 600}, "aspectRatio": {"exact": 1.33333}}
time_limit: Maximum duration in seconds for recording.
mode: WebRTC mode - "send-receive", "receive", or "send".
modality: Type of media - "video" or "audio".
rtp_params: See https://developer.mozilla.org/en-US/docs/Web/API/RTCRtpSender/setParameters. If you are changing the video resolution, you can set this to {"degradationPreference": "maintain-framerate"} to keep the frame rate consistent.
icon: Icon to display on the button instead of the wave animation. The icon should be a path/url to a .svg/.png/.jpeg file.
icon_button_color: Color of the icon button. Default is var(--color-accent) of the demo theme.
pulse_color: Color of the pulse animation. Default is var(--color-accent) of the demo theme.
button_labels: Text to display on the audio or video start, stop, waiting buttons. Dict with keys "start", "stop", "waiting" mapping to the text to display on the buttons.
"""
self.time_limit = time_limit
self.height = height
self.width = width
self.mirror_webcam = mirror_webcam
self.concurrency_limit = 1
self.rtc_configuration = rtc_configuration
self.mode = mode
self.modality = modality
self.icon_button_color = icon_button_color
self.pulse_color = pulse_color
self.rtp_params = rtp_params or {}
self.button_labels = {
"start": "",
"stop": "",
"waiting": "",
**(button_labels or {}),
}
if track_constraints is None and modality == "audio":
track_constraints = {
"echoCancellation": True,
"noiseSuppression": {"exact": True},
"autoGainControl": {"exact": True},
"sampleRate": {"ideal": 24000},
"sampleSize": {"ideal": 16},
"channelCount": {"exact": 1},
}
if track_constraints is None and modality == "video":
track_constraints = {
"facingMode": "user",
"width": {"ideal": 500},
"height": {"ideal": 500},
"frameRate": {"ideal": 30},
}
if track_constraints is None and modality == "audio-video":
track_constraints = {
"video": {
"facingMode": "user",
"width": {"ideal": 500},
"height": {"ideal": 500},
"frameRate": {"ideal": 30},
},
"audio": {
"echoCancellation": True,
"noiseSuppression": {"exact": True},
"autoGainControl": {"exact": True},
"sampleRate": {"ideal": 24000},
"sampleSize": {"ideal": 16},
"channelCount": {"exact": 1},
},
}
self.track_constraints = track_constraints
self.event_handler: Callable | StreamHandler | None = None
super().__init__(
label=label,
every=every,
inputs=inputs,
show_label=show_label,
container=container,
scale=scale,
min_width=min_width,
interactive=interactive,
visible=visible,
elem_id=elem_id,
elem_classes=elem_classes,
render=render,
key=key,
value=value,
)
# need to do this here otherwise the proxy_url is not set
self.icon = (
icon if not icon else cast(dict, self.serve_static_file(icon)).get("url")
)
def preprocess(self, payload: str) -> str:
"""
Parameters:
payload: An instance of VideoData containing the video and subtitle files.
Returns:
Passes the uploaded video as a `str` filepath or URL whose extension can be modified by `format`.
"""
return payload
def postprocess(self, value: Any) -> str:
"""
Parameters:
value: Expects a {str} or {pathlib.Path} filepath to a video which is displayed, or a {Tuple[str | pathlib.Path, str | pathlib.Path | None]} where the first element is a filepath to a video and the second element is an optional filepath to a subtitle file.
Returns:
VideoData object containing the video and subtitle files.
"""
return value
def on_additional_outputs(
self,
fn: Callable[Concatenate[P], R],
inputs: Block | Sequence[Block] | set[Block] | None = None,
outputs: Block | Sequence[Block] | set[Block] | None = None,
js: str | None = None,
concurrency_limit: int | None | Literal["default"] = "default",
concurrency_id: str | None = None,
show_progress: Literal["full", "minimal", "hidden"] = "full",
queue: bool = True,
):
inputs = inputs or []
if inputs and not isinstance(inputs, Iterable):
inputs = [inputs]
inputs = list(inputs)
def handler(webrtc_id: str, *args):
if self.additional_outputs[webrtc_id].queue.qsize() > 0:
next_outputs = self.additional_outputs[webrtc_id].queue.get_nowait()
return fn(*args, *next_outputs.args) # type: ignore
return (
tuple([None for _ in range(len(outputs))])
if isinstance(outputs, Iterable)
else None
)
return self.state_change( # type: ignore
fn=handler,
inputs=[self] + cast(list, inputs),
outputs=outputs,
js=js,
concurrency_limit=concurrency_limit,
concurrency_id=concurrency_id,
show_progress=show_progress,
queue=queue,
trigger_mode="multiple",
)
def stream(
self,
fn: (
Callable[..., Any]
| StreamHandlerImpl
| AudioVideoStreamHandlerImpl
| VideoEventHandler
| None
) = None,
inputs: Block | Sequence[Block] | set[Block] | None = None,
outputs: Block | Sequence[Block] | set[Block] | None = None,
js: str | None = None,
concurrency_limit: int | None | Literal["default"] = "default",
concurrency_id: str | None = None,
time_limit: float | None = None,
trigger: Callable | None = None,
):
from gradio.blocks import Block
if inputs is None:
inputs = []
if outputs is None:
outputs = []
if isinstance(inputs, Block):
inputs = [inputs]
if isinstance(outputs, Block):
outputs = [outputs]
self.concurrency_limit = cast(
int, (1 if concurrency_limit in ["default", None] else concurrency_limit)
)
self.event_handler = fn # type: ignore
self.time_limit = time_limit
if (
self.mode == "send-receive"
and self.modality in ["audio", "audio-video"]
and not isinstance(self.event_handler, StreamHandlerBase)
):
raise ValueError(
"In the send-receive mode for audio, the event handler must be an instance of StreamHandlerBase."
)
if self.mode == "send-receive" or self.mode == "send":
if cast(list[Block], inputs)[0] != self:
raise ValueError(
"In the webrtc stream event, the first input component must be the WebRTC component."
)
if (
len(cast(list[Block], outputs)) != 1
and cast(list[Block], outputs)[0] != self
):
raise ValueError(
"In the webrtc stream event, the only output component must be the WebRTC component."
)
for input_component in inputs[1:]: # type: ignore
if hasattr(input_component, "change"):
input_component.change( # type: ignore
self.set_input,
inputs=inputs,
outputs=None,
concurrency_id=concurrency_id,
concurrency_limit=None,
time_limit=None,
js=js,
)
return self.tick( # type: ignore
self.set_input,
inputs=inputs,
outputs=None,
concurrency_id=concurrency_id,
concurrency_limit=None,
time_limit=None,
js=js,
)
elif self.mode == "receive":
if isinstance(inputs, list) and self in cast(list[Block], inputs):
raise ValueError(
"In the receive mode stream event, the WebRTC component cannot be an input."
)
if (
len(cast(list[Block], outputs)) != 1
and cast(list[Block], outputs)[0] != self
):
raise ValueError(
"In the receive mode stream, the only output component must be the WebRTC component."
)
if trigger is None:
raise ValueError(
"In the receive mode stream event, the trigger parameter must be provided"
)
trigger(lambda: "start_webrtc_stream", inputs=None, outputs=self)
self.tick( # type: ignore
self.set_input,
inputs=[self] + list(inputs),
outputs=None,
concurrency_id=concurrency_id,
)
@server
async def offer(self, body):
return await self.handle_offer(
body, self.set_additional_outputs(body["webrtc_id"])
)
def example_payload(self) -> Any:
return {
"video": handle_file(
"https://github.com/gradio-app/gradio/raw/main/demo/video_component/files/world.mp4"
),
}
def example_value(self) -> Any:
return "https://github.com/gradio-app/gradio/raw/main/demo/video_component/files/world.mp4"
def api_info(self) -> Any:
return {"type": "number"}

273
backend/fastrtc/webrtc_connection_mixin.py Normal file
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"""Mixin for handling WebRTC connections."""
from __future__ import annotations
import asyncio
import inspect
import logging
from collections import defaultdict
from collections.abc import Callable
from dataclasses import dataclass, field
from typing import (
AsyncGenerator,
Literal,
ParamSpec,
TypeVar,
cast,
)
from aiortc import (
RTCPeerConnection,
RTCSessionDescription,
)
from aiortc.contrib.media import MediaRelay # type: ignore
from fastapi.responses import JSONResponse
from fastrtc.tracks import (
AudioCallback,
HandlerType,
ServerToClientAudio,
ServerToClientVideo,
StreamHandlerBase,
StreamHandlerImpl,
VideoCallback,
VideoStreamHandler,
)
from fastrtc.utils import (
AdditionalOutputs,
DataChannel,
create_message,
)
Track = (
VideoCallback
| VideoStreamHandler
| AudioCallback
| ServerToClientAudio
| ServerToClientVideo
)
logger = logging.getLogger(__name__)
# For the return type
R = TypeVar("R")
# For the parameter specification
P = ParamSpec("P")
@dataclass
class OutputQueue:
queue: asyncio.Queue[AdditionalOutputs] = field(default_factory=asyncio.Queue)
quit: asyncio.Event = field(default_factory=asyncio.Event)
class WebRTCConnectionMixin:
pcs: set[RTCPeerConnection] = set([])
relay = MediaRelay()
connections: dict[str, list[Track]] = defaultdict(list)
data_channels: dict[str, DataChannel] = {}
additional_outputs: dict[str, OutputQueue] = defaultdict(OutputQueue)
handlers: dict[str, HandlerType | Callable] = {}
concurrency_limit: int | float
event_handler: HandlerType
time_limit: float | int | None
modality: Literal["video", "audio", "audio-video"]
mode: Literal["send", "receive", "send-receive"]
@staticmethod
async def wait_for_time_limit(pc: RTCPeerConnection, time_limit: float):
await asyncio.sleep(time_limit)
await pc.close()
def clean_up(self, webrtc_id: str):
self.handlers.pop(webrtc_id, None)
connection = self.connections.pop(webrtc_id, [])
for conn in connection:
if isinstance(conn, AudioCallback):
if inspect.iscoroutinefunction(conn.event_handler.shutdown):
asyncio.create_task(conn.event_handler.shutdown())
conn.event_handler.reset()
else:
conn.event_handler.shutdown()
conn.event_handler.reset()
output = self.additional_outputs.pop(webrtc_id, None)
if output:
logger.debug("setting quit for webrtc id %s", webrtc_id)
output.quit.set()
self.data_channels.pop(webrtc_id, None)
return connection
def set_input(self, webrtc_id: str, *args):
if webrtc_id in self.connections:
for conn in self.connections[webrtc_id]:
conn.set_args(list(args))
async def output_stream(
self, webrtc_id: str
) -> AsyncGenerator[AdditionalOutputs, None]:
outputs = self.additional_outputs[webrtc_id]
while not outputs.quit.is_set():
try:
yield await asyncio.wait_for(outputs.queue.get(), 10)
except (asyncio.TimeoutError, TimeoutError):
logger.debug("Timeout waiting for output")
async def fetch_latest_output(self, webrtc_id: str) -> AdditionalOutputs:
outputs = self.additional_outputs[webrtc_id]
return await asyncio.wait_for(outputs.queue.get(), 10)
def set_additional_outputs(
self, webrtc_id: str
) -> Callable[[AdditionalOutputs], None]:
def set_outputs(outputs: AdditionalOutputs):
self.additional_outputs[webrtc_id].queue.put_nowait(outputs)
return set_outputs
async def handle_offer(self, body, set_outputs):
logger.debug("Starting to handle offer")
logger.debug("Offer body %s", body)
if len(self.connections) >= cast(int, self.concurrency_limit):
return JSONResponse(
status_code=429,
content={
"status": "failed",
"meta": {
"error": "concurrency_limit_reached",
"limit": self.concurrency_limit,
},
},
)
offer = RTCSessionDescription(sdp=body["sdp"], type=body["type"])
pc = RTCPeerConnection()
self.pcs.add(pc)
if isinstance(self.event_handler, StreamHandlerBase):
handler = self.event_handler.copy()
else:
handler = cast(Callable, self.event_handler)
self.handlers[body["webrtc_id"]] = handler
@pc.on("iceconnectionstatechange")
async def on_iceconnectionstatechange():
logger.debug("ICE connection state change %s", pc.iceConnectionState)
if pc.iceConnectionState == "failed":
await pc.close()
self.connections.pop(body["webrtc_id"], None)
self.pcs.discard(pc)
@pc.on("connectionstatechange")
async def _():
logger.debug("pc.connectionState %s", pc.connectionState)
if pc.connectionState in ["failed", "closed"]:
await pc.close()
connection = self.clean_up(body["webrtc_id"])
if connection:
for conn in connection:
conn.stop()
self.pcs.discard(pc)
if pc.connectionState == "connected":
if self.time_limit is not None:
asyncio.create_task(self.wait_for_time_limit(pc, self.time_limit))
@pc.on("track")
def _(track):
relay = MediaRelay()
handler = self.handlers[body["webrtc_id"]]
if self.modality == "video" and track.kind == "video":
cb = VideoCallback(
relay.subscribe(track),
event_handler=cast(Callable, handler),
set_additional_outputs=set_outputs,
mode=cast(Literal["send", "send-receive"], self.mode),
)
elif self.modality == "audio-video" and track.kind == "video":
cb = VideoStreamHandler(
relay.subscribe(track),
event_handler=handler, # type: ignore
set_additional_outputs=set_outputs,
)
elif self.modality in ["audio", "audio-video"] and track.kind == "audio":
eh = cast(StreamHandlerImpl, handler)
eh._loop = asyncio.get_running_loop()
cb = AudioCallback(
relay.subscribe(track),
event_handler=eh,
set_additional_outputs=set_outputs,
)
else:
raise ValueError("Modality must be either video, audio, or audio-video")
if body["webrtc_id"] not in self.connections:
self.connections[body["webrtc_id"]] = []
self.connections[body["webrtc_id"]].append(cb)
if body["webrtc_id"] in self.data_channels:
for conn in self.connections[body["webrtc_id"]]:
conn.set_channel(self.data_channels[body["webrtc_id"]])
if self.mode == "send-receive":
logger.debug("Adding track to peer connection %s", cb)
pc.addTrack(cb)
elif self.mode == "send":
cast(AudioCallback | VideoCallback, cb).start()
if self.mode == "receive":
if self.modality == "video":
cb = ServerToClientVideo(
cast(Callable, self.event_handler),
set_additional_outputs=set_outputs,
)
elif self.modality == "audio":
cb = ServerToClientAudio(
cast(Callable, self.event_handler),
set_additional_outputs=set_outputs,
)
else:
raise ValueError("Modality must be either video or audio")
logger.debug("Adding track to peer connection %s", cb)
pc.addTrack(cb)
self.connections[body["webrtc_id"]].append(cb)
cb.on("ended", lambda: self.clean_up(body["webrtc_id"]))
@pc.on("datachannel")
def _(channel):
logger.debug(f"Data channel established: {channel.label}")
self.data_channels[body["webrtc_id"]] = channel
async def set_channel(webrtc_id: str):
while not self.connections.get(webrtc_id):
await asyncio.sleep(0.05)
logger.debug("setting channel for webrtc id %s", webrtc_id)
for conn in self.connections[webrtc_id]:
conn.set_channel(channel)
asyncio.create_task(set_channel(body["webrtc_id"]))
@channel.on("message")
def _(message):
logger.debug(f"Received message: {message}")
if channel.readyState == "open":
channel.send(
create_message("log", data=f"Server received: {message}")
)
# handle offer
await pc.setRemoteDescription(offer)
# send answer
answer = await pc.createAnswer()
await pc.setLocalDescription(answer) # type: ignore
logger.debug("done handling offer about to return")
await asyncio.sleep(0.1)
return {
"sdp": pc.localDescription.sdp,
"type": pc.localDescription.type,
}

184
backend/fastrtc/websocket.py Normal file
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import asyncio
import audioop
import base64
import logging
from typing import Any, Awaitable, Callable, Optional, cast
import anyio
import librosa
import numpy as np
from fastapi import WebSocket
from .tracks import AsyncStreamHandler, StreamHandlerImpl
from .utils import AdditionalOutputs, DataChannel, split_output
class WebSocketDataChannel(DataChannel):
def __init__(self, websocket: WebSocket, loop: asyncio.AbstractEventLoop):
self.websocket = websocket
self.loop = loop
def send(self, message: str) -> None:
asyncio.run_coroutine_threadsafe(self.websocket.send_text(message), self.loop)
logger = logging.getLogger(__file__)
def convert_to_mulaw(
audio_data: np.ndarray, original_rate: int, target_rate: int
) -> bytes:
"""Convert audio data to 8kHz mu-law format"""
if audio_data.dtype != np.float32:
audio_data = audio_data.astype(np.float32) / 32768.0
if original_rate != target_rate:
audio_data = librosa.resample(audio_data, orig_sr=original_rate, target_sr=8000)
audio_data = (audio_data * 32768).astype(np.int16)
return audioop.lin2ulaw(audio_data, 2) # type: ignore
run_sync = anyio.to_thread.run_sync # type: ignore
class WebSocketHandler:
def __init__(
self,
stream_handler: StreamHandlerImpl,
set_handler: Callable[[str, "WebSocketHandler"], Awaitable[None]],
clean_up: Callable[[str], None],
additional_outputs_factory: Callable[
[str], Callable[[AdditionalOutputs], None]
],
):
self.stream_handler = stream_handler
self.websocket: Optional[WebSocket] = None
self._emit_task: Optional[asyncio.Task] = None
self.stream_id: Optional[str] = None
self.set_additional_outputs_factory = additional_outputs_factory
self.set_additional_outputs: Callable[[AdditionalOutputs], None]
self.set_handler = set_handler
self.quit = asyncio.Event()
self.clean_up = clean_up
def set_args(self, args: list[Any]):
self.stream_handler.set_args(args)
async def handle_websocket(self, websocket: WebSocket):
await websocket.accept()
loop = asyncio.get_running_loop()
self.loop = loop
self.websocket = websocket
self.data_channel = WebSocketDataChannel(websocket, loop)
self.stream_handler._loop = loop
self.stream_handler.set_channel(self.data_channel)
self._emit_task = asyncio.create_task(self._emit_loop())
if isinstance(self.stream_handler, AsyncStreamHandler):
start_up = self.stream_handler.start_up()
else:
start_up = anyio.to_thread.run_sync(self.stream_handler.start_up) # type: ignore
self.start_up_task = asyncio.create_task(start_up)
try:
while not self.quit.is_set():
message = await websocket.receive_json()
if message["event"] == "media":
audio_payload = base64.b64decode(message["media"]["payload"])
audio_array = np.frombuffer(
audioop.ulaw2lin(audio_payload, 2), dtype=np.int16
)
if self.stream_handler.input_sample_rate != 8000:
audio_array = audio_array.astype(np.float32) / 32768.0
audio_array = librosa.resample(
audio_array,
orig_sr=8000,
target_sr=self.stream_handler.input_sample_rate,
)
audio_array = (audio_array * 32768).astype(np.int16)
if isinstance(self.stream_handler, AsyncStreamHandler):
await self.stream_handler.receive(
(self.stream_handler.input_sample_rate, audio_array)
)
else:
await run_sync(
self.stream_handler.receive,
(self.stream_handler.input_sample_rate, audio_array),
)
elif message["event"] == "start":
if self.stream_handler.phone_mode:
self.stream_id = cast(str, message["streamSid"])
else:
self.stream_id = cast(str, message["websocket_id"])
self.set_additional_outputs = self.set_additional_outputs_factory(
self.stream_id
)
await self.set_handler(self.stream_id, self)
elif message["event"] == "stop":
self.quit.set()
self.clean_up(cast(str, self.stream_id))
return
elif message["event"] == "ping":
await websocket.send_json({"event": "pong"})
except Exception as e:
print(e)
import traceback
traceback.print_exc()
logger.debug("Error in websocket handler %s", e)
finally:
if self._emit_task:
self._emit_task.cancel()
if self.start_up_task:
self.start_up_task.cancel()
await websocket.close()
async def _emit_loop(self):
try:
while not self.quit.is_set():
if isinstance(self.stream_handler, AsyncStreamHandler):
output = await self.stream_handler.emit()
else:
output = await run_sync(self.stream_handler.emit)
if output is not None:
frame, output = split_output(output)
if output is not None:
self.set_additional_outputs(output)
if not isinstance(frame, tuple):
continue
target_rate = (
self.stream_handler.output_sample_rate
if not self.stream_handler.phone_mode
else 8000
)
mulaw_audio = convert_to_mulaw(
frame[1], frame[0], target_rate=target_rate
)
audio_payload = base64.b64encode(mulaw_audio).decode("utf-8")
if self.websocket and self.stream_id:
payload = {
"event": "media",
"media": {"payload": audio_payload},
}
if self.stream_handler.phone_mode:
payload["streamSid"] = self.stream_id
await self.websocket.send_json(payload)
await asyncio.sleep(0.02)
except asyncio.CancelledError:
logger.debug("Emit loop cancelled")
except Exception as e:
import traceback
traceback.print_exc()
logger.debug("Error in emit loop: %s", e)

3
backend/gradio_webrtc/speech_to_text/__init__.py
View File

@@ -1,3 +0,0 @@
from .stt_ import get_stt_model, stt, stt_for_chunks
__all__ = ["stt", "stt_for_chunks", "get_stt_model"]

53
backend/gradio_webrtc/speech_to_text/stt_.py
View File

@@ -1,53 +0,0 @@
from dataclasses import dataclass
from functools import lru_cache
from typing import Callable
import numpy as np
from numpy.typing import NDArray
from ..utils import AudioChunk
@dataclass
class STTModel:
encoder: Callable
decoder: Callable
@lru_cache
def get_stt_model() -> STTModel:
from silero import silero_stt
model, decoder, _ = silero_stt(language="en", version="v6", jit_model="jit_xlarge")
return STTModel(model, decoder)
def stt(audio: tuple[int, NDArray[np.int16]]) -> str:
model = get_stt_model()
sr, audio_np = audio
if audio_np.dtype != np.float32:
print("converting")
audio_np = audio_np.astype(np.float32) / 32768.0
try:
import torch
except ImportError:
raise ImportError(
"PyTorch is required to run speech-to-text for stopword detection. Run `pip install torch`."
)
audio_torch = torch.tensor(audio_np, dtype=torch.float32)
if audio_torch.ndim == 1:
audio_torch = audio_torch.unsqueeze(0)
assert audio_torch.ndim == 2, "Audio must have a batch dimension"
print("before")
res = model.decoder(model.encoder(audio_torch)[0])
print("after")
return res
def stt_for_chunks(
audio: tuple[int, NDArray[np.int16]], chunks: list[AudioChunk]
) -> str:
sr, audio_np = audio
return " ".join(
[stt((sr, audio_np[chunk["start"] : chunk["end"]])) for chunk in chunks]
)

1151
backend/gradio_webrtc/webrtc.py
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File diff suppressed because it is too large Load Diff

44
demo/README.md
View File

@@ -1,44 +0,0 @@
---
license: mit
tags:
- object-detection
- computer-vision
- yolov10
datasets:
- detection-datasets/coco
sdk: gradio
sdk_version: 5.0.0b1
---
### Model Description
[YOLOv10: Real-Time End-to-End Object Detection](https://arxiv.org/abs/2405.14458v1)
- arXiv: https://arxiv.org/abs/2405.14458v1
- github: https://github.com/THU-MIG/yolov10
### Installation
```
pip install supervision git+https://github.com/THU-MIG/yolov10.git
```
### Yolov10 Inference
```python
from ultralytics import YOLOv10
import supervision as sv
import cv2
IMAGE_PATH = 'dog.jpeg'
model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')
model.predict(IMAGE_PATH, show=True)
```
### BibTeX Entry and Citation Info
```
@article{wang2024yolov10,
title={YOLOv10: Real-Time End-to-End Object Detection},
author={Wang, Ao and Chen, Hui and Liu, Lihao and Chen, Kai and Lin, Zijia and Han, Jungong and Ding, Guiguang},
journal={arXiv preprint arXiv:2405.14458},
year={2024}
}
```

105
demo/also_return_text.py
View File

@@ -1,105 +0,0 @@
import logging
import os
import gradio as gr
import numpy as np
from gradio_webrtc import AdditionalOutputs, WebRTC
from pydub import AudioSegment
from twilio.rest import Client
# Configure the root logger to WARNING to suppress debug messages from other libraries
logging.basicConfig(level=logging.WARNING)
# Create a console handler
console_handler = logging.FileHandler("gradio_webrtc.log")
console_handler.setLevel(logging.DEBUG)
# Create a formatter
formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
console_handler.setFormatter(formatter)
# Configure the logger for your specific library
logger = logging.getLogger("gradio_webrtc")
logger.setLevel(logging.DEBUG)
logger.addHandler(console_handler)
account_sid = os.environ.get("TWILIO_ACCOUNT_SID")
auth_token = os.environ.get("TWILIO_AUTH_TOKEN")
if account_sid and auth_token:
client = Client(account_sid, auth_token)
token = client.tokens.create()
rtc_configuration = {
"iceServers": token.ice_servers,
"iceTransportPolicy": "relay",
}
else:
rtc_configuration = None
def generation(num_steps):
for i in range(num_steps):
segment = AudioSegment.from_file(
"/Users/freddy/sources/gradio/demo/scratch/audio-streaming/librispeech.mp3"
)
yield (
(
segment.frame_rate,
np.array(segment.get_array_of_samples()).reshape(1, -1),
),
AdditionalOutputs(
f"Hello, from step {i}!",
"/Users/freddy/sources/gradio/demo/scratch/audio-streaming/librispeech.mp3",
),
)
css = """.my-group {max-width: 600px !important; max-height: 600 !important;}
.my-column {display: flex !important; justify-content: center !important; align-items: center !important};"""
with gr.Blocks() as demo:
gr.HTML(
"""
<h1 style='text-align: center'>
Audio Streaming (Powered by WebRTC ⚡️)
</h1>
"""
)
with gr.Column(elem_classes=["my-column"]):
with gr.Group(elem_classes=["my-group"]):
audio = WebRTC(
label="Stream",
rtc_configuration=rtc_configuration,
mode="receive",
modality="audio",
)
num_steps = gr.Slider(
label="Number of Steps",
minimum=1,
maximum=10,
step=1,
value=5,
)
button = gr.Button("Generate")
textbox = gr.Textbox(placeholder="Output will appear here.")
audio_file = gr.Audio()
audio.stream(
fn=generation, inputs=[num_steps], outputs=[audio], trigger=button.click
)
audio.on_additional_outputs(
fn=lambda t, a: (f"State changed to {t}.", a),
outputs=[textbox, audio_file],
)
if __name__ == "__main__":
demo.launch(
allowed_paths=[
"/Users/freddy/sources/gradio/demo/scratch/audio-streaming/librispeech.mp3"
]
)

367
demo/app.py
View File

@@ -1,367 +0,0 @@
import os
import gradio as gr
_docs = {
"WebRTC": {
"description": "Stream audio/video with WebRTC",
"members": {
"__init__": {
"rtc_configuration": {
"type": "dict[str, Any] | None",
"default": "None",
"description": "The configration dictionary to pass to the RTCPeerConnection constructor. If None, the default configuration is used.",
},
"height": {
"type": "int | str | None",
"default": "None",
"description": "The height of the component, specified in pixels if a number is passed, or in CSS units if a string is passed. This has no effect on the preprocessed video file, but will affect the displayed video.",
},
"width": {
"type": "int | str | None",
"default": "None",
"description": "The width of the component, specified in pixels if a number is passed, or in CSS units if a string is passed. This has no effect on the preprocessed video file, but will affect the displayed video.",
},
"label": {
"type": "str | None",
"default": "None",
"description": "the label for this component. Appears above the component and is also used as the header if there are a table of examples for this component. If None and used in a `gr.Interface`, the label will be the name of the parameter this component is assigned to.",
},
"show_label": {
"type": "bool | None",
"default": "None",
"description": "if True, will display label.",
},
"container": {
"type": "bool",
"default": "True",
"description": "if True, will place the component in a container - providing some extra padding around the border.",
},
"scale": {
"type": "int | None",
"default": "None",
"description": "relative size compared to adjacent Components. For example if Components A and B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide as B. Should be an integer. scale applies in Rows, and to top-level Components in Blocks where fill_height=True.",
},
"min_width": {
"type": "int",
"default": "160",
"description": "minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.",
},
"interactive": {
"type": "bool | None",
"default": "None",
"description": "if True, will allow users to upload a video; if False, can only be used to display videos. If not provided, this is inferred based on whether the component is used as an input or output.",
},
"visible": {
"type": "bool",
"default": "True",
"description": "if False, component will be hidden.",
},
"elem_id": {
"type": "str | None",
"default": "None",
"description": "an optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.",
},
"elem_classes": {
"type": "list[str] | str | None",
"default": "None",
"description": "an optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.",
},
"render": {
"type": "bool",
"default": "True",
"description": "if False, component will not render be rendered in the Blocks context. Should be used if the intention is to assign event listeners now but render the component later.",
},
"key": {
"type": "int | str | None",
"default": "None",
"description": "if assigned, will be used to assume identity across a re-render. Components that have the same key across a re-render will have their value preserved.",
},
"mirror_webcam": {
"type": "bool",
"default": "True",
"description": "if True webcam will be mirrored. Default is True.",
},
},
"events": {"tick": {"type": None, "default": None, "description": ""}},
},
"__meta__": {"additional_interfaces": {}, "user_fn_refs": {"WebRTC": []}},
}
}
abs_path = os.path.join(os.path.dirname(__file__), "css.css")
with gr.Blocks(
css_paths=abs_path,
theme=gr.themes.Default(
font_mono=[
gr.themes.GoogleFont("Inconsolata"),
"monospace",
],
),
) as demo:
gr.Markdown(
"""
<h1 style='text-align: center; margin-bottom: 1rem'> Gradio WebRTC ⚡️ </h1>
<div style="display: flex; flex-direction: row; justify-content: center">
<img style="display: block; padding-right: 5px; height: 20px;" alt="Static Badge" src="https://img.shields.io/badge/version%20-%200.0.6%20-%20orange">
<a href="https://github.com/freddyaboulton/gradio-webrtc" target="_blank"><img alt="Static Badge" src="https://img.shields.io/badge/github-white?logo=github&logoColor=black"></a>
</div>
""",
elem_classes=["md-custom"],
header_links=True,
)
gr.Markdown(
"""
## Installation
```bash
pip install gradio_webrtc
```
## Examples:
1. [Object Detection from Webcam with YOLOv10](https://huggingface.co/spaces/freddyaboulton/webrtc-yolov10n) 📷
2. [Streaming Object Detection from Video with RT-DETR](https://huggingface.co/spaces/freddyaboulton/rt-detr-object-detection-webrtc) 🎥
3. [Text-to-Speech](https://huggingface.co/spaces/freddyaboulton/parler-tts-streaming-webrtc) 🗣️
4. [Conversational AI](https://huggingface.co/spaces/freddyaboulton/omni-mini-webrtc) 🤖🗣️
## Usage
The WebRTC component supports the following three use cases:
1. [Streaming video from the user webcam to the server and back](#h-streaming-video-from-the-user-webcam-to-the-server-and-back)
2. [Streaming Video from the server to the client](#h-streaming-video-from-the-server-to-the-client)
3. [Streaming Audio from the server to the client](#h-streaming-audio-from-the-server-to-the-client)
4. [Streaming Audio from the client to the server and back (conversational AI)](#h-conversational-ai)
## Streaming Video from the User Webcam to the Server and Back
```python
import gradio as gr
from gradio_webrtc import WebRTC
def detection(image, conf_threshold=0.3):
... your detection code here ...
with gr.Blocks() as demo:
image = WebRTC(label="Stream", mode="send-receive", modality="video")
conf_threshold = gr.Slider(
label="Confidence Threshold",
minimum=0.0,
maximum=1.0,
step=0.05,
value=0.30,
)
image.stream(
fn=detection,
inputs=[image, conf_threshold],
outputs=[image], time_limit=10
)
if __name__ == "__main__":
demo.launch()
```
* Set the `mode` parameter to `send-receive` and `modality` to "video".
* The `stream` event's `fn` parameter is a function that receives the next frame from the webcam
as a **numpy array** and returns the processed frame also as a **numpy array**.
* Numpy arrays are in (height, width, 3) format where the color channels are in RGB format.
* The `inputs` parameter should be a list where the first element is the WebRTC component. The only output allowed is the WebRTC component.
* The `time_limit` parameter is the maximum time in seconds the video stream will run. If the time limit is reached, the video stream will stop.
## Streaming Video from the server to the client
```python
import gradio as gr
from gradio_webrtc import WebRTC
import cv2
def generation():
url = "https://download.tsi.telecom-paristech.fr/gpac/dataset/dash/uhd/mux_sources/hevcds_720p30_2M.mp4"
cap = cv2.VideoCapture(url)
iterating = True
while iterating:
iterating, frame = cap.read()
yield frame
with gr.Blocks() as demo:
output_video = WebRTC(label="Video Stream", mode="receive", modality="video")
button = gr.Button("Start", variant="primary")
output_video.stream(
fn=generation, inputs=None, outputs=[output_video],
trigger=button.click
)
if __name__ == "__main__":
demo.launch()
```
* Set the "mode" parameter to "receive" and "modality" to "video".
* The `stream` event's `fn` parameter is a generator function that yields the next frame from the video as a **numpy array**.
* The only output allowed is the WebRTC component.
* The `trigger` parameter the gradio event that will trigger the webrtc connection. In this case, the button click event.
## Streaming Audio from the Server to the Client
```python
import gradio as gr
from pydub import AudioSegment
def generation(num_steps):
for _ in range(num_steps):
segment = AudioSegment.from_file("/Users/freddy/sources/gradio/demo/audio_debugger/cantina.wav")
yield (segment.frame_rate, np.array(segment.get_array_of_samples()).reshape(1, -1))
with gr.Blocks() as demo:
audio = WebRTC(label="Stream", mode="receive", modality="audio")
num_steps = gr.Slider(
label="Number of Steps",
minimum=1,
maximum=10,
step=1,
value=5,
)
button = gr.Button("Generate")
audio.stream(
fn=generation, inputs=[num_steps], outputs=[audio],
trigger=button.click
)
```
* Set the "mode" parameter to "receive" and "modality" to "audio".
* The `stream` event's `fn` parameter is a generator function that yields the next audio segment as a tuple of (frame_rate, audio_samples).
* The numpy array should be of shape (1, num_samples).
* The `outputs` parameter should be a list with the WebRTC component as the only element.
## Conversational AI
```python
import gradio as gr
import numpy as np
from gradio_webrtc import WebRTC, StreamHandler
from queue import Queue
import time
class EchoHandler(StreamHandler):
def __init__(self) -> None:
super().__init__()
self.queue = Queue()
def receive(self, frame: tuple[int, np.ndarray] | np.ndarray) -> None:
self.queue.put(frame)
def emit(self) -> None:
return self.queue.get()
with gr.Blocks() as demo:
with gr.Column():
with gr.Group():
audio = WebRTC(
label="Stream",
rtc_configuration=None,
mode="send-receive",
modality="audio",
)
audio.stream(fn=EchoHandler(), inputs=[audio], outputs=[audio], time_limit=15)
if __name__ == "__main__":
demo.launch()
```
* Instead of passing a function to the `stream` event's `fn` parameter, pass a `StreamHandler` implementation. The `StreamHandler` above simply echoes the audio back to the client.
* The `StreamHandler` class has two methods: `receive` and `emit`. The `receive` method is called when a new frame is received from the client, and the `emit` method returns the next frame to send to the client.
* An audio frame is represented as a tuple of (frame_rate, audio_samples) where `audio_samples` is a numpy array of shape (num_channels, num_samples).
* You can also specify the audio layout ("mono" or "stereo") in the emit method by retuning it as the third element of the tuple. If not specified, the default is "mono".
* The `time_limit` parameter is the maximum time in seconds the conversation will run. If the time limit is reached, the audio stream will stop.
* The `emit` method SHOULD NOT block. If a frame is not ready to be sent, the method should return None.
## Deployment
When deploying in a cloud environment (like Hugging Face Spaces, EC2, etc), you need to set up a TURN server to relay the WebRTC traffic.
The easiest way to do this is to use a service like Twilio.
```python
from twilio.rest import Client
import os
account_sid = os.environ.get("TWILIO_ACCOUNT_SID")
auth_token = os.environ.get("TWILIO_AUTH_TOKEN")
client = Client(account_sid, auth_token)
token = client.tokens.create()
rtc_configuration = {
"iceServers": token.ice_servers,
"iceTransportPolicy": "relay",
}
with gr.Blocks() as demo:
...
rtc = WebRTC(rtc_configuration=rtc_configuration, ...)
...
```
""",
elem_classes=["md-custom"],
header_links=True,
)
gr.Markdown(
"""
##
""",
elem_classes=["md-custom"],
header_links=True,
)
gr.ParamViewer(value=_docs["WebRTC"]["members"]["__init__"], linkify=[])
demo.load(
None,
js=r"""function() {
const refs = {};
const user_fn_refs = {
WebRTC: [], };
requestAnimationFrame(() => {
Object.entries(user_fn_refs).forEach(([key, refs]) => {
if (refs.length > 0) {
const el = document.querySelector(`.${key}-user-fn`);
if (!el) return;
refs.forEach(ref => {
el.innerHTML = el.innerHTML.replace(
new RegExp("\\b"+ref+"\\b", "g"),
`<a href="#h-${ref.toLowerCase()}">${ref}</a>`
);
})
}
})
Object.entries(refs).forEach(([key, refs]) => {
if (refs.length > 0) {
const el = document.querySelector(`.${key}`);
if (!el) return;
refs.forEach(ref => {
el.innerHTML = el.innerHTML.replace(
new RegExp("\\b"+ref+"\\b", "g"),
`<a href="#h-${ref.toLowerCase()}">${ref}</a>`
);
})
}
})
})
}
""",
)
demo.launch()

367
demo/app_.py
View File

@@ -1,367 +0,0 @@
import os
import gradio as gr
_docs = {
"WebRTC": {
"description": "Stream audio/video with WebRTC",
"members": {
"__init__": {
"rtc_configuration": {
"type": "dict[str, Any] | None",
"default": "None",
"description": "The configration dictionary to pass to the RTCPeerConnection constructor. If None, the default configuration is used.",
},
"height": {
"type": "int | str | None",
"default": "None",
"description": "The height of the component, specified in pixels if a number is passed, or in CSS units if a string is passed. This has no effect on the preprocessed video file, but will affect the displayed video.",
},
"width": {
"type": "int | str | None",
"default": "None",
"description": "The width of the component, specified in pixels if a number is passed, or in CSS units if a string is passed. This has no effect on the preprocessed video file, but will affect the displayed video.",
},
"label": {
"type": "str | None",
"default": "None",
"description": "the label for this component. Appears above the component and is also used as the header if there are a table of examples for this component. If None and used in a `gr.Interface`, the label will be the name of the parameter this component is assigned to.",
},
"show_label": {
"type": "bool | None",
"default": "None",
"description": "if True, will display label.",
},
"container": {
"type": "bool",
"default": "True",
"description": "if True, will place the component in a container - providing some extra padding around the border.",
},
"scale": {
"type": "int | None",
"default": "None",
"description": "relative size compared to adjacent Components. For example if Components A and B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide as B. Should be an integer. scale applies in Rows, and to top-level Components in Blocks where fill_height=True.",
},
"min_width": {
"type": "int",
"default": "160",
"description": "minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.",
},
"interactive": {
"type": "bool | None",
"default": "None",
"description": "if True, will allow users to upload a video; if False, can only be used to display videos. If not provided, this is inferred based on whether the component is used as an input or output.",
},
"visible": {
"type": "bool",
"default": "True",
"description": "if False, component will be hidden.",
},
"elem_id": {
"type": "str | None",
"default": "None",
"description": "an optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.",
},
"elem_classes": {
"type": "list[str] | str | None",
"default": "None",
"description": "an optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.",
},
"render": {
"type": "bool",
"default": "True",
"description": "if False, component will not render be rendered in the Blocks context. Should be used if the intention is to assign event listeners now but render the component later.",
},
"key": {
"type": "int | str | None",
"default": "None",
"description": "if assigned, will be used to assume identity across a re-render. Components that have the same key across a re-render will have their value preserved.",
},
"mirror_webcam": {
"type": "bool",
"default": "True",
"description": "if True webcam will be mirrored. Default is True.",
},
},
"events": {"tick": {"type": None, "default": None, "description": ""}},
},
"__meta__": {"additional_interfaces": {}, "user_fn_refs": {"WebRTC": []}},
}
}
abs_path = os.path.join(os.path.dirname(__file__), "css.css")
with gr.Blocks(
css_paths=abs_path,
theme=gr.themes.Default(
font_mono=[
gr.themes.GoogleFont("Inconsolata"),
"monospace",
],
),
) as demo:
gr.Markdown(
"""
<h1 style='text-align: center; margin-bottom: 1rem'> Gradio WebRTC ⚡️ </h1>
<div style="display: flex; flex-direction: row; justify-content: center">
<img style="display: block; padding-right: 5px; height: 20px;" alt="Static Badge" src="https://img.shields.io/badge/version%20-%200.0.6%20-%20orange">
<a href="https://github.com/freddyaboulton/gradio-webrtc" target="_blank"><img alt="Static Badge" src="https://img.shields.io/badge/github-white?logo=github&logoColor=black"></a>
</div>
""",
elem_classes=["md-custom"],
header_links=True,
)
gr.Markdown(
"""
## Installation
```bash
pip install gradio_webrtc
```
## Examples:
1. [Object Detection from Webcam with YOLOv10](https://huggingface.co/spaces/freddyaboulton/webrtc-yolov10n) 📷
2. [Streaming Object Detection from Video with RT-DETR](https://huggingface.co/spaces/freddyaboulton/rt-detr-object-detection-webrtc) 🎥
3. [Text-to-Speech](https://huggingface.co/spaces/freddyaboulton/parler-tts-streaming-webrtc) 🗣️
4. [Conversational AI](https://huggingface.co/spaces/freddyaboulton/omni-mini-webrtc) 🤖🗣️
## Usage
The WebRTC component supports the following three use cases:
1. [Streaming video from the user webcam to the server and back](#h-streaming-video-from-the-user-webcam-to-the-server-and-back)
2. [Streaming Video from the server to the client](#h-streaming-video-from-the-server-to-the-client)
3. [Streaming Audio from the server to the client](#h-streaming-audio-from-the-server-to-the-client)
4. [Streaming Audio from the client to the server and back (conversational AI)](#h-conversational-ai)
## Streaming Video from the User Webcam to the Server and Back
```python
import gradio as gr
from gradio_webrtc import WebRTC
def detection(image, conf_threshold=0.3):
... your detection code here ...
with gr.Blocks() as demo:
image = WebRTC(label="Stream", mode="send-receive", modality="video")
conf_threshold = gr.Slider(
label="Confidence Threshold",
minimum=0.0,
maximum=1.0,
step=0.05,
value=0.30,
)
image.stream(
fn=detection,
inputs=[image, conf_threshold],
outputs=[image], time_limit=10
)
if __name__ == "__main__":
demo.launch()
```
* Set the `mode` parameter to `send-receive` and `modality` to "video".
* The `stream` event's `fn` parameter is a function that receives the next frame from the webcam
as a **numpy array** and returns the processed frame also as a **numpy array**.
* Numpy arrays are in (height, width, 3) format where the color channels are in RGB format.
* The `inputs` parameter should be a list where the first element is the WebRTC component. The only output allowed is the WebRTC component.
* The `time_limit` parameter is the maximum time in seconds the video stream will run. If the time limit is reached, the video stream will stop.
## Streaming Video from the server to the client
```python
import gradio as gr
from gradio_webrtc import WebRTC
import cv2
def generation():
url = "https://download.tsi.telecom-paristech.fr/gpac/dataset/dash/uhd/mux_sources/hevcds_720p30_2M.mp4"
cap = cv2.VideoCapture(url)
iterating = True
while iterating:
iterating, frame = cap.read()
yield frame
with gr.Blocks() as demo:
output_video = WebRTC(label="Video Stream", mode="receive", modality="video")
button = gr.Button("Start", variant="primary")
output_video.stream(
fn=generation, inputs=None, outputs=[output_video],
trigger=button.click
)
if __name__ == "__main__":
demo.launch()
```
* Set the "mode" parameter to "receive" and "modality" to "video".
* The `stream` event's `fn` parameter is a generator function that yields the next frame from the video as a **numpy array**.
* The only output allowed is the WebRTC component.
* The `trigger` parameter the gradio event that will trigger the webrtc connection. In this case, the button click event.
## Streaming Audio from the Server to the Client
```python
import gradio as gr
from pydub import AudioSegment
def generation(num_steps):
for _ in range(num_steps):
segment = AudioSegment.from_file("/Users/freddy/sources/gradio/demo/audio_debugger/cantina.wav")
yield (segment.frame_rate, np.array(segment.get_array_of_samples()).reshape(1, -1))
with gr.Blocks() as demo:
audio = WebRTC(label="Stream", mode="receive", modality="audio")
num_steps = gr.Slider(
label="Number of Steps",
minimum=1,
maximum=10,
step=1,
value=5,
)
button = gr.Button("Generate")
audio.stream(
fn=generation, inputs=[num_steps], outputs=[audio],
trigger=button.click
)
```
* Set the "mode" parameter to "receive" and "modality" to "audio".
* The `stream` event's `fn` parameter is a generator function that yields the next audio segment as a tuple of (frame_rate, audio_samples).
* The numpy array should be of shape (1, num_samples).
* The `outputs` parameter should be a list with the WebRTC component as the only element.
## Conversational AI
```python
import gradio as gr
import numpy as np
from gradio_webrtc import WebRTC, StreamHandler
from queue import Queue
import time
class EchoHandler(StreamHandler):
def __init__(self) -> None:
super().__init__()
self.queue = Queue()
def receive(self, frame: tuple[int, np.ndarray] | np.ndarray) -> None:
self.queue.put(frame)
def emit(self) -> None:
return self.queue.get()
with gr.Blocks() as demo:
with gr.Column():
with gr.Group():
audio = WebRTC(
label="Stream",
rtc_configuration=None,
mode="send-receive",
modality="audio",
)
audio.stream(fn=EchoHandler(), inputs=[audio], outputs=[audio], time_limit=15)
if __name__ == "__main__":
demo.launch()
```
* Instead of passing a function to the `stream` event's `fn` parameter, pass a `StreamHandler` implementation. The `StreamHandler` above simply echoes the audio back to the client.
* The `StreamHandler` class has two methods: `receive` and `emit`. The `receive` method is called when a new frame is received from the client, and the `emit` method returns the next frame to send to the client.
* An audio frame is represented as a tuple of (frame_rate, audio_samples) where `audio_samples` is a numpy array of shape (num_channels, num_samples).
* You can also specify the audio layout ("mono" or "stereo") in the emit method by retuning it as the third element of the tuple. If not specified, the default is "mono".
* The `time_limit` parameter is the maximum time in seconds the conversation will run. If the time limit is reached, the audio stream will stop.
* The `emit` method SHOULD NOT block. If a frame is not ready to be sent, the method should return None.
## Deployment
When deploying in a cloud environment (like Hugging Face Spaces, EC2, etc), you need to set up a TURN server to relay the WebRTC traffic.
The easiest way to do this is to use a service like Twilio.
```python
from twilio.rest import Client
import os
account_sid = os.environ.get("TWILIO_ACCOUNT_SID")
auth_token = os.environ.get("TWILIO_AUTH_TOKEN")
client = Client(account_sid, auth_token)
token = client.tokens.create()
rtc_configuration = {
"iceServers": token.ice_servers,
"iceTransportPolicy": "relay",
}
with gr.Blocks() as demo:
...
rtc = WebRTC(rtc_configuration=rtc_configuration, ...)
...
```
""",
elem_classes=["md-custom"],
header_links=True,
)
gr.Markdown(
"""
##
""",
elem_classes=["md-custom"],
header_links=True,
)
gr.ParamViewer(value=_docs["WebRTC"]["members"]["__init__"], linkify=[])
demo.load(
None,
js=r"""function() {
const refs = {};
const user_fn_refs = {
WebRTC: [], };
requestAnimationFrame(() => {
Object.entries(user_fn_refs).forEach(([key, refs]) => {
if (refs.length > 0) {
const el = document.querySelector(`.${key}-user-fn`);
if (!el) return;
refs.forEach(ref => {
el.innerHTML = el.innerHTML.replace(
new RegExp("\\b"+ref+"\\b", "g"),
`<a href="#h-${ref.toLowerCase()}">${ref}</a>`
);
})
}
})
Object.entries(refs).forEach(([key, refs]) => {
if (refs.length > 0) {
const el = document.querySelector(`.${key}`);
if (!el) return;
refs.forEach(ref => {
el.innerHTML = el.innerHTML.replace(
new RegExp("\\b"+ref+"\\b", "g"),
`<a href="#h-${ref.toLowerCase()}">${ref}</a>`
);
})
}
})
})
}
""",
)
demo.launch()

73
demo/app_orig.py
View File

@@ -1,73 +0,0 @@
import os
import cv2
import gradio as gr
from gradio_webrtc import WebRTC
from huggingface_hub import hf_hub_download
from inference import YOLOv10
from twilio.rest import Client
model_file = hf_hub_download(
repo_id="onnx-community/yolov10n", filename="onnx/model.onnx"
)
model = YOLOv10(model_file)
account_sid = os.environ.get("TWILIO_ACCOUNT_SID")
auth_token = os.environ.get("TWILIO_AUTH_TOKEN")
if account_sid and auth_token:
client = Client(account_sid, auth_token)
token = client.tokens.create()
rtc_configuration = {
"iceServers": token.ice_servers,
"iceTransportPolicy": "relay",
}
else:
rtc_configuration = None
def detection(image, conf_threshold=0.3):
image = cv2.resize(image, (model.input_width, model.input_height))
new_image = model.detect_objects(image, conf_threshold)
return cv2.resize(new_image, (500, 500))
css = """.my-group {max-width: 600px !important; max-height: 600 !important;}
.my-column {display: flex !important; justify-content: center !important; align-items: center !important};"""
with gr.Blocks(css=css) as demo:
gr.HTML(
"""
<h1 style='text-align: center'>
YOLOv10 Webcam Stream (Powered by WebRTC ⚡️)
</h1>
"""
)
gr.HTML(
"""
<h3 style='text-align: center'>
<a href='https://arxiv.org/abs/2405.14458' target='_blank'>arXiv</a> | <a href='https://github.com/THU-MIG/yolov10' target='_blank'>github</a>
</h3>
"""
)
with gr.Column(elem_classes=["my-column"]):
with gr.Group(elem_classes=["my-group"]):
image = WebRTC(label="Stream", rtc_configuration=rtc_configuration)
conf_threshold = gr.Slider(
label="Confidence Threshold",
minimum=0.0,
maximum=1.0,
step=0.05,
value=0.30,
)
image.stream(
fn=detection, inputs=[image, conf_threshold], outputs=[image], time_limit=10
)
if __name__ == "__main__":
demo.launch()

71
demo/audio_out.py
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@@ -1,71 +0,0 @@
import os
import gradio as gr
import numpy as np
from gradio_webrtc import WebRTC
from pydub import AudioSegment
from twilio.rest import Client
account_sid = os.environ.get("TWILIO_ACCOUNT_SID")
auth_token = os.environ.get("TWILIO_AUTH_TOKEN")
if account_sid and auth_token:
client = Client(account_sid, auth_token)
token = client.tokens.create()
rtc_configuration = {
"iceServers": token.ice_servers,
"iceTransportPolicy": "relay",
}
else:
rtc_configuration = None
def generation(num_steps):
for _ in range(num_steps):
segment = AudioSegment.from_file(
"/Users/freddy/sources/gradio/demo/audio_debugger/cantina.wav"
)
yield (
segment.frame_rate,
np.array(segment.get_array_of_samples()).reshape(1, -1),
)
css = """.my-group {max-width: 600px !important; max-height: 600 !important;}
.my-column {display: flex !important; justify-content: center !important; align-items: center !important};"""
with gr.Blocks() as demo:
gr.HTML(
"""
<h1 style='text-align: center'>
Audio Streaming (Powered by WebRTC ⚡️)
</h1>
"""
)
with gr.Column(elem_classes=["my-column"]):
with gr.Group(elem_classes=["my-group"]):
audio = WebRTC(
label="Stream",
rtc_configuration=rtc_configuration,
mode="receive",
modality="audio",
)
num_steps = gr.Slider(
label="Number of Steps",
minimum=1,
maximum=10,
step=1,
value=5,
)
button = gr.Button("Generate")
audio.stream(
fn=generation, inputs=[num_steps], outputs=[audio], trigger=button.click
)
if __name__ == "__main__":
demo.launch()

64
demo/audio_out_2.py
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@@ -1,64 +0,0 @@
import os
import time
import gradio as gr
import numpy as np
from gradio_webrtc import WebRTC
from pydub import AudioSegment
from twilio.rest import Client
account_sid = os.environ.get("TWILIO_ACCOUNT_SID")
auth_token = os.environ.get("TWILIO_AUTH_TOKEN")
if account_sid and auth_token:
client = Client(account_sid, auth_token)
token = client.tokens.create()
rtc_configuration = {
"iceServers": token.ice_servers,
"iceTransportPolicy": "relay",
}
else:
rtc_configuration = None
def generation(num_steps):
for _ in range(num_steps):
segment = AudioSegment.from_file(
"/Users/freddy/sources/gradio/demo/audio_debugger/cantina.wav"
)
yield (
segment.frame_rate,
np.array(segment.get_array_of_samples()).reshape(1, -1),
)
time.sleep(3.5)
css = """.my-group {max-width: 600px !important; max-height: 600 !important;}
.my-column {display: flex !important; justify-content: center !important; align-items: center !important};"""
with gr.Blocks() as demo:
gr.HTML(
"""
<h1 style='text-align: center'>
Audio Streaming (Powered by WebRaTC ⚡️)
</h1>
"""
)
with gr.Row():
with gr.Column():
gr.Slider()
with gr.Column():
# audio = gr.Audio(interactive=False)
audio = WebRTC(
label="Stream",
rtc_configuration=rtc_configuration,
mode="receive",
modality="audio",
)
if __name__ == "__main__":
demo.launch()

161
demo/css.css
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@@ -1,161 +0,0 @@
html {
font-family: Inter;
font-size: 16px;
font-weight: 400;
line-height: 1.5;
-webkit-text-size-adjust: 100%;
background: #fff;
color: #323232;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
text-rendering: optimizeLegibility;
}
:root {
--space: 1;
--vspace: calc(var(--space) * 1rem);
--vspace-0: calc(3 * var(--space) * 1rem);
--vspace-1: calc(2 * var(--space) * 1rem);
--vspace-2: calc(1.5 * var(--space) * 1rem);
--vspace-3: calc(0.5 * var(--space) * 1rem);
}
.app {
max-width: 748px !important;
}
.prose p {
margin: var(--vspace) 0;
line-height: var(--vspace * 2);
font-size: 1rem;
}
code {
font-family: "Inconsolata", sans-serif;
font-size: 16px;
}
h1,
h1 code {
font-weight: 400;
line-height: calc(2.5 / var(--space) * var(--vspace));
}
h1 code {
background: none;
border: none;
letter-spacing: 0.05em;
padding-bottom: 5px;
position: relative;
padding: 0;
}
h2 {
margin: var(--vspace-1) 0 var(--vspace-2) 0;
line-height: 1em;
}
h3,
h3 code {
margin: var(--vspace-1) 0 var(--vspace-2) 0;
line-height: 1em;
}
h4,
h5,
h6 {
margin: var(--vspace-3) 0 var(--vspace-3) 0;
line-height: var(--vspace);
}
.bigtitle,
h1,
h1 code {
font-size: calc(8px * 4.5);
word-break: break-word;
}
.title,
h2,
h2 code {
font-size: calc(8px * 3.375);
font-weight: lighter;
word-break: break-word;
border: none;
background: none;
}
.subheading1,
h3,
h3 code {
font-size: calc(8px * 1.8);
font-weight: 600;
border: none;
background: none;
letter-spacing: 0.1em;
text-transform: uppercase;
}
h2 code {
padding: 0;
position: relative;
letter-spacing: 0.05em;
}
blockquote {
font-size: calc(8px * 1.1667);
font-style: italic;
line-height: calc(1.1667 * var(--vspace));
margin: var(--vspace-2) var(--vspace-2);
}
.subheading2,
h4 {
font-size: calc(8px * 1.4292);
text-transform: uppercase;
font-weight: 600;
}
.subheading3,
h5 {
font-size: calc(8px * 1.2917);
line-height: calc(1.2917 * var(--vspace));
font-weight: lighter;
text-transform: uppercase;
letter-spacing: 0.15em;
}
h6 {
font-size: calc(8px * 1.1667);
font-size: 1.1667em;
font-weight: normal;
font-style: italic;
font-family: "le-monde-livre-classic-byol", serif !important;
letter-spacing: 0px !important;
}
#start .md > *:first-child {
margin-top: 0;
}
h2 + h3 {
margin-top: 0;
}
.md hr {
border: none;
border-top: 1px solid var(--block-border-color);
margin: var(--vspace-2) 0 var(--vspace-2) 0;
}
.prose ul {
margin: var(--vspace-2) 0 var(--vspace-1) 0;
}
.gap {
gap: 0;
}
.md-custom {
overflow: hidden;
}

99
demo/docs.py
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@@ -1,99 +0,0 @@
_docs = {
"WebRTC": {
"description": "Stream audio/video with WebRTC",
"members": {
"__init__": {
"rtc_configuration": {
"type": "dict[str, Any] | None",
"default": "None",
"description": "The configration dictionary to pass to the RTCPeerConnection constructor. If None, the default configuration is used.",
},
"height": {
"type": "int | str | None",
"default": "None",
"description": "The height of the component, specified in pixels if a number is passed, or in CSS units if a string is passed. This has no effect on the preprocessed video file, but will affect the displayed video.",
},
"width": {
"type": "int | str | None",
"default": "None",
"description": "The width of the component, specified in pixels if a number is passed, or in CSS units if a string is passed. This has no effect on the preprocessed video file, but will affect the displayed video.",
},
"label": {
"type": "str | None",
"default": "None",
"description": "the label for this component. Appears above the component and is also used as the header if there are a table of examples for this component. If None and used in a `gr.Interface`, the label will be the name of the parameter this component is assigned to.",
},
"show_label": {
"type": "bool | None",
"default": "None",
"description": "if True, will display label.",
},
"container": {
"type": "bool",
"default": "True",
"description": "if True, will place the component in a container - providing some extra padding around the border.",
},
"scale": {
"type": "int | None",
"default": "None",
"description": "relative size compared to adjacent Components. For example if Components A and B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide as B. Should be an integer. scale applies in Rows, and to top-level Components in Blocks where fill_height=True.",
},
"min_width": {
"type": "int",
"default": "160",
"description": "minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.",
},
"interactive": {
"type": "bool | None",
"default": "None",
"description": "if True, will allow users to upload a video; if False, can only be used to display videos. If not provided, this is inferred based on whether the component is used as an input or output.",
},
"visible": {
"type": "bool",
"default": "True",
"description": "if False, component will be hidden.",
},
"elem_id": {
"type": "str | None",
"default": "None",
"description": "an optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.",
},
"elem_classes": {
"type": "list[str] | str | None",
"default": "None",
"description": "an optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.",
},
"render": {
"type": "bool",
"default": "True",
"description": "if False, component will not render be rendered in the Blocks context. Should be used if the intention is to assign event listeners now but render the component later.",
},
"key": {
"type": "int | str | None",
"default": "None",
"description": "if assigned, will be used to assume identity across a re-render. Components that have the same key across a re-render will have their value preserved.",
},
"mirror_webcam": {
"type": "bool",
"default": "True",
"description": "if True webcam will be mirrored. Default is True.",
},
"postprocess": {
"value": {
"type": "typing.Any",
"description": "Expects a {str} or {pathlib.Path} filepath to a video which is displayed, or a {Tuple[str | pathlib.Path, str | pathlib.Path | None]} where the first element is a filepath to a video and the second element is an optional filepath to a subtitle file.",
}
},
"preprocess": {
"return": {
"type": "str",
"description": "Passes the uploaded video as a `str` filepath or URL whose extension can be modified by `format`.",
},
"value": None,
},
},
"events": {"tick": {"type": None, "default": None, "description": ""}},
},
"__meta__": {"additional_interfaces": {}, "user_fn_refs": {"WebRTC": []}},
}
}

15
demo/echo_audio/README.md Normal file
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@@ -0,0 +1,15 @@
---
title: Echo Audio
emoji: 🪩
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 5.16.0
app_file: app.py
pinned: false
license: mit
short_description: Simple echo stream - simplest FastRTC demo
tags: [webrtc, websocket, gradio, secret|TWILIO_ACCOUNT_SID, secret|TWILIO_AUTH_TOKEN]
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

45
demo/echo_audio/app.py Normal file
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@@ -0,0 +1,45 @@
import numpy as np
from fastapi import FastAPI
from fastapi.responses import RedirectResponse
from fastrtc import ReplyOnPause, Stream, get_twilio_turn_credentials
from gradio.utils import get_space
def detection(audio: tuple[int, np.ndarray]):
# Implement any iterator that yields audio
# See "LLM Voice Chat" for a more complete example
yield audio
stream = Stream(
handler=ReplyOnPause(detection),
modality="audio",
mode="send-receive",
rtc_configuration=get_twilio_turn_credentials() if get_space() else None,
concurrency_limit=5 if get_space() else None,
time_limit=90 if get_space() else None,
)
app = FastAPI()
stream.mount(app)
@app.get("/")
async def index():
return RedirectResponse(
url="/ui" if not get_space() else "https://fastrtc-echo-audio.hf.space/ui/"
)
if __name__ == "__main__":
import os
if (mode := os.getenv("MODE")) == "UI":
stream.ui.launch(server_port=7860)
elif mode == "PHONE":
stream.fastphone(port=7860)
else:
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=7860)

3
demo/echo_audio/requirements.txt Normal file
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@@ -0,0 +1,3 @@
fastrtc[vad]
twilio
python-dotenv

61
demo/echo_conversation.py
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@@ -1,61 +0,0 @@
import logging
from queue import Queue
import gradio as gr
import numpy as np
from gradio_webrtc import StreamHandler, WebRTC
# Configure the root logger to WARNING to suppress debug messages from other libraries
logging.basicConfig(level=logging.WARNING)
# Create a console handler
console_handler = logging.StreamHandler()
console_handler.setLevel(logging.DEBUG)
# Create a formatter
formatter = logging.Formatter("%(name)s - %(levelname)s - %(message)s")
console_handler.setFormatter(formatter)
# Configure the logger for your specific library
logger = logging.getLogger("gradio_webrtc")
logger.setLevel(logging.DEBUG)
logger.addHandler(console_handler)
class EchoHandler(StreamHandler):
def __init__(self) -> None:
super().__init__()
self.queue = Queue()
def receive(self, frame: tuple[int, np.ndarray] | np.ndarray) -> None:
self.queue.put(frame)
def emit(self) -> None:
return self.queue.get()
def copy(self) -> StreamHandler:
return EchoHandler()
with gr.Blocks() as demo:
gr.HTML(
"""
<h1 style='text-align: center'>
Conversational AI (Powered by WebRTC ⚡️)
</h1>
"""
)
with gr.Column():
with gr.Group():
audio = WebRTC(
label="Stream",
rtc_configuration=None,
mode="send-receive",
modality="audio",
)
audio.stream(fn=EchoHandler(), inputs=[audio], outputs=[audio], time_limit=15)
if __name__ == "__main__":
demo.launch()

15
demo/gemini_audio_video/README.md Normal file
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---
title: Gemini Audio Video
emoji: ♊️
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 5.16.0
app_file: app.py
pinned: false
license: mit
short_description: Gemini understands audio and video!
tags: [webrtc, websocket, gradio, secret|TWILIO_ACCOUNT_SID, secret|TWILIO_AUTH_TOKEN, secret|GEMINI_API_KEY]
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

207
demo/gemini_audio_video/app.py Normal file
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import asyncio
import base64
import os
import time
from io import BytesIO
import gradio as gr
from gradio.utils import get_space
import numpy as np
from google import genai
from dotenv import load_dotenv
from fastrtc import (
AsyncAudioVideoStreamHandler,
Stream,
get_twilio_turn_credentials,
WebRTC,
)
from PIL import Image
load_dotenv()
def encode_audio(data: np.ndarray) -> dict:
"""Encode Audio data to send to the server"""
return {
"mime_type": "audio/pcm",
"data": base64.b64encode(data.tobytes()).decode("UTF-8"),
}
def encode_image(data: np.ndarray) -> dict:
with BytesIO() as output_bytes:
pil_image = Image.fromarray(data)
pil_image.save(output_bytes, "JPEG")
bytes_data = output_bytes.getvalue()
base64_str = str(base64.b64encode(bytes_data), "utf-8")
return {"mime_type": "image/jpeg", "data": base64_str}
class GeminiHandler(AsyncAudioVideoStreamHandler):
def __init__(
self,
) -> None:
super().__init__(
"mono",
output_sample_rate=24000,
output_frame_size=480,
input_sample_rate=16000,
)
self.audio_queue = asyncio.Queue()
self.video_queue = asyncio.Queue()
self.quit = asyncio.Event()
self.session = None
self.last_frame_time = 0
self.quit = asyncio.Event()
def copy(self) -> "GeminiHandler":
return GeminiHandler()
async def start_up(self):
client = genai.Client(
api_key=os.getenv("GEMINI_API_KEY"), http_options={"api_version": "v1alpha"}
)
config = {"response_modalities": ["AUDIO"]}
try:
async with client.aio.live.connect(
model="gemini-2.0-flash-exp", config=config
) as session:
self.session = session
print("set session")
while not self.quit.is_set():
turn = self.session.receive()
async for response in turn:
if data := response.data:
audio = np.frombuffer(data, dtype=np.int16).reshape(1, -1)
self.audio_queue.put_nowait(audio)
except Exception as e:
import traceback
traceback.print_exc()
async def video_receive(self, frame: np.ndarray):
try:
print("out")
if self.session:
print("here")
# send image every 1 second
print(time.time() - self.last_frame_time)
if time.time() - self.last_frame_time > 1:
self.last_frame_time = time.time()
print("sending image")
await self.session.send(input=encode_image(frame))
print("sent image")
if self.latest_args[1] is not None:
print("sending image2")
await self.session.send(input=encode_image(self.latest_args[1]))
print("sent image2")
except Exception as e:
print(e)
import traceback
traceback.print_exc()
self.video_queue.put_nowait(frame)
async def video_emit(self):
return await self.video_queue.get()
async def receive(self, frame: tuple[int, np.ndarray]) -> None:
_, array = frame
array = array.squeeze()
audio_message = encode_audio(array)
if self.session:
try:
await self.session.send(input=audio_message)
except Exception as e:
print(e)
import traceback
traceback.print_exc()
async def emit(self):
array = await self.audio_queue.get()
return (self.output_sample_rate, array)
async def shutdown(self) -> None:
if self.session:
self.quit.set()
await self.session._websocket.close()
self.quit.clear()
stream = Stream(
handler=GeminiHandler(),
modality="audio-video",
mode="send-receive",
rtc_configuration=get_twilio_turn_credentials()
if get_space() == "spaces"
else None,
time_limit=90 if get_space() else None,
additional_inputs=[
gr.Image(label="Image", type="numpy", sources=["upload", "clipboard"])
],
ui_args={
"icon": "https://www.gstatic.com/lamda/images/gemini_favicon_f069958c85030456e93de685481c559f160ea06b.png",
"pulse_color": "rgb(35, 157, 225)",
"icon_button_color": "rgb(35, 157, 225)",
"title": "Gemini Audio Video Chat",
},
)
css = """
#video-source {max-width: 600px !important; max-height: 600 !important;}
"""
with gr.Blocks(css=css) as demo:
gr.HTML(
"""
<div style='display: flex; align-items: center; justify-content: center; gap: 20px'>
<div style="background-color: var(--block-background-fill); border-radius: 8px">
<img src="https://www.gstatic.com/lamda/images/gemini_favicon_f069958c85030456e93de685481c559f160ea06b.png" style="width: 100px; height: 100px;">
</div>
<div>
<h1>Gen AI SDK Voice Chat</h1>
<p>Speak with Gemini using real-time audio + video streaming</p>
<p>Powered by <a href="https://gradio.app/">Gradio</a> and <a href=https://freddyaboulton.github.io/gradio-webrtc/">WebRTC</a>⚡️</p>
<p>Get an API Key <a href="https://support.google.com/googleapi/answer/6158862?hl=en">here</a></p>
</div>
</div>
"""
)
with gr.Row() as row:
with gr.Column():
webrtc = WebRTC(
label="Video Chat",
modality="audio-video",
mode="send-receive",
elem_id="video-source",
rtc_configuration=get_twilio_turn_credentials()
if get_space() == "spaces"
else None,
icon="https://www.gstatic.com/lamda/images/gemini_favicon_f069958c85030456e93de685481c559f160ea06b.png",
pulse_color="rgb(35, 157, 225)",
icon_button_color="rgb(35, 157, 225)",
)
with gr.Column():
image_input = gr.Image(
label="Image", type="numpy", sources=["upload", "clipboard"]
)
webrtc.stream(
GeminiHandler(),
inputs=[webrtc, image_input],
outputs=[webrtc],
time_limit=60 if get_space() else None,
concurrency_limit=2 if get_space() else None,
)
stream.ui = demo
if __name__ == "__main__":
if (mode := os.getenv("MODE")) == "UI":
stream.ui.launch(server_port=7860)
elif mode == "PHONE":
raise ValueError("Phone mode not supported for this demo")
else:
stream.ui.launch(server_port=7860)

4
demo/gemini_audio_video/requirements.txt Normal file
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fastrtc
python-dotenv
google-genai
twilio

15
demo/hello_computer/README.md Normal file
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@@ -0,0 +1,15 @@
---
title: Hello Computer
emoji: 💻
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 5.16.0
app_file: app.py
pinned: false
license: mit
short_description: Say computer before asking your question
tags: [webrtc, websocket, gradio, secret|TWILIO_ACCOUNT_SID, secret|TWILIO_AUTH_TOKEN, secret|SAMBANOVA_API_KEY]
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

15
demo/hello_computer/README_gradio.md Normal file
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---
title: Hello Computer (Gradio)
emoji: 💻
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 5.16.0
app_file: app.py
pinned: false
license: mit
short_description: Say computer (Gradio)
tags: [webrtc, websocket, gradio, secret|TWILIO_ACCOUNT_SID, secret|TWILIO_AUTH_TOKEN, secret|SAMBANOVA_API_KEY]
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

153
demo/hello_computer/app.py Normal file
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import base64
import json
import os
from pathlib import Path
import gradio as gr
import numpy as np
import openai
from dotenv import load_dotenv
from fastapi import FastAPI
from fastapi.responses import HTMLResponse, StreamingResponse
from fastrtc import (
AdditionalOutputs,
ReplyOnStopWords,
Stream,
WebRTCError,
get_stt_model,
get_twilio_turn_credentials,
)
from gradio.utils import get_space
from pydantic import BaseModel
load_dotenv()
curr_dir = Path(__file__).parent
client = openai.OpenAI(
api_key=os.environ.get("SAMBANOVA_API_KEY"),
base_url="https://api.sambanova.ai/v1",
)
model = get_stt_model()
def response(
audio: tuple[int, np.ndarray],
gradio_chatbot: list[dict] | None = None,
conversation_state: list[dict] | None = None,
):
gradio_chatbot = gradio_chatbot or []
conversation_state = conversation_state or []
try:
text = model.stt(audio)
print("STT in handler", text)
sample_rate, array = audio
gradio_chatbot.append(
{"role": "user", "content": gr.Audio((sample_rate, array.squeeze()))}
)
yield AdditionalOutputs(gradio_chatbot, conversation_state)
conversation_state.append({"role": "user", "content": text})
request = client.chat.completions.create(
model="Meta-Llama-3.2-3B-Instruct",
messages=conversation_state, # type: ignore
temperature=0.1,
top_p=0.1,
)
response = {"role": "assistant", "content": request.choices[0].message.content}
except Exception as e:
import traceback
traceback.print_exc()
raise WebRTCError(str(e) + "\n" + traceback.format_exc())
conversation_state.append(response)
gradio_chatbot.append(response)
yield AdditionalOutputs(gradio_chatbot, conversation_state)
chatbot = gr.Chatbot(type="messages", value=[])
state = gr.State(value=[])
stream = Stream(
ReplyOnStopWords(
response, # type: ignore
stop_words=["computer"],
input_sample_rate=16000,
),
mode="send",
modality="audio",
additional_inputs=[chatbot, state],
additional_outputs=[chatbot, state],
additional_outputs_handler=lambda *a: (a[2], a[3]),
concurrency_limit=5 if get_space() else None,
time_limit=90 if get_space() else None,
rtc_configuration=get_twilio_turn_credentials() if get_space() else None,
)
app = FastAPI()
stream.mount(app)
class Message(BaseModel):
role: str
content: str
class InputData(BaseModel):
webrtc_id: str
chatbot: list[Message]
state: list[Message]
@app.get("/")
async def _():
rtc_config = get_twilio_turn_credentials() if get_space() else None
html_content = (curr_dir / "index.html").read_text()
html_content = html_content.replace("__RTC_CONFIGURATION__", json.dumps(rtc_config))
return HTMLResponse(content=html_content)
@app.post("/input_hook")
async def _(data: InputData):
body = data.model_dump()
stream.set_input(data.webrtc_id, body["chatbot"], body["state"])
def audio_to_base64(file_path):
audio_format = "wav"
with open(file_path, "rb") as audio_file:
encoded_audio = base64.b64encode(audio_file.read()).decode("utf-8")
return f"data:audio/{audio_format};base64,{encoded_audio}"
@app.get("/outputs")
async def _(webrtc_id: str):
async def output_stream():
async for output in stream.output_stream(webrtc_id):
chatbot = output.args[0]
state = output.args[1]
data = {
"message": state[-1],
"audio": audio_to_base64(chatbot[-1]["content"].value["path"])
if chatbot[-1]["role"] == "user"
else None,
}
yield f"event: output\ndata: {json.dumps(data)}\n\n"
return StreamingResponse(output_stream(), media_type="text/event-stream")
if __name__ == "__main__":
import os
if (mode := os.getenv("MODE")) == "UI":
stream.ui.launch(server_port=7860)
elif mode == "PHONE":
raise ValueError("Phone mode not supported")
else:
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=7860)

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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Hello Computer 💻</title>
<style>
body {
font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif;
background-color: #f8f9fa;
color: #1a1a1a;
margin: 0;
padding: 20px;
height: 100vh;
box-sizing: border-box;
}
.container {
max-width: 800px;
margin: 0 auto;
height: calc(100% - 100px);
}
.logo {
text-align: center;
margin-bottom: 40px;
}
.chat-container {
background: white;
border-radius: 8px;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
padding: 20px;
height: 90%;
box-sizing: border-box;
display: flex;
flex-direction: column;
}
.chat-messages {
flex-grow: 1;
overflow-y: auto;
margin-bottom: 20px;
padding: 10px;
}
.message {
margin-bottom: 20px;
padding: 12px;
border-radius: 8px;
font-size: 14px;
line-height: 1.5;
}
.message.user {
background-color: #e9ecef;
margin-left: 20%;
}
.message.assistant {
background-color: #f1f3f5;
margin-right: 20%;
}
.controls {
text-align: center;
margin-top: 20px;
}
button {
background-color: #0066cc;
color: white;
border: none;
padding: 12px 24px;
font-family: inherit;
font-size: 14px;
cursor: pointer;
transition: all 0.3s;
border-radius: 4px;
font-weight: 500;
}
button:hover {
background-color: #0052a3;
}
#audio-output {
display: none;
}
.icon-with-spinner {
display: flex;
align-items: center;
justify-content: center;
gap: 12px;
min-width: 180px;
}
.spinner {
width: 20px;
height: 20px;
border: 2px solid #ffffff;
border-top-color: transparent;
border-radius: 50%;
animation: spin 1s linear infinite;
flex-shrink: 0;
}
@keyframes spin {
to {
transform: rotate(360deg);
}
}
.pulse-container {
display: flex;
align-items: center;
justify-content: center;
gap: 12px;
min-width: 180px;
}
.pulse-circle {
width: 20px;
height: 20px;
border-radius: 50%;
background-color: #ffffff;
opacity: 0.2;
flex-shrink: 0;
transform: translateX(-0%) scale(var(--audio-level, 1));
transition: transform 0.1s ease;
}
/* Add styles for typing indicator */
.typing-indicator {
padding: 8px;
background-color: #f1f3f5;
border-radius: 8px;
margin-bottom: 10px;
display: none;
}
.dots {
display: inline-flex;
gap: 4px;
}
.dot {
width: 8px;
height: 8px;
background-color: #0066cc;
border-radius: 50%;
animation: pulse 1.5s infinite;
opacity: 0.5;
}
.dot:nth-child(2) {
animation-delay: 0.5s;
}
.dot:nth-child(3) {
animation-delay: 1s;
}
@keyframes pulse {
0%,
100% {
opacity: 0.5;
transform: scale(1);
}
50% {
opacity: 1;
transform: scale(1.2);
}
}
/* Add styles for toast notifications */
.toast {
position: fixed;
top: 20px;
left: 50%;
transform: translateX(-50%);
padding: 16px 24px;
border-radius: 4px;
font-size: 14px;
z-index: 1000;
display: none;
box-shadow: 0 2px 5px rgba(0, 0, 0, 0.2);
}
.toast.error {
background-color: #f44336;
color: white;
}
.toast.warning {
background-color: #ffd700;
color: black;
}
</style>
</head>
<body>
<!-- Add toast element after body opening tag -->
<div id="error-toast" class="toast"></div>
<div class="container">
<div class="logo">
<h1>Hello Computer 💻</h1>
<h2 style="font-size: 1.2em; color: #666; margin-top: 10px;">Say 'Computer' before asking your question</h2>
</div>
<div class="chat-container">
<div class="chat-messages" id="chat-messages"></div>
<div class="typing-indicator" id="typing-indicator">
<div class="dots">
<div class="dot"></div>
<div class="dot"></div>
<div class="dot"></div>
</div>
</div>
</div>
<div class="controls">
<button id="start-button">Start Conversation</button>
</div>
</div>
<audio id="audio-output"></audio>
<script>
let peerConnection;
let webrtc_id;
const startButton = document.getElementById('start-button');
const chatMessages = document.getElementById('chat-messages');
let audioLevel = 0;
let animationFrame;
let audioContext, analyser, audioSource;
let messages = [];
let eventSource;
function updateButtonState() {
const button = document.getElementById('start-button');
if (peerConnection && (peerConnection.connectionState === 'connecting' || peerConnection.connectionState === 'new')) {
button.innerHTML = `
<div class="icon-with-spinner">
<div class="spinner"></div>
<span>Connecting...</span>
</div>
`;
} else if (peerConnection && peerConnection.connectionState === 'connected') {
button.innerHTML = `
<div class="pulse-container">
<div class="pulse-circle"></div>
<span>Stop Conversation</span>
</div>
`;
} else {
button.innerHTML = 'Start Conversation';
}
}
function setupAudioVisualization(stream) {
audioContext = new (window.AudioContext || window.webkitAudioContext)();
analyser = audioContext.createAnalyser();
audioSource = audioContext.createMediaStreamSource(stream);
audioSource.connect(analyser);
analyser.fftSize = 64;
const dataArray = new Uint8Array(analyser.frequencyBinCount);
function updateAudioLevel() {
analyser.getByteFrequencyData(dataArray);
const average = Array.from(dataArray).reduce((a, b) => a + b, 0) / dataArray.length;
audioLevel = average / 255;
const pulseCircle = document.querySelector('.pulse-circle');
if (pulseCircle) {
pulseCircle.style.setProperty('--audio-level', 1 + audioLevel);
}
animationFrame = requestAnimationFrame(updateAudioLevel);
}
updateAudioLevel();
}
function showError(message) {
const toast = document.getElementById('error-toast');
toast.textContent = message;
toast.className = 'toast error';
toast.style.display = 'block';
// Hide toast after 5 seconds
setTimeout(() => {
toast.style.display = 'none';
}, 5000);
}
function handleMessage(event) {
const eventJson = JSON.parse(event.data);
const typingIndicator = document.getElementById('typing-indicator');
if (eventJson.type === "error") {
showError(eventJson.message);
} else if (eventJson.type === "send_input") {
fetch('/input_hook', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({
webrtc_id: webrtc_id,
chatbot: messages,
state: messages
})
});
} else if (eventJson.type === "log") {
if (eventJson.data === "pause_detected") {
typingIndicator.style.display = 'block';
chatMessages.scrollTop = chatMessages.scrollHeight;
} else if (eventJson.data === "response_starting") {
typingIndicator.style.display = 'none';
}
}
}
async function setupWebRTC() {
const config = __RTC_CONFIGURATION__;
peerConnection = new RTCPeerConnection(config);
const timeoutId = setTimeout(() => {
const toast = document.getElementById('error-toast');
toast.textContent = "Connection is taking longer than usual. Are you on a VPN?";
toast.className = 'toast warning';
toast.style.display = 'block';
// Hide warning after 5 seconds
setTimeout(() => {
toast.style.display = 'none';
}, 5000);
}, 5000);
try {
const stream = await navigator.mediaDevices.getUserMedia({
audio: true
});
setupAudioVisualization(stream);
stream.getTracks().forEach(track => {
peerConnection.addTrack(track, stream);
});
const dataChannel = peerConnection.createDataChannel('text');
dataChannel.onmessage = handleMessage;
const offer = await peerConnection.createOffer();
await peerConnection.setLocalDescription(offer);
await new Promise((resolve) => {
if (peerConnection.iceGatheringState === "complete") {
resolve();
} else {
const checkState = () => {
if (peerConnection.iceGatheringState === "complete") {
peerConnection.removeEventListener("icegatheringstatechange", checkState);
resolve();
}
};
peerConnection.addEventListener("icegatheringstatechange", checkState);
}
});
peerConnection.addEventListener('connectionstatechange', () => {
console.log('connectionstatechange', peerConnection.connectionState);
if (peerConnection.connectionState === 'connected') {
clearTimeout(timeoutId);
const toast = document.getElementById('error-toast');
toast.style.display = 'none';
}
updateButtonState();
});
webrtc_id = Math.random().toString(36).substring(7);
const response = await fetch('/webrtc/offer', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
sdp: peerConnection.localDescription.sdp,
type: peerConnection.localDescription.type,
webrtc_id: webrtc_id
})
});
const serverResponse = await response.json();
if (serverResponse.status === 'failed') {
showError(serverResponse.meta.error === 'concurrency_limit_reached'
? `Too many connections. Maximum limit is ${serverResponse.meta.limit}`
: serverResponse.meta.error);
stop();
return;
}
await peerConnection.setRemoteDescription(serverResponse);
eventSource = new EventSource('/outputs?webrtc_id=' + webrtc_id);
eventSource.addEventListener("output", (event) => {
const eventJson = JSON.parse(event.data);
console.log(eventJson);
messages.push(eventJson.message);
addMessage(eventJson.message.role, eventJson.audio ?? eventJson.message.content);
});
} catch (err) {
clearTimeout(timeoutId);
console.error('Error setting up WebRTC:', err);
showError('Failed to establish connection. Please try again.');
stop();
}
}
function addMessage(role, content) {
const messageDiv = document.createElement('div');
messageDiv.classList.add('message', role);
if (role === 'user') {
// Create audio element for user messages
const audio = document.createElement('audio');
audio.controls = true;
audio.src = content;
messageDiv.appendChild(audio);
} else {
// Text content for assistant messages
messageDiv.textContent = content;
}
chatMessages.appendChild(messageDiv);
chatMessages.scrollTop = chatMessages.scrollHeight;
}
function stop() {
if (eventSource) {
eventSource.close();
eventSource = null;
}
if (animationFrame) {
cancelAnimationFrame(animationFrame);
}
if (audioContext) {
audioContext.close();
audioContext = null;
analyser = null;
audioSource = null;
}
if (peerConnection) {
if (peerConnection.getTransceivers) {
peerConnection.getTransceivers().forEach(transceiver => {
if (transceiver.stop) {
transceiver.stop();
}
});
}
if (peerConnection.getSenders) {
peerConnection.getSenders().forEach(sender => {
if (sender.track && sender.track.stop) sender.track.stop();
});
}
peerConnection.close();
}
updateButtonState();
audioLevel = 0;
}
startButton.addEventListener('click', () => {
if (!peerConnection || peerConnection.connectionState !== 'connected') {
setupWebRTC();
} else {
stop();
}
});
</script>
</body>
</html>

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demo/hello_computer/requirements.txt Normal file
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fastrtc[stopword]
python-dotenv
openai
twilio

16
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---
title: Llama Code Editor
emoji: 🦙
colorFrom: indigo
colorTo: pink
sdk: gradio
sdk_version: 5.16.0
app_file: app.py
pinned: false
license: mit
short_description: Create interactive HTML web pages with your voice
tags: [webrtc, websocket, gradio, secret|TWILIO_ACCOUNT_SID, secret|TWILIO_AUTH_TOKEN,
secret|SAMBANOVA_API_KEY, secret|GROQ_API_KEY]
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

45
demo/llama_code_editor/app.py Normal file
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from fastapi import FastAPI
from fastapi.responses import RedirectResponse
from fastrtc import Stream
from gradio.utils import get_space
try:
from demo.llama_code_editor.handler import (
CodeHandler,
)
from demo.llama_code_editor.ui import demo as ui
except (ImportError, ModuleNotFoundError):
from handler import CodeHandler
from ui import demo as ui
stream = Stream(
handler=CodeHandler,
modality="audio",
mode="send-receive",
concurrency_limit=10 if get_space() else None,
time_limit=90 if get_space() else None,
)
stream.ui = ui
app = FastAPI()
@app.get("/")
async def _():
url = "/ui" if not get_space() else "https://fastrtc-llama-code-editor.hf.space/ui/"
return RedirectResponse(url)
if __name__ == "__main__":
import os
if (mode := os.getenv("MODE")) == "UI":
stream.ui.launch(server_port=7860, server_name="0.0.0.0")
elif mode == "PHONE":
stream.fastphone(host="0.0.0.0", port=7860)
else:
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=7860)

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<div style="
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
min-height: 400px;
background: linear-gradient(135deg, #f5f7fa 0%, #e4e8ec 100%);
border-radius: 8px;
border: 2px dashed #cbd5e1;
padding: 2rem;
text-align: center;
color: #64748b;
font-family: system-ui, -apple-system, sans-serif;
">
<div style="
width: 80px;
height: 80px;
margin-bottom: 1.5rem;
border: 3px solid #cbd5e1;
border-radius: 12px;
position: relative;
">
<div style="
position: absolute;
top: 50%;
left: 50%;
transform: translate(-50%, -50%);
font-size: 2rem;
">📦</div>
</div>
<h2 style="
margin: 0 0 0.5rem 0;
font-size: 1.5rem;
font-weight: 600;
color: #475569;
">No Application Created</h2>
</div>

60
demo/llama_code_editor/assets/spinner.html Normal file
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<div style="
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
min-height: 400px;
background: linear-gradient(135deg, #f8fafc 0%, #f1f5f9 100%);
border-radius: 8px;
padding: 2rem;
text-align: center;
font-family: system-ui, -apple-system, sans-serif;
">
<!-- Spinner container -->
<div style="
position: relative;
width: 64px;
height: 64px;
margin-bottom: 1.5rem;
">
<!-- Static ring -->
<div style="
position: absolute;
width: 100%;
height: 100%;
border: 4px solid #e2e8f0;
border-radius: 50%;
"></div>
<!-- Animated spinner -->
<div style="
position: absolute;
width: 100%;
height: 100%;
border: 4px solid transparent;
border-top-color: #3b82f6;
border-radius: 50%;
animation: spin 1s linear infinite;
"></div>
</div>
<!-- Text content -->
<h2 style="
margin: 0 0 0.5rem 0;
font-size: 1.25rem;
font-weight: 600;
color: #475569;
">Generating your application...</h2>
<p style="
margin: 0;
font-size: 0.875rem;
color: #64748b;
">This may take a few moments</p>
<style>
@keyframes spin {
0% { transform: rotate(0deg); }
100% { transform: rotate(360deg); }
}
</style>
</div>

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import base64
import os
import re
from pathlib import Path
import numpy as np
import openai
from dotenv import load_dotenv
from fastrtc import (
AdditionalOutputs,
ReplyOnPause,
audio_to_bytes,
)
from groq import Groq
load_dotenv()
groq_client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
client = openai.OpenAI(
api_key=os.environ.get("SAMBANOVA_API_KEY"),
base_url="https://api.sambanova.ai/v1",
)
path = Path(__file__).parent / "assets"
spinner_html = open(path / "spinner.html").read()
system_prompt = "You are an AI coding assistant. Your task is to write single-file HTML applications based on a user's request. Only return the necessary code. Include all necessary imports and styles. You may also be asked to edit your original response."
user_prompt = "Please write a single-file HTML application to fulfill the following request.\nThe message:{user_message}\nCurrent code you have written:{code}"
def extract_html_content(text):
"""
Extract content including HTML tags.
"""
match = re.search(r"<!DOCTYPE html>.*?</html>", text, re.DOTALL)
return match.group(0) if match else None
def display_in_sandbox(code):
encoded_html = base64.b64encode(code.encode("utf-8")).decode("utf-8")
data_uri = f"data:text/html;charset=utf-8;base64,{encoded_html}"
return f'<iframe src="{data_uri}" width="100%" height="600px"></iframe>'
def generate(user_message: tuple[int, np.ndarray], history: list[dict], code: str):
yield AdditionalOutputs(history, spinner_html)
text = groq_client.audio.transcriptions.create(
file=("audio-file.mp3", audio_to_bytes(user_message)),
model="whisper-large-v3-turbo",
response_format="verbose_json",
).text
user_msg_formatted = user_prompt.format(user_message=text, code=code)
history.append({"role": "user", "content": user_msg_formatted})
response = client.chat.completions.create(
model="Meta-Llama-3.1-70B-Instruct",
messages=history, # type: ignore
temperature=0.1,
top_p=0.1,
)
output = response.choices[0].message.content
html_code = extract_html_content(output)
history.append({"role": "assistant", "content": output})
yield AdditionalOutputs(history, html_code)
CodeHandler = ReplyOnPause(generate) # type: ignore

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demo/llama_code_editor/requirements.in Normal file
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fastrtc[vad]
groq
openai
python-dotenv
twilio

295
demo/llama_code_editor/requirements.txt Normal file
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# This file was autogenerated by uv via the following command:
# uv pip compile demo/llama_code_editor/requirements.in -o demo/llama_code_editor/requirements.txt
aiofiles==23.2.1
# via gradio
aiohappyeyeballs==2.4.6
# via aiohttp
aiohttp==3.11.12
# via
# aiohttp-retry
# twilio
aiohttp-retry==2.9.1
# via twilio
aioice==0.9.0
# via aiortc
aiortc==1.10.1
# via fastrtc
aiosignal==1.3.2
# via aiohttp
annotated-types==0.7.0
# via pydantic
anyio==4.6.2.post1
# via
# gradio
# groq
# httpx
# openai
# starlette
attrs==25.1.0
# via aiohttp
audioread==3.0.1
# via librosa
av==12.3.0
# via aiortc
certifi==2024.8.30
# via
# httpcore
# httpx
# requests
cffi==1.17.1
# via
# aiortc
# cryptography
# pylibsrtp
# soundfile
charset-normalizer==3.4.0
# via requests
click==8.1.7
# via
# typer
# uvicorn
coloredlogs==15.0.1
# via onnxruntime
cryptography==43.0.3
# via
# aiortc
# pyopenssl
decorator==5.1.1
# via librosa
distro==1.9.0
# via
# groq
# openai
dnspython==2.7.0
# via aioice
fastapi==0.115.5
# via gradio
fastrtc==0.0.2.post4
# via -r demo/llama_code_editor/requirements.in
ffmpy==0.4.0
# via gradio
filelock==3.16.1
# via huggingface-hub
flatbuffers==24.3.25
# via onnxruntime
frozenlist==1.5.0
# via
# aiohttp
# aiosignal
fsspec==2024.10.0
# via
# gradio-client
# huggingface-hub
google-crc32c==1.6.0
# via aiortc
gradio==5.16.0
# via fastrtc
gradio-client==1.7.0
# via gradio
groq==0.18.0
# via -r demo/llama_code_editor/requirements.in
h11==0.14.0
# via
# httpcore
# uvicorn
httpcore==1.0.7
# via httpx
httpx==0.27.2
# via
# gradio
# gradio-client
# groq
# openai
# safehttpx
huggingface-hub==0.28.1
# via
# gradio
# gradio-client
humanfriendly==10.0
# via coloredlogs
idna==3.10
# via
# anyio
# httpx
# requests
# yarl
ifaddr==0.2.0
# via aioice
jinja2==3.1.4
# via gradio
jiter==0.7.1
# via openai
joblib==1.4.2
# via
# librosa
# scikit-learn
lazy-loader==0.4
# via librosa
librosa==0.10.2.post1
# via fastrtc
llvmlite==0.43.0
# via numba
markdown-it-py==3.0.0
# via rich
markupsafe==2.1.5
# via
# gradio
# jinja2
mdurl==0.1.2
# via markdown-it-py
mpmath==1.3.0
# via sympy
msgpack==1.1.0
# via librosa
multidict==6.1.0
# via
# aiohttp
# yarl
numba==0.60.0
# via librosa
numpy==2.0.2
# via
# gradio
# librosa
# numba
# onnxruntime
# pandas
# scikit-learn
# scipy
# soxr
onnxruntime==1.20.1
# via fastrtc
openai==1.54.4
# via -r demo/llama_code_editor/requirements.in
orjson==3.10.11
# via gradio
packaging==24.2
# via
# gradio
# gradio-client
# huggingface-hub
# lazy-loader
# onnxruntime
# pooch
pandas==2.2.3
# via gradio
pillow==11.0.0
# via gradio
platformdirs==4.3.6
# via pooch
pooch==1.8.2
# via librosa
propcache==0.2.1
# via
# aiohttp
# yarl
protobuf==5.28.3
# via onnxruntime
pycparser==2.22
# via cffi
pydantic==2.9.2
# via
# fastapi
# gradio
# groq
# openai
pydantic-core==2.23.4
# via pydantic
pydub==0.25.1
# via gradio
pyee==12.1.1
# via aiortc
pygments==2.18.0
# via rich
pyjwt==2.10.1
# via twilio
pylibsrtp==0.10.0
# via aiortc
pyopenssl==24.2.1
# via aiortc
python-dateutil==2.9.0.post0
# via pandas
python-dotenv==1.0.1
# via -r demo/llama_code_editor/requirements.in
python-multipart==0.0.20
# via gradio
pytz==2024.2
# via pandas
pyyaml==6.0.2
# via
# gradio
# huggingface-hub
requests==2.32.3
# via
# huggingface-hub
# pooch
# twilio
rich==13.9.4
# via typer
ruff==0.9.6
# via gradio
safehttpx==0.1.6
# via gradio
scikit-learn==1.5.2
# via librosa
scipy==1.14.1
# via
# librosa
# scikit-learn
semantic-version==2.10.0
# via gradio
shellingham==1.5.4
# via typer
six==1.16.0
# via python-dateutil
sniffio==1.3.1
# via
# anyio
# groq
# httpx
# openai
soundfile==0.12.1
# via librosa
soxr==0.5.0.post1
# via librosa
starlette==0.41.3
# via
# fastapi
# gradio
sympy==1.13.3
# via onnxruntime
threadpoolctl==3.5.0
# via scikit-learn
tomlkit==0.12.0
# via gradio
tqdm==4.67.0
# via
# huggingface-hub
# openai
twilio==9.4.5
# via -r demo/llama_code_editor/requirements.in
typer==0.13.1
# via gradio
typing-extensions==4.12.2
# via
# fastapi
# gradio
# gradio-client
# groq
# huggingface-hub
# librosa
# openai
# pydantic
# pydantic-core
# pyee
# typer
tzdata==2024.2
# via pandas
urllib3==2.2.3
# via requests
uvicorn==0.32.0
# via gradio
websockets==12.0
# via gradio-client
yarl==1.18.3
# via aiohttp

75
demo/llama_code_editor/ui.py Normal file
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@@ -0,0 +1,75 @@
from pathlib import Path
import gradio as gr
from dotenv import load_dotenv
from fastrtc import WebRTC, get_twilio_turn_credentials
from gradio.utils import get_space
try:
from demo.llama_code_editor.handler import (
CodeHandler,
display_in_sandbox,
system_prompt,
)
except (ImportError, ModuleNotFoundError):
from handler import CodeHandler, display_in_sandbox, system_prompt
load_dotenv()
path = Path(__file__).parent / "assets"
with gr.Blocks(css=".code-component {max-height: 500px !important}") as demo:
history = gr.State([{"role": "system", "content": system_prompt}])
with gr.Row():
with gr.Column(scale=1):
gr.HTML(
"""
<h1 style='text-align: center'>
Llama Code Editor
</h1>
<h2 style='text-align: center'>
Powered by SambaNova and Gradio-WebRTC ⚡️
</h2>
<p style='text-align: center'>
Create and edit single-file HTML applications with just your voice!
</p>
<p style='text-align: center'>
Each conversation is limited to 90 seconds. Once the time limit is up you can rejoin the conversation.
</p>
"""
)
webrtc = WebRTC(
rtc_configuration=get_twilio_turn_credentials()
if get_space()
else None,
mode="send",
modality="audio",
)
with gr.Column(scale=10):
with gr.Tabs():
with gr.Tab("Sandbox"):
sandbox = gr.HTML(value=open(path / "sandbox.html").read())
with gr.Tab("Code"):
code = gr.Code(
language="html",
max_lines=50,
interactive=False,
elem_classes="code-component",
)
with gr.Tab("Chat"):
cb = gr.Chatbot(type="messages")
webrtc.stream(
CodeHandler,
inputs=[webrtc, history, code],
outputs=[webrtc],
time_limit=90 if get_space() else None,
concurrency_limit=10 if get_space() else None,
)
webrtc.on_additional_outputs(
lambda history, code: (history, code, history), outputs=[history, code, cb]
)
code.change(display_in_sandbox, code, sandbox, queue=False)
if __name__ == "__main__":
demo.launch()

15
demo/llm_voice_chat/README.md Normal file
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@@ -0,0 +1,15 @@
---
title: LLM Voice Chat
emoji: 💻
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 5.16.0
app_file: app.py
pinned: false
license: mit
short_description: Talk to an LLM with ElevenLabs
tags: [webrtc, websocket, gradio, secret|TWILIO_ACCOUNT_SID, secret|TWILIO_AUTH_TOKEN, secret|GROQ_API_KEY, secret|ELEVENLABS_API_KEY]
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

15
demo/llm_voice_chat/README_gradio.md Normal file
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@@ -0,0 +1,15 @@
---
title: LLM Voice Chat (Gradio)
emoji: 💻
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 5.16.0
app_file: app.py
pinned: false
license: mit
short_description: LLM Voice by ElevenLabs (Gradio)
tags: [webrtc, websocket, gradio, secret|TWILIO_ACCOUNT_SID, secret|TWILIO_AUTH_TOKEN, secret|GROQ_API_KEY, secret|ELEVENLABS_API_KEY]
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

101
demo/llm_voice_chat/app.py Normal file
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@@ -0,0 +1,101 @@
import os
import time
import gradio as gr
import numpy as np
from dotenv import load_dotenv
from elevenlabs import ElevenLabs
from fastapi import FastAPI
from fastrtc import (
AdditionalOutputs,
ReplyOnPause,
Stream,
WebRTCError,
get_stt_model,
get_twilio_turn_credentials,
)
from gradio.utils import get_space
from groq import Groq
from numpy.typing import NDArray
load_dotenv()
groq_client = Groq()
tts_client = ElevenLabs(api_key=os.getenv("ELEVENLABS_API_KEY"))
stt_model = get_stt_model()
# See "Talk to Claude" in Cookbook for an example of how to keep
# track of the chat history.
def response(
audio: tuple[int, NDArray[np.int16 | np.float32]],
chatbot: list[dict] | None = None,
):
try:
chatbot = chatbot or []
messages = [{"role": d["role"], "content": d["content"]} for d in chatbot]
start = time.time()
text = stt_model.stt(audio)
print("transcription", time.time() - start)
print("prompt", text)
chatbot.append({"role": "user", "content": text})
yield AdditionalOutputs(chatbot)
messages.append({"role": "user", "content": text})
response_text = (
groq_client.chat.completions.create(
model="llama-3.1-8b-instant",
max_tokens=512,
messages=messages, # type: ignore
)
.choices[0]
.message.content
)
chatbot.append({"role": "assistant", "content": response_text})
for chunk in tts_client.text_to_speech.convert_as_stream(
text=response_text, # type: ignore
voice_id="JBFqnCBsd6RMkjVDRZzb",
model_id="eleven_multilingual_v2",
output_format="pcm_24000",
):
audio_array = np.frombuffer(chunk, dtype=np.int16).reshape(1, -1)
yield (24000, audio_array)
yield AdditionalOutputs(chatbot)
except Exception:
import traceback
traceback.print_exc()
raise WebRTCError(traceback.format_exc())
chatbot = gr.Chatbot(type="messages")
stream = Stream(
modality="audio",
mode="send-receive",
handler=ReplyOnPause(response, input_sample_rate=16000),
additional_outputs_handler=lambda a, b: b,
additional_inputs=[chatbot],
additional_outputs=[chatbot],
rtc_configuration=get_twilio_turn_credentials() if get_space() else None,
concurrency_limit=5 if get_space() else None,
time_limit=90 if get_space() else None,
ui_args={"title": "LLM Voice Chat (Powered by Groq, ElevenLabs, and WebRTC ⚡️)"},
)
# Mount the STREAM UI to the FastAPI app
# Because I don't want to build the UI manually
app = FastAPI()
app = gr.mount_gradio_app(app, stream.ui, path="/")
if __name__ == "__main__":
import os
os.environ["GRADIO_SSR_MODE"] = "false"
if (mode := os.getenv("MODE")) == "UI":
stream.ui.launch(server_port=7860)
elif mode == "PHONE":
stream.fastphone(host="0.0.0.0", port=7860)
else:
stream.ui.launch(server_port=7860)

6
demo/llm_voice_chat/requirements.txt Normal file
View File

@@ -0,0 +1,6 @@
fastrtc[stopword]
python-dotenv
openai
twilio
groq
elevenlabs

15
demo/object_detection/README.md Normal file
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@@ -0,0 +1,15 @@
---
title: Object Detection
emoji: 📸
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 5.16.0
app_file: app.py
pinned: false
license: mit
short_description: Use YOLOv10 to detect objects in real-time
tags: [webrtc, websocket, gradio, secret|TWILIO_ACCOUNT_SID, secret|TWILIO_AUTH_TOKEN]
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

83
demo/object_detection/app.py Normal file
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@@ -0,0 +1,83 @@
import json
from pathlib import Path
import cv2
import gradio as gr
from fastapi import FastAPI
from fastapi.responses import HTMLResponse
from fastrtc import Stream, WebRTCError, get_twilio_turn_credentials
from gradio.utils import get_space
from huggingface_hub import hf_hub_download
from pydantic import BaseModel, Field
try:
from demo.object_detection.inference import YOLOv10
except (ImportError, ModuleNotFoundError):
from inference import YOLOv10
cur_dir = Path(__file__).parent
model_file = hf_hub_download(
repo_id="onnx-community/yolov10n", filename="onnx/model.onnx"
)
model = YOLOv10(model_file)
def detection(image, conf_threshold=0.3):
try:
image = cv2.resize(image, (model.input_width, model.input_height))
print("conf_threshold", conf_threshold)
new_image = model.detect_objects(image, conf_threshold)
return cv2.resize(new_image, (500, 500))
except Exception as e:
import traceback
traceback.print_exc()
raise WebRTCError(str(e))
stream = Stream(
handler=detection,
modality="video",
mode="send-receive",
additional_inputs=[gr.Slider(minimum=0, maximum=1, step=0.01, value=0.3)],
rtc_configuration=get_twilio_turn_credentials() if get_space() else None,
concurrency_limit=2 if get_space() else None,
)
app = FastAPI()
stream.mount(app)
@app.get("/")
async def _():
rtc_config = get_twilio_turn_credentials() if get_space() else None
html_content = open(cur_dir / "index.html").read()
html_content = html_content.replace("__RTC_CONFIGURATION__", json.dumps(rtc_config))
return HTMLResponse(content=html_content)
class InputData(BaseModel):
webrtc_id: str
conf_threshold: float = Field(ge=0, le=1)
@app.post("/input_hook")
async def _(data: InputData):
stream.set_input(data.webrtc_id, data.conf_threshold)
if __name__ == "__main__":
import os
if (mode := os.getenv("MODE")) == "UI":
stream.ui.launch(server_port=7860)
elif mode == "PHONE":
stream.fastphone(host="0.0.0.0", port=7860)
else:
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=7860)

340
demo/object_detection/index.html Normal file
View File

@@ -0,0 +1,340 @@
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Object Detection</title>
<style>
body {
font-family: system-ui, -apple-system, sans-serif;
background: linear-gradient(135deg, #2d2b52 0%, #191731 100%);
color: white;
margin: 0;
padding: 20px;
height: 100vh;
box-sizing: border-box;
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
}
.container {
width: 100%;
max-width: 800px;
text-align: center;
display: flex;
flex-direction: column;
align-items: center;
}
.video-container {
width: 100%;
max-width: 500px;
aspect-ratio: 1/1;
background: rgba(255, 255, 255, 0.1);
border-radius: 12px;
overflow: hidden;
box-shadow: 0 8px 32px rgba(0, 0, 0, 0.2);
margin: 10px 0;
}
#video-output {
width: 100%;
height: 100%;
object-fit: cover;
}
button {
background: white;
color: #2d2b52;
border: none;
padding: 12px 32px;
border-radius: 24px;
font-size: 16px;
font-weight: 600;
cursor: pointer;
transition: all 0.3s ease;
box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1);
}
button:hover {
transform: translateY(-2px);
box-shadow: 0 6px 16px rgba(0, 0, 0, 0.2);
}
h1 {
font-size: 2.5em;
margin-bottom: 0.3em;
}
p {
color: rgba(255, 255, 255, 0.8);
margin-bottom: 1em;
}
.controls {
display: flex;
flex-direction: column;
gap: 12px;
align-items: center;
margin-top: 10px;
}
.slider-container {
width: 100%;
max-width: 300px;
display: flex;
flex-direction: column;
gap: 8px;
}
.slider-container label {
color: rgba(255, 255, 255, 0.8);
font-size: 14px;
}
input[type="range"] {
width: 100%;
height: 6px;
-webkit-appearance: none;
background: rgba(255, 255, 255, 0.1);
border-radius: 3px;
outline: none;
}
input[type="range"]::-webkit-slider-thumb {
-webkit-appearance: none;
width: 18px;
height: 18px;
background: white;
border-radius: 50%;
cursor: pointer;
}
/* Add styles for toast notifications */
.toast {
position: fixed;
top: 20px;
left: 50%;
transform: translateX(-50%);
padding: 16px 24px;
border-radius: 4px;
font-size: 14px;
z-index: 1000;
display: none;
box-shadow: 0 2px 5px rgba(0, 0, 0, 0.2);
}
.toast.error {
background-color: #f44336;
color: white;
}
.toast.warning {
background-color: #ffd700;
color: black;
}
</style>
</head>
<body>
<!-- Add toast element after body opening tag -->
<div id="error-toast" class="toast"></div>
<div class="container">
<h1>Real-time Object Detection</h1>
<p>Using YOLOv10 to detect objects in your webcam feed</p>
<div class="video-container">
<video id="video-output" autoplay playsinline></video>
</div>
<div class="controls">
<div class="slider-container">
<label>Confidence Threshold: <span id="conf-value">0.3</span></label>
<input type="range" id="conf-threshold" min="0" max="1" step="0.01" value="0.3">
</div>
<button id="start-button">Start</button>
</div>
</div>
<script>
let peerConnection;
let webrtc_id;
const startButton = document.getElementById('start-button');
const videoOutput = document.getElementById('video-output');
const confThreshold = document.getElementById('conf-threshold');
const confValue = document.getElementById('conf-value');
// Update confidence value display
confThreshold.addEventListener('input', (e) => {
confValue.textContent = e.target.value;
if (peerConnection) {
updateConfThreshold(e.target.value);
}
});
function updateConfThreshold(value) {
fetch('/input_hook', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({
webrtc_id: webrtc_id,
conf_threshold: parseFloat(value)
})
});
}
function showError(message) {
const toast = document.getElementById('error-toast');
toast.textContent = message;
toast.className = 'toast error';
toast.style.display = 'block';
// Hide toast after 5 seconds
setTimeout(() => {
toast.style.display = 'none';
}, 5000);
}
async function setupWebRTC() {
const config = __RTC_CONFIGURATION__;
peerConnection = new RTCPeerConnection(config);
const timeoutId = setTimeout(() => {
const toast = document.getElementById('error-toast');
toast.textContent = "Connection is taking longer than usual. Are you on a VPN?";
toast.className = 'toast warning';
toast.style.display = 'block';
// Hide warning after 5 seconds
setTimeout(() => {
toast.style.display = 'none';
}, 5000);
}, 5000);
try {
const stream = await navigator.mediaDevices.getUserMedia({
video: true
});
stream.getTracks().forEach(track => {
peerConnection.addTrack(track, stream);
});
peerConnection.addEventListener('track', (evt) => {
if (videoOutput && videoOutput.srcObject !== evt.streams[0]) {
videoOutput.srcObject = evt.streams[0];
}
});
const dataChannel = peerConnection.createDataChannel('text');
dataChannel.onmessage = (event) => {
const eventJson = JSON.parse(event.data);
if (eventJson.type === "error") {
showError(eventJson.message);
} else if (eventJson.type === "send_input") {
updateConfThreshold(confThreshold.value);
}
};
const offer = await peerConnection.createOffer();
await peerConnection.setLocalDescription(offer);
await new Promise((resolve) => {
if (peerConnection.iceGatheringState === "complete") {
resolve();
} else {
const checkState = () => {
if (peerConnection.iceGatheringState === "complete") {
peerConnection.removeEventListener("icegatheringstatechange", checkState);
resolve();
}
};
peerConnection.addEventListener("icegatheringstatechange", checkState);
}
});
webrtc_id = Math.random().toString(36).substring(7);
const response = await fetch('/webrtc/offer', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
sdp: peerConnection.localDescription.sdp,
type: peerConnection.localDescription.type,
webrtc_id: webrtc_id
})
});
const serverResponse = await response.json();
if (serverResponse.status === 'failed') {
showError(serverResponse.meta.error === 'concurrency_limit_reached'
? `Too many connections. Maximum limit is ${serverResponse.meta.limit}`
: serverResponse.meta.error);
stop();
startButton.textContent = 'Start';
return;
}
await peerConnection.setRemoteDescription(serverResponse);
// Send initial confidence threshold
updateConfThreshold(confThreshold.value);
peerConnection.addEventListener('connectionstatechange', () => {
if (peerConnection.connectionState === 'connected') {
clearTimeout(timeoutId);
const toast = document.getElementById('error-toast');
toast.style.display = 'none';
}
});
} catch (err) {
clearTimeout(timeoutId);
console.error('Error setting up WebRTC:', err);
showError('Failed to establish connection. Please try again.');
stop();
startButton.textContent = 'Start';
}
}
function stop() {
if (peerConnection) {
if (peerConnection.getTransceivers) {
peerConnection.getTransceivers().forEach(transceiver => {
if (transceiver.stop) {
transceiver.stop();
}
});
}
if (peerConnection.getSenders) {
peerConnection.getSenders().forEach(sender => {
if (sender.track && sender.track.stop) sender.track.stop();
});
}
setTimeout(() => {
peerConnection.close();
}, 500);
}
videoOutput.srcObject = null;
}
startButton.addEventListener('click', () => {
if (startButton.textContent === 'Start') {
setupWebRTC();
startButton.textContent = 'Stop';
} else {
stop();
startButton.textContent = 'Start';
}
});
</script>
</body>
</html>

10
demo/inference.py → demo/object_detection/inference.py
View File

@@ -3,7 +3,11 @@ import time
import cv2 import cv2
import numpy as np import numpy as np
import onnxruntime import onnxruntime
from utils import draw_detections
try:
from demo.object_detection.utils import draw_detections
except (ImportError, ModuleNotFoundError):
from utils import draw_detections
class YOLOv10: class YOLOv10:
@@ -51,7 +55,7 @@ class YOLOv10:
self.output_names, {self.input_names[0]: input_tensor} self.output_names, {self.input_names[0]: input_tensor}
) )
print(f"Inference time: {(time.perf_counter() - start)*1000:.2f} ms") print(f"Inference time: {(time.perf_counter() - start) * 1000:.2f} ms")
( (
boxes, boxes,
scores, scores,
@@ -71,7 +75,7 @@ class YOLOv10:
return [], [], [] return [], [], []
# Get the class with the highest confidence # Get the class with the highest confidence
class_ids = np.argmax(predictions[:, 4:], axis=1) class_ids = predictions[:, 5].astype(int)
# Get bounding boxes for each object # Get bounding boxes for each object
boxes = self.extract_boxes(predictions) boxes = self.extract_boxes(predictions)

4
demo/object_detection/requirements.txt Normal file
View File

@@ -0,0 +1,4 @@
fastrtc
opencv-python
twilio
onnxruntime-gpu

6
demo/utils.py → demo/object_detection/utils.py
View File

@@ -170,11 +170,11 @@ def draw_detections(image, boxes, scores, class_ids, mask_alpha=0.3):
for class_id, box, score in zip(class_ids, boxes, scores): for class_id, box, score in zip(class_ids, boxes, scores):
color = colors[class_id] color = colors[class_id]
draw_box(det_img, box, color) draw_box(det_img, box, color) # type: ignore
label = class_names[class_id] label = class_names[class_id]
caption = f"{label} {int(score * 100)}%" caption = f"{label} {int(score * 100)}%"
draw_text(det_img, caption, box, color, font_size, text_thickness) draw_text(det_img, caption, box, color, font_size, text_thickness) # type: ignore
return det_img return det_img
@@ -232,6 +232,6 @@ def draw_masks(
x1, y1, x2, y2 = box.astype(int) x1, y1, x2, y2 = box.astype(int)
# Draw fill rectangle in mask image # Draw fill rectangle in mask image
cv2.rectangle(mask_img, (x1, y1), (x2, y2), color, -1) cv2.rectangle(mask_img, (x1, y1), (x2, y2), color, -1) # type: ignore
return cv2.addWeighted(mask_img, mask_alpha, image, 1 - mask_alpha, 0) return cv2.addWeighted(mask_img, mask_alpha, image, 1 - mask_alpha, 0)

74
demo/old_app.py
View File

@@ -1,74 +0,0 @@
import os
import cv2
import gradio as gr
from gradio_webrtc import WebRTC
from huggingface_hub import hf_hub_download
from inference import YOLOv10
from twilio.rest import Client
model_file = hf_hub_download(
repo_id="onnx-community/yolov10n", filename="onnx/model.onnx"
)
model = YOLOv10(model_file)
account_sid = os.environ.get("TWILIO_ACCOUNT_SID")
auth_token = os.environ.get("TWILIO_AUTH_TOKEN")
if account_sid and auth_token:
client = Client(account_sid, auth_token)
token = client.tokens.create()
rtc_configuration = {
"iceServers": token.ice_servers,
"iceTransportPolicy": "relay",
}
else:
rtc_configuration = None
def detection(frame, conf_threshold=0.3):
frame = cv2.flip(frame, 0)
return cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
css = """.my-group {max-width: 600px !important; max-height: 600 !important;}
.my-column {display: flex !important; justify-content: center !important; align-items: center !important};"""
with gr.Blocks(css=css) as demo:
gr.HTML(
"""
<h1 style='text-align: center'>
YOLOv10 Webcam Stream (Powered by WebRTC ⚡️)
</h1>
"""
)
gr.HTML(
"""
<h3 style='text-align: center'>
<a href='https://arxiv.org/abs/2405.14458' target='_blank'>arXiv</a> | <a href='https://github.com/THU-MIG/yolov10' target='_blank'>github</a>
</h3>
"""
)
with gr.Column(elem_classes=["my-column"]):
with gr.Group(elem_classes=["my-group"]):
image = WebRTC(label="Stream", rtc_configuration=rtc_configuration)
conf_threshold = gr.Slider(
label="Confidence Threshold",
minimum=0.0,
maximum=1.0,
step=0.05,
value=0.30,
)
number = gr.Number()
image.stream(
fn=detection, inputs=[image, conf_threshold], outputs=[image], time_limit=10
)
image.on_additional_outputs(lambda n: n, outputs=[number])
if __name__ == "__main__":
demo.launch()

16
demo/phonic_chat/README.md Normal file
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---
title: Phonic AI Chat
emoji: 🎙️
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 5.16.0
app_file: app.py
pinned: false
license: mit
short_description: Talk to Phonic AI's speech-to-speech model
tags: [webrtc, websocket, gradio, secret|TWILIO_ACCOUNT_SID, secret|TWILIO_AUTH_TOKEN, secret|PHONIC_API_KEY]
python_version: 3.11
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

134
demo/phonic_chat/app.py Normal file
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import subprocess
subprocess.run(["pip", "install", "fastrtc==0.0.3.post7"])
import asyncio
import base64
import os
import gradio as gr
from gradio.utils import get_space
import numpy as np
from dotenv import load_dotenv
from fastrtc import (
AdditionalOutputs,
AsyncStreamHandler,
Stream,
get_twilio_turn_credentials,
WebRTCError,
audio_to_float32,
)
from fastapi import FastAPI
from phonic.client import PhonicSTSClient, get_voices
load_dotenv()
STS_URI = "wss://api.phonic.co/v1/sts/ws"
API_KEY = os.environ["PHONIC_API_KEY"]
SAMPLE_RATE = 44_100
voices = get_voices(API_KEY)
voice_ids = [voice["id"] for voice in voices]
class PhonicHandler(AsyncStreamHandler):
def __init__(self):
super().__init__(input_sample_rate=SAMPLE_RATE, output_sample_rate=SAMPLE_RATE)
self.output_queue = asyncio.Queue()
self.client = None
def copy(self) -> AsyncStreamHandler:
return PhonicHandler()
async def start_up(self):
await self.wait_for_args()
voice_id = self.latest_args[1]
try:
async with PhonicSTSClient(STS_URI, API_KEY) as client:
self.client = client
sts_stream = client.sts( # type: ignore
input_format="pcm_44100",
output_format="pcm_44100",
system_prompt="You are a helpful voice assistant. Respond conversationally.",
# welcome_message="Hello! I'm your voice assistant. How can I help you today?",
voice_id=voice_id,
)
async for message in sts_stream:
message_type = message.get("type")
if message_type == "audio_chunk":
audio_b64 = message["audio"]
audio_bytes = base64.b64decode(audio_b64)
await self.output_queue.put(
(SAMPLE_RATE, np.frombuffer(audio_bytes, dtype=np.int16))
)
if text := message.get("text"):
msg = {"role": "assistant", "content": text}
await self.output_queue.put(AdditionalOutputs(msg))
elif message_type == "input_text":
msg = {"role": "user", "content": message["text"]}
await self.output_queue.put(AdditionalOutputs(msg))
except Exception as e:
raise WebRTCError(f"Error starting up: {e}")
async def emit(self):
try:
return await self.output_queue.get()
except Exception as e:
raise WebRTCError(f"Error emitting: {e}")
async def receive(self, frame: tuple[int, np.ndarray]) -> None:
try:
if not self.client:
return
audio_float32 = audio_to_float32(frame)
await self.client.send_audio(audio_float32) # type: ignore
except Exception as e:
raise WebRTCError(f"Error sending audio: {e}")
async def shutdown(self):
if self.client:
await self.client._websocket.close()
return super().shutdown()
def add_to_chatbot(state, chatbot, message):
state.append(message)
return state, gr.skip()
state = gr.State(value=[])
chatbot = gr.Chatbot(type="messages", value=[])
stream = Stream(
handler=PhonicHandler(),
mode="send-receive",
modality="audio",
additional_inputs=[
gr.Dropdown(
choices=voice_ids,
value="katherine",
label="Voice",
info="Select a voice from the dropdown",
)
],
additional_outputs=[state, chatbot],
additional_outputs_handler=add_to_chatbot,
ui_args={
"title": "Phonic Chat (Powered by FastRTC ⚡️)",
},
rtc_configuration=get_twilio_turn_credentials() if get_space() else None,
concurrency_limit=5 if get_space() else None,
time_limit=90 if get_space() else None,
)
with stream.ui:
state.change(lambda s: s, inputs=state, outputs=chatbot)
app = FastAPI()
stream.mount(app)
if __name__ == "__main__":
if (mode := os.getenv("MODE")) == "UI":
stream.ui.launch(server_port=7860)
elif mode == "PHONE":
stream.fastphone(host="0.0.0.0", port=7860)
else:
stream.ui.launch(server_port=7860)

74
demo/phonic_chat/requirements.txt Normal file
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# This file was autogenerated by uv via the following command:
# uv pip compile requirements.in -o requirements.txt
aiohappyeyeballs==2.4.6
# via aiohttp
aiohttp==3.11.12
# via
# aiohttp-retry
# twilio
aiohttp-retry==2.9.1
# via twilio
aiosignal==1.3.2
# via aiohttp
attrs==25.1.0
# via aiohttp
certifi==2025.1.31
# via requests
cffi==1.17.1
# via sounddevice
charset-normalizer==3.4.1
# via requests
fastrtc==0.0.1
# via -r requirements.in
frozenlist==1.5.0
# via
# aiohttp
# aiosignal
idna==3.10
# via
# requests
# yarl
isort==6.0.0
# via phonic-python
loguru==0.7.3
# via phonic-python
multidict==6.1.0
# via
# aiohttp
# yarl
numpy==2.2.3
# via
# phonic-python
# scipy
phonic-python==0.1.3
# via -r requirements.in
propcache==0.3.0
# via
# aiohttp
# yarl
pycparser==2.22
# via cffi
pyjwt==2.10.1
# via twilio
python-dotenv==1.0.1
# via
# -r requirements.in
# phonic-python
requests==2.32.3
# via
# phonic-python
# twilio
scipy==1.15.2
# via phonic-python
sounddevice==0.5.1
# via phonic-python
twilio==9.4.6
# via -r requirements.in
typing-extensions==4.12.2
# via phonic-python
urllib3==2.3.0
# via requests
websockets==15.0
# via phonic-python
yarl==1.18.3
# via aiohttp

6
demo/requirements.txt
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@@ -1,6 +0,0 @@
safetensors==0.4.3
opencv-python
twilio
https://huggingface.co/datasets/freddyaboulton/bucket/resolve/main/gradio-5.0.0b3-py3-none-any.whl
https://huggingface.co/datasets/freddyaboulton/bucket/resolve/main/gradio_webrtc-0.0.1-py3-none-any.whl
onnxruntime-gpu

321
demo/space.py
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@@ -1,321 +0,0 @@
import os
import gradio as gr
_docs = {
"WebRTC": {
"description": "Stream audio/video with WebRTC",
"members": {
"__init__": {
"rtc_configuration": {
"type": "dict[str, Any] | None",
"default": "None",
"description": "The configration dictionary to pass to the RTCPeerConnection constructor. If None, the default configuration is used.",
},
"height": {
"type": "int | str | None",
"default": "None",
"description": "The height of the component, specified in pixels if a number is passed, or in CSS units if a string is passed. This has no effect on the preprocessed video file, but will affect the displayed video.",
},
"width": {
"type": "int | str | None",
"default": "None",
"description": "The width of the component, specified in pixels if a number is passed, or in CSS units if a string is passed. This has no effect on the preprocessed video file, but will affect the displayed video.",
},
"label": {
"type": "str | None",
"default": "None",
"description": "the label for this component. Appears above the component and is also used as the header if there are a table of examples for this component. If None and used in a `gr.Interface`, the label will be the name of the parameter this component is assigned to.",
},
"show_label": {
"type": "bool | None",
"default": "None",
"description": "if True, will display label.",
},
"container": {
"type": "bool",
"default": "True",
"description": "if True, will place the component in a container - providing some extra padding around the border.",
},
"scale": {
"type": "int | None",
"default": "None",
"description": "relative size compared to adjacent Components. For example if Components A and B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide as B. Should be an integer. scale applies in Rows, and to top-level Components in Blocks where fill_height=True.",
},
"min_width": {
"type": "int",
"default": "160",
"description": "minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.",
},
"interactive": {
"type": "bool | None",
"default": "None",
"description": "if True, will allow users to upload a video; if False, can only be used to display videos. If not provided, this is inferred based on whether the component is used as an input or output.",
},
"visible": {
"type": "bool",
"default": "True",
"description": "if False, component will be hidden.",
},
"elem_id": {
"type": "str | None",
"default": "None",
"description": "an optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.",
},
"elem_classes": {
"type": "list[str] | str | None",
"default": "None",
"description": "an optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.",
},
"render": {
"type": "bool",
"default": "True",
"description": "if False, component will not render be rendered in the Blocks context. Should be used if the intention is to assign event listeners now but render the component later.",
},
"key": {
"type": "int | str | None",
"default": "None",
"description": "if assigned, will be used to assume identity across a re-render. Components that have the same key across a re-render will have their value preserved.",
},
"mirror_webcam": {
"type": "bool",
"default": "True",
"description": "if True webcam will be mirrored. Default is True.",
},
},
"events": {"tick": {"type": None, "default": None, "description": ""}},
},
"__meta__": {"additional_interfaces": {}, "user_fn_refs": {"WebRTC": []}},
}
}
abs_path = os.path.join(os.path.dirname(__file__), "css.css")
with gr.Blocks(
css_paths=abs_path,
theme=gr.themes.Default(
font_mono=[
gr.themes.GoogleFont("Inconsolata"),
"monospace",
],
),
) as demo:
gr.Markdown(
"""
<h1 style='text-align: center; margin-bottom: 1rem'> Gradio WebRTC ⚡️ </h1>
<div style="display: flex; flex-direction: row; justify-content: center">
<img style="display: block; padding-right: 5px; height: 20px;" alt="Static Badge" src="https://img.shields.io/badge/version%20-%200.0.5%20-%20orange">
<a href="https://github.com/freddyaboulton/gradio-webrtc" target="_blank"><img alt="Static Badge" src="https://img.shields.io/badge/github-white?logo=github&logoColor=black"></a>
</div>
""",
elem_classes=["md-custom"],
header_links=True,
)
gr.Markdown(
"""
## Installation
```bash
pip install gradio_webrtc
```
## Examples:
1. [Object Detection from Webcam with YOLOv10](https://huggingface.co/spaces/freddyaboulton/webrtc-yolov10n) 📷
2. [Streaming Object Detection from Video with RT-DETR](https://huggingface.co/spaces/freddyaboulton/rt-detr-object-detection-webrtc) 🎥
3. [Text-to-Speech](https://huggingface.co/spaces/freddyaboulton/parler-tts-streaming-webrtc) 🗣️
## Usage
The WebRTC component supports the following three use cases:
1. Streaming video from the user webcam to the server and back
2. Streaming Video from the server to the client
3. Streaming Audio from the server to the client
Streaming Audio from client to the server and back (conversational AI) is not supported yet.
## Streaming Video from the User Webcam to the Server and Back
```python
import gradio as gr
from gradio_webrtc import WebRTC
def detection(image, conf_threshold=0.3):
... your detection code here ...
with gr.Blocks() as demo:
image = WebRTC(label="Stream", mode="send-receive", modality="video")
conf_threshold = gr.Slider(
label="Confidence Threshold",
minimum=0.0,
maximum=1.0,
step=0.05,
value=0.30,
)
image.stream(
fn=detection,
inputs=[image, conf_threshold],
outputs=[image], time_limit=10
)
if __name__ == "__main__":
demo.launch()
```
* Set the `mode` parameter to `send-receive` and `modality` to "video".
* The `stream` event's `fn` parameter is a function that receives the next frame from the webcam
as a **numpy array** and returns the processed frame also as a **numpy array**.
* Numpy arrays are in (height, width, 3) format where the color channels are in RGB format.
* The `inputs` parameter should be a list where the first element is the WebRTC component. The only output allowed is the WebRTC component.
* The `time_limit` parameter is the maximum time in seconds the video stream will run. If the time limit is reached, the video stream will stop.
## Streaming Video from the User Webcam to the Server and Back
```python
import gradio as gr
from gradio_webrtc import WebRTC
import cv2
def generation():
url = "https://download.tsi.telecom-paristech.fr/gpac/dataset/dash/uhd/mux_sources/hevcds_720p30_2M.mp4"
cap = cv2.VideoCapture(url)
iterating = True
while iterating:
iterating, frame = cap.read()
yield frame
with gr.Blocks() as demo:
output_video = WebRTC(label="Video Stream", mode="receive", modality="video")
button = gr.Button("Start", variant="primary")
output_video.stream(
fn=generation, inputs=None, outputs=[output_video],
trigger=button.click
)
if __name__ == "__main__":
demo.launch()
```
* Set the "mode" parameter to "receive" and "modality" to "video".
* The `stream` event's `fn` parameter is a generator function that yields the next frame from the video as a **numpy array**.
* The only output allowed is the WebRTC component.
* The `trigger` parameter the gradio event that will trigger the webrtc connection. In this case, the button click event.
## Streaming Audio from the Server to the Client
```python
import gradio as gr
from pydub import AudioSegment
def generation(num_steps):
for _ in range(num_steps):
segment = AudioSegment.from_file("/Users/freddy/sources/gradio/demo/audio_debugger/cantina.wav")
yield (segment.frame_rate, np.array(segment.get_array_of_samples()).reshape(1, -1))
with gr.Blocks() as demo:
audio = WebRTC(label="Stream", mode="receive", modality="audio")
num_steps = gr.Slider(
label="Number of Steps",
minimum=1,
maximum=10,
step=1,
value=5,
)
button = gr.Button("Generate")
audio.stream(
fn=generation, inputs=[num_steps], outputs=[audio],
trigger=button.click
)
```
* Set the "mode" parameter to "receive" and "modality" to "audio".
* The `stream` event's `fn` parameter is a generator function that yields the next audio segment as a tuple of (frame_rate, audio_samples).
* The numpy array should be of shape (1, num_samples).
* The `outputs` parameter should be a list with the WebRTC component as the only element.
## Deployment
When deploying in a cloud environment (like Hugging Face Spaces, EC2, etc), you need to set up a TURN server to relay the WebRTC traffic.
The easiest way to do this is to use a service like Twilio.
```python
from twilio.rest import Client
import os
account_sid = os.environ.get("TWILIO_ACCOUNT_SID")
auth_token = os.environ.get("TWILIO_AUTH_TOKEN")
client = Client(account_sid, auth_token)
token = client.tokens.create()
rtc_configuration = {
"iceServers": token.ice_servers,
"iceTransportPolicy": "relay",
}
with gr.Blocks() as demo:
...
rtc = WebRTC(rtc_configuration=rtc_configuration, ...)
...
```
""",
elem_classes=["md-custom"],
header_links=True,
)
gr.Markdown(
"""
##
""",
elem_classes=["md-custom"],
header_links=True,
)
gr.ParamViewer(value=_docs["WebRTC"]["members"]["__init__"], linkify=[])
demo.load(
None,
js=r"""function() {
const refs = {};
const user_fn_refs = {
WebRTC: [], };
requestAnimationFrame(() => {
Object.entries(user_fn_refs).forEach(([key, refs]) => {
if (refs.length > 0) {
const el = document.querySelector(`.${key}-user-fn`);
if (!el) return;
refs.forEach(ref => {
el.innerHTML = el.innerHTML.replace(
new RegExp("\\b"+ref+"\\b", "g"),
`<a href="#h-${ref.toLowerCase()}">${ref}</a>`
);
})
}
})
Object.entries(refs).forEach(([key, refs]) => {
if (refs.length > 0) {
const el = document.querySelector(`.${key}`);
if (!el) return;
refs.forEach(ref => {
el.innerHTML = el.innerHTML.replace(
new RegExp("\\b"+ref+"\\b", "g"),
`<a href="#h-${ref.toLowerCase()}">${ref}</a>`
);
})
}
})
})
}
""",
)
demo.launch()

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demo/stream_whisper.py
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@@ -1,53 +0,0 @@
import tempfile
import gradio as gr
import numpy as np
from gradio_webrtc import AdditionalOutputs, ReplyOnPause, WebRTC
from openai import OpenAI
from pydub import AudioSegment
from dotenv import load_dotenv
load_dotenv()
client = OpenAI()
def transcribe(audio: tuple[int, np.ndarray], transcript: list[dict]):
print("audio", audio)
segment = AudioSegment(
audio[1].tobytes(),
frame_rate=audio[0],
sample_width=audio[1].dtype.itemsize,
channels=1,
)
transcript.append({"role": "user", "content": gr.Audio((audio[0], audio[1].squeeze()))})
with tempfile.NamedTemporaryFile(suffix=".mp3") as temp_audio:
segment.export(temp_audio.name, format="mp3")
next_chunk = client.audio.transcriptions.create(
model="whisper-1", file=open(temp_audio.name, "rb")
).text
transcript.append({"role": "assistant", "content": next_chunk})
yield AdditionalOutputs(transcript)
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
audio = WebRTC(
label="Stream",
mode="send",
modality="audio",
)
with gr.Column():
transcript = gr.Chatbot(label="transcript", type="messages")
audio.stream(ReplyOnPause(transcribe), inputs=[audio, transcript], outputs=[audio],
time_limit=30)
audio.on_additional_outputs(lambda s: s, outputs=transcript)
if __name__ == "__main__":
demo.launch()

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demo/talk_to_claude/README.md Normal file
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---
title: Talk to Claude
emoji: 👨‍🦰
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 5.16.0
app_file: app.py
pinned: false
license: mit
short_description: Talk to Anthropic's Claude
tags: [webrtc, websocket, gradio, secret|TWILIO_ACCOUNT_SID, secret|TWILIO_AUTH_TOKEN, secret|GROQ_API_KEY, secret|ANTHROPIC_API_KEY, secret|ELEVENLABS_API_KEY]
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

140
demo/talk_to_claude/app.py Normal file
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import json
import os
import time
from pathlib import Path
import anthropic
import gradio as gr
import numpy as np
from dotenv import load_dotenv
from elevenlabs import ElevenLabs
from fastapi import FastAPI
from fastapi.responses import HTMLResponse, StreamingResponse
from fastrtc import (
AdditionalOutputs,
ReplyOnPause,
Stream,
WebRTCError,
get_tts_model,
get_twilio_turn_credentials,
)
from fastrtc.utils import audio_to_bytes
from gradio.utils import get_space
from groq import Groq
from pydantic import BaseModel
load_dotenv()
groq_client = Groq()
claude_client = anthropic.Anthropic()
tts_client = ElevenLabs(api_key=os.environ["ELEVENLABS_API_KEY"])
curr_dir = Path(__file__).parent
tts_model = get_tts_model()
def response(
audio: tuple[int, np.ndarray],
chatbot: list[dict] | None = None,
):
try:
chatbot = chatbot or []
messages = [{"role": d["role"], "content": d["content"]} for d in chatbot]
prompt = groq_client.audio.transcriptions.create(
file=("audio-file.mp3", audio_to_bytes(audio)),
model="whisper-large-v3-turbo",
response_format="verbose_json",
).text
print("prompt", prompt)
chatbot.append({"role": "user", "content": prompt})
yield AdditionalOutputs(chatbot)
messages.append({"role": "user", "content": prompt})
response = claude_client.messages.create(
model="claude-3-5-haiku-20241022",
max_tokens=512,
messages=messages, # type: ignore
)
response_text = " ".join(
block.text # type: ignore
for block in response.content
if getattr(block, "type", None) == "text"
)
chatbot.append({"role": "assistant", "content": response_text})
start = time.time()
print("starting tts", start)
for i, chunk in enumerate(tts_model.stream_tts_sync(response_text)):
print("chunk", i, time.time() - start)
yield chunk
print("finished tts", time.time() - start)
yield AdditionalOutputs(chatbot)
except Exception as e:
raise WebRTCError(str(e))
chatbot = gr.Chatbot(type="messages")
stream = Stream(
modality="audio",
mode="send-receive",
handler=ReplyOnPause(response),
additional_outputs_handler=lambda a, b: b,
additional_inputs=[chatbot],
additional_outputs=[chatbot],
rtc_configuration=get_twilio_turn_credentials() if get_space() else None,
concurrency_limit=5 if get_space() else None,
time_limit=90 if get_space() else None,
)
class Message(BaseModel):
role: str
content: str
class InputData(BaseModel):
webrtc_id: str
chatbot: list[Message]
app = FastAPI()
stream.mount(app)
@app.get("/")
async def _():
rtc_config = get_twilio_turn_credentials() if get_space() else None
html_content = (curr_dir / "index.html").read_text()
html_content = html_content.replace("__RTC_CONFIGURATION__", json.dumps(rtc_config))
return HTMLResponse(content=html_content, status_code=200)
@app.post("/input_hook")
async def _(body: InputData):
stream.set_input(body.webrtc_id, body.model_dump()["chatbot"])
return {"status": "ok"}
@app.get("/outputs")
def _(webrtc_id: str):
async def output_stream():
async for output in stream.output_stream(webrtc_id):
chatbot = output.args[0]
yield f"event: output\ndata: {json.dumps(chatbot[-1])}\n\n"
return StreamingResponse(output_stream(), media_type="text/event-stream")
if __name__ == "__main__":
import os
if (mode := os.getenv("MODE")) == "UI":
stream.ui.launch(server_port=7860, server_name="0.0.0.0")
elif mode == "PHONE":
stream.fastphone(host="0.0.0.0", port=7860)
else:
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=7860)

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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>RetroChat Audio</title>
<style>
body {
font-family: monospace;
background-color: #1a1a1a;
color: #00ff00;
margin: 0;
padding: 20px;
height: 100vh;
box-sizing: border-box;
}
.container {
display: flex;
flex-direction: column;
gap: 20px;
height: calc(100% - 100px);
margin-bottom: 20px;
}
.chat-container {
border: 2px solid #00ff00;
padding: 20px;
display: flex;
flex-direction: column;
flex-grow: 1;
box-sizing: border-box;
}
.controls-container {
border: 2px solid #00ff00;
padding: 20px;
display: flex;
align-items: center;
gap: 20px;
height: 128px;
box-sizing: border-box;
}
.visualization-container {
flex-grow: 1;
display: flex;
align-items: center;
}
.box-container {
display: flex;
justify-content: space-between;
height: 64px;
width: 100%;
}
.box {
height: 100%;
width: 8px;
background: #00ff00;
border-radius: 8px;
transition: transform 0.05s ease;
}
.chat-messages {
flex-grow: 1;
overflow-y: auto;
margin-bottom: 20px;
padding: 10px;
border: 1px solid #00ff00;
}
.message {
margin-bottom: 10px;
padding: 8px;
border-radius: 4px;
}
.message.user {
background-color: #003300;
}
.message.assistant {
background-color: #002200;
}
button {
height: 64px;
min-width: 120px;
background-color: #000;
color: #00ff00;
border: 2px solid #00ff00;
padding: 10px 20px;
font-family: monospace;
font-size: 16px;
cursor: pointer;
transition: all 0.3s;
}
button:hover {
border-width: 3px;
}
#audio-output {
display: none;
}
/* Retro CRT effect */
.crt-overlay {
position: absolute;
top: 0;
left: 0;
width: 100%;
height: 100%;
background: repeating-linear-gradient(0deg,
rgba(0, 255, 0, 0.03),
rgba(0, 255, 0, 0.03) 1px,
transparent 1px,
transparent 2px);
pointer-events: none;
}
/* Add these new styles */
.icon-with-spinner {
display: flex;
align-items: center;
justify-content: center;
gap: 12px;
min-width: 180px;
}
.spinner {
width: 20px;
height: 20px;
border: 2px solid #00ff00;
border-top-color: transparent;
border-radius: 50%;
animation: spin 1s linear infinite;
flex-shrink: 0;
}
@keyframes spin {
to {
transform: rotate(360deg);
}
}
.pulse-container {
display: flex;
align-items: center;
justify-content: center;
gap: 12px;
min-width: 180px;
}
.pulse-circle {
width: 20px;
height: 20px;
border-radius: 50%;
background-color: #00ff00;
opacity: 0.2;
flex-shrink: 0;
transform: translateX(-0%) scale(var(--audio-level, 1));
transition: transform 0.1s ease;
}
/* Add styles for typing indicator */
.typing-indicator {
padding: 8px;
background-color: #002200;
border-radius: 4px;
margin-bottom: 10px;
display: none;
}
.dots {
display: inline-flex;
gap: 4px;
}
.dot {
width: 8px;
height: 8px;
background-color: #00ff00;
border-radius: 50%;
animation: pulse 1.5s infinite;
opacity: 0.5;
}
.dot:nth-child(2) {
animation-delay: 0.5s;
}
.dot:nth-child(3) {
animation-delay: 1s;
}
@keyframes pulse {
0%,
100% {
opacity: 0.5;
transform: scale(1);
}
50% {
opacity: 1;
transform: scale(1.2);
}
}
/* Add styles for toast notifications */
.toast {
position: fixed;
top: 20px;
left: 50%;
transform: translateX(-50%);
padding: 16px 24px;
border-radius: 4px;
font-size: 14px;
z-index: 1000;
display: none;
box-shadow: 0 2px 5px rgba(0, 0, 0, 0.2);
}
.toast.error {
background-color: #f44336;
color: white;
}
.toast.warning {
background-color: #ffd700;
color: black;
}
</style>
</head>
<body>
<!-- Add toast element after body opening tag -->
<div id="error-toast" class="toast"></div>
<div class="container">
<div class="chat-container">
<div class="chat-messages" id="chat-messages"></div>
<!-- Move typing indicator outside the chat messages -->
<div class="typing-indicator" id="typing-indicator">
<div class="dots">
<div class="dot"></div>
<div class="dot"></div>
<div class="dot"></div>
</div>
</div>
</div>
<div class="controls-container">
<div class="visualization-container">
<div class="box-container">
<!-- Boxes will be dynamically added here -->
</div>
</div>
<button id="start-button">Start</button>
</div>
</div>
<audio id="audio-output"></audio>
<script>
let audioContext;
let analyser_input, analyser_output;
let dataArray_input, dataArray_output;
let animationId_input, animationId_output;
let chatHistory = [];
let peerConnection;
let webrtc_id;
const audioOutput = document.getElementById('audio-output');
const startButton = document.getElementById('start-button');
const chatMessages = document.getElementById('chat-messages');
function updateButtonState() {
if (peerConnection && (peerConnection.connectionState === 'connecting' || peerConnection.connectionState === 'new')) {
startButton.innerHTML = `
<div class="icon-with-spinner">
<div class="spinner"></div>
<span>Connecting...</span>
</div>
`;
} else if (peerConnection && peerConnection.connectionState === 'connected') {
startButton.innerHTML = `
<div class="pulse-container">
<div class="pulse-circle"></div>
<span>Stop</span>
</div>
`;
} else {
startButton.innerHTML = 'Start';
}
}
function showError(message) {
const toast = document.getElementById('error-toast');
toast.textContent = message;
toast.className = 'toast error';
toast.style.display = 'block';
// Hide toast after 5 seconds
setTimeout(() => {
toast.style.display = 'none';
}, 5000);
}
async function setupWebRTC() {
const config = __RTC_CONFIGURATION__;
peerConnection = new RTCPeerConnection(config);
const timeoutId = setTimeout(() => {
const toast = document.getElementById('error-toast');
toast.textContent = "Connection is taking longer than usual. Are you on a VPN?";
toast.className = 'toast warning';
toast.style.display = 'block';
// Hide warning after 5 seconds
setTimeout(() => {
toast.style.display = 'none';
}, 5000);
}, 5000);
try {
const stream = await navigator.mediaDevices.getUserMedia({
audio: true
});
// Set up input visualization
audioContext = new AudioContext();
analyser_input = audioContext.createAnalyser();
const inputSource = audioContext.createMediaStreamSource(stream);
inputSource.connect(analyser_input);
analyser_input.fftSize = 64;
dataArray_input = new Uint8Array(analyser_input.frequencyBinCount);
function updateAudioLevel() {
analyser_input.getByteFrequencyData(dataArray_input);
const average = Array.from(dataArray_input).reduce((a, b) => a + b, 0) / dataArray_input.length;
const audioLevel = average / 255;
const pulseCircle = document.querySelector('.pulse-circle');
if (pulseCircle) {
pulseCircle.style.setProperty('--audio-level', 1 + audioLevel);
}
animationId_input = requestAnimationFrame(updateAudioLevel);
}
updateAudioLevel();
stream.getTracks().forEach(track => {
peerConnection.addTrack(track, stream);
});
// Add connection state change listener
peerConnection.addEventListener('connectionstatechange', () => {
console.log('Connection state:', peerConnection.connectionState);
if (peerConnection.connectionState === 'connected') {
clearTimeout(timeoutId);
const toast = document.getElementById('error-toast');
toast.style.display = 'none';
}
updateButtonState();
});
// Handle incoming audio
peerConnection.addEventListener('track', (evt) => {
if (audioOutput.srcObject !== evt.streams[0]) {
audioOutput.srcObject = evt.streams[0];
audioOutput.play();
// Set up output visualization
analyser_output = audioContext.createAnalyser();
const outputSource = audioContext.createMediaStreamSource(evt.streams[0]);
outputSource.connect(analyser_output);
analyser_output.fftSize = 2048;
dataArray_output = new Uint8Array(analyser_output.frequencyBinCount);
updateVisualization();
}
});
// Create data channel for messages
const dataChannel = peerConnection.createDataChannel('text');
dataChannel.onmessage = (event) => {
const eventJson = JSON.parse(event.data);
const typingIndicator = document.getElementById('typing-indicator');
if (eventJson.type === "error") {
showError(eventJson.message);
} else if (eventJson.type === "send_input") {
fetch('/input_hook', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({
webrtc_id: webrtc_id,
chatbot: chatHistory
})
});
} else if (eventJson.type === "log") {
if (eventJson.data === "pause_detected") {
typingIndicator.style.display = 'block';
chatMessages.scrollTop = chatMessages.scrollHeight;
} else if (eventJson.data === "response_starting") {
typingIndicator.style.display = 'none';
}
}
};
// Create and send offer
const offer = await peerConnection.createOffer();
await peerConnection.setLocalDescription(offer);
await new Promise((resolve) => {
if (peerConnection.iceGatheringState === "complete") {
resolve();
} else {
const checkState = () => {
if (peerConnection.iceGatheringState === "complete") {
peerConnection.removeEventListener("icegatheringstatechange", checkState);
resolve();
}
};
peerConnection.addEventListener("icegatheringstatechange", checkState);
}
});
webrtc_id = Math.random().toString(36).substring(7);
const response = await fetch('/webrtc/offer', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
sdp: peerConnection.localDescription.sdp,
type: peerConnection.localDescription.type,
webrtc_id: webrtc_id
})
});
const serverResponse = await response.json();
if (serverResponse.status === 'failed') {
showError(serverResponse.meta.error === 'concurrency_limit_reached'
? `Too many connections. Maximum limit is ${serverResponse.meta.limit}`
: serverResponse.meta.error);
stop();
return;
}
await peerConnection.setRemoteDescription(serverResponse);
// Start visualization
updateVisualization();
// create event stream to receive messages from /output
const eventSource = new EventSource('/outputs?webrtc_id=' + webrtc_id);
eventSource.addEventListener("output", (event) => {
const eventJson = JSON.parse(event.data);
addMessage(eventJson.role, eventJson.content);
});
} catch (err) {
clearTimeout(timeoutId);
console.error('Error setting up WebRTC:', err);
showError('Failed to establish connection. Please try again.');
stop();
}
}
function addMessage(role, content) {
const messageDiv = document.createElement('div');
messageDiv.classList.add('message', role);
messageDiv.textContent = content;
chatMessages.appendChild(messageDiv);
chatMessages.scrollTop = chatMessages.scrollHeight;
chatHistory.push({ role, content });
}
// Add this after other const declarations
const boxContainer = document.querySelector('.box-container');
const numBars = 32;
for (let i = 0; i < numBars; i++) {
const box = document.createElement('div');
box.className = 'box';
boxContainer.appendChild(box);
}
// Replace the draw function with updateVisualization
function updateVisualization() {
animationId_output = requestAnimationFrame(updateVisualization);
analyser_output.getByteFrequencyData(dataArray_output);
const bars = document.querySelectorAll('.box');
for (let i = 0; i < bars.length; i++) {
const barHeight = (dataArray_output[i] / 255) * 2;
bars[i].style.transform = `scaleY(${Math.max(0.1, barHeight)})`;
}
}
function stop() {
if (peerConnection) {
if (peerConnection.getTransceivers) {
peerConnection.getTransceivers().forEach(transceiver => {
if (transceiver.stop) {
transceiver.stop();
}
});
}
if (peerConnection.getSenders) {
peerConnection.getSenders().forEach(sender => {
if (sender.track && sender.track.stop) sender.track.stop();
});
}
peerConnection.close();
}
if (animationId_input) {
cancelAnimationFrame(animationId_input);
}
if (animationId_output) {
cancelAnimationFrame(animationId_output);
}
if (audioContext) {
audioContext.close();
}
updateButtonState();
}
startButton.addEventListener('click', () => {
if (startButton.textContent === 'Start') {
setupWebRTC();
} else {
stop();
}
});
</script>
</body>
</html>

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fastrtc[vad, tts]
elevenlabs
groq
anthropic
twilio
python-dotenv

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---
title: Talk to Gemini
emoji: ♊️
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 5.16.0
app_file: app.py
pinned: false
license: mit
short_description: Talk to Gemini using Google's multimodal API
tags: [webrtc, websocket, gradio, secret|TWILIO_ACCOUNT_SID, secret|TWILIO_AUTH_TOKEN, secret|GEMINI_API_KEY]
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

15
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---
title: Talk to Gemini (Gradio UI)
emoji: ♊️
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 5.16.0
app_file: app.py
pinned: false
license: mit
short_description: Talk to Gemini (Gradio UI)
tags: [webrtc, websocket, gradio, secret|TWILIO_ACCOUNT_SID, secret|TWILIO_AUTH_TOKEN, secret|GEMINI_API_KEY]
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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import asyncio
import base64
import json
import os
import pathlib
from typing import AsyncGenerator, Literal
import gradio as gr
import numpy as np
from dotenv import load_dotenv
from fastapi import FastAPI
from fastapi.responses import HTMLResponse
from fastrtc import (
AsyncStreamHandler,
Stream,
WebRTCError,
get_twilio_turn_credentials,
)
from google import genai
from google.genai.types import (
LiveConnectConfig,
PrebuiltVoiceConfig,
SpeechConfig,
VoiceConfig,
)
from gradio.utils import get_space
from pydantic import BaseModel
current_dir = pathlib.Path(__file__).parent
load_dotenv()
def encode_audio(data: np.ndarray) -> str:
"""Encode Audio data to send to the server"""
return base64.b64encode(data.tobytes()).decode("UTF-8")
class GeminiHandler(AsyncStreamHandler):
"""Handler for the Gemini API"""
def __init__(
self,
expected_layout: Literal["mono"] = "mono",
output_sample_rate: int = 24000,
output_frame_size: int = 480,
) -> None:
super().__init__(
expected_layout,
output_sample_rate,
output_frame_size,
input_sample_rate=16000,
)
self.input_queue: asyncio.Queue = asyncio.Queue()
self.output_queue: asyncio.Queue = asyncio.Queue()
self.quit: asyncio.Event = asyncio.Event()
def copy(self) -> "GeminiHandler":
return GeminiHandler(
expected_layout="mono",
output_sample_rate=self.output_sample_rate,
output_frame_size=self.output_frame_size,
)
async def start_up(self):
if not self.phone_mode:
await self.wait_for_args()
api_key, voice_name = self.latest_args[1:]
else:
api_key, voice_name = None, "Puck"
try:
client = genai.Client(
api_key=api_key or os.getenv("GEMINI_API_KEY"),
http_options={"api_version": "v1alpha"},
)
except Exception as e:
raise WebRTCError(str(e))
config = LiveConnectConfig(
response_modalities=["AUDIO"], # type: ignore
speech_config=SpeechConfig(
voice_config=VoiceConfig(
prebuilt_voice_config=PrebuiltVoiceConfig(
voice_name=voice_name,
)
)
),
)
try:
async with client.aio.live.connect(
model="gemini-2.0-flash-exp", config=config
) as session:
async for audio in session.start_stream(
stream=self.stream(), mime_type="audio/pcm"
):
if audio.data:
array = np.frombuffer(audio.data, dtype=np.int16)
self.output_queue.put_nowait(array)
except Exception as e:
raise WebRTCError(str(e))
async def stream(self) -> AsyncGenerator[bytes, None]:
while not self.quit.is_set():
try:
audio = await asyncio.wait_for(self.input_queue.get(), 0.1)
yield audio
except (asyncio.TimeoutError, TimeoutError):
pass
async def receive(self, frame: tuple[int, np.ndarray]) -> None:
_, array = frame
array = array.squeeze()
audio_message = encode_audio(array)
self.input_queue.put_nowait(audio_message)
async def emit(self) -> tuple[int, np.ndarray]:
array = await self.output_queue.get()
return (self.output_sample_rate, array)
def shutdown(self) -> None:
self.quit.set()
self.args_set.clear()
stream = Stream(
modality="audio",
mode="send-receive",
handler=GeminiHandler(),
rtc_configuration=get_twilio_turn_credentials() if get_space() else None,
concurrency_limit=5 if get_space() else None,
time_limit=90 if get_space() else None,
additional_inputs=[
gr.Textbox(
label="API Key",
type="password",
value=os.getenv("GEMINI_API_KEY") if not get_space() else "",
),
gr.Dropdown(
label="Voice",
choices=[
"Puck",
"Charon",
"Kore",
"Fenrir",
"Aoede",
],
value="Puck",
),
],
)
class InputData(BaseModel):
webrtc_id: str
voice_name: str
api_key: str
app = FastAPI()
stream.mount(app)
@app.post("/input_hook")
async def _(body: InputData):
stream.set_input(body.webrtc_id, body.api_key, body.voice_name)
return {"status": "ok"}
@app.get("/")
async def index():
rtc_config = get_twilio_turn_credentials() if get_space() else None
html_content = (current_dir / "index.html").read_text()
html_content = html_content.replace("__RTC_CONFIGURATION__", json.dumps(rtc_config))
return HTMLResponse(content=html_content)
if __name__ == "__main__":
import os
if (mode := os.getenv("MODE")) == "UI":
stream.ui.launch(server_port=7860)
elif mode == "PHONE":
stream.fastphone(host="0.0.0.0", port=7860)
else:
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=7860)

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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Gemini Voice Chat</title>
<style>
:root {
--color-accent: #6366f1;
--color-background: #0f172a;
--color-surface: #1e293b;
--color-text: #e2e8f0;
--boxSize: 8px;
--gutter: 4px;
}
body {
margin: 0;
padding: 0;
background-color: var(--color-background);
color: var(--color-text);
font-family: system-ui, -apple-system, sans-serif;
min-height: 100vh;
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
}
.container {
width: 90%;
max-width: 800px;
background-color: var(--color-surface);
padding: 2rem;
border-radius: 1rem;
box-shadow: 0 25px 50px -12px rgba(0, 0, 0, 0.25);
}
.wave-container {
position: relative;
display: flex;
min-height: 100px;
max-height: 128px;
justify-content: center;
align-items: center;
margin: 2rem 0;
}
.box-container {
display: flex;
justify-content: space-between;
height: 64px;
width: 100%;
}
.box {
height: 100%;
width: var(--boxSize);
background: var(--color-accent);
border-radius: 8px;
transition: transform 0.05s ease;
}
.controls {
display: grid;
gap: 1rem;
margin-bottom: 2rem;
}
.input-group {
display: flex;
flex-direction: column;
gap: 0.5rem;
}
label {
font-size: 0.875rem;
font-weight: 500;
}
input,
select {
padding: 0.75rem;
border-radius: 0.5rem;
border: 1px solid rgba(255, 255, 255, 0.1);
background-color: var(--color-background);
color: var(--color-text);
font-size: 1rem;
}
button {
padding: 1rem 2rem;
border-radius: 0.5rem;
border: none;
background-color: var(--color-accent);
color: white;
font-weight: 600;
cursor: pointer;
transition: all 0.2s ease;
}
button:hover {
opacity: 0.9;
transform: translateY(-1px);
}
.icon-with-spinner {
display: flex;
align-items: center;
justify-content: center;
gap: 12px;
min-width: 180px;
}
.spinner {
width: 20px;
height: 20px;
border: 2px solid white;
border-top-color: transparent;
border-radius: 50%;
animation: spin 1s linear infinite;
flex-shrink: 0;
}
@keyframes spin {
to {
transform: rotate(360deg);
}
}
.pulse-container {
display: flex;
align-items: center;
justify-content: center;
gap: 12px;
min-width: 180px;
}
.pulse-circle {
width: 20px;
height: 20px;
border-radius: 50%;
background-color: white;
opacity: 0.2;
flex-shrink: 0;
transform: translateX(-0%) scale(var(--audio-level, 1));
transition: transform 0.1s ease;
}
/* Add styles for toast notifications */
.toast {
position: fixed;
top: 20px;
left: 50%;
transform: translateX(-50%);
padding: 16px 24px;
border-radius: 4px;
font-size: 14px;
z-index: 1000;
display: none;
box-shadow: 0 2px 5px rgba(0, 0, 0, 0.2);
}
.toast.error {
background-color: #f44336;
color: white;
}
.toast.warning {
background-color: #ffd700;
color: black;
}
</style>
</head>
<body>
<!-- Add toast element after body opening tag -->
<div id="error-toast" class="toast"></div>
<div style="text-align: center">
<h1>Gemini Voice Chat</h1>
<p>Speak with Gemini using real-time audio streaming</p>
<p>
Get a Gemini API key
<a href="https://ai.google.dev/gemini-api/docs/api-key">here</a>
</p>
</div>
<div class="container">
<div class="controls">
<div class="input-group">
<label for="api-key">API Key</label>
<input type="password" id="api-key" placeholder="Enter your API key">
</div>
<div class="input-group">
<label for="voice">Voice</label>
<select id="voice">
<option value="Puck">Puck</option>
<option value="Charon">Charon</option>
<option value="Kore">Kore</option>
<option value="Fenrir">Fenrir</option>
<option value="Aoede">Aoede</option>
</select>
</div>
</div>
<div class="wave-container">
<div class="box-container">
<!-- Boxes will be dynamically added here -->
</div>
</div>
<button id="start-button">Start Recording</button>
</div>
<audio id="audio-output"></audio>
<script>
let peerConnection;
let audioContext;
let dataChannel;
let isRecording = false;
let webrtc_id;
const startButton = document.getElementById('start-button');
const apiKeyInput = document.getElementById('api-key');
const voiceSelect = document.getElementById('voice');
const audioOutput = document.getElementById('audio-output');
const boxContainer = document.querySelector('.box-container');
const numBars = 32;
for (let i = 0; i < numBars; i++) {
const box = document.createElement('div');
box.className = 'box';
boxContainer.appendChild(box);
}
function updateButtonState() {
if (peerConnection && (peerConnection.connectionState === 'connecting' || peerConnection.connectionState === 'new')) {
startButton.innerHTML = `
<div class="icon-with-spinner">
<div class="spinner"></div>
<span>Connecting...</span>
</div>
`;
} else if (peerConnection && peerConnection.connectionState === 'connected') {
startButton.innerHTML = `
<div class="pulse-container">
<div class="pulse-circle"></div>
<span>Stop Recording</span>
</div>
`;
} else {
startButton.innerHTML = 'Start Recording';
}
}
function showError(message) {
const toast = document.getElementById('error-toast');
toast.textContent = message;
toast.className = 'toast error';
toast.style.display = 'block';
// Hide toast after 5 seconds
setTimeout(() => {
toast.style.display = 'none';
}, 5000);
}
async function setupWebRTC() {
const config = __RTC_CONFIGURATION__;
peerConnection = new RTCPeerConnection(config);
webrtc_id = Math.random().toString(36).substring(7);
const timeoutId = setTimeout(() => {
const toast = document.getElementById('error-toast');
toast.textContent = "Connection is taking longer than usual. Are you on a VPN?";
toast.className = 'toast warning';
toast.style.display = 'block';
// Hide warning after 5 seconds
setTimeout(() => {
toast.style.display = 'none';
}, 5000);
}, 5000);
try {
const stream = await navigator.mediaDevices.getUserMedia({ audio: true });
stream.getTracks().forEach(track => peerConnection.addTrack(track, stream));
// Update audio visualization setup
audioContext = new AudioContext();
analyser_input = audioContext.createAnalyser();
const source = audioContext.createMediaStreamSource(stream);
source.connect(analyser_input);
analyser_input.fftSize = 64;
dataArray_input = new Uint8Array(analyser_input.frequencyBinCount);
function updateAudioLevel() {
analyser_input.getByteFrequencyData(dataArray_input);
const average = Array.from(dataArray_input).reduce((a, b) => a + b, 0) / dataArray_input.length;
const audioLevel = average / 255;
const pulseCircle = document.querySelector('.pulse-circle');
if (pulseCircle) {
console.log("audioLevel", audioLevel);
pulseCircle.style.setProperty('--audio-level', 1 + audioLevel);
}
animationId = requestAnimationFrame(updateAudioLevel);
}
updateAudioLevel();
// Add connection state change listener
peerConnection.addEventListener('connectionstatechange', () => {
console.log('connectionstatechange', peerConnection.connectionState);
if (peerConnection.connectionState === 'connected') {
clearTimeout(timeoutId);
const toast = document.getElementById('error-toast');
toast.style.display = 'none';
}
updateButtonState();
});
// Handle incoming audio
peerConnection.addEventListener('track', (evt) => {
if (audioOutput && audioOutput.srcObject !== evt.streams[0]) {
audioOutput.srcObject = evt.streams[0];
audioOutput.play();
// Set up audio visualization on the output stream
audioContext = new AudioContext();
analyser = audioContext.createAnalyser();
const source = audioContext.createMediaStreamSource(evt.streams[0]);
source.connect(analyser);
analyser.fftSize = 2048;
dataArray = new Uint8Array(analyser.frequencyBinCount);
updateVisualization();
}
});
// Create data channel for messages
dataChannel = peerConnection.createDataChannel('text');
dataChannel.onmessage = (event) => {
const eventJson = JSON.parse(event.data);
if (eventJson.type === "error") {
showError(eventJson.message);
} else if (eventJson.type === "send_input") {
fetch('/input_hook', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({
webrtc_id: webrtc_id,
api_key: apiKeyInput.value,
voice_name: voiceSelect.value
})
});
}
};
// Create and send offer
const offer = await peerConnection.createOffer();
await peerConnection.setLocalDescription(offer);
await new Promise((resolve) => {
if (peerConnection.iceGatheringState === "complete") {
resolve();
} else {
const checkState = () => {
if (peerConnection.iceGatheringState === "complete") {
peerConnection.removeEventListener("icegatheringstatechange", checkState);
resolve();
}
};
peerConnection.addEventListener("icegatheringstatechange", checkState);
}
});
const response = await fetch('/webrtc/offer', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
sdp: peerConnection.localDescription.sdp,
type: peerConnection.localDescription.type,
webrtc_id: webrtc_id,
})
});
const serverResponse = await response.json();
if (serverResponse.status === 'failed') {
showError(serverResponse.meta.error === 'concurrency_limit_reached'
? `Too many connections. Maximum limit is ${serverResponse.meta.limit}`
: serverResponse.meta.error);
stop();
startButton.textContent = 'Start Recording';
return;
}
await peerConnection.setRemoteDescription(serverResponse);
} catch (err) {
clearTimeout(timeoutId);
console.error('Error setting up WebRTC:', err);
showError('Failed to establish connection. Please try again.');
stop();
startButton.textContent = 'Start Recording';
}
}
function updateVisualization() {
if (!analyser) return;
analyser.getByteFrequencyData(dataArray);
const bars = document.querySelectorAll('.box');
for (let i = 0; i < bars.length; i++) {
const barHeight = (dataArray[i] / 255) * 2;
bars[i].style.transform = `scaleY(${Math.max(0.1, barHeight)})`;
}
animationId = requestAnimationFrame(updateVisualization);
}
function stopWebRTC() {
if (peerConnection) {
peerConnection.close();
}
if (animationId) {
cancelAnimationFrame(animationId);
}
if (audioContext) {
audioContext.close();
}
updateButtonState();
}
startButton.addEventListener('click', () => {
if (!isRecording) {
setupWebRTC();
startButton.classList.add('recording');
} else {
stopWebRTC();
startButton.classList.remove('recording');
}
isRecording = !isRecording;
});
</script>
</body>
</html>

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fastrtc
python-dotenv
google-genai
twilio

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---
title: Talk to OpenAI
emoji: 🗣️
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 5.16.0
app_file: app.py
pinned: false
license: mit
short_description: Talk to OpenAI using their multimodal API
tags: [webrtc, websocket, gradio, secret|TWILIO_ACCOUNT_SID, secret|TWILIO_AUTH_TOKEN, secret|OPENAI_API_KEY]
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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---
title: Talk to OpenAI (Gradio UI)
emoji: 🗣️
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 5.16.0
app_file: app.py
pinned: false
license: mit
short_description: Talk to OpenAI (Gradio UI)
tags: [webrtc, websocket, gradio, secret|TWILIO_ACCOUNT_SID, secret|TWILIO_AUTH_TOKEN, secret|OPENAI_API_KEY]
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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import asyncio
import base64
import json
from pathlib import Path
import gradio as gr
import numpy as np
import openai
from dotenv import load_dotenv
from fastapi import FastAPI
from fastapi.responses import HTMLResponse, StreamingResponse
from fastrtc import (
AdditionalOutputs,
AsyncStreamHandler,
Stream,
WebRTCError,
get_twilio_turn_credentials,
)
from gradio.utils import get_space
from openai.types.beta.realtime import ResponseAudioTranscriptDoneEvent
load_dotenv()
cur_dir = Path(__file__).parent
SAMPLE_RATE = 24000
class OpenAIHandler(AsyncStreamHandler):
def __init__(
self,
) -> None:
super().__init__(
expected_layout="mono",
output_sample_rate=SAMPLE_RATE,
output_frame_size=480,
input_sample_rate=SAMPLE_RATE,
)
self.connection = None
self.output_queue = asyncio.Queue()
def copy(self):
return OpenAIHandler()
async def start_up(
self,
):
"""Connect to realtime API. Run forever in separate thread to keep connection open."""
self.client = openai.AsyncOpenAI()
try:
async with self.client.beta.realtime.connect(
model="gpt-4o-mini-realtime-preview-2024-12-17"
) as conn:
await conn.session.update(
session={"turn_detection": {"type": "server_vad"}}
)
self.connection = conn
async for event in self.connection:
if event.type == "response.audio_transcript.done":
await self.output_queue.put(AdditionalOutputs(event))
if event.type == "response.audio.delta":
await self.output_queue.put(
(
self.output_sample_rate,
np.frombuffer(
base64.b64decode(event.delta), dtype=np.int16
).reshape(1, -1),
),
)
except Exception:
import traceback
traceback.print_exc()
raise WebRTCError(str(traceback.format_exc()))
async def receive(self, frame: tuple[int, np.ndarray]) -> None:
if not self.connection:
return
try:
_, array = frame
array = array.squeeze()
audio_message = base64.b64encode(array.tobytes()).decode("utf-8")
await self.connection.input_audio_buffer.append(audio=audio_message) # type: ignore
except Exception as e:
# print traceback
print(f"Error in receive: {str(e)}")
import traceback
traceback.print_exc()
raise WebRTCError(str(traceback.format_exc()))
async def emit(self) -> tuple[int, np.ndarray] | AdditionalOutputs | None:
return await self.output_queue.get()
def reset_state(self):
"""Reset connection state for new recording session"""
self.connection = None
self.args_set.clear()
async def shutdown(self) -> None:
if self.connection:
await self.connection.close()
self.reset_state()
def update_chatbot(chatbot: list[dict], response: ResponseAudioTranscriptDoneEvent):
chatbot.append({"role": "assistant", "content": response.transcript})
return chatbot
chatbot = gr.Chatbot(type="messages")
latest_message = gr.Textbox(type="text", visible=False)
stream = Stream(
OpenAIHandler(),
mode="send-receive",
modality="audio",
additional_inputs=[chatbot],
additional_outputs=[chatbot],
additional_outputs_handler=update_chatbot,
rtc_configuration=get_twilio_turn_credentials() if get_space() else None,
concurrency_limit=5 if get_space() else None,
time_limit=90 if get_space() else None,
)
app = FastAPI()
stream.mount(app)
@app.get("/")
async def _():
rtc_config = get_twilio_turn_credentials() if get_space() else None
html_content = (cur_dir / "index.html").read_text()
html_content = html_content.replace("__RTC_CONFIGURATION__", json.dumps(rtc_config))
return HTMLResponse(content=html_content)
@app.get("/outputs")
def _(webrtc_id: str):
async def output_stream():
import json
async for output in stream.output_stream(webrtc_id):
s = json.dumps({"role": "assistant", "content": output.args[0].transcript})
yield f"event: output\ndata: {s}\n\n"
return StreamingResponse(output_stream(), media_type="text/event-stream")
if __name__ == "__main__":
import os
if (mode := os.getenv("MODE")) == "UI":
stream.ui.launch(server_port=7860)
elif mode == "PHONE":
stream.fastphone(host="0.0.0.0", port=7860)
else:
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=7860)

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demo/talk_to_openai/index.html Normal file
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>OpenAI Real-Time Chat</title>
<style>
body {
font-family: "SF Pro Display", -apple-system, BlinkMacSystemFont, sans-serif;
background-color: #0a0a0a;
color: #ffffff;
margin: 0;
padding: 20px;
height: 100vh;
box-sizing: border-box;
}
.container {
max-width: 800px;
margin: 0 auto;
height: calc(100% - 100px);
}
.logo {
text-align: center;
margin-bottom: 40px;
}
.chat-container {
border: 1px solid #333;
padding: 20px;
height: 90%;
box-sizing: border-box;
display: flex;
flex-direction: column;
}
.chat-messages {
flex-grow: 1;
overflow-y: auto;
margin-bottom: 20px;
padding: 10px;
}
.message {
margin-bottom: 20px;
padding: 12px;
border-radius: 4px;
font-size: 16px;
line-height: 1.5;
}
.message.user {
background-color: #1a1a1a;
margin-left: 20%;
}
.message.assistant {
background-color: #262626;
margin-right: 20%;
}
.controls {
text-align: center;
margin-top: 20px;
}
button {
background-color: transparent;
color: #ffffff;
border: 1px solid #ffffff;
padding: 12px 24px;
font-family: inherit;
font-size: 16px;
cursor: pointer;
transition: all 0.3s;
text-transform: uppercase;
letter-spacing: 1px;
}
button:hover {
border-width: 2px;
transform: scale(1.02);
box-shadow: 0 0 10px rgba(255, 255, 255, 0.2);
}
#audio-output {
display: none;
}
.icon-with-spinner {
display: flex;
align-items: center;
justify-content: center;
gap: 12px;
min-width: 180px;
}
.spinner {
width: 20px;
height: 20px;
border: 2px solid #ffffff;
border-top-color: transparent;
border-radius: 50%;
animation: spin 1s linear infinite;
flex-shrink: 0;
}
@keyframes spin {
to {
transform: rotate(360deg);
}
}
.pulse-container {
display: flex;
align-items: center;
justify-content: center;
gap: 12px;
min-width: 180px;
}
.pulse-circle {
width: 20px;
height: 20px;
border-radius: 50%;
background-color: #ffffff;
opacity: 0.2;
flex-shrink: 0;
transform: translateX(-0%) scale(var(--audio-level, 1));
transition: transform 0.1s ease;
}
/* Add styles for toast notifications */
.toast {
position: fixed;
top: 20px;
left: 50%;
transform: translateX(-50%);
padding: 16px 24px;
border-radius: 4px;
font-size: 14px;
z-index: 1000;
display: none;
box-shadow: 0 2px 5px rgba(0, 0, 0, 0.2);
}
.toast.error {
background-color: #f44336;
color: white;
}
.toast.warning {
background-color: #ffd700;
color: black;
}
</style>
</head>
<body>
<!-- Add toast element after body opening tag -->
<div id="error-toast" class="toast"></div>
<div class="container">
<div class="logo">
<h1>OpenAI Real-Time Chat</h1>
</div>
<div class="chat-container">
<div class="chat-messages" id="chat-messages"></div>
</div>
<div class="controls">
<button id="start-button">Start Conversation</button>
</div>
</div>
<audio id="audio-output"></audio>
<script>
let peerConnection;
let webrtc_id;
const audioOutput = document.getElementById('audio-output');
const startButton = document.getElementById('start-button');
const chatMessages = document.getElementById('chat-messages');
let audioLevel = 0;
let animationFrame;
let audioContext, analyser, audioSource;
function updateButtonState() {
const button = document.getElementById('start-button');
if (peerConnection && (peerConnection.connectionState === 'connecting' || peerConnection.connectionState === 'new')) {
button.innerHTML = `
<div class="icon-with-spinner">
<div class="spinner"></div>
<span>Connecting...</span>
</div>
`;
} else if (peerConnection && peerConnection.connectionState === 'connected') {
button.innerHTML = `
<div class="pulse-container">
<div class="pulse-circle"></div>
<span>Stop Conversation</span>
</div>
`;
} else {
button.innerHTML = 'Start Conversation';
}
}
function setupAudioVisualization(stream) {
audioContext = new (window.AudioContext || window.webkitAudioContext)();
analyser = audioContext.createAnalyser();
audioSource = audioContext.createMediaStreamSource(stream);
audioSource.connect(analyser);
analyser.fftSize = 64;
const dataArray = new Uint8Array(analyser.frequencyBinCount);
function updateAudioLevel() {
analyser.getByteFrequencyData(dataArray);
const average = Array.from(dataArray).reduce((a, b) => a + b, 0) / dataArray.length;
audioLevel = average / 255;
// Update CSS variable instead of rebuilding the button
const pulseCircle = document.querySelector('.pulse-circle');
if (pulseCircle) {
pulseCircle.style.setProperty('--audio-level', 1 + audioLevel);
}
animationFrame = requestAnimationFrame(updateAudioLevel);
}
updateAudioLevel();
}
function showError(message) {
const toast = document.getElementById('error-toast');
toast.textContent = message;
toast.style.display = 'block';
// Hide toast after 5 seconds
setTimeout(() => {
toast.style.display = 'none';
}, 5000);
}
async function setupWebRTC() {
isConnecting = true;
const config = __RTC_CONFIGURATION__;
peerConnection = new RTCPeerConnection(config);
const timeoutId = setTimeout(() => {
const toast = document.getElementById('error-toast');
toast.textContent = "Connection is taking longer than usual. Are you on a VPN?";
toast.className = 'toast warning';
toast.style.display = 'block';
// Hide warning after 5 seconds
setTimeout(() => {
toast.style.display = 'none';
}, 5000);
}, 5000);
try {
const stream = await navigator.mediaDevices.getUserMedia({
audio: true
});
setupAudioVisualization(stream);
stream.getTracks().forEach(track => {
peerConnection.addTrack(track, stream);
});
peerConnection.addEventListener('track', (evt) => {
if (audioOutput.srcObject !== evt.streams[0]) {
audioOutput.srcObject = evt.streams[0];
audioOutput.play();
}
});
const dataChannel = peerConnection.createDataChannel('text');
dataChannel.onmessage = (event) => {
const eventJson = JSON.parse(event.data);
if (eventJson.type === "error") {
showError(eventJson.message);
}
};
const offer = await peerConnection.createOffer();
await peerConnection.setLocalDescription(offer);
await new Promise((resolve) => {
if (peerConnection.iceGatheringState === "complete") {
resolve();
} else {
const checkState = () => {
if (peerConnection.iceGatheringState === "complete") {
peerConnection.removeEventListener("icegatheringstatechange", checkState);
resolve();
}
};
peerConnection.addEventListener("icegatheringstatechange", checkState);
}
});
peerConnection.addEventListener('connectionstatechange', () => {
console.log('connectionstatechange', peerConnection.connectionState);
if (peerConnection.connectionState === 'connected') {
clearTimeout(timeoutId);
const toast = document.getElementById('error-toast');
toast.style.display = 'none';
}
updateButtonState();
});
webrtc_id = Math.random().toString(36).substring(7);
const response = await fetch('/webrtc/offer', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
sdp: peerConnection.localDescription.sdp,
type: peerConnection.localDescription.type,
webrtc_id: webrtc_id
})
});
const serverResponse = await response.json();
if (serverResponse.status === 'failed') {
showError(serverResponse.meta.error === 'concurrency_limit_reached'
? `Too many connections. Maximum limit is ${serverResponse.meta.limit}`
: serverResponse.meta.error);
stop();
return;
}
await peerConnection.setRemoteDescription(serverResponse);
const eventSource = new EventSource('/outputs?webrtc_id=' + webrtc_id);
eventSource.addEventListener("output", (event) => {
const eventJson = JSON.parse(event.data);
addMessage("assistant", eventJson.content);
});
} catch (err) {
clearTimeout(timeoutId);
console.error('Error setting up WebRTC:', err);
showError('Failed to establish connection. Please try again.');
stop();
}
}
function addMessage(role, content) {
const messageDiv = document.createElement('div');
messageDiv.classList.add('message', role);
messageDiv.textContent = content;
chatMessages.appendChild(messageDiv);
chatMessages.scrollTop = chatMessages.scrollHeight;
}
function stop() {
if (animationFrame) {
cancelAnimationFrame(animationFrame);
}
if (audioContext) {
audioContext.close();
audioContext = null;
analyser = null;
audioSource = null;
}
if (peerConnection) {
if (peerConnection.getTransceivers) {
peerConnection.getTransceivers().forEach(transceiver => {
if (transceiver.stop) {
transceiver.stop();
}
});
}
if (peerConnection.getSenders) {
peerConnection.getSenders().forEach(sender => {
if (sender.track && sender.track.stop) sender.track.stop();
});
}
console.log('closing');
peerConnection.close();
}
updateButtonState();
audioLevel = 0;
}
startButton.addEventListener('click', () => {
console.log('clicked');
console.log(peerConnection, peerConnection?.connectionState);
if (!peerConnection || peerConnection.connectionState !== 'connected') {
setupWebRTC();
} else {
console.log('stopping');
stop();
}
});
</script>
</body>
</html>

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demo/talk_to_openai/requirements.txt Normal file
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fastrtc[vad]
openai
twilio
python-dotenv

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---
title: Talk to Sambanova
emoji: 💻
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 5.16.0
app_file: app.py
pinned: false
license: mit
short_description: Llama 3.2 - SambaNova API
tags: [webrtc, websocket, gradio, secret|TWILIO_ACCOUNT_SID, secret|TWILIO_AUTH_TOKEN, secret|SAMBANOVA_API_KEY]
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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demo/talk_to_sambanova/README_gradio.md Normal file
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---
title: Talk to Sambanova (Gradio)
emoji: 💻
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 5.16.0
app_file: app.py
pinned: false
license: mit
short_description: Llama 3.2 - SambaNova API (Gradio)
tags: [webrtc, websocket, gradio, secret|TWILIO_ACCOUNT_SID, secret|TWILIO_AUTH_TOKEN, secret|SAMBANOVA_API_KEY]
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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import base64
import json
import os
from pathlib import Path
import gradio as gr
import numpy as np
import openai
from dotenv import load_dotenv
from fastapi import FastAPI
from fastapi.responses import HTMLResponse, StreamingResponse
from fastrtc import (
AdditionalOutputs,
ReplyOnPause,
Stream,
WebRTCError,
get_stt_model,
get_twilio_turn_credentials,
)
from gradio.utils import get_space
from pydantic import BaseModel
load_dotenv()
curr_dir = Path(__file__).parent
client = openai.OpenAI(
api_key=os.environ.get("SAMBANOVA_API_KEY"),
base_url="https://api.sambanova.ai/v1",
)
stt_model = get_stt_model()
def response(
audio: tuple[int, np.ndarray],
gradio_chatbot: list[dict] | None = None,
conversation_state: list[dict] | None = None,
):
gradio_chatbot = gradio_chatbot or []
conversation_state = conversation_state or []
text = stt_model.stt(audio)
sample_rate, array = audio
gradio_chatbot.append(
{"role": "user", "content": gr.Audio((sample_rate, array.squeeze()))}
)
yield AdditionalOutputs(gradio_chatbot, conversation_state)
conversation_state.append({"role": "user", "content": text})
try:
request = client.chat.completions.create(
model="Meta-Llama-3.2-3B-Instruct",
messages=conversation_state, # type: ignore
temperature=0.1,
top_p=0.1,
)
response = {"role": "assistant", "content": request.choices[0].message.content}
except Exception:
import traceback
traceback.print_exc()
raise WebRTCError(traceback.format_exc())
conversation_state.append(response)
gradio_chatbot.append(response)
yield AdditionalOutputs(gradio_chatbot, conversation_state)
chatbot = gr.Chatbot(type="messages", value=[])
state = gr.State(value=[])
stream = Stream(
ReplyOnPause(
response, # type: ignore
input_sample_rate=16000,
),
mode="send",
modality="audio",
additional_inputs=[chatbot, state],
additional_outputs=[chatbot, state],
additional_outputs_handler=lambda *a: (a[2], a[3]),
concurrency_limit=20 if get_space() else None,
rtc_configuration=get_twilio_turn_credentials() if get_space() else None,
)
app = FastAPI()
stream.mount(app)
class Message(BaseModel):
role: str
content: str
class InputData(BaseModel):
webrtc_id: str
chatbot: list[Message]
state: list[Message]
@app.get("/")
async def _():
rtc_config = get_twilio_turn_credentials() if get_space() else None
html_content = (curr_dir / "index.html").read_text()
html_content = html_content.replace("__RTC_CONFIGURATION__", json.dumps(rtc_config))
return HTMLResponse(content=html_content)
@app.post("/input_hook")
async def _(data: InputData):
body = data.model_dump()
stream.set_input(data.webrtc_id, body["chatbot"], body["state"])
def audio_to_base64(file_path):
audio_format = "wav"
with open(file_path, "rb") as audio_file:
encoded_audio = base64.b64encode(audio_file.read()).decode("utf-8")
return f"data:audio/{audio_format};base64,{encoded_audio}"
@app.get("/outputs")
async def _(webrtc_id: str):
async def output_stream():
async for output in stream.output_stream(webrtc_id):
chatbot = output.args[0]
state = output.args[1]
data = {
"message": state[-1],
"audio": audio_to_base64(chatbot[-1]["content"].value["path"])
if chatbot[-1]["role"] == "user"
else None,
}
yield f"event: output\ndata: {json.dumps(data)}\n\n"
return StreamingResponse(output_stream(), media_type="text/event-stream")
if __name__ == "__main__":
import os
if (mode := os.getenv("MODE")) == "UI":
stream.ui.launch(server_port=7860)
elif mode == "PHONE":
raise ValueError("Phone mode not supported")
else:
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=7860)

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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Talk to Sambanova</title>
<style>
body {
font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif;
background-color: #f8f9fa;
color: #1a1a1a;
margin: 0;
padding: 20px;
height: 100vh;
box-sizing: border-box;
}
.container {
max-width: 800px;
margin: 0 auto;
height: 80%;
}
.logo {
text-align: center;
margin-bottom: 40px;
}
.chat-container {
background: white;
border-radius: 8px;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
padding: 20px;
height: 90%;
box-sizing: border-box;
display: flex;
flex-direction: column;
}
.chat-messages {
flex-grow: 1;
overflow-y: auto;
margin-bottom: 20px;
padding: 10px;
}
.message {
margin-bottom: 20px;
padding: 12px;
border-radius: 8px;
font-size: 14px;
line-height: 1.5;
}
.message.user {
background-color: #e9ecef;
margin-left: 20%;
}
.message.assistant {
background-color: #f1f3f5;
margin-right: 20%;
}
.controls {
text-align: center;
margin-top: 20px;
}
button {
background-color: #0066cc;
color: white;
border: none;
padding: 12px 24px;
font-family: inherit;
font-size: 14px;
cursor: pointer;
transition: all 0.3s;
border-radius: 4px;
font-weight: 500;
}
button:hover {
background-color: #0052a3;
}
#audio-output {
display: none;
}
.icon-with-spinner {
display: flex;
align-items: center;
justify-content: center;
gap: 12px;
min-width: 180px;
}
.spinner {
width: 20px;
height: 20px;
border: 2px solid #ffffff;
border-top-color: transparent;
border-radius: 50%;
animation: spin 1s linear infinite;
flex-shrink: 0;
}
@keyframes spin {
to {
transform: rotate(360deg);
}
}
.pulse-container {
display: flex;
align-items: center;
justify-content: center;
gap: 12px;
min-width: 180px;
}
.pulse-circle {
width: 20px;
height: 20px;
border-radius: 50%;
background-color: #ffffff;
opacity: 0.2;
flex-shrink: 0;
transform: translateX(-0%) scale(var(--audio-level, 1));
transition: transform 0.1s ease;
}
/* Add styles for typing indicator */
.typing-indicator {
padding: 8px;
background-color: #f1f3f5;
border-radius: 8px;
margin-bottom: 10px;
display: none;
}
.dots {
display: inline-flex;
gap: 4px;
}
.dot {
width: 8px;
height: 8px;
background-color: #0066cc;
border-radius: 50%;
animation: pulse 1.5s infinite;
opacity: 0.5;
}
.dot:nth-child(2) {
animation-delay: 0.5s;
}
.dot:nth-child(3) {
animation-delay: 1s;
}
@keyframes pulse {
0%,
100% {
opacity: 0.5;
transform: scale(1);
}
50% {
opacity: 1;
transform: scale(1.2);
}
}
/* Add styles for toast notifications */
.toast {
position: fixed;
top: 20px;
left: 50%;
transform: translateX(-50%);
padding: 16px 24px;
border-radius: 4px;
font-size: 14px;
z-index: 1000;
display: none;
box-shadow: 0 2px 5px rgba(0, 0, 0, 0.2);
}
.toast.error {
background-color: #f44336;
color: white;
}
.toast.warning {
background-color: #ffd700;
color: black;
}
</style>
</head>
<body>
<!-- Add toast element after body opening tag -->
<div id="error-toast" class="toast"></div>
<div class="container">
<div class="logo">
<h1>Talk to Sambanova 🗣️</h1>
<h2 style="font-size: 1.2em; color: #666; margin-top: 10px;">Speak to Llama 3.2 powered by Sambanova API
</h2>
</div>
<div class="chat-container">
<div class="chat-messages" id="chat-messages"></div>
<div class="typing-indicator" id="typing-indicator">
<div class="dots">
<div class="dot"></div>
<div class="dot"></div>
<div class="dot"></div>
</div>
</div>
</div>
<div class="controls">
<button id="start-button">Start Conversation</button>
</div>
</div>
<audio id="audio-output"></audio>
<script>
let peerConnection;
let webrtc_id;
const startButton = document.getElementById('start-button');
const chatMessages = document.getElementById('chat-messages');
let audioLevel = 0;
let animationFrame;
let audioContext, analyser, audioSource;
let messages = [];
let eventSource;
function updateButtonState() {
const button = document.getElementById('start-button');
if (peerConnection && (peerConnection.connectionState === 'connecting' || peerConnection.connectionState === 'new')) {
button.innerHTML = `
<div class="icon-with-spinner">
<div class="spinner"></div>
<span>Connecting...</span>
</div>
`;
} else if (peerConnection && peerConnection.connectionState === 'connected') {
button.innerHTML = `
<div class="pulse-container">
<div class="pulse-circle"></div>
<span>Stop Conversation</span>
</div>
`;
} else {
button.innerHTML = 'Start Conversation';
}
}
function setupAudioVisualization(stream) {
audioContext = new (window.AudioContext || window.webkitAudioContext)();
analyser = audioContext.createAnalyser();
audioSource = audioContext.createMediaStreamSource(stream);
audioSource.connect(analyser);
analyser.fftSize = 64;
const dataArray = new Uint8Array(analyser.frequencyBinCount);
function updateAudioLevel() {
analyser.getByteFrequencyData(dataArray);
const average = Array.from(dataArray).reduce((a, b) => a + b, 0) / dataArray.length;
audioLevel = average / 255;
const pulseCircle = document.querySelector('.pulse-circle');
if (pulseCircle) {
pulseCircle.style.setProperty('--audio-level', 1 + audioLevel);
}
animationFrame = requestAnimationFrame(updateAudioLevel);
}
updateAudioLevel();
}
function showError(message) {
const toast = document.getElementById('error-toast');
toast.textContent = message;
toast.className = 'toast error';
toast.style.display = 'block';
// Hide toast after 5 seconds
setTimeout(() => {
toast.style.display = 'none';
}, 5000);
}
function handleMessage(event) {
const eventJson = JSON.parse(event.data);
const typingIndicator = document.getElementById('typing-indicator');
if (eventJson.type === "error") {
showError(eventJson.message);
} else if (eventJson.type === "send_input") {
fetch('/input_hook', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({
webrtc_id: webrtc_id,
chatbot: messages,
state: messages
})
});
} else if (eventJson.type === "log") {
if (eventJson.data === "pause_detected") {
typingIndicator.style.display = 'block';
chatMessages.scrollTop = chatMessages.scrollHeight;
} else if (eventJson.data === "response_starting") {
typingIndicator.style.display = 'none';
}
}
}
async function setupWebRTC() {
const config = __RTC_CONFIGURATION__;
peerConnection = new RTCPeerConnection(config);
const timeoutId = setTimeout(() => {
const toast = document.getElementById('error-toast');
toast.textContent = "Connection is taking longer than usual. Are you on a VPN?";
toast.className = 'toast warning';
toast.style.display = 'block';
// Hide warning after 5 seconds
setTimeout(() => {
toast.style.display = 'none';
}, 5000);
}, 5000);
try {
const stream = await navigator.mediaDevices.getUserMedia({
audio: true
});
setupAudioVisualization(stream);
stream.getTracks().forEach(track => {
peerConnection.addTrack(track, stream);
});
const dataChannel = peerConnection.createDataChannel('text');
dataChannel.onmessage = handleMessage;
const offer = await peerConnection.createOffer();
await peerConnection.setLocalDescription(offer);
await new Promise((resolve) => {
if (peerConnection.iceGatheringState === "complete") {
resolve();
} else {
const checkState = () => {
if (peerConnection.iceGatheringState === "complete") {
peerConnection.removeEventListener("icegatheringstatechange", checkState);
resolve();
}
};
peerConnection.addEventListener("icegatheringstatechange", checkState);
}
});
peerConnection.addEventListener('connectionstatechange', () => {
console.log('connectionstatechange', peerConnection.connectionState);
if (peerConnection.connectionState === 'connected') {
clearTimeout(timeoutId);
const toast = document.getElementById('error-toast');
toast.style.display = 'none';
}
updateButtonState();
});
webrtc_id = Math.random().toString(36).substring(7);
const response = await fetch('/webrtc/offer', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
sdp: peerConnection.localDescription.sdp,
type: peerConnection.localDescription.type,
webrtc_id: webrtc_id
})
});
const serverResponse = await response.json();
if (serverResponse.status === 'failed') {
showError(serverResponse.meta.error === 'concurrency_limit_reached'
? `Too many connections. Maximum limit is ${serverResponse.meta.limit}`
: serverResponse.meta.error);
stop();
return;
}
await peerConnection.setRemoteDescription(serverResponse);
eventSource = new EventSource('/outputs?webrtc_id=' + webrtc_id);
eventSource.addEventListener("output", (event) => {
const eventJson = JSON.parse(event.data);
console.log(eventJson);
messages.push(eventJson.message);
addMessage(eventJson.message.role, eventJson.audio ?? eventJson.message.content);
});
} catch (err) {
clearTimeout(timeoutId);
console.error('Error setting up WebRTC:', err);
showError('Failed to establish connection. Please try again.');
stop();
}
}
function addMessage(role, content) {
const messageDiv = document.createElement('div');
messageDiv.classList.add('message', role);
if (role === 'user') {
// Create audio element for user messages
const audio = document.createElement('audio');
audio.controls = true;
audio.src = content;
messageDiv.appendChild(audio);
} else {
// Text content for assistant messages
messageDiv.textContent = content;
}
chatMessages.appendChild(messageDiv);
chatMessages.scrollTop = chatMessages.scrollHeight;
}
function stop() {
if (eventSource) {
eventSource.close();
eventSource = null;
}
if (animationFrame) {
cancelAnimationFrame(animationFrame);
}
if (audioContext) {
audioContext.close();
audioContext = null;
analyser = null;
audioSource = null;
}
if (peerConnection) {
if (peerConnection.getTransceivers) {
peerConnection.getTransceivers().forEach(transceiver => {
if (transceiver.stop) {
transceiver.stop();
}
});
}
if (peerConnection.getSenders) {
peerConnection.getSenders().forEach(sender => {
if (sender.track && sender.track.stop) sender.track.stop();
});
}
peerConnection.close();
}
updateButtonState();
audioLevel = 0;
}
startButton.addEventListener('click', () => {
if (!peerConnection || peerConnection.connectionState !== 'connected') {
setupWebRTC();
} else {
stop();
}
});
</script>
</body>
</html>

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demo/talk_to_sambanova/requirements.txt Normal file
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fastrtc[vad, stt]
python-dotenv
openai
twilio

65
demo/video_out.py
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import os
import cv2
import gradio as gr
from gradio_webrtc import WebRTC
from twilio.rest import Client
account_sid = os.environ.get("TWILIO_ACCOUNT_SID")
auth_token = os.environ.get("TWILIO_AUTH_TOKEN")
if account_sid and auth_token:
client = Client(account_sid, auth_token)
token = client.tokens.create()
rtc_configuration = {
"iceServers": token.ice_servers,
"iceTransportPolicy": "relay",
}
else:
rtc_configuration = None
def generation(input_video):
cap = cv2.VideoCapture(input_video)
iterating = True
while iterating:
iterating, frame = cap.read()
# flip frame vertically
frame = cv2.flip(frame, 0)
display_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
yield display_frame
with gr.Blocks() as demo:
gr.HTML(
"""
<h1 style='text-align: center'>
Video Streaming (Powered by WebRTC ⚡️)
</h1>
"""
)
with gr.Row():
with gr.Column():
input_video = gr.Video(sources="upload")
with gr.Column():
output_video = WebRTC(
label="Video Stream",
rtc_configuration=rtc_configuration,
mode="receive",
modality="video",
)
output_video.stream(
fn=generation,
inputs=[input_video],
outputs=[output_video],
trigger=input_video.upload,
)
if __name__ == "__main__":
demo.launch()

54
demo/video_out_stream.py
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import os
import cv2
import gradio as gr
from gradio_webrtc import WebRTC
from twilio.rest import Client
account_sid = os.environ.get("TWILIO_ACCOUNT_SID")
auth_token = os.environ.get("TWILIO_AUTH_TOKEN")
if account_sid and auth_token:
client = Client(account_sid, auth_token)
token = client.tokens.create()
rtc_configuration = {
"iceServers": token.ice_servers,
"iceTransportPolicy": "relay",
}
else:
rtc_configuration = None
def generation():
url = "https://download.tsi.telecom-paristech.fr/gpac/dataset/dash/uhd/mux_sources/hevcds_720p30_2M.mp4"
cap = cv2.VideoCapture(url)
iterating = True
while iterating:
iterating, frame = cap.read()
yield frame
with gr.Blocks() as demo:
gr.HTML(
"""
<h1 style='text-align: center'>
Video Streaming (Powered by WebRTC ⚡️)
</h1>
"""
)
output_video = WebRTC(
label="Video Stream",
rtc_configuration=rtc_configuration,
mode="receive",
modality="video",
)
button = gr.Button("Start", variant="primary")
output_video.stream(
fn=generation, inputs=None, outputs=[output_video], trigger=button.click
)
if __name__ == "__main__":
demo.launch()

102
demo/video_send_output.py
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import logging
import os
import random
import cv2
import gradio as gr
from gradio_webrtc import AdditionalOutputs, WebRTC
from huggingface_hub import hf_hub_download
from inference import YOLOv10
from twilio.rest import Client
# Configure the root logger to WARNING to suppress debug messages from other libraries
logging.basicConfig(level=logging.WARNING)
# Create a console handler
console_handler = logging.FileHandler("gradio_webrtc.log")
console_handler.setLevel(logging.DEBUG)
# Create a formatter
formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
console_handler.setFormatter(formatter)
# Configure the logger for your specific library
logger = logging.getLogger("gradio_webrtc")
logger.setLevel(logging.DEBUG)
logger.addHandler(console_handler)
model_file = hf_hub_download(
repo_id="onnx-community/yolov10n", filename="onnx/model.onnx"
)
model = YOLOv10(model_file)
account_sid = os.environ.get("TWILIO_ACCOUNT_SID")
auth_token = os.environ.get("TWILIO_AUTH_TOKEN")
if account_sid and auth_token:
client = Client(account_sid, auth_token)
token = client.tokens.create()
rtc_configuration = {
"iceServers": token.ice_servers,
"iceTransportPolicy": "relay",
}
else:
rtc_configuration = None
def detection(image, conf_threshold=0.3):
image = cv2.resize(image, (model.input_width, model.input_height))
new_image = model.detect_objects(image, conf_threshold)
return cv2.resize(new_image, (500, 500))
css = """.my-group {max-width: 600px !important; max-height: 600 !important;}
.my-column {display: flex !important; justify-content: center !important; align-items: center !important};"""
with gr.Blocks(css=css) as demo:
gr.HTML(
"""
<h1 style='text-align: center'>
YOLOv10 Webcam Stream (Powered by WebRTC ⚡️)
</h1>
"""
)
gr.HTML(
"""
<h3 style='text-align: center'>
<a href='https://arxiv.org/abs/2405.14458' target='_blank'>arXiv</a> | <a href='https://github.com/THU-MIG/yolov10' target='_blank'>github</a>
</h3>
"""
)
with gr.Column(elem_classes=["my-column"]):
with gr.Group(elem_classes=["my-group"]):
image = WebRTC(
label="Stream",
rtc_configuration=rtc_configuration,
mode="send-receive",
modality="video",
track_constraints={
"width": {"exact": 800},
"height": {"exact": 600},
"aspectRatio": {"exact": 1.33333},
},
rtp_params={"degradationPreference": "maintain-resolution"},
)
conf_threshold = gr.Slider(
label="Confidence Threshold",
minimum=0.0,
maximum=1.0,
step=0.05,
value=0.30,
)
number = gr.Number()
image.stream(
fn=detection, inputs=[image, conf_threshold], outputs=[image], time_limit=90
)
demo.launch()

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demo/webrtc_vs_websocket/README.md Normal file
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---
title: Webrtc Vs Websocket
emoji: 🧪
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 5.16.0
app_file: app.py
pinned: false
license: mit
short_description: Compare Round Trip Times between WebRTC and Websockets
tags: [webrtc, websocket, gradio, secret|TWILIO_ACCOUNT_SID, secret|TWILIO_AUTH_TOKEN, secret|ELEVENLABS_API_KEY, secret|GROQ_API_KEY, secret|ANTHROPIC_API_KEY]
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

147
demo/webrtc_vs_websocket/app.py Normal file
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import json
import logging
import os
from pathlib import Path
import anthropic
import gradio as gr
import numpy as np
from dotenv import load_dotenv
from elevenlabs import ElevenLabs
from fastapi import FastAPI
from fastapi.responses import HTMLResponse, StreamingResponse
from fastrtc import AdditionalOutputs, ReplyOnPause, Stream, get_twilio_turn_credentials
from fastrtc.utils import aggregate_bytes_to_16bit, audio_to_bytes
from gradio.utils import get_space
from groq import Groq
from pydantic import BaseModel
# Configure the root logger to WARNING to suppress debug messages from other libraries
logging.basicConfig(level=logging.WARNING)
# Create a console handler
console_handler = logging.FileHandler("gradio_webrtc.log")
console_handler.setLevel(logging.DEBUG)
# Create a formatter
formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
console_handler.setFormatter(formatter)
# Configure the logger for your specific library
logger = logging.getLogger("fastrtc")
logger.setLevel(logging.DEBUG)
logger.addHandler(console_handler)
load_dotenv()
groq_client = Groq()
claude_client = anthropic.Anthropic()
tts_client = ElevenLabs(api_key=os.environ["ELEVENLABS_API_KEY"])
curr_dir = Path(__file__).parent
def response(
audio: tuple[int, np.ndarray],
chatbot: list[dict] | None = None,
):
chatbot = chatbot or []
messages = [{"role": d["role"], "content": d["content"]} for d in chatbot]
prompt = groq_client.audio.transcriptions.create(
file=("audio-file.mp3", audio_to_bytes(audio)),
model="whisper-large-v3-turbo",
response_format="verbose_json",
).text
print("prompt", prompt)
chatbot.append({"role": "user", "content": prompt})
messages.append({"role": "user", "content": prompt})
response = claude_client.messages.create(
model="claude-3-5-haiku-20241022",
max_tokens=512,
messages=messages, # type: ignore
)
response_text = " ".join(
block.text # type: ignore
for block in response.content
if getattr(block, "type", None) == "text"
)
chatbot.append({"role": "assistant", "content": response_text})
yield AdditionalOutputs(chatbot)
iterator = tts_client.text_to_speech.convert_as_stream(
text=response_text,
voice_id="JBFqnCBsd6RMkjVDRZzb",
model_id="eleven_multilingual_v2",
output_format="pcm_24000",
)
for chunk in aggregate_bytes_to_16bit(iterator):
audio_array = np.frombuffer(chunk, dtype=np.int16).reshape(1, -1)
yield (24000, audio_array, "mono")
chatbot = gr.Chatbot(type="messages")
stream = Stream(
modality="audio",
mode="send-receive",
handler=ReplyOnPause(response),
additional_outputs_handler=lambda a, b: b,
additional_inputs=[chatbot],
additional_outputs=[chatbot],
rtc_configuration=get_twilio_turn_credentials() if get_space() else None,
concurrency_limit=20 if get_space() else None,
)
class Message(BaseModel):
role: str
content: str
class InputData(BaseModel):
webrtc_id: str
chatbot: list[Message]
app = FastAPI()
stream.mount(app)
@app.get("/")
async def _():
rtc_config = get_twilio_turn_credentials() if get_space() else None
html_content = (curr_dir / "index.html").read_text()
html_content = html_content.replace("__RTC_CONFIGURATION__", json.dumps(rtc_config))
return HTMLResponse(content=html_content, status_code=200)
@app.post("/input_hook")
async def _(body: InputData):
stream.set_input(body.webrtc_id, body.model_dump()["chatbot"])
return {"status": "ok"}
@app.get("/outputs")
def _(webrtc_id: str):
print("outputs", webrtc_id)
async def output_stream():
async for output in stream.output_stream(webrtc_id):
chatbot = output.args[0]
yield f"event: output\ndata: {json.dumps(chatbot[-2])}\n\n"
yield f"event: output\ndata: {json.dumps(chatbot[-1])}\n\n"
return StreamingResponse(output_stream(), media_type="text/event-stream")
if __name__ == "__main__":
import os
if (mode := os.getenv("MODE")) == "UI":
stream.ui.launch(server_port=7860, server_name="0.0.0.0")
elif mode == "PHONE":
stream.fastphone(host="0.0.0.0", port=7860)
else:
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=7860)

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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>WebRTC vs WebSocket Benchmark</title>
<script src="https://cdn.jsdelivr.net/npm/alawmulaw"></script>
<style>
body {
font-family: system-ui, -apple-system, sans-serif;
margin: 0;
padding: 20px;
background-color: #f5f5f5;
}
.container {
display: grid;
grid-template-columns: 1fr 1fr;
gap: 30px;
max-width: 1400px;
margin: 0 auto;
}
.panel {
background: white;
border-radius: 12px;
padding: 20px;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
}
.chat-container {
height: 400px;
overflow-y: auto;
border: 1px solid #e0e0e0;
border-radius: 8px;
padding: 15px;
margin-bottom: 15px;
}
.message {
margin-bottom: 10px;
padding: 8px 12px;
border-radius: 8px;
max-width: 80%;
}
.message.user {
background-color: #e3f2fd;
margin-left: auto;
}
.message.assistant {
background-color: #f5f5f5;
}
.metrics {
margin-top: 15px;
padding: 10px;
background: #f8f9fa;
border-radius: 8px;
}
.metric {
margin: 5px 0;
font-size: 14px;
}
button {
background-color: #1976d2;
color: white;
border: none;
padding: 10px 20px;
border-radius: 6px;
cursor: pointer;
font-size: 14px;
transition: background-color 0.2s;
}
button:hover {
background-color: #1565c0;
}
button:disabled {
background-color: #bdbdbd;
cursor: not-allowed;
}
h2 {
margin-top: 0;
color: #1976d2;
}
.visualizer {
width: 100%;
height: 100px;
margin: 10px 0;
background: #fafafa;
border-radius: 8px;
}
/* Add styles for disclaimer */
.disclaimer {
background-color: #fff3e0;
padding: 15px;
border-radius: 8px;
margin-bottom: 20px;
font-size: 14px;
line-height: 1.5;
max-width: 1400px;
margin: 0 auto 20px auto;
}
/* Update nav bar styles */
.nav-bar {
background-color: #f5f5f5;
padding: 10px 20px;
margin-bottom: 20px;
}
.nav-container {
max-width: 1400px;
margin: 0 auto;
display: flex;
gap: 10px;
}
.nav-button {
background-color: #1976d2;
color: white;
border: none;
padding: 8px 16px;
border-radius: 4px;
cursor: pointer;
text-decoration: none;
font-size: 14px;
transition: background-color 0.2s;
}
.nav-button:hover {
background-color: #1565c0;
}
/* Add styles for toast notifications */
.toast {
position: fixed;
top: 20px;
left: 50%;
transform: translateX(-50%);
padding: 16px 24px;
border-radius: 4px;
font-size: 14px;
z-index: 1000;
display: none;
box-shadow: 0 2px 5px rgba(0, 0, 0, 0.2);
}
.toast.error {
background-color: #f44336;
color: white;
}
.toast.warning {
background-color: #ffd700;
color: black;
}
</style>
</head>
<body>
<nav class="nav-bar">
<div class="nav-container">
<a href="./webrtc/docs" class="nav-button">WebRTC Docs</a>
<a href="./websocket/docs" class="nav-button">WebSocket Docs</a>
<a href="./telephone/docs" class="nav-button">Telephone Docs</a>
<a href="./ui" class="nav-button">UI</a>
</div>
</nav>
<div class="disclaimer">
This page compares the WebRTC Round-Trip-Time calculated from <code>getStats()</code> to the time taken to
process a ping/pong response pattern over websockets. It may not be a gold standard benchmark. Both WebRTC and
Websockets have their merits/advantages which is why FastRTC supports both. Artifacts in the WebSocket playback
audio are due to gaps in my frontend processing code and not FastRTC web server.
</div>
<div class="container">
<div class="panel">
<h2>WebRTC Connection</h2>
<div id="webrtc-chat" class="chat-container"></div>
<div id="webrtc-metrics" class="metrics">
<div class="metric">RTT (Round Trip Time): <span id="webrtc-rtt">-</span></div>
</div>
<button id="webrtc-button">Connect WebRTC</button>
</div>
<div class="panel">
<h2>WebSocket Connection</h2>
<div id="ws-chat" class="chat-container"></div>
<div id="ws-metrics" class="metrics">
<div class="metric">RTT (Round Trip Time): <span id="ws-rtt">0</span></div>
</div>
<button id="ws-button">Connect WebSocket</button>
</div>
</div>
<audio id="webrtc-audio" style="display: none;"></audio>
<audio id="ws-audio" style="display: none;"></audio>
<div id="error-toast" class="toast"></div>
<script>
// Shared utilities
function generateId() {
return Math.random().toString(36).substring(7);
}
function sendInput(id) {
return function handleMessage(event) {
const eventJson = JSON.parse(event.data);
if (eventJson.type === "send_input") {
fetch('/input_hook', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({
webrtc_id: id,
chatbot: chatHistoryWebRTC
})
});
}
}
}
let chatHistoryWebRTC = [];
let chatHistoryWebSocket = [];
function addMessage(containerId, role, content) {
const container = document.getElementById(containerId);
const messageDiv = document.createElement('div');
messageDiv.classList.add('message', role);
messageDiv.textContent = content;
container.appendChild(messageDiv);
container.scrollTop = container.scrollHeight;
if (containerId === 'webrtc-chat') {
chatHistoryWebRTC.push({ role, content });
} else {
chatHistoryWebSocket.push({ role, content });
}
}
// WebRTC Implementation
let webrtcPeerConnection;
// Add this function to collect RTT stats
async function updateWebRTCStats() {
if (!webrtcPeerConnection) return;
const stats = await webrtcPeerConnection.getStats();
stats.forEach(report => {
if (report.type === 'candidate-pair' && report.state === 'succeeded') {
const rtt = report.currentRoundTripTime * 1000; // Convert to ms
document.getElementById('webrtc-rtt').textContent = `${rtt.toFixed(2)}ms`;
}
});
}
async function setupWebRTC() {
const button = document.getElementById('webrtc-button');
button.textContent = "Stop";
const config = __RTC_CONFIGURATION__;
webrtcPeerConnection = new RTCPeerConnection(config);
const webrtcId = generateId();
const timeoutId = setTimeout(() => {
const toast = document.getElementById('error-toast');
toast.textContent = "Connection is taking longer than usual. Are you on a VPN?";
toast.className = 'toast warning';
toast.style.display = 'block';
// Hide warning after 5 seconds
setTimeout(() => {
toast.style.display = 'none';
}, 5000);
}, 5000);
try {
const stream = await navigator.mediaDevices.getUserMedia({ audio: true });
stream.getTracks().forEach(track => {
webrtcPeerConnection.addTrack(track, stream);
});
webrtcPeerConnection.addEventListener('track', (evt) => {
const audio = document.getElementById('webrtc-audio');
if (audio.srcObject !== evt.streams[0]) {
audio.srcObject = evt.streams[0];
audio.play();
}
});
const dataChannel = webrtcPeerConnection.createDataChannel('text');
dataChannel.onmessage = sendInput(webrtcId);
const offer = await webrtcPeerConnection.createOffer();
await webrtcPeerConnection.setLocalDescription(offer);
await new Promise((resolve) => {
if (webrtcPeerConnection.iceGatheringState === "complete") {
resolve();
} else {
const checkState = () => {
if (webrtcPeerConnection.iceGatheringState === "complete") {
webrtcPeerConnection.removeEventListener("icegatheringstatechange", checkState);
resolve();
}
};
webrtcPeerConnection.addEventListener("icegatheringstatechange", checkState);
}
});
const response = await fetch('/webrtc/offer', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
sdp: webrtcPeerConnection.localDescription.sdp,
type: webrtcPeerConnection.localDescription.type,
webrtc_id: webrtcId
})
});
const serverResponse = await response.json();
await webrtcPeerConnection.setRemoteDescription(serverResponse);
// Setup event source for messages
const eventSource = new EventSource('/outputs?webrtc_id=' + webrtcId);
eventSource.addEventListener("output", (event) => {
const eventJson = JSON.parse(event.data);
addMessage('webrtc-chat', eventJson.role, eventJson.content);
});
// Add periodic stats collection
const statsInterval = setInterval(updateWebRTCStats, 1000);
// Store the interval ID on the connection
webrtcPeerConnection.statsInterval = statsInterval;
webrtcPeerConnection.addEventListener('connectionstatechange', () => {
if (webrtcPeerConnection.connectionState === 'connected') {
clearTimeout(timeoutId);
const toast = document.getElementById('error-toast');
toast.style.display = 'none';
}
});
} catch (err) {
clearTimeout(timeoutId);
console.error('WebRTC setup error:', err);
}
}
function webrtc_stop() {
if (webrtcPeerConnection) {
// Clear the stats interval
if (webrtcPeerConnection.statsInterval) {
clearInterval(webrtcPeerConnection.statsInterval);
}
// Close all tracks
webrtcPeerConnection.getSenders().forEach(sender => {
if (sender.track) {
sender.track.stop();
}
});
webrtcPeerConnection.close();
webrtcPeerConnection = null;
// Reset metrics display
document.getElementById('webrtc-rtt').textContent = '-';
}
}
// WebSocket Implementation
let webSocket;
let wsMetrics = {
pingStartTime: 0,
rttValues: []
};
// Load mu-law library
// Add load promise to track when the script is ready
function resample(audioData, fromSampleRate, toSampleRate) {
const ratio = fromSampleRate / toSampleRate;
const newLength = Math.round(audioData.length / ratio);
const result = new Float32Array(newLength);
for (let i = 0; i < newLength; i++) {
const position = i * ratio;
const index = Math.floor(position);
const fraction = position - index;
if (index + 1 < audioData.length) {
result[i] = audioData[index] * (1 - fraction) + audioData[index + 1] * fraction;
} else {
result[i] = audioData[index];
}
}
return result;
}
function convertToMulaw(audioData, sampleRate) {
// Resample to 8000 Hz if needed
if (sampleRate !== 8000) {
audioData = resample(audioData, sampleRate, 8000);
}
// Convert float32 [-1,1] to int16 [-32768,32767]
const int16Data = new Int16Array(audioData.length);
for (let i = 0; i < audioData.length; i++) {
int16Data[i] = Math.floor(audioData[i] * 32768);
}
// Convert to mu-law using the library
return alawmulaw.mulaw.encode(int16Data);
}
async function setupWebSocket() {
const button = document.getElementById('ws-button');
button.textContent = "Stop";
try {
const stream = await navigator.mediaDevices.getUserMedia({
audio: {
"echoCancellation": true,
"noiseSuppression": { "exact": true },
"autoGainControl": { "exact": true },
"sampleRate": { "ideal": 24000 },
"sampleSize": { "ideal": 16 },
"channelCount": { "exact": 1 },
}
});
const wsId = generateId();
wsMetrics.startTime = performance.now();
// Create audio context and analyser for visualization
const audioContext = new AudioContext();
const analyser = audioContext.createAnalyser();
const source = audioContext.createMediaStreamSource(stream);
source.connect(analyser);
// Connect to websocket endpoint
webSocket = new WebSocket(`${window.location.protocol === 'https:' ? 'wss:' : 'ws:'}//${window.location.host}/websocket/offer`);
webSocket.onopen = () => {
// Send initial start message
webSocket.send(JSON.stringify({
event: "start",
websocket_id: wsId
}));
// Setup audio processing
const processor = audioContext.createScriptProcessor(2048, 1, 1);
source.connect(processor);
processor.connect(audioContext.destination);
processor.onaudioprocess = (e) => {
const inputData = e.inputBuffer.getChannelData(0);
const mulawData = convertToMulaw(inputData, audioContext.sampleRate);
const base64Audio = btoa(String.fromCharCode.apply(null, mulawData));
if (webSocket.readyState === WebSocket.OPEN) {
webSocket.send(JSON.stringify({
event: "media",
media: {
payload: base64Audio
}
}));
}
};
// Add ping interval
webSocket.pingInterval = setInterval(() => {
wsMetrics.pingStartTime = performance.now();
webSocket.send(JSON.stringify({
event: "ping"
}));
}, 500);
};
// Setup audio output context
const outputContext = new AudioContext({ sampleRate: 24000 });
const sampleRate = 24000; // Updated to match server sample rate
let audioQueue = [];
let isPlaying = false;
webSocket.onmessage = (event) => {
const data = JSON.parse(event.data);
if (data?.type === "send_input") {
console.log("sending input")
fetch('/input_hook', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ webrtc_id: wsId, chatbot: chatHistoryWebSocket })
});
}
if (data.event === "media") {
// Process received audio
const audioData = atob(data.media.payload);
const mulawData = new Uint8Array(audioData.length);
for (let i = 0; i < audioData.length; i++) {
mulawData[i] = audioData.charCodeAt(i);
}
// Convert mu-law to linear PCM
const linearData = alawmulaw.mulaw.decode(mulawData);
// Create an AudioBuffer
const audioBuffer = outputContext.createBuffer(1, linearData.length, sampleRate);
const channelData = audioBuffer.getChannelData(0);
// Fill the buffer with the decoded data
for (let i = 0; i < linearData.length; i++) {
channelData[i] = linearData[i] / 32768.0;
}
// Queue the audio buffer
audioQueue.push(audioBuffer);
// Start playing if not already playing
if (!isPlaying) {
playNextBuffer();
}
}
// Add pong handler
if (data.event === "pong") {
const rtt = performance.now() - wsMetrics.pingStartTime;
wsMetrics.rttValues.push(rtt);
// Keep only last 20 values for running mean
if (wsMetrics.rttValues.length > 20) {
wsMetrics.rttValues.shift();
}
const avgRtt = wsMetrics.rttValues.reduce((a, b) => a + b, 0) / wsMetrics.rttValues.length;
document.getElementById('ws-rtt').textContent = `${avgRtt.toFixed(2)}ms`;
return;
}
};
function playNextBuffer() {
if (audioQueue.length === 0) {
isPlaying = false;
return;
}
isPlaying = true;
const bufferSource = outputContext.createBufferSource();
bufferSource.buffer = audioQueue.shift();
bufferSource.connect(outputContext.destination);
bufferSource.onended = playNextBuffer;
bufferSource.start();
}
const eventSource = new EventSource('/outputs?webrtc_id=' + wsId);
eventSource.addEventListener("output", (event) => {
console.log("ws output", event);
const eventJson = JSON.parse(event.data);
addMessage('ws-chat', eventJson.role, eventJson.content);
});
} catch (err) {
console.error('WebSocket setup error:', err);
button.disabled = false;
}
}
function ws_stop() {
if (webSocket) {
webSocket.send(JSON.stringify({
event: "stop"
}));
// Clear ping interval
if (webSocket.pingInterval) {
clearInterval(webSocket.pingInterval);
}
// Reset RTT display
document.getElementById('ws-rtt').textContent = '-';
wsMetrics.rttValues = [];
// Clear the stats interval
if (webSocket.statsInterval) {
clearInterval(webSocket.statsInterval);
}
webSocket.close();
}
}
// Event Listeners
document.getElementById('webrtc-button').addEventListener('click', () => {
const button = document.getElementById('webrtc-button');
if (button.textContent === 'Connect WebRTC') {
setupWebRTC();
} else {
webrtc_stop();
button.textContent = 'Connect WebRTC';
}
});
const ws_start_button = document.getElementById('ws-button')
ws_start_button.addEventListener('click', () => {
if (ws_start_button.textContent === 'Connect WebSocket') {
setupWebSocket();
ws_start_button.textContent = 'Stop';
} else {
ws_stop();
ws_start_button.textContent = 'Connect WebSocket';
}
});
document.addEventListener("beforeunload", () => {
ws_stop();
webrtc_stop();
});
</script>
</body>
</html>

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demo/webrtc_vs_websocket/requirements.txt Normal file
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fastrtc[vad]
elevenlabs
groq
anthropic
twilio
python-dotenv

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---
title: Whisper Realtime Transcription
emoji: 👂
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 5.16.0
app_file: app.py
pinned: false
license: mit
short_description: Transcribe audio in realtime with Whisper
tags: [webrtc, websocket, gradio, secret|TWILIO_ACCOUNT_SID, secret|TWILIO_AUTH_TOKEN, secret|GROQ_API_KEY]
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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demo/whisper_realtime/README_gradio.md Normal file
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---
app_file: app.py
colorFrom: purple
colorTo: red
emoji: 👂
license: mit
pinned: false
sdk: gradio
sdk_version: 5.16.0
short_description: Transcribe audio in realtime - Gradio UI version
tags:
- webrtc
- websocket
- gradio
- secret|TWILIO_ACCOUNT_SID
- secret|TWILIO_AUTH_TOKEN
- secret|GROQ_API_KEY
title: Whisper Realtime Transcription (Gradio UI)
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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import json
from pathlib import Path
import gradio as gr
import numpy as np
from dotenv import load_dotenv
from fastapi import FastAPI
from fastapi.responses import HTMLResponse, StreamingResponse
from fastrtc import (
AdditionalOutputs,
ReplyOnPause,
Stream,
WebRTCError,
audio_to_bytes,
get_twilio_turn_credentials,
)
from gradio.utils import get_space
from groq import AsyncClient
cur_dir = Path(__file__).parent
load_dotenv()
groq_client = AsyncClient()
async def transcribe(audio: tuple[int, np.ndarray]):
try:
transcript = await groq_client.audio.transcriptions.create(
file=("audio-file.mp3", audio_to_bytes(audio)),
model="whisper-large-v3-turbo",
response_format="verbose_json",
)
yield AdditionalOutputs(transcript.text)
except Exception as e:
raise WebRTCError(str(e))
stream = Stream(
ReplyOnPause(transcribe),
modality="audio",
mode="send",
additional_outputs=[
gr.Textbox(label="Transcript"),
],
additional_outputs_handler=lambda a, b: a + " " + b,
rtc_configuration=get_twilio_turn_credentials() if get_space() else None,
concurrency_limit=5 if get_space() else None,
time_limit=90 if get_space() else None,
)
app = FastAPI()
stream.mount(app)
@app.get("/transcript")
def _(webrtc_id: str):
async def output_stream():
async for output in stream.output_stream(webrtc_id):
transcript = output.args[0]
yield f"event: output\ndata: {transcript}\n\n"
return StreamingResponse(output_stream(), media_type="text/event-stream")
@app.get("/")
def index():
rtc_config = get_twilio_turn_credentials() if get_space() else None
html_content = (cur_dir / "index.html").read_text()
html_content = html_content.replace("__RTC_CONFIGURATION__", json.dumps(rtc_config))
return HTMLResponse(content=html_content)
if __name__ == "__main__":
import os
if (mode := os.getenv("MODE")) == "UI":
stream.ui.launch(server_port=7860, server_name="0.0.0.0")
elif mode == "PHONE":
stream.fastphone(host="0.0.0.0", port=7860)
else:
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=7860)

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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Real-time Whisper Transcription</title>
<style>
:root {
--primary-gradient: linear-gradient(135deg, #f9a45c 0%, #e66465 100%);
--background-cream: #faf8f5;
--text-dark: #2d2d2d;
}
body {
font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif;
margin: 0;
padding: 0;
background-color: var(--background-cream);
color: var(--text-dark);
min-height: 100vh;
}
.hero {
background: var(--primary-gradient);
color: white;
padding: 2.5rem 2rem;
text-align: center;
}
.hero h1 {
font-size: 2.5rem;
margin: 0;
font-weight: 600;
letter-spacing: -0.5px;
}
.hero p {
font-size: 1rem;
margin-top: 0.5rem;
opacity: 0.9;
}
.container {
max-width: 1000px;
margin: 1.5rem auto;
padding: 0 2rem;
}
.transcript-container {
border-radius: 8px;
box-shadow: 0 2px 8px rgba(0, 0, 0, 0.06);
padding: 1.5rem;
height: 300px;
overflow-y: auto;
margin-bottom: 1.5rem;
border: 1px solid rgba(0, 0, 0, 0.1);
}
.controls {
text-align: center;
margin: 1.5rem 0;
}
button {
background: var(--primary-gradient);
color: white;
border: none;
padding: 10px 20px;
font-size: 0.95rem;
border-radius: 6px;
cursor: pointer;
transition: all 0.2s ease;
font-weight: 500;
min-width: 180px;
}
button:hover {
transform: translateY(-1px);
box-shadow: 0 4px 12px rgba(230, 100, 101, 0.15);
}
button:active {
transform: translateY(0);
}
/* Transcript text styling */
.transcript-container p {
margin: 0.4rem 0;
padding: 0.6rem;
background: var(--background-cream);
border-radius: 4px;
line-height: 1.4;
font-size: 0.95rem;
}
/* Custom scrollbar - made thinner */
.transcript-container::-webkit-scrollbar {
width: 6px;
}
.transcript-container::-webkit-scrollbar-track {
background: var(--background-cream);
border-radius: 3px;
}
.transcript-container::-webkit-scrollbar-thumb {
background: #e66465;
border-radius: 3px;
opacity: 0.8;
}
.transcript-container::-webkit-scrollbar-thumb:hover {
background: #f9a45c;
}
/* Add styles for toast notifications */
.toast {
position: fixed;
top: 20px;
left: 50%;
transform: translateX(-50%);
padding: 16px 24px;
border-radius: 4px;
font-size: 14px;
z-index: 1000;
display: none;
box-shadow: 0 2px 5px rgba(0, 0, 0, 0.2);
}
.toast.error {
background-color: #f44336;
color: white;
}
.toast.warning {
background-color: #ffd700;
color: black;
}
/* Add styles for audio visualization */
.icon-with-spinner {
display: flex;
align-items: center;
justify-content: center;
gap: 12px;
min-width: 180px;
}
.spinner {
width: 20px;
height: 20px;
border: 2px solid white;
border-top-color: transparent;
border-radius: 50%;
animation: spin 1s linear infinite;
flex-shrink: 0;
}
.pulse-container {
display: flex;
align-items: center;
justify-content: center;
gap: 12px;
min-width: 180px;
}
.pulse-circle {
width: 20px;
height: 20px;
border-radius: 50%;
background-color: white;
opacity: 0.2;
flex-shrink: 0;
transform: translateX(-0%) scale(var(--audio-level, 1));
transition: transform 0.1s ease;
}
@keyframes spin {
to {
transform: rotate(360deg);
}
}
</style>
</head>
<body>
<!-- Add toast element after body opening tag -->
<div id="error-toast" class="toast"></div>
<div class="hero">
<h1>Real-time Transcription</h1>
<p>Powered by Groq and FastRTC</p>
</div>
<div class="container">
<div class="transcript-container" id="transcript"></div>
<div class="controls">
<button id="start-button">Start Recording</button>
</div>
</div>
<script>
let peerConnection;
let webrtc_id;
let audioContext, analyser, audioSource;
let audioLevel = 0;
let animationFrame;
const startButton = document.getElementById('start-button');
const transcriptDiv = document.getElementById('transcript');
function showError(message) {
const toast = document.getElementById('error-toast');
toast.textContent = message;
toast.style.display = 'block';
// Hide toast after 5 seconds
setTimeout(() => {
toast.style.display = 'none';
}, 5000);
}
function handleMessage(event) {
// Handle any WebRTC data channel messages if needed
const eventJson = JSON.parse(event.data);
if (eventJson.type === "error") {
showError(eventJson.message);
}
console.log('Received message:', event.data);
}
function updateButtonState() {
if (peerConnection && (peerConnection.connectionState === 'connecting' || peerConnection.connectionState === 'new')) {
startButton.innerHTML = `
<div class="icon-with-spinner">
<div class="spinner"></div>
<span>Connecting...</span>
</div>
`;
} else if (peerConnection && peerConnection.connectionState === 'connected') {
startButton.innerHTML = `
<div class="pulse-container">
<div class="pulse-circle"></div>
<span>Stop Recording</span>
</div>
`;
} else {
startButton.innerHTML = 'Start Recording';
}
}
function setupAudioVisualization(stream) {
audioContext = new (window.AudioContext || window.webkitAudioContext)();
analyser = audioContext.createAnalyser();
audioSource = audioContext.createMediaStreamSource(stream);
audioSource.connect(analyser);
analyser.fftSize = 64;
const dataArray = new Uint8Array(analyser.frequencyBinCount);
function updateAudioLevel() {
analyser.getByteFrequencyData(dataArray);
const average = Array.from(dataArray).reduce((a, b) => a + b, 0) / dataArray.length;
audioLevel = average / 255;
const pulseCircle = document.querySelector('.pulse-circle');
if (pulseCircle) {
pulseCircle.style.setProperty('--audio-level', 1 + audioLevel);
}
animationFrame = requestAnimationFrame(updateAudioLevel);
}
updateAudioLevel();
}
async function setupWebRTC() {
const config = __RTC_CONFIGURATION__;
peerConnection = new RTCPeerConnection(config);
const timeoutId = setTimeout(() => {
const toast = document.getElementById('error-toast');
toast.textContent = "Connection is taking longer than usual. Are you on a VPN?";
toast.className = 'toast warning';
toast.style.display = 'block';
// Hide warning after 5 seconds
setTimeout(() => {
toast.style.display = 'none';
}, 5000);
}, 5000);
try {
const stream = await navigator.mediaDevices.getUserMedia({
audio: true
});
setupAudioVisualization(stream);
stream.getTracks().forEach(track => {
peerConnection.addTrack(track, stream);
});
// Add connection state change listener
peerConnection.addEventListener('connectionstatechange', () => {
console.log('connectionstatechange', peerConnection.connectionState);
if (peerConnection.connectionState === 'connected') {
clearTimeout(timeoutId);
const toast = document.getElementById('error-toast');
toast.style.display = 'none';
}
updateButtonState();
});
// Create data channel for messages
const dataChannel = peerConnection.createDataChannel('text');
dataChannel.onmessage = handleMessage;
// Create and send offer
const offer = await peerConnection.createOffer();
await peerConnection.setLocalDescription(offer);
await new Promise((resolve) => {
if (peerConnection.iceGatheringState === "complete") {
resolve();
} else {
const checkState = () => {
if (peerConnection.iceGatheringState === "complete") {
peerConnection.removeEventListener("icegatheringstatechange", checkState);
resolve();
}
};
peerConnection.addEventListener("icegatheringstatechange", checkState);
}
});
webrtc_id = Math.random().toString(36).substring(7);
const response = await fetch('/webrtc/offer', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
sdp: peerConnection.localDescription.sdp,
type: peerConnection.localDescription.type,
webrtc_id: webrtc_id
})
});
const serverResponse = await response.json();
if (serverResponse.status === 'failed') {
showError(serverResponse.meta.error === 'concurrency_limit_reached'
? `Too many connections. Maximum limit is ${serverResponse.meta.limit}`
: serverResponse.meta.error);
stop();
startButton.textContent = 'Start Recording';
return;
}
await peerConnection.setRemoteDescription(serverResponse);
// Create event stream to receive transcripts
const eventSource = new EventSource('/transcript?webrtc_id=' + webrtc_id);
eventSource.addEventListener("output", (event) => {
appendTranscript(event.data);
});
} catch (err) {
clearTimeout(timeoutId);
console.error('Error setting up WebRTC:', err);
showError('Failed to establish connection. Please try again.');
stop();
startButton.textContent = 'Start Recording';
}
}
function appendTranscript(text) {
const p = document.createElement('p');
p.textContent = text;
transcriptDiv.appendChild(p);
transcriptDiv.scrollTop = transcriptDiv.scrollHeight;
}
function stop() {
if (animationFrame) {
cancelAnimationFrame(animationFrame);
}
if (audioContext) {
audioContext.close();
audioContext = null;
analyser = null;
audioSource = null;
}
if (peerConnection) {
if (peerConnection.getTransceivers) {
peerConnection.getTransceivers().forEach(transceiver => {
if (transceiver.stop) {
transceiver.stop();
}
});
}
if (peerConnection.getSenders) {
peerConnection.getSenders().forEach(sender => {
if (sender.track && sender.track.stop) sender.track.stop();
});
}
setTimeout(() => {
peerConnection.close();
}, 500);
}
audioLevel = 0;
updateButtonState();
}
startButton.addEventListener('click', () => {
if (startButton.textContent === 'Start Recording') {
setupWebRTC();
} else {
stop();
}
});
</script>
</body>
</html>

4
demo/whisper_realtime/requirements.txt Normal file
View File

@@ -0,0 +1,4 @@
fastrtc[vad]
groq
python-dotenv
twilio

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