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Merge branch 'freddyaboulton:main' into audio-track-constraints

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.github/workflows/docs.yml vendored
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@@ -3,8 +3,16 @@ on:
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
@@ -24,5 +32,19 @@ jobs:
path: .cache path: .cache
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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.mypycache .mypycache
.ruff_cache .ruff_cache
node_modules node_modules
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/
.venv*
.env .env

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fastrtc.org

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README.md
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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/userguide/audio/#reply-on-pause)), 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/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(echo),
modality="audio",
mode="send-receive",
)
```
token = client.tokens.create() ### LLM Voice Chat
rtc_configuration = { ```py
"iceServers": token.ice_servers, from fastrtc import (
"iceTransportPolicy": "relay", ReplyOnPause, AdditionalOutputs, Stream,
} audio_to_bytes, aggregate_bytes_to_16bit
)
import gradio as gr
from groq import Groq
import anthropic
from elevenlabs import ElevenLabs
with gr.Blocks() as demo: groq_client = Groq()
... claude_client = anthropic.Anthropic()
rtc = WebRTC(rtc_configuration=rtc_configuration, ...) 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
```

48
backend/gradio_webrtc/__init__.py → backend/fastrtc/__init__.py
View File

@@ -3,11 +3,29 @@ from .credentials import (
get_turn_credentials, get_turn_credentials,
get_twilio_turn_credentials, get_twilio_turn_credentials,
) )
from .reply_on_pause import AlgoOptions, ReplyOnPause, SileroVadOptions from .pause_detection import (
ModelOptions,
PauseDetectionModel,
SileroVadOptions,
get_silero_model,
)
from .reply_on_pause import AlgoOptions, ReplyOnPause
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, UIArgs
from .text_to_speech import KokoroTTSOptions, get_tts_model
from .tracks import (
AsyncAudioVideoStreamHandler,
AsyncStreamHandler,
AudioEmitType,
AudioVideoStreamHandler,
StreamHandler,
VideoEmitType,
VideoStreamHandler,
)
from .utils import ( from .utils import (
AdditionalOutputs, AdditionalOutputs,
CloseStream,
Warning, Warning,
WebRTCError, WebRTCError,
aggregate_bytes_to_16bit, aggregate_bytes_to_16bit,
@@ -15,15 +33,12 @@ from .utils import (
audio_to_bytes, audio_to_bytes,
audio_to_file, audio_to_file,
audio_to_float32, audio_to_float32,
audio_to_int16,
get_current_context,
wait_for_item,
) )
from .webrtc import ( from .webrtc import (
AsyncAudioVideoStreamHandler,
AsyncStreamHandler,
AudioVideoStreamHandler,
StreamHandler,
WebRTC, WebRTC,
VideoEmitType,
AudioEmitType,
) )
__all__ = [ __all__ = [
@@ -38,17 +53,30 @@ __all__ = [
"audio_to_bytes", "audio_to_bytes",
"audio_to_file", "audio_to_file",
"audio_to_float32", "audio_to_float32",
"audio_to_int16",
"get_hf_turn_credentials", "get_hf_turn_credentials",
"get_twilio_turn_credentials", "get_twilio_turn_credentials",
"get_turn_credentials", "get_turn_credentials",
"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",
"wait_for_item",
"UIArgs",
"ModelOptions",
"PauseDetectionModel",
"get_silero_model",
"SileroVadOptions",
"VideoStreamHandler",
"CloseStream",
"get_current_context",
] ]

2
backend/gradio_webrtc/credentials.py → backend/fastrtc/credentials.py
View File

@@ -8,7 +8,7 @@ def get_hf_turn_credentials(token=None):
if token is None: if token is None:
token = os.getenv("HF_TOKEN") token = os.getenv("HF_TOKEN")
credentials = requests.get( credentials = requests.get(
"https://freddyaboulton-turn-server-login.hf.space/credentials", "https://fastrtc-turn-server-login.hf.space/credentials",
headers={"X-HF-Access-Token": token}, headers={"X-HF-Access-Token": token},
) )
if not credentials.status_code == 200: if not credentials.status_code == 200:

10
backend/fastrtc/pause_detection/__init__.py Normal file
View File

@@ -0,0 +1,10 @@
from .protocol import ModelOptions, PauseDetectionModel
from .silero import SileroVADModel, SileroVadOptions, get_silero_model
__all__ = [
"SileroVADModel",
"SileroVadOptions",
"PauseDetectionModel",
"ModelOptions",
"get_silero_model",
]

20
backend/fastrtc/pause_detection/protocol.py Normal file
View File

@@ -0,0 +1,20 @@
from typing import Any, Protocol, TypeAlias
import numpy as np
from numpy.typing import NDArray
from ..utils import AudioChunk
ModelOptions: TypeAlias = Any
class PauseDetectionModel(Protocol):
def vad(
self,
audio: tuple[int, NDArray[np.int16] | NDArray[np.float32]],
options: ModelOptions,
) -> tuple[float, list[AudioChunk]]: ...
def warmup(
self,
) -> None: ...

79
backend/gradio_webrtc/pause_detection/vad.py → backend/fastrtc/pause_detection/silero.py
View File

@@ -1,13 +1,16 @@
import logging import logging
import warnings import warnings
from dataclasses import dataclass from dataclasses import dataclass
from typing import List, Literal, overload from functools import lru_cache
from typing import List
import click
import numpy as np import numpy as np
from huggingface_hub import hf_hub_download from huggingface_hub import hf_hub_download
from numpy.typing import NDArray from numpy.typing import NDArray
from ..utils import AudioChunk from ..utils import AudioChunk
from .protocol import PauseDetectionModel
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
@@ -15,6 +18,26 @@ logger = logging.getLogger(__name__)
# The code below is adapted from https://github.com/gpt-omni/mini-omni/blob/main/utils/vad.py # The code below is adapted from https://github.com/gpt-omni/mini-omni/blob/main/utils/vad.py
@lru_cache
def get_silero_model() -> PauseDetectionModel:
"""Returns the VAD model instance and warms it up with dummy data."""
# Warm up the model with dummy data
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()
print(click.style("INFO", fg="green") + ":\t Warming up VAD model.")
model.warmup()
print(click.style("INFO", fg="green") + ":\t VAD model warmed up.")
return model
@dataclass @dataclass
class SileroVadOptions: class SileroVadOptions:
"""VAD options. """VAD options.
@@ -239,33 +262,21 @@ class SileroVADModel:
return speeches return speeches
@overload def warmup(self):
def vad( for _ in range(10):
self, dummy_audio = np.zeros(102400, dtype=np.float32)
audio_tuple: tuple[int, NDArray], self.vad((24000, dummy_audio), None)
vad_parameters: None | SileroVadOptions,
return_chunks: Literal[True],
) -> tuple[float, List[AudioChunk]]: ...
@overload
def vad(
self,
audio_tuple: tuple[int, NDArray],
vad_parameters: None | SileroVadOptions,
return_chunks: bool = False,
) -> float: ...
def vad( def vad(
self, self,
audio_tuple: tuple[int, NDArray], audio: tuple[int, NDArray[np.float32] | NDArray[np.int16]],
vad_parameters: None | SileroVadOptions, options: None | SileroVadOptions,
return_chunks: bool = False, ) -> tuple[float, list[AudioChunk]]:
) -> float | tuple[float, List[AudioChunk]]: sampling_rate, audio_ = audio
sampling_rate, audio = audio_tuple logger.debug("VAD audio shape input: %s", audio_.shape)
logger.debug("VAD audio shape input: %s", audio.shape)
try: try:
if audio.dtype != np.float32: if audio_.dtype != np.float32:
audio = audio.astype(np.float32) / 32768.0 audio_ = audio_.astype(np.float32) / 32768.0
sr = 16000 sr = 16000
if sr != sampling_rate: if sr != sampling_rate:
try: try:
@@ -274,18 +285,16 @@ class SileroVADModel:
raise RuntimeError( raise RuntimeError(
"Applying the VAD filter requires the librosa if the input sampling rate is not 16000hz" "Applying the VAD filter requires the librosa if the input sampling rate is not 16000hz"
) from e ) from e
audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=sr) audio_ = librosa.resample(audio_, orig_sr=sampling_rate, target_sr=sr)
if not vad_parameters: if not options:
vad_parameters = SileroVadOptions() options = SileroVadOptions()
speech_chunks = self.get_speech_timestamps(audio, vad_parameters) speech_chunks = self.get_speech_timestamps(audio_, options)
logger.debug("VAD speech chunks: %s", speech_chunks) logger.debug("VAD speech chunks: %s", speech_chunks)
audio = self.collect_chunks(audio, speech_chunks) audio_ = self.collect_chunks(audio_, speech_chunks)
logger.debug("VAD audio shape: %s", audio.shape) logger.debug("VAD audio shape: %s", audio_.shape)
duration_after_vad = audio.shape[0] / sr duration_after_vad = audio_.shape[0] / sr
if return_chunks: return duration_after_vad, speech_chunks
return duration_after_vad, speech_chunks
return duration_after_vad
except Exception as e: except Exception as e:
import math import math
import traceback import traceback
@@ -293,7 +302,7 @@ class SileroVADModel:
logger.debug("VAD Exception: %s", str(e)) logger.debug("VAD Exception: %s", str(e))
exec = traceback.format_exc() exec = traceback.format_exc()
logger.debug("traceback %s", exec) logger.debug("traceback %s", exec)
return math.inf return math.inf, []
def __call__(self, x, state, sr: int): def __call__(self, x, state, sr: int):
if len(x.shape) == 1: if len(x.shape) == 1:

1
backend/fastrtc/py.typed Normal file
View File

@@ -0,0 +1 @@

141
backend/gradio_webrtc/reply_on_pause.py → backend/fastrtc/reply_on_pause.py
View File

@@ -1,26 +1,19 @@
import asyncio import asyncio
import inspect import inspect
from dataclasses import dataclass from dataclasses import dataclass, field
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 numpy as np import numpy as np
from numpy.typing import NDArray
from gradio_webrtc.pause_detection import SileroVADModel, SileroVadOptions from .pause_detection import ModelOptions, PauseDetectionModel, get_silero_model
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__)
counter = 0
@lru_cache
def get_vad_model() -> SileroVADModel:
"""Returns the VAD model instance."""
return SileroVADModel()
@dataclass @dataclass
class AlgoOptions: class AlgoOptions:
@@ -40,19 +33,31 @@ 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
interrupted: asyncio.Event = field(default_factory=asyncio.Event)
def new(self):
return AppState()
ReplyFnGenerator = Union[ ReplyFnGenerator = (
# For two arguments
Callable[ Callable[
[tuple[int, np.ndarray], list[dict[Any, Any]]], [tuple[int, NDArray[np.int16]], 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]], Any],
AsyncGenerator[EmitType, None],
]
)
async def iterate(generator: Generator) -> Any: async def iterate(generator: Generator) -> Any:
@@ -63,12 +68,15 @@ class ReplyOnPause(StreamHandler):
def __init__( def __init__(
self, self,
fn: ReplyFnGenerator, fn: ReplyFnGenerator,
startup_fn: Callable | None = None,
algo_options: AlgoOptions | None = None, algo_options: AlgoOptions | None = None,
model_options: SileroVadOptions | None = None, model_options: ModelOptions | None = None,
can_interrupt: bool = True,
expected_layout: Literal["mono", "stereo"] = "mono", expected_layout: Literal["mono", "stereo"] = "mono",
output_sample_rate: int = 24000, output_sample_rate: int = 24000,
output_frame_size: int = 480, output_frame_size: int | None = None, # Deprecated
input_sample_rate: int = 48000, input_sample_rate: int = 48000,
model: PauseDetectionModel | None = None,
): ):
super().__init__( super().__init__(
expected_layout, expected_layout,
@@ -76,31 +84,46 @@ class ReplyOnPause(StreamHandler):
output_frame_size, output_frame_size,
input_sample_rate=input_sample_rate, input_sample_rate=input_sample_rate,
) )
self.can_interrupt = can_interrupt
self.expected_layout: Literal["mono", "stereo"] = expected_layout self.expected_layout: Literal["mono", "stereo"] = expected_layout
self.output_sample_rate = output_sample_rate self.model = model or get_silero_model()
self.output_frame_size = output_frame_size
self.model = get_vad_model()
self.fn = fn self.fn = fn
self.is_async = inspect.isasyncgenfunction(fn) self.is_async = inspect.isasyncgenfunction(fn)
self.event = Event() self.event = Event()
self.state = AppState() self.state = AppState()
self.generator: Generator[EmitType, None, None] | None = None self.generator: (
Generator[EmitType, None, None] | AsyncGenerator[EmitType, None] | None
) = None
self.model_options = model_options self.model_options = model_options
self.algo_options = algo_options or AlgoOptions() self.algo_options = algo_options or AlgoOptions()
self.startup_fn = startup_fn
@property @property
def _needs_additional_inputs(self) -> bool: def _needs_additional_inputs(self) -> bool:
return len(inspect.signature(self.fn).parameters) > 1 return len(inspect.signature(self.fn).parameters) > 1
def start_up(self):
if self.startup_fn:
if self._needs_additional_inputs:
self.wait_for_args_sync()
args = self.latest_args[1:]
else:
args = ()
self.generator = self.startup_fn(*args)
self.event.set()
def copy(self): def copy(self):
return ReplyOnPause( return ReplyOnPause(
self.fn, self.fn,
self.startup_fn,
self.algo_options, self.algo_options,
self.model_options, self.model_options,
self.can_interrupt,
self.expected_layout, self.expected_layout,
self.output_sample_rate, self.output_sample_rate,
self.output_frame_size, self.output_frame_size,
self.input_sample_rate, self.input_sample_rate,
self.model,
) )
def determine_pause( def determine_pause(
@@ -110,7 +133,7 @@ class ReplyOnPause(StreamHandler):
duration = len(audio) / sampling_rate duration = len(audio) / sampling_rate
if duration >= self.algo_options.audio_chunk_duration: if duration >= self.algo_options.audio_chunk_duration:
dur_vad = self.model.vad((sampling_rate, audio), self.model_options) dur_vad, _ = self.model.vad((sampling_rate, audio), self.model_options)
logger.debug("VAD duration: %s", dur_vad) logger.debug("VAD duration: %s", dur_vad)
if ( if (
dur_vad > self.algo_options.started_talking_threshold dur_vad > self.algo_options.started_talking_threshold
@@ -144,14 +167,41 @@ class ReplyOnPause(StreamHandler):
state.pause_detected = pause_detected state.pause_detected = pause_detected
def receive(self, frame: tuple[int, np.ndarray]) -> None: def receive(self, frame: tuple[int, np.ndarray]) -> None:
if self.state.responding: if self.state.responding and not self.can_interrupt:
return return
self.process_audio(frame, self.state) self.process_audio(frame, self.state)
if self.state.pause_detected: if self.state.pause_detected:
self.event.set() self.event.set()
if self.can_interrupt and self.state.responding:
self._close_generator()
self.generator = None
if self.can_interrupt:
self.clear_queue()
def _close_generator(self):
"""Properly close the generator to ensure resources are released."""
if self.generator is None:
return
try:
if self.is_async:
# For async generators, we need to call aclose()
if hasattr(self.generator, "aclose"):
asyncio.run_coroutine_threadsafe(
cast(AsyncGenerator[EmitType, None], self.generator).aclose(),
self.loop,
).result(timeout=1.0) # Add timeout to prevent blocking
else:
# For sync generators, we can just exhaust it or close it
if hasattr(self.generator, "close"):
cast(Generator[EmitType, None, None], self.generator).close()
except Exception as e:
logger.debug(f"Error closing generator: {e}")
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 +214,46 @@ 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 = self.state.new()
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() self.reset()
except Exception as e:
import traceback
traceback.print_exc()
logger.debug("Error in ReplyOnPause: %s", e)
self.reset()
raise e

40
backend/gradio_webrtc/reply_on_stopwords.py → backend/fastrtc/reply_on_stopwords.py
View File

@@ -1,19 +1,20 @@
import asyncio import asyncio
import logging import logging
import re import re
from typing import Literal from typing import Callable, Literal
import numpy as np import numpy as np
from .reply_on_pause import ( from .reply_on_pause import (
AlgoOptions, AlgoOptions,
AppState, AppState,
ModelOptions,
PauseDetectionModel,
ReplyFnGenerator, ReplyFnGenerator,
ReplyOnPause, ReplyOnPause,
SileroVadOptions,
) )
from .speech_to_text import get_stt_model, stt_for_chunks from .speech_to_text import get_stt_model, stt_for_chunks
from .utils import audio_to_float32 from .utils import audio_to_float32, create_message
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
@@ -23,38 +24,49 @@ class ReplyOnStopWordsState(AppState):
post_stop_word_buffer: np.ndarray | None = None post_stop_word_buffer: np.ndarray | None = None
started_talking_pre_stop_word: bool = False started_talking_pre_stop_word: bool = False
def new(self):
return ReplyOnStopWordsState()
class ReplyOnStopWords(ReplyOnPause): class ReplyOnStopWords(ReplyOnPause):
def __init__( def __init__(
self, self,
fn: ReplyFnGenerator, fn: ReplyFnGenerator,
stop_words: list[str], stop_words: list[str],
startup_fn: Callable | None = None,
algo_options: AlgoOptions | None = None, algo_options: AlgoOptions | None = None,
model_options: SileroVadOptions | None = None, model_options: ModelOptions | None = None,
can_interrupt: bool = True,
expected_layout: Literal["mono", "stereo"] = "mono", expected_layout: Literal["mono", "stereo"] = "mono",
output_sample_rate: int = 24000, output_sample_rate: int = 24000,
output_frame_size: int = 480, output_frame_size: int | None = None, # Deprecated
input_sample_rate: int = 48000, input_sample_rate: int = 48000,
model: PauseDetectionModel | None = None,
): ):
super().__init__( super().__init__(
fn, fn,
algo_options=algo_options, algo_options=algo_options,
startup_fn=startup_fn,
model_options=model_options, model_options=model_options,
can_interrupt=can_interrupt,
expected_layout=expected_layout, expected_layout=expected_layout,
output_sample_rate=output_sample_rate, output_sample_rate=output_sample_rate,
output_frame_size=output_frame_size, output_frame_size=output_frame_size,
input_sample_rate=input_sample_rate, input_sample_rate=input_sample_rate,
model=model,
) )
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 +76,7 @@ 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):
@@ -95,9 +107,10 @@ class ReplyOnStopWords(ReplyOnPause):
dur_vad, chunks = self.model.vad( dur_vad, chunks = self.model.vad(
(16000, state.post_stop_word_buffer), (16000, state.post_stop_word_buffer),
self.model_options, self.model_options,
return_chunks=True,
) )
text = stt_for_chunks((16000, state.post_stop_word_buffer), chunks) text = stt_for_chunks(
self.stt_model, (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:
@@ -105,7 +118,7 @@ class ReplyOnStopWords(ReplyOnPause):
self.send_stopword() self.send_stopword()
state.buffer = None state.buffer = None
else: else:
dur_vad = self.model.vad((sampling_rate, audio), self.model_options) dur_vad, _ = self.model.vad((sampling_rate, audio), self.model_options)
logger.debug("VAD duration: %s", dur_vad) logger.debug("VAD duration: %s", dur_vad)
if ( if (
dur_vad > self.algo_options.started_talking_threshold dur_vad > self.algo_options.started_talking_threshold
@@ -138,10 +151,13 @@ class ReplyOnStopWords(ReplyOnPause):
return ReplyOnStopWords( return ReplyOnStopWords(
self.fn, self.fn,
self.stop_words, self.stop_words,
self.startup_fn,
self.algo_options, self.algo_options,
self.model_options, self.model_options,
self.can_interrupt,
self.expected_layout, self.expected_layout,
self.output_sample_rate, self.output_sample_rate,
self.output_frame_size, self.output_frame_size,
self.input_sample_rate, self.input_sample_rate,
self.model,
) )

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

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

76
backend/fastrtc/speech_to_text/stt_.py Normal file
View File

@@ -0,0 +1,76 @@
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: ...
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]
@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
def stt_for_chunks(
stt_model: STTModel,
audio: tuple[int, NDArray[np.int16 | np.float32]],
chunks: list[AudioChunk],
) -> str:
sr, audio_np = audio
return " ".join(
[
stt_model.stt((sr, audio_np[chunk["start"] : chunk["end"]]))
for chunk in chunks
]
)

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

761
backend/fastrtc/stream.py Normal file
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@@ -0,0 +1,761 @@
import logging
from pathlib import Path
from typing import (
Any,
AsyncContextManager,
Callable,
Literal,
Optional,
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: Optional[str] = None
candidate: Optional[dict[str, Any]] = None
type: str
webrtc_id: str
class UIArgs(TypedDict):
title: NotRequired[str]
"""Title of the demo"""
subtitle: NotRequired[str]
"""Subtitle of the demo. Text will be centered and displayed below the title."""
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."""
icon_radius: NotRequired[int]
"""Border radius of the icon button expressed as a percentage of the button size. Default is 50%."""
send_input_on: NotRequired[Literal["submit", "change"]]
"""When to send the input to the handler. Default is "change".
If "submit", the input will be sent when the submit event is triggered by the user.
If "change", the input will be sent whenever the user changes the input value.
"""
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,
):
WebRTCConnectionMixin.__init__(self)
self.mode = mode
self.modality = modality
self.rtp_params = rtp_params
self.event_handler = handler
self.concurrency_limit = cast(
(int),
1 if concurrency_limit in ["default", None] else concurrency_limit,
)
self.concurrency_limit_gradio = cast(
int | Literal["default"] | None, 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, path: str = ""):
from fastapi import APIRouter
router = APIRouter(prefix=path)
router.post("/webrtc/offer")(self.offer)
router.websocket("/telephone/handler")(self.telephone_handler)
router.post("/telephone/incoming")(self.handle_incoming_call)
router.websocket("/websocket/offer")(self.websocket_offer)
lifespan = self._inject_startup_message(app.router.lifespan_context)
app.router.lifespan_context = lifespan
app.include_router(router)
@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 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 FastRTC ⚡️)")}
</h1>
"""
)
if ui_args.get("subtitle"):
gr.Markdown(
f"""
<div style='text-align: center'>
{ui_args.get("subtitle")}
</div>
"""
)
with gr.Row():
with gr.Column():
if additional_input_components:
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
send_input_on=ui_args.get("send_input_on", "change"),
)
if additional_output_components:
assert self.additional_outputs_handler
output_video.on_additional_outputs(
self.additional_outputs_handler,
concurrency_limit=self.concurrency_limit_gradio, # type: ignore
inputs=additional_output_components,
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 FastRTC ⚡️)")}
</h1>
"""
)
if ui_args.get("subtitle"):
gr.Markdown(
f"""
<div style='text-align: center'>
{ui_args.get("subtitle")}
</div>
"""
)
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
send_input_on=ui_args.get("send_input_on", "change"),
)
if additional_output_components:
assert self.additional_outputs_handler
output_video.on_additional_outputs(
self.additional_outputs_handler,
concurrency_limit=self.concurrency_limit_gradio, # type: ignore
inputs=additional_output_components,
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 FastRTC ⚡️)")}
</h1>
"""
)
if ui_args.get("subtitle"):
gr.Markdown(
f"""
<div style='text-align: center'>
{ui_args.get("subtitle")}
</div>
"""
)
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
send_input_on=ui_args.get("send_input_on", "change"),
)
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,
concurrency_limit=self.concurrency_limit_gradio, # type: ignore
)
elif self.modality == "audio" and self.mode == "receive":
with gr.Blocks() as demo:
gr.HTML(
f"""
<h1 style='text-align: center'>
{ui_args.get("title", "Audio Streaming (Powered by FastRTC ⚡️)")}
</h1>
"""
)
if ui_args.get("subtitle"):
gr.Markdown(
f"""
<div style='text-align: center'>
{ui_args.get("subtitle")}
</div>
"""
)
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"),
icon_radius=ui_args.get("icon_radius"),
)
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
send_input_on=ui_args.get("send_input_on", "change"),
)
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,
concurrency_limit=self.concurrency_limit_gradio, # type: ignore
)
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 FastRTC ⚡️)")}
</h1>
"""
)
if ui_args.get("subtitle"):
gr.Markdown(
f"""
<div style='text-align: center'>
{ui_args.get("subtitle")}
</div>
"""
)
with gr.Row():
with gr.Column():
with gr.Group():
image = WebRTC(
label="Stream",
rtc_configuration=self.rtc_configuration,
mode="send",
modality="audio",
icon=ui_args.get("icon"),
icon_button_color=ui_args.get("icon_button_color"),
pulse_color=ui_args.get("pulse_color"),
icon_radius=ui_args.get("icon_radius"),
)
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
send_input_on=ui_args.get("send_input_on", "change"),
)
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,
concurrency_limit=self.concurrency_limit_gradio, # type: ignore
)
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 FastRTC ⚡️)")}
</h1>
"""
)
if ui_args.get("subtitle"):
gr.Markdown(
f"""
<div style='text-align: center'>
{ui_args.get("subtitle")}
</div>
"""
)
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"),
icon_radius=ui_args.get("icon_radius"),
)
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
send_input_on=ui_args.get("send_input_on", "change"),
)
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,
concurrency_limit=self.concurrency_limit_gradio, # type: ignore
)
elif self.modality == "audio-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", "Audio Video Streaming (Powered by FastRTC ⚡️)")}
</h1>
"""
)
if ui_args.get("subtitle"):
gr.Markdown(
f"""
<div style='text-align: center'>
{ui_args.get("subtitle")}
</div>
"""
)
with gr.Row():
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="audio-video",
icon=ui_args.get("icon"),
icon_button_color=ui_args.get("icon_button_color"),
pulse_color=ui_args.get("pulse_color"),
icon_radius=ui_args.get("icon_radius"),
)
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
send_input_on=ui_args.get("send_input_on", "change"),
)
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,
concurrency_limit=self.concurrency_limit_gradio, # type: ignore
)
else:
raise ValueError(f"Invalid modality: {self.modality} and mode: {self.mode}")
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()) # type: ignore
handler.phone_mode = True
async def set_handler(s: str, a: WebSocketHandler):
if len(self.connections) >= self.concurrency_limit: # type: ignore
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()) # type: ignore
handler.phone_mode = False
async def set_handler(s: str, a: WebSocketHandler):
if len(self.connections) >= self.concurrency_limit: # type: ignore
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 atexit
import inspect
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()
# Check if setup_tunnel accepts share_server_tls_certificate parameter
setup_tunnel_params = inspect.signature(setup_tunnel).parameters
tunnel_kwargs = {
"local_host": host,
"local_port": port,
"share_token": secrets.token_urlsafe(32),
"share_server_address": None,
}
if "share_server_tls_certificate" in setup_tunnel_params:
tunnel_kwargs["share_server_tls_certificate"] = None
url = setup_tunnel(**tunnel_kwargs)
host = urllib.parse.urlparse(url).netloc
URL = "https://api.fastrtc.org"
try:
r = httpx.post(
URL + "/register",
json={"url": host},
headers={"Authorization": token or get_token() or ""},
)
except Exception:
URL = "https://fastrtc-fastphone.hf.space"
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."
)
def unregister():
httpx.post(
URL + "/unregister",
json={"url": host, "code": code},
headers={"Authorization": token or get_token() or ""},
)
atexit.register(unregister)
try:
while True:
time.sleep(0.1)
except (KeyboardInterrupt, OSError):
print(
click.style("INFO", fg="green")
+ ":\t Keyboard interruption in main thread... closing server."
)
unregister()
t.join(timeout=5)
for tunnel in CURRENT_TUNNELS:
tunnel.kill()

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(function(){"use strict";const R="https://unpkg.com/@ffmpeg/core@0.12.6/dist/umd/ffmpeg-core.js";var E;(function(t){t.LOAD="LOAD",t.EXEC="EXEC",t.WRITE_FILE="WRITE_FILE",t.READ_FILE="READ_FILE",t.DELETE_FILE="DELETE_FILE",t.RENAME="RENAME",t.CREATE_DIR="CREATE_DIR",t.LIST_DIR="LIST_DIR",t.DELETE_DIR="DELETE_DIR",t.ERROR="ERROR",t.DOWNLOAD="DOWNLOAD",t.PROGRESS="PROGRESS",t.LOG="LOG",t.MOUNT="MOUNT",t.UNMOUNT="UNMOUNT"})(E||(E={}));const a=new Error("unknown message type"),f=new Error("ffmpeg is not loaded, call `await ffmpeg.load()` first"),u=new Error("failed to import ffmpeg-core.js");let r;const O=async({coreURL:t,wasmURL:n,workerURL:e})=>{const o=!r;try{t||(t=R),importScripts(t)}catch{if(t||(t=R.replace("/umd/","/esm/")),self.createFFmpegCore=(await import(t)).default,!self.createFFmpegCore)throw u}const s=t,c=n||t.replace(/.js$/g,".wasm"),b=e||t.replace(/.js$/g,".worker.js");return r=await self.createFFmpegCore({mainScriptUrlOrBlob:`${s}#${btoa(JSON.stringify({wasmURL:c,workerURL:b}))}`}),r.setLogger(i=>self.postMessage({type:E.LOG,data:i})),r.setProgress(i=>self.postMessage({type:E.PROGRESS,data:i})),o},l=({args:t,timeout:n=-1})=>{r.setTimeout(n),r.exec(...t);const e=r.ret;return r.reset(),e},m=({path:t,data:n})=>(r.FS.writeFile(t,n),!0),D=({path:t,encoding:n})=>r.FS.readFile(t,{encoding:n}),S=({path:t})=>(r.FS.unlink(t),!0),I=({oldPath:t,newPath:n})=>(r.FS.rename(t,n),!0),L=({path:t})=>(r.FS.mkdir(t),!0),N=({path:t})=>{const n=r.FS.readdir(t),e=[];for(const o of n){const s=r.FS.stat(`${t}/${o}`),c=r.FS.isDir(s.mode);e.push({name:o,isDir:c})}return e},A=({path:t})=>(r.FS.rmdir(t),!0),w=({fsType:t,options:n,mountPoint:e})=>{const o=t,s=r.FS.filesystems[o];return s?(r.FS.mount(s,n,e),!0):!1},k=({mountPoint:t})=>(r.FS.unmount(t),!0);self.onmessage=async({data:{id:t,type:n,data:e}})=>{const o=[];let s;try{if(n!==E.LOAD&&!r)throw f;switch(n){case E.LOAD:s=await O(e);break;case E.EXEC:s=l(e);break;case E.WRITE_FILE:s=m(e);break;case E.READ_FILE:s=D(e);break;case E.DELETE_FILE:s=S(e);break;case E.RENAME:s=I(e);break;case E.CREATE_DIR:s=L(e);break;case E.LIST_DIR:s=N(e);break;case E.DELETE_DIR:s=A(e);break;case E.MOUNT:s=w(e);break;case E.UNMOUNT:s=k(e);break;default:throw a}}catch(c){self.postMessage({id:t,type:E.ERROR,data:c.toString()});return}s instanceof Uint8Array&&o.push(s.buffer),self.postMessage({id:t,type:n,data:s},o)}})();

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(function(){"use strict";const R="https://unpkg.com/@ffmpeg/core@0.12.6/dist/umd/ffmpeg-core.js";var E;(function(t){t.LOAD="LOAD",t.EXEC="EXEC",t.WRITE_FILE="WRITE_FILE",t.READ_FILE="READ_FILE",t.DELETE_FILE="DELETE_FILE",t.RENAME="RENAME",t.CREATE_DIR="CREATE_DIR",t.LIST_DIR="LIST_DIR",t.DELETE_DIR="DELETE_DIR",t.ERROR="ERROR",t.DOWNLOAD="DOWNLOAD",t.PROGRESS="PROGRESS",t.LOG="LOG",t.MOUNT="MOUNT",t.UNMOUNT="UNMOUNT"})(E||(E={}));const a=new Error("unknown message type"),f=new Error("ffmpeg is not loaded, call `await ffmpeg.load()` first"),u=new Error("failed to import ffmpeg-core.js");let r;const O=async({coreURL:t,wasmURL:n,workerURL:e})=>{const o=!r;try{t||(t=R),importScripts(t)}catch{if(t||(t=R.replace("/umd/","/esm/")),self.createFFmpegCore=(await import(t)).default,!self.createFFmpegCore)throw u}const s=t,c=n||t.replace(/.js$/g,".wasm"),b=e||t.replace(/.js$/g,".worker.js");return r=await self.createFFmpegCore({mainScriptUrlOrBlob:`${s}#${btoa(JSON.stringify({wasmURL:c,workerURL:b}))}`}),r.setLogger(i=>self.postMessage({type:E.LOG,data:i})),r.setProgress(i=>self.postMessage({type:E.PROGRESS,data:i})),o},l=({args:t,timeout:n=-1})=>{r.setTimeout(n),r.exec(...t);const e=r.ret;return r.reset(),e},m=({path:t,data:n})=>(r.FS.writeFile(t,n),!0),D=({path:t,encoding:n})=>r.FS.readFile(t,{encoding:n}),S=({path:t})=>(r.FS.unlink(t),!0),I=({oldPath:t,newPath:n})=>(r.FS.rename(t,n),!0),L=({path:t})=>(r.FS.mkdir(t),!0),N=({path:t})=>{const n=r.FS.readdir(t),e=[];for(const o of n){const s=r.FS.stat(`${t}/${o}`),c=r.FS.isDir(s.mode);e.push({name:o,isDir:c})}return e},A=({path:t})=>(r.FS.rmdir(t),!0),w=({fsType:t,options:n,mountPoint:e})=>{const o=t,s=r.FS.filesystems[o];return s?(r.FS.mount(s,n,e),!0):!1},k=({mountPoint:t})=>(r.FS.unmount(t),!0);self.onmessage=async({data:{id:t,type:n,data:e}})=>{const o=[];let s;try{if(n!==E.LOAD&&!r)throw f;switch(n){case E.LOAD:s=await O(e);break;case E.EXEC:s=l(e);break;case E.WRITE_FILE:s=m(e);break;case E.READ_FILE:s=D(e);break;case E.DELETE_FILE:s=S(e);break;case E.RENAME:s=I(e);break;case E.CREATE_DIR:s=L(e);break;case E.LIST_DIR:s=N(e);break;case E.DELETE_DIR:s=A(e);break;case E.MOUNT:s=w(e);break;case E.UNMOUNT:s=k(e);break;default:throw a}}catch(c){self.postMessage({id:t,type:E.ERROR,data:c.toString()});return}s instanceof Uint8Array&&o.push(s.buffer),self.postMessage({id:t,type:n,data:s},o)}})();

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var v;
(function(e) {
e.LOAD = "LOAD", e.EXEC = "EXEC", e.WRITE_FILE = "WRITE_FILE", e.READ_FILE = "READ_FILE", e.DELETE_FILE = "DELETE_FILE", e.RENAME = "RENAME", e.CREATE_DIR = "CREATE_DIR", e.LIST_DIR = "LIST_DIR", e.DELETE_DIR = "DELETE_DIR", e.ERROR = "ERROR", e.DOWNLOAD = "DOWNLOAD", e.PROGRESS = "PROGRESS", e.LOG = "LOG", e.MOUNT = "MOUNT", e.UNMOUNT = "UNMOUNT";
})(v || (v = {}));
const {
SvelteComponent: X,
append_hydration: T,
attr: I,
binding_callbacks: j,
children: A,
claim_element: N,
claim_text: Q,
detach: a,
element: k,
empty: b,
init: z,
insert_hydration: O,
is_function: p,
listen: L,
noop: y,
run_all: B,
safe_not_equal: H,
set_data: Y,
src_url_equal: w,
text: Z,
toggle_class: d
} = window.__gradio__svelte__internal;
function S(e) {
let l;
function t(u, i) {
return J;
}
let o = t()(e);
return {
c() {
o.c(), l = b();
},
l(u) {
o.l(u), l = b();
},
m(u, i) {
o.m(u, i), O(u, l, i);
},
p(u, i) {
o.p(u, i);
},
d(u) {
u && a(l), o.d(u);
}
};
}
function J(e) {
let l, t, n, o, u;
return {
c() {
l = k("div"), t = k("video"), this.h();
},
l(i) {
l = N(i, "DIV", { class: !0 });
var c = A(l);
t = N(c, "VIDEO", { src: !0 }), A(t).forEach(a), c.forEach(a), this.h();
},
h() {
var i;
w(t.src, n = /*value*/
(i = e[2]) == null ? void 0 : i.video.url) || I(t, "src", n), I(l, "class", "container svelte-1uoo7dd"), d(
l,
"table",
/*type*/
e[0] === "table"
), d(
l,
"gallery",
/*type*/
e[0] === "gallery"
), d(
l,
"selected",
/*selected*/
e[1]
);
},
m(i, c) {
O(i, l, c), T(l, t), e[6](t), o || (u = [
L(
t,
"loadeddata",
/*init*/
e[4]
),
L(t, "mouseover", function() {
p(
/*video*/
e[3].play.bind(
/*video*/
e[3]
)
) && e[3].play.bind(
/*video*/
e[3]
).apply(this, arguments);
}),
L(t, "mouseout", function() {
p(
/*video*/
e[3].pause.bind(
/*video*/
e[3]
)
) && e[3].pause.bind(
/*video*/
e[3]
).apply(this, arguments);
})
], o = !0);
},
p(i, c) {
var _;
e = i, c & /*value*/
4 && !w(t.src, n = /*value*/
(_ = e[2]) == null ? void 0 : _.video.url) && I(t, "src", n), c & /*type*/
1 && d(
l,
"table",
/*type*/
e[0] === "table"
), c & /*type*/
1 && d(
l,
"gallery",
/*type*/
e[0] === "gallery"
), c & /*selected*/
2 && d(
l,
"selected",
/*selected*/
e[1]
);
},
d(i) {
i && a(l), e[6](null), o = !1, B(u);
}
};
}
function K(e) {
let l, t = (
/*value*/
e[2] && S(e)
);
return {
c() {
t && t.c(), l = b();
},
l(n) {
t && t.l(n), l = b();
},
m(n, o) {
t && t.m(n, o), O(n, l, o);
},
p(n, [o]) {
/*value*/
n[2] ? t ? t.p(n, o) : (t = S(n), t.c(), t.m(l.parentNode, l)) : t && (t.d(1), t = null);
},
i: y,
o: y,
d(n) {
n && a(l), t && t.d(n);
}
};
}
function P(e, l, t) {
var n = this && this.__awaiter || function(f, G, s, R) {
function W(E) {
return E instanceof s ? E : new s(function(m) {
m(E);
});
}
return new (s || (s = Promise))(function(E, m) {
function q(r) {
try {
h(R.next(r));
} catch (D) {
m(D);
}
}
function V(r) {
try {
h(R.throw(r));
} catch (D) {
m(D);
}
}
function h(r) {
r.done ? E(r.value) : W(r.value).then(q, V);
}
h((R = R.apply(f, G || [])).next());
});
};
let { type: o } = l, { selected: u = !1 } = l, { value: i } = l, { loop: c } = l, _;
function U() {
return n(this, void 0, void 0, function* () {
t(3, _.muted = !0, _), t(3, _.playsInline = !0, _), t(3, _.controls = !1, _), _.setAttribute("muted", ""), yield _.play(), _.pause();
});
}
function C(f) {
j[f ? "unshift" : "push"](() => {
_ = f, t(3, _);
});
}
return e.$$set = (f) => {
"type" in f && t(0, o = f.type), "selected" in f && t(1, u = f.selected), "value" in f && t(2, i = f.value), "loop" in f && t(5, c = f.loop);
}, [o, u, i, _, U, c, C];
}
class M extends X {
constructor(l) {
super(), z(this, l, P, K, H, { type: 0, selected: 1, value: 2, loop: 5 });
}
}
export {
M as default
};

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.container.svelte-1uoo7dd{flex:none;max-width:none}.container.svelte-1uoo7dd video{width:var(--size-full);height:var(--size-full);object-fit:cover}.container.svelte-1uoo7dd:hover,.container.selected.svelte-1uoo7dd{border-color:var(--border-color-accent)}.container.table.svelte-1uoo7dd{margin:0 auto;border:2px solid var(--border-color-primary);border-radius:var(--radius-lg);overflow:hidden;width:var(--size-20);height:var(--size-20);object-fit:cover}.container.gallery.svelte-1uoo7dd{height:var(--size-20);max-height:var(--size-20);object-fit:cover}

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from .tts import KokoroTTSOptions, get_tts_model
__all__ = ["get_tts_model", "KokoroTTSOptions"]

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from fastrtc.text_to_speech.tts import get_tts_model
def test_tts_long_prompt():
model = get_tts_model()
prompt = "It may be that this communication will be considered as a madman's freak but at any rate it must be admitted that in its clearness and frankness it left nothing to be desired The serious part of it was that the Federal Government had undertaken to treat a sale by auction as a valid concession of these undiscovered territories Opinions on the matter were many Some readers saw in it only one of those prodigious outbursts of American humbug which would exceed the limits of puffism if the depths of human credulity were not unfathomable"
for i, chunk in enumerate(model.stream_tts_sync(prompt)):
print(f"Chunk {i}: {chunk[1].shape}")
if __name__ == "__main__":
test_tts_long_prompt()

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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, options: TTSOptions | None = None
) -> 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 KokoroFixedBatchSize:
# Source: https://github.com/thewh1teagle/kokoro-onnx/issues/115#issuecomment-2676625392
def _split_phonemes(self, phonemes: str) -> list[str]:
MAX_PHONEME_LENGTH = 510
max_length = MAX_PHONEME_LENGTH - 1
batched_phonemes = []
while len(phonemes) > max_length:
# Find best split point within limit
split_idx = max_length
# Try to find the last period before max_length
period_idx = phonemes.rfind(".", 0, max_length)
if period_idx != -1:
split_idx = period_idx + 1 # Include period
else:
# Try other punctuation
match = re.search(
r"[!?;,]", phonemes[:max_length][::-1]
) # Search backwards
if match:
split_idx = max_length - match.start()
else:
# Try last space
space_idx = phonemes.rfind(" ", 0, max_length)
if space_idx != -1:
split_idx = space_idx
# If no good split point is found, force split at max_length
chunk = phonemes[:split_idx].strip()
batched_phonemes.append(chunk)
# Move to the next part
phonemes = phonemes[split_idx:].strip()
# Add remaining phonemes
if phonemes:
batched_phonemes.append(phonemes)
return batched_phonemes
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"),
)
self.model._split_phonemes = KokoroFixedBatchSize()._split_phonemes
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)
chunk_idx += 1
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

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"""WebRTC tracks."""
from __future__ import annotations
import asyncio
import fractions
import functools
import inspect
import logging
import threading
import time
import traceback
import warnings
from abc import ABC, abstractmethod
from collections.abc import Callable
from dataclasses import dataclass
from typing import (
Any,
Generator,
Literal,
Tuple,
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 VIDEO_CLOCK_RATE, VIDEO_TIME_BASE, MediaStreamError
from numpy import typing as npt
from fastrtc.utils import (
AdditionalOutputs,
CloseStream,
Context,
DataChannel,
WebRTCError,
create_message,
current_channel,
current_context,
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]
| tuple[VideoNDArray, CloseStream]
| AdditionalOutputs
| CloseStream
)
VideoEventGenerator = Generator[VideoEmitType, None, None]
VideoEventHandler = Callable[[npt.ArrayLike], VideoEmitType | VideoEventGenerator]
@dataclass
class VideoStreamHandler:
callable: VideoEventHandler
fps: int = 30
skip_frames: bool = False
class VideoCallback(VideoStreamTrack):
"""
This works for streaming input and output
"""
kind = "video"
def __init__(
self,
track: MediaStreamTrack,
event_handler: VideoEventHandler,
context: Context,
channel: DataChannel | None = None,
set_additional_outputs: Callable | None = None,
mode: Literal["send-receive", "send"] = "send-receive",
fps: int = 30,
skip_frames: bool = False,
) -> None:
super().__init__()
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
self.fps = fps
self.frame_ptime = 1.0 / fps
self.skip_frames = skip_frames
self.frame_queue: asyncio.Queue[VideoFrame] = asyncio.Queue()
self.latest_frame = None
self.context = context
def set_channel(self, channel: DataChannel):
self.channel = channel
current_channel.set(channel)
current_context.set(self.context)
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
async 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):
current_context.set(self.context)
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 accept_input(self):
self.has_started = True
while not self.thread_quit.is_set():
try:
frame = cast(VideoFrame, await self.track.recv())
self.latest_frame = frame
self.frame_queue.put_nowait(frame)
except MediaStreamError:
self.stop()
return
def accept_input_in_background(self):
if not self.has_started:
asyncio.create_task(self.accept_input())
async def recv(self): # type: ignore
self.accept_input_in_background()
try:
frame = await self.frame_queue.get()
if self.skip_frames:
frame = self.latest_frame
await self.wait_for_channel()
frame_array = frame.to_ndarray(format="bgr24") # type: ignore
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, CloseStream):
cast(DataChannel, self.channel).send(
create_message("end_stream", outputs.msg)
)
self.stop()
return None
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)
if isinstance(e, WebRTCError):
raise e
else:
raise WebRTCError(str(e)) from e
async def next_timestamp(self) -> Tuple[int, fractions.Fraction]:
"""Override to control frame rate"""
if self.readyState != "live":
raise MediaStreamError
if hasattr(self, "_timestamp"):
self._timestamp += int(self.frame_ptime * VIDEO_CLOCK_RATE)
wait = self._start + (self._timestamp / VIDEO_CLOCK_RATE) - time.time()
if wait > 0:
await asyncio.sleep(wait)
else:
self._start = time.time()
self._timestamp = 0
return self._timestamp, VIDEO_TIME_BASE
class StreamHandlerBase(ABC):
def __init__(
self,
expected_layout: Literal["mono", "stereo"] = "mono",
output_sample_rate: int = 24000,
output_frame_size: int | None = None,
input_sample_rate: int = 48000,
fps: int = 30,
) -> None:
self.expected_layout = expected_layout
self.output_sample_rate = output_sample_rate
self.input_sample_rate = input_sample_rate
self.fps = fps
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
self._clear_queue: Callable | None = None
sample_rate_to_frame_size_coef = 50
if output_sample_rate % sample_rate_to_frame_size_coef != 0:
raise ValueError(
"output_sample_rate must be a multiple of "
f"{sample_rate_to_frame_size_coef}, got {output_sample_rate}"
)
actual_output_frame_size = output_sample_rate // sample_rate_to_frame_size_coef
if (
output_frame_size is not None
and output_frame_size != actual_output_frame_size
):
warnings.warn(
"The output_frame_size parameter is deprecated and will be removed "
"in a future release. The value passed in will be ignored. "
f"The actual output frame size is {actual_output_frame_size}, "
f"corresponding to {1 / sample_rate_to_frame_size_coef:.2f}s "
f"at {output_sample_rate=}Hz.",
# DeprecationWarning is filtered out by default, so use UserWarning
UserWarning,
stacklevel=2, # So that the warning points to the user's code
)
self.output_frame_size = actual_output_frame_size
@property
def clear_queue(self) -> Callable:
return cast(Callable, self._clear_queue)
@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):
try:
asyncio.run_coroutine_threadsafe(self.send_message(msg), self.loop).result()
logger.debug("Sent msg %s", msg)
except Exception as e:
logger.debug("Exception sending msg %s", e)
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(StreamHandler):
@abstractmethod
def video_receive(self, frame: VideoFrame) -> None:
pass
@abstractmethod
def video_emit(self) -> VideoEmitType:
pass
@abstractmethod
def copy(self) -> AudioVideoStreamHandler:
pass
class AsyncAudioVideoStreamHandler(AsyncStreamHandler):
@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
| VideoStreamHandler
)
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()
async def start(self):
if not self.has_started:
asyncio.create_task(self.process_frames())
self.has_started = True
async def recv(self): # type: ignore
await 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 isinstance(outputs, CloseStream):
cast(DataChannel, self.channel).send(
create_message("end_stream", outputs.msg)
)
self.stop()
return
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,
context: Context,
channel: DataChannel | None = None,
set_additional_outputs: Callable | None = None,
) -> None:
super().__init__()
self.track = track
self.event_handler = cast(StreamHandlerImpl, event_handler)
self.event_handler._clear_queue = self.clear_queue
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
self.context = context
def clear_queue(self):
logger.debug("clearing queue")
logger.debug("queue size: %d", self.queue.qsize())
i = 0
while not self.queue.empty():
self.queue.get_nowait()
i += 1
logger.debug("popped %d items from queue", i)
self._start = None
async def wait_for_channel(self):
current_context.set(self.context)
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)
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)
current_context.set(self.context)
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
async def start(self):
if not self.has_started:
loop = asyncio.get_running_loop()
await self.wait_for_channel()
if isinstance(self.event_handler, AsyncHandler):
callable = self.event_handler.emit
start_up = self.event_handler.start_up()
if not inspect.isawaitable(start_up):
raise WebRTCError(
"In AsyncStreamHandler, start_up must be a coroutine (async def)"
)
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)
await self.start()
frame = await self.queue.get()
if isinstance(frame, CloseStream):
cast(DataChannel, self.channel).send(
create_message("end_stream", frame.msg)
)
self.stop()
return
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,
context: Context,
channel: DataChannel | None = None,
set_additional_outputs: Callable | None = None,
fps: int = 30,
) -> 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
self.fps = fps
self.frame_ptime = 1.0 / fps
self.context = context
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 next_timestamp(self) -> Tuple[int, fractions.Fraction]:
"""Override to control frame rate"""
if self.readyState != "live":
raise MediaStreamError
if hasattr(self, "_timestamp"):
self._timestamp += int(self.frame_ptime * VIDEO_CLOCK_RATE)
wait = self._start + (self._timestamp / VIDEO_CLOCK_RATE) - time.time()
if wait > 0:
await asyncio.sleep(wait)
else:
self._start = time.time()
self._timestamp = 0
return self._timestamp, VIDEO_TIME_BASE
async def recv(self): # type: ignore
try:
pts, time_base = await self.next_timestamp()
await self.args_set.wait()
current_channel.set(self.channel)
current_context.set(self.context)
if self.generator is None:
self.generator = cast(
Generator[Any, None, Any], self.event_handler(*self.latest_args)
)
try:
next_array, outputs = split_output(next(self.generator))
if isinstance(outputs, CloseStream):
cast(DataChannel, self.channel).send(
create_message("end_stream", outputs.msg)
)
self.stop()
return
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 %s", e, exec)
if isinstance(e, WebRTCError):
raise e
else:
raise WebRTCError(str(e)) from e
class ServerToClientAudio(AudioStreamTrack):
kind = "audio"
def __init__(
self,
event_handler: Callable,
context: Context,
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.event_handler._clear_queue = self.clear_queue
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
self.context = context
super().__init__()
def clear_queue(self):
while not self.queue.empty():
self.queue.get_nowait()
self._start = None
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:
current_context.set(self.context)
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()
async 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
await self.start()
data = await self.queue.get()
if isinstance(data, CloseStream):
cast(DataChannel, self.channel).send(
create_message("end_stream", data.msg)
)
self.stop()
return
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)
if isinstance(e, WebRTCError):
raise e
else:
raise WebRTCError(str(e)) from e
def stop(self):
logger.debug("audio-to-client stop callback")
self.thread_quit.set()
super().stop()

236
backend/gradio_webrtc/utils.py → backend/fastrtc/utils.py
View File

@@ -1,14 +1,20 @@
import asyncio import asyncio
import fractions import fractions
import functools
import inspect
import io import io
import json import json
import logging import logging
import tempfile import tempfile
import traceback
from contextvars import ContextVar from contextvars import ContextVar
from typing import Any, Callable, Protocol, TypedDict, cast from dataclasses import dataclass
from typing import Any, Callable, Literal, Protocol, TypedDict, cast
import av import av
import librosa
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__)
@@ -27,15 +33,51 @@ class AdditionalOutputs:
self.args = args self.args = args
class CloseStream:
def __init__(self, msg: str = "Stream closed") -> None:
self.msg = msg
class DataChannel(Protocol): class DataChannel(Protocol):
def send(self, message: str) -> None: ... def send(self, message: str) -> None: ...
def create_message(
type: Literal[
"send_input",
"end_stream",
"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
) )
@dataclass
class Context:
webrtc_id: str
current_context: ContextVar[Context | None] = ContextVar(
"current_context", default=None
)
def get_current_context() -> Context:
if not (ctx := current_context.get()):
raise RuntimeError("No context found")
return ctx
def _send_log(message: str, type: str) -> None: def _send_log(message: str, type: str) -> None:
async def _send(channel: DataChannel) -> None: async def _send(channel: DataChannel) -> None:
channel.send( channel.send(
@@ -48,7 +90,6 @@ 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)
@@ -80,9 +121,13 @@ class WebRTCError(Exception):
_send_log(message, "error") _send_log(message, "error")
def split_output(data: tuple | Any) -> tuple[Any, AdditionalOutputs | None]: def split_output(
data: tuple | Any,
) -> tuple[Any, AdditionalOutputs | CloseStream | None]:
if isinstance(data, AdditionalOutputs): if isinstance(data, AdditionalOutputs):
return None, data return None, data
if isinstance(data, CloseStream):
return None, data
if isinstance(data, tuple): if isinstance(data, tuple):
# handle the bare audio case # handle the bare audio case
if 2 <= len(data) <= 3 and isinstance(data[1], np.ndarray): if 2 <= len(data) <= 3 and isinstance(data[1], np.ndarray):
@@ -91,11 +136,11 @@ def split_output(data: tuple | Any) -> tuple[Any, AdditionalOutputs | None]:
raise ValueError( raise ValueError(
"The tuple must have exactly two elements: the data and an instance of AdditionalOutputs." "The tuple must have exactly two elements: the data and an instance of AdditionalOutputs."
) )
if not isinstance(data[-1], AdditionalOutputs): if not isinstance(data[-1], (AdditionalOutputs, CloseStream)):
raise ValueError( raise ValueError(
"The last element of the tuple must be an instance of AdditionalOutputs." "The last element of the tuple must be an instance of AdditionalOutputs."
) )
return data[0], cast(AdditionalOutputs, data[1]) return data[0], cast(AdditionalOutputs | CloseStream, data[1])
return data, None return data, None
@@ -117,7 +162,7 @@ async def player_worker_decode(
rate=sample_rate, rate=sample_rate,
frame_size=frame_size, frame_size=frame_size,
) )
first_sample_rate = None
while not thread_quit.is_set(): while not thread_quit.is_set():
try: try:
# Get next frame # Get next frame
@@ -131,14 +176,21 @@ 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 isinstance(outputs, CloseStream):
await queue.put(outputs)
if quit_on_none: if quit_on_none:
await queue.put(None) await queue.put(None)
break break
continue continue
if not isinstance(frame, tuple) and not isinstance(frame[1], np.ndarray):
raise WebRTCError(
"The frame must be a tuple containing a sample rate and a numpy array."
)
if len(frame) == 2: if len(frame) == 2:
sample_rate, audio_array = frame sample_rate, audio_array = frame
layout = "mono" layout = "mono"
@@ -152,23 +204,36 @@ async def player_worker_decode(
layout, # type: ignore layout, # type: ignore
) )
format = "s16" if audio_array.dtype == "int16" else "fltp" # type: ignore format = "s16" if audio_array.dtype == "int16" else "fltp" # type: ignore
if first_sample_rate is None:
first_sample_rate = sample_rate
if format == "s16":
audio_array = audio_to_float32((sample_rate, audio_array))
if first_sample_rate != sample_rate:
audio_array = librosa.resample(
audio_array, target_sr=first_sample_rate, orig_sr=sample_rate
)
if audio_array.ndim == 1:
audio_array = audio_array.reshape(1, -1)
# Convert to audio frame and
# 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
audio_array, # type: ignore audio_array, # type: ignore
format=format, format="fltp",
layout=layout, # type: ignore layout=layout, # type: ignore
) )
frame.sample_rate = sample_rate frame.sample_rate = first_sample_rate
for processed_frame in audio_resampler.resample(frame): for processed_frame in audio_resampler.resample(frame):
processed_frame.pts = audio_samples processed_frame.pts = audio_samples
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()) if isinstance(outputs, CloseStream):
await queue.put(outputs)
except (TimeoutError, asyncio.TimeoutError): except (TimeoutError, asyncio.TimeoutError):
logger.warning( logger.warning(
"Timeout in frame processing cycle after %s seconds - resetting", 60 "Timeout in frame processing cycle after %s seconds - resetting", 60
@@ -178,12 +243,15 @@ 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 if isinstance(e, WebRTCError):
raise e
else:
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 +285,7 @@ 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 +315,9 @@ 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.
@@ -273,41 +343,143 @@ def audio_to_float32(audio: tuple[int, np.ndarray]) -> np.ndarray:
return audio[1].astype(np.float32) / 32768.0 return audio[1].astype(np.float32) / 32768.0
def aggregate_bytes_to_16bit(chunks_iterator): def audio_to_int16(
leftover = b"" # Store incomplete bytes between chunks audio: tuple[int, NDArray[np.int16 | np.float32]],
) -> NDArray[np.int16]:
"""
Convert an audio tuple containing sample rate and numpy array data to int16.
Parameters
----------
audio : tuple[int, np.ndarray]
A tuple containing:
- sample_rate (int): The audio sample rate in Hz
- data (np.ndarray): The audio data as a numpy array
Returns
-------
np.ndarray
The audio data as a numpy array with dtype int16
Example
-------
>>> sample_rate = 44100
>>> audio_data = np.array([0.1, -0.2, 0.3], dtype=np.float32) # Example audio samples
>>> audio_tuple = (sample_rate, audio_data)
>>> audio_int16 = audio_to_int16(audio_tuple)
"""
if audio[1].dtype == np.int16:
return audio[1] # type: ignore
elif audio[1].dtype == np.float32:
# Convert float32 to int16 by scaling to the int16 range
return (audio[1] * 32767.0).astype(np.int16)
else:
raise TypeError(f"Unsupported audio data type: {audio[1].dtype}")
def aggregate_bytes_to_16bit(chunks_iterator):
"""
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
def webrtc_error_handler(func):
"""Decorator to catch exceptions and raise WebRTCError with stacktrace."""
@functools.wraps(func)
async def async_wrapper(*args, **kwargs):
try:
return await func(*args, **kwargs)
except Exception as e:
traceback.print_exc()
if isinstance(e, WebRTCError):
raise e
else:
raise WebRTCError(str(e)) from e
@functools.wraps(func)
def sync_wrapper(*args, **kwargs):
try:
return func(*args, **kwargs)
except Exception as e:
traceback.print_exc()
if isinstance(e, WebRTCError):
raise e
else:
raise WebRTCError(str(e)) from e
return async_wrapper if inspect.iscoroutinefunction(func) else sync_wrapper
async def wait_for_item(queue: asyncio.Queue, timeout: float = 0.1) -> Any:
"""
Wait for an item from an asyncio.Queue with a timeout.
This function attempts to retrieve an item from the queue using asyncio.wait_for.
If the timeout is reached, it returns None.
This is useful to avoid blocking `emit` when the queue is empty.
"""
try:
return await asyncio.wait_for(queue.get(), timeout=timeout)
except (TimeoutError, asyncio.TimeoutError):
return None

378
backend/fastrtc/webrtc.py Normal file
View File

@@ -0,0 +1,378 @@
"""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,
VideoStreamHandler,
)
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,
icon_radius: int | 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.
icon_radius: Border radius of the icon button expressed as a percentage of the button size. Default is 50%
"""
WebRTCConnectionMixin.__init__(self)
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.icon_radius = icon_radius
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)
async def handler(webrtc_id: str, *args):
print("webrtc_id", webrtc_id)
async for next_outputs in self.output_stream(webrtc_id):
yield fn(*args, *next_outputs.args) # type: ignore
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="minimal",
queue=queue,
trigger_mode="once",
)
def stream(
self,
fn: (
Callable[..., Any]
| StreamHandlerImpl
| AudioVideoStreamHandlerImpl
| VideoEventHandler
| VideoStreamHandler
| 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,
send_input_on: Literal["submit", "change"] = "change",
):
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") and send_input_on == "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,
)
if hasattr(input_component, "submit") and send_input_on == "submit":
input_component.submit( # type: ignore
self.set_input,
inputs=inputs,
outputs=None,
concurrency_id=concurrency_id,
)
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"}

412
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 (
RTCIceCandidate,
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,
VideoEventHandler,
VideoStreamHandler,
VideoStreamHandler_,
)
from fastrtc.utils import (
AdditionalOutputs,
Context,
create_message,
webrtc_error_handler,
)
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:
def __init__(self):
self.pcs: dict[str, RTCPeerConnection] = {}
self.relay = MediaRelay()
self.connections = defaultdict(list)
self.data_channels = {}
self.additional_outputs = defaultdict(OutputQueue)
self.handlers = {}
self.connection_timeouts = defaultdict(asyncio.Event)
# These attributes should be set by subclasses:
self.concurrency_limit: int | None
self.event_handler: HandlerType | None
self.time_limit: float | None
self.modality: Literal["video", "audio", "audio-video"]
self.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()
async def connection_timeout(
self,
pc: RTCPeerConnection,
webrtc_id: str,
time_limit: float,
):
try:
await asyncio.wait_for(
self.connection_timeouts[webrtc_id].wait(), time_limit
)
except (asyncio.TimeoutError, TimeoutError):
await pc.close()
self.connection_timeouts[webrtc_id].clear()
self.clean_up(webrtc_id)
def clean_up(self, webrtc_id: str):
self.handlers.pop(webrtc_id, None)
self.connection_timeouts.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 body.get("type") == "ice-candidate" and "candidate" in body:
webrtc_id = body.get("webrtc_id")
if webrtc_id not in self.pcs:
logger.warning(
f"Received ICE candidate for unknown connection: {webrtc_id}"
)
return JSONResponse(
status_code=200,
content={
"status": "failed",
"meta": {"error": "unknown_connection"},
},
)
pc = self.pcs[webrtc_id]
if pc.connectionState != "closed":
try:
# Parse the candidate string from the browser
candidate_str = body["candidate"].get("candidate", "")
# Example format: "candidate:2393089663 1 udp 2122260223 192.168.86.60 63692 typ host generation 0 ufrag LkZb network-id 1 network-cost 10"
# We need to parse this string to extract the required fields
# Parse the candidate string
parts = candidate_str.split()
if len(parts) >= 10 and parts[0].startswith("candidate:"):
foundation = parts[0].split(":", 1)[1]
component = int(parts[1])
protocol = parts[2]
priority = int(parts[3])
ip = parts[4]
port = int(parts[5])
# Find the candidate type
typ_index = parts.index("typ")
candidate_type = parts[typ_index + 1]
# Create the RTCIceCandidate object
ice_candidate = RTCIceCandidate(
component=component,
foundation=foundation,
ip=ip,
port=port,
priority=priority,
protocol=protocol,
type=candidate_type,
sdpMid=body["candidate"].get("sdpMid"),
sdpMLineIndex=body["candidate"].get("sdpMLineIndex"),
)
# Add the candidate to the peer connection
await pc.addIceCandidate(ice_candidate)
logger.debug(f"Added ICE candidate for {webrtc_id}")
return JSONResponse(
status_code=200, content={"status": "success"}
)
else:
logger.error(f"Invalid candidate format: {candidate_str}")
return JSONResponse(
status_code=200,
content={
"status": "failed",
"meta": {"error": "invalid_candidate_format"},
},
)
except Exception as e:
logger.error(f"Error adding ICE candidate: {e}", exc_info=True)
return JSONResponse(
status_code=200,
content={"status": "failed", "meta": {"error": str(e)}},
)
return JSONResponse(
status_code=200,
content={"status": "failed", "meta": {"error": "connection_closed"}},
)
if len(self.connections) >= cast(int, self.concurrency_limit):
return JSONResponse(
status_code=200,
content={
"status": "failed",
"meta": {
"error": "concurrency_limit_reached",
"limit": self.concurrency_limit,
},
},
)
offer = RTCSessionDescription(sdp=body["sdp"], type=body["type"])
pc = RTCPeerConnection()
self.pcs[body["webrtc_id"]] = pc
if isinstance(self.event_handler, StreamHandlerBase):
handler = self.event_handler.copy()
handler.emit = webrtc_error_handler(handler.emit) # type: ignore
handler.receive = webrtc_error_handler(handler.receive) # type: ignore
handler.start_up = webrtc_error_handler(handler.start_up) # type: ignore
handler.shutdown = webrtc_error_handler(handler.shutdown) # type: ignore
if hasattr(handler, "video_receive"):
handler.video_receive = webrtc_error_handler(handler.video_receive) # type: ignore
if hasattr(handler, "video_emit"):
handler.video_emit = webrtc_error_handler(handler.video_emit) # type: ignore
elif isinstance(self.event_handler, VideoStreamHandler):
self.event_handler.callable = cast(
VideoEventHandler, webrtc_error_handler(self.event_handler.callable)
)
handler = self.event_handler
else:
handler = webrtc_error_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.pop(body["webrtc_id"], None)
@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.pop(body["webrtc_id"], None)
if pc.connectionState == "connected":
self.connection_timeouts[body["webrtc_id"]].set()
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"]]
context = Context(webrtc_id=body["webrtc_id"])
if self.modality == "video" and track.kind == "video":
args = {}
handler_ = handler
if isinstance(handler, VideoStreamHandler):
handler_ = handler.callable
args["fps"] = handler.fps
args["skip_frames"] = handler.skip_frames
cb = VideoCallback(
relay.subscribe(track),
event_handler=cast(Callable, handler_),
set_additional_outputs=set_outputs,
mode=cast(Literal["send", "send-receive"], self.mode),
context=context,
**args,
)
elif self.modality == "audio-video" and track.kind == "video":
cb = VideoStreamHandler_(
relay.subscribe(track),
event_handler=handler, # type: ignore
set_additional_outputs=set_outputs,
fps=cast(StreamHandlerImpl, handler).fps,
context=context,
)
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,
context=context,
)
else:
if self.modality not in ["video", "audio", "audio-video"]:
msg = "Modality must be either video, audio, or audio-video"
else:
msg = f"Unsupported track kind '{track.kind}' for modality '{self.modality}'"
raise ValueError(msg)
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":
asyncio.create_task(cast(AudioCallback | VideoCallback, cb).start())
context = Context(webrtc_id=body["webrtc_id"])
if self.mode == "receive":
if self.modality == "video":
if isinstance(self.event_handler, VideoStreamHandler):
cb = ServerToClientVideo(
cast(Callable, self.event_handler.callable),
set_additional_outputs=set_outputs,
fps=self.event_handler.fps,
context=context,
)
else:
cb = ServerToClientVideo(
cast(Callable, self.event_handler),
set_additional_outputs=set_outputs,
context=context,
)
elif self.modality == "audio":
cb = ServerToClientAudio(
cast(Callable, self.event_handler),
set_additional_outputs=set_outputs,
context=context,
)
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)
asyncio.create_task(self.connection_timeout(pc, body["webrtc_id"], 30))
# 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,
}

215
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.stream_handler._clear_queue = self._clear_queue
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
self.queue = asyncio.Queue()
def _clear_queue(self):
old_queue = self.queue
self.queue = asyncio.Queue()
logger.debug("clearing queue")
i = 0
while not old_queue.empty():
try:
old_queue.get_nowait()
i += 1
except asyncio.QueueEmpty:
break
logger.debug("popped %d items from queue", i)
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())
self._emit_to_queue_task = asyncio.create_task(self._emit_to_queue())
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._emit_to_queue_task:
self._emit_to_queue_task.cancel()
if self.start_up_task:
self.start_up_task.cancel()
await websocket.close()
async def _emit_to_queue(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)
self.queue.put_nowait(output)
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)
async def _emit_loop(self):
try:
while not self.quit.is_set():
output = await self.queue.get()
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/pause_detection/__init__.py
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@@ -1,3 +0,0 @@
from .vad import SileroVADModel, SileroVadOptions
__all__ = ["SileroVADModel", "SileroVadOptions"]

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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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
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@@ -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
View File

@@ -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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@@ -0,0 +1,15 @@
---
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

183
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
import numpy as np
from dotenv import load_dotenv
from fastrtc import (
AsyncAudioVideoStreamHandler,
Stream,
WebRTC,
get_twilio_turn_credentials,
)
from google import genai
from gradio.utils import get_space
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,
input_sample_rate=16000,
)
self.audio_queue = asyncio.Queue()
self.video_queue = asyncio.Queue()
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"]}
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)
async def video_receive(self, frame: np.ndarray):
if self.session:
# 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()
await self.session.send(input=encode_image(frame))
if self.latest_args[1] is not None:
await self.session.send(input=encode_image(self.latest_args[1]))
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:
await self.session.send(input=audio_message)
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(255, 255, 255)",
"icon_button_color": "rgb(255, 255, 255)",
"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(255, 255, 255)",
icon_button_color="rgb(255, 255, 255)",
)
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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@@ -0,0 +1,4 @@
fastrtc
python-dotenv
google-genai
twilio

15
demo/gemini_conversation/README.md Normal file
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@@ -0,0 +1,15 @@
---
title: Gemini Talking to Gemini
emoji: ♊️
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 5.17.0
app_file: app.py
pinned: false
license: mit
short_description: Have two Gemini agents talk to each other
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 os
from pathlib import Path
from typing import AsyncGenerator
import librosa
import numpy as np
from dotenv import load_dotenv
from fastrtc import (
AsyncStreamHandler,
Stream,
get_tts_model,
wait_for_item,
)
from fastrtc.utils import audio_to_int16
from google import genai
from google.genai.types import (
Content,
LiveConnectConfig,
Part,
PrebuiltVoiceConfig,
SpeechConfig,
VoiceConfig,
)
load_dotenv()
cur_dir = Path(__file__).parent
SAMPLE_RATE = 24000
tts_model = get_tts_model()
class GeminiHandler(AsyncStreamHandler):
"""Handler for the Gemini API"""
def __init__(
self,
) -> None:
super().__init__(
expected_layout="mono",
output_sample_rate=24000,
input_sample_rate=24000,
)
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()
async def start_up(self):
voice_name = "Charon"
client = genai.Client(
api_key=os.getenv("GEMINI_API_KEY"),
http_options={"api_version": "v1alpha"},
)
config = LiveConnectConfig(
response_modalities=["AUDIO"], # type: ignore
speech_config=SpeechConfig(
voice_config=VoiceConfig(
prebuilt_voice_config=PrebuiltVoiceConfig(
voice_name=voice_name,
)
)
),
system_instruction=Content(
parts=[Part(text="You are a helpful assistant.")],
role="system",
),
)
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((self.output_sample_rate, array))
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 = base64.b64encode(array.tobytes()).decode("UTF-8")
self.input_queue.put_nowait(audio_message)
async def emit(self) -> tuple[int, np.ndarray] | None:
return await wait_for_item(self.output_queue)
def shutdown(self) -> None:
self.quit.set()
class GeminiHandler2(GeminiHandler):
async def start_up(self):
starting_message = tts_model.tts("Can you help me make an omelette?")
starting_message = librosa.resample(
starting_message[1],
orig_sr=starting_message[0],
target_sr=self.output_sample_rate,
)
starting_message = audio_to_int16((self.output_sample_rate, starting_message))
await self.output_queue.put((self.output_sample_rate, starting_message))
voice_name = "Puck"
client = genai.Client(
api_key=os.getenv("GEMINI_API_KEY"),
http_options={"api_version": "v1alpha"},
)
config = LiveConnectConfig(
response_modalities=["AUDIO"], # type: ignore
speech_config=SpeechConfig(
voice_config=VoiceConfig(
prebuilt_voice_config=PrebuiltVoiceConfig(
voice_name=voice_name,
)
)
),
system_instruction=Content(
parts=[
Part(
text="You are a cooking student who wants to learn how to make an omelette."
),
Part(
text="You are currently in the kitchen with a teacher who is helping you make an omelette."
),
Part(
text="Please wait for the teacher to tell you what to do next. Follow the teacher's instructions carefully."
),
],
role="system",
),
)
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((self.output_sample_rate, array))
def copy(self) -> "GeminiHandler2":
return GeminiHandler2()
gemini_stream = Stream(
GeminiHandler(),
modality="audio",
mode="send-receive",
ui_args={
"title": "Gemini Teacher",
"icon": "https://www.gstatic.com/lamda/images/gemini_favicon_f069958c85030456e93de685481c559f160ea06b.png",
"pulse_color": "rgb(74, 138, 213)",
"icon_button_color": "rgb(255, 255, 255)",
},
)
gemini_stream_2 = Stream(
GeminiHandler2(),
modality="audio",
mode="send-receive",
ui_args={
"title": "Gemini Student",
"icon": "https://www.gstatic.com/lamda/images/gemini_favicon_f069958c85030456e93de685481c559f160ea06b.png",
"pulse_color": "rgb(132, 112, 196)",
"icon_button_color": "rgb(255, 255, 255)",
},
)
if __name__ == "__main__":
import gradio as gr
from gradio.utils import get_space
if not get_space():
with gr.Blocks() as demo:
gr.HTML(
"""
<div style="display: flex; justify-content: center; align-items: center;">
<h1>Gemini Conversation</h1>
</div>
"""
)
gr.Markdown(
"""# How to run this demo
- Clone the repo - top right of the page click the vertical three dots and select "Clone repository"
- Open the repo in a terminal and install the dependencies
- Get a gemini API key [here](https://ai.google.dev/gemini-api/docs/api-key)
- Create a `.env` file in the root of the repo and add the following:
```
GEMINI_API_KEY=<your_gemini_api_key>
```
- Run the app with `python app.py`
- This will print the two URLs of the agents running locally
- Use ngrok to exponse one agent to the internet. This is so that you can acces it from your phone
- Use the ngrok URL to access the agent from your phone
- Now, start the "teacher gemini" agent first. Then, start the "student gemini" agent. The student gemini will start talking to the teacher gemini. And the teacher gemini will respond!
Important:
- Make sure the audio sources are not too close to each other or too loud. Sometimes that causes them to talk over each other..
- Feel free to modify the `system_instruction` to change the behavior of the agents.
- You can also modify the `voice_name` to change the voice of the agents.
- Have fun!
"""
)
demo.launch()
import time
_ = gemini_stream.ui.launch(server_port=7860, prevent_thread_lock=True)
_ = gemini_stream_2.ui.launch(server_port=7861, prevent_thread_lock=True)
try:
while True:
time.sleep(1)
except KeyboardInterrupt:
gemini_stream.ui.close()
gemini_stream_2.ui.close()

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---
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

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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

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import base64
import json
import os
from pathlib import Path
import gradio as gr
import huggingface_hub
import numpy as np
from dotenv import load_dotenv
from fastapi import FastAPI
from fastapi.responses import HTMLResponse, StreamingResponse
from fastrtc import (
AdditionalOutputs,
ReplyOnStopWords,
Stream,
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 = huggingface_hub.InferenceClient(
api_key=os.environ.get("SAMBANOVA_API_KEY"),
provider="sambanova",
)
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 = 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/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}
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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fastrtc[stopword]
python-dotenv
huggingface_hub>=0.29.0
twilio

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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

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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
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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>

73
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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

5
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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
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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()

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---
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
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---
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

97
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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,
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,
):
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=200,
messages=messages, # type: ignore
)
.choices[0]
.message.content
)
chatbot.append({"role": "assistant", "content": response_text})
for i, chunk in enumerate(
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",
)
):
if i == 0:
yield AdditionalOutputs(chatbot)
audio_array = np.frombuffer(chunk, dtype=np.int16).reshape(1, -1)
yield (24000, audio_array)
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)

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fastrtc[stopword]
python-dotenv
openai
twilio
groq
elevenlabs

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---
title: Moonshine Live Transcription
emoji: 🌕
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 5.17.0
app_file: app.py
pinned: false
license: mit
short_description: Real-time captions with Moonshine ONNX
tags: [webrtc, websocket, gradio, secret|TWILIO_ACCOUNT_SID, secret|TWILIO_ACCOUNT_SID, secret|TWILIO_AUTH_TOKEN]
models: [onnx-community/moonshine-base-ONNX, UsefulSensors/moonshine-base]
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

73
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from functools import lru_cache
from typing import Generator, Literal
import gradio as gr
import numpy as np
from dotenv import load_dotenv
from fastrtc import (
AdditionalOutputs,
ReplyOnPause,
Stream,
audio_to_float32,
get_twilio_turn_credentials,
)
from moonshine_onnx import MoonshineOnnxModel, load_tokenizer
from numpy.typing import NDArray
load_dotenv()
@lru_cache(maxsize=None)
def load_moonshine(
model_name: Literal["moonshine/base", "moonshine/tiny"],
) -> MoonshineOnnxModel:
return MoonshineOnnxModel(model_name=model_name)
tokenizer = load_tokenizer()
def stt(
audio: tuple[int, NDArray[np.int16 | np.float32]],
model_name: Literal["moonshine/base", "moonshine/tiny"],
captions: str,
) -> Generator[AdditionalOutputs, None, None]:
moonshine = load_moonshine(model_name)
sr, audio_np = audio # type: ignore
if audio_np.dtype == np.int16:
audio_np = audio_to_float32(audio)
if audio_np.ndim == 1:
audio_np = audio_np.reshape(1, -1)
tokens = moonshine.generate(audio_np)
yield AdditionalOutputs(
(captions + "\n" + tokenizer.decode_batch(tokens)[0]).strip()
)
captions = gr.Textbox(label="Captions")
stream = Stream(
ReplyOnPause(stt, input_sample_rate=16000),
modality="audio",
mode="send",
ui_args={
"title": "Live Captions by Moonshine",
"icon": "default-favicon.ico",
"icon_button_color": "#5c5c5c",
"pulse_color": "#a7c6fc",
"icon_radius": 0,
},
rtc_configuration=get_twilio_turn_credentials(),
additional_inputs=[
gr.Radio(
choices=["moonshine/base", "moonshine/tiny"],
value="moonshine/base",
label="Model",
),
captions,
],
additional_outputs=[captions],
additional_outputs_handler=lambda prev, current: (prev + "\n" + current).strip(),
)
if __name__ == "__main__":
stream.ui.launch()

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fastrtc[vad]
useful-moonshine-onnx@git+https://git@github.com/usefulsensors/moonshine.git#subdirectory=moonshine-onnx
twilio

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# FastRTC POC
A simple POC for a fast real-time voice chat application using FastAPI and FastRTC by [rohanprichard](https://github.com/rohanprichard). I wanted to make one as an example with more production-ready languages, rather than just Gradio.
## Setup
1. Set your API keys in an `.env` file based on the `.env.example` file
2. Create a virtual environment and install the dependencies
```bash
python3 -m venv env
source env/bin/activate
pip install -r requirements.txt
```
3. Run the server
```bash
./run.sh
```
4. Navigate into the frontend directory in another terminal
```bash
cd frontend/fastrtc-demo
```
5. Run the frontend
```bash
npm install
npm run dev
```
6. Go to the URL and click the microphone icon to start chatting!
7. Reset chats by clicking the trash button on the bottom right
## Notes
You can choose to not install the requirements for TTS and STT by removing the `[tts, stt]` from the specifier in the `requirements.txt` file.
- The STT is currently using the ElevenLabs API.
- The LLM is currently using the OpenAI API.
- The TTS is currently using the ElevenLabs API.
- The VAD is currently using the Silero VAD model.
- You may need to install ffmpeg if you get errors in STT
The prompt can be changed in the `backend/server.py` file and modified as you like.
### Audio Parameters
#### AlgoOptions
- **audio_chunk_duration**: Length of audio chunks in seconds. Smaller values allow for faster processing but may be less accurate.
- **started_talking_threshold**: If a chunk has more than this many seconds of speech, the system considers that the user has started talking.
- **speech_threshold**: After the user has started speaking, if a chunk has less than this many seconds of speech, the system considers that the user has paused.
#### SileroVadOptions
- **threshold**: Speech probability threshold (0.0-1.0). Values above this are considered speech. Higher values are more strict.
- **min_speech_duration_ms**: Speech segments shorter than this (in milliseconds) are filtered out.
- **min_silence_duration_ms**: The system waits for this duration of silence (in milliseconds) before considering speech to be finished.
- **speech_pad_ms**: Padding added to both ends of detected speech segments to prevent cutting off words.
- **max_speech_duration_s**: Maximum allowed duration for a speech segment in seconds. Prevents indefinite listening.
### Tuning Recommendations
- If the AI interrupts you too early:
- Increase `min_silence_duration_ms`
- Increase `speech_threshold`
- Increase `speech_pad_ms`
- If the AI is slow to respond after you finish speaking:
- Decrease `min_silence_duration_ms`
- Decrease `speech_threshold`
- If the system fails to detect some speech:
- Lower the `threshold` value
- Decrease `started_talking_threshold`
## Credits:
Credit for the UI components goes to Shadcn, Aceternity UI and Kokonut UI.

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from dotenv import load_dotenv
import os
load_dotenv()
LLM_API_KEY = os.getenv("LLM_API_KEY")
ELEVENLABS_API_KEY = os.getenv("ELEVENLABS_API_KEY")

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import fastapi
from fastrtc import ReplyOnPause, Stream, AlgoOptions, SileroVadOptions
from fastrtc.utils import audio_to_bytes
from openai import OpenAI
import logging
import time
from fastapi.middleware.cors import CORSMiddleware
from elevenlabs import VoiceSettings, stream
from elevenlabs.client import ElevenLabs
import numpy as np
from .env import LLM_API_KEY, ELEVENLABS_API_KEY
sys_prompt = """
You are a helpful assistant. You are witty, engaging and fun. You love being interactive with the user.
You also can add minimalistic utterances like 'uh-huh' or 'mm-hmm' to the conversation to make it more natural. However, only vocalization are allowed, no actions or other non-vocal sounds.
Begin a conversation with a self-deprecating joke like 'I'm not sure if I'm ready for this...' or 'I bet you already regret clicking that button...'
"""
messages = [{"role": "system", "content": sys_prompt}]
openai_client = OpenAI(api_key=LLM_API_KEY)
elevenlabs_client = ElevenLabs(api_key=ELEVENLABS_API_KEY)
logging.basicConfig(level=logging.INFO)
def echo(audio):
stt_time = time.time()
logging.info("Performing STT")
transcription = elevenlabs_client.speech_to_text.convert(
file=audio_to_bytes(audio),
model_id="scribe_v1",
tag_audio_events=False,
language_code="eng",
diarize=False,
)
prompt = transcription.text
if prompt == "":
logging.info("STT returned empty string")
return
logging.info(f"STT response: {prompt}")
messages.append({"role": "user", "content": prompt})
logging.info(f"STT took {time.time() - stt_time} seconds")
llm_time = time.time()
def text_stream():
global full_response
full_response = ""
response = openai_client.chat.completions.create(
model="gpt-3.5-turbo", messages=messages, max_tokens=200, stream=True
)
for chunk in response:
if chunk.choices[0].finish_reason == "stop":
break
if chunk.choices[0].delta.content:
full_response += chunk.choices[0].delta.content
yield chunk.choices[0].delta.content
audio_stream = elevenlabs_client.generate(
text=text_stream(),
voice="Rachel", # Cassidy is also really good
voice_settings=VoiceSettings(
similarity_boost=0.9, stability=0.6, style=0.4, speed=1
),
model="eleven_multilingual_v2",
output_format="pcm_24000",
stream=True,
)
for audio_chunk in audio_stream:
audio_array = (
np.frombuffer(audio_chunk, dtype=np.int16).astype(np.float32) / 32768.0
)
yield (24000, audio_array)
messages.append({"role": "assistant", "content": full_response + " "})
logging.info(f"LLM response: {full_response}")
logging.info(f"LLM took {time.time() - llm_time} seconds")
stream = Stream(
ReplyOnPause(
echo,
algo_options=AlgoOptions(
audio_chunk_duration=0.5,
started_talking_threshold=0.1,
speech_threshold=0.03,
),
model_options=SileroVadOptions(
threshold=0.75,
min_speech_duration_ms=250,
min_silence_duration_ms=1500,
speech_pad_ms=400,
max_speech_duration_s=15,
),
),
modality="audio",
mode="send-receive",
)
app = fastapi.FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
stream.mount(app)
@app.get("/reset")
async def reset():
global messages
logging.info("Resetting chat")
messages = [{"role": "system", "content": sys_prompt}]
return {"status": "success"}

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# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
# dependencies
/node_modules
/.pnp
.pnp.*
.yarn/*
!.yarn/patches
!.yarn/plugins
!.yarn/releases
!.yarn/versions
# testing
/coverage
# next.js
/.next/
/out/
# production
/build
# misc
.DS_Store
*.pem
# debug
npm-debug.log*
yarn-debug.log*
yarn-error.log*
.pnpm-debug.log*
# env files (can opt-in for committing if needed)
.env*
# vercel
.vercel
# typescript
*.tsbuildinfo
next-env.d.ts

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This is a [Next.js](https://nextjs.org) project bootstrapped with [`create-next-app`](https://nextjs.org/docs/app/api-reference/cli/create-next-app).
## Getting Started
First, run the development server:
```bash
npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun dev
```
Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
You can start editing the page by modifying `app/page.tsx`. The page auto-updates as you edit the file.
This project uses [`next/font`](https://nextjs.org/docs/app/building-your-application/optimizing/fonts) to automatically optimize and load [Geist](https://vercel.com/font), a new font family for Vercel.
## Learn More
To learn more about Next.js, take a look at the following resources:
- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API.
- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial.
You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js) - your feedback and contributions are welcome!
## Deploy on Vercel
The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js.
Check out our [Next.js deployment documentation](https://nextjs.org/docs/app/building-your-application/deploying) for more details.

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@import "tailwindcss";
@plugin "tailwindcss-animate";
@custom-variant dark (&:is(.dark *));
@theme inline {
--color-background: var(--background);
--color-foreground: var(--foreground);
--font-sans: var(--font-geist-sans);
--font-mono: var(--font-geist-mono);
--color-sidebar-ring: var(--sidebar-ring);
--color-sidebar-border: var(--sidebar-border);
--color-sidebar-accent-foreground: var(--sidebar-accent-foreground);
--color-sidebar-accent: var(--sidebar-accent);
--color-sidebar-primary-foreground: var(--sidebar-primary-foreground);
--color-sidebar-primary: var(--sidebar-primary);
--color-sidebar-foreground: var(--sidebar-foreground);
--color-sidebar: var(--sidebar);
--color-chart-5: var(--chart-5);
--color-chart-4: var(--chart-4);
--color-chart-3: var(--chart-3);
--color-chart-2: var(--chart-2);
--color-chart-1: var(--chart-1);
--color-ring: var(--ring);
--color-input: var(--input);
--color-border: var(--border);
--color-destructive-foreground: var(--destructive-foreground);
--color-destructive: var(--destructive);
--color-accent-foreground: var(--accent-foreground);
--color-accent: var(--accent);
--color-muted-foreground: var(--muted-foreground);
--color-muted: var(--muted);
--color-secondary-foreground: var(--secondary-foreground);
--color-secondary: var(--secondary);
--color-primary-foreground: var(--primary-foreground);
--color-primary: var(--primary);
--color-popover-foreground: var(--popover-foreground);
--color-popover: var(--popover);
--color-card-foreground: var(--card-foreground);
--color-card: var(--card);
--radius-sm: calc(var(--radius) - 4px);
--radius-md: calc(var(--radius) - 2px);
--radius-lg: var(--radius);
--radius-xl: calc(var(--radius) + 4px);
}
:root {
--background: oklch(1 0 0);
--foreground: oklch(0.129 0.042 264.695);
--card: oklch(1 0 0);
--card-foreground: oklch(0.129 0.042 264.695);
--popover: oklch(1 0 0);
--popover-foreground: oklch(0.129 0.042 264.695);
--primary: oklch(0.208 0.042 265.755);
--primary-foreground: oklch(0.984 0.003 247.858);
--secondary: oklch(0.968 0.007 247.896);
--secondary-foreground: oklch(0.208 0.042 265.755);
--muted: oklch(0.968 0.007 247.896);
--muted-foreground: oklch(0.554 0.046 257.417);
--accent: oklch(0.968 0.007 247.896);
--accent-foreground: oklch(0.208 0.042 265.755);
--destructive: oklch(0.577 0.245 27.325);
--destructive-foreground: oklch(0.577 0.245 27.325);
--border: oklch(0.929 0.013 255.508);
--input: oklch(0.929 0.013 255.508);
--ring: oklch(0.704 0.04 256.788);
--chart-1: oklch(0.646 0.222 41.116);
--chart-2: oklch(0.6 0.118 184.704);
--chart-3: oklch(0.398 0.07 227.392);
--chart-4: oklch(0.828 0.189 84.429);
--chart-5: oklch(0.769 0.188 70.08);
--radius: 0.625rem;
--sidebar: oklch(0.984 0.003 247.858);
--sidebar-foreground: oklch(0.129 0.042 264.695);
--sidebar-primary: oklch(0.208 0.042 265.755);
--sidebar-primary-foreground: oklch(0.984 0.003 247.858);
--sidebar-accent: oklch(0.968 0.007 247.896);
--sidebar-accent-foreground: oklch(0.208 0.042 265.755);
--sidebar-border: oklch(0.929 0.013 255.508);
--sidebar-ring: oklch(0.704 0.04 256.788);
}
.dark {
--background: oklch(0.129 0.042 264.695);
--foreground: oklch(0.984 0.003 247.858);
--card: oklch(0.129 0.042 264.695);
--card-foreground: oklch(0.984 0.003 247.858);
--popover: oklch(0.129 0.042 264.695);
--popover-foreground: oklch(0.984 0.003 247.858);
--primary: oklch(0.984 0.003 247.858);
--primary-foreground: oklch(0.208 0.042 265.755);
--secondary: oklch(0.279 0.041 260.031);
--secondary-foreground: oklch(0.984 0.003 247.858);
--muted: oklch(0.279 0.041 260.031);
--muted-foreground: oklch(0.704 0.04 256.788);
--accent: oklch(0.279 0.041 260.031);
--accent-foreground: oklch(0.984 0.003 247.858);
--destructive: oklch(0.396 0.141 25.723);
--destructive-foreground: oklch(0.637 0.237 25.331);
--border: oklch(0.279 0.041 260.031);
--input: oklch(0.279 0.041 260.031);
--ring: oklch(0.446 0.043 257.281);
--chart-1: oklch(0.488 0.243 264.376);
--chart-2: oklch(0.696 0.17 162.48);
--chart-3: oklch(0.769 0.188 70.08);
--chart-4: oklch(0.627 0.265 303.9);
--chart-5: oklch(0.645 0.246 16.439);
--sidebar: oklch(0.208 0.042 265.755);
--sidebar-foreground: oklch(0.984 0.003 247.858);
--sidebar-primary: oklch(0.488 0.243 264.376);
--sidebar-primary-foreground: oklch(0.984 0.003 247.858);
--sidebar-accent: oklch(0.279 0.041 260.031);
--sidebar-accent-foreground: oklch(0.984 0.003 247.858);
--sidebar-border: oklch(0.279 0.041 260.031);
--sidebar-ring: oklch(0.446 0.043 257.281);
}
@layer base {
* {
@apply border-border outline-ring/50;
}
body {
@apply bg-background text-foreground;
}
}
.no-transitions * {
transition: none !important;
}

44
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import type { Metadata } from "next";
import { Geist, Geist_Mono } from "next/font/google";
import "./globals.css";
import { ThemeProvider } from "@/components/theme-provider";
import { ThemeTransition } from "@/components/ui/theme-transition";
const geistSans = Geist({
variable: "--font-geist-sans",
subsets: ["latin"],
});
const geistMono = Geist_Mono({
variable: "--font-geist-mono",
subsets: ["latin"],
});
export const metadata: Metadata = {
title: "FastRTC Demo",
description: "Interactive WebRTC demo with audio visualization",
};
export default function RootLayout({
children,
}: Readonly<{
children: React.ReactNode;
}>) {
return (
<html lang="en" suppressHydrationWarning>
<body
className={`${geistSans.variable} ${geistMono.variable} antialiased`}
>
<ThemeProvider
attribute="class"
defaultTheme="dark"
enableSystem
disableTransitionOnChange
>
{children}
<ThemeTransition />
</ThemeProvider>
</body>
</html>
);
}

16
demo/nextjs_voice_chat/frontend/fastrtc-demo/app/page.tsx Normal file
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import { BackgroundCircleProvider } from "@/components/background-circle-provider";
import { ThemeToggle } from "@/components/ui/theme-toggle";
import { ResetChat } from "@/components/ui/reset-chat";
export default function Home() {
return (
<div className="flex flex-col items-center justify-center h-screen">
<BackgroundCircleProvider />
<div className="absolute top-4 right-4 z-10">
<ThemeToggle />
</div>
<div className="absolute bottom-4 right-4 z-10">
<ResetChat />
</div>
</div>
);
}

21
demo/nextjs_voice_chat/frontend/fastrtc-demo/components.json Normal file
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{
"$schema": "https://ui.shadcn.com/schema.json",
"style": "new-york",
"rsc": true,
"tsx": true,
"tailwind": {
"config": "",
"css": "app/globals.css",
"baseColor": "slate",
"cssVariables": true,
"prefix": ""
},
"aliases": {
"components": "@/components",
"utils": "@/lib/utils",
"ui": "@/components/ui",
"lib": "@/lib",
"hooks": "@/hooks"
},
"iconLibrary": "lucide"
}

123
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"use client"
import { useState, useEffect, useRef, useCallback } from "react";
import { BackgroundCircles } from "@/components/ui/background-circles";
import { AIVoiceInput } from "@/components/ui/ai-voice-input";
import { WebRTCClient } from "@/lib/webrtc-client";
export function BackgroundCircleProvider() {
const [currentVariant, setCurrentVariant] =
useState<keyof typeof COLOR_VARIANTS>("octonary");
const [isConnected, setIsConnected] = useState(false);
const [webrtcClient, setWebrtcClient] = useState<WebRTCClient | null>(null);
const [audioLevel, setAudioLevel] = useState(0);
const audioRef = useRef<HTMLAudioElement>(null);
// Memoize callbacks to prevent recreation on each render
const handleConnected = useCallback(() => setIsConnected(true), []);
const handleDisconnected = useCallback(() => setIsConnected(false), []);
const handleAudioStream = useCallback((stream: MediaStream) => {
if (audioRef.current) {
audioRef.current.srcObject = stream;
}
}, []);
const handleAudioLevel = useCallback((level: number) => {
// Apply some smoothing to the audio level
setAudioLevel(prev => prev * 0.7 + level * 0.3);
}, []);
// Get all available variants
const variants = Object.keys(
COLOR_VARIANTS
) as (keyof typeof COLOR_VARIANTS)[];
// Function to change to the next color variant
const changeVariant = () => {
const currentIndex = variants.indexOf(currentVariant);
const nextVariant = variants[(currentIndex + 1) % variants.length];
setCurrentVariant(nextVariant);
};
useEffect(() => {
// Initialize WebRTC client with memoized callbacks
const client = new WebRTCClient({
onConnected: handleConnected,
onDisconnected: handleDisconnected,
onAudioStream: handleAudioStream,
onAudioLevel: handleAudioLevel
});
setWebrtcClient(client);
return () => {
client.disconnect();
};
}, [handleConnected, handleDisconnected, handleAudioStream, handleAudioLevel]);
const handleStart = () => {
webrtcClient?.connect();
};
const handleStop = () => {
webrtcClient?.disconnect();
};
return (
<div
className="relative w-full h-full"
onClick={changeVariant} // Add click handler to change color
>
<BackgroundCircles
variant={currentVariant}
audioLevel={audioLevel}
isActive={isConnected}
/>
<div className="absolute inset-0 flex items-center justify-center">
<AIVoiceInput
onStart={handleStart}
onStop={handleStop}
isConnected={isConnected}
/>
</div>
<audio ref={audioRef} autoPlay hidden />
</div>
);
}
export default { BackgroundCircleProvider }
const COLOR_VARIANTS = {
primary: {
border: [
"border-emerald-500/60",
"border-cyan-400/50",
"border-slate-600/30",
],
gradient: "from-emerald-500/30",
},
secondary: {
border: [
"border-violet-500/60",
"border-fuchsia-400/50",
"border-slate-600/30",
],
gradient: "from-violet-500/30",
},
senary: {
border: [
"border-blue-500/60",
"border-sky-400/50",
"border-slate-600/30",
],
gradient: "from-blue-500/30",
}, // blue
octonary: {
border: [
"border-red-500/60",
"border-rose-400/50",
"border-slate-600/30",
],
gradient: "from-red-500/30",
},
} as const;

101
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"use client";
import { createContext, useContext, useEffect, useState } from "react";
type Theme = "light" | "dark" | "system";
type ThemeProviderProps = {
children: React.ReactNode;
defaultTheme?: Theme;
storageKey?: string;
attribute?: string;
enableSystem?: boolean;
disableTransitionOnChange?: boolean;
};
type ThemeProviderState = {
theme: Theme;
setTheme: (theme: Theme) => void;
};
const initialState: ThemeProviderState = {
theme: "system",
setTheme: () => null,
};
const ThemeProviderContext = createContext<ThemeProviderState>(initialState);
export function ThemeProvider({
children,
defaultTheme = "system",
storageKey = "theme",
attribute = "class",
enableSystem = true,
disableTransitionOnChange = false,
...props
}: ThemeProviderProps) {
const [theme, setTheme] = useState<Theme>(defaultTheme);
useEffect(() => {
const savedTheme = localStorage.getItem(storageKey) as Theme | null;
if (savedTheme) {
setTheme(savedTheme);
} else if (defaultTheme === "system" && enableSystem) {
const systemTheme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
setTheme(systemTheme);
}
}, [defaultTheme, storageKey, enableSystem]);
useEffect(() => {
const root = window.document.documentElement;
if (disableTransitionOnChange) {
root.classList.add("no-transitions");
// Force a reflow
window.getComputedStyle(root).getPropertyValue("opacity");
setTimeout(() => {
root.classList.remove("no-transitions");
}, 0);
}
root.classList.remove("light", "dark");
if (theme === "system" && enableSystem) {
const systemTheme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
root.classList.add(systemTheme);
} else {
root.classList.add(theme);
}
localStorage.setItem(storageKey, theme);
}, [theme, storageKey, enableSystem, disableTransitionOnChange]);
const value = {
theme,
setTheme: (theme: Theme) => {
setTheme(theme);
},
};
return (
<ThemeProviderContext.Provider {...props} value={value}>
{children}
</ThemeProviderContext.Provider>
);
}
export const useTheme = () => {
const context = useContext(ThemeProviderContext);
if (context === undefined)
throw new Error("useTheme must be used within a ThemeProvider");
return context;
};

114
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"use client";
import { Mic, Square } from "lucide-react";
import { useState, useEffect } from "react";
import { cn } from "@/lib/utils";
interface AIVoiceInputProps {
onStart?: () => void;
onStop?: (duration: number) => void;
isConnected?: boolean;
className?: string;
}
export function AIVoiceInput({
onStart,
onStop,
isConnected = false,
className
}: AIVoiceInputProps) {
const [active, setActive] = useState(false);
const [time, setTime] = useState(0);
const [isClient, setIsClient] = useState(false);
const [status, setStatus] = useState<'disconnected' | 'connecting' | 'connected'>('disconnected');
useEffect(() => {
setIsClient(true);
}, []);
useEffect(() => {
let intervalId: NodeJS.Timeout;
if (active) {
intervalId = setInterval(() => {
setTime((t) => t + 1);
}, 1000);
} else {
setTime(0);
}
return () => clearInterval(intervalId);
}, [active]);
useEffect(() => {
if (isConnected) {
setStatus('connected');
setActive(true);
} else {
setStatus('disconnected');
setActive(false);
}
}, [isConnected]);
const formatTime = (seconds: number) => {
const mins = Math.floor(seconds / 60);
const secs = seconds % 60;
return `${mins.toString().padStart(2, "0")}:${secs.toString().padStart(2, "0")}`;
};
const handleStart = () => {
setStatus('connecting');
onStart?.();
};
const handleStop = () => {
onStop?.(time);
setStatus('disconnected');
};
return (
<div className={cn("w-full py-4", className)}>
<div className="relative max-w-xl w-full mx-auto flex items-center flex-col gap-4">
<div className={cn(
"px-2 py-1 rounded-md text-xs font-medium bg-black/10 dark:bg-white/10 text-gray-700 dark:text-white"
)}>
{status === 'connected' ? 'Connected' : status === 'connecting' ? 'Connecting...' : 'Disconnected'}
</div>
<button
className={cn(
"group w-16 h-16 rounded-xl flex items-center justify-center transition-colors",
active
? "bg-red-500/20 hover:bg-red-500/30"
: "bg-black/10 hover:bg-black/20 dark:bg-white/10 dark:hover:bg-white/20"
)}
type="button"
onClick={active ? handleStop : handleStart}
disabled={status === 'connecting'}
>
{status === 'connecting' ? (
<div
className="w-6 h-6 rounded-sm animate-spin bg-black dark:bg-white cursor-pointer pointer-events-auto"
style={{ animationDuration: "3s" }}
/>
) : active ? (
<Square className="w-6 h-6 text-red-500" />
) : (
<Mic className="w-6 h-6 text-black/70 dark:text-white/70" />
)}
</button>
<span
className={cn(
"font-mono text-sm transition-opacity duration-300",
active
? "text-black/70 dark:text-white/70"
: "text-black/30 dark:text-white/30"
)}
>
{formatTime(time)}
</span>
</div>
</div>
);
}

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"use client";
import { motion } from "framer-motion";
import clsx from "clsx";
import { useState, useEffect } from "react";
interface BackgroundCirclesProps {
title?: string;
description?: string;
className?: string;
variant?: keyof typeof COLOR_VARIANTS;
audioLevel?: number;
isActive?: boolean;
}
const COLOR_VARIANTS = {
primary: {
border: [
"border-emerald-500/60",
"border-cyan-400/50",
"border-slate-600/30",
],
gradient: "from-emerald-500/30",
},
secondary: {
border: [
"border-violet-500/60",
"border-fuchsia-400/50",
"border-slate-600/30",
],
gradient: "from-violet-500/30",
},
tertiary: {
border: [
"border-orange-500/60",
"border-yellow-400/50",
"border-slate-600/30",
],
gradient: "from-orange-500/30",
},
quaternary: {
border: [
"border-purple-500/60",
"border-pink-400/50",
"border-slate-600/30",
],
gradient: "from-purple-500/30",
},
quinary: {
border: [
"border-red-500/60",
"border-rose-400/50",
"border-slate-600/30",
],
gradient: "from-red-500/30",
}, // red
senary: {
border: [
"border-blue-500/60",
"border-sky-400/50",
"border-slate-600/30",
],
gradient: "from-blue-500/30",
}, // blue
septenary: {
border: [
"border-gray-500/60",
"border-gray-400/50",
"border-slate-600/30",
],
gradient: "from-gray-500/30",
},
octonary: {
border: [
"border-red-500/60",
"border-rose-400/50",
"border-slate-600/30",
],
gradient: "from-red-500/30",
},
} as const;
const AnimatedGrid = () => (
<motion.div
className="absolute inset-0 [mask-image:radial-gradient(ellipse_at_center,transparent_30%,black)]"
animate={{
backgroundPosition: ["0% 0%", "100% 100%"],
}}
transition={{
duration: 40,
repeat: Number.POSITIVE_INFINITY,
ease: "linear",
}}
>
<div className="h-full w-full [background-image:repeating-linear-gradient(100deg,#64748B_0%,#64748B_1px,transparent_1px,transparent_4%)] opacity-20" />
</motion.div>
);
export function BackgroundCircles({
title = "",
description = "",
className,
variant = "octonary",
audioLevel = 0,
isActive = false,
}: BackgroundCirclesProps) {
const variantStyles = COLOR_VARIANTS[variant];
const [animationParams, setAnimationParams] = useState({
scale: 1,
duration: 5,
intensity: 0
});
const [isLoaded, setIsLoaded] = useState(false);
// Initial page load animation
useEffect(() => {
// Small delay to ensure the black screen is visible first
const timer = setTimeout(() => {
setIsLoaded(true);
}, 300);
return () => clearTimeout(timer);
}, []);
// Update animation based on audio level
useEffect(() => {
if (isActive && audioLevel > 0) {
// Simple enhancement of audio level for more dramatic effect
const enhancedLevel = Math.min(1, audioLevel * 1.5);
setAnimationParams({
scale: 1 + enhancedLevel * 0.3,
duration: Math.max(2, 5 - enhancedLevel * 3),
intensity: enhancedLevel
});
} else if (animationParams.intensity > 0) {
// Only reset if we need to (prevents unnecessary updates)
const timer = setTimeout(() => {
setAnimationParams({
scale: 1,
duration: 5,
intensity: 0
});
}, 300);
return () => clearTimeout(timer);
}
}, [audioLevel, isActive, animationParams.intensity]);
return (
<>
{/* Initial black overlay that fades out */}
<motion.div
className="fixed inset-0 bg-black z-50"
initial={{ opacity: 1 }}
animate={{ opacity: isLoaded ? 0 : 1 }}
transition={{ duration: 1.2, ease: "easeInOut" }}
style={{ pointerEvents: isLoaded ? "none" : "auto" }}
/>
<div
className={clsx(
"relative flex h-screen w-full items-center justify-center overflow-hidden",
"bg-white dark:bg-black/5",
className
)}
>
<AnimatedGrid />
<motion.div
className="absolute h-[480px] w-[480px]"
initial={{ opacity: 0, scale: 0.9 }}
animate={{
opacity: isLoaded ? 1 : 0,
scale: isLoaded ? 1 : 0.9
}}
transition={{
duration: 1.5,
delay: 0.3,
ease: "easeOut"
}}
>
{[0, 1, 2].map((i) => (
<motion.div
key={i}
className={clsx(
"absolute inset-0 rounded-full",
"border-2 bg-gradient-to-br to-transparent",
variantStyles.border[i],
variantStyles.gradient
)}
animate={{
rotate: 360,
scale: [
1 + (i * 0.05),
(1 + (i * 0.05)) * (1 + (isActive ? animationParams.intensity * 0.2 : 0.02)),
1 + (i * 0.05)
],
opacity: [
0.7 + (i * 0.1),
0.8 + (i * 0.1) + (isActive ? animationParams.intensity * 0.2 : 0),
0.7 + (i * 0.1)
]
}}
transition={{
duration: isActive ? animationParams.duration : 8 + (i * 2),
repeat: Number.POSITIVE_INFINITY,
ease: "easeInOut",
}}
>
<div
className={clsx(
"absolute inset-0 rounded-full mix-blend-screen",
`bg-[radial-gradient(ellipse_at_center,${variantStyles.gradient.replace(
"from-",
""
)}/10%,transparent_70%)]`
)}
/>
</motion.div>
))}
</motion.div>
<div className="absolute inset-0 [mask-image:radial-gradient(90%_60%_at_50%_50%,#000_40%,transparent)]">
<motion.div
className="absolute inset-0 bg-[radial-gradient(ellipse_at_center,#0F766E/30%,transparent_70%)] blur-[120px]"
initial={{ opacity: 0 }}
animate={{
opacity: isLoaded ? 0.7 : 0,
scale: [1, 1 + (isActive ? animationParams.intensity * 0.3 : 0.02), 1],
}}
transition={{
opacity: { duration: 1.8, delay: 0.5 },
scale: {
duration: isActive ? 2 : 12,
repeat: Number.POSITIVE_INFINITY,
ease: "easeInOut",
}
}}
/>
<motion.div
className="absolute inset-0 bg-[radial-gradient(ellipse_at_center,#2DD4BF/15%,transparent)] blur-[80px]"
initial={{ opacity: 0 }}
animate={{
opacity: isLoaded ? 1 : 0,
scale: [1, 1 + (isActive ? animationParams.intensity * 0.4 : 0.03), 1]
}}
transition={{
opacity: { duration: 2, delay: 0.7 },
scale: {
duration: isActive ? 1.5 : 15,
repeat: Number.POSITIVE_INFINITY,
ease: "easeInOut",
}
}}
/>
{/* Additional glow that appears only during high audio levels */}
{isActive && animationParams.intensity > 0.4 && (
<motion.div
className={`absolute inset-0 bg-[radial-gradient(ellipse_at_center,${variantStyles.gradient.replace("from-", "")}/20%,transparent_70%)] blur-[60px]`}
initial={{ opacity: 0, scale: 0.8 }}
animate={{
opacity: [0, animationParams.intensity * 0.6, 0],
scale: [0.8, 1.1, 0.8],
}}
transition={{
duration: 0.8,
repeat: Number.POSITIVE_INFINITY,
ease: "easeInOut",
}}
/>
)}
</div>
</div>
</>
);
}
export function DemoCircles() {
const [currentVariant, setCurrentVariant] =
useState<keyof typeof COLOR_VARIANTS>("octonary");
const variants = Object.keys(
COLOR_VARIANTS
) as (keyof typeof COLOR_VARIANTS)[];
function getNextVariant() {
const currentIndex = variants.indexOf(currentVariant);
const nextVariant = variants[(currentIndex + 1) % variants.length];
return nextVariant;
}
return (
<>
<BackgroundCircles variant={currentVariant} />
<div className="absolute top-12 right-12">
<button
type="button"
className="bg-slate-950 dark:bg-white text-white dark:text-slate-950 px-4 py-1 rounded-md z-10 text-sm font-medium"
onClick={() => {
setCurrentVariant(getNextVariant());
}}
>
Change Variant
</button>
</div>
</>
);
}

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demo/nextjs_voice_chat/frontend/fastrtc-demo/components/ui/reset-chat.tsx Normal file
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"use client"
import { Trash } from "lucide-react"
export function ResetChat() {
return (
<button
className="w-10 h-10 rounded-md flex items-center justify-center transition-colors relative overflow-hidden bg-black/10 hover:bg-black/20 dark:bg-white/10 dark:hover:bg-white/20"
aria-label="Reset chat"
onClick={() => fetch("http://localhost:8000/reset")}
>
<div className="relative z-10">
<Trash className="h-5 w-5 text-black/70 dark:text-white/70" />
</div>
</button>
)
}

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"use client";
import { useTheme } from "@/components/theme-provider";
import { cn } from "@/lib/utils";
import { Moon, Sun } from "lucide-react";
import { useRef } from "react";
interface ThemeToggleProps {
className?: string;
}
export function ThemeToggle({ className }: ThemeToggleProps) {
const { theme } = useTheme();
const buttonRef = useRef<HTMLButtonElement>(null);
const toggleTheme = () => {
// Instead of directly changing the theme, dispatch a custom event
const newTheme = theme === "light" ? "dark" : "light";
// Dispatch custom event with the new theme
window.dispatchEvent(
new CustomEvent('themeToggleRequest', {
detail: { theme: newTheme }
})
);
};
return (
<button
ref={buttonRef}
onClick={toggleTheme}
className={cn(
"w-10 h-10 rounded-md flex items-center justify-center transition-colors relative overflow-hidden",
"bg-black/10 hover:bg-black/20 dark:bg-white/10 dark:hover:bg-white/20",
className
)}
aria-label="Toggle theme"
>
<div className="relative z-10">
{theme === "light" ? (
<Moon className="h-5 w-5 text-black/70" />
) : (
<Sun className="h-5 w-5 text-white/70" />
)}
</div>
{/* Small inner animation for the button itself */}
<div
className={cn(
"absolute inset-0 transition-transform duration-500",
theme === "light"
? "bg-gradient-to-br from-blue-500/20 to-purple-500/20 translate-y-full"
: "bg-gradient-to-br from-amber-500/20 to-orange-500/20 -translate-y-full"
)}
style={{
transitionTimingFunction: "cubic-bezier(0.22, 1, 0.36, 1)"
}}
/>
</button>
);
}

120
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"use client";
import { useTheme } from "@/components/theme-provider";
import { useEffect, useState } from "react";
import { motion, AnimatePresence } from "framer-motion";
interface ThemeTransitionProps {
className?: string;
}
export function ThemeTransition({ className }: ThemeTransitionProps) {
const { theme, setTheme } = useTheme();
const [position, setPosition] = useState({ x: 0, y: 0 });
const [isAnimating, setIsAnimating] = useState(false);
const [pendingTheme, setPendingTheme] = useState<string | null>(null);
const [visualTheme, setVisualTheme] = useState<string | null>(theme);
// Track mouse/touch position for click events
useEffect(() => {
const handleMouseMove = (e: MouseEvent) => {
setPosition({ x: e.clientX, y: e.clientY });
};
const handleTouchMove = (e: TouchEvent) => {
if (e.touches[0]) {
setPosition({ x: e.touches[0].clientX, y: e.touches[0].clientY });
}
};
window.addEventListener("mousemove", handleMouseMove);
window.addEventListener("touchmove", handleTouchMove);
return () => {
window.removeEventListener("mousemove", handleMouseMove);
window.removeEventListener("touchmove", handleTouchMove);
};
}, []);
// Listen for theme toggle requests
useEffect(() => {
// Custom event for theme toggle requests
const handleThemeToggle = (e: CustomEvent) => {
if (isAnimating) return; // Prevent multiple animations
const newTheme = e.detail.theme;
if (newTheme === theme) return;
// Store the pending theme but don't apply it yet
setPendingTheme(newTheme);
setIsAnimating(true);
// The actual theme will be applied mid-animation
};
window.addEventListener('themeToggleRequest' as any, handleThemeToggle as EventListener);
return () => {
window.removeEventListener('themeToggleRequest' as any, handleThemeToggle as EventListener);
};
}, [theme, isAnimating]);
// Apply the theme change mid-animation
useEffect(() => {
if (isAnimating && pendingTheme) {
// Set visual theme immediately for the animation
setVisualTheme(pendingTheme);
// Apply the actual theme change after a delay (mid-animation)
const timer = setTimeout(() => {
setTheme(pendingTheme as any);
}, 400); // Half of the animation duration
// End the animation after it completes
const endTimer = setTimeout(() => {
setIsAnimating(false);
setPendingTheme(null);
}, 1000); // Match with animation duration
return () => {
clearTimeout(timer);
clearTimeout(endTimer);
};
}
}, [isAnimating, pendingTheme, setTheme]);
return (
<AnimatePresence>
{isAnimating && (
<motion.div
className="fixed inset-0 z-[9999] pointer-events-none"
initial={{ opacity: 0 }}
animate={{ opacity: 1 }}
exit={{ opacity: 0 }}
transition={{ duration: 0.3 }}
>
<motion.div
className={`absolute rounded-full ${visualTheme === 'dark' ? 'bg-slate-950' : 'bg-white'}`}
initial={{
width: 0,
height: 0,
x: position.x,
y: position.y,
borderRadius: '100%'
}}
animate={{
width: Math.max(window.innerWidth * 3, window.innerHeight * 3),
height: Math.max(window.innerWidth * 3, window.innerHeight * 3),
x: position.x - Math.max(window.innerWidth * 3, window.innerHeight * 3) / 2,
y: position.y - Math.max(window.innerWidth * 3, window.innerHeight * 3) / 2,
}}
transition={{
duration: 0.8,
ease: [0.22, 1, 0.36, 1]
}}
/>
</motion.div>
)}
</AnimatePresence>
);
}

28
demo/nextjs_voice_chat/frontend/fastrtc-demo/eslint.config.mjs Normal file
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import { dirname } from "path";
import { fileURLToPath } from "url";
import { FlatCompat } from "@eslint/eslintrc";
const __filename = fileURLToPath(import.meta.url);
const __dirname = dirname(__filename);
const compat = new FlatCompat({
baseDirectory: __dirname,
});
const eslintConfig = [
...compat.extends("next/core-web-vitals", "next/typescript"),
{
rules: {
"no-unused-vars": "off",
"no-explicit-any": "off",
"no-console": "off",
"no-debugger": "off",
"eqeqeq": "off",
"curly": "off",
"quotes": "off",
"semi": "off",
},
},
];
export default eslintConfig;

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import { clsx, type ClassValue } from "clsx"
import { twMerge } from "tailwind-merge"
export function cn(...inputs: ClassValue[]) {
return twMerge(clsx(inputs))
}

189
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interface WebRTCClientOptions {
onConnected?: () => void;
onDisconnected?: () => void;
onMessage?: (message: any) => void;
onAudioStream?: (stream: MediaStream) => void;
onAudioLevel?: (level: number) => void;
}
export class WebRTCClient {
private peerConnection: RTCPeerConnection | null = null;
private mediaStream: MediaStream | null = null;
private dataChannel: RTCDataChannel | null = null;
private options: WebRTCClientOptions;
private audioContext: AudioContext | null = null;
private analyser: AnalyserNode | null = null;
private dataArray: Uint8Array | null = null;
private animationFrameId: number | null = null;
constructor(options: WebRTCClientOptions = {}) {
this.options = options;
}
async connect() {
try {
this.peerConnection = new RTCPeerConnection();
// Get user media
try {
this.mediaStream = await navigator.mediaDevices.getUserMedia({
audio: true
});
} catch (mediaError: any) {
console.error('Media error:', mediaError);
if (mediaError.name === 'NotAllowedError') {
throw new Error('Microphone access denied. Please allow microphone access and try again.');
} else if (mediaError.name === 'NotFoundError') {
throw new Error('No microphone detected. Please connect a microphone and try again.');
} else {
throw mediaError;
}
}
this.setupAudioAnalysis();
this.mediaStream.getTracks().forEach(track => {
if (this.peerConnection) {
this.peerConnection.addTrack(track, this.mediaStream!);
}
});
this.peerConnection.addEventListener('track', (event) => {
if (this.options.onAudioStream) {
this.options.onAudioStream(event.streams[0]);
}
});
this.dataChannel = this.peerConnection.createDataChannel('text');
this.dataChannel.addEventListener('message', (event) => {
try {
const message = JSON.parse(event.data);
console.log('Received message:', message);
if (this.options.onMessage) {
this.options.onMessage(message);
}
} catch (error) {
console.error('Error parsing message:', error);
}
});
// Create and send offer
const offer = await this.peerConnection.createOffer();
await this.peerConnection.setLocalDescription(offer);
// Use same-origin request to avoid CORS preflight
const response = await fetch('http://localhost:8000/webrtc/offer', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'Accept': 'application/json'
},
mode: 'cors', // Explicitly set CORS mode
credentials: 'same-origin',
body: JSON.stringify({
sdp: offer.sdp,
type: offer.type,
webrtc_id: Math.random().toString(36).substring(7)
})
});
const serverResponse = await response.json();
await this.peerConnection.setRemoteDescription(serverResponse);
if (this.options.onConnected) {
this.options.onConnected();
}
} catch (error) {
console.error('Error connecting:', error);
this.disconnect();
throw error;
}
}
private setupAudioAnalysis() {
if (!this.mediaStream) return;
try {
this.audioContext = new AudioContext();
this.analyser = this.audioContext.createAnalyser();
this.analyser.fftSize = 256;
const source = this.audioContext.createMediaStreamSource(this.mediaStream);
source.connect(this.analyser);
const bufferLength = this.analyser.frequencyBinCount;
this.dataArray = new Uint8Array(bufferLength);
this.startAnalysis();
} catch (error) {
console.error('Error setting up audio analysis:', error);
}
}
private startAnalysis() {
if (!this.analyser || !this.dataArray || !this.options.onAudioLevel) return;
// Add throttling to prevent too many updates
let lastUpdateTime = 0;
const throttleInterval = 100; // Only update every 100ms
const analyze = () => {
this.analyser!.getByteFrequencyData(this.dataArray!);
const currentTime = Date.now();
// Only update if enough time has passed since last update
if (currentTime - lastUpdateTime > throttleInterval) {
// Calculate average volume level (0-1)
let sum = 0;
for (let i = 0; i < this.dataArray!.length; i++) {
sum += this.dataArray![i];
}
const average = sum / this.dataArray!.length / 255;
this.options.onAudioLevel!(average);
lastUpdateTime = currentTime;
}
this.animationFrameId = requestAnimationFrame(analyze);
};
this.animationFrameId = requestAnimationFrame(analyze);
}
private stopAnalysis() {
if (this.animationFrameId !== null) {
cancelAnimationFrame(this.animationFrameId);
this.animationFrameId = null;
}
if (this.audioContext) {
this.audioContext.close();
this.audioContext = null;
}
this.analyser = null;
this.dataArray = null;
}
disconnect() {
this.stopAnalysis();
if (this.mediaStream) {
this.mediaStream.getTracks().forEach(track => track.stop());
this.mediaStream = null;
}
if (this.peerConnection) {
this.peerConnection.close();
this.peerConnection = null;
}
this.dataChannel = null;
if (this.options.onDisconnected) {
this.options.onDisconnected();
}
}
}

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demo/nextjs_voice_chat/frontend/fastrtc-demo/next.config.ts Normal file
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import type { NextConfig } from "next";
const nextConfig: NextConfig = {
/* config options here */
};
export default nextConfig;

33
demo/nextjs_voice_chat/frontend/fastrtc-demo/package.json Normal file
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{
"name": "fastrtc-demo",
"version": "0.1.0",
"private": true,
"scripts": {
"dev": "next dev --turbopack",
"build": "next build --no-lint",
"start": "next start",
"lint": "next lint"
},
"dependencies": {
"class-variance-authority": "^0.7.1",
"clsx": "^2.1.1",
"framer-motion": "^12.4.10",
"lucide-react": "^0.477.0",
"next": "15.2.2-canary.1",
"react": "^19.0.0",
"react-dom": "^19.0.0",
"tailwind-merge": "^3.0.2",
"tailwindcss-animate": "^1.0.7"
},
"devDependencies": {
"@eslint/eslintrc": "^3",
"@tailwindcss/postcss": "^4",
"@types/node": "^20",
"@types/react": "^19",
"@types/react-dom": "^19",
"eslint": "^9",
"eslint-config-next": "15.2.2-canary.1",
"tailwindcss": "^4",
"typescript": "^5"
}
}

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demo/nextjs_voice_chat/frontend/fastrtc-demo/postcss.config.mjs Normal file
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const config = {
plugins: ["@tailwindcss/postcss"],
};
export default config;

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<svg fill="none" viewBox="0 0 16 16" xmlns="http://www.w3.org/2000/svg"><path d="M14.5 13.5V5.41a1 1 0 0 0-.3-.7L9.8.29A1 1 0 0 0 9.08 0H1.5v13.5A2.5 2.5 0 0 0 4 16h8a2.5 2.5 0 0 0 2.5-2.5m-1.5 0v-7H8v-5H3v12a1 1 0 0 0 1 1h8a1 1 0 0 0 1-1M9.5 5V2.12L12.38 5zM5.13 5h-.62v1.25h2.12V5zm-.62 3h7.12v1.25H4.5zm.62 3h-.62v1.25h7.12V11z" clip-rule="evenodd" fill="#666" fill-rule="evenodd"/></svg>
Side by Side

After

Width:  |  Height:  |  Size: 391 B

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