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add code (#102)

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Co-authored-by: Freddy Boulton <freddyboulton@hf-freddy.local>
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Freddy Boulton
2025-02-28 12:47:20 -05:00
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commit 4b44e67032
4 changed files with 183 additions and 344 deletions
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demo/voice_text_editor/README.md Normal file
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---
title: Voice Text Editor
emoji: 📝
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 5.16.0
app_file: app.py
pinned: false
license: mit
short_description: Edit text documents with your voice!
tags: [webrtc, websocket, gradio, secret|HF_TOKEN, secret|SAMBANOVA_API_KEY]
---
# Voice Text Editor
Edit text documents with your voice!

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demo/voice_text_editor/app.py Normal file
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import os
import gradio as gr
from dotenv import load_dotenv
from fastrtc import AdditionalOutputs, ReplyOnPause, Stream, get_stt_model
from openai import OpenAI
load_dotenv()
sambanova_client = OpenAI(
api_key=os.getenv("SAMBANOVA_API_KEY"), base_url="https://api.sambanova.ai/v1"
)
stt_model = get_stt_model()
SYSTEM_PROMPT = """You are an intelligent voice-activated text editor assistant. Your purpose is to help users create and modify text documents through voice commands.
For each interaction:
1. You will receive the current state of a text document and a voice input from the user.
2. Determine if the input is:
a) A command to modify the document (e.g., "delete the last line", "capitalize that")
b) Content to be added to the document (e.g., "buy 12 eggs at the store")
c) A modification to existing content (e.g., "actually make that 24" to change "12" to "24")
3. Return ONLY the new document state after the changes have been applied.
Example:
CURRENT DOCUMENT:
Meeting notes:
- Buy GPUs
- Meet with Joe
USER INPUT: Make that 100 GPUS
NEW DOCUMENT STATE:
Meeting notes:
- Buy 100 GPUs
- Meet with Joe
Example 2:
CURRENT DOCUMENT:
Project Proposal
USER INPUT: Make that a header
NEW DOCUMENT STATE:
# Project Proposal
When handling commands:
- Apply the requested changes precisely to the document
- Support operations like adding, deleting, modifying, and moving text
- Understand contextual references like "that", "the last line", "the second paragraph"
When handling content additions:
- Add the new text at the appropriate location (usually at the end or cursor position)
- Format it appropriately based on the document context
- If the user says to "add" or "insert" do not remove text that was already in the document.
When handling content modifications:
- Identify what part of the document the user is referring to
- Apply the requested change while preserving the rest of the content
- Be smart about contextual references (e.g., "make that 24" should know to replace a number)
NEVER include any text in the new document state that is not part of the user's input.
NEVER include the phrase "CURRENT DOCUMENT" in the new document state.
NEVER reword the user's input unless you are explicitly asked to do so.
"""
def edit(audio, current_document: str):
prompt = stt_model.stt(audio)
print(f"Prompt: {prompt}")
response = sambanova_client.chat.completions.create(
model="Meta-Llama-3.3-70B-Instruct",
messages=[
{"role": "system", "content": SYSTEM_PROMPT},
{
"role": "user",
"content": f"CURRENT DOCUMENT:\n\n{current_document}\n\nUSER INPUT: {prompt}",
},
],
max_tokens=200,
)
doc = response.choices[0].message.content
yield AdditionalOutputs(doc)
doc = gr.Textbox(value="", label="Current Document")
stream = Stream(
ReplyOnPause(edit),
modality="audio",
mode="send",
additional_inputs=[doc],
additional_outputs=[doc],
additional_outputs_handler=lambda prev, current: current,
ui_args={"title": "Voice Text Editor with FastRTC 🗣️"},
)
if __name__ == "__main__":
if (mode := os.getenv("MODE")) == "UI":
stream.ui.launch(server_port=7860)
elif mode == "PHONE":
stream.fastphone(host="0.0.0.0", port=7860)
else:
stream.ui.launch(server_port=7860)

104
docs/userguide/audio.md
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@@ -151,97 +151,41 @@ The API is similar to `ReplyOnPause` with the addition of a `stop_words` paramet
It is also possible to create asynchronous stream handlers. This is very convenient for accessing async APIs from major LLM developers, like Google and OpenAI. The main difference is that `receive`, `emit`, and `start_up` are now defined with `async def`. It is also possible to create asynchronous stream handlers. This is very convenient for accessing async APIs from major LLM developers, like Google and OpenAI. The main difference is that `receive`, `emit`, and `start_up` are now defined with `async def`.
Here is a complete example of using `AsyncStreamHandler` for using the Google Gemini real time API: Here is aa simple example of using `AsyncStreamHandler`:
=== "Code" === "Code"
``` py ``` py
from fastrtc import AsyncStreamHandler from fastrtc import AsyncStreamHandler, wait_for_item
import asyncio import asyncio
import base64
import os
import google.generativeai as genai
from google.generativeai.types import (
LiveConnectConfig, SpeechConfig,
VoiceConfig, PrebuiltVoiceConfig
)
class GeminiHandler(AsyncStreamHandler): class AsyncEchoHandler(AsyncStreamHandler):
"""Handler for the Gemini API""" """Handler for the Gemini API"""
def __init__(self) -> None:
super().__init__()
self.queue = asyncio.Queue()
def __init__( async def receive(self, frame: tuple[int, np.ndarray]) -> None:
self, self.queue.put(frame)
expected_layout: Literal["mono"] = "mono",
output_sample_rate: int = 24000,
output_frame_size: int = 480,
) -> None:
super().__init__(
expected_layout,
output_sample_rate,
output_frame_size,
input_sample_rate=16000,
)
self.input_queue: asyncio.Queue = asyncio.Queue()
self.output_queue: asyncio.Queue = asyncio.Queue()
self.quit: asyncio.Event = asyncio.Event()
def copy(self) -> "GeminiHandler": async def emit(self) -> None: # (2)
return GeminiHandler( return await wait_for_item(self.queue)
expected_layout="mono",
output_sample_rate=self.output_sample_rate, def copy(self):
output_frame_size=self.output_frame_size, return AsyncEchoHandler()
)
async def shutdown(self): # (3)
async def start_up(self): pass
await self.wait_for_args()
api_key, voice_name = self.latest_args[1:] def start_up(self) -> None: # (4)
client = genai.Client( pass
api_key=api_key or 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,
)
)
),
)
async with client.aio.live.connect(
model="gemini-2.0-flash-exp", config=config
) as session:
async for audio in session.start_stream(
stream=self.stream(), mime_type="audio/pcm"
):
if audio.data:
array = np.frombuffer(audio.data, dtype=np.int16)
self.output_queue.put_nowait(array)
async def stream(self) -> AsyncGenerator[bytes, None]:
while not self.quit.is_set():
try:
audio = await asyncio.wait_for(self.input_queue.get(), 0.1)
yield audio
except (asyncio.TimeoutError, TimeoutError):
pass
async def receive(self, frame: tuple[int, np.ndarray]) -> None:
_, array = frame
array = array.squeeze()
audio_message = encode_audio(array)
self.input_queue.put_nowait(audio_message)
async def emit(self) -> tuple[int, np.ndarray]:
array = await self.output_queue.get()
return (self.output_sample_rate, array)
def shutdown(self) -> None:
self.quit.set()
self.args_set.clear()
``` ```
!!! tip
See [Talk To Gemini](https://huggingface.co/spaces/fastrtc/talk-to-gemini), [Talk To Openai](https://huggingface.co/spaces/fastrtc/talk-to-openai) for complete examples of `AsyncStreamHandler`s.
## Text To Speech ## Text To Speech
You can use an on-device text to speech model if you have the `tts` extra installed. You can use an on-device text to speech model if you have the `tts` extra installed.

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docs/userguide/gradio.md
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@@ -7,274 +7,37 @@ The automatic gradio UI is a great way to test your stream. However, you may wan
To build a standalone Gradio application, you can use the `WebRTC` component and implement the `stream` event. To build a standalone Gradio application, you can use the `WebRTC` component and implement the `stream` event.
Similarly to the `Stream` object, you must set the `mode` and `modality` arguments and pass in a `handler`. Similarly to the `Stream` object, you must set the `mode` and `modality` arguments and pass in a `handler`.
Below are some common examples of how to use the `WebRTC` component. In the `stream` event, you pass in your handler as well as the input and output components.
``` py
import gradio as gr
from fastrtc import WebRTC, ReplyOnPause
def response(audio: tuple[int, np.ndarray]):
"""This function must yield audio frames"""
...
yield audio
=== "Reply On Pause" with gr.Blocks() as demo:
``` py gr.HTML(
import gradio as gr """
from gradio_webrtc import WebRTC, ReplyOnPause <h1 style='text-align: center'>
Chat (Powered by WebRTC ⚡️)
def response(audio: tuple[int, np.ndarray]): # (1) </h1>
"""This function must yield audio frames""" """
...
for numpy_array in generated_audio:
yield (sampling_rate, numpy_array, "mono") # (2)
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-receive", # (3)
modality="audio",
)
audio.stream(fn=ReplyOnPause(response),
inputs=[audio], outputs=[audio], # (4)
time_limit=60) # (5)
demo.launch()
```
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.
=== "AsyncStreamHandler"
``` py
import asyncio
import base64
import logging
import os
import gradio as gr
import numpy as np
from google import genai
from gradio_webrtc import (
AsyncStreamHandler,
WebRTC,
async_aggregate_bytes_to_16bit,
get_twilio_turn_credentials,
) )
with gr.Column():
class GeminiHandler(AsyncStreamHandler): with gr.Group():
def __init__( audio = WebRTC(
self, expected_layout="mono", output_sample_rate=24000, output_frame_size=480
) -> None:
super().__init__(
expected_layout,
output_sample_rate,
output_frame_size,
input_sample_rate=16000,
)
self.client: genai.Client | None = None
self.input_queue = asyncio.Queue()
self.output_queue = asyncio.Queue()
self.quit = asyncio.Event()
self.connected = asyncio.Event()
def copy(self) -> "GeminiHandler":
return GeminiHandler(
expected_layout=self.expected_layout,
output_sample_rate=self.output_sample_rate,
output_frame_size=self.output_frame_size,
)
async def stream(self):
while not self.quit.is_set():
audio = await self.input_queue.get()
yield audio
async def connect(self, api_key: str):
client = genai.Client(api_key=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.connected.set()
async for audio in session.start_stream(
stream=self.stream(), mime_type="audio/pcm"
):
if audio.data:
yield audio.data
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 generator(self):
async for audio_response in async_aggregate_bytes_to_16bit(
self.connect(api_key=self.latest_args[1])
):
self.output_queue.put_nowait(audio_response)
async def emit(self):
if not self.args_set.is_set():
await self.wait_for_args()
if not self.connected.is_set():
asyncio.create_task(self.generator())
await self.connected.wait()
array = await self.output_queue.get()
return (self.output_sample_rate, array)
def shutdown(self) -> None:
self.quit.set()
with gr.Blocks() as demo:
gr.HTML(
"""
<div style='text-align: center'>
<h1>Gen AI SDK Voice Chat</h1>
<p>Speak with Gemini using real-time audio streaming</p>
<p>Get an API Key <a href="https://support.google.com/googleapi/answer/6158862?hl=en">here</a></p>
</div>
"""
)
with gr.Row() as api_key_row:
api_key = gr.Textbox(
label="API Key",
placeholder="Enter your API Key",
value=os.getenv("GOOGLE_API_KEY", ""),
type="password",
)
with gr.Row(visible=False) as row:
webrtc = WebRTC(
label="Audio",
modality="audio",
mode="send-receive", mode="send-receive",
rtc_configuration=get_twilio_turn_credentials(), modality="audio",
pulse_color="rgb(35, 157, 225)",
icon_button_color="rgb(35, 157, 225)",
icon="https://www.gstatic.com/lamda/images/gemini_favicon_f069958c85030456e93de685481c559f160ea06b.png",
) )
audio.stream(fn=ReplyOnPause(response),
webrtc.stream( inputs=[audio], outputs=[audio],
GeminiHandler(), time_limit=60)
inputs=[webrtc, api_key], demo.launch()
outputs=[webrtc], ```
time_limit=90,
concurrency_limit=2,
)
api_key.submit(
lambda: (gr.update(visible=False), gr.update(visible=True)),
None,
[api_key_row, row],
)
```
=== "Server-To-Client Audio"
``` 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 Streaming"
``` 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 Video"
``` 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.
!!! tip
You can configure the `time_limit` and `concurrency_limit` parameters of the `stream` event similar to the `Stream` object.
## Additional Outputs ## Additional Outputs
@@ -285,7 +48,7 @@ This is common for displaying a multimodal text/audio conversation in a Chatbot
=== "Code" === "Code"
``` py title="Additional Outputs" ``` py title="Additional Outputs"
from gradio_webrtc import AdditionalOutputs, WebRTC from fastrtc import AdditionalOutputs, WebRTC
def transcribe(audio: tuple[int, np.ndarray], def transcribe(audio: tuple[int, np.ndarray],
transformers_convo: list[dict], transformers_convo: list[dict],