🗣️👀 Gemini Audio Video Chat
Stream BOTH your webcam video and audio feeds to Google Gemini. You can also upload images to augment your conversation!
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🗣️ Google Gemini Real Time Voice API
Talk to Gemini in real time using Google's voice API.
Demo |
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🗣️ OpenAI Real Time Voice API
Talk to ChatGPT in real time using OpenAI's voice API.
Demo |
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🤖 Hello Computer
Say computer before asking your question!
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🤖 Llama Code Editor
Create and edit HTML pages with just your voice! Powered by SambaNova systems.
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🗣️ Talk to Claude
Use the Anthropic and Play.Ht APIs to have an audio conversation with Claude.
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🎵 Whisper Transcription
Have whisper transcribe your speech in real time!
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📷 Yolov10 Object Detection
Run the Yolov10 model on a user webcam stream in real time!
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🗣️ Kyutai Moshi
Kyutai's moshi is a novel speech-to-speech model for modeling human conversations.
Demo |
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🗣️ Hello Llama: Stop Word Detection
A code editor built with Llama 3.3 70b that is triggered by the phrase "Hello Llama". Build a Siri-like coding assistant in 100 lines of code!
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## Usage
This is a shortened version of the official [usage guide](https://freddyaboulton.github.io/gradio-webrtc/user-guide/).
- `.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.
- `.mount(app)`: Mount the stream on a [FastAPI](https://fastapi.tiangolo.com/) app. Perfect for integrating with your already existing production system.
## Quickstart
### Echo Audio
```python
from fastrtc import Stream, ReplyOnPause
import numpy as np
def echo(audio: tuple[int, np.ndarray]):
# 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
stream = Stream(
handler=ReplyOnPause(echo),
modality="audio",
mode="send-receive",
)
```
### LLM Voice Chat
```py
from fastrtc import (
ReplyOnPause, AdditionalOutputs, Stream,
audio_to_bytes, aggregate_bytes_to_16bit
)
import gradio as gr
from groq import Groq
import anthropic
from elevenlabs import ElevenLabs
groq_client = Groq()
claude_client = anthropic.Anthropic()
tts_client = ElevenLabs()
# See "Talk to Claude" in Cookbook for an example of how to keep
# track of the chat history.
def response(
audio: tuple[int, np.ndarray],
):
prompt = groq_client.audio.transcriptions.create(
file=("audio-file.mp3", audio_to_bytes(audio)),
model="whisper-large-v3-turbo",
response_format="verbose_json",
).text
response = claude_client.messages.create(
model="claude-3-5-haiku-20241022",
max_tokens=512,
messages=[{"role": "user", "content": prompt}],
)
response_text = " ".join(
block.text
for block in response.content
if getattr(block, "type", None) == "text"
)
iterator = tts_client.text_to_speech.convert_as_stream(
text=response_text,
voice_id="JBFqnCBsd6RMkjVDRZzb",
model_id="eleven_multilingual_v2",
output_format="pcm_24000"
)
for chunk in aggregate_bytes_to_16bit(iterator):
audio_array = np.frombuffer(chunk, dtype=np.int16).reshape(1, -1)
yield (24000, audio_array)
stream = Stream(
modality="audio",
mode="send-receive",
handler=ReplyOnPause(response),
)
```
### Webcam Stream
```python
from fastrtc import Stream
import numpy as np
def flip_vertically(image):
return np.flip(image, axis=0)
stream = Stream(
handler=flip_vertically,
modality="video",
mode="send-receive",
)
```
### Object Detection
```python
from fastrtc import Stream
import gradio as gr
import cv2
from huggingface_hub import hf_hub_download
from .inference import YOLOv10
model_file = hf_hub_download(
repo_id="onnx-community/yolov10n", filename="onnx/model.onnx"
)
# git clone https://huggingface.co/spaces/fastrtc/object-detection
# for YOLOv10 implementation
model = YOLOv10(model_file)
def detection(image, conf_threshold=0.3):
image = cv2.resize(image, (model.input_width, model.input_height))
new_image = model.detect_objects(image, conf_threshold)
return cv2.resize(new_image, (500, 500))
stream = Stream(
handler=detection,
modality="video",
mode="send-receive",
additional_inputs=[
gr.Slider(minimum=0, maximum=1, step=0.01, value=0.3)
]
)
```
## Running the Stream
Run:
### Gradio
```py
stream.ui.launch()
```
### Telephone (Audio Only)
```py
stream.fastphone()
```
### FastAPI
```py
app = FastAPI()
stream.mount(app)
# Optional: Add routes
@app.get("/")
async def _():
return HTMLResponse(content=open("index.html").read())
# uvicorn app:app --host 0.0.0.0 --port 8000
```
