fastrtc-compact 0.0.1

The compact version of FastRTC, the realtime communication library for Python

FastRTC-Compact

A lean, Gradio-free fork of FastRTC.

Turn any Python function into a real-time audio and video stream over WebRTC or WebSockets — without the Gradio, librosa and their dependencies baggage of upstream.

This is a fork. It tracks FastRTC v0.0.34 and strips everything not needed for a production, bring-your-own-frontend deployment. The public Stream API and the fastrtc import path are unchanged, so code that mounts a stream on FastAPI works as-is.

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What's different from upstream

Removed / out of scope

• Gradio — the entire auto-UI (.ui.launch()), the Gradio WebRTC component, and the bundled Svelte / compiled frontend assets.
• librosa + numba + llvmlite — replaced by soxr for audio resampling. Same resampler quality (soxr_hq), roughly 400 MB lighter, and no one-time JIT compilation stall on the first call.
• fastphone() — the free temporary phone number (HF token + Gradio tunneling) has been removed.
• HuggingFace Spaces tooling — upload_space.py and related helpers.

Kept

• WebRTC and WebSocket endpoints via .mount(app).
• Voice Activity Detection and turn-taking (ReplyOnPause, optional vad extra).
• Optional TTS / STT / stop-word extras.

The result is a much smaller dependency tree and image footprint, suitable for packaging as a plain FastAPI / WebRTC library.

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Installation

This fork is distributed from git (it is not published to PyPI under this name):

pip install "git+https://github.com/abdurrafay0610/FastRTC-Compact.git"

To use built-in pause detection (see ReplyOnPause) and text to speech (see Text To Speech), add the vad and tts extras:

pip install "fastrtc-compact[vad,tts] @ git+https://github.com/abdurrafay0610/FastRTC-Compact.git"

Other optional extras: stt, stopword.

Naming: the distribution is fastrtc-compact, but the import path is unchanged — from fastrtc import Stream, ReplyOnPause. It is a drop-in replacement for code already written against FastRTC.

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Key Features

• 🗣️ Automatic voice detection & turn-taking — only worry about the logic for responding to the user; ReplyOnPause handles detecting when they've finished speaking.
• 🔌 WebRTC support — .mount(app) adds a /webrtc/offer endpoint to a FastAPI app for your own frontend.
• ⚡️ WebSocket support — the same .mount(app) adds a /websocket/offer endpoint.
• 🤖 Fully customizable backend — a Stream mounts onto any FastAPI app, so you can extend it to fit your production system.

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Quickstart

Echo Audio

from fastrtc import Stream, ReplyOnPause
import numpy as np

def echo(audio: tuple[int, np.ndarray]):
    # The function is passed the audio until the user pauses.
    # Implement any iterator that yields audio.
    yield audio

stream = Stream(
    handler=ReplyOnPause(echo),
    modality="audio",
    mode="send-receive",
)

LLM Voice Chat

from fastrtc import (
    ReplyOnPause, Stream,
    audio_to_bytes, aggregate_bytes_to_16bit,
)
import numpy as np
from groq import Groq
import anthropic
from elevenlabs import ElevenLabs

groq_client = Groq()
claude_client = anthropic.Anthropic()
tts_client = ElevenLabs()


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
    reply = 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 reply.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

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",
)

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Running the Stream

Mount the stream on a FastAPI app and serve your own frontend:

from fastapi import FastAPI
from fastapi.responses import HTMLResponse
from fastrtc import Stream, ReplyOnPause

app = FastAPI()
stream = Stream(handler=ReplyOnPause(...), modality="audio", mode="send-receive")
stream.mount(app)

# Optional: serve your frontend
@app.get("/")
async def index():
    return HTMLResponse(content=open("index.html").read())

# uvicorn app:app --host 0.0.0.0 --port 8000

mount() registers the following routes (prefixed with the optional path argument):

Endpoint	Protocol	Purpose
/webrtc/offer	HTTP POST	WebRTC SDP / ICE exchange
/websocket/offer	WebSocket	WebSocket streaming
/telephone/incoming	HTTP POST	Twilio inbound call webhook *
/telephone/handler	WebSocket	Twilio media-stream handler *

* Telephone (Twilio): dial-in routes are still mounted, so you can point your own Twilio number at /telephone/incoming. The zero-config fastphone() temporary number is gone. (Pending decision: keep these routes for phone dial-in, or strip them for a browser-only deployment.)

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Examples

For end-to-end demos (Gemini / OpenAI / Claude voice chat, Whisper transcription, object detection, and more), see the upstream FastRTC cookbook. Those demos are built against the original library and its Gradio UI; adapt the handler logic to a .mount(app) deployment when porting them to this fork.

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Credits

Forked from FastRTC by the Gradio team. MIT licensed.