How to Contribute to the HuggingFace Speech-to-Speech Project: A Complete Guide

Contributing to the HuggingFace speech-to-speech project involves setting up a local development environment, understanding the modular VAD→STT→LLM→TTS pipeline architecture, and submitting focused pull requests that add backends, fix issues, or improve documentation.

The HuggingFace speech-to-speech repository is a production-ready, low-latency voice assistant pipeline built entirely in Python. Whether you want to add a new TTS backend, optimize queue handling, or improve documentation, this guide walks you through the contribution workflow using actual source paths and patterns from the codebase.

Setting Up Your Development Environment

Before writing code, you need the repository cloned with dependencies installed and the test suite passing.

git clone https://github.com/huggingface/speech-to-speech.git
cd speech-to-speech
uv sync                 # installs in editable mode from pyproject.toml

pytest                  # run unit tests

ruff check              # linting check

This setup (lines 44‑51 of the README) ensures your changes integrate cleanly with the existing CI pipeline.

Understanding the Core Architecture

The speech-to-speech pipeline uses three fundamental concepts that every contributor must understand:

Concept	Purpose	Location
Argument classes	Typed dataclass objects exposing every CLI flag, parsed with HfArgumentParser	src/speech_to_speech/arguments_classes/*
Handlers	BaseHandler subclasses encoding each pipeline stage (VAD, STT, LLM, TTS)	src/speech_to_speech/VAD/*, STT/*, LLM/*, TTS/*
Pipeline builder	Orchestrates queues, events, and handler chains across four deployment modes	src/speech_to_speech/s2s_pipeline.py

Pipeline Execution Flow

In src/speech_to_speech/s2s_pipeline.py, the build_pipeline function constructs the communication graph:

• Argument pre‑parsing (lines 57‑70): Determines which LLM argument class to register based on --lm_model selection
• Handler chain construction via _build_pipeline_handlers (lines 78‑94): Wires VAD → STT → TranscriptionNotifier → LLM → LMOutputProcessor → TTS
• Mode‑specific setup (lines 106‑140): Selects between local, raw-websocket, realtime, or socket modes

Contributing a New Backend Handler

Adding support for a new speech-to-text or text-to-speech model follows a consistent pattern.

Adding a Custom STT Handler

1. Create your handler class in src/speech_to_speech/STT/:

# src/speech_to_speech/STT/my_stt_handler.py

from speech_to_speech.baseHandler import BaseHandler
from speech_to_speech.pipeline.handler_types import STTIn, STTOut

class MySTTHandler(BaseHandler[STTIn, STTOut]):
    def __init__(self, stop_event, queue_in, queue_out, *, model_name: str):
        super().__init__(stop_event, queue_in, queue_out)
        self.model_name = model_name
        self._load_model()

    def _run_once(self, item: STTIn) -> None:
        transcription = self.model.transcribe(item.audio_bytes)
        self.queue_out.put(STTOut(text=transcription, language=item.language))

2. Add an arguments dataclass in src/speech_to_speech/arguments_classes/stt_arguments.py (or create a new module).

3. Register in the pipeline builder at lines 80‑78 of s2s_pipeline.py:

elif module_kwargs.stt == "my_custom":
    from speech_to_speech.STT.my_stt_handler import MySTTHandler
    return MySTTHandler(
        stop_event,
        queue_in=audio_chunks_queue,
        queue_out=transcription_queue,
        model_name=module_kwargs.stt_model_name,
    )

Adding a Custom TTS Handler

The pattern is identical for TTS in src/speech_to_speech/TTS/:

# Register in get_tts_handler (lines 49-63 of s2s_pipeline.py)

elif module_kwargs.tts == "my_custom":
    from speech_to_speech.TTS.my_custom_tts_handler import MyCustomTTSHandler
    return MyCustomTTSHandler(
        stop_event,
        queue_in=lm_response_queue,
        queue_out=send_audio_chunks_queue,
        voice=module_kwargs.tts_voice,
    )

Testing Your Changes Locally

Before submitting, verify your implementation against the actual pipeline.

Run the Realtime Server Demo

# Terminal 1: Start the WebSocket server

export OPENAI_API_KEY=sk-...   # or leave unset for local LLM

speech-to-speech                # starts at ws://localhost:8765/v1/realtime

# Terminal 2: Connect with the demo client

python scripts/listen_and_play_realtime.py --host 127.0.0.1 --port 8765

Test Local Microphone Mode

speech-to-speech --local_mac_optimal_settings

Write a Unit Test for Your Handler

def test_my_stt_handler():
    from speech_to_speech.STT.my_stt_handler import MySTTHandler
    from speech_to_speech.utils.thread_manager import ThreadManager
    from threading import Event
    from queue import Queue

    in_q = Queue()
    out_q = Queue()
    stop_evt = Event()
    
    handler = MySTTHandler(stop_evt, in_q, out_q, model_name="test-model")
    tm = ThreadManager([handler])
    tm.start()
    
    in_q.put(STTIn(audio_bytes=b"test audio data", sample_rate=16000))
    result = out_q.get(timeout=5)
    
    assert result.text == "expected transcription"
    tm.stop()

Suggested First-Time Contributions to the Speech-to-Speech Project

Area	Specific Tasks
Documentation	Expand README sections on custom backend development; add queue flow diagrams
Test coverage	Add edge‑case tests for empty audio chunks and graceful shutdown in all four modes
Handler refactor	Extract common BaseHandler boilerplate into shared mix‑ins
New backend	Implement a lightweight TTS option (e.g., gTTS) with --tts=gtts flag
Performance	Benchmark --num_pipelines scaling and profile queue bottlenecks

Submitting Your Pull Request

Follow the workflow defined in the README (lines 72‑75):

1. Fork the repository and create a feature branch: git checkout -b add-my-backend
2. Make focused changes — limit each PR to one logical addition or fix
3. Validate locally: pytest && ruff check
4. Update documentation for any new CLI flags or API changes
5. Open a PR targeting main with clear description: "Closes #123: Adds MyCustom TTS backend"
6. Monitor CI — GitHub Actions runs tests and style checks automatically

The HuggingFace team prefers small, focused PRs over large refactorings. For substantial architectural changes, open an issue first to discuss approach.

Key Files Every Contributor Should Know

File	Function
src/speech_to_speech/s2s_pipeline.py	Central orchestration: argument parsing, handler wiring, mode selection
src/speech_to_speech/arguments_classes/module_arguments.py	Global CLI options and backend registry
src/speech_to_speech/baseHandler.py	Abstract base class for all pipeline stage handlers
src/speech_to_speech/utils/thread_manager.py	Thread lifecycle management for handler processes
src/speech_to_speech/VAD/vad_handler.py	Reference Silero VAD implementation
src/speech_to_speech/STT/parakeet_tdt_handler.py	Default local STT backend pattern
src/speech_to_speech/TTS/qwen3_tts_handler.py	Default TTS backend pattern
README.md	Installation, quick‑start, and contribution guidelines

Summary

• Clone and install with uv sync, then run pytest and ruff check to validate your environment
• Understand the three pillars: argument classes, BaseHandler subclasses, and the build_pipeline orchestrator in s2s_pipeline.py
• Add backends by implementing handlers in the appropriate VAD/, STT/, LLM/, or TTS/ directory, registering them in s2s_pipeline.py, and adding corresponding argument classes
• Test locally using speech-to-speech with the demo client or --local_mac_optimal_settings
• Submit focused PRs referencing issues, with passing CI and updated documentation

Frequently Asked Questions

What skills do I need to contribute to the HuggingFace speech-to-speech project?

You need intermediate Python experience, familiarity with asyncio or threading, and basic understanding of audio processing concepts. The codebase uses dataclasses, HfArgumentParser, and typed queues extensively — patterns common in modern ML engineering but approachable with standard Python knowledge.

Can I add a commercial API backend like Azure Speech or AWS Polly?

Yes. Create a handler subclass in the appropriate STT/ or TTS/ directory that calls the commercial API in _run_once(). Add your API key handling to the arguments classes, and register the backend in s2s_pipeline.py. Follow the pattern in existing handlers like qwen3_tts_handler.py for queue management.

How does the ThreadManager ensure clean shutdown?

The ThreadManager (in src/speech_to_speech/utils/thread_manager.py) collects all handler threads and provides start() and stop() methods. On stop(), it sets the shared stop_event that each handler checks in its main loop, allowing graceful termination rather than forced thread killing.

Should I write tests for my new backend contribution?

Yes — include unit tests that instantiate your handler with ThreadManager, feed sample data through the input queue, and verify expected output on the result queue. Test edge cases like empty inputs and rapid start/stop cycles to match the project's quality standards.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}