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huggingface/speech-to-speech

AI Agent

Build local voice agents with open-source models

12k Stars1.5k Forks136 Open Issues12k WatchersPythonApache-2.0
AI AgentAI AppDeveloper ToolUI Framework
Review Readiness

This repository page has enough original analysis, source evidence, and comparison context to be treated as an indexable Git-Stars review.

100

review score

Indexable review
Decision Snapshot

Problem solved

It solves the fragmentation of building voice agents by providing a fully integrated, swappable pipeline that works with open-source models locally or with hosted providers, avoiding vendor lock-in and enabling privacy-sensitive or offline deployments.

Deployment reality

The available setup signal starts with: pip install speech-to-speech. Treat this as a starting point, then ask Codex or Claude to inspect the README, environment variables, runtime version, and deployment target before production use.

Commercial use

The recorded license is Apache-2.0. This is a useful commercial-use signal, but teams should still verify license text, dependencies, model/API terms, and trademark constraints.

Capability ceiling

Developers can build real-time conversational voice agents, such as customer support bots, personal assistants, or interactive robots (e.g., Reachy Mini). The ceiling includes production-grade, low-latency voice interaction with custom LLMs, STT, and TTS components, all controllable via CLI and compatible with any OpenAI Realtime client.

Source and compliance noteLast synced: Aug 12, 2026

Git-Stars is independent and not affiliated with GitHub or this project. Analysis may be AI-assisted and based on public repository metadata plus short README-derived summaries. We do not mirror full README files, docs, issues, or social comments.

Original GitHub sourceMethodologyEditorial Policy
Editorial Evaluation

huggingface/speech-to-speech is tracked as a Python project in the AI Agent, AI App, Developer Tool, UI Framework area. This evaluation combines public GitHub metadata, category signals, short source summaries, and Git-Stars editorial rules rather than copying project documentation.

Momentum check: the repository has 12k total stars, with +0 today, +0 this week, and +6.2k this month. These growth windows help distinguish durable adoption from short-lived visibility spikes.

Maintenance check: current activity is Active; the latest push was 11 days ago, and the open issue queue is 136, about 1.11% of total stars. Treat this as an adoption signal, not a substitute for engineering due diligence.

Adoption check: 1.5k forks and 12k watchers suggest how often the project is reused or followed. License signal: Apache-2.0. Always verify license compatibility before commercial or internal use.

Practical fit: this project is most relevant when you need AI prototypes, LLM workflows, and agent-style applications. Be cautious when you need a legal review, security audit, or production SLA.

Source check: Git-Stars currently has 2 explicit source reference(s) for this report and a recent growth signal of 6.2k. Follow the original GitHub repository for final install, security, and release information.

Evidence Checklist

Analysis

Ready

Original problem, capability, and verdict guidance are available.

Sources

Ready

Repository metadata and README/source references are attached.

License

Ready

Apache-2.0 is recorded for review.

Maintenance

Ready

Recent activity is visible in repository metadata.

Alternatives

Ready

Enough nearby projects exist for comparison.

Best For
  • AI prototypes, LLM workflows, and agent-style applications
  • Python teams evaluating ecosystem-native tooling
  • teams that prefer mature projects with broad adoption signals
  • use cases where recent maintenance matters
Avoid When
  • you need a legal review, security audit, or production SLA
Adoption Signals

Momentum

12k Stars

Reuse

1.5k Forks

Attention

12k Watchers

Maintenance

active

License

Apache-2.0

Open issues

136

Overview

A low-latency, modular voice-agent pipeline (VAD -> STT -> LLM -> TTS) that exposes an OpenAI Realtime-compatible WebSocket API. Every component is swappable, supporting local and hosted models.

Key Features

- Fully modular pipeline: VAD, STT, LLM, TTS components are swappable via CLI flags. - OpenAI Realtime-compatible WebSocket API for easy client integration. - Supports multiple backends including local models (e.g., Parakeet TDT, Qwen3-TTS) and hosted providers.

Tool Positioning

AI Agent

Agent frameworks, autonomous workflows, and tool-use systems

AI App

End-user AI applications and AI-native product examples

Developer Tool

Tools that improve coding, testing, build, and local workflow

UI Framework

Frontend frameworks, design systems, and interface libraries

Quick Start
pip install speech-to-speech
View on GitHub
Project Activity

80

Health Score

Active

Commit Activity

Aug 7, 2024

Created

Aug 11, 2026

Last push

Source Trail

GitHub repository metadata

metadata

GitHub README

readme_summary

Star History

+233

Today's growth

+1.3k

7-day growth

+5.0k

30-day growth

Jul 29, 2026Aug 12, 2026
Community Health
1.5k

Forks

136

Open

12k

Watchers

Owner
SS

huggingface

GitHub profile
Topics & Language
Pythonaiassistantlanguage-modelmachine-learningpythonspeechspeech-synthesisspeech-to-textspeech-translation
Ecosystem & Usage
GitHub Repository
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License
Apache-2.0
CreatedAug 7, 2024
Last pushAug 11, 2026
Last syncedAug 12, 2026
Repository Standards

✓

License

✓

Forked

✓ Active

Maintained

AI AnalysisAnalyzed by Git-Stars

Problem Solved

It solves the fragmentation of building voice agents by providing a fully integrated, swappable pipeline that works with open-source models locally or with hosted providers, avoiding vendor lock-in and enabling privacy-sensitive or offline deployments.

Capabilities

Developers can build real-time conversational voice agents, such as customer support bots, personal assistants, or interactive robots (e.g., Reachy Mini). The ceiling includes production-grade, low-latency voice interaction with custom LLMs, STT, and TTS components, all controllable via CLI and compatible with any OpenAI Realtime client.

Bottom Line

This framework is ideal for developers who want to build voice agents with full control over components and data privacy, using open-source models. It is not for those seeking a fully managed, no-code solution. The key trade-off is flexibility vs. operational complexity of self-hosting.

Full AI Analysis