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Speech To Speech
Speech To Speech is a Hugging Face toolkit for building low-latency voice agents from separate voice-activity detection, speech-to-text, language-model, and text-to-speech components.
Its components are swappable, and the language-model step can use a hosted provider or a server on your own hardware. The project also exposes an OpenAI Realtime-compatible API, so an existing realtime client can connect to a different voice-agent stack. Use this as a first read, not a recommendation. Open the original project before trusting details like terms, limits, privacy, cost, setup, or safety.
What it is
A modular voice-agent pipeline
The pipeline connects speech detection, transcription, an LLM, and generated speech through queues. Each stage has selectable backends, so builders can change one layer without replacing the whole voice interface.
Why it stands out
A familiar realtime client interface
The same pipeline can sit behind a compatible realtime WebSocket or WebRTC client, while the LLM layer can move between local Transformers or MLX models, a self-hosted server, and a compatible hosted provider.
Availability
Python package and source repository
Install it as a Python package on Python 3.10 or newer, then use local microphone, realtime, raw WebSocket, TCP, or Docker paths. Backend support varies by platform, and the package is currently marked alpha.
Why it matters
What makes it useful
Voice-agent experiments often become tied to one provider or one bundled stack. Speech To Speech separates the listening, reasoning, and speaking layers, making it easier to replace a model, compare a hosted route with a local server, or keep the client while changing what runs behind it.
What to know
Where it fits
This fits builders who want a working voice pipeline and a standard client-facing API without first adopting a larger realtime communications platform. It can run with local speech components and a self-served LLM, or mix local components with an OpenAI-compatible provider.
Notable points
What stands out
Available paths include realtime WebSocket and WebRTC clients, direct local microphone use, raw WebSocket and TCP modes, several speech backends, local Transformers or MLX language models, and OpenAI-compatible hosted or self-hosted servers.
Before using
What to review
The package metadata currently marks the project alpha, so test the exact platform, backend combination, and interruption behavior needed for the intended use.
Hardware and installation needs vary by speech and language model. Some Linux Qwen3-TTS setups need a CUDA-matched wheel, while some fully local setups use a separate LLM server.
The default quickstart uses an external language-model API key. Check which audio, transcripts, prompts, and generated text leave the machine when mixing local and hosted components.
Model weights, optional backends, and hosted services can carry terms that differ from the repository's Apache-2.0 code license.
Reader fit
Who may find it relevant
Builders prototyping voice agents who want to swap speech or language-model components without rebuilding the client connection.
People comparing a hosted LLM route with a more local voice setup built from separate models and servers.
Less relevant for readers looking for a finished consumer voice assistant or a no-code service.
Editorial note
Why LifeHubber lists it
Speech To Speech turns a vague "local voice agent" idea into concrete choices: which parts listen, reason, and speak, which can stay on your hardware, and which still depend on an outside provider.
Source links
Source materials
Reader note
Before relying on this entry
LifeHubber lists entries to help readers inspect AI projects, not to endorse them or prove they are safe, suitable, accurate, maintained, or right for a specific use. We do not verify every entry in depth. Before relying on anything listed, review the original materials, terms, privacy practices, limits, and risks that matter for your situation.
What to explore next
Compare what sits around the voice pipeline.
Speech To Speech narrows the build to listening, an LLM, generated speech, and a realtime API. These next pages add broader media infrastructure, a larger pipeline ecosystem, or a model designed for overlapping conversation.
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