speech-to-speech

Master Hugging Face speech-to-speech CLI commands. Explore run, demo, benchmark, and realtime options for seamless voice interaction. Get started now.

Run the Hugging Face Speech-to-Speech pipeline offline. Cache models locally or use explicit paths to operate without a network connection and maintain your workflow.

Discover supported TTS backends in Hugging Face Speech-to-Speech: chatTTS, MMS, pocket, kokoro, and qwen3. Get installation guides for seamless setup.

Configure your Speech-to-Speech project easily. Learn how to select TTS backends like MLX GGML or Torch within the huggingface repository for optimal performance.

Learn how to skip STT and send audio directly to audio-capable LLMs in Hugging Face Speech-to-Speech by setting --stt none. Stream raw audio for efficient processing.

Learn how to use the Chat Completions API for your LLM backend with huggingface speech-to-speech. Manage your LLM lifecycle easily and efficiently.

Learn how to use vLLM with the Responses API backend for speech-to-speech pipelines. Point the speech-to-speech library to your vLLM HTTP endpoint using responses_api_base_url and responses_api_model_name.

Easily configure the Responses API for your LLM backend in huggingface/speech-to-speech. Learn to set API keys, model names, and endpoint URLs for seamless integration.

Integrate an OpenAI-compatible API with the huggingface speech-to-speech pipeline. Connect to Ollama, Azure OpenAI, or self-hosted LLMs using the responses_api backend.

Master selecting LLM backends for speech-to-speech with this guide. Easily switch between transformers, mlx-lm, and more using CLI flags or Python functions.

Integrate Faster Whisper as your STT backend for enhanced speech-to-speech performance. Learn how to set up this powerful tool easily for your projects. Improve your audio processing today.

Learn how to use Whisper as an STT backend for your speech-to-speech pipeline. Configure model parameters and generation settings easily via the CLI for efficient transcription.