Description
#HuggingFace #Real-time Voice AI #Speech Recognition #Large Language Models #Speech Synthesis
speech-to-speech is a real-time voice AI pipeline open-sourced by Hugging Face, covering aspects such as speech recognition, language model processing, speech synthesis, and audio output. The entire system can run on your own GPU, without relying on cloud-based voice APIs that charge by the minute, character count, or number of calls.
It is not bound to a specific model but provides a modular solution that can be freely combined. Users can select ASR, LLM, and TTS models based on their needs for language, latency, memory usage, and audio quality to build voice assistants, intelligent customer service, virtual characters, and real-time dialogue applications.
- Local GPU Operation: Models and audio data can be deployed on your own devices or servers, reducing reliance on third-party real-time voice APIs.
- Multiple Speech Recognition Models: Supports combinations of ASR solutions such as Whisper, Faster-Whisper, Distil-Whisper, Paraformer, etc.
- Free Choice of Language Models: Can connect to models like Llama, Mistral, Qwen, and services compatible with OpenAI API format.
- Multiple Speech Synthesis Solutions: Supports models like Parler-TTS, MeloTTS, ChatTTS, Qwen3-TTS, which can be tested for audio quality and latency.
- Modular Combination: Recognition, inference, and synthesis modules can be replaced separately, making it easy to compare the speed and effects of different model combinations.
- Reduce End-to-End Latency: Stream processing is performed separately for speech recognition, model generation, speech synthesis, and playback stages, reducing serial waiting times.
- LMS Mode: Supports direct audio token generation by the language model, skipping the traditional text-to-speech stage, for exploring end-to-end voice model architectures.
- Suitable for Real-time Applications: Can be used in scenarios such as voice chatbots, customer service systems, game characters, companion assistants, and real-time translation.
- Reduce API Costs: After local deployment, the main costs come from GPU devices or computing power rental, eliminating separate API call fees based on voice duration.
- One Command Installation: Can be installed via pip install speech-to-speech Python package.
- Apache 2.0 License: Source code is open, usable for research, deployment, and commercial projects that comply with the license requirements.
- Installation Command: pip install speech-to-speech
speech-to-speech is a real-time voice AI pipeline open-sourced by Hugging Face, covering aspects such as speech recognition, language model processing, speech synthesis, and audio output. The entire system can run on your own GPU, without relying on cloud-based voice APIs that charge by the minute, character count, or number of calls.
It is not bound to a specific model but provides a modular solution that can be freely combined. Users can select ASR, LLM, and TTS models based on their needs for language, latency, memory usage, and audio quality to build voice assistants, intelligent customer service, virtual characters, and real-time dialogue applications.
Software Features
- Complete Real-time Voice Link: Integrates speech recognition, language modeling, text-to-speech, and audio playback into a single workflow.- Local GPU Operation: Models and audio data can be deployed on your own devices or servers, reducing reliance on third-party real-time voice APIs.
- Multiple Speech Recognition Models: Supports combinations of ASR solutions such as Whisper, Faster-Whisper, Distil-Whisper, Paraformer, etc.
- Free Choice of Language Models: Can connect to models like Llama, Mistral, Qwen, and services compatible with OpenAI API format.
- Multiple Speech Synthesis Solutions: Supports models like Parler-TTS, MeloTTS, ChatTTS, Qwen3-TTS, which can be tested for audio quality and latency.
- Modular Combination: Recognition, inference, and synthesis modules can be replaced separately, making it easy to compare the speed and effects of different model combinations.
- Reduce End-to-End Latency: Stream processing is performed separately for speech recognition, model generation, speech synthesis, and playback stages, reducing serial waiting times.
- LMS Mode: Supports direct audio token generation by the language model, skipping the traditional text-to-speech stage, for exploring end-to-end voice model architectures.
- Suitable for Real-time Applications: Can be used in scenarios such as voice chatbots, customer service systems, game characters, companion assistants, and real-time translation.
- Reduce API Costs: After local deployment, the main costs come from GPU devices or computing power rental, eliminating separate API call fees based on voice duration.
- One Command Installation: Can be installed via pip install speech-to-speech Python package.
- Apache 2.0 License: Source code is open, usable for research, deployment, and commercial projects that comply with the license requirements.
Download Links
- GitHub: https://github.com/huggingface/speech-to-speech- Installation Command: pip install speech-to-speech
