💻 programming

speech-to-speech

Open source speech-to-speech conversion module

#natural language processing
#Open source
#speech recognition
#speech synthesis
speech-to-speech

Product Details

speech-to-speech is an open source modular GPT4-o project that implements speech-to-speech conversion through continuous parts such as speech activity detection, speech-to-text, language model, and text-to-speech. It leverages the Transformers library and models available on the Hugging Face hub, providing a high degree of modularity and flexibility.

Main Features

1
Voice Activity Detection (VAD): Using silero VAD v5.
2
Speech-to-text (STT): using Whisper models, including distilled versions.
3
Language Model (LM): Any available instruction model can be selected on Hugging Face Hub.
4
Text-to-Speech (TTS): Using Parler-TTS, different checkpoints are supported.
5
Modular design: Each component is implemented as a class and can be reimplemented according to specific needs.
6
Supports server/client method and local method running.

How to Use

1
Clone the repository to your local environment.
2
Install required dependencies.
3
Configure model parameters and generation parameters as needed.
4
Choose how to run: server/client method or local method.
5
In the case of a server/client approach, run the model on the server first and then handle the audio input and output on the client.
6
If the method is local, run using the loopback address.
7
Use Torch Compile to optimize the performance of Whisper and Parler-TTS.
8
Using the model via the command line, specify different parameters to control the behavior of different parts.

Target Users

The target audience is developers and researchers, especially those interested in speech recognition, natural language processing and speech synthesis technologies. This product is suitable for them because it provides a flexible, customizable open source tool that can be used for research or development of related applications.

Examples

Developers can use this model to create a voice assistant to achieve voice interaction.

Researchers can use this model to conduct experiments and research on speech recognition and speech synthesis.

Educational institutions can integrate it into teaching tools to improve students' understanding of speech technology.

Quick Access

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Categories

💻 programming
› AI speech synthesis
› AI speech recognition

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