SpeechTokenizer
The SpeechTokenizer utilizes an Encoder-Decoder architecture with residual vector quantization to unify semantic and acoustic tokens, streamlining the processing of speech information. It features models that operate at 16kHz trained on LibriSpeech and Common Voice datasets to improve semantic and timbre representation. Compatible with Python 3.8+ and PyTorch, the installation is straightforward via pip or GitHub. Users can access guidance on model loading, representation extraction, and decoding. Extensive documentation and open-source resources such as USLM increase the tool's accessibility and utility for developers.
