#HuBERT

The project utilizes HuBERT and custom quantizers to support developers in implementing voice cloning. It includes code samples, pretrained models, and input preparation guidelines. Available tools like audio-webui and community contributions in German and Polish are included. Designed for Python 3.10, it also offers resources for training custom models with semantic data, aiming for precise and realistic voice cloning.

Delve into the unofficial Pytorch version of Google's SoundStorm, featuring mask-based discrete diffusion for audio generation. This approach utilizes HuBERT for semantic tokens and uses a shallow u-net to integrate different codebooks in parallel audio processing. For detailed insights, refer to the InsturctTTS paper. Upcoming updates will incorporate MASKGIT to align with Google's methodology, serving developers and researchers focused on advanced audio processing.

This project uses extensive Chinese audio data from sources like YouTube and Podcasts to train models such as wav2vec 2.0 and HuBERT via Fairseq. These models, available in BASE and LARGE versions, enhance speech recognition and are evaluated on datasets like Aishell and WenetSpeech. Accessible on Hugging Face, these models are suitable for diverse applications, showing improved performance in varied noise and recording settings.

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.