Wavlm Base Plus

🚀 WavLM-Base-Plus

The base model pretrained on 16kHz sampled speech audio, offering potential for various speech processing tasks.

This is Microsoft's WavLM. It is a base model pretrained on 16kHz sampled speech audio. When using the model, ensure that your speech input is also sampled at 16kHz.

⚠️ Important Note

This model does not have a tokenizer as it was pretrained on audio alone. To use this model for speech recognition, a tokenizer should be created and the model should be fine - tuned on labeled text data. Check out [this blog](https://huggingface.co/blog/fine - tune-wav2vec2-english) for a more in - depth explanation of how to fine - tune the model.

The model was pre - trained on:

• 60,000 hours of Libri - Light
• 10,000 hours of GigaSpeech
• 24,000 hours of VoxPopuli

Paper: WavLM: Large - Scale Self - Supervised Pre - Training for Full Stack Speech Processing

Authors: Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei

Abstract Self - supervised learning (SSL) achieves great success in speech recognition, while limited exploration has been attempted for other speech processing tasks. As speech signal contains multi - faceted information including speaker identity, paralinguistics, spoken content, etc., learning universal representations for all speech tasks is challenging. In this paper, we propose a new pre - trained model, WavLM, to solve full - stack downstream speech tasks. WavLM is built based on the HuBERT framework, with an emphasis on both spoken content modeling and speaker identity preservation. We first equip the Transformer structure with gated relative position bias to improve its capability on recognition tasks. For better speaker discrimination, we propose an utterance mixing training strategy, where additional overlapped utterances are created unsupervisely and incorporated during model training. Lastly, we scale up the training dataset from 60k hours to 94k hours. WavLM Large achieves state - of - the - art performance on the SUPERB benchmark, and brings significant improvements for various speech processing tasks on their representative benchmarks.

The original model can be found under https://github.com/microsoft/unilm/tree/master/wavlm.

🚀 Quick Start

This is an English pre - trained speech model that needs to be fine - tuned on a downstream task like speech recognition or audio classification before it can be used in inference. The model was pre - trained in English and thus should perform well only in English. It has been shown to work well on the SUPERB benchmark.

⚠️ Important Note

The model was pre - trained on phonemes rather than characters. This means that one should ensure that the input text is converted to a sequence of phonemes before fine - tuning.

💻 Usage Examples

Speech Recognition

To fine - tune the model for speech recognition, see [the official speech recognition example](https://github.com/huggingface/transformers/tree/master/examples/pytorch/speech - recognition).

Speech Classification

To fine - tune the model for speech classification, see [the official audio classification example](https://github.com/huggingface/transformers/tree/master/examples/pytorch/audio - classification).

Speaker Verification

TODO

Speaker Diarization

TODO

📄 License

The official license can be found here

📚 Documentation

The model was contributed by cywang and patrickvonplaten.