speechbrain.lobes.models.huggingface_transformers.wavlm module

This lobe enables the integration of huggingface pretrained wavlm models.

Reference: https://arxiv.org/abs/2006.11477 Reference: https://arxiv.org/abs/1904.05862 Reference: https://arxiv.org/abs/2110.13900 Transformer from HuggingFace needs to be installed: https://huggingface.co/transformers/installation.html

Authors

  • Titouan Parcollet 2021

  • Boumadane Abdelmoumene 2021

  • Ha Nguyen 2023

Summary

Classes:

WavLM

This lobe enables the integration of HuggingFace and SpeechBrain pretrained WavLM models.

Reference

class speechbrain.lobes.models.huggingface_transformers.wavlm.WavLM(source, save_path, output_norm=False, freeze=False, freeze_feature_extractor=False, apply_spec_augment=False, output_all_hiddens=False)[source]

Bases: Wav2Vec2

This lobe enables the integration of HuggingFace and SpeechBrain pretrained WavLM models.

Source paper WavLM: https://arxiv.org/abs/2110.13900 Transformer from HuggingFace needs to be installed: https://huggingface.co/transformers/installation.html

The model can be used as a fixed feature extractor or can be finetuned. It will download automatically the model from HuggingFace or use a local path.

For now, HuggingFace’s HuBERT and WavLM model can be loaded using the exact code for Wav2Vec2 model. For this reason, HuBERT and WavLM can be fine inheriting the Wav2Vec2 class.

Parameters:

  • source (str) – HuggingFace hub name: e.g “microsoft/wavlm-large”

  • save_path (str) – Path (dir) of the downloaded model.

  • output_norm (bool (default: True)) – If True, a layer_norm (affine) will be applied to the output obtained from the wavlm model.

  • freeze (bool (default: True)) – If True, the model is frozen. If False, the model will be trained alongside with the rest of the pipeline.

  • freeze_feature_extractor (bool (default: False)) – When freeze = False and freeze_feature_extractor True, the feature_extractor module of the model is Frozen. If False all the wavlm model will be trained including feature_extractor module.

  • apply_spec_augment (bool (default: False)) – If True, the model will apply spec augment on the output of feature extractor (inside huggingface WavLMModel() class). If False, the model will not apply spec augment. We set this to false to prevent from doing it twice.

  • output_all_hiddens (bool (default: False)) – If True, the forward function outputs the hidden states from all transformer layers. For example wavlm-base has 12 transformer layers and the output is of shape (13, B, T, C), where a projection of the CNN output is added to the beginning. If False, the forward function outputs the hidden states only from the last transformer layer.

Example

>>> import torch
>>> inputs = torch.rand([10, 600])
>>> model_hub = "microsoft/wavlm-large"
>>> save_path = "savedir"
>>> model = WavLM(model_hub, save_path)
>>> outputs = model(inputs)