speechbrain.lobes.models.huggingface_wav2vec module¶
This lobe enables the integration of huggingface pretrained wav2vec2 models.
Reference: https://arxiv.org/abs/2006.11477 Reference: https://arxiv.org/abs/1904.05862 Transformer from HuggingFace needs to be installed: https://huggingface.co/transformers/installation.html
- Authors
Titouan Parcollet 2021
Reference¶
- class speechbrain.lobes.models.huggingface_wav2vec.HuggingFaceWav2Vec2(source, save_path, output_norm=True, freeze=True, freeze_feature_extractor=False, pretrain=True, apply_spec_augment=False)[source]¶
Bases:
torch.nn.modules.module.Module
This lobe enables the integration of HuggingFace pretrained wav2vec2.0 models.
Source paper: https://arxiv.org/abs/2006.11477 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.
- Parameters
source (str) – HuggingFace hub name: e.g “facebook/wav2vec2-large-lv60”
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 wav2vec 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 featue_extractor module of the model is Frozen. If False all the wav2vec model will be trained including featue_extractor module.
pretrain (bool (default: True)) – If True, the model is pretrained with the specified source. If False, the randomly-initialized model is instantiated.
apply_spec_augment (bool (default: False)) – If True, the model will apply spec augment on the output of feature extractor (inside huggingface Wav2VecModel() class). If False, the model will not apply spec augment. We set this to false to prevent from doing it twice.
Example
>>> inputs = torch.rand([10, 600]) >>> model_hub = "facebook/wav2vec2-base-960h" >>> save_path = "savedir" >>> model = HuggingFaceWav2Vec2(model_hub, save_path) >>> outputs = model(inputs) >>> outputs.shape torch.Size([10, 1, 768])
- forward(wav)[source]¶
Takes an input waveform and return its corresponding wav2vec encoding.
- Parameters
wav (torch.Tensor (signal)) – A batch of audio signals to transform to features.
- extract_features(wav)[source]¶
Takes an input waveform and return its corresponding wav2vec encoding.
- Parameters
wav (torch.Tensor (signal)) – A batch of audio signals to transform to features.