speechbrain.lobes.models.fairseq_wav2vec module

This lobe enables the integration of fairseq pretrained wav2vec models.

Reference: https://arxiv.org/abs/2006.11477 Reference: https://arxiv.org/abs/1904.05862 FairSeq >= 1.0.0 needs to be installed: https://fairseq.readthedocs.io/en/latest/

Authors

• Titouan Parcollet 2021

• Salima Mdhaffar 2021

Summary

Classes:

FairseqWav2Vec1	This lobes enables the integration of fairseq pretrained wav2vec1.0 models.
FairseqWav2Vec2	This lobe enables the integration of fairseq pretrained wav2vec2.0 models.

Reference

class speechbrain.lobes.models.fairseq_wav2vec.FairseqWav2Vec2(pretrained_path, save_path, input_norm=None, output_norm=True, freeze=True, pretrain=True)

Bases: torch.nn.modules.module.Module

This lobe enables the integration of fairseq pretrained wav2vec2.0 models.

Source paper: https://arxiv.org/abs/2006.11477 FairSeq >= 1.0.0 needs to be installed: https://fairseq.readthedocs.io/en/latest/

The model can be used as a fixed features extractor or can be finetuned. It will download automatically the model if a url is given (e.g FairSeq repository from GitHub).

Parameters

• pretrained_path (str) – Path of the pretrained wav2vec2 model. It can be a url or a local path.

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

• input_norm (bool (default: None)) – If True, a layer_norm (affine) will be applied to the input waveform. By default, it is extracted from the checkpoint of the downloaded model in order to match the pretraining conditions. However, if this information is not given in the checkpoint, it has to be given manually.

• 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.

• pretrain (bool (default: True)) – If True, the model is pretrained with the specified source. If False, the randomly-initialized model is instantiated.

Example

>>> inputs = torch.rand([10, 600])
>>> model_url = "https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_small.pt"
>>> save_path = "models_checkpoints/wav2vec2.pt"
>>> model = FairseqWav2Vec2(model_url, save_path)
>>> outputs = model(inputs)
>>> outputs.shape
torch.Size([10, 100,  768])

forward(wav)

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)

Extracts the wav2vect embeddings

reset_layer(model)

Reinitializes the parameters of the network

training: bool

class speechbrain.lobes.models.fairseq_wav2vec.FairseqWav2Vec1(pretrained_path, save_path, output_norm=True, freeze=True, pretrain=True)

Bases: torch.nn.modules.module.Module

This lobes enables the integration of fairseq pretrained wav2vec1.0 models.

Parameters

• pretrained_path (str) – Path of the pretrained wav2vec1 model. It can be a url or a local path.

• save_path (str) – Path and filename 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.

• pretrain (bool (default: True)) – If True, the model is pretrained with the specified source. If False, the randomly-initialized model is instantiated.

Example

>>> inputs = torch.rand([10, 600])
>>> model_url = ""
>>> save_path = "models_checkpoints/wav2vec.pt"
>>> model = FairseqWav2Vec1(model_url, save_path)
>>> outputs = model(inputs)
>>> outputs.shape
torch.Size([10, 100, 512])

forward(wav)

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)

Extracts the wav2vect embeddings

training: bool

reset_layer(model)

Reinitializes the parameters of the network