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:
This lobes enables the integration of fairseq pretrained wav2vec1.0 models. |
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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, dropout=None)[source]
Bases:
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.
dropout (float (default: None)) – If different from None (0.0 to 1.0), it will override the given fairseq dropout rates. This is useful if the wav2vec2 model has been trained without dropout and one wants to reactivate it for downstream task fine-tuning (better performance observed).
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)[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.
- class speechbrain.lobes.models.fairseq_wav2vec.FairseqWav2Vec1(pretrained_path, save_path, output_norm=True, freeze=True, pretrain=True)[source]
Bases:
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)[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.