speechbrain.lobes.models.huggingface_transformers.discrete_wav2vec2 module

This lobe enables the integration of pretrained discrete wav2vec2 model.

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

Author

  • Pooneh Mousavi 2023

Summary

Classes:

DiscreteWav2Vec2

This lobe enables the integration of HuggingFace and SpeechBrain pretrained Discrete Wav2Vec2 models.

Reference

class speechbrain.lobes.models.huggingface_transformers.discrete_wav2vec2.DiscreteWav2Vec2(source, save_path, kmeans_filename, kmeans_cache_dir, kmeans_repo_id='speechbrain/SSL_Quantization', output_norm=False, freeze=False, freeze_feature_extractor=False, apply_spec_augment=False, output_all_hiddens=True, ssl_layer_num=-1)[source]

Bases: Wav2Vec2

This lobe enables the integration of HuggingFace and SpeechBrain pretrained Discrete Wav2Vec2 models.

Source paper wav2vec2.0: 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 Discrete feature extractor or can be finetuned. It will download automatically the model from HuggingFace or use a local path.

Parameters:

  • source (str) – HuggingFace hub name: e.g “facebook/wav2vec2-large-lv60”

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

  • kmeans_repo_id (str) – Huggingface repository if that contains the pretrained kmean model

  • kmeans_filename (str) – Name of the file in HF repo that need to be downloaded.

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

  • output_norm (bool (default: True)) – If True, a layer_norm (affine) will be applied to the output obtained from the Wav2Vec2 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 Wav2Vec2 model will be trained including featue_extractor module.

  • apply_spec_augment (bool (default: False)) – If True, the model will apply spec augment on the output of feature extractor (inside huggingface Wav2Vec2 Model() 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: True)) – If True, the forward function outputs the hidden states from all transformer layers. For example facebook/wav2vec2-large-lv60 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.

  • ssl_layer_num ((int) (default: -1)) – determine the output of which layer of the SSL model should be used for clustering.

Example

>>> import torch
>>> inputs = torch.rand([10, 600])
>>> model_hub = "facebook/wav2vec2-large-lv60"
>>> save_path = "savedir"
>>> ssl_layer_num = -1
>>> kmeans_repo_id = "speechbrain/SSL_Quantization"
>>> kmeans_filename = "LibriSpeech_wav2vec_k128_L7.pt"
>>> kmeans_cache_dir="savedir"
>>> model = DiscreteWav2Vec2(model_hub, save_path,freeze = True,ssl_layer_num=ssl_layer_num,kmeans_repo_id=kmeans_repo_id, kmeans_filename=kmeans_filename, kmeans_cache_dir=kmeans_cache_dir)
>>> embs, tokens = model(inputs)
>>> embs.shape
torch.Size([10, 1, 1024])
>>> tokens.shape
torch.Size([10, 1])
load_kmeans(repo_id, filename, cache_dir)[source]

Load a Pretrained kmeans model from HF.

Parameters:

  • repo_id (str) – The hugingface repo id that contains the model.

  • filename (str) – The name of the checkpoints in the repo that need to be downloaded.

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

  • Returns –

  • --------- –

  • kmeans_model (MiniBatchKMeans:) – pretrained Kmeans model loaded from the HF.

forward(wav, wav_lens=None)[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.

  • wav_len (tensor) – The relative length of the wav given in SpeechBrain format.

  • Returns –

  • --------- –

  • tokens (torch.Tensor) – A (Batch x Seq) tensor of audio tokens

  • emb (torch.Tensor) – A (Batch x Seq x embedding_dim ) cluster_centers embeddings for each tokens

training: bool