Source code for speechbrain.lobes.models.huggingface_transformers.hubert

"""This lobe enables the integration of huggingface pretrained hubert models.

Transformer from HuggingFace needs to be installed:

 * Titouan Parcollet 2021
 * Boumadane Abdelmoumene 2021
 * Ha Nguyen 2023

import logging

from speechbrain.lobes.models.huggingface_transformers.wav2vec2 import Wav2Vec2

logger = logging.getLogger(__name__)

[docs] class HuBERT(Wav2Vec2): """This lobe enables the integration of HuggingFace and SpeechBrain pretrained HuBERT models. Source paper HuBERT: Transformer from HuggingFace needs to be installed: 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. Arguments --------- source : str HuggingFace hub name: e.g "facebook/hubert-base-ls960" 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 HuBERT 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 HuBERT 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 HubertModel() 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 facebook/hubert-base-ls960 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 = "facebook/hubert-base-ls960" >>> save_path = "savedir" >>> model = HuBERT(model_hub, save_path) >>> outputs = model(inputs) """ def __init__( self, source, save_path, output_norm=False, freeze=False, freeze_feature_extractor=False, apply_spec_augment=False, output_all_hiddens=False, ): super().__init__( source=source, save_path=save_path, output_norm=output_norm, freeze=freeze, freeze_feature_extractor=freeze_feature_extractor, apply_spec_augment=apply_spec_augment, output_all_hiddens=output_all_hiddens, )