espnet2.asr.encoder.hubert_encoder.FairseqHubertEncoder

source

class espnet2.asr.encoder.hubert_encoder.FairseqHubertEncoder(input_size: int, hubert_url: str = './', hubert_dir_path: str = './', output_size: int = 256, normalize_before: bool = False, freeze_finetune_updates: int = 0, dropout_rate: float = 0.0, activation_dropout: float = 0.1, attention_dropout: float = 0.0, mask_length: int = 10, mask_prob: float = 0.75, mask_selection: str = 'static', mask_other: int = 0, apply_mask: bool = True, mask_channel_length: int = 64, mask_channel_prob: float = 0.5, mask_channel_other: int = 0, mask_channel_selection: str = 'static', layerdrop: float = 0.1, feature_grad_mult: float = 0.0)

Bases: AbsEncoder

FairSeq Hubert encoder module, used for loading pretrained weight and finetuning

• Parameters:
  • input_size – input dim
  • hubert_url – url to Hubert pretrained model
  • hubert_dir_path – directory to download the Wav2Vec2.0 pretrained model.
  • output_size – dimension of attention
  • normalize_before – whether to use layer_norm before the first block
  • freeze_finetune_updates – steps that freeze all layers except output layer before tuning the whole model (nessasary to prevent overfit).
  • dropout_rate – dropout rate
  • activation_dropout – dropout rate in activation function
  • attention_dropout – dropout rate in attention

Hubert specific Args: : Please refer to: https://github.com/pytorch/fairseq/blob/master/fairseq/models/hubert/hubert.py

Initialize internal Module state, shared by both nn.Module and ScriptModule.

forward(xs_pad: Tensor, ilens: Tensor, prev_states: Tensor = None) → Tuple[Tensor, Tensor, Tensor | None]

Forward Hubert ASR Encoder.

• Parameters:
  • xs_pad – input tensor (B, L, D)
  • ilens – input length (B)
  • prev_states – Not to be used now.
• Returns: position embedded tensor and mask

output_size() → int

reload_pretrained_parameters()