Shortcuts

Source code for torchaudio.models.wav2vec2.utils.import_huggingface

"""Import Hugging Face transformers's wav2vec2.0 pretrained weights to torchaudios's format.
"""
import logging

from torch.nn import Module

from ..model import wav2vec2_model, Wav2Vec2Model

_LG = logging.getLogger(__name__)


def _get_config(cfg):
    config = {
        "extractor_mode": f"{cfg.feat_extract_norm}_norm",
        "extractor_conv_layer_config": list(zip(cfg.conv_dim, cfg.conv_kernel, cfg.conv_stride)),
        "extractor_conv_bias": cfg.conv_bias,
        "encoder_embed_dim": cfg.hidden_size,
        "encoder_projection_dropout": cfg.feat_proj_dropout,
        "encoder_pos_conv_kernel": cfg.num_conv_pos_embeddings,
        "encoder_pos_conv_groups": cfg.num_conv_pos_embedding_groups,
        "encoder_num_layers": cfg.num_hidden_layers,
        "encoder_num_heads": cfg.num_attention_heads,
        "encoder_attention_dropout": cfg.attention_dropout,
        "encoder_ff_interm_features": cfg.intermediate_size,
        "encoder_ff_interm_dropout": cfg.activation_dropout,
        "encoder_dropout": cfg.hidden_dropout,
        "encoder_layer_norm_first": cfg.do_stable_layer_norm,
        "encoder_layer_drop": cfg.layerdrop,
    }
    return config


def _build(config, original):
    if original.__class__.__name__ == "Wav2Vec2ForCTC":
        aux_num_out = original.config.vocab_size
        wav2vec2 = original.wav2vec2
    else:
        _LG.warning("The model is not an instance of Wav2Vec2ForCTC. " '"lm_head" module is not imported.')
        aux_num_out = None
        wav2vec2 = original
    imported = wav2vec2_model(**config, aux_num_out=aux_num_out)
    imported.feature_extractor.load_state_dict(wav2vec2.feature_extractor.state_dict())
    imported.encoder.feature_projection.load_state_dict(wav2vec2.feature_projection.state_dict())
    imported.encoder.transformer.load_state_dict(wav2vec2.encoder.state_dict())
    if original.__class__.__name__ == "Wav2Vec2ForCTC":
        imported.aux.load_state_dict(original.lm_head.state_dict())
    return imported


[docs]def import_huggingface_model(original: Module) -> Wav2Vec2Model: """import_huggingface_model(original: torch.nn.Module) -> torchaudio.models.Wav2Vec2Model Build Wav2Vec2Model from the corresponding model object of Hugging Face's `Transformers`_. Args: original (torch.nn.Module): An instance of ``Wav2Vec2ForCTC`` from ``transformers``. Returns: Wav2Vec2Model: Imported model. Example >>> from torchaudio.models.wav2vec2.utils import import_huggingface_model >>> >>> original = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h") >>> model = import_huggingface_model(original) >>> >>> waveforms, _ = torchaudio.load("audio.wav") >>> logits, _ = model(waveforms) .. _Transformers: https://huggingface.co/transformers/ """ _LG.info("Importing model.") _LG.info("Loading model configuration.") config = _get_config(original.config) _LG.debug(" - config: %s", config) _LG.info("Building model.") imported = _build(config, original) return imported

Docs

Access comprehensive developer documentation for PyTorch

View Docs

Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

Resources

Find development resources and get your questions answered

View Resources