• Docs >
  • Module code >
  • torchaudio.models.wav2vec2.utils.import_fairseq >
  • Nightly (unstable)
Shortcuts

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

"""Import fariseq's wav2vec2.0 pretrained weights to torchaudios's format.

For this module to work, you need `fairseq`.
"""
import re

from torch.nn import Module

from ..model import wav2vec2_model, Wav2Vec2Model


def _parse_config(w2v_model):
    encoder = w2v_model.encoder
    conv_layers = w2v_model.feature_extractor.conv_layers

    extractor_mode = "layer_norm"
    if "GroupNorm" in conv_layers[0][2].__class__.__name__:
        extractor_mode = "group_norm"
    else:
        extractor_mode = "layer_norm"

    conv_layer_config = [(l[0].out_channels, l[0].kernel_size[0], l[0].stride[0]) for l in conv_layers]

    if all(l[0].bias is None for l in conv_layers):
        conv_bias = False
    elif all(l[0].bias is not None for l in conv_layers):
        conv_bias = True
    else:
        raise ValueError("Either all the convolutions layers have bias term or none of them should.")

    config = {
        "extractor_mode": extractor_mode,
        "extractor_conv_layer_config": conv_layer_config,
        "extractor_conv_bias": conv_bias,
        "encoder_embed_dim": w2v_model.post_extract_proj.out_features,
        "encoder_projection_dropout": w2v_model.dropout_input.p,
        "encoder_pos_conv_kernel": encoder.pos_conv[0].kernel_size[0],
        "encoder_pos_conv_groups": encoder.pos_conv[0].groups,
        "encoder_num_layers": len(encoder.layers),
        "encoder_num_heads": encoder.layers[0].self_attn.num_heads,
        "encoder_attention_dropout": encoder.layers[0].self_attn.dropout_module.p,
        "encoder_ff_interm_features": encoder.layers[0].fc1.out_features,
        "encoder_ff_interm_dropout": encoder.layers[0].dropout2.p,
        "encoder_dropout": encoder.layers[0].dropout3.p,
        "encoder_layer_norm_first": encoder.layer_norm_first,
        "encoder_layer_drop": encoder.layerdrop,
    }
    return config


def _map_key(key):
    key_ = key
    if key.startswith("w2v_model."):
        key = key.replace("w2v_model.", "")
    if re.match(r"(mask_emb|quantizer|project_q|final_proj|mask_emb)", key):
        return None
    # Feature Extractor
    # Group norm when "extractor_mode" is "default".
    # (Only the first layer)
    # "conv_layers.0.2.weight" -> "conv_layers.0.layer_norm.weight"
    # "conv_layers.0.2.bias"   -> "conv_layers.0.layer_norm.bias"
    match = re.match(r"feature_extractor\.conv_layers\.0\.2\.(weight|bias)", key)
    if match:
        return f"feature_extractor.conv_layers.0.layer_norm.{match.group(1)}"
    # Convolutions
    # "conv_layers.X.0.weight" -> "conv_layers.X.conv.weight"
    # "conv_layers.X.0.bias"   -> "conv_layers.X.conv.bias"
    match = re.match(r"feature_extractor\.conv_layers\.(\d+)\.0\.(weight|bias)", key)
    if match:
        return f"feature_extractor.conv_layers.{match.group(1)}.conv.{match.group(2)}"
    # Layer norm when "extractor_mode" is "layer_norm".
    # "conv_layers.X.2.1.weight" -> "conv_layers.X.layer_norm.weight"
    # "conv_layers.X.2.1.bias"   -> "conv_layers.X.layer_norm.bias"
    match = re.match(r"feature_extractor\.conv_layers\.(\d+)\.2\.1\.(weight|bias)", key)
    if match:
        return f"feature_extractor.conv_layers.{match.group(1)}.layer_norm.{match.group(2)}"
    match = re.match(r"post_extract_proj\.(weight|bias)", key)
    # Encoder - Feature projection
    if match:
        return f"encoder.feature_projection.projection.{match.group(1)}"
    match = re.match(r"layer_norm\.(weight|bias)", key)
    if match:
        return f"encoder.feature_projection.layer_norm.{match.group(1)}"
    # Encoder - Transformer - Convolutional positional embedding
    match = re.match(r"encoder\.pos_conv\.0\.(bias|weight_g|weight_v)", key)
    if match:
        return f"encoder.transformer.pos_conv_embed.conv.{match.group(1)}"
    match = re.match(r"encoder\.layer_norm\.(weight|bias)", key)
    if match:
        return f"encoder.transformer.layer_norm.{match.group(1)}"
    # Encoder - Transformer - Self attention layers
    match = re.match(r"encoder\.layers\.(\d+)\.self_attn\.((k_|v_|q_|out_)proj\.(weight|bias))", key)
    if match:
        return f"encoder.transformer.layers.{match.group(1)}.attention.{match.group(2)}"
    match = re.match(r"encoder\.layers\.(\d+)\.self_attn_layer_norm\.(weight|bias)", key)
    if match:
        return f"encoder.transformer.layers.{match.group(1)}.layer_norm.{match.group(2)}"
    match = re.match(r"encoder\.layers\.(\d+)\.fc1\.(weight|bias)", key)
    if match:
        return f"encoder.transformer.layers.{match.group(1)}.feed_forward.intermediate_dense.{match.group(2)}"
    match = re.match(r"encoder\.layers\.(\d+)\.fc2\.(weight|bias)", key)
    if match:
        return f"encoder.transformer.layers.{match.group(1)}.feed_forward.output_dense.{match.group(2)}"
    match = re.match(r"encoder\.layers\.(\d+)\.final_layer_norm\.(weight|bias)", key)
    if match:
        return f"encoder.transformer.layers.{match.group(1)}.final_layer_norm.{match.group(2)}"
    match = re.match(r"proj\.(weight|bias)", key)
    # Auxiliary Module
    # Only relevant when loading fine-tuned models
    if match:
        return f"aux.{match.group(1)}"
    # HuBERT Extension
    if key in ["label_embs_concat"]:
        return key
    raise ValueError(f"Unexpected key: {key_}")


def _convert_state_dict(state_dict):
    converted = {}
    for k, v in state_dict.items():
        k = _map_key(k)
        if k is not None:
            converted[k] = v
    return converted


[docs]def import_fairseq_model(original: Module) -> Wav2Vec2Model: """Builds :class:`Wav2Vec2Model` from the corresponding model object of `fairseq <https://github.com/pytorch/fairseq>`_. Args: original (torch.nn.Module): An instance of fairseq's Wav2Vec2.0 or HuBERT model. One of ``fairseq.models.wav2vec.wav2vec2_asr.Wav2VecEncoder``, ``fairseq.models.wav2vec.wav2vec2.Wav2Vec2Model`` or ``fairseq.models.hubert.hubert_asr.HubertEncoder``. Returns: Wav2Vec2Model: Imported model. Example - Loading pretrain-only model >>> from torchaudio.models.wav2vec2.utils import import_fairseq_model >>> >>> # Load model using fairseq >>> model_file = 'wav2vec_small.pt' >>> model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task([model_file]) >>> original = model[0] >>> imported = import_fairseq_model(original) >>> >>> # Perform feature extraction >>> waveform, _ = torchaudio.load('audio.wav') >>> features, _ = imported.extract_features(waveform) >>> >>> # Compare result with the original model from fairseq >>> reference = original.feature_extractor(waveform).transpose(1, 2) >>> torch.testing.assert_allclose(features, reference) Example - Fine-tuned model >>> from torchaudio.models.wav2vec2.utils import import_fairseq_model >>> >>> # Load model using fairseq >>> model_file = 'wav2vec_small_960h.pt' >>> model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task([model_file]) >>> original = model[0] >>> imported = import_fairseq_model(original.w2v_encoder) >>> >>> # Perform encoding >>> waveform, _ = torchaudio.load('audio.wav') >>> emission, _ = imported(waveform) >>> >>> # Compare result with the original model from fairseq >>> mask = torch.zeros_like(waveform) >>> reference = original(waveform, mask)['encoder_out'].transpose(0, 1) >>> torch.testing.assert_allclose(emission, reference) """ class_ = original.__class__.__name__ if class_ == "Wav2Vec2Model": return _import_wav2vec2_pretraining(original) if class_ == "Wav2VecEncoder": return _import_wav2vec2_finetuning(original) if class_ == "HubertModel": return _import_hubert_pretraining(original) if class_ == "HubertEncoder": return _import_hubert_finetuning(original) raise ValueError(f"Expected an instance of `Wav2Vec2Model` or `Wav2VecEncoder`. Found: {class_}")
def _import_wav2vec2_finetuning(original: Module) -> Wav2Vec2Model: config = _parse_config(original.w2v_model) model = wav2vec2_model(**config, aux_num_out=original.proj.out_features) model.load_state_dict(_convert_state_dict(original.state_dict())) return model def _import_wav2vec2_pretraining(original: Module) -> Wav2Vec2Model: config = _parse_config(original) model = wav2vec2_model(**config, aux_num_out=None) model.load_state_dict(_convert_state_dict(original.state_dict()), strict=False) return model def _import_hubert_finetuning(original: Module) -> Wav2Vec2Model: config = _parse_config(original.w2v_model) model = wav2vec2_model(**config, aux_num_out=original.proj.out_features) model.load_state_dict(_convert_state_dict(original.state_dict()), strict=False) return model def _import_hubert_pretraining(original: Module) -> Wav2Vec2Model: config = _parse_config(original) model = wav2vec2_model(**config, aux_num_out=None) model.load_state_dict(_convert_state_dict(original.state_dict()), strict=False) return model

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