impl.py

from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple

import torch
from torch import Tensor
from torch.nn import functional as F, Module
from torchaudio._internal import load_state_dict_from_url
from torchaudio.models import wav2vec2_model, Wav2Vec2Model, wavlm_model

from . import utils


__all__ = []


class _Wav2Vec2Model(Module):
    """Wrapper class for :py:class:`~torchaudio.models.Wav2Vec2Model`.

    This is used for layer normalization at the input
    """

    def __init__(self, model: Wav2Vec2Model):
        super().__init__()
        self.model = model

    def forward(self, waveforms: Tensor, lengths: Optional[Tensor] = None) -> Tuple[Tensor, Optional[Tensor]]:
        waveforms = F.layer_norm(waveforms, waveforms.shape)
        return self.model(waveforms, lengths)

    @torch.jit.export
    def extract_features(
        self,
        waveforms: Tensor,
        lengths: Optional[Tensor] = None,
        num_layers: Optional[int] = None,
    ) -> Tuple[List[Tensor], Optional[Tensor]]:
        waveforms = F.layer_norm(waveforms, waveforms.shape)
        return self.model.extract_features(waveforms, lengths, num_layers)


@dataclass
class Wav2Vec2Bundle:
    """Data class that bundles associated information to use pretrained :py:class:`~torchaudio.models.Wav2Vec2Model`.

    This class provides interfaces for instantiating the pretrained model along with
    the information necessary to retrieve pretrained weights and additional data
    to be used with the model.

    Torchaudio library instantiates objects of this class, each of which represents
    a different pretrained model. Client code should access pretrained models via these
    instances.

    Please see below for the usage and the available values.

    Example - Feature Extraction
        >>> import torchaudio
        >>>
        >>> bundle = torchaudio.pipelines.HUBERT_BASE
        >>>
        >>> # Build the model and load pretrained weight.
        >>> model = bundle.get_model()
        Downloading:
        100%|███████████████████████████████| 360M/360M [00:06<00:00, 60.6MB/s]
        >>>
        >>> # Resample audio to the expected sampling rate
        >>> waveform = torchaudio.functional.resample(waveform, sample_rate, bundle.sample_rate)
        >>>
        >>> # Extract acoustic features
        >>> features, _ = model.extract_features(waveform)
    """  # noqa: E501

    _path: str
    _params: Dict[str, Any]
    _sample_rate: float
    _normalize_waveform: bool

    @property
    def sample_rate(self) -> float:
        """Sample rate of the audio that the model is trained on.

        :type: float
        """
        return self._sample_rate

    def _get_state_dict(self, dl_kwargs):
        url = f"https://download.pytorch.org/torchaudio/models/{self._path}"
        dl_kwargs = {} if dl_kwargs is None else dl_kwargs
        state_dict = load_state_dict_from_url(url, **dl_kwargs)
        return state_dict

    def get_model(self, *, dl_kwargs=None) -> Module:
        """Construct the model and load the pretrained weight.

        The weight file is downloaded from the internet and cached with
        :func:`torch.hub.load_state_dict_from_url`

        Args:
            dl_kwargs (dictionary of keyword arguments): Passed to :func:`torch.hub.load_state_dict_from_url`.

        Returns:
            Variation of :py:class:`~torchaudio.models.Wav2Vec2Model`.

            For the models listed below, an additional layer normalization is performed on the input.

            For all other models, a :py:class:`~torchaudio.models.Wav2Vec2Model` instance is returned.

            - WAV2VEC2_LARGE_LV60K
            - WAV2VEC2_ASR_LARGE_LV60K_10M
            - WAV2VEC2_ASR_LARGE_LV60K_100H
            - WAV2VEC2_ASR_LARGE_LV60K_960H
            - WAV2VEC2_XLSR53
            - HUBERT_LARGE
            - HUBERT_XLARGE
            - HUBERT_ASR_LARGE
            - HUBERT_ASR_XLARGE
            - WAVLM_LARGE
        """
        model_type = self._params.pop("model_type", None)
        if model_type == "WavLM":
            model = wavlm_model(**self._params)
        else:
            model = wav2vec2_model(**self._params)
        model.load_state_dict(self._get_state_dict(dl_kwargs))
        if self._normalize_waveform:
            model = _Wav2Vec2Model(model)
        model.eval()
        return model


@dataclass
class Wav2Vec2ASRBundle(Wav2Vec2Bundle):
    """Data class that bundles associated information to use pretrained
    :py:class:`~torchaudio.models.Wav2Vec2Model`.

    This class provides interfaces for instantiating the pretrained model along with
    the information necessary to retrieve pretrained weights and additional data
    to be used with the model.

    Torchaudio library instantiates objects of this class, each of which represents
    a different pretrained model. Client code should access pretrained models via these
    instances.

    Please see below for the usage and the available values.

    Example - ASR
        >>> import torchaudio
        >>>
        >>> bundle = torchaudio.pipelines.HUBERT_ASR_LARGE
        >>>
        >>> # Build the model and load pretrained weight.
        >>> model = bundle.get_model()
        Downloading:
        100%|███████████████████████████████| 1.18G/1.18G [00:17<00:00, 73.8MB/s]
        >>>
        >>> # Check the corresponding labels of the output.
        >>> labels = bundle.get_labels()
        >>> print(labels)
        ('-', '|', 'E', 'T', 'A', 'O', 'N', 'I', 'H', 'S', 'R', 'D', 'L', 'U', 'M', 'W', 'C', 'F', 'G', 'Y', 'P', 'B', 'V', 'K', "'", 'X', 'J', 'Q', 'Z')
        >>>
        >>> # Resample audio to the expected sampling rate
        >>> waveform = torchaudio.functional.resample(waveform, sample_rate, bundle.sample_rate)
        >>>
        >>> # Infer the label probability distribution
        >>> emissions, _ = model(waveform)
        >>>
        >>> # Pass emission to decoder
        >>> # `ctc_decode` is for illustration purpose only
        >>> transcripts = ctc_decode(emissions, labels)
    """  # noqa: E501

    _labels: Tuple[str]
    _remove_aux_axis: Tuple[int] = (1, 2, 3)

    def get_labels(
        self,
        *,
        blank: str = "-",
    ) -> Tuple[str]:
        """The output class labels (only applicable to fine-tuned bundles)

        The first is blank token, and it is customizable.

        Args:
            blank (str, optional): Blank token. (default: ``'-'``)

        Returns:
            Tuple[str]:
            For models fine-tuned on ASR, returns the tuple of strings representing
            the output class labels.

        Example
            >>> import torchaudio
            >>> torchaudio.models.HUBERT_ASR_LARGE.get_labels()
            ('-', '|', 'E', 'T', 'A', 'O', 'N', 'I', 'H', 'S', 'R', 'D', 'L', 'U', 'M', 'W', 'C', 'F', 'G', 'Y', 'P', 'B', 'V', 'K', "'", 'X', 'J', 'Q', 'Z')
        """  # noqa: E501
        return (blank, *self._labels)

    def _get_state_dict(self, dl_kwargs):
        state_dict = super()._get_state_dict(dl_kwargs)
        if self._remove_aux_axis:
            # Remove the seemingly unnecessary axis
            # For ASR task, the pretrained weights originated from fairseq has unrelated dimensions at index 1, 2, 3
            # It's originated from the Dictionary implementation of fairseq, which was intended for NLP tasks,
            # but not used during the ASR training.
            # https://github.com/pytorch/fairseq/blob/c5ff181125c7e6126b49a85e5ebdd5f5b6a07914/fairseq/data/dictionary.py#L21-L37
            # https://github.com/pytorch/fairseq/blob/c5ff181125c7e6126b49a85e5ebdd5f5b6a07914/fairseq/criterions/ctc.py#L126-L129
            #
            # Also, some pretrained weights originated from voxpopuli has an extra dimensions that almost never used and
            # that resembles mistake.
            # The label `1` shows up in the training dataset of German (1 out of 16M),
            # English (1 / 28M), Spanish (1 / 9.4M), Romanian (1 / 4.7M) and Polish (6 / 5.8M)
            for key in ["aux.weight", "aux.bias"]:
                t = state_dict[key]
                state_dict[key] = torch.stack([t[i] for i in range(t.size(0)) if i not in self._remove_aux_axis])
        return state_dict


WAV2VEC2_BASE = Wav2Vec2Bundle(
    _path="wav2vec2_fairseq_base_ls960.pth",
    _params={
        "extractor_mode": "group_norm",
        "extractor_conv_layer_config": [
            (512, 10, 5),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 2, 2),
            (512, 2, 2),
        ],
        "extractor_conv_bias": False,
        "encoder_embed_dim": 768,
        "encoder_projection_dropout": 0.1,
        "encoder_pos_conv_kernel": 128,
        "encoder_pos_conv_groups": 16,
        "encoder_num_layers": 12,
        "encoder_num_heads": 12,
        "encoder_attention_dropout": 0.1,
        "encoder_ff_interm_features": 3072,
        "encoder_ff_interm_dropout": 0.0,
        "encoder_dropout": 0.1,
        "encoder_layer_norm_first": False,
        "encoder_layer_drop": 0.05,
        "aux_num_out": None,
    },
    _sample_rate=16000,
    _normalize_waveform=False,
)
WAV2VEC2_BASE.__doc__ = """Wav2vec 2.0 model ("base" architecture),
pre-trained on 960 hours of unlabeled audio from *LibriSpeech* dataset :cite:`7178964`
(the combination of "train-clean-100", "train-clean-360", and "train-other-500"), not fine-tuned.

Originally published by the authors of *wav2vec 2.0* :cite:`baevski2020wav2vec` under MIT License and
redistributed with the same license.
[`License <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/LICENSE>`__,
`Source <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/examples/wav2vec#pre-trained-models>`__]

Please refer to :py:class:`torchaudio.pipelines.Wav2Vec2Bundle` for the usage.
"""  # noqa: E501

WAV2VEC2_ASR_BASE_10M = Wav2Vec2ASRBundle(
    _path="wav2vec2_fairseq_base_ls960_asr_ll10m.pth",
    _params={
        "extractor_mode": "group_norm",
        "extractor_conv_layer_config": [
            (512, 10, 5),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 2, 2),
            (512, 2, 2),
        ],
        "extractor_conv_bias": False,
        "encoder_embed_dim": 768,
        "encoder_projection_dropout": 0.1,
        "encoder_pos_conv_kernel": 128,
        "encoder_pos_conv_groups": 16,
        "encoder_num_layers": 12,
        "encoder_num_heads": 12,
        "encoder_attention_dropout": 0.1,
        "encoder_ff_interm_features": 3072,
        "encoder_ff_interm_dropout": 0.0,
        "encoder_dropout": 0.1,
        "encoder_layer_norm_first": False,
        "encoder_layer_drop": 0.05,
        "aux_num_out": 29,
    },
    _labels=utils._get_en_labels(),
    _sample_rate=16000,
    _normalize_waveform=False,
)
WAV2VEC2_ASR_BASE_10M.__doc__ = """Wav2vec 2.0 model ("base" architecture with an extra linear module),
pre-trained on 960 hours of unlabeled audio from *LibriSpeech* dataset :cite:`7178964`
(the combination of "train-clean-100", "train-clean-360", and "train-other-500"), and
fine-tuned for ASR on 10 minutes of transcribed audio from *Libri-Light* dataset
:cite:`librilight` ("train-10min" subset).

Originally published by the authors of *wav2vec 2.0* :cite:`baevski2020wav2vec` under MIT License and
redistributed with the same license.
[`License <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/LICENSE>`__,
`Source <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/examples/wav2vec#pre-trained-models>`__]

Please refer to :py:class:`torchaudio.pipelines.Wav2Vec2ASRBundle` for the usage.
"""  # noqa: E501

WAV2VEC2_ASR_BASE_100H = Wav2Vec2ASRBundle(
    "wav2vec2_fairseq_base_ls960_asr_ls100.pth",
    {
        "extractor_mode": "group_norm",
        "extractor_conv_layer_config": [
            (512, 10, 5),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 2, 2),
            (512, 2, 2),
        ],
        "extractor_conv_bias": False,
        "encoder_embed_dim": 768,
        "encoder_projection_dropout": 0.1,
        "encoder_pos_conv_kernel": 128,
        "encoder_pos_conv_groups": 16,
        "encoder_num_layers": 12,
        "encoder_num_heads": 12,
        "encoder_attention_dropout": 0.1,
        "encoder_ff_interm_features": 3072,
        "encoder_ff_interm_dropout": 0.0,
        "encoder_dropout": 0.1,
        "encoder_layer_norm_first": False,
        "encoder_layer_drop": 0.05,
        "aux_num_out": 29,
    },
    _labels=utils._get_en_labels(),
    _sample_rate=16000,
    _normalize_waveform=False,
)

WAV2VEC2_ASR_BASE_100H.__doc__ = """Wav2vec 2.0 model ("base" architecture with an extra linear module),
pre-trained on 960 hours of unlabeled audio from *LibriSpeech* dataset :cite:`7178964`
(the combination of "train-clean-100", "train-clean-360", and "train-other-500"), and
fine-tuned for ASR on 100 hours of transcribed audio from "train-clean-100" subset.

Originally published by the authors of *wav2vec 2.0* :cite:`baevski2020wav2vec` under MIT License and
redistributed with the same license.
[`License <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/LICENSE>`__,
`Source <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/examples/wav2vec#pre-trained-models>`__]

Please refer to :py:class:`torchaudio.pipelines.Wav2Vec2ASRBundle` for the usage.
"""  # noqa: E501

WAV2VEC2_ASR_BASE_960H = Wav2Vec2ASRBundle(
    "wav2vec2_fairseq_base_ls960_asr_ls960.pth",
    {
        "extractor_mode": "group_norm",
        "extractor_conv_layer_config": [
            (512, 10, 5),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 2, 2),
            (512, 2, 2),
        ],
        "extractor_conv_bias": False,
        "encoder_embed_dim": 768,
        "encoder_projection_dropout": 0.1,
        "encoder_pos_conv_kernel": 128,
        "encoder_pos_conv_groups": 16,
        "encoder_num_layers": 12,
        "encoder_num_heads": 12,
        "encoder_attention_dropout": 0.1,
        "encoder_ff_interm_features": 3072,
        "encoder_ff_interm_dropout": 0.0,
        "encoder_dropout": 0.1,
        "encoder_layer_norm_first": False,
        "encoder_layer_drop": 0.05,
        "aux_num_out": 29,
    },
    _labels=utils._get_en_labels(),
    _sample_rate=16000,
    _normalize_waveform=False,
)
WAV2VEC2_ASR_BASE_960H.__doc__ = """Wav2vec 2.0 model ("base" architecture with an extra linear module),
pre-trained on 960 hours of unlabeled audio from *LibriSpeech* dataset :cite:`7178964`
(the combination of "train-clean-100", "train-clean-360", and "train-other-500"), and
fine-tuned for ASR on the same audio with the corresponding transcripts.

Originally published by the authors of *wav2vec 2.0* :cite:`baevski2020wav2vec` under MIT License and
redistributed with the same license.
[`License <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/LICENSE>`__,
`Source <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/examples/wav2vec#pre-trained-models>`__]

Please refer to :py:class:`torchaudio.pipelines.Wav2Vec2ASRBundle` for the usage.
"""  # noqa: E501

WAV2VEC2_LARGE = Wav2Vec2Bundle(
    "wav2vec2_fairseq_large_ls960.pth",
    {
        "extractor_mode": "group_norm",
        "extractor_conv_layer_config": [
            (512, 10, 5),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 2, 2),
            (512, 2, 2),
        ],
        "extractor_conv_bias": False,
        "encoder_embed_dim": 1024,
        "encoder_projection_dropout": 0.1,
        "encoder_pos_conv_kernel": 128,
        "encoder_pos_conv_groups": 16,
        "encoder_num_layers": 24,
        "encoder_num_heads": 16,
        "encoder_attention_dropout": 0.1,
        "encoder_ff_interm_features": 4096,
        "encoder_ff_interm_dropout": 0.0,
        "encoder_dropout": 0.0,
        "encoder_layer_norm_first": False,
        "encoder_layer_drop": 0.2,
        "aux_num_out": None,
    },
    _sample_rate=16000,
    _normalize_waveform=False,
)
WAV2VEC2_LARGE.__doc__ = """Wav2vec 2.0 model ("large" architecture),
pre-trained on 960 hours of unlabeled audio from *LibriSpeech* dataset :cite:`7178964`
(the combination of "train-clean-100", "train-clean-360", and "train-other-500"), not fine-tuned.

Originally published by the authors of *wav2vec 2.0* :cite:`baevski2020wav2vec` under MIT License and
redistributed with the same license.
[`License <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/LICENSE>`__,
`Source <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/examples/wav2vec#pre-trained-models>`__]

Please refer to :py:class:`torchaudio.pipelines.Wav2Vec2Bundle` for the usage.
"""  # noqa: E501

WAV2VEC2_ASR_LARGE_10M = Wav2Vec2ASRBundle(
    "wav2vec2_fairseq_large_ls960_asr_ll10m.pth",
    {
        "extractor_mode": "group_norm",
        "extractor_conv_layer_config": [
            (512, 10, 5),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 2, 2),
            (512, 2, 2),
        ],
        "extractor_conv_bias": False,
        "encoder_embed_dim": 1024,
        "encoder_projection_dropout": 0.1,
        "encoder_pos_conv_kernel": 128,
        "encoder_pos_conv_groups": 16,
        "encoder_num_layers": 24,
        "encoder_num_heads": 16,
        "encoder_attention_dropout": 0.1,
        "encoder_ff_interm_features": 4096,
        "encoder_ff_interm_dropout": 0.0,
        "encoder_dropout": 0.0,
        "encoder_layer_norm_first": False,
        "encoder_layer_drop": 0.2,
        "aux_num_out": 29,
    },
    _labels=utils._get_en_labels(),
    _sample_rate=16000,
    _normalize_waveform=False,
)
WAV2VEC2_ASR_LARGE_10M.__doc__ = """Wav2vec 2.0 model ("large" architecture with an extra linear module),
pre-trained on 960 hours of unlabeled audio from *LibriSpeech* dataset :cite:`7178964`
(the combination of "train-clean-100", "train-clean-360", and "train-other-500"), and
fine-tuned for ASR on 10 minutes of transcribed audio from *Libri-Light* dataset
:cite:`librilight` ("train-10min" subset).

Originally published by the authors of *wav2vec 2.0* :cite:`baevski2020wav2vec` under MIT License and
redistributed with the same license.
[`License <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/LICENSE>`__,
`Source <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/examples/wav2vec#pre-trained-models>`__]

Please refer to :py:class:`torchaudio.pipelines.Wav2Vec2ASRBundle` for the usage.
"""  # noqa: E501

WAV2VEC2_ASR_LARGE_100H = Wav2Vec2ASRBundle(
    "wav2vec2_fairseq_large_ls960_asr_ls100.pth",
    {
        "extractor_mode": "group_norm",
        "extractor_conv_layer_config": [
            (512, 10, 5),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 2, 2),
            (512, 2, 2),
        ],
        "extractor_conv_bias": False,
        "encoder_embed_dim": 1024,
        "encoder_projection_dropout": 0.1,
        "encoder_pos_conv_kernel": 128,
        "encoder_pos_conv_groups": 16,
        "encoder_num_layers": 24,
        "encoder_num_heads": 16,
        "encoder_attention_dropout": 0.1,
        "encoder_ff_interm_features": 4096,
        "encoder_ff_interm_dropout": 0.0,
        "encoder_dropout": 0.0,
        "encoder_layer_norm_first": False,
        "encoder_layer_drop": 0.2,
        "aux_num_out": 29,
    },
    _labels=utils._get_en_labels(),
    _sample_rate=16000,
    _normalize_waveform=False,
)
WAV2VEC2_ASR_LARGE_100H.__doc__ = """Wav2vec 2.0 model ("large" architecture with an extra linear module),
pre-trained on 960 hours of unlabeled audio from *LibriSpeech* dataset :cite:`7178964`
(the combination of "train-clean-100", "train-clean-360", and "train-other-500"), and
fine-tuned for ASR on 100 hours of transcribed audio from
the same dataset ("train-clean-100" subset).

Originally published by the authors of *wav2vec 2.0* :cite:`baevski2020wav2vec` under MIT License and
redistributed with the same license.
[`License <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/LICENSE>`__,
`Source <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/examples/wav2vec#pre-trained-models>`__]

Please refer to :py:class:`torchaudio.pipelines.Wav2Vec2ASRBundle` for the usage.
"""  # noqa: E501

WAV2VEC2_ASR_LARGE_960H = Wav2Vec2ASRBundle(
    "wav2vec2_fairseq_large_ls960_asr_ls960.pth",
    {
        "extractor_mode": "group_norm",
        "extractor_conv_layer_config": [
            (512, 10, 5),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 2, 2),
            (512, 2, 2),
        ],
        "extractor_conv_bias": False,
        "encoder_embed_dim": 1024,
        "encoder_projection_dropout": 0.1,
        "encoder_pos_conv_kernel": 128,
        "encoder_pos_conv_groups": 16,
        "encoder_num_layers": 24,
        "encoder_num_heads": 16,
        "encoder_attention_dropout": 0.1,
        "encoder_ff_interm_features": 4096,
        "encoder_ff_interm_dropout": 0.0,
        "encoder_dropout": 0.0,
        "encoder_layer_norm_first": False,
        "encoder_layer_drop": 0.2,
        "aux_num_out": 29,
    },
    _labels=utils._get_en_labels(),
    _sample_rate=16000,
    _normalize_waveform=False,
)
WAV2VEC2_ASR_LARGE_960H.__doc__ = """Wav2vec 2.0 model ("large" architecture with an extra linear module),
pre-trained on 960 hours of unlabeled audio from *LibriSpeech* dataset :cite:`7178964`
(the combination of "train-clean-100", "train-clean-360", and "train-other-500"), and
fine-tuned for ASR on the same audio with the corresponding transcripts.

Originally published by the authors of *wav2vec 2.0* :cite:`baevski2020wav2vec` under MIT License and
redistributed with the same license.
[`License <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/LICENSE>`__,
`Source <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/examples/wav2vec#pre-trained-models>`__]

Please refer to :py:class:`torchaudio.pipelines.Wav2Vec2ASRBundle` for the usage.
"""  # noqa:  E501

WAV2VEC2_LARGE_LV60K = Wav2Vec2Bundle(
    "wav2vec2_fairseq_large_lv60k.pth",
    {
        "extractor_mode": "layer_norm",
        "extractor_conv_layer_config": [
            (512, 10, 5),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 2, 2),
            (512, 2, 2),
        ],
        "extractor_conv_bias": True,
        "encoder_embed_dim": 1024,
        "encoder_projection_dropout": 0.1,
        "encoder_pos_conv_kernel": 128,
        "encoder_pos_conv_groups": 16,
        "encoder_num_layers": 24,
        "encoder_num_heads": 16,
        "encoder_attention_dropout": 0.1,
        "encoder_ff_interm_features": 4096,
        "encoder_ff_interm_dropout": 0.0,
        "encoder_dropout": 0.0,
        "encoder_layer_norm_first": True,
        "encoder_layer_drop": 0.0,
        "aux_num_out": None,
    },
    _sample_rate=16000,
    _normalize_waveform=True,
)
WAV2VEC2_LARGE_LV60K.__doc__ = """Wav2vec 2.0 model ("large-lv60k" architecture),
pre-trained on 60,000 hours of unlabeled audio from *Libri-Light* dataset :cite:`librilight`,
not fine-tuned.

Originally published by the authors of *wav2vec 2.0* :cite:`baevski2020wav2vec` under MIT License and
redistributed with the same license.
[`License <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/LICENSE>`__,
`Source <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/examples/wav2vec#pre-trained-models>`__]

Please refer to :py:class:`torchaudio.pipelines.Wav2Vec2Bundle` for the usage.
"""  # noqa: E501

WAV2VEC2_ASR_LARGE_LV60K_10M = Wav2Vec2ASRBundle(
    "wav2vec2_fairseq_large_lv60k_asr_ll10m.pth",
    {
        "extractor_mode": "layer_norm",
        "extractor_conv_layer_config": [
            (512, 10, 5),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 2, 2),
            (512, 2, 2),
        ],
        "extractor_conv_bias": True,
        "encoder_embed_dim": 1024,
        "encoder_projection_dropout": 0.1,
        "encoder_pos_conv_kernel": 128,
        "encoder_pos_conv_groups": 16,
        "encoder_num_layers": 24,
        "encoder_num_heads": 16,
        "encoder_attention_dropout": 0.1,
        "encoder_ff_interm_features": 4096,
        "encoder_ff_interm_dropout": 0.0,
        "encoder_dropout": 0.0,
        "encoder_layer_norm_first": True,
        "encoder_layer_drop": 0.0,
        "aux_num_out": 29,
    },
    _labels=utils._get_en_labels(),
    _sample_rate=16000,
    _normalize_waveform=True,
)
WAV2VEC2_ASR_LARGE_LV60K_10M.__doc__ = """Wav2vec 2.0 model ("large-lv60k" architecture with an extra linear module),
pre-trained on 60,000 hours of unlabeled audio from *Libri-Light* dataset :cite:`librilight`, and
fine-tuned for ASR on 10 minutes of transcribed audio from the same dataset ("train-10min" subset).

Originally published by the authors of *wav2vec 2.0* :cite:`baevski2020wav2vec` under MIT License and
redistributed with the same license.
[`License <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/LICENSE>`__,
`Source <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/examples/wav2vec#pre-trained-models>`__]

Please refer to :py:class:`torchaudio.pipelines.Wav2Vec2ASRBundle` for the usage.
"""  # noqa: E501

WAV2VEC2_ASR_LARGE_LV60K_100H = Wav2Vec2ASRBundle(
    "wav2vec2_fairseq_large_lv60k_asr_ls100.pth",
    {
        "extractor_mode": "layer_norm",
        "extractor_conv_layer_config": [
            (512, 10, 5),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 2, 2),
            (512, 2, 2),
        ],
        "extractor_conv_bias": True,
        "encoder_embed_dim": 1024,
        "encoder_projection_dropout": 0.1,
        "encoder_pos_conv_kernel": 128,
        "encoder_pos_conv_groups": 16,
        "encoder_num_layers": 24,
        "encoder_num_heads": 16,
        "encoder_attention_dropout": 0.1,
        "encoder_ff_interm_features": 4096,
        "encoder_ff_interm_dropout": 0.0,
        "encoder_dropout": 0.0,
        "encoder_layer_norm_first": True,
        "encoder_layer_drop": 0.0,
        "aux_num_out": 29,
    },
    _labels=utils._get_en_labels(),
    _sample_rate=16000,
    _normalize_waveform=True,
)
WAV2VEC2_ASR_LARGE_LV60K_100H.__doc__ = """Wav2vec 2.0 model ("large-lv60k" architecture with an extra linear module),
pre-trained on 60,000 hours of unlabeled audio from *Libri-Light* dataset :cite:`librilight`, and
fine-tuned for ASR on 100 hours of transcribed audio from
*LibriSpeech* dataset :cite:`7178964` ("train-clean-100" subset).

Originally published by the authors of *wav2vec 2.0* :cite:`baevski2020wav2vec` under MIT License and
redistributed with the same license.
[`License <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/LICENSE>`__,
`Source <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/examples/wav2vec#pre-trained-models>`__]

Please refer to :py:class:`torchaudio.pipelines.Wav2Vec2ASRBundle` for the usage.
"""  # noqa: E501

WAV2VEC2_ASR_LARGE_LV60K_960H = Wav2Vec2ASRBundle(
    "wav2vec2_fairseq_large_lv60k_asr_ls960.pth",
    {
        "extractor_mode": "layer_norm",
        "extractor_conv_layer_config": [
            (512, 10, 5),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 2, 2),
            (512, 2, 2),
        ],
        "extractor_conv_bias": True,
        "encoder_embed_dim": 1024,
        "encoder_projection_dropout": 0.1,
        "encoder_pos_conv_kernel": 128,
        "encoder_pos_conv_groups": 16,
        "encoder_num_layers": 24,
        "encoder_num_heads": 16,
        "encoder_attention_dropout": 0.1,
        "encoder_ff_interm_features": 4096,
        "encoder_ff_interm_dropout": 0.0,
        "encoder_dropout": 0.0,
        "encoder_layer_norm_first": True,
        "encoder_layer_drop": 0.0,
        "aux_num_out": 29,
    },
    _labels=utils._get_en_labels(),
    _sample_rate=16000,
    _normalize_waveform=True,
)
WAV2VEC2_ASR_LARGE_LV60K_960H.__doc__ = """Wav2vec 2.0 model ("large-lv60k" architecture with an extra linear module),
pre-trained on 60,000 hours of unlabeled audio from *Libri-Light* :cite:`librilight` dataset, and
fine-tuned for ASR on 960 hours of transcribed audio from *LibriSpeech* dataset :cite:`7178964`
(the combination of "train-clean-100", "train-clean-360", and "train-other-500").

Originally published by the authors of *wav2vec 2.0* :cite:`baevski2020wav2vec` under MIT License and
redistributed with the same license.
[`License <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/LICENSE>`__,
`Source <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/examples/wav2vec#pre-trained-models>`__]

Please refer to :py:class:`torchaudio.pipelines.Wav2Vec2ASRBundle` for the usage.
"""  # noqa: E501

WAV2VEC2_XLSR53 = Wav2Vec2Bundle(
    "wav2vec2_fairseq_large_xlsr53.pth",
    {
        "extractor_mode": "layer_norm",
        "extractor_conv_layer_config": [
            (512, 10, 5),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 2, 2),
            (512, 2, 2),
        ],
        "extractor_conv_bias": True,
        "encoder_embed_dim": 1024,
        "encoder_projection_dropout": 0.0,
        "encoder_pos_conv_kernel": 128,
        "encoder_pos_conv_groups": 16,
        "encoder_num_layers": 24,
        "encoder_num_heads": 16,
        "encoder_attention_dropout": 0.0,
        "encoder_ff_interm_features": 4096,
        "encoder_ff_interm_dropout": 0.0,
        "encoder_dropout": 0.0,
        "encoder_layer_norm_first": True,
        "encoder_layer_drop": 0.0,
        "aux_num_out": None,
    },
    _sample_rate=16000,
    _normalize_waveform=True,
)
WAV2VEC2_XLSR53.__doc__ = """Wav2vec 2.0 model ("base" architecture),
pre-trained on 56,000 hours of unlabeled audio from multiple datasets (
*Multilingual LibriSpeech* :cite:`Pratap_2020`,
*CommonVoice* :cite:`ardila2020common` and
*BABEL* :cite:`Gales2014SpeechRA`),
not fine-tuned.

Originally published by the authors of
*Unsupervised Cross-lingual Representation Learning for Speech Recognition*
:cite:`conneau2020unsupervised` under MIT License and redistributed with the same license.
[`License <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/LICENSE>`__,
`Source <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/examples/wav2vec#pre-trained-models>`__]

Please refer to :py:class:`torchaudio.pipelines.Wav2Vec2Bundle` for the usage.
"""  # noqa: E501

HUBERT_BASE = Wav2Vec2Bundle(
    "hubert_fairseq_base_ls960.pth",
    {
        "extractor_mode": "group_norm",
        "extractor_conv_layer_config": [
            (512, 10, 5),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 2, 2),
            (512, 2, 2),
        ],
        "extractor_conv_bias": False,
        "encoder_embed_dim": 768,
        "encoder_projection_dropout": 0.1,
        "encoder_pos_conv_kernel": 128,
        "encoder_pos_conv_groups": 16,
        "encoder_num_layers": 12,
        "encoder_num_heads": 12,
        "encoder_attention_dropout": 0.1,
        "encoder_ff_interm_features": 3072,
        "encoder_ff_interm_dropout": 0.0,
        "encoder_dropout": 0.1,
        "encoder_layer_norm_first": False,
        "encoder_layer_drop": 0.05,
        "aux_num_out": None,
    },
    _sample_rate=16000,
    _normalize_waveform=False,
)
HUBERT_BASE.__doc__ = """HuBERT model ("base" architecture),
pre-trained on 960 hours of unlabeled audio from *LibriSpeech* dataset :cite:`7178964`
(the combination of "train-clean-100", "train-clean-360", and "train-other-500"), not fine-tuned.

Originally published by the authors of *HuBERT* :cite:`hsu2021hubert` under MIT License and
redistributed with the same license.
[`License <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/LICENSE>`__,
`Source <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/examples/hubert#pre-trained-and-fine-tuned-asr-models>`__]

Please refer to :py:class:`torchaudio.pipelines.Wav2Vec2Bundle` for the usage.
"""  # noqa: E501

HUBERT_LARGE = Wav2Vec2Bundle(
    "hubert_fairseq_large_ll60k.pth",
    {
        "extractor_mode": "layer_norm",
        "extractor_conv_layer_config": [
            (512, 10, 5),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 2, 2),
            (512, 2, 2),
        ],
        "extractor_conv_bias": False,
        "encoder_embed_dim": 1024,
        "encoder_projection_dropout": 0.0,
        "encoder_pos_conv_kernel": 128,
        "encoder_pos_conv_groups": 16,
        "encoder_num_layers": 24,
        "encoder_num_heads": 16,
        "encoder_attention_dropout": 0.0,
        "encoder_ff_interm_features": 4096,
        "encoder_ff_interm_dropout": 0.0,
        "encoder_dropout": 0.0,
        "encoder_layer_norm_first": True,
        "encoder_layer_drop": 0.0,
        "aux_num_out": None,
    },
    _sample_rate=16000,
    _normalize_waveform=True,
)
HUBERT_LARGE.__doc__ = """HuBERT model ("large" architecture),
pre-trained on 60,000 hours of unlabeled audio from *Libri-Light* dataset :cite:`librilight`,
not fine-tuned.

Originally published by the authors of *HuBERT* :cite:`hsu2021hubert` under MIT License and
redistributed with the same license.
[`License <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/LICENSE>`__,
`Source <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/examples/hubert#pre-trained-and-fine-tuned-asr-models>`__]

Please refer to :py:class:`torchaudio.pipelines.Wav2Vec2Bundle` for the usage.
"""  # noqa: E501

HUBERT_XLARGE = Wav2Vec2Bundle(
    "hubert_fairseq_xlarge_ll60k.pth",
    {
        "extractor_mode": "layer_norm",
        "extractor_conv_layer_config": [
            (512, 10, 5),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 2, 2),
            (512, 2, 2),
        ],
        "extractor_conv_bias": False,
        "encoder_embed_dim": 1280,
        "encoder_projection_dropout": 0.0,
        "encoder_pos_conv_kernel": 128,
        "encoder_pos_conv_groups": 16,
        "encoder_num_layers": 48,
        "encoder_num_heads": 16,
        "encoder_attention_dropout": 0.0,
        "encoder_ff_interm_features": 5120,
        "encoder_ff_interm_dropout": 0.0,
        "encoder_dropout": 0.0,
        "encoder_layer_norm_first": True,
        "encoder_layer_drop": 0.0,
        "aux_num_out": None,
    },
    _sample_rate=16000,
    _normalize_waveform=True,
)
HUBERT_XLARGE.__doc__ = """HuBERT model ("extra large" architecture),
pre-trained on 60,000 hours of unlabeled audio from *Libri-Light* dataset :cite:`librilight`,
not fine-tuned.

Originally published by the authors of *HuBERT* :cite:`hsu2021hubert` under MIT License and
redistributed with the same license.
[`License <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/LICENSE>`__,
`Source <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/examples/hubert#pre-trained-and-fine-tuned-asr-models>`__]

Please refer to :py:class:`torchaudio.pipelines.Wav2Vec2Bundle` for the usage.
"""  # noqa: E501

HUBERT_ASR_LARGE = Wav2Vec2ASRBundle(
    "hubert_fairseq_large_ll60k_asr_ls960.pth",
    {
        "extractor_mode": "layer_norm",
        "extractor_conv_layer_config": [
            (512, 10, 5),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 2, 2),
            (512, 2, 2),
        ],
        "extractor_conv_bias": False,
        "encoder_embed_dim": 1024,
        "encoder_projection_dropout": 0.0,
        "encoder_pos_conv_kernel": 128,
        "encoder_pos_conv_groups": 16,
        "encoder_num_layers": 24,
        "encoder_num_heads": 16,
        "encoder_attention_dropout": 0.0,
        "encoder_ff_interm_features": 4096,
        "encoder_ff_interm_dropout": 0.1,
        "encoder_dropout": 0.0,
        "encoder_layer_norm_first": True,
        "encoder_layer_drop": 0.1,
        "aux_num_out": 29,
    },
    _labels=utils._get_en_labels(),
    _sample_rate=16000,
    _normalize_waveform=True,
)
HUBERT_ASR_LARGE.__doc__ = """HuBERT model ("large" architecture),
pre-trained on 60,000 hours of unlabeled audio from *Libri-Light* dataset :cite:`librilight`, and
fine-tuned for ASR on 960 hours of transcribed audio from *LibriSpeech* dataset :cite:`7178964`
(the combination of "train-clean-100", "train-clean-360", and "train-other-500").

Originally published by the authors of *HuBERT* :cite:`hsu2021hubert` under MIT License and
redistributed with the same license.
[`License <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/LICENSE>`__,
`Source <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/examples/hubert#pre-trained-and-fine-tuned-asr-models>`__]

Please refer to :py:class:`torchaudio.pipelines.Wav2Vec2ASRBundle` for the usage.
"""  # noqa: E501

HUBERT_ASR_XLARGE = Wav2Vec2ASRBundle(
    "hubert_fairseq_xlarge_ll60k_asr_ls960.pth",
    {
        "extractor_mode": "layer_norm",
        "extractor_conv_layer_config": [
            (512, 10, 5),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 2, 2),
            (512, 2, 2),
        ],
        "extractor_conv_bias": False,
        "encoder_embed_dim": 1280,
        "encoder_projection_dropout": 0.0,
        "encoder_pos_conv_kernel": 128,
        "encoder_pos_conv_groups": 16,
        "encoder_num_layers": 48,
        "encoder_num_heads": 16,
        "encoder_attention_dropout": 0.0,
        "encoder_ff_interm_features": 5120,
        "encoder_ff_interm_dropout": 0.1,
        "encoder_dropout": 0.0,
        "encoder_layer_norm_first": True,
        "encoder_layer_drop": 0.1,
        "aux_num_out": 29,
    },
    _labels=utils._get_en_labels(),
    _sample_rate=16000,
    _normalize_waveform=True,
)
HUBERT_ASR_XLARGE.__doc__ = """HuBERT model ("extra large" architecture),
pre-trained on 60,000 hours of unlabeled audio from
*Libri-Light* dataset :cite:`librilight`, and
fine-tuned for ASR on 960 hours of transcribed audio from
*LibriSpeech* dataset :cite:`7178964`
(the combination of "train-clean-100", "train-clean-360", and "train-other-500").

Originally published by the authors of *HuBERT* :cite:`hsu2021hubert` under MIT License and
redistributed with the same license.
[`License <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/LICENSE>`__,
`Source <https://github.com/pytorch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/examples/hubert#pre-trained-and-fine-tuned-asr-models>`__]

Please refer to :py:class:`torchaudio.pipelines.Wav2Vec2ASRBundle` for the usage.
"""  # noqa: E501


VOXPOPULI_ASR_BASE_10K_DE = Wav2Vec2ASRBundle(
    "wav2vec2_voxpopuli_base_10k_asr_de.pt",
    {
        "extractor_mode": "group_norm",
        "extractor_conv_layer_config": [
            (512, 10, 5),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 2, 2),
            (512, 2, 2),
        ],
        "extractor_conv_bias": False,
        "encoder_embed_dim": 768,
        "encoder_projection_dropout": 0.0,
        "encoder_pos_conv_kernel": 128,
        "encoder_pos_conv_groups": 16,
        "encoder_num_layers": 12,
        "encoder_num_heads": 12,
        "encoder_attention_dropout": 0.0,
        "encoder_ff_interm_features": 3072,
        "encoder_ff_interm_dropout": 0.1,
        "encoder_dropout": 0.0,
        "encoder_layer_norm_first": False,
        "encoder_layer_drop": 0.1,
        "aux_num_out": 32,
    },
    _labels=utils._get_de_labels(),
    _sample_rate=16000,
    _normalize_waveform=False,
    _remove_aux_axis=(1, 2, 3, 35),
)
VOXPOPULI_ASR_BASE_10K_DE.__doc__ = """wav2vec 2.0 model ("base" architecture),
pre-trained on 10k hours of unlabeled audio from *VoxPopuli* dataset :cite:`voxpopuli`
("10k" subset, consisting of 23 languages), and
fine-tuned for ASR on 282 hours of transcribed audio from "de" subset.

Originally published by the authors of *VoxPopuli* :cite:`voxpopuli` under CC BY-NC 4.0 and
redistributed with the same license.
[`License <https://github.com/facebookresearch/voxpopuli/tree/160e4d7915bad9f99b2c35b1d3833e51fd30abf2#license>`__,
`Source <https://github.com/facebookresearch/voxpopuli/tree/160e4d7915bad9f99b2c35b1d3833e51fd30abf2#asr-and-lm>`__]

Please refer to :py:class:`torchaudio.pipelines.Wav2Vec2ASRBundle` for the usage.
"""  # noqa: E501


VOXPOPULI_ASR_BASE_10K_EN = Wav2Vec2ASRBundle(
    "wav2vec2_voxpopuli_base_10k_asr_en.pt",
    {
        "extractor_mode": "group_norm",
        "extractor_conv_layer_config": [
            (512, 10, 5),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 2, 2),
            (512, 2, 2),
        ],
        "extractor_conv_bias": False,
        "encoder_embed_dim": 768,
        "encoder_projection_dropout": 0.0,
        "encoder_pos_conv_kernel": 128,
        "encoder_pos_conv_groups": 16,
        "encoder_num_layers": 12,
        "encoder_num_heads": 12,
        "encoder_attention_dropout": 0.0,
        "encoder_ff_interm_features": 3072,
        "encoder_ff_interm_dropout": 0.1,
        "encoder_dropout": 0.0,
        "encoder_layer_norm_first": False,
        "encoder_layer_drop": 0.1,
        "aux_num_out": 28,
    },
    _labels=utils._get_vp_en_labels(),
    _sample_rate=16000,
    _normalize_waveform=False,
    _remove_aux_axis=(1, 2, 3, 31),
)
VOXPOPULI_ASR_BASE_10K_EN.__doc__ = """wav2vec 2.0 model ("base" architecture),
pre-trained on 10k hours of unlabeled audio from *VoxPopuli* dataset :cite:`voxpopuli`
("10k" subset, consisting of 23 languages), and
fine-tuned for ASR on 543 hours of transcribed audio from "en" subset.

Originally published by the authors of *VoxPopuli* :cite:`voxpopuli` under CC BY-NC 4.0 and
redistributed with the same license.
[`License <https://github.com/facebookresearch/voxpopuli/tree/160e4d7915bad9f99b2c35b1d3833e51fd30abf2#license>`__,
`Source <https://github.com/facebookresearch/voxpopuli/tree/160e4d7915bad9f99b2c35b1d3833e51fd30abf2#asr-and-lm>`__]

Please refer to :py:class:`torchaudio.pipelines.Wav2Vec2ASRBundle` for the usage.
"""  # noqa: E501


VOXPOPULI_ASR_BASE_10K_ES = Wav2Vec2ASRBundle(
    "wav2vec2_voxpopuli_base_10k_asr_es.pt",
    {
        "extractor_mode": "group_norm",
        "extractor_conv_layer_config": [
            (512, 10, 5),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 2, 2),
            (512, 2, 2),
        ],
        "extractor_conv_bias": False,
        "encoder_embed_dim": 768,
        "encoder_projection_dropout": 0.0,
        "encoder_pos_conv_kernel": 128,
        "encoder_pos_conv_groups": 16,
        "encoder_num_layers": 12,
        "encoder_num_heads": 12,
        "encoder_attention_dropout": 0.0,
        "encoder_ff_interm_features": 3072,
        "encoder_ff_interm_dropout": 0.1,
        "encoder_dropout": 0.0,
        "encoder_layer_norm_first": False,
        "encoder_layer_drop": 0.1,
        "aux_num_out": 35,
    },
    _labels=utils._get_es_labels(),
    _sample_rate=16000,
    _normalize_waveform=False,
    _remove_aux_axis=(1, 2, 3, 35),
)
VOXPOPULI_ASR_BASE_10K_ES.__doc__ = """wav2vec 2.0 model ("base" architecture),
pre-trained on 10k hours of unlabeled audio from *VoxPopuli* dataset :cite:`voxpopuli`
("10k" subset, consisting of 23 languages), and
fine-tuned for ASR on 166 hours of transcribed audio from "es" subset.

Originally published by the authors of *VoxPopuli* :cite:`voxpopuli` under CC BY-NC 4.0 and
redistributed with the same license.
[`License <https://github.com/facebookresearch/voxpopuli/tree/160e4d7915bad9f99b2c35b1d3833e51fd30abf2#license>`__,
`Source <https://github.com/facebookresearch/voxpopuli/tree/160e4d7915bad9f99b2c35b1d3833e51fd30abf2#asr-and-lm>`__]

Please refer to :py:class:`torchaudio.pipelines.Wav2Vec2ASRBundle` for the usage.
"""  # noqa: E501

VOXPOPULI_ASR_BASE_10K_FR = Wav2Vec2ASRBundle(
    "wav2vec2_voxpopuli_base_10k_asr_fr.pt",
    {
        "extractor_mode": "group_norm",
        "extractor_conv_layer_config": [
            (512, 10, 5),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 2, 2),
            (512, 2, 2),
        ],
        "extractor_conv_bias": False,
        "encoder_embed_dim": 768,
        "encoder_projection_dropout": 0.0,
        "encoder_pos_conv_kernel": 128,
        "encoder_pos_conv_groups": 16,
        "encoder_num_layers": 12,
        "encoder_num_heads": 12,
        "encoder_attention_dropout": 0.0,
        "encoder_ff_interm_features": 3072,
        "encoder_ff_interm_dropout": 0.1,
        "encoder_dropout": 0.0,
        "encoder_layer_norm_first": False,
        "encoder_layer_drop": 0.1,
        "aux_num_out": 43,
    },
    _labels=utils._get_fr_labels(),
    _sample_rate=16000,
    _normalize_waveform=False,
)
VOXPOPULI_ASR_BASE_10K_FR.__doc__ = """wav2vec 2.0 model ("base" architecture),
pre-trained on 10k hours of unlabeled audio from *VoxPopuli* dataset :cite:`voxpopuli`
("10k" subset, consisting of 23 languages), and
fine-tuned for ASR on 211 hours of transcribed audio from "fr" subset.

Originally published by the authors of *VoxPopuli* :cite:`voxpopuli` under CC BY-NC 4.0 and
redistributed with the same license.
[`License <https://github.com/facebookresearch/voxpopuli/tree/160e4d7915bad9f99b2c35b1d3833e51fd30abf2#license>`__,
`Source <https://github.com/facebookresearch/voxpopuli/tree/160e4d7915bad9f99b2c35b1d3833e51fd30abf2#asr-and-lm>`__]

Please refer to :py:class:`torchaudio.pipelines.Wav2Vec2ASRBundle` for the usage.
"""  # noqa: E501


VOXPOPULI_ASR_BASE_10K_IT = Wav2Vec2ASRBundle(
    "wav2vec2_voxpopuli_base_10k_asr_it.pt",
    {
        "extractor_mode": "group_norm",
        "extractor_conv_layer_config": [
            (512, 10, 5),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 2, 2),
            (512, 2, 2),
        ],
        "extractor_conv_bias": False,
        "encoder_embed_dim": 768,
        "encoder_projection_dropout": 0.0,
        "encoder_pos_conv_kernel": 128,
        "encoder_pos_conv_groups": 16,
        "encoder_num_layers": 12,
        "encoder_num_heads": 12,
        "encoder_attention_dropout": 0.0,
        "encoder_ff_interm_features": 3072,
        "encoder_ff_interm_dropout": 0.1,
        "encoder_dropout": 0.0,
        "encoder_layer_norm_first": False,
        "encoder_layer_drop": 0.1,
        "aux_num_out": 37,
    },
    _labels=utils._get_it_labels(),
    _sample_rate=16000,
    _normalize_waveform=False,
    _remove_aux_axis=(1, 2, 3),
)
VOXPOPULI_ASR_BASE_10K_IT.__doc__ = """wav2vec 2.0 model ("base" architecture),
pre-trained on 10k hours of unlabeled audio from *VoxPopuli* dataset :cite:`voxpopuli`
("10k" subset, consisting of 23 languages), and
fine-tuned for ASR on 91 hours of transcribed audio from "it" subset.

Originally published by the authors of *VoxPopuli* :cite:`voxpopuli` under CC BY-NC 4.0 and
redistributed with the same license.
[`License <https://github.com/facebookresearch/voxpopuli/tree/160e4d7915bad9f99b2c35b1d3833e51fd30abf2#license>`__,
`Source <https://github.com/facebookresearch/voxpopuli/tree/160e4d7915bad9f99b2c35b1d3833e51fd30abf2#asr-and-lm>`__]

Please refer to :py:class:`torchaudio.pipelines.Wav2Vec2ASRBundle` for the usage.
"""  # noqa: E501


WAVLM_BASE = Wav2Vec2Bundle(
    "wavlm_base.pth",
    {
        "extractor_mode": "group_norm",
        "extractor_conv_layer_config": [
            (512, 10, 5),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 2, 2),
            (512, 2, 2),
        ],
        "extractor_conv_bias": False,
        "encoder_embed_dim": 768,
        "encoder_projection_dropout": 0.1,
        "encoder_pos_conv_kernel": 128,
        "encoder_pos_conv_groups": 16,
        "encoder_num_layers": 12,
        "encoder_num_heads": 12,
        "encoder_max_distance": 800,
        "encoder_num_buckets": 320,
        "encoder_attention_dropout": 0.1,
        "encoder_ff_interm_features": 3072,
        "encoder_ff_interm_dropout": 0.0,
        "encoder_dropout": 0.1,
        "encoder_layer_norm_first": False,
        "encoder_layer_drop": 0.05,
        "aux_num_out": None,
        "model_type": "WavLM",
    },
    _sample_rate=16000,
    _normalize_waveform=False,
)
WAVLM_BASE.__doc__ = """WavLM Base model ("base" architecture),
pre-trained on 960 hours of unlabeled audio from *LibriSpeech* dataset :cite:`7178964`, not fine-tuned.

Originally published by the authors of *WavLM* :cite:`chen2022wavlm` under MIT License and
redistributed with the same license.
[`License <https://github.com/microsoft/unilm/blob/65f15af2a307ebb64cfb25adf54375b002e6fe8d/LICENSE>`__,
`Source https://github.com/microsoft/unilm/tree/65f15af2a307ebb64cfb25adf54375b002e6fe8d/wavlm#pre-trained-models>`__]

Please refer to :py:class:`torchaudio.pipelines.Wav2Vec2Bundle` for the usage.
"""  # noqa: E501


WAVLM_BASE_PLUS = Wav2Vec2Bundle(
    "wavlm_base_plus.pth",
    {
        "extractor_mode": "group_norm",
        "extractor_conv_layer_config": [
            (512, 10, 5),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 2, 2),
            (512, 2, 2),
        ],
        "extractor_conv_bias": False,
        "encoder_embed_dim": 768,
        "encoder_projection_dropout": 0.1,
        "encoder_pos_conv_kernel": 128,
        "encoder_pos_conv_groups": 16,
        "encoder_num_layers": 12,
        "encoder_num_heads": 12,
        "encoder_max_distance": 800,
        "encoder_num_buckets": 320,
        "encoder_attention_dropout": 0.1,
        "encoder_ff_interm_features": 3072,
        "encoder_ff_interm_dropout": 0.0,
        "encoder_dropout": 0.1,
        "encoder_layer_norm_first": False,
        "encoder_layer_drop": 0.05,
        "aux_num_out": None,
        "model_type": "WavLM",
    },
    _sample_rate=16000,
    _normalize_waveform=False,
)
WAVLM_BASE_PLUS.__doc__ = """WavLM Base+ model ("base" architecture),
pre-trained on 60,000 hours of Libri-Light dataset :cite:`librilight`, 10,000 hours of GigaSpeech :cite:`GigaSpeech2021`,
and 24,000 hours of *VoxPopuli* :cite:`voxpopuli`, not fine-tuned.

Originally published by the authors of *WavLM* :cite:`chen2022wavlm` under MIT License and
redistributed with the same license.
[`License <https://github.com/microsoft/unilm/blob/65f15af2a307ebb64cfb25adf54375b002e6fe8d/LICENSE>`__,
`Source https://github.com/microsoft/unilm/tree/65f15af2a307ebb64cfb25adf54375b002e6fe8d/wavlm#pre-trained-models>`__]

Please refer to :py:class:`torchaudio.pipelines.Wav2Vec2Bundle` for the usage.
"""  # noqa: E501


WAVLM_LARGE = Wav2Vec2Bundle(
    "wavlm_large.pth",
    {
        "extractor_mode": "layer_norm",
        "extractor_conv_layer_config": [
            (512, 10, 5),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 3, 2),
            (512, 2, 2),
            (512, 2, 2),
        ],
        "extractor_conv_bias": False,
        "encoder_embed_dim": 1024,
        "encoder_projection_dropout": 0.1,
        "encoder_pos_conv_kernel": 128,
        "encoder_pos_conv_groups": 16,
        "encoder_num_layers": 24,
        "encoder_num_heads": 16,
        "encoder_max_distance": 800,
        "encoder_num_buckets": 320,
        "encoder_attention_dropout": 0.1,
        "encoder_ff_interm_features": 4096,
        "encoder_ff_interm_dropout": 0.0,
        "encoder_dropout": 0.1,
        "encoder_layer_norm_first": False,
        "encoder_layer_drop": 0.05,
        "aux_num_out": None,
        "model_type": "WavLM",
    },
    _sample_rate=16000,
    _normalize_waveform=True,
)
WAVLM_LARGE.__doc__ = """WavLM Large model ("large" architecture),
pre-trained on 60,000 hours of Libri-Light dataset :cite:`librilight`, 10,000 hours of GigaSpeech :cite:`GigaSpeech2021`,
and 24,000 hours of *VoxPopuli* :cite:`voxpopuli`, not fine-tuned.

Originally published by the authors of *WavLM* :cite:`chen2022wavlm` under MIT License and
redistributed with the same license.
[`License <https://github.com/microsoft/unilm/blob/65f15af2a307ebb64cfb25adf54375b002e6fe8d/LICENSE>`__,
`Source https://github.com/microsoft/unilm/tree/65f15af2a307ebb64cfb25adf54375b002e6fe8d/wavlm#pre-trained-models>`__]

Please refer to :py:class:`torchaudio.pipelines.Wav2Vec2Bundle` for the usage.
"""  # noqa: E501