Wav2Vec2ASRBundle

class torchaudio.pipelines.Wav2Vec2ASRBundle

Data class that bundles associated information to use pretrained 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)

Tutorials using Wav2Vec2ASRBundle:

Speech Recognition with Wav2Vec2

Speech Recognition with Wav2Vec2

ASR Inference with CTC Decoder

ASR Inference with CTC Decoder

Forced Alignment with Wav2Vec2

Forced Alignment with Wav2Vec2

Properties

sample_rate

property Wav2Vec2ASRBundle.sample_rate: float

Sample rate of the audio that the model is trained on.

Type:

float

Methods

get_labels

Wav2Vec2ASRBundle.get_labels(*, blank: str = '-') → Tuple[str, ...]

The output class labels.

The first is blank token, and it is customizable.

Parameters:

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

Returns:

For models fine-tuned on ASR, returns the tuple of strings representing the output class labels.

Return type:

Tuple[str, …]

Example

>>> from torchaudio.pipelines import HUBERT_ASR_LARGE as bundle
>>> bundle.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')

get_model

Wav2Vec2ASRBundle.get_model(*, dl_kwargs=None) → Module

Construct the model and load the pretrained weight.

The weight file is downloaded from the internet and cached with torch.hub.load_state_dict_from_url()

Parameters:

dl_kwargs (dictionary of keyword arguments) – Passed to torch.hub.load_state_dict_from_url().

Returns:

Variation of Wav2Vec2Model.

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

For all other models, a Wav2Vec2Model instance is returned.

• WAV2VEC2_LARGE_LV60K

• WAV2VEC2_ASR_LARGE_LV60K_10M

• WAV2VEC2_ASR_LARGE_LV60K_100H

• WAV2VEC2_ASR_LARGE_LV60K_960H

• WAV2VEC2_XLSR53

• WAV2VEC2_XLSR_300M

• WAV2VEC2_XLSR_1B

• WAV2VEC2_XLSR_2B

• HUBERT_LARGE

• HUBERT_XLARGE

• HUBERT_ASR_LARGE

• HUBERT_ASR_XLARGE

• WAVLM_LARGE