Wav2Vec2Bundle

class torchaudio.pipelines.Wav2Vec2Bundle

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 - 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)

Properties

sample_rate

property Wav2Vec2Bundle.sample_rate: float

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

Type:

float

Methods

get_model

Wav2Vec2Bundle.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