Wav2Vec2Bundle¶
- class torchaudio.pipelines.Wav2Vec2Bundle[source]¶
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)
sample_rate¶
get_model¶
- Wav2Vec2Bundle.get_model(*, dl_kwargs=None) Wav2Vec2Model [source]¶
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()
.