XLS-R model with 300 million parameters, pre-trained on 436,000 hours of unlabeled audio from multiple datasets ( Multilingual LibriSpeech, CommonVoice, VoxLingua107, BABEL, and VoxPopuli ) in 128 languages, not fine-tuned. Wav2vec 2.0 model ("base" architecture), pre-trained on 56,000 hours of unlabeled audio from multiple datasets ( Multilingual LibriSpeech, CommonVoice and BABEL ), not fine-tuned. Wav2vec 2.0 model ("large-lv60k" architecture), pre-trained on 60,000 hours of unlabeled audio from Libri-Light dataset, not fine-tuned. Wav2vec 2.0 model ("large" architecture), pre-trained on 960 hours of unlabeled audio from LibriSpeech dataset (the combination of "train-clean-100", "train-clean-360", and "train-other-500"), not fine-tuned. Wav2vec 2.0 model ("base" architecture), pre-trained on 960 hours of unlabeled audio from LibriSpeech dataset (the combination of "train-clean-100", "train-clean-360", and "train-other-500"), not fine-tuned. Wav2Vec2Bundle instantiates models that generate acoustic features that can be used for downstream inference and fine-tuning.ĭata class that bundles associated information to use pretrained Wav2Vec2Model. Wav2vec 2.0 / HuBERT / WavLM - SSL ¶ Interface ¶ Online ASR with Emformer RNN-T Pretrained Models ¶ĪSR pipeline based on Emformer-RNNT, pretrained on LibriSpeech dataset, capable of performing both streaming and non-streaming inference. Still, because they share the same interface, the usage is the same. For example, SourceSeparationBundle defines the interface for performing source separation, but its instance CONVTASNET_BASE_LIBRI2MIX instantiates a model of ConvTasNet while HDEMUCS_HIGH_MUSDB instantiates a model of HDemucs. Different instances of same Bundle share the interface, but their implementations are not constrained to be of same types. To make this information tied to a pre-trained model and easily accessible, torchaudio.pipelines module uses the concept of a Bundle class, which defines a set of APIs to instantiate pipelines, and the interface of the pipelines.Ī pre-trained model and associated pipelines are expressed as an instance of Bundle. This requires to carrying over information used during the training, such as the type of transforms and the their parameters (for example, sampling rate the number of FFT bins). When using pre-trained models to perform a task, in addition to instantiating the model with pre-trained weights, the client code also needs to build pipelines for feature extractions and post processing in the same way they were done during the training. The torchaudio.pipelines module packages pre-trained models with support functions and meta-data into simple APIs tailored to perform specific tasks. HuBERT Pre-training and Fine-tuning (ASR).Music Source Separation with Hybrid Demucs.Speech Enhancement with MVDR Beamforming.
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