torchaudio.prototype.pipelines¶
The pipelines subpackage contains APIs to models with pretrained weights and relevant utilities.
RNN-T Streaming/Non-Streaming ASR¶
EMFORMER_RNNT_BASE_MUSTC¶
- torchaudio.prototype.pipelines.EMFORMER_RNNT_BASE_MUSTC¶
Pre-trained Emformer-RNNT-based ASR pipeline capable of performing both streaming and non-streaming inference. The underlying model is constructed by
torchaudio.models.emformer_rnnt_base()and utilizes weights trained on MuST-C release v2.0 [Cattoni et al., 2021] dataset using training scripttrain.pyhere withnum_symbols=501. Please refer totorchaudio.pipelines.RNNTBundlefor usage instructions.
EMFORMER_RNNT_BASE_TEDLIUM3¶
- torchaudio.prototype.pipelines.EMFORMER_RNNT_BASE_TEDLIUM3¶
Pre-trained Emformer-RNNT-based ASR pipeline capable of performing both streaming and non-streaming inference.
The underlying model is constructed by
torchaudio.models.emformer_rnnt_base()and utilizes weights trained on TED-LIUM Release 3 dataset using training scripttrain.pyhere withnum_symbols=501.Please refer to
torchaudio.pipelines.RNNTBundlefor usage instructions.
HiFiGAN Vocoder¶
Interface¶
HiFiGANVocoderBundle defines HiFiGAN Vocoder pipeline capable of transforming mel spectrograms into waveforms.
Data class that bundles associated information to use pretrained |
Pretrained Models¶
HiFiGAN Vocoder pipeline, trained on The LJ Speech Dataset [Ito and Johnson, 2017]. |
