speechbrain.inference.separation module
Specifies the inference interfaces for speech separation modules.
- Authors:
Aku Rouhe 2021
Peter Plantinga 2021
Loren Lugosch 2020
Mirco Ravanelli 2020
Titouan Parcollet 2021
Abdel Heba 2021
Andreas Nautsch 2022, 2023
Pooneh Mousavi 2023
Sylvain de Langen 2023
Adel Moumen 2023
Pradnya Kandarkar 2023
Summary
Classes:
A "ready-to-use" speech separation model. |
Reference
- class speechbrain.inference.separation.SepformerSeparation(modules=None, hparams=None, run_opts=None, freeze_params=True)[source]
Bases:
Pretrained
A “ready-to-use” speech separation model.
Uses Sepformer architecture.
Example
>>> tmpdir = getfixture("tmpdir") >>> model = SepformerSeparation.from_hparams( ... source="speechbrain/sepformer-wsj02mix", ... savedir=tmpdir) >>> mix = torch.randn(1, 400) >>> est_sources = model.separate_batch(mix) >>> print(est_sources.shape) torch.Size([1, 400, 2])
- MODULES_NEEDED = ['encoder', 'masknet', 'decoder']
- separate_batch(mix)[source]
Run source separation on batch of audio.
- Parameters:
mix (torch.Tensor) – The mixture of sources.
- Returns:
Separated sources
- Return type:
tensor
- separate_file(path, savedir=None)[source]
Separate sources from file.
- Parameters:
path (str) – Path to file which has a mixture of sources. It can be a local path, a web url, or a huggingface repo.
savedir (path) – Path where to store the wav signals (when downloaded from the web).
- Returns:
Separated sources
- Return type:
tensor