torchaudio¶
This library is part of the PyTorch project. PyTorch is an open source machine learning framework.
Features described in this documentation are classified by release status:
Stable: These features will be maintained long-term and there should generally be no major performance limitations or gaps in documentation. We also expect to maintain backwards compatibility (although breaking changes can happen and notice will be given one release ahead of time).
Beta: Features are tagged as Beta because the API may change based on user feedback, because the performance needs to improve, or because coverage across operators is not yet complete. For Beta features, we are committing to seeing the feature through to the Stable classification. We are not, however, committing to backwards compatibility.
Prototype: These features are typically not available as part of binary distributions like PyPI or Conda, except sometimes behind run-time flags, and are at an early stage for feedback and testing.
The torchaudio package consists of I/O, popular datasets and common audio transformations.
Package Reference
- torchaudio
- torchaudio.backend
- torchaudio.functional
- spectrogram
- amplitude_to_DB
- create_fb_matrix
- create_dct
- mu_law_encoding
- mu_law_decoding
- complex_norm
- angle
- magphase
- phase_vocoder
- lfilter
- biquad
- lowpass_biquad
- highpass_biquad
- allpass_biquad
- equalizer_biquad
- bandpass_biquad
- bandreject_biquad
- band_biquad
- treble_biquad
- bass_biquad
- deemph_biquad
- riaa_biquad
- contrast
- dcshift
- overdrive
- phaser
- flanger
- mask_along_axis
- mask_along_axis_iid
- compute_deltas
- detect_pitch_frequency
- sliding_window_cmn
- vad
- torchaudio.transforms
- torchaudio.datasets
- torchaudio.models
- torchaudio.sox_effects
- torchaudio.compliance.kaldi
- torchaudio.kaldi_io
- torchaudio.utils
PyTorch Libraries
