Audio Versions Save

Data manipulation and transformation for audio signal processing, powered by PyTorch

v0.8.0

3 years ago

Highlights

This release supports Python 3.9.

I/O Improvements

Continuing from the previous release, torchaudio improves the audio I/O mechanism. In this release, we have four major updates.

  1. Backend migration. We have migrated the default backend for audio I/O. The new default backend is “sox_io” (for Linux/macOS). The interface for “soundfile” backend has been also changed to align that of “sox_io”. Following the change of default backends, the legacy backend/interface have been marked as deprecated. The legacy backend/interface are still accessible, though it is strongly discouraged to use them. For the detail on the migration, please refer to #903.

  2. File-like object support. We have added file-like object support to I/O functions and sox_effects. You can perform the info, load, save and apply_effects_file operation on file-like objects.

    # Query audio metadata over HTTP
# Will only fetch the first few kB
with requests.get(URL, stream=True) as response:
  metadata = torchaudio.info(response.raw)

# Load audio from tar file
# No need to extract TAR file.
with tarfile.open(TAR_PATH, mode='r') as tarfile_:
  fileobj = tarfile_.extractfile(SAMPLE_TAR_ITEM)
  waveform, sample_rate = torchaudio.load(fileobj)

# Saving to Bytes buffer
# Using BytesIO, you can perform in-memory encoding/decoding.
buffer_ = io.BytesIO()
torchaudio.save(buffer_, waveform, sample_rate, format="wav")

# Apply effects (lowpass filter / resampling) while loading audio from S3
client = boto3.client('s3')
response = client.get_object(Bucket=S3_BUCKET, Key=S3_KEY)
waveform, sample_rate = torchaudio.sox_effects.apply_effect_file(
  response['Body'], [["lowpass", "-1", "300"], ["rate", "8000"]])

  3. [Beta] Codec Application. Built upon the file-like object support, we added functional.apply_codec function, which can degrades audio data by applying audio codecs supported by “sox_io” backend, in in-memory fashion.

    # Apply MP3 codec
degraded = F.apply_codec(
  waveform, sample_rate, format="mp3", compression=-9)
# Apply GSM codec
degraded = F.apply_codec(waveform, sample_rate, format="gsm")

  4. Encoding options. We have added encoding options to save function of new backends. Now you can change the format and encodings with format, encoding and bits_per_sample options

    # Save without any encoding option.
# The function will pick the encoding which the provided data fit
# For Tensor of float32 type, that is 32-bit floating-point PCM.
torchaudio.save("data.wav", waveform, sample_rate)

# Save as 16-bit signed integer Linear PCM
# The resulting file occupies half the storage but loses precision
torchaudio.save(
  "data.wav", waveform, sample_rate, encoding="PCM_S", bits_per_sample=16)

  5. More format support to "sox_io"’s save function. We have added support for GSM, HTK, AMB, and AMR-NB formats to "sox_io"’s save function.

Switch to CMake-based build

torchaudio was utilizing CMake to build third party dependencies. Now torchaudio uses CMake to build its C++ extension. This will open the door to integrate torchaudio in non-Python environments (such as C++ applications and mobile). We will work on adding example applications and mobile integrations in upcoming releases.

Backwards Incompatible Changes

  • Removed deprecated transform and target_transform arguments from VCTK and YESNO datasets. (#1120) If you were relying on the previous behavior, we recommend that you apply the transforms in the collate function.
  • Removed torchaudio.datasets.utils.walk_files (#1111) and replaced by Path and glob. (#1069, #1101). If you relied on the function, we recommend that you use glob instead.
  • Removed torchaudio.data.utils.unicode_csv_reader. (#1086) If you relied on the function, we recommend that you replace by csv.reader.
  • Disabled CommonVoice download as users are required to sign user agreement. Please download and extract the dataset manually, and replace the root argument by the subfolder for the version and language of interest, see #1082 for more details. (#1018, #1079, #1080, #1082)
  • Removed legacy sox effects (#977, #1001). Please migrate to apply_effects_file or apply_effects_tensor.
  • Switched the default backend to the ones with new interfaces (#978). If you were relying on the previous behavior, you can return to the previous behavior by following instructions in #975 for one more release.

New Features

  • Added GSM, HTK, AMB, AMR-NB and AMR-WB format support to “sox_io” backend. (#1276, #1291, #1277, #1275, #1066)
  • Added encoding options (format, bits_per_sample and encoding) to save function. (#1226, #1177, #1129, #1104)
  • Added new attributes (bits_per_sample and encoding) to the info function return type (AudioMetaData) (#1177, #1206, #1324)
  • Added format override to libsox-based file input. (load, info, sox_effects.apply_effects_file) (#1104)
  • Added file-like object support in “sox_io”, and “soundfile” backend and sox_effects.apply_effects_file. (#1115)
  • [Beta] Added the Kaldi Pitch feature. (#1243, #1260)
  • [Beta] Added the SpectralCentroid transform. (#1167, #1216, #1316)
  • [Beta] Added codec transformation apply_codec. (#1200)

Improvements

  • Exposed normalization method to Mel transforms. (#1212)
  • Exposed additional STFT arguments to Spectrogram (#892) and to MelSpectrogram (#1211).
  • Added support for pathlib.Path to apply_effects_file (#1048) and to CMUARCTIC (#1025), YESNO (#1015), COMMONVOICE (#1027), VCTK and LJSPEECH (#1028), GTZAN (#1032), SPEECHCOMMANDS (#1039), TEDLIUM (#1045), LIBRITTS and LIBRISPEECH (#1046).
  • Added SpeechCommands train/valid/test split. (#966, #1012)

Internals

  • Replaced if-elseif-else with switch in sox C++ code. (#1270)
  • Refactored C++ interface for sox_io's get_info_file (#1232) and get_encodinginfo (#1233).
  • Add explicit functional import in init. (#1228)
  • Refactored YESNO dataset (#1127), LJSPEECH dataset (#1143).
  • Removed Python 2.7 reference from setup.py. (#1182)
  • Merged flake8 configurations into single .flake8 file. (#1172, #1214)
  • Updated calls to torch.stft to use return_complex=True. (#1096, #1013)
  • Cleaned up handling of optional args in C++ with c10:optional. (#1043)
  • Removed unused imports in sox effects. (#1052)
  • Introduced functional submodule to organize functionals. (#1003)
  • [Testing] Refactored MelSpectrogram librosa compatibility test to decouple from other tests. (#1267)
  • [Testing] Moved batch tests for functionals. (#1254)
  • [Testing] Refactored tests for backend (#1239) and for functionals (#1237).
  • [Testing] Removed dependency on pytest from testing (#1157, #1188)
  • [Testing] Refactored unitests for VCTK (#1134), SPEECHCOMMANDS (#1136), LIBRISPEECH (#1140), TEDLIUM (#1135), LJSPEECH (#1138), LIBRITTS (#1139), CMUARCTIC (#1147), GTZAN(#1148), COMMONVOICE and YESNO (#1133).
  • [Testing] Removed dependency on COMMONVOICE dataset from tests. (#1132)
  • [Build] Fixed Python 3.9 support (#1242)
  • [Build] Switched to cmake for build. (#1187, #1246, #1249)
  • [Build] Restructured C++ code to allow per file registration of custom ops. (#1221)
  • [Build] Added logging to sox/CMakeLists.txt. (#1190)
  • [Build] Disabled C++11 ABI when necessary for libtorch compatibility. (#880)
  • [Build] Reorganized libsox source and build directory to accommodate additional third party code. (#1161, #1176)
  • [Build] Refactored sox source files and moved into dedicated subfolder. (#1106)
  • [Build] Enabled custom clean function for python setup.py clean. (#1142)
  • [CI] Documented undocumented parameters. Added CI check. (#1248)
  • [CI] Fixed sphinx warnings in documentation. Turned warnings into errors. (#1247)
  • [CI] Print CPU info before running unit test. (#1218)
  • [CI] Fixed clang-format job and fixed newly detected formatting issues. (#981, #1198, #1222)
  • [CI] Updated unit test base Docker image. (#1193)
  • [CI] Disabled CCI cache which is now known to be flaky. (#1189)
  • [CI] Disabled torchscript BC test which is known to fail. (#1192)
  • [CI] Stripped version suffix for pytorch. (#1185)
  • [CI] Ran smoke test with CPU package for pytorch due to known issue with CUDA 11. (#1105)
  • [CI] Added missing empty line at the end of config.yml. (#1020)
  • [CI] Added automatic documentation build and push to branch in CI. (#1006, #1034, #1041, #1049, #1091, #1093, #1098, #1100, #1121)
  • [CI] Ran GPU test for all pull requests and fixed current setup. (#998, #1014, #1191)
  • [CI] Skipped tests that is known to fail on macOS Python 3.6/3.7. (#999)
  • [CI] Changed the order of installation and aligned with Windows. (#987)
  • [CI] Fixed documentation rendering by using Sphinx 2.4.4. (#974)
  • [Doc] Added subcategories to functional documentation. (#1325)
  • [Doc] Added a version selector in documentation. (#1273)
  • [Doc] Updated compilation recommendation in README. (#1263)
  • [Doc] Added CONTRIBUTING.md. (#1241)
  • [Doc] Added instructions to install parametrized package. (#1164)
  • [Doc] Fixed the return type for load functions. (#1122)
  • [Doc] Added missing modules and minor fixes. (#1022, #1056, #1117)
  • [Doc] Fixed spelling and links in README. (#1029, #1037, #1062, #1110, #1261)
  • [Doc] Grouped filtering functionals in documentation page. (#1005, #1004)
  • [Doc] Updated the compatibility matrix with torchaudio 0.7 (#979)
  • [Doc] Added description of prototype/beta/stable features. (#968)

Bug Fixes

  • Fixed amplitude_to_DB clamping behaviour on batches. (#1113)
  • Disabled audio devices in sox builds which could interfere in the build process when detected. (#1153)
  • Fixed COMMONVOICE for French where the audio file extension was missing on load. (#1126)
  • Disabled OpenMP support for libsox which can produce errors when used in DataLoader. (#1026)
  • Fixed noise_down_time argument in VAD by properly propagating it. (#1017)
  • Removed print-freq option to compute validation loss at each epoch in wav2letter pipeline. (#997)
  • Migrated from torch.rfft to torch.fft.rfft and cfloat following change in pytorch. (#941)
  • Fixed interactive ASR demo to aligned with latest version of FAIRSeq. (#996)

Deprecations

  • The normalized argument is unused and will be removed from griffinlim. (#1036)
  • The previous sox and soundfile backend remain available for one release, see #903 for details. (#975)

Performance

  • Added C++ lfilter core loop for faster iteration on CPU. (#1244)
  • Leveraged julius resampling implementation to make resampling faster. (#1087)

v0.7.2

3 years ago

Highlights

This release introduces support for python 3.9. There is no 0.7.1 release, and the following changes are compared to 0.7.0.

Improvements

  • Add python 3.9 support (#1061)

Bug Fixes

  • Temporarily disable OpenMP support for libsox (#1054)

Deprecations

  • Disallow download=True in CommonVoice (#1076)

v0.7.0

3 years ago

Highlights

Example Pipelines

torchaudio is expanding its support for models and end-to-end applications. Please file an issue on github to provide feedback on them.

  • Speech Recognition: Building on the addition of the Wav2Letter model for speech recognition in the last release, we added a training example pipelines for speech recognition that uses the LibriSpeech dataset.
  • Text-to-Speech: With the goal of supporting text-to-speech applications, we added a vocoder based on the WaveRNN model. WaveRNN model is based on the implementation from this repository. The original implementation was introduced in "Efficient Neural Audio Synthesis". We provide an example training pipeline in the example folder that uses the LibriTTS dataset added to torchaudio in this release.
  • Source Separation: We also support source separation with the addition of the ConvTasNet model, based on the paper "Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation." An example training pipeline is provided with the wsj0-mix dataset.

I/O Improvements

As you are likely already aware from the last release we’re currently in the process of making sox_io, which ships with new features such as TorchScript support and performance improvements, the new default. If you want to benefit from these features now, we encourage you to migrate. For more information see issue #903.

Backwards Incompatible Changes

  • Switched all %-based string formatting to str.format to adopt changes in PyTorch, leading to improved error messages for TorchScript (#850)
  • Split sox_utils.list_formats() for read and write (#811)
  • Made directory traversal order alphabetical and breadth-first, consistent across operating systems (#814)
  • Changed GTZAN so that it only traverses filenames belonging to the dataset (#791)

New Features

  • Added ConvTasNet model (#920, #933) with pipeline (#894)
  • Added canonical pipeline with wav2letter (#632)
  • The WaveRNN model (#705, #797, #801, #810, #836) is available with a canonical pipeline (#749, #802, #831, #863)
  • Added all 3 releases of tedlium dataset (#882, #934, #945, #895)
  • Added VCTK_092 dataset (#812)
  • Added LibriTTS (#790, #820)
  • Added SPHERE support to sox_io backend (#871)
  • Added torchscript sox effects (#760)
  • Added a flag to change the interface of soundfile backend to the one identical to sox_io backend. (#922)

Improvements

  • Added soundfile compatibility backend. (#922)
  • Improved the speed of torchaudio.compliance.kaldi.fbank (#947)
  • Improved the speed of phaser (#660)
  • Added warning when a Mel filter is all zero (#914)
  • Added pathlib.Path support to sox_io backend (#907)
  • Simplified C++ registration with TORCH_LIBRARY (#840)
  • Merged sox effect and sox_io C++ implementation (#779)

Internal

  • CI: Added test to validate torchscript backward compatibility (#838)
  • CI: Used mocked datasets to test CMUArctic (#829), CommonVoice (#827), Speech Commands (#824), LJSpeech (#826), LibriSpeech (#825), YESNO (#792, #832)
  • CI: Made *nix unit test fail if C++ extension is not available (#847, #849)
  • CI: Separated I/O in testing. (#813, #773, #783)
  • CI: Added smoke tests to sox_io and sox_effects (#806)
  • CI: Tested utilities have been refactored (#805, #808, #809, #817, #822, #831)
  • Doc: Added how to run tests (#843)
  • Doc: Added 0.6.0 to version matrix in README (#833)

Bug Fixes

  • Fixed device in interactive ASR example (#900)
  • Fixed incorrect extension parsing (#885)
  • Fixed dither with noise_shaping = True (#865)
  • Run unit test with non-editable installation (#845), and set zip_safe = False to disable egg installation (#842)
  • Sorted GTZAN dataset and use on-the-fly data in GTZAN test (#819)

Deprecations

  • Removed istft wrapper in favor of torch.istft. (#841)
  • Deprecated SoxEffect and SoxEffectsChain (#787)
  • I/O: Deprecated sox backend. (#904)
  • I/O: Deprecated the current interface of soundfile. (#922)
  • I/O: Deprecated load_wav functions. (#905)

v0.6.0

3 years ago

Highlights

torchaudio now includes a new model module (with wav2letter included), new functionals (contrast, cvm, dcshift, overdrive, vad, phaser, flanger, biquad), datasets (GTZAN, CMU), and a new optional sox backend with support for torchscript. torchaudio now also supports Windows, with the soundfile backend.

torchaudio requires python 3.6 or more recent.

Backwards Incompatible Changes

  • We reorganized the C++ resources (#630) and replaced C++ bindings for sox_effects init/list/shutdown with torch binding (#748).
  • We removed code specific to python 2 (#691), and we no longer tests against python 2 (#575) and 3.5 (#577)

New Features

  • We now support Windows. (#604, #637, #642, #655, #743)
  • We now have a model module which includes wav2letter. (#462, #722)
  • We added the GTZAN and CMU datasets. (#668, #710)
  • We now have the contrast functional (#551), cvm (#540), dcshift (#558), overdrive (#569), vad (#578, #599), phaser (#587, #607, #702), flanger (#651, #702), biquad (#661).
  • We added a new sox_io backend (#718, #728, #734, #727, #763, #752, #731, #732, #726, #780) that is compatible with torchscript with a new AudioMetaData class (#761).
  • MelSpectrogram now has power and normalized parameters (#633), and slaney normalization (#589, #641).
  • lfilter now has a clamp option. (#600)
  • Griffin-Lim can now have zero momentum. (#601)
  • sliding_window_cmn now supports batching. (#570)
  • Downloaded datasets now verify checksums. (#499)

Improvements

  • We added ogg/vorbis/opus support to binary distribution (#750, #755).
  • We replaced the use of torch.norm in spectrogram to improve performance (#747).
  • We now use fused operations in lfilter for faster computation. (#517, #564)
  • STFT is now called directly from torchaudio. (#531)
  • We redesigned the backend mechanism to support torchscript, by restructuring the code (#695, #696, #700, #706, #707, #698), adding dynamic listing (#697)
  • torchaudio can be built along with sox, or can use external sox. (#625, #669, #739)
  • We redesigned the sox_effects module. (#708)
  • We added more details to compilation instructions. (#667)
  • We updated the README with instructions on changing the backend. (#553)
  • We now have a version compatibility matrix in README. (#685)
  • We now use cmake to build third party libraries (#753).
  • We now use CircleCI instead of travis (#576, #584, #598, #603, #636, #738) and we test on GPU (#586, #777).
  • We run the test suite against nightlies. (#538, #678)
  • We redesigned our test suite: with new helper functions (#514, #519, #521, #565, #616, #690, #692, #694), standard pytorch test utilities (#513, #640, #643, #645, #646, #652, #650, #712), separated CPU and GPU tests (#513, #528, #644), more descriptive names (#532), clearer organization (#539, #541, #542, #664, #672, #687, #703, #716, #732), standardized name (#559), and backend aware (#719). This is detailed in a new README for testing (#566, #759).
  • We now support typing, for datasets (#511, #522), for backends (#527), for init (#526), and inline (#530), with mypy configuration (#524, #544, #590).

Bug Fixes

  • We removed in place operations so that Griffin-Lim can be backpropagated through. (#730)
  • We fixed kaldi MFCC on GPU. (#681)
  • We removed multiple definitions of SoxEffect in C++. (#635)
  • We fixed the docstring of masking. (#612)
  • We replaced views by reshape for batching. (#594)
  • We fixed missing conda environment when testing in python 3.8. (#582)
  • We ensure that sox is not exposed in windows. (#579)
  • We corrected the instructions to install nightlies. (#547, #552)
  • We fix the seed of mask_along_iid. (#529)
  • We correctly report GPU tests as skipped instead of passed. (#516)

Deprecations

  • Since sox_effects is now automatically initialized and shutdown (#572, #693), we are deprecating these functions (#709).
  • ISTFT is migrating to torch. (#523)

v0.5.1

3 years ago

Highlights

  • Updated pinned version of PyTorch to v1.5.1

v0.5.0

4 years ago

Highlights

torchaudio includes new transforms (e.g. Griffin-Lim and inverse Mel scale), new filters (e.g. all pass, fade, band pass/reject, band, treble, deemph, riaa), and datasets (LJ Speech and SpeechCommands).

Backwards Incompatible Changes

  • torchaudio no longer supports python 2. We removed future and six imports. We added inline typing. (#413, #478, #479, #482, #486)
  • We fixed CommonVoice dataset download, and updated to the latest version. (#498)
  • We now skip data point with missing data in VCTK dataset. (#484)

New Features

  • We now have the Vol transforms, and DB_to_amplitude.(#468, #469)
  • We now have the InverseMelScale (#448)
  • We now have the Griffin-Lim functional. (#365)
  • We now support allpass, fade, bandpass, bandreject, band, treble, deemph, riaa. (#444, #449, #464, #470, #508)
  • We now offer LJSpeech and SpeechCommands datasets. (#439, #437)

Improvements

  • We added inline typing to SoxEffects and Kaldi compliance. (#490, #497)
  • We refactored the tests. (#480, #485, #496, #491, #501, #502, #503, #506, #507, #509)
  • We now run tests with sox only when sox is available. (#419)
  • We extended batch support to MelScale, MelSpectrogram, MFCC, Resample. (#391, #435)
  • The speed of torchaudio.functional.istft was improved. (#471)
  • We now have transform and functional tests for AmplitudeToDB. (#463)
  • We now ignore pycharm and OSX files in git. (#461)
  • TimeStretch now has a batch test. (#459)
  • Docstrings in transforms were polished. (#442)
  • TimeStretch and AmplitudeToDB are now torch.nn.Module. (#456)
  • Resample is now jitable. (#441)
  • We support python 3.8. (#397)
  • Add cuda test for complex norm. (#421)
  • Dither is jitable with the latest version of pytorch. (#417)
  • Batching uses view instead of reshape. (#409)
  • We refactored the jitability test. (#395)
  • In .circleci, we removed a conditional block that wasn't doing anything. (#399)
  • We now have Windows CI for building. (#394 and #398)
  • We corrected the use of standard variable names in code. (#393)
  • We adopted native-Python code generation convention. (#378)
  • torchaudio.istft creates tensors directly on device. (#377)
  • torchaudio.compliance.kaldi.resample_waveform is now jitable. (#362)
  • The runtime of torchaudio.functional.lfilter was decreased. (#374)

Bug Fixes

  • We fixed flake8 errors. (#504, #505)
  • We fixed Windows test by only testing with cpu-only binaries. (#489)
  • Spelling correction in docstrings for transforms.FrequencyMasking and transforms.TimeMasking. (#474)
  • In .circleci, we switched to use token for conda uploads. (#460)
  • The default value of dither parameter was changed. (#453)
  • TimeStretch moves device correctly. (#457)
  • Adding dev-other option in librispeech. (#433)
  • In build script, we install the correct version of pytorch for pip. (#412)
  • Upgrading dataset DeprecationWarning to UserWarning so that the user gets the warning. (#402)
  • Make power of spectrogram a float to work with complex norm. (#392)
  • Fix random seed for flaky test_griffinlim test. (#388)
  • Apply 'nightly' branch filter to binary uploads. (#385)
  • Fixed build errors: added explicitly utf8 decoration, added explicit utf_8_encoder definition if not available, explicitly cast to int. (#380)

Deprecations

  • None

v0.4.0

4 years ago

torchaudio 0.4 improves on current transformations, datasets, and backend support.

  • We introduce an interactive speech recognition demo. (#266, #229, #248)
  • SoX is now optional, and a new extensible backend dispatch mechanism exposes SoundFile as an alternative to SoX.
  • The interface for datasets has been unified. This enables the addition of two large datasets: LibriSpeech and Common Voice.
  • New filters such as biquad, data augmentation such as time and frequency masking, and transforms such as gain and dither, and new feature computation such as deltas, are now available.
  • Transformations now support batches and are jitable.

We would like to thank again our contributors and the wider community for their significant contributions to this release. In particular we'd like to thank @keunwoochoi, @ksanjeevan, and all the other maintainers and contributors of torchaudio-contrib for their significant and valuable additions around augmentations (#285) and batching (#327).

Breaking Changes

  • torchaudio now requires PyTorch 1.3.0 or newer, see https://pytorch.org/ for installation instructions. (#312)
  • We make jit compilation optional for functions and use nn.Module where possible. (#314, #326, #342, #369)
  • By unifying the interface for datasets, we changed the interface for VCTK and YESNO (#303, #316). In particular, the construction parameters downsample, transform, target_transform, and return_dict are being deprecated.
  • SoxEffectsChain.EFFECTS_AVAILABLE replaced by SoxEffectsChain().EFFECTS_AVAILABLE (#355)
  • This is the last version to support Python 2.

New Features

  • SoX is now optional, and a new extensible backend dispatch mechanism exposes SoundFile as an alternative to SoX. This makes it possible to use torchaudio even when SoX or SoundFile are not installed or available. (#355)
  • We now have a unified dataset interface that loads in memory only one item at a time enabling new large datasets: LibriSpeech and CommonVoice. (#303, #316, #330)
  • We introduce a pitch detection algorithm: torchaudio.functional.detect_pitch_frequency. (#313, #322)
  • We offer data augmentations in torchaudio.transforms: TimeStretch, FrequencyMasking, TimeMasking. (#285, #333, #348)
  • We introduce a complex norm transform: torchaudio.transform.ComplexNorm. (#285, #333)
  • We now have a new audio feature generation for computing deltas: torchaudio.functional.compute_deltas. (#268, #326)
  • We introduce torchaudio.functional.gain and torchaudio.functional.dither (#319, #360). We welcome work to continue the effort to implement features available in SoX, see #260.
  • We now include equalizer_biquad (#315, #340), lowpass_biquad, highpass_biquad (#275), lfilter, and biquad (#275, #291, #326) in torchaudio.functional.
  • MFCC is available as torchaudio.functional.mfcc. (#228)

Improvements

  • We now support batching in transforms. (#327, #337, #404)
  • Functions are now jitable, and nn.Module is used where possible. (#314, #326, #342, #362, #369, #395)
  • Downloads of large files are now automatically resumed with new download function. (#320)
  • New tests for ISTFT are added. (#279)
  • We introduce nightly builds. (#301)
  • We now have smoke tests for builds. (#346, #359)

Bug Fixes

  • Fix mismatch between MelScale and librosa. (#294)
  • Fix torchaudio.compliance.kaldi.resample_waveform where internal variables where not moved to the GPU when used. (#277)
  • Fix a bug that occurred when importing torchaudio built outside of a git repository. (#276)
  • Fix istft where the dtype and device of parameters were not created on the same device as the tensor provided by the user. (#264)
  • Fix size mismatch when saving and loading from state dictionary (load_state_dict). (#246)
  • Clarified internal naming convention within transforms and functionals. (#298)
  • Fix build script to be more tolerant to download drops. (#280, #284, #305)
  • Correct documentation for SoxEffectsChain. (#283)
  • Fix resample error with cuda tensors. (#277)
  • Fix error when importing version outside of git. (#276)
  • Fix missing asound in linux build. (#254)
  • Fix deprecated torch. (#254)
  • Fix link in README. (#253)
  • Fix window device in ISTFT. (#240)
  • Documentation: Fix range in documentation for torchaudio.load to [-1, 1]. (#283)

v0.3.2

4 years ago

This release is to update the dependency to PyTorch 1.3.1.

v0.3.1

4 years ago

This release is to update the dependency to PyTorch 1.3.0.

Minor Fix

  • Updated settings for curl in build scripts (#280, #284, #297).

v0.3.0

4 years ago

Highlights

torchaudio as an extension of PyTorch

torchaudio has been redesigned to be an extension of PyTorch and part of the domain APIs (DAPI) ecosystem. Domain specific libraries such as this one are kept separated in order to maintain a coherent environment for each of them. As such, torchaudio is an ML library that provides relevant signal processing functionality, but it is not a general signal processing library. The full rationale of this new standardization can be found in the README.md.

In light of these changes some transforms have been removed or have different argument names and conventions. See the section on backwards breaking changes for a migration guide.

We provide binaries via pip and conda. They require PyTorch 1.2.0 and newer. See https://pytorch.org/ for installation instructions.

Community

We would like to thank our contributors and the wider community for their significant contributions to this release. We are happy to see an active community around torchaudio and are eager to further grow and support it.

In particular we'd like to thank @keunwoochoi, @ksanjeevan, and all the other maintainers and contributors of torchaudio-contrib for their significant and valuable additions around standardization and the support of complex numbers (https://github.com/pytorch/audio/pull/131, https://github.com/pytorch/audio/issues/110, https://github.com/keunwoochoi/torchaudio-contrib/issues/61, https://github.com/keunwoochoi/torchaudio-contrib/issues/36).

Kaldi Compliance Interface

An implementation of basic transforms with a Kaldi-like interface.

We added the functions spectrogram, fbank, and resample_waveform (https://github.com/pytorch/audio/pull/119, https://github.com/pytorch/audio/pull/127, and https://github.com/pytorch/audio/pull/134). For more details see the documentation on torchaudio.compliance.kaldi which mirrors the arguments and outputs of Kaldi features.

As an example we can look at the sinc interpolation resampling similar to Kaldi’s implementation. In the figure below, the blue dots are the original signal and red dots are the downsampled signal with half the original frequency. The red dot elements are approximately every other original element.

resampling

specgram = torchaudio.compliance.kaldi.spectrogram(waveform, frame_length=...)
fbank = torchaudio.compliance.kaldi.fbank(waveform, num_mel_bins=...)
resampled_waveform = torchaudio.compliance.kaldi.resample_waveform(waveform, orig_freq=...)

Inverse short time Fourier transform

Constructing a signal from a spectrogram can be used in applications like source separation or to generate audio signals to listen to. More specifically torchaudio.functional.istft is the inverse of torch.stft. It has the same parameters (+ additional optional parameter of length) and returns the least squares estimation of an original signal.

torch.manual_seed(0)
n_fft = 5
waveform = torch.rand(2, 5)
stft = torch.stft(waveform, n_fft=n_fft)
approx_waveform = torchaudio.functional.istft(stft, n_fft=n_fft, length=waveform.size(1))
>>> waveform
tensor([[0.4963, 0.7682, 0.0885, 0.1320, 0.3074],
        [0.6341, 0.4901, 0.8964, 0.4556, 0.6323]])
>>> approx_waveform
tensor([[0.4963, 0.7682, 0.0885, 0.1320, 0.3074],
        [0.6341, 0.4901, 0.8964, 0.4556, 0.6323]])

Breaking Changes

  • Removed Compose: Please use core abstractions such as nn.Sequential() or a for-loop over a list of transforms.
  • SPECTROGRAM, F2M, and MEL have been removed. Please use Spectrogram, MelScale, and MelSpectrogram
  • Removed formatting transforms ( LC2CL and BLC2CBL): While the LC layout might be common in signal processing, support for it is out of scope of this library and transforms such as LC2CL only aid their proliferation. Please use transpose if you need this behavior.
  • Removed Scale, PadTrim, DownmixMono: Please use division in place of Scale torch.nn.functional.pad/trim in place of PadTrim , torch.mean on the channel dimension in place of DownmixMono.
  • torchaudio.legacy has been removed. Please use torchaudio.load and torchaudio.save
  • Spectrogram used to be of dimension (channel, time, freq) and is now (channel, freq, time). Similarly for MelScale, MelSpectrogram, and MFCC, time is the last dimension. Please see our README for an explanation of the rationale behind these changes. Please use transpose to get the previous behavior.
  • MuLawExpanding was renamed to MuLawDecoding as the inverse of MuLawEncoding ( https://github.com/pytorch/audio/pull/159)
  • SpectrogramToDB was renamed to AmplitudeToDB ( https://github.com/pytorch/audio/pull/170). The input does not necessarily have to be a spectrogram and as such can be used in many more cases as the name should reflect.

New Features

Performance

JIT and CUDA

Bug Fixes