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+ The examples in this directory demonstrate applied use of the torchaudio library. They are included only for reference purposes.
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+
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+ > [ !NOTE]
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+ > ** We have transitioned TorchAudio into a
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+ > maintenance phase. Our main goals were to reduce redundancies with the rest of the
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+ > PyTorch ecosystem, make it easier to maintain, and create a version of
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+ > TorchAudio that is more tightly scoped to its strengths: processing audio
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+ > data for ML. Because of this transition, the examples included here will no longer be maintained
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+ > going forward. The scripts may not work properly as is. For hands on examples of features in the
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+ > library that * are* still supported, see the "tutorials" subdirectory. Please see
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+ > [ our community message] ( https://github.com/pytorch/audio/issues/3902 )
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+ > for more details.**
Original file line number Diff line number Diff line change @@ -2,9 +2,6 @@ cmake_minimum_required(VERSION 3.5)
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project (libtorchaudio-cpp-example)
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- SET (BUILD_SOX ON CACHE BOOL "Build libsox into libtorchaudio" )
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-
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- SET (BUILD_KALDI OFF CACHE BOOL "Build Kaldi into libtorchaudio" )
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SET (BUILD_RNNT ON CACHE BOOL "Build RNN transducer into libtorchaudio" )
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SET (BUILD_TORCHAUDIO_PYTHON_EXTENSION OFF CACHE BOOL "Build Python binding" )
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# Libtorchaudio Examples
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- * [ Augmentation] ( ./augmentation )
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* [ Speech Recognition with wav2vec2.0] ( ./speech_recognition )
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## Build
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cd build
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cmake -GNinja \
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-DCMAKE_PREFIX_PATH=" $( python -c ' import torch;print(torch.utils.cmake_prefix_path)' ) " \
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- -DBUILD_SOX=ON \
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- -DBUILD_KALDI=OFF \
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-DBUILD_RNNT=ON \
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..
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cmake --build .
Original file line number Diff line number Diff line change @@ -11,7 +11,5 @@ cd "${build_dir}"
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git submodule update
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cmake -GNinja \
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-DCMAKE_PREFIX_PATH=" $( python -c ' import torch;print(torch.utils.cmake_prefix_path)' ) " \
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- -DBUILD_SOX=ON \
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- -DBUILD_KALDI=OFF \
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..
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cmake --build .
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# Speech Recognition with wav2vec2.0
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- This example demonstarates how you can use torchaudio's I/O features and models to run speech recognition in C++ application.
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-
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- ** NOTE**
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- This example uses ` "sox_io" ` backend for loading audio, which does not work on Windows. To make it work on
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- Windows, you need to replace the part of loading audio and converting it to Tensor object.
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+ This example demonstrates how you can use torchaudio's models to run speech recognition in a C++ application.
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## 1. Create a transcription pipeline TorchScript file
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from ._hubert_datamodule import HuBERTDataModule
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+ from ._wav2vec2_datamodule import Wav2Vec2DataModule
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__all__ = [
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"HuBERTDataModule" ,
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