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The aim of torchaudio is to apply PyTorch to the audio domain. By supporting PyTorch, torchaudio follows the same philosophy of providing strong GPU acceleration, having a focus on trainable features through the autograd system, and having consistent style (tensor names and dimension names). Therefore, it is primarily a machine learning library and not a general signal processing library. The benefits of PyTorch can be seen in torchaudio through having all the computations be through PyTorch operations which makes it easy to use and feel like a natural extension.
Features
- AM inference with CUDA CTC Beam Seach Decoder
- On device audio-visual automatic speech recognition
- Loading waveform Tensors from files and saving them
- Documentation available
- CTC Forced Alignment API
- Forced alignment for multilingual data
- Streaming media decoding with StreamReader
Project Samples
License
BSD LicenseFollow TorchAudio
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