TorchAudio: Building Blocks for Audio and Speech Processing (2110.15018v2)

Abstract: This document describes version 0.10 of TorchAudio: building blocks for machine learning applications in the audio and speech processing domain. The objective of TorchAudio is to accelerate the development and deployment of machine learning applications for researchers and engineers by providing off-the-shelf building blocks. The building blocks are designed to be GPU-compatible, automatically differentiable, and production-ready. TorchAudio can be easily installed from Python Package Index repository and the source code is publicly available under a BSD-2-Clause License (as of September 2021) at https://github.com/pytorch/audio. In this document, we provide an overview of the design principles, functionalities, and benchmarks of TorchAudio. We also benchmark our implementation of several audio and speech operations and models. We verify through the benchmarks that our implementations of various operations and models are valid and perform similarly to other publicly available implementations.

• The paper introduces TorchAudio, a toolkit that integrates GPU compute, automatic differentiation, and production-ready modules into PyTorch.
• It presents extensive benchmarks demonstrating competitive runtime and accuracy compared to alternatives like librosa.
• The toolkit’s seamless PyTorch integration and active community support drive rapid innovation in audio and speech processing research.

Overview of TorchAudio: Building Blocks for Audio and Speech Processing

The paper presents a comprehensive overview of TorchAudio, an open-source toolkit developed to facilitate machine learning applications in audio and speech processing. This toolkit aims to streamline the workflow for researchers and engineers by providing foundational building blocks that are GPU-compatible, automatically differentiable, and suitable for production environments.

Key Features and Design Principles

TorchAudio integrates seamlessly with the PyTorch ecosystem, offering functionality that leverages PyTorch's core features, such as neural network containers and data handling utilities. The design adheres to three primary principles:

1. GPU Compute Capability: Ensuring that resource-intensive tasks such as convolution operations are executable on GPUs, significantly improving efficiency.
2. Automatic Differentiability: Allowing these operations to be embedded in neural network architectures, thereby supporting end-to-end learning.
3. Production Readiness: Enabling models developed with TorchAudio to be easily deployed across diverse platforms, including mobile devices.

The toolkit emphasizes stability, providing canonical implementations of audio/speech tasks rather than aiming to cover all cutting-edge technologies. This focus on quality ensures that TorchAudio serves as a reliable baseline for developing new models and applications.

Functional Components

TorchAudio encompasses a suite of functionalities subdivided into four primary categories:

• Audio I/O: Facilitates the loading and saving of audio data using a user-friendly interface, incorporating the capabilities of SoX and other backend options.
• Audio/Speech Datasets: Provides streamlined access to multiple commonly used datasets, integrating smoothly with PyTorch's data pipelines to enhance efficiency.
• Audio/Speech Operations: Supports a wide range of audio processing tasks through submodules such as functional, transform, and sox_effects, covering operations from basic utilities to sophisticated feature extraction and transformations.
• Machine Learning Models: Implements numerous standard models for tasks including speech recognition and text-to-speech conversion, ensuring these models are readily deployable within PyTorch projects.

Empirical Evaluations

The paper presents empirical evaluations of key modules within TorchAudio, benchmarking them against established alternatives. Results indicate that TorchAudio meets or surpasses the performance of other implementations in terms of both runtime efficiency and model accuracy. For instance, when benchmarked against librosa, TorchAudio operations showed competitive runtime performance, especially with GPU acceleration.

Specific models such as WaveRNN and Tacotron2 demonstrated parity in performance metrics like PESQ, STOI, and MCD when compared with leading implementations. Conv-TasNet's evaluation revealed slight improvements over competing models in terms of Si-SDRi and SDRi, underscoring TorchAudio's capability in handling complex tasks efficiently.

Community and Development

TorchAudio has fostered a vibrant community of developers and users, contributing to its rapid evolution and adoption in diverse projects. The open-source nature and extensive documentation encourage external contributions, driving advancements in the toolkit's capabilities and usability. With regular updates and a clear roadmap outlined on its GitHub repository, TorchAudio remains poised for continued growth and development in response to the evolving demands of audio and speech processing applications.

Conclusion

TorchAudio stands as a robust toolkit for audio and speech processing within the PyTorch ecosystem. Its design principles ensure it is both performant and accessible, catering to a wide range of research and development needs. As the audio and speech processing fields advance, TorchAudio is well-positioned to support innovative applications and discoveries in machine learning.