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Openspeech

Openspeech is an open-source Python toolkit that provides reference implementations of automatic speech recognition models and recipes for training end-to-end speech recognizers, using PyTorch Lightning and Hydra.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Openspeech — OpenViST project profile in Audio & Voice AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Openspeech when you need a code-based starting point for training or adapting an end-to-end speech-recognition model, and can work with its Python and machine-learning dependencies.

Who it is for

  • Researchers comparing or experimenting with end-to-end ASR model implementations.
  • Practitioners seeking language-specific recipes as a starting point for speech-recognition training.
  • Developers adapting provided model modules or integrating a custom dataset.

Documented uses

  • Start an end-to-end ASR experiment from a reference model implementation.
  • Use recipes for English, Chinese or Korean speech-recognition tasks.
  • Adjust supported model hyperparameters or compose a custom model from provided modules.
  • Prepare a new dataset by defining LightningDataModule and Tokenizer classes.
Before you choose

Requirements & limits

The repository is public and identifies the project as open source under the MIT license. The README documents installation through PyPI or from source.

  • The recommended Python version is 3.7 or higher.
  • The documented stack includes PyTorch Lightning for training workflows and Hydra for hierarchical configuration.
  • The README names LibriSpeech, AISHELL-1 and KsponSpeech as example datasets for English, Chinese and Korean recipes.
  • A custom dataset requires defining LightningDataModule and Tokenizer classes.
  • The dependency list includes optional or setup-specific components such as warp-rnnt, ctcdecode and Apex for 16-bit training.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install Openspeech from PyPI

Prepare the documented Python package installation path.

Before you start

  • Python 3.7 or higher is recommended.
  • A separate virtual environment is recommended.
  • The README lists NumPy, PyTorch, librosa, torchaudio, SentencePiece, PyTorch Lightning, Hydra, warp-rnnt and ctcdecode among prerequisites.

Steps

  1. Create a virtual environment using virtualenv or conda.

  2. Install the prerequisites appropriate to the intended setup, following their project-specific installation instructions.

  3. Install the package with the command below.

    Documented command
    pip install openspeech-core

Expected outcome

The documented PyPI installation command installs the Openspeech package.

Guide limitations

  • The source does not specify a single compatible version set for all dependencies.
  • Installation guidance does not document how to select or run a model recipe.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
openspeech-team/openspeech · Official homepage title · retained in its source language
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