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Speechbrain

SpeechBrain is an open-source Python toolkit built on PyTorch for developing speech and audio-processing systems, including recognition, diarization, speaker verification, enhancement and separation.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Speechbrain — 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 SpeechBrain when you need Python and PyTorch building blocks, training recipes or pretrained models for a speech-processing project, rather than a ready-made hosted transcription interface.

Who it is for

  • Researchers comparing or adapting speech models
  • Developers prototyping speech and audio applications
  • Educators and learners exploring conversational AI and speech processing

Documented uses

  • Transcribe audio using a pretrained speech-recognition model.
  • Train a speech model from scratch or fine-tune a pretrained model.
  • Build workflows for speaker diarization, recognition or verification.
  • Develop speech enhancement, separation and other audio-processing systems.
Before you choose

Requirements & limits

The project is open source under the Apache-2.0 license. Its README documents installation through PyPI or an editable GitHub checkout.

  • The toolkit is Python-based and built on PyTorch.
  • Users can choose between pretrained-model inference, fine-tuning and training from scratch.
  • Training recipes organize experiments around task and dataset directories with YAML parameter files.
  • The README links to tutorials and API documentation for learning and implementation.

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 SpeechBrain from PyPI

Install the toolkit and make it available to a Python project.

Before you start

  • Python environment with pip

Steps

  1. Run the command below.

    Documented command
    pip install speechbrain

  2. Import the package in Python with import speechbrain as sb.

Expected outcome

SpeechBrain is installed and can be imported in Python.

Guide limitations

  • These steps cover installation and import only, not model training or inference.
Documentation 1 ↗
Source-based practical guide

Transcribe a WAV file with a pretrained ASR model

Load the documented pretrained ASR model and request transcription of an example WAV file.

Before you start

  • SpeechBrain installed in a Python environment
  • The pretrained model hosted at speechbrain/asr-conformer-transformerlm-librispeech
  • An audio file accessible to the Python process

Steps

  1. Import EncoderDecoderASR from speechbrain.inference.

  2. Load the model with EncoderDecoderASR.from_hparams, specifying the model source and a save directory.

  3. Call transcribe_file with the path to the WAV file.

Expected outcome

The model's transcription method is called for the selected audio file.

Guide limitations

  • The README demonstrates the workflow with its example WAV path; it does not specify performance or accuracy guarantees.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
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Sources checked
13 October 2026
Original website title
speechbrain/speechbrain · Official homepage title · retained in its source language
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