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speechbrain/speechbrain

A PyTorch-based Speech Toolkit

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Python Apache-2.0Created Apr 28, 2020

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Overview

A PyTorch-based Speech Toolkit

Capability facts

Languages
python

Source: github.language+pyproject.toml · Jul 11, 2026

Categories

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Python runtimePython

Source: README excerpt (regex_v1, Jul 11, 2026)

2. Access SpeechBrain in your Python code:
Source link

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README

🚀 Quick Start

To get started with SpeechBrain, follow these simple steps:


Install via PyPI

  1. Install SpeechBrain using PyPI:

    pip install speechbrain
    
  2. Access SpeechBrain in your Python code:

    import speechbrain as sb
    

Install from GitHub

This installation is recommended for users who wish to conduct experiments and customize the toolkit according to their needs.

  1. Clone the GitHub repository and install the requirements:

    git clone https://github.com/speechbrain/speechbrain.git
    cd speechbrain
    pip install -r requirements.txt
    pip install --editable .
    
  2. Access SpeechBrain in your Python code:

    import speechbrain as sb
    

Any modifications made to the speechbrain package will be automatically reflected, thanks to the --editable flag.


✔️ Test Installation

Ensure your installation is correct by running the following commands:

pytest tests
pytest --doctest-modules speechbrain

📜 License

  • SpeechBrain is released under the Apache License, version 2.0, a popular BSD-like license.
  • You are free to redistribute SpeechBrain for both free and commercial purposes, with the condition of retaining license headers. Unlike the GPL, the Apache License is not viral, meaning you are not obligated to release modifications to the source code.

🔮Future Plans

We have ambitious plans for the future, with a focus on the following priorities:

  • Scale Up: We aim to provide comprehensive recipes and technologies for training massive models on extensive datasets.

  • Scale Down: While scaling up delivers unprecedented performance, we recognize the challenges of deploying large models in production scenarios. We are focusing on real-time, streamable, and small-footprint Conversational AI.

  • Multimodal Large Language Models: We envision a future where a single foundation model can handle a wide range of text, speech, and audio tasks. Our core team is focused on enabling the training of advanced multimodal LLMs.