Key Features

Open, simple, flexible, well-documented, and with competitive performance.


SpeechBrain supports state-of-the-art technologies for speech recognition, enhancement, separation, text-to-speech, speaker recognition, speech-to-speech translation, spoken language understanding, and beyond.


SpeechBrain encompasses a wide range of audio technologies, including vocoding, audio augmentation, feature extraction, sound event detection, beamforming, and other multi-microphone signal processing capabilities.


SpeechBrain offers user-friendly tools for training Language Models, supporting technologies ranging from basic n-gram LMs to modern Large Language Models. Our platform seamlessly integrates them into speech processing pipelines and facilitates the creation of customizable chatbots.


SpeechBrain leverages the most advanced deep learning technologies, including methods for self-supervised learning, continual learning, diffusion models, Bayesian deep learning, and interpretable neural networks.

Research & Development

SpeechBrain is engineered to accelerate the research and development of Conversational AI technologies. It comes with pre-built recipes for popular datasets. Extensive documentation and tutorials are available to support newcomers.


SpeechBrain offers pre-trained models with user-friendly interfaces, making tasks like transcription, speaker verification, speech enhancement, and source separation easier than ever.

Why SpeechBrain?

Adapts to your needs.

You can install SpeechBrain via PyPI for quick access to its functionalities, or through a local install for accessing recipes and delving deeper into the toolkit.
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  # From PyPI
  pip install speechbrain

  # Local installation
  git clone
  cd speechbrain
  pip install -r requirements.txt
  pip install --editable .

A single command.

Each SpeechBrain recipe defines all hyperparameters into a single YAML file. The training process is then orchestrated by a Python script.
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  cd recipes/{dataset}/{task}/train

  # Train the model using the default recipe
  python hparams/train.yaml

  # Train the model with a hyperparameter tweak
  python hparams/train.yaml --learning_rate=0.1

Built for research.

SpeechBrain is designed for research and development. Hence, flexibility, transparency, and replicability are core concepts to enhance our daily workflows. Users can easily define custom deep learning models, losses, training/evaluation loops, and input pipelines/transformations, and easily integrate into existing pipelines.
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  class ASR_Brain(sb.Brain):
    def compute_forward(self, batch, stage):

      # Compute features (mfcc, fbanks, etc.) on the fly
      features = self.hparams.compute_features(batch.wavs)

      # Improve robustness with pre-built augmentations
      features = self.hparams.augment(features)

      # Apply your custom model
      return self.modules.myCustomModel(features)

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