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Exciting News (January, 2024): Discover what is new in SpeechBrain 1.0 here!

🗣️💬 What SpeechBrain Offers

SpeechBrain is an open-source PyTorch toolkit that accelerates Conversational AI development, i.e., the technology behind speech assistants, chatbots, and large language models.
It is crafted for fast and easy creation of advanced technologies for Speech and Text Processing.

🌐 Vision

With the rise of deep learning, once-distant domains like speech processing and NLP are now very close. A well-designed neural network and large datasets are all you need.
We think it is now time for a holistic toolkit that, mimicking the human brain, jointly supports diverse technologies for complex Conversational AI systems.
This spans speech recognition, speaker recognition, speech enhancement, speech separation, language modeling, dialogue, and beyond.
Aligned with our long-term goal of natural human-machine conversation, including for non-verbal individuals, we have recently added support for the EEG modality.

📚 Training Recipes

We share over 200 competitive training recipes on more than 40 datasets supporting 20 speech and text processing tasks (see below).
We support both training from scratch and fine-tuning pretrained models such as Whisper, Wav2Vec2, WavLM, Hubert, GPT2, Llama2, and beyond. The models on HuggingFace can be easily plugged in and fine-tuned.
For any task, you train the model using these commands:

python train.py hparams/train.yaml

The hyperparameters are encapsulated in a YAML file, while the training process is orchestrated through a Python script.
We maintained a consistent code structure across different tasks.
For better replicability, training logs and checkpoints are hosted on Dropbox.

<a href="https://huggingface.co/speechbrain" target="_blank"> <img src="https://huggingface.co/front/assets/huggingface_logo.svg" alt="drawing" width="40"/> </a> Pretrained Models and Inference

Access over 100 pretrained models hosted on HuggingFace.
Each model comes with a user-friendly interface for seamless inference. For example, transcribing speech using a pretrained model requires just three lines of code:

from speechbrain.inference import EncoderDecoderASR

asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-conformer-transformerlm-librispeech", savedir="pretrained_models/asr-transformer-transformerlm-librispeech")
asr_model.transcribe_file("speechbrain/asr-conformer-transformerlm-librispeech/example.wav")

<a href="https://speechbrain.github.io/" target="_blank"> <img src="https://upload.wikimedia.org/wikipedia/commons/thumb/d/d0/Google_Colaboratory_SVG_Logo.svg/1200px-Google_Colaboratory_SVG_Logo.svg.png" alt="drawing" width="50"/> </a> Documentation

We are deeply dedicated to promoting inclusivity and education.
We have authored over 30 tutorials that not only describe how SpeechBrain works but also help users familiarize themselves with Conversational AI.
Every class or function has clear explanations and examples that you can run. Check out the documentation for more details 📚.

🎯 Use Cases

🚀 Research Acceleration: Speeding up academic and industrial research. You can develop and integrate new models effortlessly, comparing their performance against our baselines.
⚡️ Rapid Prototyping: Ideal for quick prototyping in time-sensitive projects.
🎓 Educational Tool: SpeechBrain's simplicity makes it a valuable educational resource. It is used by institutions like Mila, Concordia University, Avignon University, and many others for student training.

🚀 Quick Start

To get started with SpeechBrain, follow these simple steps:

🛠️ Installation

Install via PyPI

Install SpeechBrain using PyPI:
```
pip install speechbrain
```
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.

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 .

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

🏃‍♂️ Running an Experiment

In SpeechBrain, you can train a model for any task using the following steps:

cd recipes/<dataset>/<task>/
python experiment.py params.yaml

The results will be saved in the output_folder specified in the YAML file.

📘 Learning SpeechBrain

Website: Explore general information on the official website.
Tutorials: Start with basic tutorials covering fundamental functionalities. Find advanced tutorials and topics in the Tutorial notebooks category in the SpeechBrain documentation.
Documentation: Detailed information on the SpeechBrain API, contribution guidelines, and code is available in the documentation.

🔧 Supported Technologies

SpeechBrain is a versatile framework designed for implementing a wide range of technologies within the field of Conversational AI.
It excels not only in individual task implementations but also in combining various technologies into complex pipelines.

🎙️ Speech/Audio Processing

| Tasks | Datasets | Technologies/Models | | ------------- |-------------| -----| | Speech Recognition | AISHELL-1, CommonVoice, DVoice, LibriSpeech, MEDIA, RescueSpeech, Switchboard, TIMIT, Tedlium2, Voicebank | CTC, Transducers, Transformers, Seq2Seq, Beamsearch techniques for CTC,seq2seq,transducers), Rescoring, Conformer, Branchformer, Hyperconformer, Kaldi2-FST | | Speaker Recognition | VoxCeleb | ECAPA-TDNN, ResNET, Xvectors, PLDA, Score Normalization | | Speech Separation | WSJ0Mix, LibriMix, WHAM!, WHAMR!, Aishell1Mix, BinauralWSJ0Mix | SepFormer, RESepFormer, SkiM, DualPath RNN, ConvTasNET | | Speech Enhancement | DNS, Voicebank | SepFormer, MetricGAN, MetricGAN-U, [SEGAN](https://arxiv.org/abs/1703.0

Speechbrain

Install / Use

README