README

| 📘 Tutorials | 🌐 Website | 📚 Documentation | 🤝 Contributing | 🤗 HuggingFace | ▶️ YouTube | 🐦 X |

!GitHub Repo stars Please, help our community project. Star on GitHub!

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
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.

    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:

python
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")


      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. Y