Asr Crdnn Rnnlm Librispeech

🚀 CRDNN with CTC/Attention and RNNLM trained on LibriSpeech

This repository offers all essential tools for automatic speech recognition using an end - to - end system pretrained on LibriSpeech (EN) within SpeechBrain. For an enhanced experience, explore SpeechBrain.

The performance of the model is as follows:

✨ Features

This ASR system consists of 3 different yet interconnected blocks:

• Tokenizer (unigram): Transforms words into subword units and is trained with the train transcriptions of LibriSpeech.
• Neural language model (RNNLM): Trained on the full 10M words dataset.
• Acoustic model (CRDNN + CTC/Attention): The CRDNN architecture comprises N blocks of convolutional neural networks with normalization and pooling in the frequency domain. A bidirectional LSTM is then connected to a final DNN to obtain the final acoustic representation, which is fed to the CTC and attention decoders.

The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling transcribe_file if necessary.

📦 Installation

First of all, please install SpeechBrain with the following command:

pip install speechbrain

Please note that we encourage you to read our tutorials and learn more about SpeechBrain.

💻 Usage Examples

Basic Usage

Transcribing your own audio files (in English)

from speechbrain.inference.ASR import EncoderDecoderASR

asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-crdnn-rnnlm-librispeech", savedir="pretrained_models/asr-crdnn-rnnlm-librispeech")
asr_model.transcribe_file('speechbrain/asr-crdnn-rnnlm-librispeech/example.wav')

Advanced Usage

Inference on GPU

To perform inference on the GPU, add run_opts={"device":"cuda"} when calling the from_hparams method.

Parallel Inference on a Batch

Please, see this Colab notebook to figure out how to transcribe in parallel a batch of input sentences using a pre - trained model.

Training

The model was trained with SpeechBrain (Commit hash: '2abd9f01'). To train it from scratch, follow these steps:

1. Clone SpeechBrain:

git clone https://github.com/speechbrain/speechbrain/

2. Install it:

cd speechbrain
pip install -r requirements.txt
pip install -e .

3. Run Training:

cd recipes/LibriSpeech/ASR/seq2seq/
python train.py hparams/train_BPE_1000.yaml --data_folder=your_data_folder

You can find our training results (models, logs, etc) here.

📚 Documentation

Limitations

The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.

📄 License

This project is licensed under the "apache - 2.0" license.

🔗 About SpeechBrain

• Website: https://speechbrain.github.io/
• Code: https://github.com/speechbrain/speechbrain/
• HuggingFace: https://huggingface.co/speechbrain/

📖 Citing SpeechBrain

Please, cite SpeechBrain if you use it for your research or business.

@misc{speechbrain,
  title={{SpeechBrain}: A General-Purpose Speech Toolkit},
  author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},
  year={2021},
  eprint={2106.04624},
  archivePrefix={arXiv},
  primaryClass={eess.AS},
  note={arXiv:2106.04624}
}