speechbrain/asr-wav2vec2-dvoice-wolof

wav2vec 2.0 with CTC/Attention trained on DVoice Wolof (No LM)

This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on a ALFFA Wolof dataset within SpeechBrain. For a better experience, we encourage you to learn more about SpeechBrain.

DVoice Release	Val. CER	Val. WER	Test CER	Test WER
v2.0	4.81	16.25	4.83	16.05

Pipeline description

This ASR system is composed of 2 different but linked blocks:

• Tokenizer (unigram) that transforms words into subword units and is trained with the train transcriptions.
• Acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model (facebook/wav2vec2-large-xlsr-53) is combined with two DNN layers and finetuned on the Darija dataset. The obtained final acoustic representation is given to the CTC greedy decoder. 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 needed.

Install SpeechBrain

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

pip install speechbrain transformers

Please notice that we encourage you to read the SpeechBrain tutorials and learn more about SpeechBrain.

Transcribing your own audio files (in Wolof)

from speechbrain.inference.ASR import EncoderASR
asr_model = EncoderASR.from_hparams(source="speechbrain/asr-wav2vec2-dvoice-wolof", savedir="pretrained_models/asr-wav2vec2-dvoice-wolof")
asr_model.transcribe_file('speechbrain/asr-wav2vec2-dvoice-wolof/example_wolof.wav')

Inference on GPU

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

Training

The model was trained with SpeechBrain. 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/DVoice/ASR/CTC
python train_with_wav2vec2.py hparams/train_wol_with_wav2vec.yaml --data_folder=/localscratch/ALFFA_PUBLIC/ASR/WOLOF/data/

Limitations

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

Referencing SpeechBrain

@misc{SB2021,
    author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua },
    title = {SpeechBrain},
    year = {2021},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}},
  }

About DVoice

DVoice is a community initiative that aims to provide African low resources languages with data and models to facilitate their use of voice technologies. The lack of data on these languages makes it necessary to collect data using methods that are specific to each one. Two different approaches are currently used: the DVoice platforms (https://dvoice.ma and https://dvoice.sn), which are based on Mozilla Common Voice, for collecting authentic recordings from the community, and transfer learning techniques for automatically labeling recordings that are retrieved from social media. The DVoice platform currently manages 7 languages including Darija (Moroccan Arabic dialect) whose dataset appears on this version, Wolof, Mandingo, Serere, Pular, Diola, and Soninke.

For this project, AIOX Labs and the SI2M Laboratory are joining forces to build the future of technologies together.

About AIOX Labs

Based in Rabat, London, and Paris, AIOX-Labs mobilizes artificial intelligence technologies to meet the business needs and data projects of companies.

• He is at the service of the growth of groups, the optimization of processes, or the improvement of the customer experience.
• AIOX-Labs is multi-sector, from fintech to industry, including retail and consumer goods.
• Business-ready data products with a solid algorithmic base and adaptability for the specific needs of each client.
• A complementary team made up of doctors in AI and business experts with a solid scientific base and international publications.

Website: https://www.aiox-labs.com/

SI2M Laboratory

The Information Systems, Intelligent Systems, and Mathematical Modeling Research Laboratory (SI2M) is an academic research laboratory of the National Institute of Statistics and Applied Economics (INSEA). The research areas of the laboratories are Information Systems, Intelligent Systems, Artificial Intelligence, Decision Support, Network, and System Security, and Mathematical Modelling.

Website: SI2M Laboratory

About SpeechBrain

SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains. Website: https://speechbrain.github.io/ GitHub: https://github.com/speechbrain/speechbrain

Referencing SpeechBrain

@misc{SB2021,
    author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua },
    title = {SpeechBrain},
    year = {2021},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}},
  }

Acknowledgements

This research was supported through computational resources of HPC-MARWAN (www.marwan.ma/hpc) provided by CNRST, Rabat, Morocco. We deeply thank this institution.