readme: ---
  language: "sw"
  thumbnail:
  pipeline_tag: automatic-speech-recognition
  tags:
  - CTC
  - pytorch
  - speechbrain
  - Transformer
  license: "apache-2.0"
  datasets:
  - Dvoice
  metrics:
  - wer
  - cer
  ---
  
  
  <iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
  <br/><br/>
  
  # wav2vec 2.0 with CTC/Attention trained on DVoice Swahili (No LM)
  This repository provides all the necessary tools to perform automatic speech
  recognition from an end-to-end system pretrained on a [DVoice-VoxLingua107](https://zenodo.org/record/6342622) Swahili dataset within
  SpeechBrain. For a better experience, we encourage you to learn more about
  [SpeechBrain](https://speechbrain.github.io).
  
  | DVoice Release | Val. CER | Val. WER | Test CER | Test WER |
  |:-------------:|:---------------------------:| -----:| -----:| -----:|
  | v2.0 | 8.83 | 22.78 | 9.46 | 23.16 |
  
  # 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](https://huggingface.co/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](https://speechbrain.github.io).
  
  # Transcribing your own audio files (in Swahili)
  ```python
  from speechbrain.pretrained import EncoderASR
  asr_model = EncoderASR.from_hparams(source="speechbrain/asr-wav2vec2-dvoice-swahili", savedir="pretrained_models/asr-wav2vec2-dvoice-swahili")
  asr_model.transcribe_file('speechbrain/asr-wav2vec2-dvoice-swahili/example_swahili.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:
  ```bash
  git clone https://github.com/speechbrain/speechbrain/
  ```
  2. Install it:
  ```bash
  cd speechbrain
  pip install -r requirements.txt
  pip install -e .
  ```
  3. Run Training:
  ```bash
  cd recipes/DVoice/ASR/CTC
  python train_with_wav2vec2.py hparams/train_sw_with_wav2vec.yaml --data_folder=/localscratch/dvoice_recipe_data/
  ```
  Please, read the README.md carefully before running the experiment to make sure the dataset is structured as expected.
  You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1vNT7RjRuELs7pumBHmfYsrOp9m46D0ym?usp=sharing).
  
  # Limitations
  The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
  
  # 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](https://dvoice.ma) and [https://dvoice.sn](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/](https://www.aiox-labs.com/)
  
  # SI2M Laboratory
  The Information Systems, Intelligent S