joshuarwanda/swahili-stt-model

This repository contains code worked on during the Mozilla Common Voice hackathon hosted by Africa's Talking on Oct 13th 2023 # Mozilla Common Voice Speech-to-Text Hackathon This repository contains code worked on during the Mozilla Common Voice hackathon hosted by Africa's Talking on Oct 13th 2023 ## The Data The data used was sourced by Mozilla in a crowd sourcing activity spanning several years. The latest version can be found here We converted the `tsv` files to `json` for compatibility with **Nvidia Nemo**. If using **Coqui-stt** `csv` is required and you would need a different script to achieve the conversion To convert tsv to json run: ``` python tsv_to_json.py \ --tsv=sw/train.tsv \ --folder=sw/clips \ --sampling_count=-1 ``` To convert the mp3 files to wav run: ``` python scripts/decode_resample.py --manifest=sw/train.json --destination_folder=./train ``` Do this for train, dev and test data sets ## Replicating results ### Setup The current version of Coqui-STT used in this project depends on Tensorflow 1.15.4. To replicate create a new env in python3.7 and install the packages in *requirements.txt* ``` pip install -r requirements.txt ``` ### Evaluation The model was evluated on a dataset from zindi for the MCVxAfrica's Talking Hackathon. To download it and extract it to the `sw/` directory of this repo. You can then run: ``` python3 -m scripts/mp3-2-wav.py sw/test/ ``` to convert the evaluation, and for that matter any folder of .mp3 files to .wav To transcribe the eval set use the command: ``` python scripts/multiprocess_transcribe.py ``` this will run multiple transcription tasks on the set You can then run the transcription script dictated in coqui-stt documentation. To format the submission for the hackathon, assuming the data directories are well set up, run: ``` python3 scripts/format_sub.py ```