README.md

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license: apache-2.0
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# Pre-training wav2vec2 models for Welsh speech recognition

At the moment, the best Welsh speech recognition models are achieved from fine-tuning https://huggingface.co/facebook/wav2vec2-large-xlsr-53 and https://huggingface.co/facebook/wav2vec2-xls-r-1b models by Facebook/Meta AI. 

This model is experimental in investigating pretraining better models with more Welsh language speech that could lower WER scores even further in subsequently fine-tuned models. The work draws heavily on resources and documentation from the HuggingFace examples:

https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-pretraining

This initial base model has been pre-trained with scripts at 

https://github.com/techiaith/docker-wav2vec2-cy/tree/main/train/pre-train
 
using English speech from LibriSpeech's minimal subsets (`validation` and `test`), and 184 hours and 47 minutes of Welsh speech from various playlists on YouTube. The script [`build_youtube_playlists_corpus.sh`](https://github.com/techiaith/docker-wav2vec2-cy/blob/main/inference/python/build_youtube_playlists_corpus.sh) lists the playlists used.

Until we have collected thousands of hours of Welsh speech, rather than hundreds, the WER scores, after fine-tuning, will remain very high. The following WERs are from tests on a Welsh Common Voice test set as well a [second set of YouTube videos with corrected transcriptions](https://git.techiaith.bangor.ac.uk/data-porth-technolegau-iaith/corpws-profi-adnabod-lleferydd/-/tree/master/data/trawsgrifio).

| Test Set | WER | CER | WER (+LM) | CER (+LM)|
| -------- | --- | --- | --------- | -------- |
| CV CY 10 | 94.83  | 85.55  | 92.31  | 82.25 |
| YouTube  | 95.43  | 90.26  | 93.60  | 89.33 |