| license: apache-2.0 | |
| # 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 | | |