
whisper tiny fine-tuned on a very big collection of vietnamese speech datasets

TODO:
- [ ] training then publish checkpoint (*no ETA*)
- [ ] evaluate WER on Common Voice & FLEURS
- [ ] convert to `openai-whisper`, `whisper.cpp`, `faster-whisper`
- [ ] convert to ONNX: to try `k2-fsa/sherpa-onnx` &amp; `zhuzilin/whisper-openvino`

fine-tuning script for `tiny` (compatible colab &amp; kaggle):
[![](https://colab.research.google.com/assets/colab-badge.svg)](src/fine-tune-tiny.ipynb)

{"language": ["vi"], "license": "apache-2.0", "library_name": "transformers", "datasets": ["doof-ferb/vlsp2020_vinai_100h", "doof-ferb/fpt_fosd", "doof-ferb/infore1_25hours", "doof-ferb/infore2_audiobooks", "quocanh34/viet_vlsp", "linhtran92/final_dataset_500hrs_wer0", "linhtran92/viet_youtube_asr_corpus_v2", "google/fleurs", "mozilla-foundation/common_voice_16_1", "vivos"], "metrics": ["wer"], "base_model": "openai/whisper-tiny", "pipeline_tag": "automatic-speech-recognition", "inference": false}
transformers whisper automatic-speech-recognition vi dataset:doof-ferb/vlsp2020_vinai_100h dataset:doof-ferb/fpt_fosd dataset:doof-ferb/infore1_25hours dataset:doof-ferb/infore2_audiobooks dataset:quocanh34/viet_vlsp dataset:linhtran92/final_dataset_500hrs_wer0 dataset:linhtran92/viet_youtube_asr_corpus_v2 dataset:google/fleurs dataset:mozilla-foundation/common_voice_16_1 dataset:vivos base_model:openai/whisper-tiny license:apache-2.0 region:us