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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: openai/whisper-large-v3 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- wer |
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model-index: |
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- name: whisper-large-v3-ft-cv-cy-en |
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results: [] |
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datasets: |
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- techiaith/commonvoice_18_0_cy_en |
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language: |
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- cy |
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- en |
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pipeline_tag: automatic-speech-recognition |
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--- |
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# whisper-large-v3-ft-cv-cy-en |
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This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the |
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[techiaith/commonvoice_18_0_cy_en](https://huggingface.co/datasets/techiaith/commonvoice_18_0_cy_en) dataset. Both the |
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English and Welsh data have been used to fine-tune the whisper model for transcribing both languages as well as improved |
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language detection. |
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It achieves a success rate of **98.86% for language detection** on recordings from a [Common Voice bilingual test set](https://huggingface.co/datasets/techiaith/commonvoice_18_0_cy_en/viewer/default/test) |
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While, it achieves the following WER results for transcribing using the same test set: |
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- Welsh: 26.20 |
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- English: 15.37 |
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- Average: 20.70 |
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N.B. the desired transcript language is not given to the fine-tuned model during testing. |
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## Usage |
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```python |
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from transformers import pipeline |
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transcriber = pipeline("automatic-speech-recognition", model="techiaith/whisper-large-v3-ft-cv-cy-en") |
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result = transcriber(<path or url to soundfile>) |
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print (result) |
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``` |
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`{'text': 'Mae hen wlad fy nhadau yn annwyl i mi.'}` |
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