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---
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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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- whisper
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datasets:
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- techiaith/commonvoice_18_0_cy
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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-train-all-plus-other-with-excluded
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results:
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- task:
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name: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: DewiBrynJones/commonvoice_18_0_cy default
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type: DewiBrynJones/commonvoice_18_0_cy
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args: default
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metrics:
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- name: Wer
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type: wer
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value: 0.185
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language:
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- cy
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pipeline_tag: automatic-speech-recognition
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---
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# whisper-large-v3-ft-cv-cy |
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This model is a version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) fine-tuned with the |
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`train_all` and `other_with_excluded` custom splits from [techiaith/commonvoice_18_0_cy](https://huggingface.co/datasets/techiaith/commonvoice_18_0_cy) |
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It achieves the following results on the Common Voice for Welsh release 18's standard test set: |
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- WER: 18.50 |
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- CER: 5.32 |
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N.B. this model performs considerably worse on English language speech, but better on Welsh than a [bilingual model](https://huggingface.co/techiaith/whisper-large-v3-ft-cv-cy-en) |
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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") |
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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.'}` |
