---
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
- whisper
datasets:
- techiaith/commonvoice_18_0_cy
metrics:
- wer
model-index:
- name: whisper-large-v3-ft-cv-cy-train-all-plus-other-with-excluded
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: DewiBrynJones/commonvoice_18_0_cy default
      type: DewiBrynJones/commonvoice_18_0_cy
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 0.185
language:
- cy
pipeline_tag: automatic-speech-recognition
---

# whisper-large-v3-ft-cv-cy

This model is a version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) fine-tuned with the 
`train_all` and `other_with_excluded` custom splits from [techiaith/commonvoice_18_0_cy](https://huggingface.co/datasets/techiaith/commonvoice_18_0_cy)

It achieves the following results on the Common Voice for Welsh release 18's standard test set:

 - WER: 18.50
 - CER: 5.32

 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)


## Usage

```python
from transformers import pipeline

transcriber = pipeline("automatic-speech-recognition", model="techiaith/whisper-large-v3-ft-cv-cy")
result = transcriber(<path or url to soundfile>)
print (result)
```

`{'text': 'Mae hen wlad fy nhadau yn annwyl i mi.'}`