File size: 1,419 Bytes
cc0e0d6 aeecc20 cc0e0d6 aeecc20 cc0e0d6 aeecc20 cc0e0d6 aeecc20 cc0e0d6 aeecc20 cc0e0d6 aeecc20 cc0e0d6 aeecc20 cc0e0d6 aeecc20 cc0e0d6 aeecc20 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 |
---
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
- verbatim
metrics:
- wer
model-index:
- name: whisper-large-v3-ft-btb-cv-cy
results: []
datasets:
- techiaith/banc-trawsgrifiadau-bangor
- techiaith/commonvoice_18_0_cy
language:
- cy
pipeline_tag: automatic-speech-recognition
---
# whisper-large-v3-ft-btb-cv-cy
This model is a version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) finedtuned with
transcriptions of Welsh language spontaneous speech [Banc Trawsgrifiadau Bangor (btb)](https://huggingface.co/datasets/techiaith/banc-trawsgrifiadau-bangor)
ac well as recordings of read speach from [Welsh Common Voice version 18 (cv)](https://huggingface.co/datasets/techiaith/commonvoice_18_0_cy)
for additional training.
As such this model is suitable for more verbatim transcribing of spontaneous or unplanned speech.
It achieves the following results on the [Banc Trawsgrifiadau Bangor'r test set](https://huggingface.co/datasets/techiaith/banc-trawsgrifiadau-bangor/viewer/default/test)
- WER: 29.72
- CER: 11.01
## Usage
```python
from transformers import pipeline
transcriber = pipeline("automatic-speech-recognition", model="techiaith/whisper-large-v3-ft-btb-cv-cy")
result = transcriber(<path or url to soundfile>)
print (result)
```
`{'text': 'ymm, yn y pum mlynadd dwitha 'ma ti 'di... Ie. ...bod drw dipyn felly do?'}`
|