Whisper Large — Welsh & English (techiaith/whisper-large-ft-cy-en)

A fine-tuned openai/whisper-large-v2 model for Welsh and English automatic speech recognition, with Welsh-to-English speech translation capability.

Supported Tasks

Task	Description
Welsh transcription	Welsh audio → Welsh text
English transcription	English audio (UK/Irish accents) → English text
Welsh→English translation (experimental)	Welsh audio → English text — limited quality, not recommended for production (see below)

Evaluation Results

WER / CER (lower is better) on the held-out benchmark. Average across the 4 transcription test sets: WER 20.29, CER 7.33.

Welsh Transcription

Test Set	WER	CER
cymen-arfor/lleisiau-arfor (spontaneous)	29.20	11.59
techiaith/banc-trawsgrifiadau-bangor (mixed)	26.71	9.91
techiaith/commonvoice-23-0-cy (read)	15.10	4.35

English Transcription

Test Set	WER	CER
techiaith/commonvoice-23-0-en/GB-IE (read, UK/Irish)	10.16	3.47

Welsh→English Translation

The model can translate Welsh speech directly into English text (task="translate"). This capability is experimental and has not been formally evaluated; its translation quality is limited and it is not recommended for production use.

For higher-quality English, transcribe the Welsh audio with this model and translate the resulting Welsh text with a dedicated Welsh→English machine translation model.

Training Data

Dataset	Language	Split	Revision
techiaith/banc-trawsgrifiadau-bangor	Welsh	train	26.07
techiaith/corpws-clllc-wlga	Welsh	clips	5d00294c31
techiaith/commonvoice_23_0_cy	Welsh	train+dev[50%:]	main
cymen-arfor/lleisiau-arfor	Welsh	train	main
techiaith/commonvoice_vad_cy	Welsh	train	main
techiaith/commonvoice_23_0_en__GB_IE	English	train[:10%]	main
techiaith/commonvoice_23_0_cy_en	Welsh	train[:10%]	main

Training Configuration

Parameter	Value
Base model	openai/whisper-large-v2
Learning rate	1e-05
LR scheduler	cosine
Warmup steps	500
Max steps	15000
Weight decay	0.01
Batch size	16 × 2 accumulation × 2 GPUs = 64 effective
FP16	True
SpecAugment	True
Curation applied	True
Early stopping patience	10
Selection metric	eval_btb_wer

Usage

from transformers import pipeline

pipe = pipeline(
    "automatic-speech-recognition",
    model="techiaith/whisper-large-ft-cy-en",
)

# Welsh transcription
result = pipe("welsh_audio.wav", generate_kwargs={"language": "cy", "task": "transcribe"})

# English transcription
result = pipe("english_audio.wav", generate_kwargs={"language": "en", "task": "transcribe"})

# Welsh to English translation
result = pipe("welsh_audio.wav", generate_kwargs={"language": "cy", "task": "translate"})

CTranslate2 Version

A CTranslate2 (int8 quantised) version is available at techiaith/whisper-large-ft-cy-en-ct2 for faster inference.

Acknowledgements

Developed by Uned Technolegau Iaith, Prifysgol Bangor / Language Technologies Unit, Bangor University.

Funded by the Welsh Government.