whipa-large-cv

Model details

This Whisper-for-IPA (WhIPA) model is a fine-tuned version of openai/whisper-large-v2 on a subset of the CommonVoice11 dataset (1k samples each from Greek, Finnish, Hungarian, Japanese, Maltese, Polish, Tamil) with G2P-based IPA transcriptions. It (6) achieves the following results on the evaluation set:

• Loss: 0.5570
• Lvnshtn: 5.9371
• cer: 0.1709
• cer_norm: 0.1604
• ped: 5.5229
• per: 0.1810
• pfer: 5.7470
• LhPD_(mipa): 2.0216
• wefed: 17.9254
• wefer: 0.5507

Epoch	lvnshtn	cer	cer_norm	ped	per	pfer	LhPD_(mipa)	wefed	wefer	time
2	8.382857142857137	0.24163589269510524	0.22863069089892205	7.757142857142851	0.2524059166580333	7.5604320437314785	2.6376535390328346	22.74642857142856	0.7052453948748261	3199.799334049225
4	6.691428571428563	0.19601619003578613	0.17116995643519559	6.197142857142849	0.20629669844107124	6.507800864530915	2.243887904361885	20.86178571428572	0.5902481277563488	6436.061186313629
6	5.9371428571428515	0.17092720391633578	0.1603608557874088	5.522857142857141	0.18096545655791033	5.746953179375352	2.021641668726793	17.925357142857134	0.5507118071695863	9660.67699599266
8	6.082857142857139	0.17536400979804245	0.1629698988097059	5.642857142857143	0.18555627617952597	5.993579904596805	2.100972125284737	18.620357142857138	0.57805324454652	12875.556059837341
10	6.134285714285711	0.1762981563905388	0.1632004044495094	5.668571428571428	0.1856530781701276	5.956850376357517	2.097938104613861	18.657499999999995	0.5731345760321149	16079.743387937546

Model description

For deployment and description, please refer to https://github.com/jshrdt/whipa.

from transformers import WhisperForConditionalGeneration, WhisperTokenizer, WhisperProcessor

whipa_model = WhisperForConditionalGeneration.from_pretrained("jshrdt/whipa-large-cv")

whipa_model.generation_config.language = "<|ip|>"
whipa_model.generation_config.task = "transcribe"

whipa_tokenizer = WhisperTokenizer.from_pretrained("jshrdt/whipa-large-cv", task="transcribe")
whipa_processor = WhisperProcessor.from_pretrained("jshrdt/whipa-large-cv", task="transcribe")

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