windanam_mms-1b-tts_v2

This model is a fine-tuned version of facebook/mms-1b-all on FulaSpeechCorpora-splited-noi_augmented and FulaNewsTextCorporaTTS dataset. It achieves the following results on the evaluation set:

If you want to test quickly the model, instead of using the huggingface Inference API, use this space: https://huggingface.co/spaces/cawoylel/MMS-ASR-Fula

How to use

Load Model

pipe = pipeline("automatic-speech-recognition", model="cawoylel/windanam_mms-1b-tts_v2")

Run Model

transcription = pipe(your_audio_file)

The following hyperparameters were used during training:

Training Loss	Epoch	Step	Validation Loss	Wer
0.8993	0.06	2500	0.6557	0.3266
0.8056	0.12	5000	0.5806	0.3130
0.7663	0.17	7500	0.5514	0.2804
0.736	0.23	10000	0.5333	0.2729
0.7111	0.29	12500	0.5200	0.2686
0.6867	0.35	15000	0.5043	0.2657
0.6942	0.41	17500	0.4933	0.2617
0.6695	0.47	20000	0.4814	0.2595
0.6464	0.52	22500	0.4733	0.2576
0.694	0.58	25000	0.4618	0.2553
0.6223	0.64	27500	0.4574	0.2544
0.6692	0.7	30000	0.4495	0.2526
0.6337	0.76	32500	0.4454	0.2518
0.6055	0.81	35000	0.4396	0.2501
0.6436	0.87	37500	0.4341	0.2498
0.6389	0.93	40000	0.4304	0.2488
0.6308	0.99	42500	0.4260	0.2486
0.6167	1.05	45000	0.4248	0.2469
0.6253	1.11	47500	0.4201	0.2467
0.603	1.16	50000	0.4179	0.2457
0.5864	1.22	52500	0.4187	0.2449
0.6276	1.28	55000	0.4147	0.2446
0.5901	1.34	57500	0.4151	0.2439
0.5877	1.4	60000	0.4126	0.2436
0.604	1.45	62500	0.4111	0.2435
0.6182	1.51	65000	0.4097	0.2436
0.5745	1.57	67500	0.4074	0.2433
0.5843	1.63	70000	0.4076	0.2429
0.578	1.69	72500	0.4062	0.2429
0.5799	1.75	75000	0.4060	0.2425
0.6017	1.8	77500	0.4047	0.2423
0.5688	1.86	80000	0.4052	0.2417
0.5837	1.92	82500	0.4039	0.2418
0.5801	1.98	85000	0.4038	0.2414
0.5965	2.04	87500	0.4030	0.2413
0.5827	2.09	90000	0.4022	0.2414
0.6043	2.15	92500	0.4020	0.2414
0.609	2.21	95000	0.4021	0.2414
0.5827	2.27	97500	0.4021	0.2415
0.5755	2.33	100000	0.4020	0.2416