wav2vec2-large-xlsr-hindi-demo-colab_2

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice dataset. It achieves the following results on the evaluation set:

• Loss: 3.8793
• Wer: 1.1357

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0003
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 20
• num_epochs: 50
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
22.381	1.11	20	22.1964	1.0
7.6212	2.22	40	4.0591	1.0
3.6951	3.32	60	3.6782	1.0
3.5574	4.43	80	3.6776	1.0
3.5374	5.54	100	3.5649	1.0
3.5512	6.65	120	3.5266	1.0
3.5075	7.76	140	3.6860	1.0
3.5097	8.86	160	3.4941	1.0
3.481	9.97	180	3.4659	1.0
3.5623	11.11	200	3.7254	1.0
3.4404	12.22	220	3.5225	1.0
3.432	13.32	240	3.5706	1.0
3.4177	14.43	260	3.3833	1.0
3.3735	15.54	280	3.4140	1.0
3.31	16.65	300	3.2702	1.0
3.2256	17.76	320	3.2405	1.0
3.0546	18.86	340	3.1644	1.0
2.7233	19.97	360	2.9753	1.0
2.2822	21.11	380	3.1119	1.1183
1.8027	22.22	400	3.0035	1.2378
1.5274	23.32	420	2.8536	1.2227
1.2313	24.43	440	2.9544	1.0951
1.0956	25.54	460	2.8814	1.0661
0.9456	26.65	480	3.1192	1.1589
0.7893	27.76	500	3.2919	1.1833
0.7256	28.86	520	3.0864	1.0951
0.6051	29.97	540	3.5888	1.1821
0.6087	31.11	560	3.4579	1.1392
0.5529	32.22	580	3.1998	1.0708
0.5211	33.32	600	3.4655	1.1311
0.4506	34.43	620	3.4338	1.1694
0.4101	35.54	640	3.5189	1.1450
0.4484	36.65	660	3.6585	1.1601
0.4038	37.76	680	3.6314	1.1497
0.3539	38.86	700	3.6955	1.1485
0.3898	39.97	720	3.5738	1.1148
0.35	41.11	740	3.6594	1.1195
0.3328	42.22	760	3.6894	1.1299
0.3264	43.32	780	3.7290	1.1021
0.3364	44.43	800	3.7256	1.1543
0.3071	45.54	820	3.8834	1.1415
0.3074	46.65	840	3.8077	1.1450
0.3064	47.76	860	3.8733	1.1346
0.3223	48.86	880	3.8780	1.1323
0.275	49.97	900	3.8793	1.1357

Framework versions

• Transformers 4.11.3
• Pytorch 1.10.0+cu111
• Datasets 1.13.3
• Tokenizers 0.10.3