wav2vec2-xls-r-300m-italian

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - IT dataset. It achieves the following results on the evaluation set:

Loss: inf
Wer: 0.1710

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: 64
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 5.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
No log	0.04	100	inf	1.0
No log	0.09	200	inf	0.9983
No log	0.13	300	inf	0.7672
No log	0.18	400	inf	0.6919
2.9929	0.22	500	inf	0.6266
2.9929	0.26	600	inf	0.5513
2.9929	0.31	700	inf	0.5081
2.9929	0.35	800	inf	0.4945
2.9929	0.39	900	inf	0.4720
0.5311	0.44	1000	inf	0.4387
0.5311	0.48	1100	inf	0.4411
0.5311	0.53	1200	inf	0.4429
0.5311	0.57	1300	inf	0.4322
0.5311	0.61	1400	inf	0.4532
0.4654	0.66	1500	inf	0.4492
0.4654	0.7	1600	inf	0.3879
0.4654	0.75	1700	inf	0.3836
0.4654	0.79	1800	inf	0.3743
0.4654	0.83	1900	inf	0.3687
0.4254	0.88	2000	inf	0.3793
0.4254	0.92	2100	inf	0.3766
0.4254	0.97	2200	inf	0.3705
0.4254	1.01	2300	inf	0.3272
0.4254	1.05	2400	inf	0.3185
0.3997	1.1	2500	inf	0.3244
0.3997	1.14	2600	inf	0.3082
0.3997	1.18	2700	inf	0.3040
0.3997	1.23	2800	inf	0.3028
0.3997	1.27	2900	inf	0.3112
0.3668	1.32	3000	inf	0.3110
0.3668	1.36	3100	inf	0.3067
0.3668	1.4	3200	inf	0.2961
0.3668	1.45	3300	inf	0.3081
0.3668	1.49	3400	inf	0.2936
0.3645	1.54	3500	inf	0.3037
0.3645	1.58	3600	inf	0.2974
0.3645	1.62	3700	inf	0.3010
0.3645	1.67	3800	inf	0.2985
0.3645	1.71	3900	inf	0.2976
0.3624	1.76	4000	inf	0.2928
0.3624	1.8	4100	inf	0.2860
0.3624	1.84	4200	inf	0.2922
0.3624	1.89	4300	inf	0.2866
0.3624	1.93	4400	inf	0.2776
0.3527	1.97	4500	inf	0.2792
0.3527	2.02	4600	inf	0.2858
0.3527	2.06	4700	inf	0.2767
0.3527	2.11	4800	inf	0.2824
0.3527	2.15	4900	inf	0.2799
0.3162	2.19	5000	inf	0.2673
0.3162	2.24	5100	inf	0.2962
0.3162	2.28	5200	inf	0.2736
0.3162	2.33	5300	inf	0.2652
0.3162	2.37	5400	inf	0.2551
0.3063	2.41	5500	inf	0.2680
0.3063	2.46	5600	inf	0.2558
0.3063	2.5	5700	inf	0.2598
0.3063	2.54	5800	inf	0.2518
0.3063	2.59	5900	inf	0.2541
0.2913	2.63	6000	inf	0.2507
0.2913	2.68	6100	inf	0.2500
0.2913	2.72	6200	inf	0.2435
0.2913	2.76	6300	inf	0.2376
0.2913	2.81	6400	inf	0.2348
0.2797	2.85	6500	inf	0.2512
0.2797	2.9	6600	inf	0.2382
0.2797	2.94	6700	inf	0.2523
0.2797	2.98	6800	inf	0.2522
0.2797	3.03	6900	inf	0.2409
0.2766	3.07	7000	inf	0.2453
0.2766	3.12	7100	inf	0.2326
0.2766	3.16	7200	inf	0.2286
0.2766	3.2	7300	inf	0.2342
0.2766	3.25	7400	inf	0.2305
0.2468	3.29	7500	inf	0.2238
0.2468	3.33	7600	inf	0.2321
0.2468	3.38	7700	inf	0.2305
0.2468	3.42	7800	inf	0.2174
0.2468	3.47	7900	inf	0.2201
0.2439	3.51	8000	inf	0.2133
0.2439	3.55	8100	inf	0.2217
0.2439	3.6	8200	inf	0.2189
0.2439	3.64	8300	inf	0.2105
0.2439	3.69	8400	inf	0.2118
0.2357	3.73	8500	inf	0.2093
0.2357	3.77	8600	inf	0.2103
0.2357	3.82	8700	inf	0.2035
0.2357	3.86	8800	inf	0.2019
0.2357	3.91	8900	inf	0.2032
0.2217	3.95	9000	inf	0.2056
0.2217	3.99	9100	inf	0.2022
0.2217	4.04	9200	inf	0.1932
0.2217	4.08	9300	inf	0.1935
0.2217	4.12	9400	inf	0.1906
0.2025	4.17	9500	inf	0.1879
0.2025	4.21	9600	inf	0.1882
0.2025	4.26	9700	inf	0.1854
0.2025	4.3	9800	inf	0.1865
0.2025	4.34	9900	inf	0.1844
0.1869	4.39	10000	inf	0.1822
0.1869	4.43	10100	inf	0.1815
0.1869	4.48	10200	inf	0.1812
0.1869	4.52	10300	inf	0.1792
0.1869	4.56	10400	inf	0.1797
0.1863	4.61	10500	inf	0.1774
0.1863	4.65	10600	inf	0.1767
0.1863	4.7	10700	inf	0.1765
0.1863	4.74	10800	inf	0.1753
0.1863	4.78	10900	inf	0.1731
0.178	4.83	11000	inf	0.1727
0.178	4.87	11100	inf	0.1724
0.178	4.91	11200	inf	0.1722
0.178	4.96	11300	inf	0.1712

Framework versions

Transformers 4.16.0.dev0
Pytorch 1.10.1+cu102
Datasets 1.17.1.dev0
Tokenizers 0.11.0

加载中…

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0003

train_batch_size: 64

eval_batch_size: 8

seed: 42

optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08

lr_scheduler_type: linear

lr_scheduler_warmup_steps: 500

num_epochs: 5.0

mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
No log	0.04	100	inf	1.0
No log	0.09	200	inf	0.9983
No log	0.13	300	inf	0.7672
No log	0.18	400	inf	0.6919
2.9929	0.22	500	inf	0.6266
2.9929	0.26	600	inf	0.5513
2.9929	0.31	700	inf	0.5081
2.9929	0.35	800	inf	0.4945
2.9929	0.39	900	inf	0.4720
0.5311	0.44	1000	inf	0.4387
0.5311	0.48	1100	inf	0.4411
0.5311	0.53	1200	inf	0.4429
0.5311	0.57	1300	inf	0.4322
0.5311	0.61	1400	inf	0.4532
0.4654	0.66	1500	inf	0.4492
0.4654	0.7	1600	inf	0.3879
0.4654	0.75	1700	inf	0.3836
0.4654	0.79	1800	inf	0.3743
0.4654	0.83	1900	inf	0.3687
0.4254	0.88	2000	inf	0.3793
0.4254	0.92	2100	inf	0.3766
0.4254	0.97	2200	inf	0.3705
0.4254	1.01	2300	inf	0.3272
0.4254	1.05	2400	inf	0.3185
0.3997	1.1	2500	inf	0.3244
0.3997	1.14	2600	inf	0.3082
0.3997	1.18	2700	inf	0.3040
0.3997	1.23	2800	inf	0.3028
0.3997	1.27	2900	inf	0.3112
0.3668	1.32	3000	inf	0.3110
0.3668	1.36	3100	inf	0.3067
0.3668	1.4	3200	inf	0.2961
0.3668	1.45	3300	inf	0.3081
0.3668	1.49	3400	inf	0.2936
0.3645	1.54	3500	inf	0.3037
0.3645	1.58	3600	inf	0.2974
0.3645	1.62	3700	inf	0.3010
0.3645	1.67	3800	inf	0.2985
0.3645	1.71	3900	inf	0.2976
0.3624	1.76	4000	inf	0.2928
0.3624	1.8	4100	inf	0.2860
0.3624	1.84	4200	inf	0.2922
0.3624	1.89	4300	inf	0.2866
0.3624	1.93	4400	inf	0.2776
0.3527	1.97	4500	inf	0.2792
0.3527	2.02	4600	inf	0.2858
0.3527	2.06	4700	inf	0.2767
0.3527	2.11	4800	inf	0.2824
0.3527	2.15	4900	inf	0.2799
0.3162	2.19	5000	inf	0.2673
0.3162	2.24	5100	inf	0.2962
0.3162	2.28	5200	inf	0.2736
0.3162	2.33	5300	inf	0.2652
0.3162	2.37	5400	inf	0.2551
0.3063	2.41	5500	inf	0.2680
0.3063	2.46	5600	inf	0.2558
0.3063	2.5	5700	inf	0.2598
0.3063	2.54	5800	inf	0.2518
0.3063	2.59	5900	inf	0.2541
0.2913	2.63	6000	inf	0.2507
0.2913	2.68	6100	inf	0.2500
0.2913	2.72	6200	inf	0.2435
0.2913	2.76	6300	inf	0.2376
0.2913	2.81	6400	inf	0.2348
0.2797	2.85	6500	inf	0.2512
0.2797	2.9	6600	inf	0.2382
0.2797	2.94	6700	inf	0.2523
0.2797	2.98	6800	inf	0.2522
0.2797	3.03	6900	inf	0.2409
0.2766	3.07	7000	inf	0.2453
0.2766	3.12	7100	inf	0.2326
0.2766	3.16	7200	inf	0.2286
0.2766	3.2	7300	inf	0.2342
0.2766	3.25	7400	inf	0.2305
0.2468	3.29	7500	inf	0.2238
0.2468	3.33	7600	inf	0.2321
0.2468	3.38	7700	inf	0.2305
0.2468	3.42	7800	inf	0.2174
0.2468	3.47	7900	inf	0.2201
0.2439	3.51	8000	inf	0.2133
0.2439	3.55	8100	inf	0.2217
0.2439	3.6	8200	inf	0.2189
0.2439	3.64	8300	inf	0.2105
0.2439	3.69	8400	inf	0.2118
0.2357	3.73	8500	inf	0.2093
0.2357	3.77	8600	inf	0.2103
0.2357	3.82	8700	inf	0.2035
0.2357	3.86	8800	inf	0.2019
0.2357	3.91	8900	inf	0.2032
0.2217	3.95	9000	inf	0.2056
0.2217	3.99	9100	inf	0.2022
0.2217	4.04	9200	inf	0.1932
0.2217	4.08	9300	inf	0.1935
0.2217	4.12	9400	inf	0.1906
0.2025	4.17	9500	inf	0.1879
0.2025	4.21	9600	inf	0.1882
0.2025	4.26	9700	inf	0.1854
0.2025	4.3	9800	inf	0.1865
0.2025	4.34	9900	inf	0.1844
0.1869	4.39	10000	inf	0.1822
0.1869	4.43	10100	inf	0.1815
0.1869	4.48	10200	inf	0.1812
0.1869	4.52	10300	inf	0.1792
0.1869	4.56	10400	inf	0.1797
0.1863	4.61	10500	inf	0.1774
0.1863	4.65	10600	inf	0.1767
0.1863	4.7	10700	inf	0.1765
0.1863	4.74	10800	inf	0.1753
0.1863	4.78	10900	inf	0.1731
0.178	4.83	11000	inf	0.1727
0.178	4.87	11100	inf	0.1724
0.178	4.91	11200	inf	0.1722
0.178	4.96	11300	inf	0.1712

Framework versions

Transformers 4.16.0.dev0

Pytorch 1.10.1+cu102

Datasets 1.17.1.dev0

Tokenizers 0.11.0

dbdmg/wav2vec2-xls-r-300m-italian

wav2vec2-xls-r-300m-italian

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

wav2vec2-xls-r-300m-italian

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions