wav2vec2-xls-r-1b-italian-robust

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the Common Voice 7 & Libri Speech datasets. It achieves the following results on the evaluation set:

• Loss: 0.2428
• Wer: 0.2960

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: 5e-05
• train_batch_size: 32
• eval_batch_size: 16
• 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: 10.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
No log	0.07	400	1.0053	0.8058
1.5087	0.13	800	0.9127	0.8104
0.9552	0.2	1200	1.0360	0.8836
0.9555	0.27	1600	0.9980	0.8577
1.0259	0.34	2000	1.0103	0.8842
1.0259	0.4	2400	0.9119	0.8466
1.0365	0.47	2800	0.9000	0.8281
1.0069	0.54	3200	0.7976	0.7875
0.9688	0.61	3600	0.8126	0.8051
0.9638	0.67	4000	0.7921	0.7903
0.9638	0.74	4400	0.7703	0.7783
0.9327	0.81	4800	0.7253	0.7463
0.8992	0.88	5200	0.6841	0.7171
0.8693	0.94	5600	0.6867	0.7250
0.8433	1.01	6000	0.7077	0.7302
0.8433	1.08	6400	0.6685	0.7091
0.8499	1.14	6800	0.6355	0.6825
0.8159	1.21	7200	0.6283	0.6800
0.8001	1.28	7600	0.6288	0.6743
0.7883	1.35	8000	0.5995	0.6633
0.7883	1.41	8400	0.6195	0.6726
0.7863	1.48	8800	0.6039	0.6588
0.7713	1.55	9200	0.5842	0.6490
0.7572	1.62	9600	0.5975	0.6533
0.7442	1.68	10000	0.5508	0.6233
0.7442	1.75	10400	0.5521	0.6209
0.7296	1.82	10800	0.5760	0.6245
0.7205	1.89	11200	0.5593	0.6144
0.7106	1.95	11600	0.5672	0.6220
0.7146	2.02	12000	0.5134	0.5911
0.7146	2.09	12400	0.5069	0.5811
0.6944	2.15	12800	0.5022	0.5962
0.6817	2.22	13200	0.4989	0.5813
0.6721	2.29	13600	0.4941	0.5742
0.6774	2.36	14000	0.4775	0.5676
0.6774	2.42	14400	0.4694	0.5525
0.6621	2.49	14800	0.4720	0.5514
0.6599	2.56	15200	0.4714	0.5553
0.6591	2.63	15600	0.4578	0.5397
0.645	2.69	16000	0.4619	0.5452
0.645	2.76	16400	0.4578	0.5343
0.6431	2.83	16800	0.4514	0.5328
0.636	2.9	17200	0.4526	0.5325
0.6433	2.96	17600	0.4561	0.5325
0.6356	3.03	18000	0.4386	0.5191
0.6356	3.1	18400	0.4291	0.5065
0.6175	3.16	18800	0.4306	0.5170
0.6187	3.23	19200	0.4256	0.5036
0.607	3.3	19600	0.4198	0.5027
0.6004	3.37	20000	0.4149	0.4906
0.6004	3.43	20400	0.4114	0.4902
0.6002	3.5	20800	0.4116	0.4967
0.5926	3.57	21200	0.4066	0.4843
0.5836	3.64	21600	0.3956	0.4791
0.588	3.7	22000	0.3941	0.4729
0.588	3.77	22400	0.3972	0.4799
0.5739	3.84	22800	0.4018	0.4790
0.5778	3.91	23200	0.3936	0.4750
0.5768	3.97	23600	0.3936	0.4751
0.5651	4.04	24000	0.3953	0.4706
0.5651	4.11	24400	0.3906	0.4659
0.5704	4.17	24800	0.3807	0.4557
0.5594	4.24	25200	0.3817	0.4610
0.5509	4.31	25600	0.3755	0.4553
0.5439	4.38	26000	0.3705	0.4471
0.5439	4.44	26400	0.3744	0.4487
0.5426	4.51	26800	0.3716	0.4483
0.5393	4.58	27200	0.3600	0.4356
0.5408	4.65	27600	0.3573	0.4307
0.5327	4.71	28000	0.3638	0.4382
0.5327	4.78	28400	0.3587	0.4316
0.5324	4.85	28800	0.3598	0.4290
0.5378	4.91	29200	0.3508	0.4243
0.5246	4.98	29600	0.3522	0.4260
0.5284	5.05	30000	0.3520	0.4268
0.5284	5.12	30400	0.3506	0.4224
0.5154	5.18	30800	0.3556	0.4223
0.5138	5.25	31200	0.3526	0.4276
0.51	5.32	31600	0.3440	0.4220
0.5065	5.39	32000	0.3367	0.4120
0.5065	5.45	32400	0.3406	0.4136
0.5087	5.52	32800	0.3370	0.4125
0.503	5.59	33200	0.3387	0.4134
0.5085	5.66	33600	0.3346	0.4068
0.5044	5.72	34000	0.3325	0.4057
0.5044	5.79	34400	0.3304	0.4026
0.4879	5.86	34800	0.3274	0.4002
0.4924	5.92	35200	0.3286	0.3980
0.4991	5.99	35600	0.3231	0.3952
0.487	6.06	36000	0.3324	0.4005
0.487	6.13	36400	0.3264	0.3952
0.4754	6.19	36800	0.3234	0.3905
0.4683	6.26	37200	0.3149	0.3840
0.4653	6.33	37600	0.3122	0.3824
0.4667	6.4	38000	0.3151	0.3855
0.4667	6.46	38400	0.3217	0.3859
0.4628	6.53	38800	0.3085	0.3831
0.4644	6.6	39200	0.3121	0.3791
0.4612	6.67	39600	0.3093	0.3790
0.4552	6.73	40000	0.3087	0.3749
0.4552	6.8	40400	0.3027	0.3679
0.4544	6.87	40800	0.3048	0.3672
0.4507	6.93	41200	0.2963	0.3614
0.4489	7.0	41600	0.3086	0.3718
0.4367	7.07	42000	0.3100	0.3754
0.4367	7.14	42400	0.3057	0.3701
0.4376	7.2	42800	0.2930	0.3614
0.428	7.27	43200	0.2907	0.3516
0.4241	7.34	43600	0.2916	0.3590
0.4312	7.41	44000	0.2904	0.3523
0.4312	7.47	44400	0.2908	0.3476
0.4292	7.54	44800	0.2858	0.3467
0.426	7.61	45200	0.2864	0.3484
0.4225	7.68	45600	0.2820	0.3441
0.422	7.74	46000	0.2834	0.3441
0.422	7.81	46400	0.2784	0.3420
0.4158	7.88	46800	0.2814	0.3390
0.4139	7.94	47200	0.2777	0.3384
0.4076	8.01	47600	0.2741	0.3381
0.3997	8.08	48000	0.2738	0.3320
0.3997	8.15	48400	0.2720	0.3303
0.4009	8.21	48800	0.2705	0.3357
0.3928	8.28	49200	0.2708	0.3265
0.3923	8.35	49600	0.2678	0.3283
0.3897	8.42	50000	0.2649	0.3241
0.3897	8.48	50400	0.2640	0.3218
0.3879	8.55	50800	0.2616	0.3197
0.3805	8.62	51200	0.2599	0.3170
0.3874	8.69	51600	0.2592	0.3168
0.3799	8.75	52000	0.2589	0.3157
0.3799	8.82	52400	0.2566	0.3137
0.3834	8.89	52800	0.2552	0.3141
0.3811	8.95	53200	0.2523	0.3108
0.3821	9.02	53600	0.2539	0.3112
0.3636	9.09	54000	0.2529	0.3070
0.3636	9.16	54400	0.2500	0.3078
0.3706	9.22	54800	0.2510	0.3067
0.367	9.29	55200	0.2497	0.3069
0.3618	9.36	55600	0.2493	0.3043
0.3624	9.43	56000	0.2491	0.3040
0.3624	9.49	56400	0.2466	0.3016
0.3557	9.56	56800	0.2460	0.3014
0.3536	9.63	57200	0.2470	0.2997
0.3584	9.7	57600	0.2441	0.2989
0.3563	9.76	58000	0.2442	0.2970
0.3563	9.83	58400	0.2436	0.2966
0.3492	9.9	58800	0.2431	0.2967
0.3483	9.96	59200	0.2428	0.2960

Framework versions

• Transformers 4.17.0.dev0
• Pytorch 1.10.2+cu102
• Datasets 1.18.3
• Tokenizers 0.11.0