wav2vec2-xls-r-gn-cv7

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

Loss: 1.7197
Wer: 0.7434

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: 0.0001
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: 100
training_steps: 13000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
3.4669	6.24	100	3.3003	1.0
3.3214	12.48	200	3.2090	1.0
3.1619	18.73	300	2.6322	1.0
1.751	24.97	400	1.4089	0.9803
0.7997	31.24	500	0.9996	0.9211
0.4996	37.48	600	0.9879	0.8553
0.3677	43.73	700	0.9543	0.8289
0.2851	49.97	800	1.0627	0.8487
0.2556	56.24	900	1.0933	0.8355
0.2268	62.48	1000	0.9191	0.8026
0.1914	68.73	1100	0.9582	0.7961
0.1749	74.97	1200	1.0502	0.8092
0.157	81.24	1300	0.9998	0.7632
0.1505	87.48	1400	1.0076	0.7303
0.1278	93.73	1500	0.9321	0.75
0.1078	99.97	1600	1.0383	0.7697
0.1156	106.24	1700	1.0302	0.7763
0.1107	112.48	1800	1.0419	0.7763
0.091	118.73	1900	1.0694	0.75
0.0829	124.97	2000	1.0257	0.7829
0.0865	131.24	2100	1.2108	0.7368
0.0907	137.48	2200	1.0458	0.7697
0.0897	143.73	2300	1.1504	0.7895
0.0766	149.97	2400	1.1663	0.7237
0.0659	156.24	2500	1.1320	0.7632
0.0699	162.48	2600	1.2586	0.7434
0.0613	168.73	2700	1.1815	0.8158
0.0598	174.97	2800	1.3299	0.75
0.0577	181.24	2900	1.2035	0.7171
0.0576	187.48	3000	1.2134	0.7434
0.0518	193.73	3100	1.3406	0.7566
0.0524	199.97	3200	1.4251	0.75
0.0467	206.24	3300	1.3533	0.7697
0.0428	212.48	3400	1.2463	0.7368
0.0453	218.73	3500	1.4532	0.7566
0.0473	224.97	3600	1.3152	0.7434
0.0451	231.24	3700	1.2232	0.7368
0.0361	237.48	3800	1.2938	0.7171
0.045	243.73	3900	1.4148	0.7434
0.0422	249.97	4000	1.3786	0.7961
0.036	256.24	4100	1.4488	0.7697
0.0352	262.48	4200	1.2294	0.6776
0.0326	268.73	4300	1.2796	0.6974
0.034	274.97	4400	1.3805	0.7303
0.0305	281.24	4500	1.4994	0.7237
0.0325	287.48	4600	1.4330	0.6908
0.0338	293.73	4700	1.3091	0.7368
0.0306	299.97	4800	1.2174	0.7171
0.0299	306.24	4900	1.3527	0.7763
0.0287	312.48	5000	1.3651	0.7368
0.0274	318.73	5100	1.4337	0.7368
0.0258	324.97	5200	1.3831	0.6908
0.022	331.24	5300	1.3556	0.6974
0.021	337.48	5400	1.3836	0.7237
0.0241	343.73	5500	1.4352	0.7039
0.0229	349.97	5600	1.3904	0.7105
0.026	356.24	5700	1.4131	0.7171
0.021	362.48	5800	1.5426	0.6974
0.0191	368.73	5900	1.5960	0.7632
0.0227	374.97	6000	1.6240	0.7368
0.0204	381.24	6100	1.4301	0.7105
0.0175	387.48	6200	1.5554	0.75
0.0183	393.73	6300	1.6044	0.7697
0.0183	399.97	6400	1.5963	0.7368
0.016	406.24	6500	1.5679	0.7829
0.0178	412.48	6600	1.5928	0.7697
0.014	418.73	6700	1.7000	0.7632
0.0182	424.97	6800	1.5340	0.75
0.0148	431.24	6900	1.9274	0.7368
0.0148	437.48	7000	1.6437	0.7697
0.0173	443.73	7100	1.5468	0.75
0.0109	449.97	7200	1.6083	0.75
0.0167	456.24	7300	1.6732	0.75
0.0139	462.48	7400	1.5097	0.7237
0.013	468.73	7500	1.5947	0.7171
0.0128	474.97	7600	1.6260	0.7105
0.0166	481.24	7700	1.5756	0.7237
0.0127	487.48	7800	1.4506	0.6908
0.013	493.73	7900	1.4882	0.7368
0.0125	499.97	8000	1.5589	0.7829
0.0141	506.24	8100	1.6328	0.7434
0.0115	512.48	8200	1.6586	0.7434
0.0117	518.73	8300	1.6043	0.7105
0.009	524.97	8400	1.6508	0.7237
0.0108	531.24	8500	1.4507	0.6974
0.011	537.48	8600	1.5942	0.7434
0.009	543.73	8700	1.8121	0.7697
0.0112	549.97	8800	1.6923	0.7697
0.0073	556.24	8900	1.7096	0.7368
0.0098	562.48	9000	1.7052	0.7829
0.0088	568.73	9100	1.6956	0.7566
0.0099	574.97	9200	1.4909	0.7171
0.0075	581.24	9300	1.6307	0.7697
0.0077	587.48	9400	1.6196	0.7961
0.0088	593.73	9500	1.6119	0.7566
0.0085	599.97	9600	1.4512	0.7368
0.0086	606.24	9700	1.5992	0.7237
0.0109	612.48	9800	1.4706	0.7368
0.0098	618.73	9900	1.3824	0.7171
0.0091	624.97	10000	1.4776	0.6974
0.0072	631.24	10100	1.4896	0.7039
0.0087	637.48	10200	1.5467	0.7368
0.007	643.73	10300	1.5493	0.75
0.0076	649.97	10400	1.5706	0.7303
0.0085	656.24	10500	1.5748	0.7237
0.0075	662.48	10600	1.5081	0.7105
0.0068	668.73	10700	1.4967	0.6842
0.0117	674.97	10800	1.4986	0.7105
0.0054	681.24	10900	1.5587	0.7303
0.0059	687.48	11000	1.5886	0.7171
0.0071	693.73	11100	1.5746	0.7171
0.0048	699.97	11200	1.6166	0.7237
0.0048	706.24	11300	1.6098	0.7237
0.0056	712.48	11400	1.5834	0.7237
0.0048	718.73	11500	1.5653	0.7171
0.0045	724.97	11600	1.6252	0.7237
0.0068	731.24	11700	1.6794	0.7171
0.0044	737.48	11800	1.6881	0.7039
0.008	743.73	11900	1.7393	0.75
0.0045	749.97	12000	1.6869	0.7237
0.0047	756.24	12100	1.7105	0.7303
0.0057	762.48	12200	1.7439	0.7303
0.004	768.73	12300	1.7871	0.7434
0.0061	774.97	12400	1.7812	0.7303
0.005	781.24	12500	1.7410	0.7434
0.0056	787.48	12600	1.7220	0.7303
0.0064	793.73	12700	1.7141	0.7434
0.0042	799.97	12800	1.7139	0.7368
0.0049	806.24	12900	1.7211	0.7434
0.0044	812.48	13000	1.7197	0.7434

Framework versions

Transformers 4.15.0
Pytorch 1.10.0+cu111
Datasets 1.18.0
Tokenizers 0.10.3

lgris/wav2vec2-xls-r-gn-cv7

wav2vec2-xls-r-gn-cv7

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

为此模型做种