README.md

metadata

language:
  - zh-HK
license: apache-2.0
tags:
  - automatic-speech-recognition
  - common_voice
  - generated_from_trainer
datasets:
  - common_voice
model-index:
  - name: ''
    results: []

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

• Loss: 0.8089
• Wer: 1.2499
• Cer: 0.3173

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: 4
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 2000
• num_epochs: 100.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
69.8341	1.34	500	80.0722	1.0	1.0
6.6418	2.68	1000	6.6346	1.0	1.0
6.2419	4.02	1500	6.2909	1.0	1.0
6.0813	5.36	2000	6.1150	1.0	1.0
5.9677	6.7	2500	6.0301	1.1386	1.0028
5.9296	8.04	3000	5.8975	1.2113	1.0058
5.6434	9.38	3500	5.5404	2.1624	1.0171
5.1974	10.72	4000	4.5440	2.1702	0.9366
4.3601	12.06	4500	3.3839	2.2464	0.8998
3.9321	13.4	5000	2.8785	2.3097	0.8400
3.6462	14.74	5500	2.5108	1.9623	0.6663
3.5156	16.09	6000	2.2790	1.6479	0.5706
3.32	17.43	6500	2.1450	1.8337	0.6244
3.1918	18.77	7000	1.8536	1.9394	0.6017
3.1139	20.11	7500	1.7205	1.9112	0.5638
2.8995	21.45	8000	1.5478	1.0624	0.3250
2.7572	22.79	8500	1.4068	1.1412	0.3367
2.6881	24.13	9000	1.3312	2.0100	0.5683
2.5993	25.47	9500	1.2553	2.0039	0.6450
2.5304	26.81	10000	1.2422	2.0394	0.5789
2.4352	28.15	10500	1.1582	1.9970	0.5507
2.3795	29.49	11000	1.1160	1.8255	0.4844
2.3287	30.83	11500	1.0775	1.4123	0.3780
2.2622	32.17	12000	1.0704	1.7445	0.4894
2.2225	33.51	12500	1.0272	1.7237	0.5058
2.1843	34.85	13000	0.9756	1.8042	0.5028
2.1	36.19	13500	0.9527	1.8909	0.6055
2.0741	37.53	14000	0.9418	1.9026	0.5880
2.0179	38.87	14500	0.9363	1.7977	0.5246
2.0615	40.21	15000	0.9635	1.8112	0.5599
1.9448	41.55	15500	0.9249	1.7250	0.4914
1.8966	42.89	16000	0.9023	1.5829	0.4319
1.8662	44.24	16500	0.9002	1.4833	0.4230
1.8136	45.58	17000	0.9076	1.1828	0.2987
1.7908	46.92	17500	0.8774	1.5773	0.4258
1.7354	48.26	18000	0.8727	1.5037	0.4024
1.6739	49.6	18500	0.8636	1.1239	0.2789
1.6457	50.94	19000	0.8516	1.2269	0.3104
1.5847	52.28	19500	0.8399	1.3309	0.3360
1.5971	53.62	20000	0.8441	1.3153	0.3335
1.602	54.96	20500	0.8590	1.2932	0.3433
1.5063	56.3	21000	0.8334	1.1312	0.2875
1.4631	57.64	21500	0.8474	1.1698	0.2999
1.4997	58.98	22000	0.8638	1.4279	0.3854
1.4301	60.32	22500	0.8550	1.2737	0.3300
1.3798	61.66	23000	0.8266	1.1802	0.2934
1.3454	63.0	23500	0.8235	1.3816	0.3711
1.3678	64.34	24000	0.8550	1.6427	0.5035
1.3761	65.68	24500	0.8510	1.6709	0.4907
1.2668	67.02	25000	0.8515	1.5842	0.4505
1.2835	68.36	25500	0.8283	1.5353	0.4221
1.2961	69.7	26000	0.8339	1.5743	0.4369
1.2656	71.05	26500	0.8331	1.5331	0.4217
1.2556	72.39	27000	0.8242	1.4708	0.4109
1.2043	73.73	27500	0.8245	1.4469	0.4031
1.2722	75.07	28000	0.8202	1.4924	0.4096
1.202	76.41	28500	0.8290	1.3807	0.3719
1.1679	77.75	29000	0.8195	1.4097	0.3749
1.1967	79.09	29500	0.8059	1.2074	0.3077
1.1241	80.43	30000	0.8137	1.2451	0.3270
1.1414	81.77	30500	0.8117	1.2031	0.3121
1.132	83.11	31000	0.8234	1.4266	0.3901
1.0982	84.45	31500	0.8064	1.3712	0.3607
1.0797	85.79	32000	0.8167	1.3356	0.3562
1.0119	87.13	32500	0.8215	1.2754	0.3268
1.0216	88.47	33000	0.8163	1.2512	0.3184
1.0375	89.81	33500	0.8137	1.2685	0.3290
0.9794	91.15	34000	0.8220	1.2724	0.3255
1.0207	92.49	34500	0.8165	1.2906	0.3361
1.0169	93.83	35000	0.8153	1.2819	0.3305
1.0127	95.17	35500	0.8187	1.2832	0.3252
0.9978	96.51	36000	0.8111	1.2612	0.3210
0.9923	97.85	36500	0.8076	1.2278	0.3122
1.0451	99.2	37000	0.8086	1.2451	0.3156

Framework versions

• Transformers 4.17.0.dev0
• Pytorch 1.10.2+cu102
• Datasets 1.18.4.dev0
• Tokenizers 0.11.0