README.md

metadata

language:
  - uk
license: mit
tags:
  - automatic-speech-recognition
  - common_voice
  - generated_from_trainer
datasets:
  - common_voice
model-index:
  - name: wav2vec2-xls-r-300m-uk
    results:
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice uk
          type: common_voice
          args: uk
        metrics:
          - name: Test WER
            type: wer
            value: 12.22

wav2vec2-xls-r-300m-uk

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0927
• Wer: 0.1222
• Cer: 0.0204

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: 3e-05
• train_batch_size: 40
• eval_batch_size: 40
• seed: 42
• gradient_accumulation_steps: 6
• total_train_batch_size: 240
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 100
• num_epochs: 100
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Cer	Validation Loss	Wer
9.0008	1.68	200	1.0	3.7590	1.0
3.4972	3.36	400	1.0	3.3933	1.0
3.3432	5.04	600	1.0	3.2617	1.0
3.2421	6.72	800	1.0	3.0712	1.0
1.9839	7.68	1000	0.1400	0.7204	0.6561
0.8017	9.36	1200	0.0766	0.3734	0.4159
0.5554	11.04	1400	0.0583	0.2621	0.3237
0.4309	12.68	1600	0.0486	0.2085	0.2753
0.3697	14.36	1800	0.0421	0.1746	0.2427
0.3293	16.04	2000	0.0388	0.1597	0.2243
0.2934	17.72	2200	0.0358	0.1428	0.2083
0.2704	19.4	2400	0.0333	0.1326	0.1949
0.2547	21.08	2600	0.0322	0.1255	0.1882
0.2366	22.76	2800	0.0309	0.1211	0.1815
0.2183	24.44	3000	0.0294	0.1159	0.1727
0.2115	26.13	3200	0.0280	0.1117	0.1661
0.1968	27.8	3400	0.0274	0.1063	0.1622
0.1922	29.48	3600	0.0269	0.1082	0.1598
0.1847	31.17	3800	0.0260	0.1061	0.1550
0.1715	32.84	4000	0.0252	0.1014	0.1496
0.1689	34.53	4200	0.0250	0.1012	0.1492
0.1655	36.21	4400	0.0243	0.0999	0.1450
0.1585	37.88	4600	0.0239	0.0967	0.1432
0.1492	39.57	4800	0.0237	0.0978	0.1421
0.1491	41.25	5000	0.0236	0.0963	0.1412
0.1453	42.93	5200	0.0230	0.0979	0.1373
0.1386	44.61	5400	0.0227	0.0959	0.1353
0.1387	46.29	5600	0.0226	0.0927	0.1355
0.1329	47.97	5800	0.0224	0.0951	0.1341
0.1295	49.65	6000	0.0219	0.0950	0.1306
0.1287	51.33	6200	0.0216	0.0937	0.1290
0.1277	53.02	6400	0.0215	0.0963	0.1294
0.1201	54.69	6600	0.0213	0.0959	0.1282
0.1199	56.38	6800	0.0215	0.0944	0.1286
0.1221	58.06	7000	0.0209	0.0938	0.1249
0.1145	59.68	7200	0.0208	0.0941	0.1254
0.1143	61.36	7400	0.0209	0.0941	0.1249
0.1143	63.04	7600	0.0209	0.0940	0.1248
0.1137	64.72	7800	0.0205	0.0931	0.1234
0.1125	66.4	8000	0.0204	0.0927	0.1222

Framework versions

• Transformers 4.25.1
• Pytorch 1.13.1+cu117
• Datasets 2.8.0
• Tokenizers 0.13.2