metadata

license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - dv
  - robust-speech-event
  - model_for_talk
datasets:
  - mozilla-foundation/common_voice_8_0
base_model: facebook/wav2vec2-xls-r-1b
model-index:
  - name: wav2vec2-xls-r-1b-dv
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Common Voice 8
          type: mozilla-foundation/common_voice_8_0
          args: dv
        metrics:
          - type: wer
            value: 21.32
            name: Test WER
          - type: cer
            value: 3.43
            name: Test CER

wav2vec2-xls-r-1b-dv

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the common_voice dataset. It achieves the following results on the evaluation set:

Loss: 0.1702
Wer: 0.2123

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: 4.5e-05
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: 500
num_epochs: 30
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
3.8412	0.66	400	0.7160	0.7913
0.6832	1.33	800	0.3401	0.5268
0.4624	1.99	1200	0.2671	0.4683
0.3832	2.65	1600	0.2395	0.4410
0.3443	3.32	2000	0.2410	0.4296
0.324	3.98	2400	0.2302	0.4143
0.2934	4.64	2800	0.2402	0.4136
0.2773	5.31	3200	0.2134	0.4088
0.2638	5.97	3600	0.2072	0.4037
0.2479	6.63	4000	0.2036	0.3876
0.2424	7.3	4400	0.2037	0.3767
0.2249	7.96	4800	0.1959	0.3802
0.2169	8.62	5200	0.1943	0.3813
0.2109	9.29	5600	0.1944	0.3691
0.1991	9.95	6000	0.1870	0.3589
0.1917	10.61	6400	0.1834	0.3485
0.1862	11.28	6800	0.1857	0.3486
0.1744	11.94	7200	0.1812	0.3330
0.171	12.6	7600	0.1797	0.3436
0.1599	13.27	8000	0.1839	0.3319
0.1597	13.93	8400	0.1737	0.3385
0.1494	14.59	8800	0.1807	0.3239
0.1444	15.26	9200	0.1750	0.3155
0.1382	15.92	9600	0.1705	0.3084
0.1299	16.58	10000	0.1777	0.2999
0.1306	17.25	10400	0.1765	0.3056
0.1239	17.91	10800	0.1676	0.2864
0.1149	18.57	11200	0.1774	0.2861
0.1134	19.24	11600	0.1654	0.2699
0.1101	19.9	12000	0.1621	0.2651
0.1038	20.56	12400	0.1686	0.2610
0.1038	21.23	12800	0.1722	0.2559
0.0988	21.89	13200	0.1708	0.2486
0.0949	22.55	13600	0.1696	0.2453
0.0913	23.22	14000	0.1677	0.2424
0.0879	23.88	14400	0.1640	0.2359
0.0888	24.54	14800	0.1697	0.2347
0.0826	25.21	15200	0.1709	0.2314
0.0819	25.87	15600	0.1679	0.2256
0.0793	26.53	16000	0.1701	0.2214
0.0773	27.2	16400	0.1682	0.2176
0.0783	27.86	16800	0.1685	0.2165
0.074	28.52	17200	0.1688	0.2155
0.0753	29.19	17600	0.1695	0.2110
0.0699	29.85	18000	0.1702	0.2123

Framework versions

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