metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - common_voice_8_0
metrics:
  - wer
model-index:
  - name: wav2vec2-large-xls-r-1b-frisian-cv-8-10h
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_8_0
          type: common_voice_8_0
          config: fy-NL
          split: validation
          args: fy-NL
        metrics:
          - name: Wer
            type: wer
            value: 0.09612912441079846
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_8_0
          type: common_voice_8_0
          config: fy-NL
          split: test
          args: fy-NL
        metrics:
          - name: Wer
            type: wer
            value: 0.08830755889579418

wav2vec2-large-xls-r-1b-frisian-cv-8-10h

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the common_voice_8_0 dataset. It achieves the following results on the evaluation set:

Loss: 0.1207
Wer: 0.0961

And on the test set:

Wer: 0.0883

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: 8
seed: 42
optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 50
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
5.6342	1.32	300	2.9760	1.0
2.2716	2.63	600	0.6877	0.6024
1.1303	3.95	900	0.3522	0.3450
0.9038	5.26	1200	0.2714	0.2603
0.846	6.58	1500	0.2143	0.2036
0.8044	7.89	1800	0.1829	0.1788
0.7069	9.21	2100	0.1751	0.1667
0.6995	10.53	2400	0.1741	0.1727
0.7115	11.84	2700	0.1591	0.1486
0.677	13.16	3000	0.1636	0.1459
0.6032	14.47	3300	0.1535	0.1439
0.6218	15.79	3600	0.1427	0.1406
0.6519	17.11	3900	0.1498	0.1488
0.5739	18.42	4200	0.1438	0.1319
0.567	19.74	4500	0.1379	0.1322
0.4982	21.05	4800	0.1315	0.1237
0.5825	22.37	5100	0.1349	0.1252
0.5085	23.68	5400	0.1297	0.1233
0.4946	25.0	5700	0.1343	0.1127
0.5677	26.32	6000	0.1323	0.1228
0.4858	27.63	6300	0.1292	0.1098
0.4709	28.95	6600	0.1267	0.1204
0.3241	30.26	6900	0.1315	0.1274
0.2796	31.58	7200	0.1315	0.1202
0.3171	32.89	7500	0.1315	0.1200
0.2591	34.21	7800	0.1322	0.1106
0.2716	35.53	8100	0.1233	0.1030
0.2446	36.84	8400	0.1273	0.1087
0.2377	38.16	8700	0.1243	0.1101
0.2183	39.47	9000	0.1230	0.1116
0.2059	40.79	9300	0.1240	0.1001
0.1916	42.11	9600	0.1223	0.1003
0.196	43.42	9900	0.1246	0.0965
0.1969	44.74	10200	0.1222	0.1038
0.1951	46.05	10500	0.1208	0.1003
0.1809	47.37	10800	0.1213	0.1003
0.1793	48.68	11100	0.1202	0.0959
0.1837	50.0	11400	0.1207	0.0961

Framework versions

Transformers 4.28.1
Pytorch 2.0.0+cu117
Datasets 2.11.0
Tokenizers 0.13.3