README.md

metadata

library_name: transformers
language:
  - lg
license: mit
base_model: facebook/w2v-bert-2.0
tags:
  - generated_from_trainer
datasets:
  - yogera
metrics:
  - wer
model-index:
  - name: wav2vec2-bert
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Yogera
          type: yogera
        metrics:
          - name: Wer
            type: wer
            value: 0.12906588824020016

wav2vec2-bert

This model is a fine-tuned version of facebook/w2v-bert-2.0 on the Yogera dataset. It achieves the following results on the evaluation set:

• Loss: 0.2157
• Wer: 0.1291
• Cer: 0.0296

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: 16
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 100
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.6428	1.0	257	0.1958	0.2392	0.0488
0.1608	2.0	514	0.1623	0.1868	0.0393
0.1216	3.0	771	0.1471	0.1663	0.0368
0.1001	4.0	1028	0.1483	0.1601	0.0351
0.0859	5.0	1285	0.1471	0.1497	0.0332
0.0742	6.0	1542	0.1478	0.1468	0.0315
0.0641	7.0	1799	0.1642	0.1476	0.0326
0.0544	8.0	2056	0.1520	0.1461	0.0322
0.0489	9.0	2313	0.1596	0.1386	0.0312
0.0452	10.0	2570	0.1521	0.1408	0.0320
0.04	11.0	2827	0.1754	0.1395	0.0306
0.0371	12.0	3084	0.1703	0.1405	0.0309
0.0329	13.0	3341	0.1657	0.1447	0.0318
0.0323	14.0	3598	0.1695	0.1327	0.0298
0.0282	15.0	3855	0.1852	0.1356	0.0310
0.0237	16.0	4112	0.1728	0.1399	0.0308
0.0229	17.0	4369	0.1810	0.1301	0.0291
0.02	18.0	4626	0.1781	0.1367	0.0304
0.0204	19.0	4883	0.2039	0.1329	0.0293
0.0186	20.0	5140	0.1929	0.1366	0.0302
0.0164	21.0	5397	0.2022	0.1356	0.0301
0.0154	22.0	5654	0.1787	0.1307	0.0293
0.0127	23.0	5911	0.2086	0.1296	0.0290
0.0129	24.0	6168	0.2094	0.1281	0.0287
0.0108	25.0	6425	0.2148	0.1254	0.0280
0.0122	26.0	6682	0.2091	0.1339	0.0305
0.0106	27.0	6939	0.2030	0.1315	0.0295
0.0102	28.0	7196	0.2092	0.1241	0.0282
0.0088	29.0	7453	0.2078	0.1290	0.0287
0.008	30.0	7710	0.2112	0.1298	0.0282
0.0084	31.0	7967	0.1972	0.1305	0.0295
0.0074	32.0	8224	0.2130	0.1337	0.0293
0.0062	33.0	8481	0.2141	0.1308	0.0297
0.0065	34.0	8738	0.2151	0.1319	0.0296
0.0079	35.0	8995	0.2070	0.1253	0.0279
0.0059	36.0	9252	0.2229	0.1267	0.0285
0.0071	37.0	9509	0.2218	0.1295	0.0297
0.0066	38.0	9766	0.2157	0.1291	0.0296

Framework versions

• Transformers 4.45.2
• Pytorch 2.1.0+cu118
• Datasets 3.0.1
• Tokenizers 0.20.1