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: 0.1597164303586322

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: 0.2858
• Wer: 0.1597
• Cer: 0.0355

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: 8
• eval_batch_size: 4
• 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
0.8824	1.0	198	0.2803	0.2968	0.0591
0.2156	2.0	396	0.2128	0.2389	0.0493
0.1589	3.0	594	0.2110	0.2207	0.0458
0.1277	4.0	792	0.1942	0.1964	0.0422
0.1055	5.0	990	0.1698	0.1873	0.0390
0.087	6.0	1188	0.1771	0.1879	0.0428
0.0738	7.0	1386	0.1850	0.1856	0.0406
0.0589	8.0	1584	0.1799	0.1681	0.0381
0.0573	9.0	1782	0.1882	0.1863	0.0400
0.0481	10.0	1980	0.2275	0.1664	0.0359
0.0425	11.0	2178	0.2135	0.1696	0.0379
0.039	12.0	2376	0.2035	0.1600	0.0354
0.0351	13.0	2574	0.2095	0.1683	0.0366
0.0326	14.0	2772	0.2070	0.1589	0.0353
0.0302	15.0	2970	0.2526	0.1708	0.0367
0.0308	16.0	3168	0.2441	0.1642	0.0367
0.0255	17.0	3366	0.2504	0.1678	0.0365
0.0213	18.0	3564	0.2844	0.1721	0.0377
0.0225	19.0	3762	0.2602	0.1721	0.0383
0.02	20.0	3960	0.2746	0.1610	0.0351
0.0181	21.0	4158	0.2767	0.1668	0.0364
0.0149	22.0	4356	0.2442	0.1633	0.0355
0.0136	23.0	4554	0.2765	0.1677	0.0362
0.0156	24.0	4752	0.2858	0.1597	0.0355

Framework versions

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