metadata

license: mit
tags:
  - generated_from_trainer
base_model: facebook/w2v-bert-2.0
datasets:
  - generator
metrics:
  - wer
model-index:
  - name: wav2vec2-bert-fon
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: generator
          type: generator
          config: default
          split: train
          args: default
        metrics:
          - type: wer
            value: 0.13241653693132677
            name: Wer

wav2vec2-bert-fon

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

Loss: 0.1612
Wer: 0.1324

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: 2
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 4
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
No log	0.18	250	1.2212	0.8079
2.1756	0.35	500	0.6697	0.6058
2.1756	0.53	750	0.5137	0.4606
0.5041	0.7	1000	0.4337	0.4234
0.5041	0.88	1250	0.3452	0.3529
0.426	1.05	1500	0.2770	0.2910
0.426	1.23	1750	0.2681	0.2439
0.2916	1.4	2000	0.2423	0.2155
0.2916	1.58	2250	0.2342	0.2077
0.2591	1.75	2500	0.1986	0.1791
0.2591	1.93	2750	0.1864	0.1597
0.2261	2.1	3000	0.1712	0.1419
0.2261	2.28	3250	0.1786	0.1497
0.1564	2.45	3500	0.1612	0.1324
0.1564	2.63	3750	0.1730	0.1591
0.1542	2.8	4000	0.1558	0.1364
0.1542	2.98	4250	0.1493	0.1581
0.1559	3.15	4500	0.1489	0.1347
0.1559	3.33	4750	0.2036	0.1486
0.1992	3.5	5000	0.2644	0.1582
0.1992	3.68	5250	0.2401	0.1878
0.291	3.85	5500	0.2409	0.1749

Framework versions

Transformers 4.39.3
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.15.2