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wav2vec-base-Millad_TIMIT
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.3772
- Wer: 0.6859
- Cer: 0.3217
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5000
- num_epochs: 60
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| No log | 2.36 | 2000 | 2.6233 | 1.0130 | 0.6241 |
| No log | 4.73 | 4000 | 2.2206 | 0.9535 | 0.5032 |
| No log | 7.09 | 6000 | 2.3036 | 0.9368 | 0.5063 |
| 1.235 | 9.46 | 8000 | 1.9932 | 0.9275 | 0.5032 |
| 1.235 | 11.82 | 10000 | 2.0207 | 0.8922 | 0.4498 |
| 1.235 | 14.18 | 12000 | 1.6171 | 0.7993 | 0.3976 |
| 1.235 | 16.55 | 14000 | 1.6729 | 0.8309 | 0.4209 |
| 0.2779 | 18.91 | 16000 | 1.7043 | 0.8141 | 0.4340 |
| 0.2779 | 21.28 | 18000 | 1.7426 | 0.7658 | 0.3960 |
| 0.2779 | 23.64 | 20000 | 1.5230 | 0.7361 | 0.3830 |
| 0.2779 | 26.0 | 22000 | 1.4286 | 0.7658 | 0.3794 |
| 0.1929 | 28.37 | 24000 | 1.4450 | 0.7379 | 0.3644 |
| 0.1929 | 30.73 | 26000 | 1.5922 | 0.7491 | 0.3826 |
| 0.1929 | 33.1 | 28000 | 1.4443 | 0.7454 | 0.3617 |
| 0.1929 | 35.46 | 30000 | 1.5450 | 0.7268 | 0.3621 |
| 0.1394 | 37.83 | 32000 | 1.9268 | 0.7491 | 0.3763 |
| 0.1394 | 40.19 | 34000 | 1.7094 | 0.7342 | 0.3783 |
| 0.1394 | 42.55 | 36000 | 1.4024 | 0.7082 | 0.3494 |
| 0.1394 | 44.92 | 38000 | 1.4467 | 0.6840 | 0.3395 |
| 0.104 | 47.28 | 40000 | 1.4145 | 0.6933 | 0.3407 |
| 0.104 | 49.65 | 42000 | 1.3901 | 0.6970 | 0.3403 |
| 0.104 | 52.01 | 44000 | 1.3589 | 0.6636 | 0.3348 |
| 0.104 | 54.37 | 46000 | 1.3716 | 0.6952 | 0.3340 |
| 0.0781 | 56.74 | 48000 | 1.4025 | 0.6896 | 0.3312 |
| 0.0781 | 59.1 | 50000 | 1.3772 | 0.6859 | 0.3217 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.12.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
