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wav2vec2-large-xlsr-53-demo1
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice dataset. It achieves the following results on the evaluation set:
- Loss: 0.9692
- Wer: 0.8462
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: 0.0003
- train_batch_size: 5
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 10
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
12.978 | 0.06 | 100 | 3.5377 | 1.0 |
3.5026 | 0.13 | 200 | 3.4366 | 1.0 |
3.4084 | 0.19 | 300 | 3.3831 | 1.0 |
3.3551 | 0.26 | 400 | 3.2563 | 1.0 |
3.2668 | 0.32 | 500 | 3.2109 | 1.0 |
2.9398 | 0.38 | 600 | 2.4548 | 0.9987 |
2.2204 | 0.45 | 700 | 1.8870 | 1.0135 |
1.7401 | 0.51 | 800 | 1.6816 | 1.0247 |
1.5748 | 0.57 | 900 | 1.4741 | 0.9953 |
1.4539 | 0.64 | 1000 | 1.4573 | 0.9852 |
1.3612 | 0.7 | 1100 | 1.3534 | 0.9529 |
1.3328 | 0.77 | 1200 | 1.3380 | 0.9320 |
1.2459 | 0.83 | 1300 | 1.2984 | 0.9247 |
1.1976 | 0.89 | 1400 | 1.2515 | 0.9252 |
1.1593 | 0.96 | 1500 | 1.2345 | 0.9030 |
1.1094 | 1.02 | 1600 | 1.2135 | 0.9305 |
1.0485 | 1.09 | 1700 | 1.2045 | 0.9121 |
0.9893 | 1.15 | 1800 | 1.1876 | 0.8990 |
1.0099 | 1.21 | 1900 | 1.1663 | 0.8889 |
0.982 | 1.28 | 2000 | 1.1674 | 0.8901 |
0.9975 | 1.34 | 2100 | 1.1181 | 0.8812 |
0.952 | 1.4 | 2200 | 1.1119 | 0.8817 |
0.9311 | 1.47 | 2300 | 1.0786 | 0.8773 |
0.9398 | 1.53 | 2400 | 1.1016 | 0.8720 |
0.9148 | 1.6 | 2500 | 1.0878 | 0.8778 |
0.9114 | 1.66 | 2600 | 1.1004 | 0.8712 |
0.902 | 1.72 | 2700 | 1.0223 | 0.8744 |
0.8978 | 1.79 | 2800 | 1.0616 | 0.8459 |
0.8675 | 1.85 | 2900 | 1.0974 | 0.8643 |
0.8373 | 1.92 | 3000 | 1.0389 | 0.8547 |
0.8575 | 1.98 | 3100 | 1.0388 | 0.8480 |
0.8313 | 2.04 | 3200 | 1.0001 | 0.8648 |
0.7357 | 2.11 | 3300 | 1.0222 | 0.8705 |
0.743 | 2.17 | 3400 | 1.0859 | 0.8765 |
0.7306 | 2.23 | 3500 | 1.0109 | 0.8515 |
0.7525 | 2.3 | 3600 | 0.9942 | 0.8619 |
0.7308 | 2.36 | 3700 | 1.0004 | 0.8578 |
0.7266 | 2.43 | 3800 | 1.0003 | 0.8497 |
0.737 | 2.49 | 3900 | 1.0146 | 0.8505 |
0.7202 | 2.55 | 4000 | 1.0172 | 0.8653 |
0.6945 | 2.62 | 4100 | 0.9894 | 0.8415 |
0.6633 | 2.68 | 4200 | 0.9894 | 0.8496 |
0.6972 | 2.75 | 4300 | 0.9805 | 0.8505 |
0.6872 | 2.81 | 4400 | 0.9939 | 0.8509 |
0.7238 | 2.87 | 4500 | 0.9740 | 0.8532 |
0.6847 | 2.94 | 4600 | 0.9692 | 0.8462 |
Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3