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wav2vec2-1b-adapters-mer-drL-v1.2
This model is a fine-tuned version of facebook/mms-1b-all on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: nan
- Wer: 1.0
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: 0.001
- 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: 100
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 4.2542 | 0.02 | 100 | inf | 0.6865 |
| 1.0706 | 0.05 | 200 | inf | 0.6425 |
| 1.054 | 0.07 | 300 | inf | 0.6273 |
| 1.131 | 0.1 | 400 | inf | 0.6269 |
| 1.091 | 0.12 | 500 | inf | 0.6120 |
| 1.209 | 0.14 | 600 | inf | 0.6234 |
| 1.032 | 0.17 | 700 | inf | 0.6036 |
| 1.0873 | 0.19 | 800 | inf | 0.5984 |
| 1.1786 | 0.21 | 900 | inf | 0.6164 |
| 1.0065 | 0.24 | 1000 | inf | 0.5984 |
| 1.1682 | 0.26 | 1100 | inf | 0.9431 |
| 2.716 | 0.29 | 1200 | inf | 1.0 |
| 4.6494 | 0.31 | 1300 | inf | 1.0 |
| 6.5668 | 0.33 | 1400 | inf | 1.0 |
| 8.2033 | 0.36 | 1500 | inf | 0.9997 |
| 8.4349 | 0.38 | 1600 | inf | 1.0 |
| 8.8253 | 0.41 | 1700 | inf | 1.0000 |
| 10.0956 | 0.43 | 1800 | inf | 1.0000 |
| 10.1367 | 0.45 | 1900 | inf | 1.0000 |
| 10.2369 | 0.48 | 2000 | inf | 1.0000 |
| 10.185 | 0.5 | 2100 | inf | 1.0000 |
| 10.1101 | 0.53 | 2200 | inf | 1.0000 |
| 10.1882 | 0.55 | 2300 | inf | 1.0000 |
| 10.1791 | 0.57 | 2400 | inf | 1.0000 |
| 10.095 | 0.6 | 2500 | inf | 1.0000 |
| 10.0778 | 0.62 | 2600 | inf | 1.0000 |
| 10.1407 | 0.64 | 2700 | inf | 1.0000 |
| 10.2779 | 0.67 | 2800 | inf | 1.0000 |
| 10.0577 | 0.69 | 2900 | inf | 1.0000 |
| 10.1342 | 0.72 | 3000 | inf | 1.0000 |
| 10.0638 | 0.74 | 3100 | inf | 1.0000 |
| 10.19 | 0.76 | 3200 | inf | 1.0000 |
| 10.2655 | 0.79 | 3300 | inf | 1.0000 |
| 10.1485 | 0.81 | 3400 | inf | 1.0000 |
| 10.2903 | 0.84 | 3500 | inf | 1.0000 |
| 10.1934 | 0.86 | 3600 | inf | 1.0000 |
| 10.038 | 0.88 | 3700 | inf | 1.0000 |
| 10.1181 | 0.91 | 3800 | inf | 1.0000 |
| 10.1547 | 0.93 | 3900 | inf | 1.0000 |
| 10.2849 | 0.95 | 4000 | inf | 1.0000 |
| 10.1119 | 0.98 | 4100 | inf | 1.0000 |
| 10.2269 | 1.0 | 4200 | inf | 1.0000 |
| 10.1069 | 1.03 | 4300 | inf | 1.0000 |
| 10.2036 | 1.05 | 4400 | inf | 1.0000 |
| 10.1252 | 1.07 | 4500 | inf | 1.0000 |
| 10.0869 | 1.1 | 4600 | inf | 1.0000 |
| 9.9904 | 1.12 | 4700 | inf | 1.0000 |
| 10.1395 | 1.15 | 4800 | inf | 1.0000 |
| 10.0352 | 1.17 | 4900 | inf | 1.0000 |
| 10.3128 | 1.19 | 5000 | inf | 1.0000 |
| 10.1161 | 1.22 | 5100 | inf | 1.0000 |
| 10.1318 | 1.24 | 5200 | inf | 1.0000 |
| 10.1863 | 1.27 | 5300 | inf | 1.0000 |
| 10.1645 | 1.29 | 5400 | inf | 1.0000 |
| 10.3267 | 1.31 | 5500 | inf | 1.0000 |
| 9.9707 | 1.34 | 5600 | inf | 1.0000 |
| 10.2071 | 1.36 | 5700 | inf | 1.0000 |
| 10.0865 | 1.38 | 5800 | inf | 1.0000 |
| 10.3051 | 1.41 | 5900 | inf | 1.0000 |
| 10.203 | 1.43 | 6000 | inf | 1.0000 |
| 10.0152 | 1.46 | 6100 | inf | 1.0000 |
| 10.1636 | 1.48 | 6200 | inf | 1.0000 |
| 10.1885 | 1.5 | 6300 | inf | 1.0000 |
| 10.1876 | 1.53 | 6400 | inf | 1.0000 |
| 10.1075 | 1.55 | 6500 | inf | 1.0000 |
| 10.1307 | 1.58 | 6600 | inf | 1.0000 |
| 10.3877 | 1.6 | 6700 | inf | 1.0000 |
| 10.1684 | 1.62 | 6800 | inf | 1.0000 |
| 10.0601 | 1.65 | 6900 | inf | 1.0000 |
| 10.3244 | 1.67 | 7000 | inf | 1.0000 |
| 10.2978 | 1.69 | 7100 | inf | 1.0000 |
| 10.2394 | 1.72 | 7200 | inf | 1.0000 |
| 10.0721 | 1.74 | 7300 | inf | 1.0000 |
| 10.1697 | 1.77 | 7400 | inf | 1.0000 |
| 10.3378 | 1.79 | 7500 | inf | 1.0000 |
| 10.1207 | 1.81 | 7600 | inf | 1.0000 |
| 10.1188 | 1.84 | 7700 | inf | 1.0000 |
| 10.0966 | 1.86 | 7800 | inf | 1.0000 |
| 10.2581 | 1.89 | 7900 | inf | 1.0000 |
| 10.219 | 1.91 | 8000 | inf | 1.0000 |
| 10.272 | 1.93 | 8100 | inf | 1.0000 |
| 10.1932 | 1.96 | 8200 | inf | 1.0000 |
| 10.0127 | 1.98 | 8300 | nan | 1.0 |
| 0.0 | 2.01 | 8400 | nan | 1.0 |
| 0.0 | 2.03 | 8500 | nan | 1.0 |
| 0.0 | 2.05 | 8600 | nan | 1.0 |
| 0.0 | 2.08 | 8700 | nan | 1.0 |
| 0.0 | 2.1 | 8800 | nan | 1.0 |
| 0.0 | 2.12 | 8900 | nan | 1.0 |
| 0.0 | 2.15 | 9000 | nan | 1.0 |
| 0.0 | 2.17 | 9100 | nan | 1.0 |
| 0.0 | 2.2 | 9200 | nan | 1.0 |
| 0.0 | 2.22 | 9300 | nan | 1.0 |
| 0.0 | 2.24 | 9400 | nan | 1.0 |
| 0.0 | 2.27 | 9500 | nan | 1.0 |
| 0.0 | 2.29 | 9600 | nan | 1.0 |
| 0.0 | 2.32 | 9700 | nan | 1.0 |
| 0.0 | 2.34 | 9800 | nan | 1.0 |
| 0.0 | 2.36 | 9900 | nan | 1.0 |
| 0.0 | 2.39 | 10000 | nan | 1.0 |
| 0.0 | 2.41 | 10100 | nan | 1.0 |
| 0.0 | 2.43 | 10200 | nan | 1.0 |
| 0.0 | 2.46 | 10300 | nan | 1.0 |
| 0.0 | 2.48 | 10400 | nan | 1.0 |
| 0.0 | 2.51 | 10500 | nan | 1.0 |
| 0.0 | 2.53 | 10600 | nan | 1.0 |
| 0.0 | 2.55 | 10700 | nan | 1.0 |
| 0.0 | 2.58 | 10800 | nan | 1.0 |
| 0.0 | 2.6 | 10900 | nan | 1.0 |
| 0.0 | 2.63 | 11000 | nan | 1.0 |
| 0.0 | 2.65 | 11100 | nan | 1.0 |
| 0.0 | 2.67 | 11200 | nan | 1.0 |
| 0.0 | 2.7 | 11300 | nan | 1.0 |
| 0.0 | 2.72 | 11400 | nan | 1.0 |
| 0.0 | 2.75 | 11500 | nan | 1.0 |
| 0.0 | 2.77 | 11600 | nan | 1.0 |
| 0.0 | 2.79 | 11700 | nan | 1.0 |
| 0.0 | 2.82 | 11800 | nan | 1.0 |
| 0.0 | 2.84 | 11900 | nan | 1.0 |
| 0.0 | 2.86 | 12000 | nan | 1.0 |
| 0.0 | 2.89 | 12100 | nan | 1.0 |
| 0.0 | 2.91 | 12200 | nan | 1.0 |
| 0.0 | 2.94 | 12300 | nan | 1.0 |
| 0.0 | 2.96 | 12400 | nan | 1.0 |
| 0.0 | 2.98 | 12500 | nan | 1.0 |
| 0.0 | 3.01 | 12600 | nan | 1.0 |
| 0.0 | 3.03 | 12700 | nan | 1.0 |
| 0.0 | 3.06 | 12800 | nan | 1.0 |
| 0.0 | 3.08 | 12900 | nan | 1.0 |
| 0.0 | 3.1 | 13000 | nan | 1.0 |
| 0.0 | 3.13 | 13100 | nan | 1.0 |
| 0.0 | 3.15 | 13200 | nan | 1.0 |
| 0.0 | 3.17 | 13300 | nan | 1.0 |
| 0.0 | 3.2 | 13400 | nan | 1.0 |
| 0.0 | 3.22 | 13500 | nan | 1.0 |
| 0.0 | 3.25 | 13600 | nan | 1.0 |
| 0.0 | 3.27 | 13700 | nan | 1.0 |
| 0.0 | 3.29 | 13800 | nan | 1.0 |
| 0.0 | 3.32 | 13900 | nan | 1.0 |
| 0.0 | 3.34 | 14000 | nan | 1.0 |
| 0.0 | 3.37 | 14100 | nan | 1.0 |
| 0.0 | 3.39 | 14200 | nan | 1.0 |
| 0.0 | 3.41 | 14300 | nan | 1.0 |
| 0.0 | 3.44 | 14400 | nan | 1.0 |
| 0.0 | 3.46 | 14500 | nan | 1.0 |
| 0.0 | 3.49 | 14600 | nan | 1.0 |
| 0.0 | 3.51 | 14700 | nan | 1.0 |
| 0.0 | 3.53 | 14800 | nan | 1.0 |
| 0.0 | 3.56 | 14900 | nan | 1.0 |
| 0.0 | 3.58 | 15000 | nan | 1.0 |
| 0.0 | 3.6 | 15100 | nan | 1.0 |
| 0.0 | 3.63 | 15200 | nan | 1.0 |
| 0.0 | 3.65 | 15300 | nan | 1.0 |
| 0.0 | 3.68 | 15400 | nan | 1.0 |
| 0.0 | 3.7 | 15500 | nan | 1.0 |
| 0.0 | 3.72 | 15600 | nan | 1.0 |
| 0.0 | 3.75 | 15700 | nan | 1.0 |
| 0.0 | 3.77 | 15800 | nan | 1.0 |
| 0.0 | 3.8 | 15900 | nan | 1.0 |
| 0.0 | 3.82 | 16000 | nan | 1.0 |
| 0.0 | 3.84 | 16100 | nan | 1.0 |
| 0.0 | 3.87 | 16200 | nan | 1.0 |
| 0.0 | 3.89 | 16300 | nan | 1.0 |
| 0.0 | 3.92 | 16400 | nan | 1.0 |
| 0.0 | 3.94 | 16500 | nan | 1.0 |
| 0.0 | 3.96 | 16600 | nan | 1.0 |
| 0.0 | 3.99 | 16700 | nan | 1.0 |
Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.3
- Tokenizers 0.13.3
