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WavLM-large-CORAA-pt
This model is a fine-tuned version of microsoft/wavlm-large on CORAA dataset. It achieves the following results on the evaluation set:
- Loss: 0.6144
- Wer: 0.3840
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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 40000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| No log | 0.04 | 1000 | 1.9230 | 0.9960 |
| 5.153 | 0.08 | 2000 | 1.3733 | 0.8444 |
| 5.153 | 0.13 | 3000 | 1.1992 | 0.7362 |
| 1.367 | 0.17 | 4000 | 1.1289 | 0.6957 |
| 1.367 | 0.21 | 5000 | 1.0357 | 0.6470 |
| 1.1824 | 0.25 | 6000 | 1.0216 | 0.6201 |
| 1.1824 | 0.29 | 7000 | 0.9338 | 0.6036 |
| 1.097 | 0.33 | 8000 | 0.9149 | 0.5760 |
| 1.097 | 0.38 | 9000 | 0.8885 | 0.5541 |
| 1.0254 | 0.42 | 10000 | 0.8678 | 0.5366 |
| 1.0254 | 0.46 | 11000 | 0.8349 | 0.5323 |
| 0.9782 | 0.5 | 12000 | 0.8230 | 0.5155 |
| 0.9782 | 0.54 | 13000 | 0.8245 | 0.5049 |
| 0.9448 | 0.59 | 14000 | 0.7802 | 0.4990 |
| 0.9448 | 0.63 | 15000 | 0.7650 | 0.4900 |
| 0.9092 | 0.67 | 16000 | 0.7665 | 0.4796 |
| 0.9092 | 0.71 | 17000 | 0.7568 | 0.4795 |
| 0.8764 | 0.75 | 18000 | 0.7403 | 0.4615 |
| 0.8764 | 0.8 | 19000 | 0.7219 | 0.4644 |
| 0.8498 | 0.84 | 20000 | 0.7180 | 0.4502 |
| 0.8498 | 0.88 | 21000 | 0.7017 | 0.4436 |
| 0.8278 | 0.92 | 22000 | 0.6992 | 0.4395 |
| 0.8278 | 0.96 | 23000 | 0.7021 | 0.4329 |
| 0.8077 | 1.0 | 24000 | 0.6892 | 0.4265 |
| 0.8077 | 1.05 | 25000 | 0.6940 | 0.4248 |
| 0.7486 | 1.09 | 26000 | 0.6767 | 0.4202 |
| 0.7486 | 1.13 | 27000 | 0.6734 | 0.4150 |
| 0.7459 | 1.17 | 28000 | 0.6650 | 0.4152 |
| 0.7459 | 1.21 | 29000 | 0.6559 | 0.4078 |
| 0.7304 | 1.26 | 30000 | 0.6536 | 0.4088 |
| 0.7304 | 1.3 | 31000 | 0.6537 | 0.4025 |
| 0.7183 | 1.34 | 32000 | 0.6462 | 0.4008 |
| 0.7183 | 1.38 | 33000 | 0.6381 | 0.3973 |
| 0.7059 | 1.42 | 34000 | 0.6266 | 0.3930 |
| 0.7059 | 1.46 | 35000 | 0.6280 | 0.3921 |
| 0.6983 | 1.51 | 36000 | 0.6248 | 0.3897 |
| 0.6983 | 1.55 | 37000 | 0.6275 | 0.3872 |
| 0.6892 | 1.59 | 38000 | 0.6199 | 0.3852 |
| 0.6892 | 1.63 | 39000 | 0.6180 | 0.3842 |
| 0.691 | 1.67 | 40000 | 0.6144 | 0.3840 |
Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
