hubert-librispeech-clean-100h-demo-dist
This model is a fine-tuned version of facebook/hubert-large-ll60k on the LIBRISPEECH_ASR - CLEAN dataset. It achieves the following results on the evaluation set:
- Loss: 0.0984
- Wer: 0.0883
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: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 2.9031 | 0.11 | 100 | 2.9220 | 1.0 |
| 2.6437 | 0.22 | 200 | 2.6268 | 1.0 |
| 0.3934 | 0.34 | 300 | 0.4860 | 0.4182 |
| 0.3531 | 0.45 | 400 | 0.3088 | 0.2894 |
| 0.2255 | 0.56 | 500 | 0.2568 | 0.2426 |
| 0.3379 | 0.67 | 600 | 0.2073 | 0.2011 |
| 0.2419 | 0.78 | 700 | 0.1849 | 0.1838 |
| 0.2128 | 0.9 | 800 | 0.1662 | 0.1690 |
| 0.1341 | 1.01 | 900 | 0.1600 | 0.1541 |
| 0.0946 | 1.12 | 1000 | 0.1431 | 0.1404 |
| 0.1643 | 1.23 | 1100 | 0.1373 | 0.1304 |
| 0.0663 | 1.35 | 1200 | 0.1293 | 0.1307 |
| 0.162 | 1.46 | 1300 | 0.1247 | 0.1266 |
| 0.1433 | 1.57 | 1400 | 0.1246 | 0.1262 |
| 0.1581 | 1.68 | 1500 | 0.1219 | 0.1154 |
| 0.1036 | 1.79 | 1600 | 0.1127 | 0.1081 |
| 0.1352 | 1.91 | 1700 | 0.1087 | 0.1040 |
| 0.0471 | 2.02 | 1800 | 0.1085 | 0.1005 |
| 0.0945 | 2.13 | 1900 | 0.1066 | 0.0973 |
| 0.0843 | 2.24 | 2000 | 0.1102 | 0.0964 |
| 0.0774 | 2.35 | 2100 | 0.1079 | 0.0940 |
| 0.0952 | 2.47 | 2200 | 0.1056 | 0.0927 |
| 0.0635 | 2.58 | 2300 | 0.1026 | 0.0920 |
| 0.0665 | 2.69 | 2400 | 0.1012 | 0.0905 |
| 0.034 | 2.8 | 2500 | 0.1009 | 0.0900 |
| 0.0251 | 2.91 | 2600 | 0.0993 | 0.0883 |
Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3
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