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wav2vec2-librispeech-clean-100h-demo-dist
This model is a fine-tuned version of facebook/wav2vec2-large-lv60 on the LIBRISPEECH_ASR - CLEAN dataset. It achieves the following results on the evaluation set:
- Loss: 0.0572
- Wer: 0.0417
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 |
|---|---|---|---|---|
| 3.399 | 0.11 | 100 | 3.6153 | 1.0 |
| 2.8892 | 0.22 | 200 | 2.8963 | 1.0 |
| 2.8284 | 0.34 | 300 | 2.8574 | 1.0 |
| 0.7347 | 0.45 | 400 | 0.6158 | 0.4850 |
| 0.1138 | 0.56 | 500 | 0.2038 | 0.1560 |
| 0.248 | 0.67 | 600 | 0.1274 | 0.1024 |
| 0.2586 | 0.78 | 700 | 0.1108 | 0.0876 |
| 0.0733 | 0.9 | 800 | 0.0936 | 0.0762 |
| 0.044 | 1.01 | 900 | 0.0834 | 0.0662 |
| 0.0393 | 1.12 | 1000 | 0.0792 | 0.0622 |
| 0.0941 | 1.23 | 1100 | 0.0769 | 0.0627 |
| 0.036 | 1.35 | 1200 | 0.0731 | 0.0603 |
| 0.0768 | 1.46 | 1300 | 0.0713 | 0.0559 |
| 0.0518 | 1.57 | 1400 | 0.0686 | 0.0537 |
| 0.0815 | 1.68 | 1500 | 0.0639 | 0.0515 |
| 0.0603 | 1.79 | 1600 | 0.0636 | 0.0500 |
| 0.056 | 1.91 | 1700 | 0.0609 | 0.0480 |
| 0.0265 | 2.02 | 1800 | 0.0621 | 0.0465 |
| 0.0496 | 2.13 | 1900 | 0.0607 | 0.0449 |
| 0.0436 | 2.24 | 2000 | 0.0591 | 0.0446 |
| 0.0421 | 2.35 | 2100 | 0.0590 | 0.0428 |
| 0.0641 | 2.47 | 2200 | 0.0603 | 0.0443 |
| 0.0466 | 2.58 | 2300 | 0.0580 | 0.0429 |
| 0.0132 | 2.69 | 2400 | 0.0574 | 0.0423 |
| 0.0073 | 2.8 | 2500 | 0.0586 | 0.0417 |
| 0.0021 | 2.91 | 2600 | 0.0574 | 0.0412 |
Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3
