wav2vec2-base-self-331-colab
This model is a fine-tuned version of facebook/wav2vec2-base-960h on the audiofolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.3282
- Wer: 0.1501
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: 1e-05
- 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: 500
- num_epochs: 300
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 2.3444 | 30.77 | 200 | 2.1940 | 0.9841 |
| 1.972 | 61.54 | 400 | 1.4582 | 0.8167 |
| 1.3875 | 92.31 | 600 | 0.8476 | 0.5902 |
| 0.9092 | 123.08 | 800 | 0.5445 | 0.3636 |
| 0.6382 | 153.85 | 1000 | 0.4129 | 0.2641 |
| 0.5789 | 184.62 | 1200 | 0.3497 | 0.1876 |
| 0.4632 | 215.38 | 1400 | 0.3478 | 0.1616 |
| 0.4474 | 246.15 | 1600 | 0.3394 | 0.1486 |
| 0.429 | 276.92 | 1800 | 0.3282 | 0.1501 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for St4n/wav2vec2-base-self-331-colab
Base model
facebook/wav2vec2-base-960h