wav2vec2-base-hyperVQ-timit-fine-tuned
This model is a fine-tuned version of wav2vec2-pretrained-base-hyperVQ on the TIMIT_ASR - NA dataset. It achieves the following results on the evaluation set:
- Loss: 3.3628
- Wer: 0.9993
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 3.2725 | 10.0 | 1450 | 3.4699 | 1.0006 |
| 3.1682 | 20.0 | 2900 | 3.3628 | 0.9993 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.3.0.dev20231229+cu118
- Datasets 2.16.0
- Tokenizers 0.15.0
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Dataset used to train subatomicseer/wav2vec2-base-hyperVQ-timit-fine-tuned
Evaluation results
- Wer on TIMIT_ASR - NAtest set self-reported0.999