# Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("pnparam/dys_asr")
model = AutoModelForCTC.from_pretrained("pnparam/dys_asr")Quick Links
dys_asr
This model is a fine-tuned version of facebook/wav2vec2-large-960h-lv60-self on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0256
- Wer: 1.0
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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 7
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 11.2099 | 1.36 | 500 | 2.6451 | 1.0 |
| 2.1112 | 2.72 | 1000 | 0.5892 | 1.5389 |
| 0.4699 | 4.09 | 1500 | 0.0740 | 1.0467 |
| 0.1584 | 5.45 | 2000 | 0.0305 | 1.0187 |
| 0.1005 | 6.81 | 2500 | 0.0256 | 1.0 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.13.1+cu116
- Datasets 1.18.3
- Tokenizers 0.13.2
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="pnparam/dys_asr")