# Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("pnparam/loso_F04")
model = AutoModelForCTC.from_pretrained("pnparam/loso_F04")Quick Links
loso_F04
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0791
- Wer: 1.4780
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 |
|---|---|---|---|---|
| 9.9264 | 0.96 | 500 | 3.6742 | 1.0 |
| 2.6962 | 1.91 | 1000 | 1.7830 | 2.6233 |
| 1.1118 | 2.87 | 1500 | 0.5233 | 1.8458 |
| 0.3692 | 3.82 | 2000 | 0.1670 | 1.2423 |
| 0.1671 | 4.78 | 2500 | 0.1289 | 1.3700 |
| 0.0897 | 5.74 | 3000 | 0.1031 | 1.5110 |
| 0.0656 | 6.69 | 3500 | 0.0791 | 1.4780 |
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/loso_F04")