# Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("Priyanship/base_sami_22k_cont_pt_ftpseudo_shuff142wr20esp5")
model = AutoModelForCTC.from_pretrained("Priyanship/base_sami_22k_cont_pt_ftpseudo_shuff142wr20esp5")Quick Links
base_sami_22k_cont_pt_ftpseudo_shuff142wr20esp5
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1150.0363
- Wer: 1.0
- Cer: 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: 0.0005
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 20.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 4511.6611 | 1.0 | 1080 | 1149.9353 | 1.0 | 1.0 |
| 3591.4514 | 2.0 | 2160 | 1155.4658 | 1.0 | 1.0 |
| 3649.9262 | 3.0 | 3240 | 1155.6082 | 1.0 | 1.0 |
| 3789.6616 | 4.0 | 4320 | 1146.7284 | 1.0 | 1.0 |
| 3752.8502 | 5.0 | 5400 | 1158.9340 | 1.0 | 1.0 |
| 3673.8093 | 6.0 | 6480 | 1151.3530 | 1.0 | 1.0 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1
- Datasets 3.2.0
- Tokenizers 0.21.0
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Priyanship/base_sami_22k_cont_pt_ftpseudo_shuff142wr20esp5")