metadata
library_name: peft
language:
- it
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- easycall-v2-disordersvoice
metrics:
- wer
model-index:
- name: Whisper Medium
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: easycall-v2-disordersvoice
type: easycall-v2-disordersvoice
split: None
metrics:
- type: wer
value: 18.95910780669145
name: Wer
Whisper Medium
This model is a fine-tuned version of openai/whisper-medium on the easycall-v2-disordersvoice dataset. It achieves the following results on the evaluation set:
- Loss: 0.2372
- Wer: 18.9591
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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use adafactor and the args are: No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 7
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| No log | 1.0 | 151 | 0.3071 | 39.0335 |
| No log | 2.0 | 302 | 0.2418 | 20.0743 |
| No log | 3.0 | 453 | 0.2288 | 18.0917 |
| 0.3944 | 4.0 | 604 | 0.2240 | 19.0830 |
| 0.3944 | 5.0 | 755 | 0.2298 | 17.5960 |
| 0.3944 | 6.0 | 906 | 0.2339 | 18.8352 |
| 0.0257 | 7.0 | 1057 | 0.2372 | 18.9591 |
Framework versions
- PEFT 0.14.0
- Transformers 4.48.1
- Pytorch 2.2.2+cu121
- Datasets 2.19.2
- Tokenizers 0.21.0