|
--- |
|
library_name: transformers |
|
license: apache-2.0 |
|
base_model: openai/whisper-medium |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- wer |
|
model-index: |
|
- name: Whisper Medium with 1000 orders SSD superU |
|
results: [] |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# Whisper Medium with 1000 orders SSD superU |
|
|
|
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co./openai/whisper-medium) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 3.0551 |
|
- Wer: 114.5969 |
|
|
|
## 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: 1e-05 |
|
- 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_steps: 500 |
|
- training_steps: 2000 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer | |
|
|:-------------:|:------:|:----:|:---------------:|:--------:| |
|
| 3.3178 | 0.3831 | 100 | 3.1949 | 211.3345 | |
|
| 2.5484 | 0.7663 | 200 | 2.3725 | 319.0767 | |
|
| 2.0593 | 1.1494 | 300 | 2.2722 | 248.3126 | |
|
| 2.0603 | 1.5326 | 400 | 2.2479 | 253.4519 | |
|
| 2.0258 | 1.9157 | 500 | 2.2259 | 302.7082 | |
|
| 1.6141 | 2.2989 | 600 | 2.2450 | 191.7982 | |
|
| 1.729 | 2.6820 | 700 | 2.2441 | 221.9182 | |
|
| 1.4741 | 3.0651 | 800 | 2.3114 | 169.6906 | |
|
| 1.351 | 3.4483 | 900 | 2.3248 | 199.7299 | |
|
| 1.4636 | 3.8314 | 1000 | 2.3174 | 177.5240 | |
|
| 1.1092 | 4.2146 | 1100 | 2.4755 | 143.9276 | |
|
| 1.0713 | 4.5977 | 1200 | 2.4954 | 122.6198 | |
|
| 1.0927 | 4.9808 | 1300 | 2.4714 | 153.5291 | |
|
| 0.7693 | 5.3640 | 1400 | 2.6916 | 124.3843 | |
|
| 0.7594 | 5.7471 | 1500 | 2.7017 | 125.2438 | |
|
| 0.576 | 6.1303 | 1600 | 2.8599 | 117.2104 | |
|
| 0.5304 | 6.5134 | 1700 | 2.9010 | 120.1431 | |
|
| 0.508 | 6.8966 | 1800 | 2.9175 | 120.5536 | |
|
| 0.3794 | 7.2797 | 1900 | 3.0556 | 111.9378 | |
|
| 0.3816 | 7.6628 | 2000 | 3.0551 | 114.5969 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.46.2 |
|
- Pytorch 2.2.2+cu121 |
|
- Datasets 3.1.0 |
|
- Tokenizers 0.20.3 |
|
|
