---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- generator
metrics:
- wer
model-index:
- name: whisper-medium_ro-80mel
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: generator
      type: generator
      config: default
      split: None
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 0.6935
---

<!-- 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_ro-80mel

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0839
- Wer: 0.6935
- Cer: 1.202

## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 8

### Training results

| Training Loss | Epoch  | Step  | Cer    | Validation Loss | Wer    |
|:-------------:|:------:|:-----:|:------:|:---------------:|:------:|
| 0.1665        | 0.7723 | 2500  | 0.9148 | 0.0839          | 0.7855 |
| 0.108         | 1.5443 | 5000  | 2.035  | 0.0722          | 2.1169 |
| 0.0805        | 2.3163 | 7500  | 2.5247 | 0.0718          | 2.4453 |
| 0.0589        | 3.0883 | 10000 | 2.8635 | 0.0583          | 2.8751 |
| 0.0516        | 3.8606 | 12500 | 3.1281 | 0.0511          | 3.2984 |
| 0.0415        | 4.6326 | 15000 | 3.2897 | 0.0540          | 3.365  |
| 0.0368        | 5.4047 | 17500 | 0.0543 | 3.4251          | 3.298  |
| 0.0315        | 6.1767 | 20000 | 0.0559 | 3.2634          | 3.2452 |
| 0.0316        | 6.9490 | 22500 | 0.0541 | 3.6173          | 3.4674 |
| 0.0267        | 7.7210 | 25000 | 0.0547 | 3.3599          | 3.2115 |

"eval_runtime": 113429.374,
"eval_samples": 27174,
"eval_samples_per_second": 0.24,
"eval_steps_per_second": 0.24,
"test_samples": 12987,
"train_samples": 207181
    
### Framework versions

- Transformers 4.57.0
- Pytorch 2.9.1+cu128
- Datasets 4.4.1
- Tokenizers 0.22.1
