---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- swagen
metrics:
- wer
model-index:
- name: whisper-medium-swagen-combined-25hrs-model
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: swagen
      type: swagen
    metrics:
    - name: Wer
      type: wer
      value: 0.25892857142857145
---

<!-- 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-swagen-combined-25hrs-model

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co./openai/whisper-medium) on the swagen dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3662
- Wer: 0.2589

## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use 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
- num_epochs: 30.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 2.8233        | 0.0993 | 200  | 0.8047          | 0.4897 |
| 1.9329        | 0.1986 | 400  | 0.6191          | 0.4011 |
| 1.6927        | 0.2980 | 600  | 0.5421          | 0.3791 |
| 1.6183        | 0.3973 | 800  | 0.4889          | 0.3210 |
| 1.4431        | 0.4966 | 1000 | 0.4684          | 0.2866 |
| 1.4117        | 0.5959 | 1200 | 0.4258          | 0.2650 |
| 1.2699        | 0.6952 | 1400 | 0.4222          | 0.2665 |
| 1.0532        | 0.7945 | 1600 | 0.4108          | 0.2513 |
| 1.0589        | 0.8939 | 1800 | 0.3982          | 0.2291 |
| 1.1856        | 0.9932 | 2000 | 0.3853          | 0.2355 |
| 0.6692        | 1.0929 | 2200 | 0.4001          | 0.2650 |
| 0.6505        | 1.1922 | 2400 | 0.3919          | 0.2389 |
| 0.6613        | 1.2915 | 2600 | 0.3809          | 0.2385 |
| 0.6194        | 1.3908 | 2800 | 0.3873          | 0.2343 |
| 0.6358        | 1.4901 | 3000 | 0.3850          | 0.2142 |
| 0.6208        | 1.5894 | 3200 | 0.3779          | 0.2388 |
| 0.5932        | 1.6888 | 3400 | 0.3725          | 0.2040 |
| 0.5797        | 1.7881 | 3600 | 0.3712          | 0.2092 |
| 0.5707        | 1.8874 | 3800 | 0.3738          | 0.2342 |
| 0.5928        | 1.9867 | 4000 | 0.3662          | 0.2589 |
| 0.2626        | 2.0864 | 4200 | 0.3803          | 0.2697 |
| 0.2557        | 2.1857 | 4400 | 0.3853          | 0.2102 |
| 0.3342        | 2.2850 | 4600 | 0.3891          | 0.2062 |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0