---
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-10hrs-model
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: swagen
      type: swagen
    metrics:
    - name: Wer
      type: wer
      value: 0.3075245365321701
---

<!-- 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-10hrs-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: 0.4674
- Wer: 0.3075

## 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    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 2.6418        | 0.2385 | 200  | 0.8312          | 0.4835 |
| 1.9381        | 0.4769 | 400  | 0.6574          | 0.4050 |
| 1.7528        | 0.7154 | 600  | 0.5706          | 0.3545 |
| 1.7421        | 0.9538 | 800  | 0.5140          | 0.3504 |
| 0.9259        | 1.1931 | 1000 | 0.5175          | 0.3262 |
| 0.8254        | 1.4316 | 1200 | 0.4978          | 0.3096 |
| 0.9616        | 1.6700 | 1400 | 0.4755          | 0.2999 |
| 0.7893        | 1.9085 | 1600 | 0.4674          | 0.3075 |
| 0.3547        | 2.1478 | 1800 | 0.4848          | 0.3158 |
| 0.3568        | 2.3863 | 2000 | 0.4913          | 0.2616 |
| 0.3759        | 2.6247 | 2200 | 0.4685          | 0.3104 |


### Framework versions

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