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---
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
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