---
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-medium-sds200
  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-sds200

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3857
- Wer: 23.6639

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.4466        | 0.4327 | 1000 | 0.4548          | 27.8436 |
| 0.3417        | 0.8654 | 2000 | 0.4154          | 26.0740 |
| 0.2122        | 1.2981 | 3000 | 0.3984          | 25.3984 |
| 0.1734        | 1.7309 | 4000 | 0.3851          | 24.1424 |
| 0.1015        | 2.1636 | 5000 | 0.3857          | 23.6639 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1