---
license: apache-2.0
base_model: openai/whisper-medium
tags:
- whisper-event
- generated_from_trainer
datasets:
- common_voice_14_0
metrics:
- wer
model-index:
- name: Whisper da-nst
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_14_0
      type: common_voice_14_0
      config: da
      split: test
      args: da
    metrics:
    - name: Wer
      type: wer
      value: 35.3093792833366
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Whisper da-nst

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co./openai/whisper-medium) on the common_voice_14_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7234
- Wer: 35.3094

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0133        | 4.04  | 1000 | 0.6362          | 48.9279 |
| 0.0025        | 9.04  | 2000 | 0.6635          | 37.4731 |
| 0.0001        | 14.03 | 3000 | 0.6959          | 34.1296 |
| 0.0001        | 19.03 | 4000 | 0.7166          | 35.1821 |
| 0.0           | 24.03 | 5000 | 0.7234          | 35.3094 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1