---
library_name: transformers
language:
- da
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- alexandrainst/ftspeech
metrics:
- wer
model-index:
- name: Whisper tiny FTSpeech - Julie
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: ftspeech
      type: alexandrainst/ftspeech
      args: 'split: test'
    metrics:
    - name: Wer
      type: wer
      value: 97.17612214675995
---

<!-- 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 tiny FTSpeech - Julie

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the ftspeech dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6006
- Wer: 97.1761

## 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: Use OptimizerNames.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: 200
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.9429        | 0.0080 | 500  | 0.9411          | 87.9967 |
| 0.7782        | 0.0161 | 1000 | 0.7891          | 91.5049 |
| 0.7176        | 0.0241 | 1500 | 0.7164          | 89.9351 |
| 0.6545        | 0.0321 | 2000 | 0.6686          | 85.8745 |
| 0.6171        | 0.0402 | 2500 | 0.6395          | 91.9062 |
| 0.5767        | 0.0482 | 3000 | 0.6176          | 94.2052 |
| 0.546         | 0.0562 | 3500 | 0.6006          | 97.1761 |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.21.0