---
library_name: transformers
language:
- dk
license: apache-2.0
base_model: openai/whisper-large
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- alexandrainst/ftspeech
metrics:
- wer
model-index:
- name: Whisper Large FTSpeech - Your Name
  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: 24.476331512025737
---

<!-- 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 Large FTSpeech - Your Name

This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the ftspeech dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3820
- Wer: 24.4763

## 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: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.5793        | 0.0032 | 200  | 0.5536          | 30.4519 |
| 0.4187        | 0.0064 | 400  | 0.4508          | 27.5208 |
| 0.3587        | 0.0096 | 600  | 0.4125          | 25.5569 |
| 0.3477        | 0.0129 | 800  | 0.3907          | 24.9318 |
| 0.3786        | 0.0161 | 1000 | 0.3820          | 24.4763 |


### Framework versions

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