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---
base_model: openai/whisper-tiny
datasets:
- fleurs
language:
- nl
library_name: transformers
license: apache-2.0
metrics:
- wer
tags:
- hf-asr-leaderboard
- generated_from_trainer
model-index:
- name: Whisper Tiny Dutch Punctuation 5k - Chee Li
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Google Fleurs
type: fleurs
config: nl_nl
split: None
args: 'config: nl split: test'
metrics:
- type: wer
value: 193.08638996138995
name: Wer
---
<!-- 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 Dutch Punctuation 5k - Chee Li
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Google Fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0318
- Wer: 193.0864
## 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: 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
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.2297 | 5.1546 | 1000 | 0.8384 | 125.7963 |
| 0.0387 | 10.3093 | 2000 | 0.9217 | 167.4590 |
| 0.0112 | 15.4639 | 3000 | 1.0067 | 201.5565 |
| 0.0081 | 20.6186 | 4000 | 1.0318 | 193.0864 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.20.3
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