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---
base_model: openai/whisper-base
datasets:
- fleurs
language:
- de
library_name: transformers
license: apache-2.0
metrics:
- wer
tags:
- hf-asr-leaderboard
- generated_from_trainer
model-index:
- name: Whisper Base German Punctuation 4k - Chee Li
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Google Fleurs
type: fleurs
config: de_de
split: None
args: 'config: de split: test'
metrics:
- type: wer
value: 42.652697521196735
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 Base German Punctuation 4k - Chee Li
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Google Fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6091
- Wer: 42.6527
## 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 |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.0578 | 4.7619 | 1000 | 0.4862 | 36.8202 |
| 0.0052 | 9.5238 | 2000 | 0.5652 | 36.5610 |
| 0.0028 | 14.2857 | 3000 | 0.5972 | 41.4808 |
| 0.0023 | 19.0476 | 4000 | 0.6091 | 42.6527 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.20.3
|