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---
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
should probably proofread and complete it, then remove this comment. -->
# 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
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