metadata
language:
- da
license: apache-2.0
tags:
- generated_from_trainer
- hf-asr-leaderboard
- whisper-event
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
base_model: openai/whisper-medium
model-index:
- name: Whisper Medium Danish (CV11 + FLEAURS)
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: mozilla-foundation/common_voice_11_0
type: mozilla-foundation/common_voice_11_0
config: da
split: test
metrics:
- type: wer
value: 13.708574434508153
name: Wer
Whisper Medium Danish (CV11 + FLEAURS)
This model is a fine-tuned version of openai/whisper-medium on the mozilla-foundation/common_voice_11_0,google/fleurs da,da_dk dataset. It achieves the following results on the evaluation set:
- Loss: 0.5814
- Wer: 13.7086
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: 8e-06
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0265 | 3.14 | 1000 | 0.3690 | 14.7607 |
| 0.0063 | 6.29 | 2000 | 0.4342 | 14.0926 |
| 0.0016 | 9.43 | 3000 | 0.4847 | 14.3609 |
| 0.002 | 12.58 | 4000 | 0.4919 | 14.1715 |
| 0.0013 | 15.72 | 5000 | 0.5114 | 14.2294 |
| 0.0014 | 18.87 | 6000 | 0.5197 | 13.9137 |
| 0.0003 | 22.01 | 7000 | 0.5422 | 14.1978 |
| 0.0001 | 25.16 | 8000 | 0.5659 | 13.8716 |
| 0.0001 | 28.3 | 9000 | 0.5772 | 13.7296 |
| 0.0001 | 31.45 | 10000 | 0.5814 | 13.7086 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2