metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
- wer_norm
model-index:
- name: openai/whisper-medium
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_11_0
type: common_voice_11_0
config: fr
split: test
args: fr
metrics:
- name: Wer
type: wer
value: 15.89689189275029
- name: Wer norm
type: wer
value: 11.1406
openai/whisper-medium
This model is a fine-tuned version of openai/whisper-medium on the common_voice_11_0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2664
- Wer: 15.8969
- Wer Norm: 11.1406
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: 32
- eval_batch_size: 16
- 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: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Wer Norm |
|---|---|---|---|---|---|
| 0.2695 | 0.2 | 1000 | 0.3080 | 17.8083 | 12.9791 |
| 0.2099 | 0.4 | 2000 | 0.2981 | 17.4792 | 12.4242 |
| 0.1978 | 0.6 | 3000 | 0.2864 | 16.7767 | 12.0913 |
| 0.1455 | 0.8 | 4000 | 0.2752 | 16.4597 | 11.8966 |
| 0.1712 | 1.0 | 5000 | 0.2664 | 15.8969 | 11.1406 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2