metadata
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- common_voice_9_0
metrics:
- wer
model-index:
- name: cv9-special-batch8-small-concat-Fleur-Norm
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_9_0
type: common_voice_9_0
config: id
split: test
args: id
metrics:
- name: Wer
type: wer
value: 12.003680699332874
cv9-special-batch8-small-concat-Fleur-Norm
This model is a fine-tuned version of openai/whisper-small on the common_voice_9_0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2567
- Wer: 12.0037
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: 4
- 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
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.2878 | 0.72 | 1000 | 0.2612 | 14.8930 |
| 0.1133 | 1.43 | 2000 | 0.2420 | 12.9101 |
| 0.0512 | 2.15 | 3000 | 0.2399 | 12.2107 |
| 0.0389 | 2.86 | 4000 | 0.2449 | 11.9669 |
| 0.0161 | 3.58 | 5000 | 0.2567 | 12.0037 |
Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3