metadata
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
datasets:
- fleurs
metrics:
- wer
model-index:
- name: breeze-listen-dsw-base-te
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: fleurs
type: fleurs
config: te_in
split: test
args: te_in
metrics:
- name: Wer
type: wer
value: 37.9026667282895
breeze-listen-dsw-base-te
This model is a fine-tuned version of openai/whisper-base on the fleurs dataset. It achieves the following results on the evaluation set:
- Loss: 0.5269
- Wer: 37.9027
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
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.2937 | 2.03 | 200 | 0.3237 | 42.5614 |
| 0.1611 | 5.02 | 400 | 0.2756 | 38.9148 |
| 0.0889 | 8.01 | 600 | 0.2930 | 38.1106 |
| 0.0456 | 11.0 | 800 | 0.3372 | 37.4544 |
| 0.0229 | 13.03 | 1000 | 0.3982 | 37.9258 |
| 0.0103 | 16.02 | 1200 | 0.4473 | 38.2678 |
| 0.0042 | 19.02 | 1400 | 0.4836 | 37.8980 |
| 0.0025 | 22.01 | 1600 | 0.5083 | 37.7317 |
| 0.002 | 24.04 | 1800 | 0.5220 | 37.8010 |
| 0.0018 | 27.03 | 2000 | 0.5269 | 37.9027 |
Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.0