|
|
--- |
|
|
library_name: transformers |
|
|
language: |
|
|
- en |
|
|
license: apache-2.0 |
|
|
base_model: openai/whisper-base |
|
|
tags: |
|
|
- generated_from_trainer |
|
|
metrics: |
|
|
- wer |
|
|
- precision |
|
|
- recall |
|
|
- f1 |
|
|
model-index: |
|
|
- name: ./whisper-base-ea_base_v2 |
|
|
results: [] |
|
|
--- |
|
|
|
|
|
<!-- 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-base-ea_base_v2 |
|
|
|
|
|
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Afrispeech-200 dataset. |
|
|
It achieves the following results on the evaluation set: |
|
|
- Loss: 1.0084 |
|
|
- Wer Ortho: 0.3014 |
|
|
- Wer: 0.2499 |
|
|
- Cer: 0.1058 |
|
|
- Precision: 0.8391 |
|
|
- Recall: 0.8445 |
|
|
- F1: 0.8412 |
|
|
|
|
|
## 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: 16 |
|
|
- eval_batch_size: 8 |
|
|
- seed: 42 |
|
|
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
|
|
- lr_scheduler_type: constant_with_warmup |
|
|
- lr_scheduler_warmup_steps: 500 |
|
|
- training_steps: 4000 |
|
|
- mixed_precision_training: Native AMP |
|
|
|
|
|
### Training results |
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | Cer | Precision | Recall | F1 | |
|
|
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:---------:|:------:|:------:| |
|
|
| 0.1669 | 2.1186 | 500 | 0.7136 | 0.2789 | 0.2257 | 0.1014 | 0.8596 | 0.8608 | 0.8593 | |
|
|
| 0.0787 | 4.2373 | 1000 | 0.7690 | 0.2915 | 0.2394 | 0.1081 | 0.8514 | 0.8536 | 0.8515 | |
|
|
| 0.0243 | 6.3559 | 1500 | 0.8373 | 0.2916 | 0.2405 | 0.1010 | 0.8461 | 0.8531 | 0.8491 | |
|
|
| 0.012 | 8.4746 | 2000 | 0.8798 | 0.2880 | 0.2372 | 0.1005 | 0.8484 | 0.8551 | 0.8512 | |
|
|
| 0.0081 | 10.5932 | 2500 | 0.9189 | 0.2966 | 0.2459 | 0.1047 | 0.8422 | 0.8477 | 0.8443 | |
|
|
| 0.0038 | 12.7119 | 3000 | 0.9609 | 0.2955 | 0.2460 | 0.1025 | 0.8428 | 0.8493 | 0.8455 | |
|
|
| 0.0048 | 14.8305 | 3500 | 0.9747 | 0.2971 | 0.2483 | 0.1051 | 0.8423 | 0.8478 | 0.8445 | |
|
|
| 0.0039 | 16.9492 | 4000 | 1.0084 | 0.3014 | 0.2499 | 0.1058 | 0.8391 | 0.8445 | 0.8412 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.51.3 |
|
|
- Pytorch 2.6.0+cu124 |
|
|
- Datasets 3.6.0 |
|
|
- Tokenizers 0.21.1 |
|
|
|
