|
--- |
|
base_model: openai/whisper-base |
|
language: |
|
- en |
|
license: apache-2.0 |
|
metrics: |
|
- wer |
|
tags: |
|
- hf-asr-leaderboard |
|
- generated_from_trainer |
|
model-index: |
|
- name: Whisper Small Five 20K - Chee Li |
|
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 Small Five 20K - Chee Li |
|
|
|
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co./openai/whisper-base) on the Google Fleurs dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.5771 |
|
- Wer: 22.0375 |
|
|
|
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_steps: 2500 |
|
- training_steps: 20000 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer | |
|
|:-------------:|:-------:|:-----:|:---------------:|:-------:| |
|
| 0.4014 | 1.0560 | 1000 | 0.4369 | 25.7071 | |
|
| 0.2677 | 2.1119 | 2000 | 0.3905 | 22.1327 | |
|
| 0.1651 | 3.1679 | 3000 | 0.3856 | 21.2139 | |
|
| 0.1102 | 4.2239 | 4000 | 0.3920 | 20.4471 | |
|
| 0.0514 | 5.2798 | 5000 | 0.4072 | 21.2883 | |
|
| 0.0255 | 6.3358 | 6000 | 0.4273 | 21.4687 | |
|
| 0.0184 | 7.3918 | 7000 | 0.4442 | 21.6251 | |
|
| 0.01 | 8.4477 | 8000 | 0.4635 | 21.3397 | |
|
| 0.0051 | 9.5037 | 9000 | 0.4805 | 21.3867 | |
|
| 0.0043 | 10.5597 | 10000 | 0.4924 | 21.5508 | |
|
| 0.0025 | 11.6156 | 11000 | 0.5054 | 21.5847 | |
|
| 0.0023 | 12.6716 | 12000 | 0.5166 | 22.0703 | |
|
| 0.0016 | 13.7276 | 13000 | 0.5292 | 21.7509 | |
|
| 0.0012 | 14.7835 | 14000 | 0.5375 | 21.7925 | |
|
| 0.001 | 15.8395 | 15000 | 0.5480 | 21.9325 | |
|
| 0.0008 | 16.8955 | 16000 | 0.5565 | 21.8866 | |
|
| 0.0008 | 17.9514 | 17000 | 0.5638 | 21.9423 | |
|
| 0.0005 | 19.0074 | 18000 | 0.5709 | 21.9916 | |
|
| 0.0005 | 20.0634 | 19000 | 0.5755 | 22.0397 | |
|
| 0.0004 | 21.1193 | 20000 | 0.5771 | 22.0375 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.43.4 |
|
- Pytorch 2.3.1+cu121 |
|
- Datasets 2.20.0 |
|
- Tokenizers 0.19.1 |
|
|
