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---
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 - 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 - 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: 0.4348
- Wer: 21.6569
## 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: 850
- training_steps: 8000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.3047 | 1.0560 | 1000 | 0.4050 | 22.7955 |
| 0.1771 | 2.1119 | 2000 | 0.3811 | 21.0509 |
| 0.1176 | 3.1679 | 3000 | 0.3864 | 21.3429 |
| 0.0852 | 4.2239 | 4000 | 0.3962 | 20.7720 |
| 0.0443 | 5.2798 | 5000 | 0.4100 | 21.4523 |
| 0.0265 | 6.3358 | 6000 | 0.4234 | 21.3823 |
| 0.0254 | 7.3918 | 7000 | 0.4310 | 21.7783 |
| 0.0188 | 8.4477 | 8000 | 0.4348 | 21.6569 |
### Framework versions
- Transformers 4.43.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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