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---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- Leonel-Maia/fongbe-splitted
metrics:
- wer
model-index:
- name: whisper-small-splitted
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Leonel-Maia/fongbe-splitted
type: Leonel-Maia/fongbe-splitted
metrics:
- name: Wer
type: wer
value: 0.11257288805358778
---
<!-- 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-splitted
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Leonel-Maia/fongbe-splitted dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1084
- Wer: 0.1126
## 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
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 60.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 0.1324 | 2.1094 | 500 | 0.1909 | 0.3307 |
| 0.0383 | 4.2187 | 1000 | 0.1153 | 0.1131 |
| 0.0184 | 6.3281 | 1500 | 0.1084 | 0.1126 |
| 0.0098 | 8.4374 | 2000 | 0.1122 | 0.1014 |
| 0.0076 | 10.5468 | 2500 | 0.1101 | 0.0966 |
| 0.0075 | 12.6562 | 3000 | 0.1156 | 0.0992 |
### Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
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