---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-small-CV-Fleurs-lg-313hrs-v1
  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-CV-Fleurs-lg-313hrs-v1

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Cer: 0.0803
- Loss: 0.7644
- Wer: 0.2848

## 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: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use adamw_hf with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step    | Cer    | Validation Loss | Wer    |
|:-------------:|:-----:|:-------:|:------:|:---------------:|:------:|
| 0.9959        | 1.0   | 30487   | 0.4780 | 0.5805          | 1.0507 |
| 0.2917        | 2.0   | 60974   | 0.8109 | 0.4413          | 1.4684 |
| 0.1945        | 3.0   | 91461   | 0.1449 | 0.4016          | 0.4604 |
| 0.139         | 4.0   | 121948  | 0.0933 | 0.3980          | 0.3740 |
| 0.0986        | 5.0   | 152435  | 0.0910 | 0.4101          | 0.3542 |
| 0.0698        | 6.0   | 182922  | 0.0935 | 0.4245          | 0.3447 |
| 0.0515        | 7.0   | 213409  | 0.0824 | 0.4571          | 0.3246 |
| 0.0412        | 8.0   | 243896  | 0.0843 | 0.4769          | 0.3185 |
| 0.0362        | 9.0   | 274383  | 0.0812 | 0.4909          | 0.3106 |
| 0.0339        | 10.0  | 304870  | 0.0819 | 0.5001          | 0.3119 |
| 0.0301        | 11.0  | 335357  | 0.0848 | 0.5248          | 0.3142 |
| 0.0243        | 12.0  | 365844  | 0.0843 | 0.5246          | 0.3045 |
| 0.0199        | 13.0  | 396331  | 0.0801 | 0.5518          | 0.3004 |
| 0.0167        | 14.0  | 426818  | 0.0857 | 0.5877          | 0.3085 |
| 0.0143        | 15.0  | 457305  | 0.0806 | 0.5835          | 0.3024 |
| 0.0124        | 16.0  | 487792  | 0.0819 | 0.5982          | 0.2995 |
| 0.011         | 17.0  | 518279  | 0.0845 | 0.5933          | 0.3022 |
| 0.0099        | 18.0  | 548766  | 0.0866 | 0.6195          | 0.2996 |
| 0.0088        | 19.0  | 579253  | 0.0825 | 0.6577          | 0.2966 |
| 0.0079        | 20.0  | 609740  | 0.0843 | 0.6416          | 0.2991 |
| 0.0073        | 21.0  | 640227  | 0.0810 | 0.6536          | 0.2938 |
| 0.0065        | 22.0  | 670714  | 0.0829 | 0.6708          | 0.2990 |
| 0.006         | 23.0  | 701201  | 0.0867 | 0.6726          | 0.2978 |
| 0.0056        | 24.0  | 731688  | 0.0819 | 0.6944          | 0.2921 |
| 0.0053        | 25.0  | 762175  | 0.0824 | 0.6845          | 0.2942 |
| 0.0049        | 26.0  | 792662  | 0.0840 | 0.6856          | 0.2926 |
| 0.0046        | 27.0  | 823149  | 0.0829 | 0.6926          | 0.2914 |
| 0.0042        | 28.0  | 853636  | 0.0832 | 0.7022          | 0.2866 |
| 0.004         | 29.0  | 884123  | 0.0800 | 0.7230          | 0.2898 |
| 0.0037        | 30.0  | 914610  | 0.0824 | 0.7287          | 0.2925 |
| 0.0034        | 31.0  | 945097  | 0.0801 | 0.7363          | 0.2860 |
| 0.0033        | 32.0  | 975584  | 0.0803 | 0.7497          | 0.2866 |
| 0.0032        | 33.0  | 1006071 | 0.0814 | 0.7478          | 0.2827 |
| 0.0029        | 34.0  | 1036558 | 0.0791 | 0.7292          | 0.2845 |
| 0.0028        | 35.0  | 1067045 | 0.0829 | 0.7657          | 0.2891 |
| 0.0026        | 36.0  | 1097532 | 0.0803 | 0.7644          | 0.2848 |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.1.0+cu118
- Datasets 3.2.0
- Tokenizers 0.21.0