---
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