---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
datasets:
- razhan/DOLMA-speech
metrics:
- wer
model-index:
- name: whisper-base-hac
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: razhan/DOLMA-speech hawrami
      type: razhan/DOLMA-speech
      args: hawrami
    metrics:
    - name: Wer
      type: wer
      value: 0.47917770477906113
---

<!-- 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-hac

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the razhan/DOLMA-speech hawrami dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3272
- Wer: 0.4792
- Cer: 0.1039

## 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: 192
- eval_batch_size: 128
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 384
- total_eval_batch_size: 256
- 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: 1
- num_epochs: 5.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 1.9468        | 1.0   | 27   | 0.9999          | 0.9633 | 0.3797 |
| 0.6132        | 2.0   | 54   | 0.4681          | 0.6078 | 0.1469 |
| 0.3976        | 3.0   | 81   | 0.3668          | 0.5161 | 0.1128 |
| 0.3485        | 4.0   | 108  | 0.3360          | 0.4889 | 0.1065 |
| 0.3292        | 5.0   | 135  | 0.3272          | 0.4792 | 0.1039 |


### Framework versions

- Transformers 4.49.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0