---
base_model: openai/whisper-tiny.en
datasets:
- lalipa/jv_id_asr_split
library_name: transformers
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: finetune
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: lalipa/jv_id_asr_split jv_id_asr_source
      type: lalipa/jv_id_asr_split
      config: jv_id_asr_source
      split: validation
      args: jv_id_asr_source
    metrics:
    - type: wer
      value: 0.7835602493955974
      name: Wer
---

<!-- 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. -->

# finetune

This model is a fine-tuned version of [openai/whisper-tiny.en](https://huggingface.co/openai/whisper-tiny.en) on the lalipa/jv_id_asr_split jv_id_asr_source dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7784
- Wer: 0.7836
- Cer: 0.2535

## 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: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- 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: 30
- training_steps: 150

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
| 3.6903        | 0.2041 | 30   | 2.9875          | 1.0127 | 0.4365 |
| 2.533         | 0.4082 | 60   | 2.2360          | 0.8879 | 0.2921 |
| 2.0604        | 0.6122 | 90   | 1.9514          | 0.8253 | 0.2670 |
| 1.852         | 0.8163 | 120  | 1.8182          | 0.7949 | 0.2581 |
| 1.7929        | 1.0204 | 150  | 1.7784          | 0.7836 | 0.2535 |


### Framework versions

- Transformers 4.46.0.dev0
- Pytorch 2.4.1
- Datasets 3.0.1
- Tokenizers 0.20.0