---
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: finetune_v15
  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. -->

# finetune_v15

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co./openai/whisper-large-v3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7837
- Wer: 193.6017

## 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: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- training_steps: 80
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer      |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 6.1538  | 10   | 0.7300          | 34.1589  |
| No log        | 12.3077 | 20   | 0.7090          | 39.9381  |
| No log        | 18.4615 | 30   | 0.7617          | 33.2559  |
| No log        | 24.6154 | 40   | 0.7676          | 33.4107  |
| 0.223         | 30.7692 | 50   | 0.7749          | 199.6646 |
| 0.223         | 36.9231 | 60   | 0.7764          | 164.3189 |
| 0.223         | 43.0769 | 70   | 0.7827          | 202.6574 |
| 0.223         | 49.2308 | 80   | 0.7837          | 193.6017 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.2.0
- Datasets 2.20.0
- Tokenizers 0.19.1