---
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: finetune_v8
  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_v8

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: 0.4224
- Wer: 102.2241

## 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        | 10.0  | 10   | 0.5239          | 19.1617  |
| No log        | 20.0  | 20   | 0.4346          | 18.0496  |
| No log        | 30.0  | 30   | 0.4050          | 17.1942  |
| No log        | 40.0  | 40   | 0.4204          | 18.4773  |
| 0.0997        | 50.0  | 50   | 0.4294          | 20.6159  |
| 0.0997        | 60.0  | 60   | 0.4282          | 19.6749  |
| 0.0997        | 70.0  | 70   | 0.4246          | 23.9521  |
| 0.0997        | 80.0  | 80   | 0.4224          | 102.2241 |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.2.0
- Datasets 2.20.0
- Tokenizers 0.19.1