---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-large
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: w_large
  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. -->

# w_large

This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6144
- Wer: 69.9421

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer      |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.7439        | 0.4548 | 1000 | 0.7228          | 107.9816 |
| 0.6638        | 0.9095 | 2000 | 0.6496          | 82.4336  |
| 0.413         | 1.3643 | 3000 | 0.6292          | 76.3384  |
| 0.4303        | 1.8190 | 4000 | 0.6144          | 69.9421  |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu118
- Datasets 3.0.0
- Tokenizers 0.19.1