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

# whisper-large-v3-multids-v3

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: 0.0675
- Wer: 1.7195

## 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-06
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- training_steps: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 0.3186        | 3.0215  | 250  | 0.1316          | 3.0916 |
| 0.1075        | 7.0085  | 500  | 0.0966          | 2.3375 |
| 0.0834        | 10.03   | 750  | 0.0832          | 2.0758 |
| 0.0774        | 14.017  | 1000 | 0.0762          | 1.8596 |
| 0.0693        | 18.004  | 1250 | 0.0721          | 1.7943 |
| 0.065         | 21.0255 | 1500 | 0.0696          | 1.7406 |
| 0.0634        | 25.0125 | 1750 | 0.0681          | 1.7324 |
| 0.0612        | 28.034  | 2000 | 0.0675          | 1.7195 |


### Framework versions

- Transformers 4.45.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 3.0.1.dev0
- Tokenizers 0.19.1