---
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- common_voice_18_0
metrics:
- wer
model-index:
- name: whisper-large-v3-pt-3000h-3
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_18_0
      type: common_voice_18_0
      config: pt
      split: None
      args: pt
    metrics:
    - name: Wer
      type: wer
      value: 0.10366752081998719
---

<!-- 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-pt-3000h-3

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co./openai/whisper-large-v3) on the common_voice_18_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1486
- Wer: 0.1037

## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 2.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.13          | 0.9998 | 691  | 0.1486          | 0.1037 |


### Framework versions

- Transformers 4.44.0.dev0
- Pytorch 2.4.0+cu124
- Datasets 2.18.1.dev0
- Tokenizers 0.19.1