---
library_name: transformers
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: 0.10419602818705957
---

<!-- 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: 0.2671
- Wer: 0.1042

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 10.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.1388        | 0.9996 | 691  | 0.1501          | 0.1074 |
| 0.108         | 1.9993 | 1382 | 0.1619          | 0.1153 |
| 0.091         | 2.9989 | 2073 | 0.1697          | 0.1124 |
| 0.0461        | 4.0    | 2765 | 0.1764          | 0.1120 |
| 0.0264        | 4.9996 | 3456 | 0.2024          | 0.1133 |
| 0.0203        | 5.9993 | 4147 | 0.2200          | 0.1099 |
| 0.0129        | 6.9989 | 4838 | 0.2277          | 0.1114 |
| 0.0091        | 8.0    | 5530 | 0.2552          | 0.1067 |
| 0.0063        | 8.9996 | 6221 | 0.2565          | 0.1054 |
| 0.0019        | 9.9964 | 6910 | 0.2671          | 0.1042 |


### Framework versions

- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu124
- Datasets 2.18.1.dev0
- Tokenizers 0.19.1