---
library_name: transformers
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- generated_from_trainer
datasets:
- fsicoli/common_voice_18_0
metrics:
- wer
model-index:
- name: Whisper Turbo Train
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 18.0
      type: fsicoli/common_voice_18_0
      split: None
    metrics:
    - name: Wer
      type: wer
      value: 15.246076710047603
---

<!-- 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 Turbo Train

This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the Common Voice 18.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1156
- Wer: 15.2461

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 8000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.3715        | 0.4257 | 1000 | 0.3457          | 40.4692 |
| 0.251         | 0.8514 | 2000 | 0.2181          | 27.7065 |
| 0.1569        | 1.2771 | 3000 | 0.1814          | 24.1533 |
| 0.1436        | 1.7029 | 4000 | 0.1531          | 20.3812 |
| 0.0931        | 2.1286 | 5000 | 0.1374          | 18.4662 |
| 0.0891        | 2.5543 | 6000 | 0.1252          | 16.9349 |
| 0.0738        | 2.9800 | 7000 | 0.1199          | 15.5610 |
| 0.0544        | 3.4057 | 8000 | 0.1156          | 15.2461 |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.1.0
- Datasets 3.0.1
- Tokenizers 0.20.0