---
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba
metrics:
- wer
model-index:
- name: whisper-large-v3-pt-1000h
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba
        default
      type: fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 0.11132023872721715
---

<!-- 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-1000h

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba default dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5576
- Wer: 0.1113

## 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: 5e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- training_steps: 82000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.2717        | 0.39  | 10000 | 0.4143          | 0.1341 |
| 0.2646        | 0.79  | 20000 | 0.4141          | 0.1284 |
| 0.2244        | 1.18  | 30000 | 0.5361          | 0.1253 |
| 0.2056        | 1.57  | 40000 | 0.4714          | 0.1223 |
| 0.2034        | 1.97  | 50000 | 0.4937          | 0.1195 |
| 0.1717        | 2.36  | 60000 | 0.5127          | 0.1178 |
| 0.1692        | 2.75  | 70000 | 0.6040          | 0.1146 |
| 0.121         | 3.15  | 80000 | 0.5361          | 0.1130 |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.2.1
- Datasets 2.18.1.dev0
- Tokenizers 0.15.2