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---

license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- fsicoli/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: fsicoli/common_voice_18_0 pt
      type: fsicoli/common_voice_18_0
      config: pt
      split: None
      args: pt
    metrics:
    - name: Wer
      type: wer
      value: 0.10174567584881486
---


<!-- 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 fsicoli/common_voice_18_0 pt dataset.

It achieves the following results on the evaluation set:

- Loss: 0.1478

- Wer: 0.1017



## 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 |

| 0.0844        | 1.9998 | 1382 | 0.1478          | 0.1017 |





### Framework versions



- Transformers 4.44.0.dev0

- Pytorch 2.4.0+cu124

- Datasets 2.18.1.dev0

- Tokenizers 0.19.1