---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- Jungwonchang/spgispeech_xs
base_model: openai/whisper-large-v2
model-index:
- name: openai/whisper-large-v2, all the parameters updated for 5 epochs
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Test set for spgispeech
      type: kensho/spgispeech
      config: test
      split: test
    metrics:
    - type: wer
      value: 6.85
      name: WER
    - type: cer
      value: 2.02
      name: CER
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# openai/whisper-large-v2, all the parameters updated for 5 epochs

This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the 2 hour dataset of SPGIspeech(custom dataset) dataset.

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- 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: 50
- training_steps: 120
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.15.0