transformers safetensors whisper automatic-speech-recognition generated_from_trainer en dataset:Jungwonchang/spgispeech_xs base_model:openai/whisper-base.en license:apache-2.0 model-index endpoints_compatible region:us
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- Jungwonchang/spgispeech_xs
base_model: openai/whisper-base.en
model-index:
- name: openai/whisper-base.en, 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: 8.8
      name: WER
    - type: cer
      value: 2.96
      name: CER
---

<!-- 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. -->

# openai/whisper-base.en, all the parameters updated for 5 epochs

This model is a fine-tuned version of [openai/whisper-base.en](https://huggingface.co/openai/whisper-base.en) 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: 32
- eval_batch_size: 16
- seed: 42
- 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
