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output1

Automatic Speech Recognition
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output1
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---

library_name: transformers
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- common_voice_16_1
metrics:
- wer
model-index:
- name: output1
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_16_1
      type: common_voice_16_1
      config: ko
      split: test
      args: ko
    metrics:
    - name: Wer
      type: wer
      value: 140.13953488372093
---


<!-- 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. -->

# output1

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the common_voice_16_1 dataset.

It achieves the following results on the evaluation set:

- Loss: 1.0385

- Wer: 140.1395



## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500

- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer      |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0034        | 25.0  | 1000 | 0.9055          | 100.2326 |
| 0.001         | 50.0  | 2000 | 0.9852          | 113.7674 |
| 0.0005        | 75.0  | 3000 | 1.0243          | 139.9070 |
| 0.0004        | 100.0 | 4000 | 1.0385          | 140.1395 |


### Framework versions

- Transformers 4.45.0.dev0
- Pytorch 2.4.1+cpu
- Datasets 3.0.0
- Tokenizers 0.19.1