---
library_name: transformers
language:
- zh
license: apache-2.0
base_model: openai/whisper-base
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- aimpower/mandarin_stutter_speech
metrics:
- wer
model-index:
- name: Whisper Base ZH - Dongim Lee
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: AImpower Mandarin Stutter Speech
      type: aimpower/mandarin_stutter_speech
      config: zh
      split: test
    metrics:
    - name: Wer
      type: wer
      value: 87.24489795918367
---

<!-- 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 Base ZH - Dongim Lee

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the AImpower Mandarin Stutter Speech dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4500
- Wer: 87.2449

## 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     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.2036        | 2.0704 | 1000 | 0.3756          | 86.5816 |
| 0.0885        | 4.1408 | 2000 | 0.3903          | 86.2245 |
| 0.0367        | 6.2112 | 3000 | 0.4295          | 86.8878 |
| 0.0242        | 8.2816 | 4000 | 0.4500          | 87.2449 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1