---
language:
- zh
license: apache-2.0
base_model: openai/whisper-medium
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- whucedar/datasets_stt_1
metrics:
- wer
model-index:
- name: zh-CN-model-medium-1 - whucedar
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: zh-CN
      type: whucedar/datasets_stt_1
      args: 'config: zh, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 188.47926267281105
---

<!-- 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. -->

# zh-CN-model-medium-1 - whucedar

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the zh-CN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1323
- Wer: 188.4793

## 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: 50
- training_steps: 500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer      |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.0241        | 1.5873 | 100  | 0.1363          | 158.9862 |
| 0.0042        | 3.1746 | 200  | 0.1312          | 239.6313 |
| 0.0043        | 4.7619 | 300  | 0.1316          | 215.2074 |
| 0.0013        | 6.3492 | 400  | 0.1312          | 203.6866 |
| 0.0006        | 7.9365 | 500  | 0.1323          | 188.4793 |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.1+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1