---
language:
- zh
license: apache-2.0
base_model: openai/whisper-medium
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- whucedar/datasets_stt_2
metrics:
- wer
model-index:
- name: zh-CN-model-medium-3 - whucedar
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: zh-CN
      type: whucedar/datasets_stt_2
      args: 'config: zh, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 92.0589784096893
---

<!-- 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-3 - 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: 0.2745
- Wer: 92.0590

## 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     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.2079        | 0.8306 | 1000 | 0.2799          | 78.5571 |
| 0.0942        | 1.6611 | 2000 | 0.2712          | 76.6719 |
| 0.0291        | 2.4917 | 3000 | 0.2717          | 85.0026 |
| 0.0069        | 3.3223 | 4000 | 0.2745          | 92.0590 |


### Framework versions

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