---
language:
- zh
base_model: Kathernie/whisper-small-all
tags:
- generated_from_trainer
datasets:
- custom_datset
model-index:
- name: Whisper Small Chinese MOE Response
  results: []
---

<!-- 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 Small Chinese MOE Response

This model is a fine-tuned version of [Kathernie/whisper-small-all](https://huggingface.co/Kathernie/whisper-small-all) on the MOE Response Chinese dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2640
- Cer: 11.0180

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- training_steps: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Cer     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.2252        | 1.0811  | 200  | 0.2339          | 12.4230 |
| 0.1268        | 2.1622  | 400  | 0.2223          | 10.9194 |
| 0.056         | 3.2432  | 600  | 0.2242          | 10.8701 |
| 0.023         | 4.3243  | 800  | 0.2387          | 11.3384 |
| 0.01          | 5.4054  | 1000 | 0.2546          | 11.2645 |
| 0.0044        | 6.4865  | 1200 | 0.2515          | 11.2891 |
| 0.0028        | 7.5676  | 1400 | 0.2552          | 10.9440 |
| 0.0017        | 8.6486  | 1600 | 0.2623          | 11.2645 |
| 0.0017        | 9.7297  | 1800 | 0.2624          | 10.9933 |
| 0.001         | 10.8108 | 2000 | 0.2640          | 11.0180 |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.2.0
- Datasets 2.20.0
- Tokenizers 0.19.1