---
language:
- zh
base_model: sit-justin/whisper-small-test
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 [sit-justin/whisper-small-test](https://huggingface.co/sit-justin/whisper-small-test) on the MOE Response Chinese dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0158
- Cer: 2.5487

## 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    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.0804        | 0.5405 | 200  | 0.0771          | 5.5753 |
| 0.0695        | 1.0811 | 400  | 0.0632          | 5.4650 |
| 0.0374        | 1.6216 | 600  | 0.0467          | 4.6808 |
| 0.0228        | 2.1622 | 800  | 0.0439          | 4.9994 |
| 0.0142        | 2.7027 | 1000 | 0.0339          | 3.4310 |
| 0.0068        | 3.2432 | 1200 | 0.0257          | 5.1587 |
| 0.005         | 3.7838 | 1400 | 0.0216          | 2.7815 |
| 0.0019        | 4.3243 | 1600 | 0.0176          | 2.3772 |
| 0.0023        | 4.8649 | 1800 | 0.0158          | 2.5242 |
| 0.0012        | 5.4054 | 2000 | 0.0158          | 2.5487 |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1