---
language:
- zh
license: apache-2.0
base_model: sit-justin/whisper-medium-p2-moe
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-medium-p2-moe](https://huggingface.co/sit-justin/whisper-medium-p2-moe) on the MOE Response Chinese dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0360
- Cer: 2.4470

## 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     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.1079        | 0.0852 | 200  | 0.1211          | 10.1923 |
| 0.1371        | 0.1704 | 400  | 0.1223          | 8.1479  |
| 0.1069        | 0.2556 | 600  | 0.1130          | 9.1791  |
| 0.1131        | 0.3409 | 800  | 0.1012          | 8.6100  |
| 0.0918        | 0.4261 | 1000 | 0.0844          | 6.0797  |
| 0.0846        | 0.5113 | 1200 | 0.0708          | 4.6004  |
| 0.0606        | 0.5965 | 1400 | 0.0594          | 4.1761  |
| 0.0545        | 0.6817 | 1600 | 0.0488          | 5.9528  |
| 0.044         | 0.7669 | 1800 | 0.0398          | 2.6056  |
| 0.0376        | 0.8522 | 2000 | 0.0360          | 2.4470  |


### Framework versions

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