---
language:
- zh
license: apache-2.0
base_model: openai/whisper-base
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_0
metrics:
- wer
model-index:
- name: Whisper Base Chinese-Mandarin
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: mozilla-foundation/common_voice_16_0 zh-CN
      type: mozilla-foundation/common_voice_16_0
      config: zh-CN
      split: test
      args: zh-CN
    metrics:
    - name: Wer
      type: wer
      value: 89.13440626359287
---

<!-- 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 Base Chinese-Mandarin

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the mozilla-foundation/common_voice_16_0 zh-CN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5263
- Wer: 89.1344

## 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: 5e-07
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer     |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.9769        | 1.02  | 500   | 0.6812          | 94.6411 |
| 0.8022        | 3.0   | 1000  | 0.6262          | 92.5794 |
| 0.9109        | 4.02  | 1500  | 0.6009          | 92.6229 |
| 0.7132        | 6.0   | 2000  | 0.5845          | 92.3967 |
| 0.8416        | 7.02  | 2500  | 0.5725          | 91.7616 |
| 0.6527        | 9.0   | 3000  | 0.5636          | 91.4659 |
| 0.812         | 10.02 | 3500  | 0.5561          | 90.8917 |
| 0.6584        | 12.0  | 4000  | 0.5504          | 90.7960 |
| 0.7825        | 13.02 | 4500  | 0.5455          | 90.4045 |
| 0.6174        | 15.0  | 5000  | 0.5416          | 90.0565 |
| 0.7925        | 16.02 | 5500  | 0.5381          | 90.0217 |
| 0.5983        | 18.0  | 6000  | 0.5355          | 89.7695 |
| 0.741         | 19.02 | 6500  | 0.5331          | 89.7086 |
| 0.5831        | 21.0  | 7000  | 0.5312          | 89.4998 |
| 0.7414        | 22.02 | 7500  | 0.5296          | 89.5259 |
| 0.5902        | 24.0  | 8000  | 0.5284          | 89.3084 |
| 0.7242        | 25.02 | 8500  | 0.5275          | 89.4041 |
| 0.5815        | 27.0  | 9000  | 0.5268          | 89.1518 |
| 0.717         | 28.02 | 9500  | 0.5265          | 89.2562 |
| 0.5887        | 30.0  | 10000 | 0.5263          | 89.1344 |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.0