---
language:
- cn
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- Svetlana0303/my_CN_ds
metrics:
- wer
model-index:
- name: Whisper Small CN - my voice
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: my_CN_ds
      type: Svetlana0303/my_CN_ds
      args: 'split: test'
    metrics:
    - name: Wer
      type: wer
      value: 100.0
---

<!-- 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 CN - my voice

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the my_CN_ds dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7879
- Wer: 100.0

## 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: 50
- training_steps: 400
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer   |
|:-------------:|:-----:|:----:|:---------------:|:-----:|
| 0.0           | 100.0 | 100  | 0.7750          | 100.0 |
| 0.0           | 200.0 | 200  | 0.7819          | 100.0 |
| 0.0           | 300.0 | 300  | 0.7860          | 100.0 |
| 0.0           | 400.0 | 400  | 0.7879          | 100.0 |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1