---
license: apache-2.0
tags:
- whisper-small
- asr
- zh-TW
datasets:
- mozilla-foundation/common_voice_11_0
model-index:
- name: Whisper Small TW
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: mozilla-foundation/common_voice_11_0
      type: mozilla-foundation/common_voice_11_0
      config: zh-TW
      split: test
    metrics:
    - type: wer
      value: 9.78
      name: WER
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Whisper Medium TW 

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 dataset.

## Training and evaluation data

Training:
- [mozilla-foundation/common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) (train+validation)

Evaluation:
- [mozilla-foundation/common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) (test)

## Training procedure

- Datasets were augmented using [audiomentations](https://github.com/iver56/audiomentations) via PitchShift, TimeStretch, Gain, AddGaussianNoise transformations at `p=0.3`.
- A space is added between each Chinese character, as demonstrated in the original paper. Effectively, WER == CER in this case.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: Adam
- generation_max_length: 225
- warmup_steps: 500
- max_steps: 2400
- fp16: True
- evaluation_strategy: "steps"

### Framework versions

- Transformers 4.27.1
- Pytorch 2.0.1+cu120
- Datasets 2.13.1