readme: ---
  language:
  - zh
  license: apache-2.0
  tags:
  - whisper-event
  - generated_from_trainer
  datasets:
  - mozilla-foundation/common_voice_11_0
  model-index:
  - name: Whisper Small zh-HK - Alvin
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: mozilla-foundation/common_voice_11_0 zh-HK
        type: mozilla-foundation/common_voice_11_0
        config: zh-HK
        split: test
        args: zh-HK
      metrics:
      - name: Cer
        type: cer
        value: 10.11
  ---
  <!-- 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 zh-HK - Alvin
  
  This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. This version has a lower CER (by 1%) compared to the previous one. 
  
  ## Training and evaluation data
  For training, three datasets were used:
  - Common Voice 11 Canto Train Set
  - CantoMap: Winterstein, Grégoire, Tang, Carmen and Lai, Regine (2020) "CantoMap: a Hong Kong Cantonese MapTask Corpus", in Proceedings of The 12th Language Resources and Evaluation Conference, Marseille: European Language Resources Association, p. 2899-2906.
  - Cantonse-ASR: Yu, Tiezheng, Frieske, Rita, Xu, Peng, Cahyawijaya, Samuel, Yiu, Cheuk Tung, Lovenia, Holy, Dai, Wenliang, Barezi, Elham, Chen, Qifeng, Ma, Xiaojuan, Shi, Bertram, Fung, Pascale (2022) "Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset", 2022. Link: https://arxiv.org/pdf/2201.02419.pdf
  
  ## Training Hyperparameters
  - learning_rate: 5e-5
  - train_batch_size: 25 (on 2 GPUs)
  - eval_batch_size: 8
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 16x2x2=64
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - training_steps: 14000
  - mixed_precision_training: Native AMP
  - augmentation: SpecAugment
  
  ## Training Results
  
  | Training Loss | Epoch | Step | Validation Loss | Cer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 0.4610        | 0.55  | 2000 | 0.3106          | 13.08 |
  | 0.3441        | 1.11  | 4000 | 0.2875          | 11.79 |
  | 0.3466        | 1.66  | 6000 | 0.2820          | 11.44 |
  | 0.2539        | 2.22  | 8000 | 0.2777          | 10.59 |
  | 0.2312        | 2.77  | 10000 | 0.2822          | 10.60 |
  | 0.1639        | 3.32  | 12000 | 0.2859          | 10.17 |
  | 0.1569        | 3.88  | 14000 | 0.2866          | 10.11 |
  