Create README.md

README.md

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+---
+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: Normalized 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 Large V2 zh-HK - Alvin
+
+This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset. This is trained with PEFT LoRA+BNB INT8.
+
+## 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: 60 (on 1 3090 GPU)
+- eval_batch_size: 10
+- gradient_accumulation_steps: 1
+- total_train_batch_size: 60x1x1=60
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- training_steps: 15000
+- augmentation: SpecAugment
+
+## Training Results
+
+| Training Loss | Epoch | Step | Validation Loss | Normalized 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