README.md

---
language:
- zh
license: apache-2.0
tags:
- whisper-event
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: 7.766
metrics:
- cer
pipeline_tag: automatic-speech-recognition
---
<!-- 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 with a Normalized CER of 7.77%

To use the model, use the following code. It should be able to inference with less than 4GB VRAM (batch size of 1).
```
from peft import PeftModel, PeftConfig
from transformers import WhisperForConditionalGeneration, Seq2SeqTrainer, WhisperTokenizer, WhisperProcessor

peft_model_id = "alvanlii/whisper-largev2-cantonese-peft-lora"
peft_config = PeftConfig.from_pretrained(peft_model_id)
model = WhisperForConditionalGeneration.from_pretrained(
    peft_config.base_model_name_or_path, load_in_8bit=True, device_map="auto"
)
model = PeftModel.from_pretrained(model, peft_model_id)

task = "transcribe"
tokenizer = WhisperTokenizer.from_pretrained(peft_config.base_model_name_or_path, task=task)
processor = WhisperProcessor.from_pretrained(peft_config.base_model_name_or_path, task=task)
feature_extractor = processor.feature_extractor
forced_decoder_ids = processor.get_decoder_prompt_ids(language=language, task=task)
pipe = AutomaticSpeechRecognitionPipeline(model=model, tokenizer=tokenizer, feature_extractor=feature_extractor)

audio = # load audio here
text = pipe(audio, generate_kwargs={"forced_decoder_ids": forced_decoder_ids}, max_new_tokens=255)["text"]
```

## 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: 1e-3
- train_batch_size: 60 (on 1 3090 GPU)
- eval_batch_size: 10
- gradient_accumulation_steps: 1
- total_train_batch_size: 60x1x1=60
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 12000
- augmentation: SpecAugment

## Training Results

| Training Loss | Epoch | Step | Validation Loss | Normalized CER |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|
| 0.8604        | 1.99  | 12000 | 0.2129         | 0.07766        |