---
language:
- zh
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
base_model: openai/whisper-small
model-index:
- name: Whisper Small zh-HK - Alvin
  results:
  - task:
      type: automatic-speech-recognition
      name: 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:
    - type: cer
      value: 10.11
      name: Normalized CER
---
<!-- 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

## Using the Model
```
import librosa

import torch
from transformers import WhisperForConditionalGeneration, WhisperProcessor

y, sr = librosa.load('audio.mp3', sr=16000)

MODEL_NAME = "alvanlii/whisper-small-cantonese"

processor = WhisperProcessor.from_pretrained(MODEL_NAME)
model = WhisperForConditionalGeneration.from_pretrained(MODEL_NAME)

model.config.forced_decoder_ids = None
model.config.suppress_tokens = []
model.config.use_cache = False

processed_in = processor(y, sampling_rate=sr, return_tensors="pt")
gout = model.generate(
    input_features=processed_in.input_features, 
    output_scores=True, return_dict_in_generate=True
)
transcription = processor.batch_decode(gout.sequences, skip_special_tokens=True)[0]
print(transcription)
```
- Alternatively, you can use huggingface pipelines
```
from transformers import pipeline
MODEL_NAME = "alvanlii/whisper-small-cantonese" 
lang = "zh"
pipe = pipeline(
    task="automatic-speech-recognition",
    model=MODEL_NAME,
    chunk_length_s=30,
    device=device,
)
pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=lang, task="transcribe")
text = pipe(file)["text"]
```
## 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: 25x2x2=100
- 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 | 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          | 10.11 |