---
language:
- zh
license: apache-2.0
datasets:
- mozilla-foundation/common_voice_16_0
model-index:
- name: Wav2Vec2-BERT - Alvin
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: mozilla-foundation/common_voice_16_0 yue
      type: mozilla-foundation/common_voice_16_0
      config: yue
      split: test
      args: yue
    metrics:
    - name: Normalized CER
      type: cer
      value: 20.5
---


# Wav2Vec2-BERT - Alvin

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0). This has a CER of 20.5

## Training and evaluation data
For training, three datasets were used:
- Common Voice 16 `zh-HK` and `yue` 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

## Code Example
```
from transformers import pipeline
bert_asr = pipeline(
    "automatic-speech-recognition", model="alvanlii/wav2vec2-BERT-cantonese", device="cuda"
)
text = pipe(file)["text"]
```


## Training Hyperparameters
- learning_rate: 1e-4
- train_batch_size: 4 (on 1 3090)
- eval_batch_size: 1
- gradient_accumulation_steps: 32
- total_train_batch_size: 32x4=128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_warmup_steps: 500

## Training Results

| Training Loss | Step | Validation Loss | CER    |
|:-------------:|:----:|:---------------:|:------:|
|2.416|1200|1.615|0.4246
|1.313|4200|0.9049|0.2745
|1.090|7200|0.7463|0.2388
|0.907|9600|0.6820|0.2172