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---
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: CER
type: cer
value: 10.27
---
# 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 10.27 on Common Voice 16 (yue) test set (without punctuations).
## 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"]
```
or
```
import torch
import soundfile as sf
from transformers import AutoModelForCTC, Wav2Vec2BertProcessor
model_name = "alvanlii/wav2vec2-BERT-cantonese"
asr_model = AutoModelForCTC.from_pretrained(model_name).to(device)
processor = Wav2Vec2BertProcessor.from_pretrained(model_name)
audio_input, _ = sf.read(file)
inputs = processor([audio_input], sampling_rate=16_000).input_features
features = torch.tensor(inputs)
with torch.no_grad():
logits = asr_model(features).logits
predicted_ids = torch.argmax(logits, dim=-1)
predictions = processor.batch_decode(predicted_ids, skip_special_tokens=True)
```
## Training Hyperparameters
- learning_rate: 5e-5
- 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: 1500
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