|
--- |
|
language: vi |
|
datasets: |
|
- youtube-vi-13k-hours |
|
tags: |
|
- speech |
|
license: cc-by-nc-4.0 |
|
--- |
|
|
|
# Vietnamese Self-Supervised Learning Wav2Vec2 model |
|
|
|
## Model |
|
|
|
We use wav2vec2 architecture for doing Self-Supervised learning |
|
|
|
<img src="https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/wav2vec2.png" width=75% height=75%> |
|
|
|
## Data |
|
|
|
Our self-supervised model is pre-trained on a massive audio set of 13k hours of Vietnamese youtube audio, which includes: |
|
- Clean audio |
|
- Noise audio |
|
- Conversation |
|
- Multi-gender and dialects |
|
|
|
|
|
## Download |
|
|
|
We have already upload our pre-trained model to the Huggingface. The base model trained 35 epochs and the large model trained 20 epochs in about 30 days using TPU V3-8. |
|
|
|
- [Based version](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vi) ~ 95M params |
|
- [Large version](https://huggingface.co/nguyenvulebinh/wav2vec2-large-vi) ~ 317M params |
|
|
|
## Usage |
|
|
|
```python |
|
from transformers import Wav2Vec2ForPreTraining, Wav2Vec2Processor |
|
|
|
model_name = 'nguyenvulebinh/wav2vec2-base-vi' |
|
# model_name = 'nguyenvulebinh/wav2vec2-large-vi' |
|
|
|
model = Wav2Vec2ForPreTraining.from_pretrained(model_name) |
|
processor = Wav2Vec2Processor.from_pretrained(model_name) |
|
|
|
``` |
|
|
|
Since our model has the same architecture as the English wav2vec2 version, you can use [this notebook](https://colab.research.google.com/drive/1FjTsqbYKphl9kL-eILgUc-bl4zVThL8F?usp=sharing) for more information on how to fine-tune the model. |
|
|
|
## Finetuned version |
|
|
|
### VLSP 2020 ASR dataset |
|
|
|
Benchmark WER result on VLSP T1 testset: |
|
|
|
| | [base model](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vi-vlsp2020) | [large model](https://huggingface.co/nguyenvulebinh/wav2vec2-large-vi-vlsp2020) | |
|
|---|---|---| |
|
|without LM| 8.66 | 6.90 | |
|
|with 5-grams LM| 6.53 | 5.32 | |
|
|
|
Usage |
|
|
|
```python |
|
#pytorch |
|
#!pip install transformers==4.20.0 |
|
#!pip install https://github.com/kpu/kenlm/archive/master.zip |
|
#!pip install pyctcdecode==0.4.0 |
|
from transformers.file_utils import cached_path, hf_bucket_url |
|
from importlib.machinery import SourceFileLoader |
|
from transformers import Wav2Vec2ProcessorWithLM |
|
from IPython.lib.display import Audio |
|
import torchaudio |
|
import torch |
|
|
|
# Load model & processor |
|
model_name = "nguyenvulebinh/wav2vec2-base-vi-vlsp2020" |
|
# model_name = "nguyenvulebinh/wav2vec2-large-vi-vlsp2020" |
|
model = SourceFileLoader("model", cached_path(hf_bucket_url(model_name,filename="model_handling.py"))).load_module().Wav2Vec2ForCTC.from_pretrained(model_name) |
|
processor = Wav2Vec2ProcessorWithLM.from_pretrained(model_name) |
|
|
|
# Load an example audio (16k) |
|
audio, sample_rate = torchaudio.load(cached_path(hf_bucket_url(model_name, filename="t2_0000006682.wav"))) |
|
input_data = processor.feature_extractor(audio[0], sampling_rate=16000, return_tensors='pt') |
|
|
|
# Infer |
|
output = model(**input_data) |
|
|
|
# Output transcript without LM |
|
print(processor.tokenizer.decode(output.logits.argmax(dim=-1)[0].detach().cpu().numpy())) |
|
|
|
# Output transcript with LM |
|
print(processor.decode(output.logits.cpu().detach().numpy()[0], beam_width=100).text) |
|
``` |
|
|
|
## Acknowledgment |
|
|
|
- We would like to thank the Google TPU Research Cloud (TRC) program and Soonson Kwon (Google ML Ecosystem programs Lead) for their support. |
|
- Special thanks to my colleagues at [VietAI](https://vietai.org/) and [VAIS](https://vais.vn/) for their advice. |
|
|
|
## Contact |
|
|
|
nguyenvulebinh@gmail.com / binh@vietai.org |
|
|
|
[](https://twitter.com/intent/follow?screen_name=nguyenvulebinh) |
|
|
|
|
|
|
|
|
|
|
|
|
