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