readme: ---
  language: vi
  datasets:
  - vlsp-asr-2020
  tags:
  - audio
  - automatic-speech-recognition
  license: cc-by-nc-4.0
  ---
  
  ## Model description
  
  Our models use wav2vec2 architecture, pre-trained on 13k hours of Vietnamese youtube audio (un-label data) and fine-tuned on 250 hours labeled of VLSP ASR dataset on 16kHz sampled speech audio. You can find more description [here](https://github.com/nguyenvulebinh/vietnamese-wav2vec2)
  
  ## 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 = 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)
  ```
  
  ### Model Parameters License
  
  The ASR model parameters are made available for non-commercial use only, under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. You can find details at: https://creativecommons.org/licenses/by-nc/4.0/legalcode
  
  
  ### Contact 
  
  nguyenvulebinh@gmail.com
  
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