readme: ---
  license: mit
  ---
  Pretrained on 10k hours WenetSpeech L subset. More details in  [TencentGameMate/chinese_speech_pretrain](https://github.com/TencentGameMate/chinese_speech_pretrain)
  
  This model does not have a tokenizer as it was pretrained on audio alone. 
  In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data.
  
  python package:
  transformers==4.16.2
  
  ```python
  
  
  import torch
  import torch.nn.functional as F
  import soundfile as sf
  
  from transformers import (
      Wav2Vec2FeatureExtractor,
      HubertModel,
  )
  
  
  model_path=""
  wav_path=""
  
  feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_path)
  model = HubertModel.from_pretrained(model_path)
  
  # for pretrain: Wav2Vec2ForPreTraining
  # model = Wav2Vec2ForPreTraining.from_pretrained(model_path)
  
  model = model.to(device)
  model = model.half()
  model.eval()
  
  wav, sr = sf.read(wav_path)
  input_values = feature_extractor(wav, return_tensors="pt").input_values
  input_values = input_values.half()
  input_values = input_values.to(device)
  
  with torch.no_grad():
      outputs = model(input_values)
      last_hidden_state = outputs.last_hidden_state
  
  
  ```