readme: ---
  language: en
  datasets:
  - LIUM/tedlium
  tags:
  - speech
  - audio
  - automatic-speech-recognition
  ---
  Finetuned from [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self).
  
  # Installation
  1. PyTorch installation: https://pytorch.org/
  2. Install transformers: https://huggingface.co/docs/transformers/installation
  
  e.g., installation by conda
  ```
  >> conda create -n wav2vec2 python=3.8
  >> conda install pytorch cudatoolkit=11.3 -c pytorch
  >> conda install -c conda-forge transformers
  ```
  
  # Usage
  ```python
  # Load the model and processor
  from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
  import numpy as np
  import torch
  
  model = Wav2Vec2ForCTC.from_pretrained(r'yongjian/wav2vec2-large-a') # Note: PyTorch Model
  processor = Wav2Vec2Processor.from_pretrained(r'yongjian/wav2vec2-large-a')
  
  # Load input
  np_wav = np.random.normal(size=(16000)).clip(-1, 1) # change it to your sample
  
  # Inference
  sample_rate = processor.feature_extractor.sampling_rate
  with torch.no_grad():
      model_inputs = processor(np_wav, sampling_rate=sample_rate, return_tensors="pt", padding=True)
      logits = model(model_inputs.input_values, attention_mask=model_inputs.attention_mask).logits # use .cuda() for GPU acceleration
      pred_ids = torch.argmax(logits, dim=-1).cpu()
      pred_text = processor.batch_decode(pred_ids)
  print('Transcription:', pred_text)
  ```
  
  # Code
  GitHub Repo:
  https://github.com/CassiniHuy/wav2vec2_finetune