readme: ## Usage
  The model can be used directly (without a language model) as follows:
  ```python
  import soundfile as sf
  import torch
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  import argparse
  def parse_transcription(wav_file):
      # load pretrained model
      processor = Wav2Vec2Processor.from_pretrained("addy88/wav2vec2-nepali-stt")
      model = Wav2Vec2ForCTC.from_pretrained("addy88/wav2vec2-nepali-stt")
      # load audio
      audio_input, sample_rate = sf.read(wav_file)
      # pad input values and return pt tensor
      input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values
      # INFERENCE
      # retrieve logits & take argmax
      logits = model(input_values).logits
      predicted_ids = torch.argmax(logits, dim=-1)
      # transcribe
      transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
      print(transcription)
  ```