readme: ---
  license: apache-2.0
  datasets:
  - ASCEND
  language:
  - zh
  metrics:
  - cer
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  ---
  
  
  ## inference
  
  The model can be used directly (without a language model) as follows...
  
  Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library:
  
  ```python
  from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
  from datasets import load_dataset
  import torch
  import torchaudio
  
  
  
  # load model and processor
  processor = Wav2Vec2Processor.from_pretrained("gymeee/demo_code_switching")
  model = Wav2Vec2ForCTC.from_pretrained("gymeee/demo_code_switching")
  
  # load speech
  speech_array, sampling_rate = torchaudio.load("speech.wav")
  # tokenize
  input_values = processor(speech_array[0], return_tensors="pt", padding="longest").input_values  # Batch size 1
  
  # retrieve logits
  logits = model(input_values).logits
  
  # take argmax and decode
  predicted_ids = torch.argmax(logits, dim=-1)
  transcription = processor.batch_decode(predicted_ids)
  
  transcription