Out of curiosity I took the inference part from the notebook and looped out the predicted text together with the original text.
for i in range(len(common_voice_test["input_values"])):
  input_dict = processor(common_voice_test["input_values"][i], return_tensors="pt", padding=True)

  logits = model(input_dict.input_values.to("cuda")).logits

  pred_ids = torch.argmax(logits, dim=-1)[0]
  print(str(i)+"\t"+processor.decode(pred_ids) + "\t" + common_voice_test_transcription["sentence"][i].lower())

I got 76 lines (out of 2027) before Colaboratory disconnected me. I had to remove 76 warnings about sampling_rate not being provided. I tried to send sampling_rate to 16000 but then it looked I got a different prediction result (but it could just being that you get different prediction result each time you run).

  
      docs.google.com
  
  
    

Wav2Vec Common Voice Swedish 5

Sheet1

Recognized speech,Original text
0,draknade också som en mån ligärning,det räknar jag också som en manlig gärning.
1,vi bisegrdet om i ni ör rmignter,de pissade på min jävla matta.
2,allesammansprang eller flaxade frammod pojtende medd fumat...