code: def predict_from_audio(
      processor,
      model,
      speaker_embedding,
      vocoder,
      audio,
      mic_audio=None,
      sr=16000,
  ):
      # audio = tuple (sample_rate, frames) or (sample_rate, (frames, channels))
      if mic_audio is not None:
          sampling_rate, waveform = mic_audio
      elif audio is not None:
          sampling_rate, waveform = audio
      else:
          return sr, np.zeros(0).astype(np.int16)
  
      waveform = process_audio(sampling_rate, waveform)
      inputs = processor(
          audio=waveform, sampling_rate=sr, return_tensors="pt"
      )
  
      speech = model.generate_speech(
          inputs["input_values"], speaker_embedding, vocoder=vocoder
      )
  
      speech = (speech.numpy() * 32767).astype(np.int16)
      return sr, speech
  
imports: from __future__ import annotations
  import base64
  import io
  import time
  from io import BytesIO
  import numpy
  import scipy
  import wavio
  import soundfile
  import torch
  import librosa
  from src.tts_sentence_parsing import init_sentence_state, get_sentence
  from src.tts_utils import prepare_speech, get_no_audio, chunk_speed_change
label_desc: Use image node_id 7 for calling a global function with example usage: predict_from_audio(processor, model, speaker_embedding, vocoder, audio, mic_audio, sr) and returns: sr,  speech, sr,  np