code: def generate_coarse(
      x_semantic,
      model,
      history_prompt=None,
      temp=0.7,
      top_k=None,
      top_p=None,
      silent=False,
      max_coarse_history=630,  # min 60 (faster), max 630 (more context)
      sliding_window_len=60,
      base=None,
      use_kv_caching=True,
  ):
      """Generate coarse audio codes from semantic tokens.
  
      Args:
          x_semantic (np.ndarray): The semantic tokens to generate coarse audio codes from.
          model (BarkModel): The BarkModel to use for generating the coarse audio codes.
          history_prompt (tuple): A tuple of (semantic_history, coarse_history, fine_history) to use as a prompt for the generation.
          temp (float): The temperature to use for the generation.
          top_k (int): The number of top tokens to consider for the generation.
          top_p (float): The cumulative probability to consider for the generation.
          silent (bool): Whether to silence the tqdm progress bar.
          max_coarse_history (int): The maximum number of coarse audio codes to use as history.
          sliding_window_len (int): The length of the sliding window to use for the generation.
          base (tuple): A tuple of (semantic_history, coarse_history, fine_history) to use as a base for the generation.
          use_kv_caching (bool): Whether to use key-value caching for the generation.
  
      Returns:
          np.ndarray: The generated coarse audio codes.
      """
      assert (
          isinstance(x_semantic, np.ndarray)
          and len(x_semantic.shape) == 1
          and len(x_semantic) > 0
          and x_semantic.min() >= 0
          and x_semantic.max() <= model.config.SEMANTIC_VOCAB_SIZE - 1
      )
      assert 60 <= max_coarse_history <= 630
      assert max_coarse_history + sliding_window_len <= 1024 - 256
      semantic_to_coarse_ratio = (
          model.config.COARSE_RATE_HZ
          / model.config.SEMANTIC_RATE_HZ
          * model.config.N_COARSE_CODEBOOKS
      )
      max_semantic_history = int(
          np.floor(max_coarse_history / semantic_to_coarse_ratio)
      )
      if all(v is not None for v in history_prompt) or base is not None:
          if history_prompt is not None:
              x_history = history_prompt
              x_semantic_history = x_history[0]
              x_coarse_history = x_history[1]
          if base is not None:
              x_semantic_history = base[0]
              x_coarse_history = base[1]
          assert (
              isinstance(x_semantic_history, np.ndarray)
              and len(x_semantic_history.shape) == 1
              and len(x_semantic_history) > 0
              and x_semantic_history.min() >= 0
              and x_semantic_history.max()
              <= model.config.SEMANTIC_VOCAB_SIZE - 1
              and isinstance(x_coarse_history, np.ndarray)
              and len(x_coarse_history.shape) == 2
              and x_coarse_history.shape[0]
              == model.config.N_COARSE_CODEBOOKS
              and x_coarse_history.shape[-1] >= 0
              and x_coarse_history.min() >= 0
              and x_coarse_history.max()
              <= model.config.CODEBOOK_SIZE - 1
              and (
                  round(
                      x_coarse_history.shape[-1]
                      / len(x_semantic_history),
                      1,
                  )
                  == round(
                      semantic_to_coarse_ratio
                      / model.config.N_COARSE_CODEBOOKS,
                      1,
                  )
              )
          )
          x_coarse_history = (
              _flatten_codebooks(
                  x_coarse_history, model.config.CODEBOOK_SIZE
              )
              + model.config.SEMANTIC_VOCAB_SIZE
          )
          # trim histories correctly
          n_semantic_hist_provided = np.min(
              [
                  max_semantic_history,
                  len(x_semantic_history) - len(x_semantic_history) % 2,
                  int(
                      np.floor(
                          len(x_coarse_history)
                          / semantic_to_coarse_ratio
                      )
                  ),
              ]
          )
          n_coarse_hist_provided = int(
              round(n_semantic_hist_provided * semantic_to_coarse_ratio)
          )
          x_semantic_history = x_semantic_history[
              -n_semantic_hist_provided:
          ].astype(np.int32)
          x_coarse_history = x_coarse_history[
              -n_coarse_hist_provided:
          ].astype(np.int32)
          # TODO: bit of a hack for time alignment (sounds better)
          x_coarse_history = x_coarse_history[:-2]
      else:
          x_semantic_history = np.array([], dtype=np.int32)
          x_coarse_history = np.array([], dtype=np.int32)
      # start loop
      n_steps = int(
          round(
              np.floor(
                  len(x_semantic)
                  * semantic_to_coarse_ratio
                  / model.config.N_COARSE_CODEBOOKS
              )
              * model.config.N_COARSE_CODEBOOKS
          )
      )
      assert (
          n_steps > 0 and n_steps % model.config.N_COARSE_CODEBOOKS == 0
      )
      x_semantic = np.hstack([x_semantic_history, x_semantic]).astype(
          np.int32
      )
      x_coarse = x_coarse_history.astype(np.int32)
      base_semantic_idx = len(x_semantic_history)
      with inference_mode():
          x_semantic_in = torch.from_numpy(x_semantic)[None].to(
              model.device
          )
          x_coarse_in = torch.from_numpy(x_coarse)[None].to(
              model.device
          )
          n_window_steps = int(np.ceil(n_steps / sliding_window_len))
          n_step = 0
          for _ in tqdm.tqdm(
              range(n_window_steps),
              total=n_window_steps,
              disable=silent,
          ):
              semantic_idx = base_semantic_idx + int(
                  round(n_step / semantic_to_coarse_ratio)
              )
              # pad from right side
              x_in = x_semantic_in[
                  :, np.max([0, semantic_idx - max_semantic_history]) :
              ]
              x_in = x_in[:, :256]
              x_in = F.pad(
                  x_in,
                  (0, 256 - x_in.shape[-1]),
                  "constant",
                  model.config.COARSE_SEMANTIC_PAD_TOKEN,
              )
              x_in = torch.hstack(
                  [
                      x_in,
                      torch.tensor([model.config.COARSE_INFER_TOKEN])[
                          None
                      ].to(model.device),
                      x_coarse_in[:, -max_coarse_history:],
                  ]
              )
              kv_cache = None
              for _ in range(sliding_window_len):
                  if n_step >= n_steps:
                      continue
                  is_major_step = (
                      n_step % model.config.N_COARSE_CODEBOOKS == 0
                  )
  
                  if use_kv_caching and kv_cache is not None:
                      x_input = x_in[:, [-1]]
                  else:
                      x_input = x_in
  
                  logits, kv_cache = model.coarse_model(
                      x_input,
                      use_cache=use_kv_caching,
                      past_kv=kv_cache,
                  )
                  logit_start_idx = (
                      model.config.SEMANTIC_VOCAB_SIZE
                      + (1 - int(is_major_step))
                      * model.config.CODEBOOK_SIZE
                  )
                  logit_end_idx = (
                      model.config.SEMANTIC_VOCAB_SIZE
                      + (2 - int(is_major_step))
                      * model.config.CODEBOOK_SIZE
                  )
                  relevant_logits = logits[
                      0, 0, logit_start_idx:logit_end_idx
                  ]
                  if top_p is not None:
                      # faster to convert to numpy
                      logits_device = relevant_logits.device
                      logits_dtype = relevant_logits.type()
                      relevant_logits = (
                          relevant_logits.detach()
                          .cpu()
                          .type(torch.float32)
                          .numpy()
                      )
                      sorted_indices = np.argsort(relevant_logits)[::-1]
                      sorted_logits = relevant_logits[sorted_indices]
                      cumulative_probs = np.cumsum(
                          torch.nn.functional.softmax(sorted_logits)
                      )
                      sorted_indices_to_remove = (
                          cumulative_probs > top_p
                      )
                      sorted_indices_to_remove[
                          1:
                      ] = sorted_indices_to_remove[:-1].copy()
                      sorted_indices_to_remove[0] = False
                      relevant_logits[
                          sorted_indices[sorted_indices_to_remove]
                      ] = -np.inf
                      relevant_logits = torch.from_numpy(
                          relevant_logits
                      )
                      relevant_logits = relevant_logits.to(
                          logits_device
                      ).type(logits_dtype)
                  if top_k is not None:
                      v, _ = torch.topk(
                          relevant_logits,
                          min(top_k, relevant_logits.size(-1)),
                      )
                      relevant_logits[relevant_logits < v[-1]] = -float(
                          "Inf"
                      )
                  probs = torch.nn.functional.softmax(
                      relevant_logits / temp, dim=-1
                  )
                  item_next = torch.multinomial(probs, num_samples=1)
                  item_next += logit_start_idx
                  x_coarse_in = torch.cat(
                      (x_coarse_in, item_next[None]), dim=1
                  )
                  x_in = torch.cat((x_in, item_next[None]), dim=1)
                  del logits, relevant_logits, probs, item_next
                  n_step += 1
              del x_in
          del x_semantic_in
      gen_coarse_arr = (
          x_coarse_in.detach()
          .cpu()
          .numpy()
          .squeeze()[len(x_coarse_history) :]
      )
      del x_coarse_in
      assert len(gen_coarse_arr) == n_steps
      gen_coarse_audio_arr = (
          gen_coarse_arr.reshape(-1, model.config.N_COARSE_CODEBOOKS).T
          - model.config.SEMANTIC_VOCAB_SIZE
      )
      for n in range(1, model.config.N_COARSE_CODEBOOKS):
          gen_coarse_audio_arr[n, :] -= n * model.config.CODEBOOK_SIZE
      clear_cuda_cache()
      return gen_coarse_audio_arr
  
imports: import logging
  import os
  import re
  from glob import glob
  from typing import Dict, List
  import librosa
  import numpy
  import torch
  import torchaudio
  import tqdm
  from encodec.utils import convert_audio
  from scipy.special import softmax
  from torch.nn import functional
  from TTS.tts.layers.bark.hubert.hubert_manager import HubertManager
  from TTS.tts.layers.bark.hubert.kmeans_hubert import CustomHubert
  from TTS.tts.layers.bark.hubert.tokenizer import HubertTokenizer
  from TTS.tts.layers.bark.load_model import clear_cuda_cache, inference_mode
label_desc: Use image node_id 13 for calling a global function with example usage: generate_coarse(x_semantic, model, history_prompt, temp, top_k, top_p, silent, max_coarse_history, sliding_window_len, base, use_kv_caching) and returns: gen_coarse_audio_arr