code: def finetune_segmentation(
      n_gpus=1,
      max_epochs=40,
      batch_size=64,
      learning_rate=1e-5,
      gradient_clip=0.5,
      patience=10,
      num_workers=8,
      auth_token=None,
      exp_folder="./exp/pyannote_diarization_finetuned",
      ft_protocol="chime7_finetune.SpeakerDiarization.only_words",
  ):
      model = Model.from_pretrained(
          "pyannote/segmentation", use_auth_token=auth_token
      )
  
      dataset = get_protocol(ft_protocol, {"audio": FileFinder()})
  
      augmentation = Compose(
          transforms=[  # using pitch-shifting and bandstopfiltering
              # slows significantly training, but you can try
              # as it will likely improve results a bit.
              Gain(
                  min_gain_in_db=-6.0,
                  max_gain_in_db=6.0,
                  p=0.5,
              ),
              PeakNormalization(apply_to="only_too_loud_sounds"),
              PolarityInversion(p=0.5),
              AddColoredNoise(p=0.2),
          ],
          output_type="dict",
      )
      task = Segmentation(
          dataset,
          duration=model.specifications.duration,
          max_num_speakers=len(model.specifications.classes),
          batch_size=batch_size,
          num_workers=num_workers,
          loss="bce",
          vad_loss="bce",
          augmentation=augmentation,
      )
      model.task = task
      model.setup(stage="fit")
  
      def configure_optimizers(self):
          return Adam(self.parameters(), lr=learning_rate)
  
      model.configure_optimizers = MethodType(
          configure_optimizers, model
      )
      # we monitor diarization error rate on the validation set
      # and use to keep the best checkpoint and stop early
      monitor, direction = task.val_monitor
      checkpoint = ModelCheckpoint(
          monitor=monitor,
          mode=direction,
          save_top_k=1,
          every_n_epochs=1,
          save_last=False,
          save_weights_only=False,
          filename="{epoch}",
          verbose=False,
      )
      early_stopping = EarlyStopping(
          monitor=monitor,
          mode=direction,
          min_delta=0.0,
          patience=patience,
          strict=True,
          verbose=False,
      )
  
      callbacks = [RichProgressBar(), checkpoint, early_stopping]
      Path(exp_folder).mkdir(parents=True, exist_ok=True)
  
      trainer = Trainer(
          accelerator="gpu",
          devices=list(range(n_gpus)),
          callbacks=callbacks,
          max_epochs=max_epochs,
          gradient_clip_val=gradient_clip,
          default_root_dir=exp_folder,
      )
      trainer.fit(model)
      shutil.copyfile(
          checkpoint.best_model_path,
          os.path.join(
              Path(checkpoint.best_model_path).parent, "best.ckpt"
          ),
      )
  
file_path: espnet/egs2/chime7_task1/diar_asr1/local/pyannote_finetune.py
imports: import argparse
  import os.path
  import shutil
  from pathlib import Path
  from types import MethodType
  from pyannote.audio import Inference, Model
  from pyannote.audio.tasks import Segmentation
  from pyannote.database import FileFinder, get_protocol
  from pytorch_lightning import Trainer
  from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint, RichProgressBar
  from torch.optim import Adam
  from torch_audiomentations import AddColoredNoise, Compose, Gain, PeakNormalization, PolarityInversion
label_desc: Use image node_id 1 for calling a global function with example usage: finetune_segmentation(n_gpus, max_epochs, batch_size, learning_rate, gradient_clip, patience, num_workers, auth_token, exp_folder, ft_protocol) and returns: Adam