code: def generate_fake_training_dataset(
      path,
      instrument_list=["vocals", "other"],
      n_channels=2,
      n_songs=2,
      fs=44100,
      duration=6,
  ):
      """
      generates a fake training dataset in path:
      - generates audio files
      - generates a csv file describing the dataset
      """
      aa = AudioAdapter.default()
      rng = np.random.RandomState(seed=0)
      dataset_df = pd.DataFrame(
          columns=["mix_path"]
          + [f"{instr}_path" for instr in instrument_list]
          + ["duration"]
      )
      for song in range(n_songs):
          song_path = join(path, "train", f"song{song}")
          makedirs(song_path, exist_ok=True)
          dataset_df.loc[song, "duration"] = duration
          for instr in instrument_list + ["mix"]:
              filename = join(song_path, f"{instr}.wav")
              data = rng.rand(duration * fs, n_channels) - 0.5
              aa.save(filename, data, fs)
              dataset_df.loc[song, f"{instr}_path"] = join(
                  "train", f"song{song}", f"{instr}.wav"
              )
      dataset_df.to_csv(join(path, "train", "train.csv"), index=False)
  
file_path: spleeter/tests/test_train.py
imports: import json
  import os
  from os import makedirs
  from os.path import join
  from tempfile import TemporaryDirectory
  import numpy
  import pandas
  from typer.testing import CliRunner
  from spleeter.__main__ import spleeter
  from spleeter.audio.adapter import AudioAdapter
label_desc: Use image node_id 1 for calling a global function with example usage: generate_fake_training_dataset(path, instrument_list, n_channels, n_songs, fs, duration) without return types