Update file_utils

utils/file_utils.py

@@ -1,4 +1,3 @@
-import json
 import os
 import os.path as osp
 import pickle
@@ -7,10 +6,7 @@ from typing import Any
 
 import h5py
 import numpy as np
-import pandas as pd
 import torch
-import torchaudio
-from torchtyping import TensorType
 
 num_channels, num_frames, height, width = None, None, None, None
 
@@ -73,45 +69,3 @@ def load_pth(pth_path: str) -> Any:
 def save_pth(data: Any, pth_path: str):
     """Save data in a pth (PyTorch) file."""
     torch.save(data, pth_path)
-
-
-def load_csv(csv_path: str, header: Any = None) -> pd.DataFrame:
-    """Load a csv file."""
-    try:
-        data = pd.read_csv(csv_path, header=header)
-    except pd.errors.EmptyDataError:
-        data = pd.DataFrame()
-    return data
-
-
-def save_csv(data: Any, csv_path: str):
-    """Save data in a csv file."""
-    pd.DataFrame(data).to_csv(csv_path, header=False, index=False)
-
-
-def load_json(json_path: str, header: Any = None) -> pd.DataFrame:
-    """Load a json file."""
-    with open(json_path, "r") as f:
-        data = json.load(f)
-    return data
-
-
-def save_json(data: Any, json_path: str):
-    """Save data in a json file."""
-    with open(json_path, "w") as json_file:
-        json.dump(data, json_file)
-
-
-def load_audio(audio_path: str, **kwargs):
-    """Load an audio file."""
-    waveform, sample_rate = torchaudio.load(audio_path, **kwargs)
-    return waveform, sample_rate
-
-
-def save_audio(
-    data: TensorType["num_channels", "num_frames"],
-    audio_path: str,
-    sample_rate: int = 44100,
-):
-    """Save data in an audio file."""
-    torchaudio.save(audio_path, data, sample_rate)