misc audio support

codes/utils/util.py

@@ -1,7 +1,10 @@
 import os
+import pathlib
 import sys
 import time
 import math
+
+import scipy
 import torch.nn.functional as F
 from datetime import datetime
 import random
@@ -10,6 +13,8 @@ from collections import OrderedDict
 import numpy as np
 import cv2
 import torch
+import torchaudio
+from audio2numpy import open_audio
 from torchvision.utils import make_grid
 from shutil import get_terminal_size
 import scp
@@ -541,3 +546,59 @@ def optimizer_to(opt, device):
                     subparam.data = subparam.data.to(device)
                     if subparam._grad is not None:
                         subparam._grad.data = subparam._grad.data.to(device)
+
+#''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''
+#'''                       AUDIO UTILS                          '''
+#''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''
+
+
+def find_audio_files(base_path, globs=['*.wav', '*.mp3', '*.ogg', '*.flac']):
+    path = pathlib.Path(base_path)
+    paths = []
+    for glob in globs:
+        paths.extend([str(f) for f in path.rglob(glob)])
+    return paths
+
+
+def load_audio(audiopath, sampling_rate, raw_data=None):
+    if raw_data is not None:
+        # Assume the data is wav format. SciPy's reader can read raw WAV data from a BytesIO wrapper.
+        audio, lsr = load_wav_to_torch(raw_data)
+    else:
+        if audiopath[-4:] == '.wav':
+            audio, lsr = load_wav_to_torch(audiopath)
+        else:
+            audio, lsr = open_audio(audiopath)
+            audio = torch.FloatTensor(audio)
+
+    # Remove any channel data.
+    if len(audio.shape) > 1:
+        if audio.shape[0] < 5:
+            audio = audio[0]
+        else:
+            assert audio.shape[1] < 5
+            audio = audio[:, 0]
+
+    if lsr != sampling_rate:
+        audio = torchaudio.functional.resample(audio, lsr, sampling_rate)
+
+    # Check some assumptions about audio range. This should be automatically fixed in load_wav_to_torch, but might not be in some edge cases, where we should squawk.
+    # '2' is arbitrarily chosen since it seems like audio will often "overdrive" the [-1,1] bounds.
+    if torch.any(audio > 2) or not torch.any(audio < 0):
+        print(f"Error with {audiopath}. Max={audio.max()} min={audio.min()}")
+    audio.clip_(-1, 1)
+
+    return audio
+
+
+def load_wav_to_torch(full_path):
+    sampling_rate, data = scipy.io.wavfile.read(full_path)
+    if data.dtype == np.int32:
+        norm_fix = 2 ** 31
+    elif data.dtype == np.int16:
+        norm_fix = 2 ** 15
+    elif data.dtype == np.float16 or data.dtype == np.float32:
+        norm_fix = 1.
+    else:
+        raise NotImplemented(f"Provided data dtype not supported: {data.dtype}")
+    return (torch.FloatTensor(data.astype(np.float32)) / norm_fix, sampling_rate)