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import os
import glob
import torch
import torchaudio
from tqdm import tqdm
class StyleDataset(torch.utils.data.Dataset):
def __init__(
self,
audio_dir: str,
subset: str = "train",
sample_rate: int = 24000,
length: int = 131072,
) -> None:
super().__init__()
self.audio_dir = audio_dir
self.subset = subset
self.sample_rate = sample_rate
self.length = length
self.style_dirs = glob.glob(os.path.join(audio_dir, subset, "*"))
self.style_dirs = [sd for sd in self.style_dirs if os.path.isdir(sd)]
self.num_classes = len(self.style_dirs)
self.class_labels = {"broadcast" : 0, "telephone": 1, "neutral": 2, "bright": 3, "warm": 4}
self.examples = []
for n, style_dir in enumerate(self.style_dirs):
# get all files in style dir
style_filepaths = glob.glob(os.path.join(style_dir, "*.wav"))
style_name = os.path.basename(style_dir)
for style_filepath in tqdm(style_filepaths, ncols=120):
# load audio file
x, sr = torchaudio.load(style_filepath)
# sum to mono if needed
if x.shape[0] > 1:
x = x.mean(dim=0, keepdim=True)
# resample
if sr != self.sample_rate:
x = torchaudio.transforms.Resample(sr, self.sample_rate)(x)
# crop length after resample
if x.shape[-1] >= self.length:
x = x[...,:self.length]
# store example
example = (x, self.class_labels[style_name])
self.examples.append(example)
print(f"Loaded {len(self.examples)} examples for {subset} subset.")
def __len__(self):
return len(self.examples)
def __getitem__(self, idx):
example = self.examples[idx]
x = example[0]
y = example[1]
return x, y
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