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# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# Author: Alexandre Défossez @adefossez, 2020
import json
from pathlib import Path
import math
import os
import tqdm
import sys
import torchaudio
torchaudio.set_audio_backend("sox_io")
import soundfile as sf
import torch as th
from torch.nn import functional as F
# If used, this should be saved somewhere as it takes quite a bit
# of time to generate
def find_audio_files(path, exts=[".wav"], progress=True):
audio_files = []
for root, folders, files in os.walk(path, followlinks=True):
for file in files:
file = Path(root) / file
if file.suffix.lower() in exts:
audio_files.append(str(os.path.abspath(file)))
meta = []
if progress:
audio_files = tqdm.tqdm(audio_files, ncols=80)
for file in audio_files:
siginfo, _ = torchaudio.info(file)
length = siginfo.length // siginfo.channels
meta.append((file, length))
meta.sort()
return meta
class Audioset:
def __init__(self, files, length=None, stride=None, pad=True, augment=None):
"""
files should be a list [(file, length)]
"""
self.files = files
self.num_examples = []
self.length = length
self.stride = stride or length
self.augment = augment
for file, file_length in self.files:
if length is None:
examples = 1
elif file_length < length:
examples = 1 if pad else 0
elif pad:
examples = int(
math.ceil((file_length - self.length) / self.stride) + 1)
else:
examples = (file_length - self.length) // self.stride + 1
self.num_examples.append(examples)
def __len__(self):
return sum(self.num_examples)
def __getitem__(self, index):
for (file, _), examples in zip(self.files, self.num_examples):
if index >= examples:
index -= examples
continue
num_frames = 0
offset = 0
if self.length is not None:
offset = self.stride * index
num_frames = self.length
# out = th.Tensor(sf.read(str(file), start=offset, frames=num_frames)[0]).unsqueeze(0)
out = torchaudio.load(str(file), frame_offset=offset,
num_frames=num_frames)[0]
if self.augment:
out = self.augment(out.squeeze(0).numpy()).unsqueeze(0)
if num_frames:
out = F.pad(out, (0, num_frames - out.shape[-1]))
return out[0]
if __name__ == "__main__":
json.dump(find_audio_files(sys.argv[1]), sys.stdout, indent=4)
print()
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