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"""
Torch dataset object for synthetically rendered spatial data.
"""
import os
import json
import random
from pathlib import Path
import logging
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scaper
import torch
import torchaudio
import torchaudio.transforms as AT
from random import randrange
class FSDSoundScapesDataset(torch.utils.data.Dataset): # type: ignore
"""
Base class for FSD Sound Scapes dataset
"""
_labels = [
"Acoustic_guitar", "Applause", "Bark", "Bass_drum",
"Burping_or_eructation", "Bus", "Cello", "Chime", "Clarinet",
"Computer_keyboard", "Cough", "Cowbell", "Double_bass",
"Drawer_open_or_close", "Electric_piano", "Fart", "Finger_snapping",
"Fireworks", "Flute", "Glockenspiel", "Gong", "Gunshot_or_gunfire",
"Harmonica", "Hi-hat", "Keys_jangling", "Knock", "Laughter", "Meow",
"Microwave_oven", "Oboe", "Saxophone", "Scissors", "Shatter",
"Snare_drum", "Squeak", "Tambourine", "Tearing", "Telephone",
"Trumpet", "Violin_or_fiddle", "Writing"]
def __init__(self, input_dir, dset='', sr=None,
resample_rate=None, max_num_targets=1):
assert dset in ['train', 'val', 'test'], \
"`dset` must be one of ['train', 'val', 'test']"
self.dset = dset
self.max_num_targets = max_num_targets
self.fg_dir = os.path.join(input_dir, 'FSDKaggle2018/%s' % dset)
if dset in ['train', 'val']:
self.bg_dir = os.path.join(
input_dir,
'TAU-acoustic-sounds/'
'TAU-urban-acoustic-scenes-2019-development')
else:
self.bg_dir = os.path.join(
input_dir,
'TAU-acoustic-sounds/'
'TAU-urban-acoustic-scenes-2019-evaluation')
logging.info("Loading %s dataset: fg_dir=%s bg_dir=%s" %
(dset, self.fg_dir, self.bg_dir))
self.samples = sorted(list(
Path(os.path.join(input_dir, 'jams', dset)).glob('[0-9]*')))
jamsfile = os.path.join(self.samples[0], 'mixture.jams')
_, jams, _, _ = scaper.generate_from_jams(
jamsfile, fg_path=self.fg_dir, bg_path=self.bg_dir)
_sr = jams['annotations'][0]['sandbox']['scaper']['sr']
assert _sr == sr, "Sampling rate provided does not match the data"
if resample_rate is not None:
self.resampler = AT.Resample(sr, resample_rate)
self.sr = resample_rate
else:
self.resampler = lambda a: a
self.sr = sr
def _get_label_vector(self, labels):
"""
Generates a multi-hot vector corresponding to `labels`.
"""
vector = torch.zeros(len(FSDSoundScapesDataset._labels))
for label in labels:
idx = FSDSoundScapesDataset._labels.index(label)
assert vector[idx] == 0, "Repeated labels"
vector[idx] = 1
return vector
def __len__(self):
return len(self.samples)
def __getitem__(self, idx):
sample_path = self.samples[idx]
jamsfile = os.path.join(sample_path, 'mixture.jams')
mixture, jams, ann_list, event_audio_list = scaper.generate_from_jams(
jamsfile, fg_path=self.fg_dir, bg_path=self.bg_dir)
isolated_events = {}
for e, a in zip(ann_list, event_audio_list[1:]):
# 0th event is background
isolated_events[e[2]] = a
gt_events = list(pd.read_csv(
os.path.join(sample_path, 'gt_events.csv'), sep='\t')['label'])
mixture = torch.from_numpy(mixture).permute(1, 0)
mixture = self.resampler(mixture.to(torch.float))
if self.dset == 'train':
labels = random.sample(gt_events, randrange(1,self.max_num_targets+1))
elif self.dset == 'val':
labels = gt_events[:idx%self.max_num_targets+1]
elif self.dset == 'test':
labels = gt_events[:self.max_num_targets]
label_vector = self._get_label_vector(labels)
gt = torch.zeros_like(
torch.from_numpy(event_audio_list[1]).permute(1, 0))
for l in labels:
gt = gt + torch.from_numpy(isolated_events[l]).permute(1, 0)
gt = self.resampler(gt.to(torch.float))
return mixture, label_vector, gt #, jams
def tensorboard_add_sample(writer, tag, sample, step, params):
"""
Adds a sample of FSDSynthDataset to tensorboard.
"""
if params['resample_rate'] is not None:
sr = params['resample_rate']
else:
sr = params['sr']
resample_rate = 16000 if sr > 16000 else sr
m, l, gt, o = sample
m, gt, o = (
torchaudio.functional.resample(_, sr, resample_rate).cpu()
for _ in (m, gt, o))
def _add_audio(a, audio_tag, axis, plt_title):
for i, ch in enumerate(a):
axis.plot(ch, label='mic %d' % i)
writer.add_audio(
'%s/mic %d' % (audio_tag, i), ch.unsqueeze(0), step, resample_rate)
axis.set_title(plt_title)
axis.legend()
for b in range(m.shape[0]):
label = []
for i in range(len(l[b, :])):
if l[b, i] == 1:
label.append(FSDSoundScapesDataset._labels[i])
# Add waveforms
rows = 3 # input, output, gt
fig = plt.figure(figsize=(10, 2 * rows))
axes = fig.subplots(rows, 1, sharex=True)
_add_audio(m[b], '%s/sample_%d/0_input' % (tag, b), axes[0], "Mixed")
_add_audio(o[b], '%s/sample_%d/1_output' % (tag, b), axes[1], "Output (%s)" % label)
_add_audio(gt[b], '%s/sample_%d/2_gt' % (tag, b), axes[2], "GT (%s)" % label)
writer.add_figure('%s/sample_%d/waveform' % (tag, b), fig, step)
def tensorboard_add_metrics(writer, tag, metrics, label, step):
"""
Add metrics to tensorboard.
"""
vals = np.asarray(metrics['scale_invariant_signal_noise_ratio'])
writer.add_histogram('%s/%s' % (tag, 'SI-SNRi'), vals, step)
label_names = [FSDSoundScapesDataset._labels[torch.argmax(_)] for _ in label]
for l, v in zip(label_names, vals):
writer.add_histogram('%s/%s' % (tag, l), v, step)
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