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datasets_atst_sed.py
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datasets_atst_sed.py
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from torch.utils.data import Dataset
import pandas as pd
import os
import numpy as np
import torchaudio
import random
import torch
import glob
import h5py
import torch.nn as nn
from pathlib import Path
from torchaudio.transforms import MelSpectrogram, AmplitudeToDB
class CustomAudioTransform:
def __repr__(self):
return self.__class__.__name__ + '()'
class MinMax(CustomAudioTransform):
def __init__(self, min, max):
self.min=min
self.max=max
def __call__(self,input):
min_,max_ = None,None
if self.min is None:
min_ = torch.min(input)
max_ = torch.max(input)
else:
min_ = self.min
max_ = self.max
input = (input - min_)/(max_- min_) *2. - 1.
return input
class ATSTNorm(nn.Module):
def __init__(self):
super(ATSTNorm, self).__init__()
# Audio feature extraction
self.amp_to_db = AmplitudeToDB(stype="power", top_db=80)
self.scaler = MinMax(min=-79.6482,max=50.6842) # TorchScaler("instance", "minmax", [0, 1])
def amp2db(self, spec):
return self.amp_to_db(spec).clamp(min=-50, max=80)
def forward(self, spec):
spec = self.scaler(self.amp2db(spec))
return spec
def to_mono(mixture, random_ch=False):
if mixture.ndim > 1: # multi channel
if not random_ch:
mixture = torch.mean(mixture, 0)
else: # randomly select one channel
indx = np.random.randint(0, mixture.shape[0] - 1)
mixture = mixture[indx]
return mixture
def pad_audio(audio, target_len, fs):
if audio.shape[-1] < target_len:
audio = torch.nn.functional.pad(
audio, (0, target_len - audio.shape[-1]), mode="constant"
)
padded_indx = [target_len / len(audio)]
onset_s = 0.000
elif len(audio) > target_len:
rand_onset = random.randint(0, len(audio) - target_len)
audio = audio[rand_onset:rand_onset + target_len]
onset_s = round(rand_onset / fs, 3)
padded_indx = [target_len / len(audio)]
else:
onset_s = 0.000
padded_indx = [1.0]
offset_s = round(onset_s + (target_len / fs), 3)
return audio, onset_s, offset_s, padded_indx
def process_labels(df, onset, offset):
df["onset"] = df["onset"] - onset
df["offset"] = df["offset"] - onset
df["onset"] = df.apply(lambda x: max(0, x["onset"]), axis=1)
df["offset"] = df.apply(lambda x: min(10, x["offset"]), axis=1)
df_new = df[(df.onset < df.offset)]
return df_new.drop_duplicates()
def read_audio(file, multisrc, random_channel, pad_to):
mixture, fs = torchaudio.load(file)
if not multisrc:
mixture = to_mono(mixture, random_channel)
if pad_to is not None:
mixture, onset_s, offset_s, padded_indx = pad_audio(mixture, pad_to, fs)
else:
padded_indx = [1.0]
onset_s = None
offset_s = None
mixture = mixture.float()
return mixture, onset_s, offset_s, padded_indx
class SEDTransform:
def __init__(self, feat_params):
self.transform = MelSpectrogram(
sample_rate=feat_params["sample_rate"],
n_fft=feat_params["n_window"],
win_length=feat_params["n_window"],
hop_length=feat_params["hop_length"],
f_min=feat_params["f_min"],
f_max=feat_params["f_max"],
n_mels=feat_params["n_mels"],
window_fn=torch.hamming_window,
wkwargs={"periodic": False},
power=1,
)
def __call__(self, x):
return self.transform(x)
class ATSTTransform:
def __init__(self):
self.transform = MelSpectrogram(16000, f_min=60, f_max=7800, hop_length=160, win_length=1024, n_fft=1024, n_mels=64)
self.to_db = ATSTNorm()
def __call__(self, x):
# to_db applied in the trainer files
return self.transform(x)
class StronglyAnnotatedSet(Dataset):
def __init__(
self,
audio_folder,
tsv_entries,
encoder,
pad_to=10,
fs=16000,
return_filename=False,
random_channel=False,
multisrc=False,
feat_params=None,
forbidden_list=None,
):
self.encoder = encoder
self.fs = fs
self.pad_to = pad_to * fs
self.return_filename = return_filename
self.random_channel = random_channel
self.multisrc = multisrc
self.sed_transform = SEDTransform(feat_params)
self.atst_transform = ATSTTransform()
tsv_entries = tsv_entries.dropna()
examples = {}
if forbidden_list is not None:
forbidden_list = open(forbidden_list, "r").readlines()
forbidden_list = [f.strip() for f in forbidden_list]
else:
forbidden_list = []
for i, r in tsv_entries.iterrows():
filename = r["filename"].split(".")[0]
if filename in forbidden_list:
continue
if r["filename"] not in examples.keys():
examples[r["filename"]] = {
"mixture": os.path.join(audio_folder, r["filename"]),
"events": [],
}
if not np.isnan(r["onset"]):
examples[r["filename"]]["events"].append(
{
"event_label": r["event_label"],
"onset": r["onset"],
"offset": r["offset"],
}
)
else:
if not np.isnan(r["onset"]):
examples[r["filename"]]["events"].append(
{
"event_label": r["event_label"],
"onset": r["onset"],
"offset": r["offset"],
}
)
# we construct a dictionary for each example
self.examples = examples
self.examples_list = list(examples.keys())
print("Number of examples: ", len(self.examples_list))
def __len__(self):
return len(self.examples_list)
def __getitem__(self, item):
c_ex = self.examples[self.examples_list[item]]
mixture, onset_s, offset_s, padded_indx = read_audio(
c_ex["mixture"], self.multisrc, self.random_channel, self.pad_to
)
# labels
labels = c_ex["events"]
# to steps
labels_df = pd.DataFrame(labels)
labels_df = process_labels(labels_df, onset_s, offset_s)
# check if labels exists:
if not len(labels_df):
max_len_targets = self.encoder.n_frames
strong = torch.zeros(max_len_targets, len(self.encoder.labels)).float()
else:
strong = self.encoder.encode_strong_df(labels_df)
strong = torch.from_numpy(strong).float()
# sed_feat = self.sed_transform(mixture)
atst_feat = self.atst_transform(mixture)
out_args = [mixture, atst_feat, strong.transpose(0, 1), padded_indx]
if self.return_filename:
out_args.append(c_ex["mixture"])
return out_args
class WeakSet(Dataset):
def __init__(
self,
audio_folder,
tsv_entries,
encoder,
pad_to=10,
fs=16000,
return_filename=False,
random_channel=False,
multisrc=False,
feat_params=None
):
self.encoder = encoder
self.fs = fs
self.pad_to = pad_to * fs
self.return_filename = return_filename
self.random_channel = random_channel
self.multisrc = multisrc
self.sed_transform = SEDTransform(feat_params)
self.atst_transform = ATSTTransform()
examples = {}
for i, r in tsv_entries.iterrows():
if r["filename"] not in examples.keys():
examples[r["filename"]] = {
"mixture": os.path.join(audio_folder, r["filename"]),
"events": r["event_labels"].split(","),
}
self.examples = examples
self.examples_list = list(examples.keys())
print(len(self.examples))
def __len__(self):
return len(self.examples_list)
def __getitem__(self, item):
file = self.examples_list[item]
c_ex = self.examples[file]
mixture, _, _, padded_indx = read_audio(
c_ex["mixture"], self.multisrc, self.random_channel, self.pad_to
)
# labels
labels = c_ex["events"]
# check if labels exists:
max_len_targets = self.encoder.n_frames
weak = torch.zeros(max_len_targets, len(self.encoder.labels))
if len(labels):
weak_labels = self.encoder.encode_weak(labels)
weak[0, :] = torch.from_numpy(weak_labels).float()
# sed_feat = self.sed_transform(mixture)
atst_feat = self.atst_transform(mixture)
out_args = [mixture, atst_feat, weak.transpose(0, 1), padded_indx]
if self.return_filename:
out_args.append(c_ex["mixture"])
return out_args
class UnlabeledSet(Dataset):
def __init__(
self,
unlabeled_folder,
encoder,
pad_to=10,
fs=16000,
return_filename=False,
random_channel=False,
multisrc=False,
feat_params=None
):
self.encoder = encoder
self.fs = fs
self.pad_to = pad_to * fs if pad_to is not None else None
self.examples = glob.glob(os.path.join(unlabeled_folder, "*.wav"))
print(len(self.examples))
self.return_filename = return_filename
self.random_channel = random_channel
self.multisrc = multisrc
self.sed_transform = SEDTransform(feat_params)
self.atst_transform = ATSTTransform()
def __len__(self):
return len(self.examples)
def __getitem__(self, item):
c_ex = self.examples[item]
mixture, _, _, padded_indx = read_audio(
c_ex, self.multisrc, self.random_channel, self.pad_to
)
max_len_targets = self.encoder.n_frames
strong = torch.zeros(max_len_targets, len(self.encoder.labels)).float()
# sed_feat = self.sed_transform(mixture)
atst_feat = self.atst_transform(mixture)
out_args = [mixture, atst_feat, strong.transpose(0, 1), padded_indx]
if self.return_filename:
out_args.append(c_ex)
return out_args