openmusic / audioldm_train /utilities /data /dataset_original_mos.py
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import sys
sys.path.append("src")
import os
import pandas as pd
import yaml
import audioldm_train.utilities.audio as Audio
from audioldm_train.utilities.tools import load_json
from audioldm_train.dataset_plugin import *
from librosa.filters import mel as librosa_mel_fn
import random
from torch.utils.data import Dataset
import torch.nn.functional
import torch
import numpy as np
import torchaudio
import json
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
return torch.log(torch.clamp(x, min=clip_val) * C)
def dynamic_range_decompression_torch(x, C=1):
return torch.exp(x) / C
def spectral_normalize_torch(magnitudes):
output = dynamic_range_compression_torch(magnitudes)
return output
def spectral_de_normalize_torch(magnitudes):
output = dynamic_range_decompression_torch(magnitudes)
return output
class AudioDataset(Dataset):
def __init__(
self,
config=None,
split="train",
waveform_only=False,
add_ons=[],
dataset_json=None,
):
"""
Dataset that manages audio recordings
:param audio_conf: Dictionary containing the audio loading and preprocessing settings
:param dataset_json_file
"""
self.config = config
self.split = split
self.pad_wav_start_sample = 0 # If none, random choose
self.trim_wav = False
self.waveform_only = waveform_only
self.add_ons = [eval(x) for x in add_ons]
print("Add-ons:", self.add_ons)
self.build_setting_parameters()
# For an external dataset
if dataset_json is not None:
self.data = dataset_json["data"]
self.id2label, self.index_dict, self.num2label = {}, {}, {}
else:
self.metadata_root = load_json(self.config["metadata_root"])
self.dataset_name = self.config["data"][self.split]
assert split in self.config["data"].keys(), (
"The dataset split %s you specified is not present in the config. You can choose from %s"
% (split, self.config["data"].keys())
)
self.build_dataset()
self.build_id_to_label()
self.build_dsp()
self.label_num = len(self.index_dict)
print("Dataset initialize finished")
def __getitem__(self, index):
(
fname,
waveform,
stft,
log_mel_spec,
label_vector, # the one-hot representation of the audio class
# the metadata of the sampled audio file and the mixup audio file (if exist)
(datum, mix_datum),
random_start,
) = self.feature_extraction(index)
text = self.get_sample_text_caption(datum, mix_datum, label_vector)
data = {
"text": text, # list
"fname": self.text_to_filename(text) if (not fname) else fname, # list
# tensor, [batchsize, class_num]
"label_vector": "" if (label_vector is None) else label_vector.float(),
# tensor, [batchsize, 1, samples_num]
"waveform": "" if (waveform is None) else waveform.float(),
# tensor, [batchsize, t-steps, f-bins]
"stft": "" if (stft is None) else stft.float(),
# tensor, [batchsize, t-steps, mel-bins]
"log_mel_spec": "" if (log_mel_spec is None) else log_mel_spec.float(),
"duration": self.duration,
"sampling_rate": self.sampling_rate,
"random_start_sample_in_original_audio_file": random_start,
"mos": 5
}
for add_on in self.add_ons:
data.update(add_on(self.config, data, self.data[index]))
if data["text"] is None:
print("Warning: The model return None on key text", fname)
data["text"] = ""
return data
def text_to_filename(self, text):
return text.replace(" ", "_").replace("'", "_").replace('"', "_")
def get_dataset_root_path(self, dataset):
assert dataset in self.metadata_root.keys()
return self.metadata_root[dataset]
def get_dataset_metadata_path(self, dataset, key):
# key: train, test, val, class_label_indices
try:
if dataset in self.metadata_root["metadata"]["path"].keys():
return self.metadata_root["metadata"]["path"][dataset][key]
except:
raise ValueError(
'Dataset %s does not metadata "%s" specified' % (dataset, key)
)
def __len__(self):
return len(self.data)
def feature_extraction(self, index):
if index > len(self.data) - 1:
print(
"The index of the dataloader is out of range: %s/%s"
% (index, len(self.data))
)
index = random.randint(0, len(self.data) - 1)
# Read wave file and extract feature
while True:
try:
label_indices = np.zeros(self.label_num, dtype=np.float32)
datum = self.data[index]
(
log_mel_spec,
stft,
waveform,
random_start,
) = self.read_audio_file(datum["wav"])
mix_datum = None
if self.label_num > 0 and "labels" in datum.keys():
for label_str in datum["labels"].split(","):
label_indices[int(self.index_dict[label_str])] = 1.0
# If the key "label" is not in the metadata, return all zero vector
label_indices = torch.FloatTensor(label_indices)
break
except Exception as e:
index = (index + 1) % len(self.data)
print(
"Error encounter during audio feature extraction: ", e, datum["wav"]
)
continue
# The filename of the wav file
fname = datum["wav"]
# t_step = log_mel_spec.size(0)
# waveform = torch.FloatTensor(waveform[..., : int(self.hopsize * t_step)])
waveform = torch.FloatTensor(waveform)
return (
fname,
waveform,
stft,
log_mel_spec,
label_indices,
(datum, mix_datum),
random_start,
)
# def augmentation(self, log_mel_spec):
# assert torch.min(log_mel_spec) < 0
# log_mel_spec = log_mel_spec.exp()
# log_mel_spec = torch.transpose(log_mel_spec, 0, 1)
# # this is just to satisfy new torchaudio version.
# log_mel_spec = log_mel_spec.unsqueeze(0)
# if self.freqm != 0:
# log_mel_spec = self.frequency_masking(log_mel_spec, self.freqm)
# if self.timem != 0:
# log_mel_spec = self.time_masking(
# log_mel_spec, self.timem) # self.timem=0
# log_mel_spec = (log_mel_spec + 1e-7).log()
# # squeeze back
# log_mel_spec = log_mel_spec.squeeze(0)
# log_mel_spec = torch.transpose(log_mel_spec, 0, 1)
# return log_mel_spec
def build_setting_parameters(self):
# Read from the json config
self.melbins = self.config["preprocessing"]["mel"]["n_mel_channels"]
# self.freqm = self.config["preprocessing"]["mel"]["freqm"]
# self.timem = self.config["preprocessing"]["mel"]["timem"]
self.sampling_rate = self.config["preprocessing"]["audio"]["sampling_rate"]
self.hopsize = self.config["preprocessing"]["stft"]["hop_length"]
self.duration = self.config["preprocessing"]["audio"]["duration"]
self.target_length = int(self.duration * self.sampling_rate / self.hopsize)
self.mixup = self.config["augmentation"]["mixup"]
# Calculate parameter derivations
# self.waveform_sample_length = int(self.target_length * self.hopsize)
# if (self.config["balance_sampling_weight"]):
# self.samples_weight = np.loadtxt(
# self.config["balance_sampling_weight"], delimiter=","
# )
if "train" not in self.split:
self.mixup = 0.0
# self.freqm = 0
# self.timem = 0
def _relative_path_to_absolute_path(self, metadata, dataset_name):
root_path = self.get_dataset_root_path(dataset_name)
for i in range(len(metadata["data"])):
assert "wav" in metadata["data"][i].keys(), metadata["data"][i]
assert metadata["data"][i]["wav"][0] != "/", (
"The dataset metadata should only contain relative path to the audio file: "
+ str(metadata["data"][i]["wav"])
)
metadata["data"][i]["wav"] = os.path.join(
root_path, metadata["data"][i]["wav"]
)
return metadata
def build_dataset(self):
self.data = []
print("Build dataset split %s from %s" % (self.split, self.dataset_name))
if type(self.dataset_name) is str:
data_json = load_json(
self.get_dataset_metadata_path(self.dataset_name, key=self.split)
)
data_json = self._relative_path_to_absolute_path(
data_json, self.dataset_name
)
self.data = data_json["data"]
elif type(self.dataset_name) is list:
for dataset_name in self.dataset_name:
data_json = load_json(
self.get_dataset_metadata_path(dataset_name, key=self.split)
)
data_json = self._relative_path_to_absolute_path(
data_json, dataset_name
)
self.data += data_json["data"]
else:
raise Exception("Invalid data format")
print("Data size: {}".format(len(self.data)))
def build_dsp(self):
self.mel_basis = {}
self.hann_window = {}
self.filter_length = self.config["preprocessing"]["stft"]["filter_length"]
self.hop_length = self.config["preprocessing"]["stft"]["hop_length"]
self.win_length = self.config["preprocessing"]["stft"]["win_length"]
self.n_mel = self.config["preprocessing"]["mel"]["n_mel_channels"]
self.sampling_rate = self.config["preprocessing"]["audio"]["sampling_rate"]
self.mel_fmin = self.config["preprocessing"]["mel"]["mel_fmin"]
self.mel_fmax = self.config["preprocessing"]["mel"]["mel_fmax"]
self.STFT = Audio.stft.TacotronSTFT(
self.config["preprocessing"]["stft"]["filter_length"],
self.config["preprocessing"]["stft"]["hop_length"],
self.config["preprocessing"]["stft"]["win_length"],
self.config["preprocessing"]["mel"]["n_mel_channels"],
self.config["preprocessing"]["audio"]["sampling_rate"],
self.config["preprocessing"]["mel"]["mel_fmin"],
self.config["preprocessing"]["mel"]["mel_fmax"],
)
# self.stft_transform = torchaudio.transforms.Spectrogram(
# n_fft=1024, hop_length=160
# )
# self.melscale_transform = torchaudio.transforms.MelScale(
# sample_rate=16000, n_stft=1024 // 2 + 1, n_mels=64
# )
def build_id_to_label(self):
id2label = {}
id2num = {}
num2label = {}
class_label_indices_path = self.get_dataset_metadata_path(
dataset=self.config["data"]["class_label_indices"],
key="class_label_indices",
)
if class_label_indices_path is not None:
df = pd.read_csv(class_label_indices_path)
for _, row in df.iterrows():
index, mid, display_name = row["index"], row["mid"], row["display_name"]
id2label[mid] = display_name
id2num[mid] = index
num2label[index] = display_name
self.id2label, self.index_dict, self.num2label = id2label, id2num, num2label
else:
self.id2label, self.index_dict, self.num2label = {}, {}, {}
def resample(self, waveform, sr):
waveform = torchaudio.functional.resample(waveform, sr, self.sampling_rate)
return waveform
def normalize_wav(self, waveform):
waveform = waveform - np.mean(waveform)
waveform = waveform / (np.max(np.abs(waveform)) + 1e-8)
return waveform * 0.5 # Manually limit the maximum amplitude into 0.5
def random_segment_wav(self, waveform, target_length):
waveform_length = waveform.shape[-1]
assert waveform_length > 100, "Waveform is too short, %s" % waveform_length
# Too short
if (waveform_length - target_length) <= 0:
return waveform, 0
for i in range(10):
random_start = int(self.random_uniform(0, waveform_length - target_length))
if torch.max(
torch.abs(waveform[:, random_start : random_start + target_length])
> 1e-4
):
break
return waveform[:, random_start : random_start + target_length], random_start
def pad_wav(self, waveform, target_length):
waveform_length = waveform.shape[-1]
assert waveform_length > 100, "Waveform is too short, %s" % waveform_length
if waveform_length == target_length:
return waveform
# Pad
temp_wav = np.zeros((1, target_length), dtype=np.float32)
if self.pad_wav_start_sample is None:
rand_start = int(self.random_uniform(0, target_length - waveform_length))
else:
rand_start = 0
temp_wav[:, rand_start : rand_start + waveform_length] = waveform
return temp_wav
def trim_wav(self, waveform):
if np.max(np.abs(waveform)) < 0.0001:
return waveform
def detect_leading_silence(waveform, threshold=0.0001):
chunk_size = 1000
waveform_length = waveform.shape[0]
start = 0
while start + chunk_size < waveform_length:
if np.max(np.abs(waveform[start : start + chunk_size])) < threshold:
start += chunk_size
else:
break
return start
def detect_ending_silence(waveform, threshold=0.0001):
chunk_size = 1000
waveform_length = waveform.shape[0]
start = waveform_length
while start - chunk_size > 0:
if np.max(np.abs(waveform[start - chunk_size : start])) < threshold:
start -= chunk_size
else:
break
if start == waveform_length:
return start
else:
return start + chunk_size
start = detect_leading_silence(waveform)
end = detect_ending_silence(waveform)
return waveform[start:end]
def read_wav_file(self, filename):
# waveform, sr = librosa.load(filename, sr=None, mono=True) # 4 times slower
waveform, sr = torchaudio.load(filename)
waveform, random_start = self.random_segment_wav(
waveform, target_length=int(sr * self.duration)
)
waveform = self.resample(waveform, sr)
# random_start = int(random_start * (self.sampling_rate / sr))
waveform = waveform.numpy()[0, ...]
waveform = self.normalize_wav(waveform)
if self.trim_wav:
waveform = self.trim_wav(waveform)
waveform = waveform[None, ...]
waveform = self.pad_wav(
waveform, target_length=int(self.sampling_rate * self.duration)
)
return waveform, random_start
def read_audio_file(self, filename, filename2=None):
if os.path.exists(filename):
waveform, random_start = self.read_wav_file(filename)
else:
print(
'Non-fatal Warning [dataset.py]: The wav path "',
filename,
'" is not find in the metadata. Use empty waveform instead. This is normal in the inference process.',
)
target_length = int(self.sampling_rate * self.duration)
waveform = torch.zeros((1, target_length))
random_start = 0
# log_mel_spec, stft = self.wav_feature_extraction_torchaudio(waveform) # this line is faster, but this implementation is not aligned with HiFi-GAN
if not self.waveform_only:
log_mel_spec, stft = self.wav_feature_extraction(waveform)
else:
# Load waveform data only
# Use zero array to keep the format unified
log_mel_spec, stft = None, None
return log_mel_spec, stft, waveform, random_start
def get_sample_text_caption(self, datum, mix_datum, label_indices):
text = self.label_indices_to_text(datum, label_indices)
if mix_datum is not None:
text += " " + self.label_indices_to_text(mix_datum, label_indices)
return text
def mel_spectrogram_train(self, y):
if torch.min(y) < -1.0:
print("train min value is ", torch.min(y))
if torch.max(y) > 1.0:
print("train max value is ", torch.max(y))
if self.mel_fmax not in self.mel_basis:
mel = librosa_mel_fn(
self.sampling_rate,
self.filter_length,
self.n_mel,
self.mel_fmin,
self.mel_fmax,
)
self.mel_basis[str(self.mel_fmax) + "_" + str(y.device)] = (
torch.from_numpy(mel).float().to(y.device)
)
self.hann_window[str(y.device)] = torch.hann_window(self.win_length).to(
y.device
)
y = torch.nn.functional.pad(
y.unsqueeze(1),
(
int((self.filter_length - self.hop_length) / 2),
int((self.filter_length - self.hop_length) / 2),
),
mode="reflect",
)
y = y.squeeze(1)
stft_spec = torch.stft(
y,
self.filter_length,
hop_length=self.hop_length,
win_length=self.win_length,
window=self.hann_window[str(y.device)],
center=False,
pad_mode="reflect",
normalized=False,
onesided=True,
return_complex=True,
)
stft_spec = torch.abs(stft_spec)
mel = spectral_normalize_torch(
torch.matmul(
self.mel_basis[str(self.mel_fmax) + "_" + str(y.device)], stft_spec
)
)
return mel[0], stft_spec[0]
# This one is significantly slower than "wav_feature_extraction_torchaudio" if num_worker > 1
def wav_feature_extraction(self, waveform):
waveform = waveform[0, ...]
waveform = torch.FloatTensor(waveform)
# log_mel_spec, stft, energy = Audio.tools.get_mel_from_wav(waveform, self.STFT)[0]
log_mel_spec, stft = self.mel_spectrogram_train(waveform.unsqueeze(0))
log_mel_spec = torch.FloatTensor(log_mel_spec.T)
stft = torch.FloatTensor(stft.T)
log_mel_spec, stft = self.pad_spec(log_mel_spec), self.pad_spec(stft)
return log_mel_spec, stft
# @profile
# def wav_feature_extraction_torchaudio(self, waveform):
# waveform = waveform[0, ...]
# waveform = torch.FloatTensor(waveform)
# stft = self.stft_transform(waveform)
# mel_spec = self.melscale_transform(stft)
# log_mel_spec = torch.log(mel_spec + 1e-7)
# log_mel_spec = torch.FloatTensor(log_mel_spec.T)
# stft = torch.FloatTensor(stft.T)
# log_mel_spec, stft = self.pad_spec(log_mel_spec), self.pad_spec(stft)
# return log_mel_spec, stft
def pad_spec(self, log_mel_spec):
n_frames = log_mel_spec.shape[0]
p = self.target_length - n_frames
# cut and pad
if p > 0:
m = torch.nn.ZeroPad2d((0, 0, 0, p))
log_mel_spec = m(log_mel_spec)
elif p < 0:
log_mel_spec = log_mel_spec[0 : self.target_length, :]
if log_mel_spec.size(-1) % 2 != 0:
log_mel_spec = log_mel_spec[..., :-1]
return log_mel_spec
def _read_datum_caption(self, datum):
caption_keys = [x for x in datum.keys() if ("caption" in x)]
random_index = torch.randint(0, len(caption_keys), (1,))[0].item()
return datum[caption_keys[random_index]]
def _is_contain_caption(self, datum):
caption_keys = [x for x in datum.keys() if ("caption" in x)]
return len(caption_keys) > 0
def label_indices_to_text(self, datum, label_indices):
if self._is_contain_caption(datum):
return self._read_datum_caption(datum)
elif "label" in datum.keys():
name_indices = torch.where(label_indices > 0.1)[0]
# description_header = "This audio contains the sound of "
description_header = ""
labels = ""
for id, each in enumerate(name_indices):
if id == len(name_indices) - 1:
labels += "%s." % self.num2label[int(each)]
else:
labels += "%s, " % self.num2label[int(each)]
return description_header + labels
else:
return "" # TODO, if both label and caption are not provided, return empty string
def random_uniform(self, start, end):
val = torch.rand(1).item()
return start + (end - start) * val
def frequency_masking(self, log_mel_spec, freqm):
bs, freq, tsteps = log_mel_spec.size()
mask_len = int(self.random_uniform(freqm // 8, freqm))
mask_start = int(self.random_uniform(start=0, end=freq - mask_len))
log_mel_spec[:, mask_start : mask_start + mask_len, :] *= 0.0
return log_mel_spec
def time_masking(self, log_mel_spec, timem):
bs, freq, tsteps = log_mel_spec.size()
mask_len = int(self.random_uniform(timem // 8, timem))
mask_start = int(self.random_uniform(start=0, end=tsteps - mask_len))
log_mel_spec[:, :, mask_start : mask_start + mask_len] *= 0.0
return log_mel_spec
if __name__ == "__main__":
import torch
from tqdm import tqdm
from pytorch_lightning import seed_everything
from torch.utils.data import DataLoader
seed_everything(0)
def write_json(my_dict, fname):
# print("Save json file at "+fname)
json_str = json.dumps(my_dict)
with open(fname, "w") as json_file:
json_file.write(json_str)
def load_json(fname):
with open(fname, "r") as f:
data = json.load(f)
return data
config = yaml.load(
open(
"/mnt/bn/lqhaoheliu/project/audio_generation_diffusion/config/vae_48k_256/ds_8_kl_1.0_ch_16.yaml",
"r",
),
Loader=yaml.FullLoader,
)
add_ons = config["data"]["dataloader_add_ons"]
# load_json(data)
dataset = AudioDataset(
config=config, split="train", waveform_only=False, add_ons=add_ons
)
loader = DataLoader(dataset, batch_size=1, num_workers=0, shuffle=True)
for cnt, each in tqdm(enumerate(loader)):
# print(each["waveform"].size(), each["log_mel_spec"].size())
# print(each['freq_energy_percentile'])
import ipdb
ipdb.set_trace()
# pass