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import sys
sys.path.append("src")
import os
import math
import pandas as pd
import zlib
import yaml
import audioldm_train.utilities.audio as Audio
from audioldm_train.utilities.tools import load_json
from audioldm_train.dataset_plugin import *
import librosa
from librosa.filters import mel as librosa_mel_fn
import threading
import random
import lmdb
from torch.utils.data import Dataset
import torch.nn.functional
import torch
from pydub import AudioSegment
import numpy as np
import torchaudio
import io
import json
from .datum_all_pb2 import Datum_all as Datum_lmdb
from .datum_mos_pb2 import Datum_mos as Datum_lmdb_mos
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,
lmdb_path,
key_path,
mos_path,
lock=True
):
self.config = config
# self.lock = threading.Lock()
"""
Dataset that manages audio recordings
"""
self.pad_wav_start_sample = 0
self.trim_wav = False
self.build_setting_parameters()
self.build_dsp()
self.lmdb_path = [_.encode("utf-8") for _ in lmdb_path]
self.lmdb_env = [lmdb.open(_, readonly=True, lock=False) for _ in self.lmdb_path]
self.mos_txn_env = lmdb.open(mos_path, readonly=True, lock=False)
self.key_path = [_.encode("utf-8") for id, _ in enumerate(key_path)]
self.keys = []
for _ in range(len(key_path)):
with open(self.key_path[_]) as f:
for line in f:
key = line.strip()
self.keys.append((_, key.split()[0].encode('utf-8')))
# only for test !!!
# if _ > 20:
# break
# self.keys : [(id, key), ..., ...]
# self.lmdb_env = lmdb.open(self.lmdb_path, readonly=True, lock=False)
# self.txn = self.lmdb_env.begin()
print(f"Dataset initialize finished, dataset_length : {len(self.keys)}")
print(f"Initialize of filter start: ")
with open('filter_all.lst', 'r') as f:
self.filter = {}
for _ in f.readlines():
self.filter[_.strip()] = 1
print(f"Initialize of filter finished")
#print(f"Initialize of fusion start: ")
#with open('new_file.txt', 'r') as f:
# self.refined_caption = {}
# for _ in f.readlines():
# try:
# a, b = _.strip().split("@")
# b = b.strip('"\n')
# b = b.replace('\n', ',')
# self.refined_caption[a] = b
# except:
# pass
#print(f"Initialize of fusion finished")
def __getitem__(self, index):
(
# name of file, while we use dir of fine here
fname,
# wav of sr = 16000
waveform,
# mel
stft,
# log mel
log_mel_spec,
label_vector,
# donot start at the begining
random_start,
# dict or single string which describes the wav file
caption,
# mos score for single music clip
mos
) = self.feature_extraction(index)
data = {
"text": [caption], # list ... dict ?
"fname": [fname], # list
# 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,
"label_vector": label_vector,
"mos":mos
}
if data["text"] is None:
print("Warning: The model return None on key text", fname)
data["text"] = ""
return data
def __len__(self):
return len(self.keys)
def feature_extraction(self, index):
if index > len(self.keys) - 1:
print(
"The index of the dataloader is out of range: %s/%s"
% (index, len(self.data))
)
index = random.randint(0, len(self.keys) - 1)
waveform = np.array([])
tyu = 0
flag = 0
last_index = index
while(flag == 0):
id_, k = self.keys[index]
try:
if self.filter[k.decode()] == 1:
index = random.randint(0, len(self.keys) - 1)
else:
flag = 1
except:
flag = 1
index = last_index
while len(waveform) < 1000:
id_, k = self.keys[index]
with self.lmdb_env[id_].begin(write=False) as txn:
cursor = txn.cursor()
try:
cursor.set_key(k)
datum_tmp = Datum_lmdb()
datum_tmp.ParseFromString(cursor.value())
zobj = zlib.decompressobj() # obj for decompressing data streams that won’t fit into memory at once.
decompressed_bytes = zobj.decompress(datum_tmp.wav_file)
# decompressed_bytes = zlib.decompress(file)
waveform = np.frombuffer(decompressed_bytes, dtype=np.float32)
except:
tyu += 1
pass
tyu += 1
last_index = index
index = random.randint(0, len(self.keys) - 1)
if tyu > 1:
print('error')
index = last_index
flag = 0
val = 623787092.84794
while (flag == 0):
id_, k = self.keys[index]
with self.mos_txn_env.begin(write=False) as txn:
cursor = txn.cursor()
try:
if cursor.set_key(k):
datum_mos = Datum_lmdb_mos()
datum_mos.ParseFromString(cursor.value())
mos = datum_mos.mos
else:
mos = -1.0
except :
mos = -1.0
if 'pixa_' in k.decode() or 'ifly_' in k.decode():
mos = 5.0
if np.random.rand() < math.exp(5.0 * mos) / val:
flag = 1
last_index = index
index = random.randint(0, len(self.keys) - 1)
index = last_index
caption_original = datum_tmp.caption_original
try:
caption_generated = datum_tmp.caption_generated[0]
except:
caption_generated = 'None'
assert len(caption_generated) > 1
caption_original = caption_original.lower()
caption_generated = caption_generated.lower()
caption = 'music'
if ("msd_" in k.decode()):
caption = caption_generated if caption_original == "none" else caption_original
elif ("audioset_" in k.decode()):
caption = caption_generated if caption_generated != "none" else caption_original
elif ("mtt_" in k.decode()):
caption = caption_generated if caption_original == "none" else caption_original
elif ("fma_" in k.decode()):
caption = caption_generated if caption_generated != "none" else caption_original
elif ("pixa_" in k.decode() or "ifly_" in k.decode()):
caption = caption_generated if caption_generated != "none" else caption_original
else:
caption = caption_original
prefix = 'medium quality'
if ("pixa_" in k.decode() or "ifly_" in k.decode()):
if caption == 'none':
prefix = 'high quality'
caption = ''
else:
prefix = 'high quality'
mos = 5.00
else:
mos = float(mos)
if mos > 3.55 and mos < 4.05:
prefix = "medium quality"
elif mos >= 4.05:
prefix = "high quality"
elif mos <= 3.55:
prefix = "low quality"
else:
print(f'mos score for key : {k.decode()} miss, please check')
#if 'low quality' or 'quality is low' in caption:
# prefix = 'low quality'
caption = prefix + ', ' + caption
miu = 3.80
sigma = 0.20
if miu - 2 * sigma <= mos < miu - sigma:
vq_mos = 2
elif miu - sigma <= mos < miu + sigma:
vq_mos = 3
elif miu + sigma <= mos < miu + 2 * sigma:
vq_mos = 4
elif mos >= miu + 2 * sigma:
vq_mos = 5
else:
vq_mos = 1
"""
tags = datum_tmp.tags.decode()
caption_writing = datum_tmp.caption_writing.decode()
caption_paraphrase = datum_tmp.caption_paraphrase.decode()
caption_attribute_prediction = datum_tmp.caption_attribute_prediction.decode()
caption_summary = datum_tmp.caption_summary.decode()
"""
(
log_mel_spec,
stft,
waveform,
random_start,
) = self.read_audio_file(waveform, k.decode())
fname = self.keys[index]
# t_step = log_mel_spec.size(0)
# waveform = torch.FloatTensor(waveform[..., : int(self.hopsize * t_step)])
waveform = torch.FloatTensor(waveform)
label_vector = torch.FloatTensor(np.zeros(0, dtype=np.float32))
# finally:
# self.lock.release()
# import pdb
# pdb.set_trace()
return (
fname,
waveform,
stft,
log_mel_spec,
label_vector,
random_start,
caption,
vq_mos
)
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 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"],
)
def resample(self, waveform, sr):
waveform = torchaudio.functional.resample(waveform, sr, self.sampling_rate)
# waveform = librosa.resample(waveform, sr, self.sampling_rate)
return waveform
# if sr == 16000:
# return waveform
# if sr == 32000 and self.sampling_rate == 16000:
# waveform = waveform[::2]
# return waveform
# if sr == 48000 and self.sampling_rate == 16000:
# waveform = waveform[::3]
# return waveform
# else:
# raise ValueError(
# "We currently only support 16k audio generation. You need to resample you audio file to 16k, 32k, or 48k: %s, %s"
# % (sr, self.sampling_rate)
# )
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 = torch.tensor(waveform)
waveform = waveform.unsqueeze(0)
waveform_length = waveform.shape[-1]
# assert waveform_length > 100, "Waveform is too short, %s" % waveform_length
if waveform_length < 100:
waveform = torch.nn.functional.pad(waveform, (0, target_length - 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):
# print(waveform)
# import pdb
# pdb.set_trace()
waveform_length = waveform.shape[-1]
# assert waveform_length > 100, "Waveform is too short, %s" % waveform_length
if waveform_length < 100:
waveform = torch.nn.functional.pad(waveform, (0, target_length - 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, file, k):
#zobj = zlib.decompressobj() # obj for decompressing data streams that won’t fit into memory at once.
#decompressed_bytes = zobj.decompress(file)
# decompressed_bytes = zlib.decompress(file)
#waveform = np.frombuffer(decompressed_bytes, dtype=np.float32)
waveform = file
# # waveform, sr = librosa.load(filename, sr=None, mono=True) # 4 times slower
# if "msd" in k or "fma" in k:
# try:
# waveform = torch.tensor([(np.array(file.get_array_of_samples(array_type_override='i')) / 2147483648)], dtype=torch.float32)
# except:
# waveform = torch.tensor([(np.array(file.get_array_of_samples(array_type_override='h')) / 32768)], dtype=torch.float32)
# else:
# waveform = torch.tensor([(np.array(file.get_array_of_samples(array_type_override='h')) / 32768)], dtype=torch.float32)
# # else:
# # raise AttributeError
# waveform = torch.tensor([(np.array(file.get_array_of_samples(array_type_override='h')) / 32768)], dtype=torch.float32)
# import pdb
# pdb.set_trace()
sr = 16000
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 mix_two_waveforms(self, waveform1, waveform2):
mix_lambda = np.random.beta(5, 5)
mix_waveform = mix_lambda * waveform1 + (1 - mix_lambda) * waveform2
return self.normalize_wav(mix_waveform), mix_lambda
def read_audio_file(self, file, k):
# target_length = int(self.sampling_rate * self.duration)
# import pdb
# pdb.set_trace()
# print(type(file))
waveform, random_start = self.read_wav_file(file, k)
# log_mel_spec, stft = self.wav_feature_extraction_torchaudio(waveform) # this line is faster, but this implementation is not aligned with HiFi-GAN
log_mel_spec, stft = self.wav_feature_extraction(waveform)
return log_mel_spec, stft, waveform, random_start
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))
# import pdb
# pdb.set_trace()
if self.mel_fmax not in self.mel_basis:
# import pdb
# pdb.set_trace()
mel = librosa_mel_fn(
sr=self.sampling_rate,
n_fft=self.filter_length,
n_mels=self.n_mel,
fmin=self.mel_fmin,
fmax=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)
# import pdb
# pdb.set_trace()
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
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
class AudioDataset_infer(Dataset):
def __init__(
self,
config,
caption_list,
lock=True
):
self.config = config
# self.lock = threading.Lock()
"""
Dataset that manage caption writings
"""
self.captions = []
with open(caption_list, 'r') as f:
for _ ,line in enumerate(f):
key = line.strip()
self.captions.append(key.split()[0])
self.duration = self.duration = self.config["preprocessing"]["audio"]["duration"]
self.sampling_rate = self.config["variables"]["sampling_rate"]
self.target_length = int(self.sampling_rate * self.duration)
self.waveform = torch.zeros((1, self.target_length))
def __getitem__(self, index):
fname = [f"sample_{index}"]
data = {
"text": [self.captions[index]], # list ... dict ?
"fname": fname, # list
# tensor, [batchsize, 1, samples_num]
"waveform": "",
# tensor, [batchsize, t-steps, f-bins]
"stft": "",
# tensor, [batchsize, t-steps, mel-bins]
"log_mel_spec": "",
"duration": self.duration,
"sampling_rate": self.sampling_rate,
"random_start_sample_in_original_audio_file": 0,
"label_vector": torch.FloatTensor(np.zeros(0, dtype=np.float32)),
"mos":mos
}
if data["text"] is None:
print("Warning: The model return None on key text", fname)
data["text"] = ""
return data
def __len__(self):
return len(self.captions)
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