File size: 8,427 Bytes
3e9fcf2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 |
import multiprocessing
import os
import shutil
import librosa as lb
import numpy as np
import soundfile as sf
from deepmultilingualpunctuation import PunctuationModel
from pyannote.audio import Pipeline
from rpunct import RestorePuncts
from tqdm import tqdm
class UncleanYeeter:
def __init__(self):
"""
all the models and persistent stuff
"""
self.diarizer = Pipeline.from_pretrained("pyannote/speaker-diarization@2.1")
def create_list_of_samples_marked_for_deletion(self, list_of_audios):
marked_for_yeeting = list()
for audio_file in tqdm(list_of_audios):
try:
wav, sr = sf.read(audio_file)
except RuntimeError:
print(f"PROBLEMATIC FILE: {audio_file}")
continue
wav = to_mono(wav)
# check duration
if 5 < len(wav) / sr < 15:
continue
# check SNR
if wada_snr(wav) < 20.0:
continue
# check amount of speakers
try:
output = self.diarizer(audio_file)
except ValueError:
print("Diarizer is unhappy")
continue
speakers = set()
for _, _, speaker in output.itertracks(yield_label=True):
speakers.add(speaker)
if len(speakers) > 1:
continue
marked_for_yeeting.append(audio_file.split("/")[-1])
# save list of files to be yoten to a file for later yeeting
with open("files_to_keep.txt", "a", encoding="utf8") as file:
file.write("\n".join(marked_for_yeeting) + "\n")
print(marked_for_yeeting)
class Punctuator:
def __init__(self, lang="eng"):
if lang == "en":
model = RestorePuncts()
self.punctuate_transcripts = model.punctuate # pass a string into it and you get a punctuated string returned
else:
model = PunctuationModel()
self.punctuate_transcripts = model.restore_punctuation # pass a string into it and you get a punctuated string returned
def wada_snr(wav):
# Direct blind estimation of the SNR of a speech signal.
#
# Paper on WADA SNR:
# http://www.cs.cmu.edu/~robust/Papers/KimSternIS08.pdf
#
# This function was adapted from this matlab code:
# https://labrosa.ee.columbia.edu/projects/snreval/#9
# init
eps = 1e-10
# next 2 lines define a fancy curve derived from a gamma distribution -- see paper
db_vals = np.arange(-20, 101)
g_vals = np.array(
[0.40974774, 0.40986926, 0.40998566, 0.40969089, 0.40986186, 0.40999006, 0.41027138, 0.41052627, 0.41101024, 0.41143264, 0.41231718, 0.41337272, 0.41526426, 0.4178192, 0.42077252, 0.42452799, 0.42918886, 0.43510373, 0.44234195, 0.45161485, 0.46221153, 0.47491647, 0.48883809, 0.50509236, 0.52353709, 0.54372088, 0.56532427,
0.58847532, 0.61346212, 0.63954496, 0.66750818, 0.69583724, 0.72454762, 0.75414799, 0.78323148, 0.81240985, 0.84219775, 0.87166406, 0.90030504, 0.92880418, 0.95655449, 0.9835349, 1.01047155, 1.0362095, 1.06136425, 1.08579312, 1.1094819, 1.13277995, 1.15472826, 1.17627308, 1.19703503, 1.21671694, 1.23535898, 1.25364313,
1.27103891, 1.28718029, 1.30302865, 1.31839527, 1.33294817, 1.34700935, 1.3605727, 1.37345513, 1.38577122, 1.39733504, 1.40856397, 1.41959619, 1.42983624, 1.43958467, 1.44902176, 1.45804831, 1.46669568, 1.47486938, 1.48269965, 1.49034339, 1.49748214, 1.50435106, 1.51076426, 1.51698915, 1.5229097, 1.528578, 1.53389835, 1.5391211,
1.5439065, 1.54858517, 1.55310776, 1.55744391, 1.56164927, 1.56566348, 1.56938671, 1.57307767, 1.57654764, 1.57980083, 1.58304129, 1.58602496, 1.58880681, 1.59162477, 1.5941969, 1.59693155, 1.599446, 1.60185011, 1.60408668, 1.60627134, 1.60826199, 1.61004547, 1.61192472, 1.61369656, 1.61534074, 1.61688905, 1.61838916, 1.61985374,
1.62135878, 1.62268119, 1.62390423, 1.62513143, 1.62632463, 1.6274027, 1.62842767, 1.62945532, 1.6303307, 1.63128026, 1.63204102])
# peak normalize, get magnitude, clip lower bound
wav = np.array(wav)
wav = wav / abs(wav).max()
abs_wav = abs(wav)
abs_wav[abs_wav < eps] = eps
# calcuate statistics
# E[|z|]
v1 = max(eps, abs_wav.mean())
# E[log|z|]
v2 = np.log(abs_wav).mean()
# log(E[|z|]) - E[log(|z|)]
v3 = np.log(v1) - v2
# table interpolation
wav_snr_idx = None
if any(g_vals < v3):
wav_snr_idx = np.where(g_vals < v3)[0].max()
# handle edge cases or interpolate
if wav_snr_idx is None:
wav_snr = db_vals[0]
elif wav_snr_idx == len(db_vals) - 1:
wav_snr = db_vals[-1]
else:
wav_snr = db_vals[wav_snr_idx] + \
(v3 - g_vals[wav_snr_idx]) / (g_vals[wav_snr_idx + 1] - g_vals[wav_snr_idx]) * (db_vals[wav_snr_idx + 1] - db_vals[wav_snr_idx])
# Calculate SNR
dEng = sum(wav ** 2)
dFactor = 10 ** (wav_snr / 10)
dNoiseEng = dEng / (1 + dFactor) # Noise energy
dSigEng = dEng * dFactor / (1 + dFactor) # Signal energy
snr = 10 * np.log10(dSigEng / dNoiseEng)
return snr
def to_mono(x):
"""
make sure we deal with a 1D array
"""
if len(x.shape) == 2:
return lb.to_mono(np.transpose(x))
else:
return x
def clean_mls_ger():
clean_mls("mls_german", "de")
def clean_mls_fr():
clean_mls("mls_french", "fr")
def clean_mls_it():
clean_mls("mls_italian", "it")
def clean_mls_eng():
clean_mls("mls_english", "en")
def clean_mls(lang_dir, lang):
punco = Punctuator(lang=lang)
new_file = ""
shutil.copy(f"/mount/resources/speech/corpora/MultiLingLibriSpeech/{lang_dir}/train/transcripts.txt", f"/mount/resources/speech/corpora/MultiLingLibriSpeech/{lang_dir}/train/orig_transcripts.txt")
with open(f"/mount/resources/speech/corpora/MultiLingLibriSpeech/{lang_dir}/train/transcripts.txt", "r", encoding="utf8") as file:
sentence_list = file.read().split("\n")
for sentence in tqdm(sentence_list):
if sentence.strip() == "":
continue
sent_id = sentence.split()[0]
punc_sent = punco.punctuate_transcripts(" ".join(sentence.split()[1:]))
new_file = new_file + f"{sent_id}\t{punc_sent}\n"
with open(f"/mount/resources/speech/corpora/MultiLingLibriSpeech/{lang_dir}/train/transcripts.txt", "w", encoding="utf8") as file:
file.write(new_file)
def build_path_to_transcript_dict_gigaspeech():
path_to_transcript = dict()
root = "/mount/resources/speech/corpora/GigaSpeech/"
with open(os.path.join(root, "transcripts.txt"), "r", encoding="utf8") as file:
lookup = file.read()
for line in lookup.split("\n"):
if line.strip() != "":
norm_transcript = line.split("\t")[1]
wav_path = os.path.join(root, "wavs", line.split("\t")[0])
if os.path.exists(wav_path):
path_to_transcript[wav_path] = norm_transcript
return path_to_transcript
def split_list(lst, n):
if n <= 0:
return []
quotient, remainder = divmod(len(lst), n)
shards = [lst[i * quotient + min(i, remainder):(i + 1) * quotient + min(i + 1, remainder)] for i in range(n)]
return shards
def yonkus(shard):
yeet = UncleanYeeter()
yeet.create_list_of_samples_marked_for_deletion(shard)
if __name__ == '__main__':
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "6"
print(f"Making GPU {os.environ['CUDA_VISIBLE_DEVICES']} the only visible device.")
list_of_files = os.listdir("/mount/resources/speech/corpora/GigaSpeech/wavs")
absolute_list_of_files = list()
for filo in list_of_files:
absolute_list_of_files.append(f"/mount/resources/speech/corpora/GigaSpeech/wavs/{filo}")
processes = list()
for sublist in split_list(absolute_list_of_files, 20):
processes.append(multiprocessing.Process(args=(sublist,), target=yonkus, daemon=True))
processes[-1].start()
for processo in processes:
processo.join()
# clean_mls_it()
# clean_mls_fr()
# clean_mls_ger()
# clean_mls_eng()
|