Datasets:
Tasks:
Automatic Speech Recognition
Languages:
Swedish
File size: 27,741 Bytes
c75d108 1a9a056 c75d108 1a9a056 c75d108 1a9a056 c75d108 1a9a056 c75d108 1a9a056 c75d108 1a9a056 c75d108 1a9a056 c75d108 1a9a056 c75d108 1a9a056 c75d108 1a9a056 c75d108 1a9a056 c75d108 1a9a056 c75d108 1a9a056 c75d108 1a9a056 c75d108 1a9a056 c75d108 1a9a056 c75d108 1a9a056 c75d108 1a9a056 c75d108 1a9a056 c75d108 1a9a056 c75d108 1a9a056 c75d108 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 |
# coding=utf-8
# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
# Copyright 2022, 2023 Jim O'Regan for Språkbanken Tal
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""Datasets loader for Waxholm speech corpus"""
from io import BytesIO
import os
import soundfile as sf
from collections import namedtuple
from copy import deepcopy
from difflib import SequenceMatcher
import datasets
from datasets.features import Audio
TRAIN_LIST = "alloktrainfiles"
TEST_LIST = "testfiles"
_DESCRIPTION = """\
The Waxholm corpus was collected in 1993 - 1994 at the department of Speech, Hearing and Music (TMH), KTH.
"""
_CITATION = """
@article{bertenstam1995spoken,
title={Spoken dialogue data collected in the {W}axholm project},
author={Bertenstam, Johan and Blomberg, Mats and Carlson, Rolf and Elenius, Kjell and Granstr{\"o}m, Bj{\"o}rn and Gustafson, Joakim and Hunnicutt, Sheri and H{\"o}gberg, Jesper and Lindell, Roger and Neovius, Lennart and Nord, Lennart and de~Serpa-Leitao, Antonio and Str{\"o}m, Nikko},
journal={STH-QPSR, KTH},
volume={1},
pages={49--74},
year={1995}
}
@inproceedings{bertenstam1995waxholm,
title={The {W}axholm application database.},
author={Bertenstam, J and Blomberg, Mats and Carlson, Rolf and Elenius, Kjell and Granstr{\"o}m, Bj{\"o}rn and Gustafson, Joakim and Hunnicutt, Sheri and H{\"o}gberg, Jesper and Lindell, Roger and Neovius, Lennart and Nord, Lennart and de~Serpa-Leitao, Antonio and Str{\"o}m, Nikko},
booktitle={EUROSPEECH},
year={1995}
}"""
_URL = "http://www.speech.kth.se/waxholm/waxholm2.html"
class FRExpected(Exception):
"""Exception to raise when FR line was expected, but not read"""
def __init__(self, line):
msg = "Unknown line type (does not begin with 'FR'): "
super().__init__(msg + line)
class WaxholmDataset(datasets.GeneratorBasedBuilder):
"""Dataset script for Waxholm."""
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="waxholm"),
]
def _info(self):
features = datasets.Features(
{
"id": datasets.Value("string"),
"text": datasets.Value("string"),
"phonemes": datasets.Sequence(datasets.Value("string")),
"audio": datasets.Audio(sampling_rate=16_000)
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_URL,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"split": "train",
"files": TRAIN_LIST
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"split": "test",
"files": TEST_LIST
},
),
]
def _generate_examples(self, split, files):
with open(f"./waxholm/{files}") as input_file:
for line in input_file.readlines():
line = line.strip()
parts = line.split(".")
subdir = parts[0]
audio_file = f"./waxholm/scenes_formatted/{subdir}/{line}"
if not os.path.exists(audio_file):
print(f"{audio_file} does not exist: skipping")
continue
text_file = f"{audio_file}.mix"
if not os.path.exists(text_file):
print(f"{text_file} does not exist: skipping")
continue
mix = Mix(text_file)
samples, sr = smp_read_sf(audio_file)
buffer = BytesIO()
sf.write(buffer, samples, sr, format="wav")
blank = Audio()
yield line, {
"id": line,
"text": mix.text,
"phonemes": mix.get_phoneme_list(),
"audio": {
"bytes": buffer.getvalue(),
"sampling_rate": sr,
}
}
def fix_text(text: str) -> str:
replacements = text.maketrans("{}|\\[]", "äåöÖÄÅ")
return text.translate(replacements)
Label = namedtuple('Label', ['start', 'end', 'label'])
class FR:
def __init__(self, text="", **kwargs): # C901
if text and text != "":
self.from_text(text)
else:
for arg in kwargs:
prms = ["pm", "pm_type", "type", "frame",
"seconds", "phone", "phone_type",
"word", "pseudoword"]
if arg in prms:
self.__dict__[arg] = kwargs[arg]
else:
print(f"Unrecognised argument: {arg}")
def from_text(self, text: str):
if not text.startswith("FR"):
raise FRExpected(text)
parts = [a.strip() for a in text.split("\t")]
self.frame = parts[0][2:].strip()
if parts[-1].strip().endswith(" sec"):
self.seconds = parts[-1].strip()[0:-4]
def split_phone(phone):
if phone.startswith("$#"):
phtype = 'I'
phone_type = fix_text(phone[0:2])
phone_out = fix_text(phone[2:])
elif phone.startswith("$") or phone.startswith("#"):
phtype = 'I'
phone_type = fix_text(phone[0:1])
phone_out = fix_text(phone[1:])
else:
print(phone)
return None
return {
"type": phtype,
"phone_type": phone_type,
"phone": phone_out
}
for subpart in parts[1:-1]:
subpart = subpart.strip()
if subpart.startswith("$#") or subpart.startswith("$") or subpart.startswith("#"):
phparts = split_phone(subpart)
if phparts is not None:
self.type = phparts['type']
self.phone_type = phparts['phone_type']
self.phone = phparts['phone']
elif subpart.startswith(">pm "):
phparts = split_phone(subpart[4:])
if phparts is not None:
self.pm_type = phparts['phone_type']
self.pm = phparts['phone']
elif subpart.startswith(">pm. "):
phparts = split_phone(subpart[5:])
if phparts is not None:
self.pm_type = phparts['phone_type']
self.pm = phparts['phone']
elif subpart.startswith(">w "):
self.type = 'B'
self.word = fix_text(subpart[3:])
self.pseudoword = False
elif subpart.startswith(">w. "):
self.type = 'B'
self.word = fix_text(subpart[4:])
self.pseudoword = False
elif subpart == "> XklickX" or subpart == "> XutandX":
self.type = 'B'
self.word = subpart[2:]
self.pseudoword = True
elif subpart.startswith("X"):
if hasattr(self, 'type'):
print(self.type, self.type == 'B')
self.type = getattr(self, 'type', 'B')
self.word = fix_text(subpart)
self.pseudoword = True
elif subpart == "OK":
self.type = 'E'
elif subpart == "PROBLEMS":
self.type = 'E'
def get_type(self):
if "type" in self.__dict__:
return self.type
else:
return ""
def __repr__(self):
parts = []
parts.append(f"type: {self.get_type()}")
parts.append(f"frame: {self.frame}")
if self.get_type() != 'E':
parts.append(f"phone: {self.get_phone()}")
if 'word' in self.__dict__:
parts.append(f"word: {self.word}")
if 'pm_type' in self.__dict__:
parts.append(f"pm_type: {self.pm_type}")
if 'pm' in self.__dict__:
parts.append(f"pm: {self.pm}")
if 'seconds' in self.__dict__:
parts.append(f"sec: {self.seconds}")
return "FR(" + ", ".join(parts) + ")"
def fix_type(self):
if self.is_type("B") and self.get_word() == "":
self.pm_type = "$"
self.phone_type = "$"
self.type = "I"
def get_phone(self, fix_accents=True):
def fix_accents(phone, fix_accents=True):
if not fix_accents:
return phone
return phone.replace("'", "ˈ").replace('"', "ˌ")
if 'pm' in self.__dict__:
return fix_accents(self.pm, fix_accents)
elif 'phone' in self.__dict__:
return fix_accents(self.phone, fix_accents)
else:
return None
def is_silence_word(self, noise=False):
if 'word' in self.__dict__:
if not noise:
return self.word == "XX"
else:
return self.word.startswith("X") and self.word.endswith("X")
else:
return False
def is_type(self, type):
if "type" in self.__dict__:
return type == self.type
else:
return False
def has_seconds(self):
return "seconds" in self.__dict__
def get_seconds(self):
if not self.has_seconds() and "frame" in self.__dict__:
return int(self.frame) / 16000.0
else:
return self.seconds
def get_word(self):
if self.has_word():
return self.word
else:
return ""
def has_word(self):
return "word" in self.__dict__
def has_pseudoword(self):
return "pseudoword" in self.__dict__
def merge_frs(fr1, fr2, check_time=False):
"""
Merge FRS entries for plosives: by default, the
period of glottal closure and the burst are separately
annotated.
"""
if fr2.has_word():
return None
if check_time:
if fr1.get_seconds() != fr2.get_seconds():
return None
if _is_glottal_closure(fr1.get_phone(), fr2.get_phone()):
if not fr1.has_word():
return fr2
else:
word = None
if fr1.has_word():
word = fr1.word
pword = None
if fr1.has_pseudoword():
pword = fr1.pseudoword
return FR(pm=fr2.pm, pm_type=fr2.pm_type, type=fr2.type,
frame=fr2.frame, seconds=fr2.seconds, phone=fr2.phone,
phone_type=fr2.phone_type, word=word, pseudoword=pword)
SILS = {
"K": "k",
"G": "g",
"T": "t",
"D": "d",
"2T": "2t",
"2D": "2d",
"P": "p",
"B": "b"
}
def _is_glottal_closure(cur, next):
return cur in SILS and next == SILS[cur]
def _replace_glottal_closures(input):
input += ' '
for sil in SILS:
input = input.replace(f"{sil} {SILS[sil]} ", f"{SILS[sil]} ")
return input[:-1]
def _fix_duration_markers(input):
input += ' '
input = input.replace(":+ ", ": ")
return input[:-1]
class Mix():
def __init__(self, filepath: str, stringfile=None, fix_type=True):
self.fr = []
self.path = filepath
if stringfile is None:
with open(filepath) as inpf:
self.read_data(inpf.readlines())
else:
self.read_data(stringfile.split("\n"))
if fix_type:
for fr in self.fr:
fr.fix_type()
def read_data(self, inpf): # C901
"""read data from text of a .mix file"""
saw_text = False
saw_phoneme = False
saw_labels = False
for line in inpf:
if line.startswith("Waxholm dialog."):
self.filepath = line[15:].strip()
if line.startswith("TEXT:"):
saw_text = True
continue
if saw_text:
self.text = fix_text(line.strip())
saw_text = False
if line.startswith("PHONEME:"):
saw_phoneme = True
self.phoneme = fix_text(line[8:].strip())
if line[8:].strip().endswith("."):
saw_phoneme = False
continue
if saw_phoneme:
self.phoneme = fix_text(line.strip())
if line[8:].strip().endswith("."):
saw_phoneme = False
if line.startswith("FR "):
if saw_labels:
saw_labels = False
self.fr.append(FR(text=line))
if line.startswith("Labels: "):
self.labels = line[8:].strip()
saw_labels = True
if saw_labels and line.startswith(" "):
self.labels += line.strip()
def check_fr(self, verbose=False) -> bool:
"""
Simple sanity check: that there were FR lines,
and that the first was a start type, and
last was an end type.
"""
if 'fr' not in self.__dict__:
return False
if len(self.fr) == 0:
return False
start_end = self.fr[0].is_type("B") and self.fr[-1].is_type("E")
if verbose and not start_end:
if not self.fr[0].is_type("B"):
print(f"{self.path}: missing start type")
if not self.fr[-1].is_type("E"):
print(f"{self.path}: missing end type")
return start_end
def get_times(self, as_frames=False):
"""
get the times of each phoneme
"""
if not self.check_fr(verbose=True):
return []
if as_frames:
times = [int(x.frame) for x in self.fr]
else:
times = [float(x.seconds) for x in self.fr]
return times
def get_time_pairs(self, as_frames=False):
"""
get a list of tuples containing start and end times
By default, the times are in seconds; if `as_frames`
is set, the number of frames are returned instead.
"""
times = self.get_times(as_frames=as_frames)
starts = times[0:-1]
ends = times[1:]
return [x for x in zip(starts, ends)]
def prune_empty_presilences(self, verbose=False, include_noises=False):
"""
Remove empty silence markers (i.e., those with no distinct duration)
"""
self.orig_fr = deepcopy(self.fr)
i = 0
warned = False
def check_cur(cur, next):
if verbose and not cur.has_seconds():
print(f"Missing seconds: {self.path}\nLine: {cur}")
if verbose and not next.has_seconds():
print(f"Missing seconds: {self.path}\nLine: {next}")
return cur.get_seconds() == next.get_seconds() and cur.is_silence_word()
todel = []
while i < len(self.fr) - 1:
if check_cur(self.fr[i], self.fr[i + 1]):
if verbose:
if not warned:
warned = True
print(f"Empty silence in {self.path}:")
print(self.fr[i])
todel.append(i)
i += 1
if todel is not None and todel != []:
for chaff in todel.reverse():
del(self.fr[chaff])
def prune_empty_postsilences(self, verbose=False, include_noises=False):
"""
Remove empty silence markers (i.e., those with no distinct duration)
"""
if not "orig_fr" in self.__dict__:
self.orig_fr = deepcopy(self.fr)
i = 1
warned = False
def check_cur(cur, prev):
if verbose and not cur.has_seconds():
print(f"Missing seconds: {self.path}\nLine: {cur}")
if verbose and not prev.has_seconds():
print(f"Missing seconds: {self.path}\nLine: {prev}")
return cur.get_seconds() == prev.get_seconds() and cur.is_silence_word()
todel = []
while i < len(self.fr):
if check_cur(self.fr[i], self.fr[i - 1]):
if verbose:
if not warned:
warned = True
print(f"Empty silence in {self.path}:")
print(self.fr[i])
todel.append(i)
i += 1
if todel is not None and todel != []:
for chaff in todel.reverse():
del(self.fr[chaff])
def prune_empty_segments(self, verbose=False):
"""
Remove empty segments (i.e., those with no distinct duration)
"""
if not "orig_fr" in self.__dict__:
self.orig_fr = deepcopy(self.fr)
times = self.get_time_pairs(as_frames=True)
if len(times) != (len(self.fr) - 1):
print("Uh oh: time pairs and items don't match")
else:
keep = []
for fr in zip(self.fr[:-1], times):
cur_time = fr[1]
if cur_time[0] == cur_time[1]:
if verbose:
print(f"Empty segment {fr[0].get_phone()} ({cur_time[0]} --> {cur_time[1]})")
else:
keep.append(fr[0])
keep.append(self.fr[-1])
self.fr = keep
def prune_empty_silences(self, verbose = False):
self.prune_empty_presilences(verbose)
self.prune_empty_postsilences(verbose)
def merge_plosives(self, verbose=False):
"""
Merge plosives in FRs
(in Waxholm, as in TIMIT, the silence before the burst and the burst
are annotated separately).
"""
if not "orig_fr" in self.__dict__:
self.orig_fr = deepcopy(self.fr)
tmp = []
i = 0
while i < len(self.fr)-1:
merged = merge_frs(self.fr[i], self.fr[i+1])
if merged is not None:
if verbose:
print(f"Merging {self.fr[i]} and {self.fr[i+1]}")
i += 1
tmp.append(merged)
else:
tmp.append(self.fr[i])
i += 1
tmp.append(self.fr[-1])
self.fr = tmp
def get_phone_label_tuples(self, as_frames=False, fix_accents=True):
times = self.get_time_pairs(as_frames=as_frames)
if self.check_fr():
labels = [fr.get_phone(fix_accents) for fr in self.fr[0:-1]]
else:
labels = []
if len(times) == len(labels):
out = []
for z in zip(times, labels):
out.append((z[0][0], z[0][1], z[1]))
return out
else:
return []
def get_merged_plosives(self, noop=False, prune_empty=True):
"""
Returns a list of phones with plosives merged
(in Waxholm, as in TIMIT, the silence before the burst and the burst
are annotated separately).
If `noop` is True, it simply returns the output of `prune_empty_labels()`
"""
if noop:
if not prune_empty:
print("Warning: not valid to set noop to True and prune_empty to false")
print("Ignoring prune_empty")
return self.prune_empty_labels()
i = 0
out = []
if prune_empty:
labels = self.prune_empty_labels()
else:
labels = self.get_phone_label_tuples()
while i < len(labels)-1:
cur = labels[i]
next = labels[i+1]
if _is_glottal_closure(cur[2], next[2]):
tmp = Label(start = cur[0], end = next[1], label = next[2])
out.append(tmp)
i += 2
else:
tmp = Label(start = cur[0], end = cur[1], label = cur[2])
out.append(tmp)
i += 1
return out
def get_word_label_tuples(self, verbose=True):
times = self.get_time_pairs()
if len(times) == len(self.fr[0:-1]):
out = []
labels_raw = [x for x in zip(times, self.fr[0:-1])]
i = 0
cur = None
while i < len(labels_raw) - 1:
if labels_raw[i][1].is_type("B"):
if cur is not None:
out.append(cur)
if labels_raw[i+1][1].is_type("B"):
if verbose and labels_raw[i][1].get_word() == "":
print("Expected word", labels_raw[i][1])
out.append((labels_raw[i][0][0], labels_raw[i][0][1], labels_raw[i][1].get_word()))
cur = None
i += 1
continue
else:
if verbose and labels_raw[i][1].get_word() == "":
print("Expected word", labels_raw[i][1])
cur = (labels_raw[i][0][0], labels_raw[i][0][1], labels_raw[i][1].get_word())
if labels_raw[i+1][1].is_type("B"):
if cur is not None:
cur = (cur[0], labels_raw[i][0][1], cur[2])
i += 1
out.append(cur)
return out
else:
return []
def get_dictionary(self, fix_accents=True):
"""
Get pronunciation dictionary entries from the .mix file.
These entries are based on the corrected pronunciations; for
the lexical pronunciations, use the `phoneme` property.
"""
output = {}
current_phones = []
prev_word = ''
for fr in self.fr:
if 'word' in fr.__dict__:
phone = fr.get_phone(fix_accents)
if prev_word != "":
if prev_word not in output:
output[prev_word] = []
output[prev_word].append(current_phones.copy())
current_phones.clear()
prev_word = fr.word
current_phones.append(phone)
elif fr.is_type("I"):
phone = fr.get_phone(fix_accents)
current_phones.append(phone)
else:
if prev_word not in output:
output[prev_word] = []
output[prev_word].append(current_phones.copy())
return output
def get_dictionary_list(self, fix_accents=True):
"""
Get pronunciation dictionary entries from the .mix file.
These entries are based on the corrected pronunciations; for
the lexical pronunciations, use the `phoneme` property.
This version creates a list of tuples (word, phones) that
preserves the order of the entries.
"""
output = []
current_phones = []
prev_word = ''
for fr in self.fr:
if 'word' in fr.__dict__:
phone = fr.get_phone(fix_accents)
if prev_word != "":
output.append((prev_word, " ".join(current_phones)))
current_phones.clear()
prev_word = fr.word
current_phones.append(phone)
elif fr.is_type("I"):
phone = fr.get_phone(fix_accents)
current_phones.append(phone)
else:
output.append((prev_word, " ".join(current_phones)))
return output
def get_phoneme_string(self, insert_pauses=True, fix_accents=True):
"""
Get an opinionated phoneme string
Args:
insert_pauses (bool, optional): Insert pauses between words. Defaults to True.
fix_accents (bool, optional): IPA-ify accents. Defaults to True.
"""
dict_list = self.get_dictionary_list(fix_accents)
skip = ['p:', '.']
if insert_pauses:
phone_strings = [x[1] for x in dict_list if x[1] not in skip]
joined = ' p: '.join(phone_strings)
else:
phone_strings = [x[1] for x in dict_list if x[1] != "."]
joined = ' '.join(phone_strings)
joined = _replace_glottal_closures(joined)
joined = _fix_duration_markers(joined)
return joined
def get_phoneme_list(self, insert_pauses=True, fix_accents=True):
return self.get_phoneme_string(insert_pauses, fix_accents).split(' ')
def get_compare_dictionary(self, fix_accents=True, merge_plosives=True, only_changed=True):
"""
Get pronunciation dictionary for comparision: i.e., where there is a difference
between the canonical pronunciation and what was spoken
"""
if merge_plosives:
self.merge_plosives()
orig = self.get_dictionary_list(fix_accents)
self.prune_empty_segments()
new = self.get_dictionary_list(fix_accents)
if len(orig) != len(new):
words_orig = [w[0] for w in orig]
words_new = [w[0] for w in new]
skippables = []
for tag, i, j, _, _ in SequenceMatcher(None, words_orig, words_new).get_opcodes():
if tag in ('delete', 'replace'):
skippables += [a for a in range(i, j)]
for c in skippables.reverse():
del(orig[c])
out = []
i = 0
while i < len(orig):
if orig[i][0] == new[i][0]:
if orig[i][1] == new[i][1]:
if not only_changed:
out.append(orig)
else:
out.append((orig[i][0], orig[i][1], new[i][1]))
i += 1
return out
def smp_probe(filename: str) -> bool:
with open(filename, "rb") as f:
return f.read(9) == b"file=samp"
def smp_headers(filename: str):
with open(filename, "rb") as f:
f.seek(0)
raw_headers = f.read(1024)
raw_headers = raw_headers.rstrip(b'\x00')
asc_headers = raw_headers.decode("ascii")
asc_headers.rstrip('\x00')
tmp = [a for a in asc_headers.split("\r\n")]
back = -1
while abs(back) > len(tmp) + 1:
if tmp[back] == '=':
break
back -= 1
tmp = tmp[0:back-1]
return dict(a.split("=") for a in tmp)
def smp_read_sf(filename: str):
headers = smp_headers(filename)
if headers["msb"] == "last":
ENDIAN = "LITTLE"
else:
ENDIAN = "BIG"
data, sr = sf.read(filename, channels=int(headers["nchans"]),
samplerate=16000, endian=ENDIAN, start=512,
dtype="int16", format="RAW", subtype="PCM_16")
return (data, sr)
def _write_wav(filename, arr):
import wave
with wave.open(filename, "w") as f:
f.setnchannels(1)
f.setsampwidth(2)
f.setframerate(16000)
f.writeframes(arr)
#arr, sr = smp_read_sf("/Users/joregan/Playing/waxholm/scenes_formatted//fp2060/fp2060.pr.09.smp")
#write_wav("out.wav", arr)
|