mirror of
https://github.com/morpheus65535/bazarr
synced 2024-12-27 01:57:33 +00:00
88 lines
3.2 KiB
Python
88 lines
3.2 KiB
Python
|
# -*- coding: utf-8 -*-
|
||
|
|
import logging
|
||
|
|
import math
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
from .sklearn_shim import TransformerMixin
|
||
|
|
|
||
|
|
logging.basicConfig(level=logging.INFO)
|
||
|
|
logger = logging.getLogger(__name__)
|
||
|
|
|
||
|
|
|
||
|
|
class FailedToFindAlignmentException(Exception):
|
||
|
|
pass
|
||
|
|
|
||
|
|
|
||
|
|
class FFTAligner(TransformerMixin):
|
||
|
|
def __init__(self):
|
||
|
|
self.best_offset_ = None
|
||
|
|
self.best_score_ = None
|
||
|
|
self.get_score_ = False
|
||
|
|
|
||
|
|
def fit(self, refstring, substring, get_score=False):
|
||
|
|
refstring, substring = [
|
||
|
|
list(map(int, s))
|
||
|
|
if isinstance(s, str) else s
|
||
|
|
for s in [refstring, substring]
|
||
|
|
]
|
||
|
|
refstring, substring = map(
|
||
|
|
lambda s: 2 * np.array(s).astype(float) - 1, [refstring, substring])
|
||
|
|
total_bits = math.log(len(substring) + len(refstring), 2)
|
||
|
|
total_length = int(2 ** math.ceil(total_bits))
|
||
|
|
extra_zeros = total_length - len(substring) - len(refstring)
|
||
|
|
subft = np.fft.fft(np.append(np.zeros(extra_zeros + len(refstring)), substring))
|
||
|
|
refft = np.fft.fft(np.flip(np.append(refstring, np.zeros(len(substring) + extra_zeros)), 0))
|
||
|
|
convolve = np.real(np.fft.ifft(subft * refft))
|
||
|
|
best_idx = np.argmax(convolve)
|
||
|
|
self.best_offset_ = len(convolve) - 1 - best_idx - len(substring)
|
||
|
|
self.best_score_ = convolve[best_idx]
|
||
|
|
self.get_score_ = get_score
|
||
|
|
return self
|
||
|
|
|
||
|
|
def transform(self, *_):
|
||
|
|
if self.get_score_:
|
||
|
|
return self.best_score_, self.best_offset_
|
||
|
|
else:
|
||
|
|
return self.best_offset_
|
||
|
|
|
||
|
|
|
||
|
|
class MaxScoreAligner(TransformerMixin):
|
||
|
|
def __init__(self, base_aligner, sample_rate=None, max_offset_seconds=None):
|
||
|
|
if isinstance(base_aligner, type):
|
||
|
|
self.base_aligner = base_aligner()
|
||
|
|
else:
|
||
|
|
self.base_aligner = base_aligner
|
||
|
|
self.max_offset_seconds = max_offset_seconds
|
||
|
|
if sample_rate is None or max_offset_seconds is None:
|
||
|
|
self.max_offset_samples = None
|
||
|
|
else:
|
||
|
|
self.max_offset_samples = abs(max_offset_seconds * sample_rate)
|
||
|
|
self._scores = []
|
||
|
|
|
||
|
|
def fit(self, refstring, subpipes):
|
||
|
|
if not isinstance(subpipes, list):
|
||
|
|
subpipes = [subpipes]
|
||
|
|
for subpipe in subpipes:
|
||
|
|
if hasattr(subpipe, 'transform'):
|
||
|
|
substring = subpipe.transform(None)
|
||
|
|
else:
|
||
|
|
substring = subpipe
|
||
|
|
self._scores.append((
|
||
|
|
self.base_aligner.fit_transform(
|
||
|
|
refstring, substring, get_score=True
|
||
|
|
),
|
||
|
|
subpipe
|
||
|
|
))
|
||
|
|
return self
|
||
|
|
|
||
|
|
def transform(self, *_):
|
||
|
|
scores = self._scores
|
||
|
|
if self.max_offset_samples is not None:
|
||
|
|
scores = list(filter(lambda s: abs(s[0][1]) <= self.max_offset_samples, scores))
|
||
|
|
if len(scores) == 0:
|
||
|
|
raise FailedToFindAlignmentException('Synchronization failed; consider passing '
|
||
|
|
'--max-offset-seconds with a number larger than '
|
||
|
|
'{}'.format(self.max_offset_seconds))
|
||
|
|
(score, offset), subpipe = max(scores, key=lambda x: x[0][0])
|
||
|
|
return offset, subpipe
|