mirror of
https://github.com/morpheus65535/bazarr
synced 2024-12-26 09:37:25 +00:00
302 lines
12 KiB
Python
302 lines
12 KiB
Python
# -*- coding: utf-8 -*-
|
|
from contextlib import contextmanager
|
|
import logging
|
|
import io
|
|
import subprocess
|
|
import sys
|
|
from datetime import timedelta
|
|
|
|
import ffmpeg
|
|
import numpy as np
|
|
from .sklearn_shim import TransformerMixin
|
|
from .sklearn_shim import Pipeline
|
|
import tqdm
|
|
|
|
from .constants import *
|
|
from .ffmpeg_utils import ffmpeg_bin_path, subprocess_args
|
|
from .subtitle_parser import make_subtitle_parser
|
|
from .subtitle_transformers import SubtitleScaler
|
|
|
|
logging.basicConfig(level=logging.INFO)
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
def make_subtitle_speech_pipeline(
|
|
fmt='srt',
|
|
encoding=DEFAULT_ENCODING,
|
|
caching=False,
|
|
max_subtitle_seconds=DEFAULT_MAX_SUBTITLE_SECONDS,
|
|
start_seconds=DEFAULT_START_SECONDS,
|
|
scale_factor=DEFAULT_SCALE_FACTOR,
|
|
parser=None,
|
|
**kwargs
|
|
):
|
|
if parser is None:
|
|
parser = make_subtitle_parser(
|
|
fmt,
|
|
encoding=encoding,
|
|
caching=caching,
|
|
max_subtitle_seconds=max_subtitle_seconds,
|
|
start_seconds=start_seconds
|
|
)
|
|
assert parser.encoding == encoding
|
|
assert parser.max_subtitle_seconds == max_subtitle_seconds
|
|
assert parser.start_seconds == start_seconds
|
|
return Pipeline([
|
|
('parse', parser),
|
|
('scale', SubtitleScaler(scale_factor)),
|
|
('speech_extract', SubtitleSpeechTransformer(
|
|
sample_rate=SAMPLE_RATE,
|
|
start_seconds=start_seconds,
|
|
framerate_ratio=scale_factor,
|
|
))
|
|
])
|
|
|
|
|
|
def _make_auditok_detector(sample_rate, frame_rate):
|
|
try:
|
|
from auditok import \
|
|
BufferAudioSource, ADSFactory, AudioEnergyValidator, StreamTokenizer
|
|
except ImportError as e:
|
|
logger.error("""Error: auditok not installed!
|
|
Consider installing it with `pip install auditok`. Note that auditok
|
|
is GPLv3 licensed, which means that successfully importing it at
|
|
runtime creates a derivative work that is GPLv3 licensed. For personal
|
|
use this is fine, but note that any commercial use that relies on
|
|
auditok must be open source as per the GPLv3!*
|
|
*Not legal advice. Consult with a lawyer.
|
|
""")
|
|
raise e
|
|
bytes_per_frame = 2
|
|
frames_per_window = frame_rate // sample_rate
|
|
validator = AudioEnergyValidator(
|
|
sample_width=bytes_per_frame, energy_threshold=50)
|
|
tokenizer = StreamTokenizer(
|
|
validator=validator, min_length=0.2*sample_rate,
|
|
max_length=int(5*sample_rate),
|
|
max_continuous_silence=0.25*sample_rate)
|
|
|
|
def _detect(asegment):
|
|
asource = BufferAudioSource(data_buffer=asegment,
|
|
sampling_rate=frame_rate,
|
|
sample_width=bytes_per_frame,
|
|
channels=1)
|
|
ads = ADSFactory.ads(audio_source=asource, block_dur=1./sample_rate)
|
|
ads.open()
|
|
tokens = tokenizer.tokenize(ads)
|
|
length = (len(asegment)//bytes_per_frame
|
|
+ frames_per_window - 1)//frames_per_window
|
|
media_bstring = np.zeros(length+1, dtype=int)
|
|
for token in tokens:
|
|
media_bstring[token[1]] += 1
|
|
media_bstring[token[2]+1] -= 1
|
|
return (np.cumsum(media_bstring)[:-1] > 0).astype(float)
|
|
return _detect
|
|
|
|
|
|
def _make_webrtcvad_detector(sample_rate, frame_rate):
|
|
import webrtcvad
|
|
vad = webrtcvad.Vad()
|
|
vad.set_mode(3) # set non-speech pruning aggressiveness from 0 to 3
|
|
window_duration = 1. / sample_rate # duration in seconds
|
|
frames_per_window = int(window_duration * frame_rate + 0.5)
|
|
bytes_per_frame = 2
|
|
|
|
def _detect(asegment):
|
|
media_bstring = []
|
|
failures = 0
|
|
for start in range(0, len(asegment) // bytes_per_frame,
|
|
frames_per_window):
|
|
stop = min(start + frames_per_window,
|
|
len(asegment) // bytes_per_frame)
|
|
try:
|
|
is_speech = vad.is_speech(
|
|
asegment[start * bytes_per_frame: stop * bytes_per_frame],
|
|
sample_rate=frame_rate)
|
|
except:
|
|
is_speech = False
|
|
failures += 1
|
|
# webrtcvad has low recall on mode 3, so treat non-speech as "not sure"
|
|
media_bstring.append(1. if is_speech else 0.5)
|
|
return np.array(media_bstring)
|
|
|
|
return _detect
|
|
|
|
|
|
class VideoSpeechTransformer(TransformerMixin):
|
|
def __init__(self, vad, sample_rate, frame_rate, start_seconds=0, ffmpeg_path=None, ref_stream=None, vlc_mode=False, gui_mode=False):
|
|
self.vad = vad
|
|
self.sample_rate = sample_rate
|
|
self.frame_rate = frame_rate
|
|
self.start_seconds = start_seconds
|
|
self.ffmpeg_path = ffmpeg_path
|
|
self.ref_stream = ref_stream
|
|
self.vlc_mode = vlc_mode
|
|
self.gui_mode = gui_mode
|
|
self.video_speech_results_ = None
|
|
|
|
def try_fit_using_embedded_subs(self, fname):
|
|
embedded_subs = []
|
|
embedded_subs_times = []
|
|
if self.ref_stream is None:
|
|
# check first 5; should cover 99% of movies
|
|
streams_to_try = map('0:s:{}'.format, range(5))
|
|
else:
|
|
streams_to_try = [self.ref_stream]
|
|
for stream in streams_to_try:
|
|
ffmpeg_args = [ffmpeg_bin_path('ffmpeg', self.gui_mode, ffmpeg_resources_path=self.ffmpeg_path)]
|
|
ffmpeg_args.extend([
|
|
'-loglevel', 'fatal',
|
|
'-nostdin',
|
|
'-i', fname,
|
|
'-map', '{}'.format(stream),
|
|
'-f', 'srt',
|
|
'-'
|
|
])
|
|
process = subprocess.Popen(ffmpeg_args, **subprocess_args(include_stdout=True))
|
|
output = io.BytesIO(process.communicate()[0])
|
|
if process.returncode != 0:
|
|
break
|
|
pipe = make_subtitle_speech_pipeline(start_seconds=self.start_seconds).fit(output)
|
|
speech_step = pipe.steps[-1][1]
|
|
embedded_subs.append(speech_step.subtitle_speech_results_)
|
|
embedded_subs_times.append(speech_step.max_time_)
|
|
if len(embedded_subs) == 0:
|
|
raise ValueError('Video file appears to lack subtitle stream')
|
|
# use longest set of embedded subs
|
|
self.video_speech_results_ = embedded_subs[int(np.argmax(embedded_subs_times))]
|
|
|
|
def fit(self, fname, *_):
|
|
if 'subs' in self.vad and (self.ref_stream is None or self.ref_stream.startswith('0:s:')):
|
|
try:
|
|
logger.info('Checking video for subtitles stream...')
|
|
self.try_fit_using_embedded_subs(fname)
|
|
logger.info('...success!')
|
|
return self
|
|
except Exception as e:
|
|
logger.info(e)
|
|
try:
|
|
total_duration = float(ffmpeg.probe(
|
|
fname, cmd=ffmpeg_bin_path('ffprobe', self.gui_mode, ffmpeg_resources_path=self.ffmpeg_path)
|
|
)['format']['duration']) - self.start_seconds
|
|
except Exception as e:
|
|
logger.warning(e)
|
|
total_duration = None
|
|
if 'webrtc' in self.vad:
|
|
detector = _make_webrtcvad_detector(self.sample_rate, self.frame_rate)
|
|
elif 'auditok' in self.vad:
|
|
detector = _make_auditok_detector(self.sample_rate, self.frame_rate)
|
|
else:
|
|
raise ValueError('unknown vad: %s' % self.vad)
|
|
media_bstring = []
|
|
ffmpeg_args = [ffmpeg_bin_path('ffmpeg', self.gui_mode, ffmpeg_resources_path=self.ffmpeg_path)]
|
|
if self.start_seconds > 0:
|
|
ffmpeg_args.extend([
|
|
'-ss', str(timedelta(seconds=self.start_seconds)),
|
|
])
|
|
ffmpeg_args.extend([
|
|
'-loglevel', 'fatal',
|
|
'-nostdin',
|
|
'-i', fname
|
|
])
|
|
if self.ref_stream is not None and self.ref_stream.startswith('0:a:'):
|
|
ffmpeg_args.extend(['-map', self.ref_stream])
|
|
ffmpeg_args.extend([
|
|
'-f', 's16le',
|
|
'-ac', '1',
|
|
'-acodec', 'pcm_s16le',
|
|
'-ar', str(self.frame_rate),
|
|
'-'
|
|
])
|
|
process = subprocess.Popen(ffmpeg_args, **subprocess_args(include_stdout=True))
|
|
bytes_per_frame = 2
|
|
frames_per_window = bytes_per_frame * self.frame_rate // self.sample_rate
|
|
windows_per_buffer = 10000
|
|
simple_progress = 0.
|
|
|
|
@contextmanager
|
|
def redirect_stderr(enter_result=None):
|
|
yield enter_result
|
|
tqdm_extra_args = {}
|
|
should_print_redirected_stderr = self.gui_mode
|
|
if self.gui_mode:
|
|
try:
|
|
from contextlib import redirect_stderr
|
|
tqdm_extra_args['file'] = sys.stdout
|
|
except ImportError:
|
|
should_print_redirected_stderr = False
|
|
pbar_output = io.StringIO()
|
|
with redirect_stderr(pbar_output):
|
|
with tqdm.tqdm(total=total_duration, disable=self.vlc_mode, **tqdm_extra_args) as pbar:
|
|
while True:
|
|
in_bytes = process.stdout.read(frames_per_window * windows_per_buffer)
|
|
if not in_bytes:
|
|
break
|
|
newstuff = len(in_bytes) / float(bytes_per_frame) / self.frame_rate
|
|
if total_duration is not None and simple_progress + newstuff > total_duration:
|
|
newstuff = total_duration - simple_progress
|
|
simple_progress += newstuff
|
|
pbar.update(newstuff)
|
|
if self.vlc_mode and total_duration is not None:
|
|
print("%d" % int(simple_progress * 100. / total_duration))
|
|
sys.stdout.flush()
|
|
if should_print_redirected_stderr:
|
|
assert self.gui_mode
|
|
# no need to flush since we pass -u to do unbuffered output for gui mode
|
|
print(pbar_output.read())
|
|
in_bytes = np.frombuffer(in_bytes, np.uint8)
|
|
media_bstring.append(detector(in_bytes))
|
|
if len(media_bstring) == 0:
|
|
raise ValueError(
|
|
'Unable to detect speech. Perhaps try specifying a different stream / track, or a different vad.'
|
|
)
|
|
self.video_speech_results_ = np.concatenate(media_bstring)
|
|
return self
|
|
|
|
def transform(self, *_):
|
|
return self.video_speech_results_
|
|
|
|
|
|
class SubtitleSpeechTransformer(TransformerMixin):
|
|
def __init__(self, sample_rate, start_seconds=0, framerate_ratio=1.):
|
|
self.sample_rate = sample_rate
|
|
self.start_seconds = start_seconds
|
|
self.framerate_ratio = framerate_ratio
|
|
self.subtitle_speech_results_ = None
|
|
self.max_time_ = None
|
|
|
|
def fit(self, subs, *_):
|
|
max_time = 0
|
|
for sub in subs:
|
|
max_time = max(max_time, sub.end.total_seconds())
|
|
self.max_time_ = max_time - self.start_seconds
|
|
samples = np.zeros(int(max_time * self.sample_rate) + 2, dtype=float)
|
|
for sub in subs:
|
|
start = int(round((sub.start.total_seconds() - self.start_seconds) * self.sample_rate))
|
|
duration = sub.end.total_seconds() - sub.start.total_seconds()
|
|
end = start + int(round(duration * self.sample_rate))
|
|
samples[start:end] = min(1. / self.framerate_ratio, 1.)
|
|
self.subtitle_speech_results_ = samples
|
|
return self
|
|
|
|
def transform(self, *_):
|
|
return self.subtitle_speech_results_
|
|
|
|
|
|
class DeserializeSpeechTransformer(TransformerMixin):
|
|
def __init__(self):
|
|
self.deserialized_speech_results_ = None
|
|
|
|
def fit(self, fname, *_):
|
|
speech = np.load(fname)
|
|
if hasattr(speech, 'files'):
|
|
if 'speech' in speech.files:
|
|
speech = speech['speech']
|
|
else:
|
|
raise ValueError('could not find "speech" array in '
|
|
'serialized file; only contains: %s' % speech.files)
|
|
self.deserialized_speech_results_ = speech
|
|
return self
|
|
|
|
def transform(self, *_):
|
|
return self.deserialized_speech_results_
|