6377635546

Committed 02 Oct 2023 08:21AM UTC coverage: 70.396% (-1.6%) from 71.99%

Build # 6377635546

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github

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web-flow

Commit Message

Merge pull request #900 from EsupPortail/develop

[DONE] #3.4.0

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/pod/video_encode_transcript/transcript_model.py

import numpy as np
import shlex
import subprocess
import json

import sys
import os
from timeit import default_timer as timer
import datetime as dt
from datetime import timedelta

from webvtt import WebVTT, Caption

try:
    from shhlex import quote
except ImportError:
    from pipes import quote

import logging

try:
    from ..custom import settings_local
except ImportError:
    from .. import settings as settings_local

DEBUG = getattr(settings_local, "DEBUG", False)

TRANSCRIPTION_MODEL_PARAM = getattr(settings_local, "TRANSCRIPTION_MODEL_PARAM", False)
USE_TRANSCRIPTION = getattr(settings_local, "USE_TRANSCRIPTION", False)
if USE_TRANSCRIPTION:
    TRANSCRIPTION_TYPE = getattr(settings_local, "TRANSCRIPTION_TYPE", "VOSK")
    if TRANSCRIPTION_TYPE == "VOSK":
        from vosk import Model, KaldiRecognizer
    elif TRANSCRIPTION_TYPE == "STT":
        from stt import Model

TRANSCRIPTION_NORMALIZE = getattr(settings_local, "TRANSCRIPTION_NORMALIZE", False)
TRANSCRIPTION_NORMALIZE_TARGET_LEVEL = getattr(
    settings_local, "TRANSCRIPTION_NORMALIZE_TARGET_LEVEL", -16.0
)

TRANSCRIPTION_AUDIO_SPLIT_TIME = getattr(
    settings_local, "TRANSCRIPTION_AUDIO_SPLIT_TIME", 600
)  # 10min
# time in sec for phrase length
TRANSCRIPTION_STT_SENTENCE_MAX_LENGTH = getattr(
    settings_local, "TRANSCRIPTION_STT_SENTENCE_MAX_LENGTH", 3
)
TRANSCRIPTION_STT_SENTENCE_BLANK_SPLIT_TIME = getattr(
    settings_local, "TRANSCRIPTION_STT_SENTENCE_BLANK_SPLIT_TIME", 0.5
)
log = logging.getLogger(__name__)


def get_model(lang):
    """Get model for STT or Vosk software to transcript audio."""
    transript_model = Model(TRANSCRIPTION_MODEL_PARAM[TRANSCRIPTION_TYPE][lang]["model"])
    if TRANSCRIPTION_TYPE == "STT":
        if TRANSCRIPTION_MODEL_PARAM[TRANSCRIPTION_TYPE][lang].get("beam_width"):
            transript_model.setBeamWidth(
                TRANSCRIPTION_MODEL_PARAM[TRANSCRIPTION_TYPE][lang]["beam_width"]
            )
        if TRANSCRIPTION_MODEL_PARAM[TRANSCRIPTION_TYPE][lang].get("scorer"):
            print(
                "Loading scorer from files {}".format(
                    TRANSCRIPTION_MODEL_PARAM[TRANSCRIPTION_TYPE][lang]["scorer"]
                ),
                file=sys.stderr,
            )
            scorer_load_start = timer()
            transript_model.enableExternalScorer(
                TRANSCRIPTION_MODEL_PARAM[TRANSCRIPTION_TYPE][lang]["scorer"]
            )
            scorer_load_end = timer() - scorer_load_start
            print("Loaded scorer in {:.3}s.".format(scorer_load_end), file=sys.stderr)
            if TRANSCRIPTION_MODEL_PARAM[TRANSCRIPTION_TYPE][lang].get(
                "lm_alpha"
            ) and TRANSCRIPTION_MODEL_PARAM[TRANSCRIPTION_TYPE][lang].get("lm_beta"):
                transript_model.setScorerAlphaBeta(
                    TRANSCRIPTION_MODEL_PARAM[TRANSCRIPTION_TYPE][lang]["lm_alpha"],
                    TRANSCRIPTION_MODEL_PARAM[TRANSCRIPTION_TYPE][lang]["lm_beta"],
                )
    return transript_model


def start_transcripting(mp3filepath, duration, lang):
    """
    Normalize the audio if set, get the model according to the lang and start transcript.
    """
    if TRANSCRIPTION_NORMALIZE:
        mp3filepath = normalize_mp3(mp3filepath)
    transript_model = get_model(lang)
    msg, webvtt, all_text = start_main_transcript(mp3filepath, duration, transript_model)
    if DEBUG:
        print(msg)
        print(webvtt)
        print("\n%s\n" % all_text)

    return msg, webvtt


def start_main_transcript(mp3filepath, duration, transript_model):
    """Call transcription depending software type."""
    if TRANSCRIPTION_TYPE == "STT":
        msg, webvtt, all_text = main_stt_transcript(
            mp3filepath, duration, transript_model
        )
    elif TRANSCRIPTION_TYPE == "VOSK":
        msg, webvtt, all_text = main_vosk_transcript(
            mp3filepath, duration, transript_model
        )
    return msg, webvtt, all_text


def convert_samplerate(audio_path, desired_sample_rate, trim_start, duration):
    """Convert audio to subaudio and add good sample rate."""
    sox_cmd = "sox {} --type raw --bits 16 --channels 1 --rate {} ".format(
        quote(audio_path), desired_sample_rate
    )
    sox_cmd += "--encoding signed-integer --endian little --compression 0.0 "
    sox_cmd += "--no-dither - trim {} {}".format(trim_start, duration)

    try:
        output = subprocess.check_output(shlex.split(sox_cmd), stderr=subprocess.PIPE)

    except subprocess.CalledProcessError as e:
        raise RuntimeError("SoX returned non-zero status: {}".format(e.stderr))
    except OSError as e:
        raise OSError(
            e.errno,
            "SoX not found, use {}hz files or install it: {}".format(
                desired_sample_rate, e.strerror
            ),
        )

    return np.frombuffer(output, np.int16)


def normalize_mp3(mp3filepath):
    """Normalize the audio to good format and sound level."""
    filename, file_extension = os.path.splitext(mp3filepath)
    mp3normfile = "{}{}{}".format(filename, "_norm", file_extension)
    normalize_cmd = "ffmpeg-normalize {} ".format(quote(mp3filepath))
    normalize_cmd += "-c:a libmp3lame -b:a 192k --normalization-type ebu "
    # normalize_cmd += \
    # '--loudness-range-target 7.0 --true-peak 0.0 --offset 0.0 '
    normalize_cmd += "--target-level {} -f -o {}".format(
        TRANSCRIPTION_NORMALIZE_TARGET_LEVEL, quote(mp3normfile)
    )
    if DEBUG:
        print(normalize_cmd)
    try:
        subprocess.check_output(shlex.split(normalize_cmd), stderr=subprocess.PIPE)
        return mp3normfile
    except subprocess.CalledProcessError as e:
        log.error("ffmpeg-normalize returned non-zero status: {}".format(e.stderr))
        return mp3filepath
    except OSError as e:
        log.error("ffmpeg-normalize not found {}".format(e.strerror))
        return mp3filepath


# #################################
# TRANSCRIPT VIDEO : MAIN FUNCTION
# #################################


def convert_vosk_samplerate(audio_path, desired_sample_rate, trim_start, duration):
    """Convert audio to the good sample rate."""
    sox_cmd = "sox {} --type raw --bits 16 --channels 1 --rate {} ".format(
        quote(audio_path), desired_sample_rate
    )
    sox_cmd += "--encoding signed-integer --endian little --compression 0.0 "
    sox_cmd += "--no-dither - trim {} {}".format(trim_start, duration)

    try:
        output = subprocess.Popen(shlex.split(sox_cmd), stdout=subprocess.PIPE)

    except subprocess.CalledProcessError as e:
        raise RuntimeError("SoX returned non-zero status: {}".format(e.stderr))
    except OSError as e:
        raise OSError(
            e.errno,
            "SoX not found, use {}hz files or install it: {}".format(
                desired_sample_rate, e.strerror
            ),
        )
    return output


def get_word_result_from_data(results, audio, rec):
    """Get subsound from audio and add transcription to result parameter."""
    while True:
        data = audio.stdout.read(4000)
        if len(data) == 0:
            break
        if rec.AcceptWaveform(data):
            results.append(rec.Result())
    results.append(rec.Result())


def words_to_vtt(
    words,
    start_trim,
    duration,
    is_first_caption,
    text_caption,
    start_caption,
    last_word_added,
    all_text,
    webvtt,
):
    """Convert word and time to webvtt captions."""
    for index, word in enumerate(words):
        start_key = "start_time"
        word_duration = word.get("duration", 0)
        last_word = words[-1]
        last_word_duration = last_word.get("duration", 0)
        if TRANSCRIPTION_TYPE == "VOSK":
            start_key = "start"
            word_duration = word["end"] - word["start"]
            last_word_duration = words[-1]["end"] - words[-1]["start"]
        next_word = None
        blank_duration = 0
        if word != words[-1] and (index + 1) < len(words):
            next_word = words[index + 1]
            blank_duration = ((next_word[start_key]) - start_caption) - (
                ((word[start_key]) - start_caption) + word_duration
            )
        all_text += word["word"] + " "
        # word : <class 'dict'> {'word': 'bonjour', 'start ':
        # 0.58, 'duration': 7.34}
        text_caption.append(word["word"])
        if not (
            (((word[start_key]) - start_caption) < TRANSCRIPTION_STT_SENTENCE_MAX_LENGTH)
            and (
                next_word is not None
                and (blank_duration < TRANSCRIPTION_STT_SENTENCE_BLANK_SPLIT_TIME)
            )
        ):
            # on créé le caption
            if is_first_caption:
                # A revoir, fusion de la nouvelle ligne avec
                # l'ancienne...
                is_first_caption = False
                text_caption = get_text_caption(text_caption, last_word_added)

            stop_caption = word[start_key] + word_duration

            # on evite le chevauchement
            change_previous_end_caption(webvtt, start_caption)

            caption = Caption(
                format_time_caption(start_caption),
                format_time_caption(stop_caption),
                " ".join(text_caption),
            )

            webvtt.captions.append(caption)
            # on remet tout à zero pour la prochaine phrase
            start_caption = word[start_key]
            text_caption = []
            last_word_added = word["word"]
    if start_trim + TRANSCRIPTION_AUDIO_SPLIT_TIME > duration:
        # on ajoute ici la dernière phrase de la vidéo
        stop_caption = start_trim + words[-1][start_key] + last_word_duration
        caption = Caption(
            format_time_caption(start_caption),
            format_time_caption(stop_caption),
            " ".join(text_caption),
        )
        webvtt.captions.append(caption)
    return all_text, webvtt


def main_vosk_transcript(norm_mp3_file, duration, transript_model):
    """Vosk transcription."""
    msg = ""
    inference_start = timer()
    msg += "\nInference start %0.3fs." % inference_start
    desired_sample_rate = 16000

    rec = KaldiRecognizer(transript_model, desired_sample_rate)
    rec.SetWords(True)

    webvtt = WebVTT()
    all_text = ""
    for start_trim in range(0, duration, TRANSCRIPTION_AUDIO_SPLIT_TIME):
        audio = convert_vosk_samplerate(
            norm_mp3_file,
            desired_sample_rate,
            start_trim,
            TRANSCRIPTION_AUDIO_SPLIT_TIME,  # dur
        )
        msg += "\nRunning inference."
        results = []
        get_word_result_from_data(results, audio, rec)
        for res in results:
            words = json.loads(res).get("result")
            text = json.loads(res).get("text")
            if not words:
                continue
            start_caption = words[0]["start"]
            stop_caption = words[-1]["end"]
            caption = Caption(
                format_time_caption(start_caption),
                format_time_caption(stop_caption),
                text,
            )
            webvtt.captions.append(caption)
            """
            text_caption = []
            is_first_caption = True
            all_text, webvtt = words_to_vtt(
                words,
                start_trim,
                duration,
                is_first_caption,
                text_caption,
                start_caption,
                last_word_added,
                all_text,
                webvtt,
            )
            """
    inference_end = timer() - inference_start

    msg += "\nInference took %0.3fs." % inference_end
    return msg, webvtt, all_text


def main_stt_transcript(norm_mp3_file, duration, transript_model):
    """STT transcription."""
    msg = ""
    inference_start = timer()
    msg += "\nInference start %0.3fs." % inference_start
    desired_sample_rate = transript_model.sampleRate()
    webvtt = WebVTT()
    last_word_added = ""
    metadata = None
    all_text = ""
    for start_trim in range(0, duration, TRANSCRIPTION_AUDIO_SPLIT_TIME):
        end_trim = (
            duration
            if start_trim + TRANSCRIPTION_AUDIO_SPLIT_TIME > duration
            else (
                start_trim
                + TRANSCRIPTION_AUDIO_SPLIT_TIME
                + TRANSCRIPTION_STT_SENTENCE_MAX_LENGTH
            )
        )

        dur = (
            (TRANSCRIPTION_AUDIO_SPLIT_TIME + TRANSCRIPTION_STT_SENTENCE_MAX_LENGTH)
            if (
                (
                    start_trim
                    + TRANSCRIPTION_AUDIO_SPLIT_TIME
                    + TRANSCRIPTION_STT_SENTENCE_MAX_LENGTH
                )
                < duration
            )
            else (duration - start_trim)
        )

        msg += "\ntake audio from %s to %s - %s" % (start_trim, end_trim, dur)

        audio = convert_samplerate(norm_mp3_file, desired_sample_rate, start_trim, dur)
        msg += "\nRunning inference."

        metadata = transript_model.sttWithMetadata(audio)

        for transcript in metadata.transcripts:
            msg += "\nConfidence : %s" % transcript.confidence
            words = words_from_candidate_transcript(transcript)
            start_caption = start_trim + words[0]["start_time"]
            text_caption = []
            is_first_caption = True
            all_text, webvtt = words_to_vtt(
                words,
                start_trim,
                duration,
                is_first_caption,
                text_caption,
                start_caption,
                last_word_added,
                all_text,
                webvtt,
            )
    inference_end = timer() - inference_start

    msg += "\nInference took %0.3fs." % inference_end
    return msg, webvtt, all_text


def change_previous_end_caption(webvtt, start_caption):
    """Change the end time for caption."""
    if len(webvtt.captions) > 0:
        prev_end = dt.datetime.strptime(webvtt.captions[-1].end, "%H:%M:%S.%f")
        td_prev_end = timedelta(
            hours=prev_end.hour,
            minutes=prev_end.minute,
            seconds=prev_end.second,
            microseconds=prev_end.microsecond,
        ).total_seconds()
        if td_prev_end > start_caption:
            webvtt.captions[-1].end = format_time_caption(start_caption)


def format_time_caption(time_caption):
    """Format time for webvtt caption."""
    return (
        dt.datetime.utcfromtimestamp(0) + timedelta(seconds=float(time_caption))
    ).strftime("%H:%M:%S.%f")[:-3]


def get_text_caption(text_caption, last_word_added):
    """get the text for a caption."""
    try:
        first_index = text_caption.index(last_word_added)
        return text_caption[first_index + 1 :]
    except ValueError:
        return text_caption


def words_from_candidate_transcript(metadata):
    """Get words list from transcription."""
    word = ""
    word_list = []
    word_start_time = 0
    # Loop through each character
    for i, token in enumerate(metadata.tokens):
        # Append character to word if it's not a space
        if token.text != " ":
            if len(word) == 0:
                # Log the start time of the new word
                word_start_time = token.start_time

            word = word + token.text
        # Word boundary is either a space or the last character in the array
        if token.text == " " or i == len(metadata.tokens) - 1:
            word_duration = token.start_time - word_start_time

            if word_duration < 0:
                word_duration = 0

            each_word = dict()
            each_word["word"] = word
            each_word["start_time"] = round(word_start_time, 4)
            each_word["duration"] = round(word_duration, 4)

            word_list.append(each_word)
            # Reset
            word = ""
            word_start_time = 0

    return word_list

1	import numpy as np	×
2	import shlex	×
3	import subprocess	×
4	import json	×
5
6	import sys	×
7	import os	×
8	from timeit import default_timer as timer	×
9	import datetime as dt	×
10	from datetime import timedelta	×
11
12	from webvtt import WebVTT, Caption	×
13
14	try:	×
15	from shhlex import quote	×
16	except ImportError:	×
17	from pipes import quote	×
18
19	import logging	×
20
21	try:	×
22	from ..custom import settings_local	×
23	except ImportError:	×
24	from .. import settings as settings_local	×
25
26	DEBUG = getattr(settings_local, "DEBUG", False)	×
27
28	TRANSCRIPTION_MODEL_PARAM = getattr(settings_local, "TRANSCRIPTION_MODEL_PARAM", False)	×
29	USE_TRANSCRIPTION = getattr(settings_local, "USE_TRANSCRIPTION", False)	×
30	if USE_TRANSCRIPTION:	×
31	TRANSCRIPTION_TYPE = getattr(settings_local, "TRANSCRIPTION_TYPE", "VOSK")	×
32	if TRANSCRIPTION_TYPE == "VOSK":	×
33	from vosk import Model, KaldiRecognizer	×
34	elif TRANSCRIPTION_TYPE == "STT":	×
35	from stt import Model	×
36
37	TRANSCRIPTION_NORMALIZE = getattr(settings_local, "TRANSCRIPTION_NORMALIZE", False)	×
38	TRANSCRIPTION_NORMALIZE_TARGET_LEVEL = getattr(	×
39	settings_local, "TRANSCRIPTION_NORMALIZE_TARGET_LEVEL", -16.0
40	)
41
42	TRANSCRIPTION_AUDIO_SPLIT_TIME = getattr(	×
43	settings_local, "TRANSCRIPTION_AUDIO_SPLIT_TIME", 600
44	) # 10min
45	# time in sec for phrase length
46	TRANSCRIPTION_STT_SENTENCE_MAX_LENGTH = getattr(	×
47	settings_local, "TRANSCRIPTION_STT_SENTENCE_MAX_LENGTH", 3
48	)
49	TRANSCRIPTION_STT_SENTENCE_BLANK_SPLIT_TIME = getattr(	×
50	settings_local, "TRANSCRIPTION_STT_SENTENCE_BLANK_SPLIT_TIME", 0.5
51	)
52	log = logging.getLogger(__name__)	×
53
54
55	def get_model(lang):	×
56	"""Get model for STT or Vosk software to transcript audio."""
57	transript_model = Model(TRANSCRIPTION_MODEL_PARAM[TRANSCRIPTION_TYPE][lang]["model"])	×
58	if TRANSCRIPTION_TYPE == "STT":	×
59	if TRANSCRIPTION_MODEL_PARAM[TRANSCRIPTION_TYPE][lang].get("beam_width"):	×
60	transript_model.setBeamWidth(	×
61	TRANSCRIPTION_MODEL_PARAM[TRANSCRIPTION_TYPE][lang]["beam_width"]
62	)
63	if TRANSCRIPTION_MODEL_PARAM[TRANSCRIPTION_TYPE][lang].get("scorer"):	×
64	print(	×
65	"Loading scorer from files {}".format(
66	TRANSCRIPTION_MODEL_PARAM[TRANSCRIPTION_TYPE][lang]["scorer"]
67	),
68	file=sys.stderr,
69	)
70	scorer_load_start = timer()	×
71	transript_model.enableExternalScorer(	×
72	TRANSCRIPTION_MODEL_PARAM[TRANSCRIPTION_TYPE][lang]["scorer"]
73	)
74	scorer_load_end = timer() - scorer_load_start	×
75	print("Loaded scorer in {:.3}s.".format(scorer_load_end), file=sys.stderr)	×
76	if TRANSCRIPTION_MODEL_PARAM[TRANSCRIPTION_TYPE][lang].get(	×
77	"lm_alpha"
78	) and TRANSCRIPTION_MODEL_PARAM[TRANSCRIPTION_TYPE][lang].get("lm_beta"):
79	transript_model.setScorerAlphaBeta(	×
80	TRANSCRIPTION_MODEL_PARAM[TRANSCRIPTION_TYPE][lang]["lm_alpha"],
81	TRANSCRIPTION_MODEL_PARAM[TRANSCRIPTION_TYPE][lang]["lm_beta"],
82	)
83	return transript_model	×
84
85
86	def start_transcripting(mp3filepath, duration, lang):	×
87	"""
88	Normalize the audio if set, get the model according to the lang and start transcript.
89	"""
90	if TRANSCRIPTION_NORMALIZE:	×
91	mp3filepath = normalize_mp3(mp3filepath)	×
92	transript_model = get_model(lang)	×
93	msg, webvtt, all_text = start_main_transcript(mp3filepath, duration, transript_model)	×
94	if DEBUG:	×
95	print(msg)	×
96	print(webvtt)	×
97	print("\n%s\n" % all_text)	×
98
99	return msg, webvtt	×
100
101
102	def start_main_transcript(mp3filepath, duration, transript_model):	×
103	"""Call transcription depending software type."""
104	if TRANSCRIPTION_TYPE == "STT":	×
105	msg, webvtt, all_text = main_stt_transcript(	×
106	mp3filepath, duration, transript_model
107	)
108	elif TRANSCRIPTION_TYPE == "VOSK":	×
109	msg, webvtt, all_text = main_vosk_transcript(	×
110	mp3filepath, duration, transript_model
111	)
112	return msg, webvtt, all_text	×
113
114
115	def convert_samplerate(audio_path, desired_sample_rate, trim_start, duration):	×
116	"""Convert audio to subaudio and add good sample rate."""
117	sox_cmd = "sox {} --type raw --bits 16 --channels 1 --rate {} ".format(	×
118	quote(audio_path), desired_sample_rate
119	)
120	sox_cmd += "--encoding signed-integer --endian little --compression 0.0 "	×
121	sox_cmd += "--no-dither - trim {} {}".format(trim_start, duration)	×
122
123	try:	×
124	output = subprocess.check_output(shlex.split(sox_cmd), stderr=subprocess.PIPE)	×
125
126	except subprocess.CalledProcessError as e:	×
127	raise RuntimeError("SoX returned non-zero status: {}".format(e.stderr))	×
128	except OSError as e:	×
129	raise OSError(	×
130	e.errno,
131	"SoX not found, use {}hz files or install it: {}".format(
132	desired_sample_rate, e.strerror
133	),
134	)
135
136	return np.frombuffer(output, np.int16)	×
137
138
139	def normalize_mp3(mp3filepath):	×
140	"""Normalize the audio to good format and sound level."""
141	filename, file_extension = os.path.splitext(mp3filepath)	×
142	mp3normfile = "{}{}{}".format(filename, "_norm", file_extension)	×
143	normalize_cmd = "ffmpeg-normalize {} ".format(quote(mp3filepath))	×
144	normalize_cmd += "-c:a libmp3lame -b:a 192k --normalization-type ebu "	×
145	# normalize_cmd += \
146	# '--loudness-range-target 7.0 --true-peak 0.0 --offset 0.0 '
147	normalize_cmd += "--target-level {} -f -o {}".format(	×
148	TRANSCRIPTION_NORMALIZE_TARGET_LEVEL, quote(mp3normfile)
149	)
150	if DEBUG:	×
151	print(normalize_cmd)	×
152	try:	×
153	subprocess.check_output(shlex.split(normalize_cmd), stderr=subprocess.PIPE)	×
154	return mp3normfile	×
155	except subprocess.CalledProcessError as e:	×
156	log.error("ffmpeg-normalize returned non-zero status: {}".format(e.stderr))	×
157	return mp3filepath	×
158	except OSError as e:	×
159	log.error("ffmpeg-normalize not found {}".format(e.strerror))	×
160	return mp3filepath	×
161
162
163	# #################################
164	# TRANSCRIPT VIDEO : MAIN FUNCTION
165	# #################################
166
167
168	def convert_vosk_samplerate(audio_path, desired_sample_rate, trim_start, duration):	×
169	"""Convert audio to the good sample rate."""
170	sox_cmd = "sox {} --type raw --bits 16 --channels 1 --rate {} ".format(	×
171	quote(audio_path), desired_sample_rate
172	)
173	sox_cmd += "--encoding signed-integer --endian little --compression 0.0 "	×
174	sox_cmd += "--no-dither - trim {} {}".format(trim_start, duration)	×
175
176	try:	×
177	output = subprocess.Popen(shlex.split(sox_cmd), stdout=subprocess.PIPE)	×
178
179	except subprocess.CalledProcessError as e:	×
180	raise RuntimeError("SoX returned non-zero status: {}".format(e.stderr))	×
181	except OSError as e:	×
182	raise OSError(	×
183	e.errno,
184	"SoX not found, use {}hz files or install it: {}".format(
185	desired_sample_rate, e.strerror
186	),
187	)
188	return output	×
189
190
191	def get_word_result_from_data(results, audio, rec):	×
192	"""Get subsound from audio and add transcription to result parameter."""
193	while True:
194	data = audio.stdout.read(4000)	×
195	if len(data) == 0:	×
196	break	×
197	if rec.AcceptWaveform(data):	×
198	results.append(rec.Result())	×
199	results.append(rec.Result())	×
200
201
202	def words_to_vtt(	×
203	words,
204	start_trim,
205	duration,
206	is_first_caption,
207	text_caption,
208	start_caption,
209	last_word_added,
210	all_text,
211	webvtt,
212	):
213	"""Convert word and time to webvtt captions."""
214	for index, word in enumerate(words):	×
215	start_key = "start_time"	×
216	word_duration = word.get("duration", 0)	×
217	last_word = words[-1]	×
218	last_word_duration = last_word.get("duration", 0)	×
219	if TRANSCRIPTION_TYPE == "VOSK":	×
220	start_key = "start"	×
221	word_duration = word["end"] - word["start"]	×
222	last_word_duration = words[-1]["end"] - words[-1]["start"]	×
223	next_word = None	×
224	blank_duration = 0	×
225	if word != words[-1] and (index + 1) < len(words):	×
226	next_word = words[index + 1]	×
227	blank_duration = ((next_word[start_key]) - start_caption) - (	×
228	((word[start_key]) - start_caption) + word_duration
229	)
230	all_text += word["word"] + " "	×
231	# word : <class 'dict'> {'word': 'bonjour', 'start ':
232	# 0.58, 'duration': 7.34}
233	text_caption.append(word["word"])	×
234	if not (	×
235	(((word[start_key]) - start_caption) < TRANSCRIPTION_STT_SENTENCE_MAX_LENGTH)
236	and (
237	next_word is not None
238	and (blank_duration < TRANSCRIPTION_STT_SENTENCE_BLANK_SPLIT_TIME)
239	)
240	):
241	# on créé le caption
242	if is_first_caption:	×
243	# A revoir, fusion de la nouvelle ligne avec
244	# l'ancienne...
245	is_first_caption = False	×
246	text_caption = get_text_caption(text_caption, last_word_added)	×
247
248	stop_caption = word[start_key] + word_duration	×
249
250	# on evite le chevauchement
251	change_previous_end_caption(webvtt, start_caption)	×
252
253	caption = Caption(	×
254	format_time_caption(start_caption),
255	format_time_caption(stop_caption),
256	" ".join(text_caption),
257	)
258
259	webvtt.captions.append(caption)	×
260	# on remet tout à zero pour la prochaine phrase
261	start_caption = word[start_key]	×
262	text_caption = []	×
263	last_word_added = word["word"]	×
264	if start_trim + TRANSCRIPTION_AUDIO_SPLIT_TIME > duration:	×
265	# on ajoute ici la dernière phrase de la vidéo
266	stop_caption = start_trim + words[-1][start_key] + last_word_duration	×
267	caption = Caption(	×
268	format_time_caption(start_caption),
269	format_time_caption(stop_caption),
270	" ".join(text_caption),
271	)
272	webvtt.captions.append(caption)	×
273	return all_text, webvtt	×
274
275
276	def main_vosk_transcript(norm_mp3_file, duration, transript_model):	×
277	"""Vosk transcription."""
278	msg = ""	×
279	inference_start = timer()	×
280	msg += "\nInference start %0.3fs." % inference_start	×
281	desired_sample_rate = 16000	×
282
283	rec = KaldiRecognizer(transript_model, desired_sample_rate)	×
284	rec.SetWords(True)	×
285
286	webvtt = WebVTT()	×
287	all_text = ""	×
288	for start_trim in range(0, duration, TRANSCRIPTION_AUDIO_SPLIT_TIME):	×
289	audio = convert_vosk_samplerate(	×
290	norm_mp3_file,
291	desired_sample_rate,
292	start_trim,
293	TRANSCRIPTION_AUDIO_SPLIT_TIME, # dur
294	)
295	msg += "\nRunning inference."	×
296	results = []	×
297	get_word_result_from_data(results, audio, rec)	×
298	for res in results:	×
299	words = json.loads(res).get("result")	×
300	text = json.loads(res).get("text")	×
301	if not words:	×
302	continue	×
303	start_caption = words[0]["start"]	×
304	stop_caption = words[-1]["end"]	×
305	caption = Caption(	×
306	format_time_caption(start_caption),
307	format_time_caption(stop_caption),
308	text,
309	)
310	webvtt.captions.append(caption)	×
311	"""	×
312	text_caption = []
313	is_first_caption = True
314	all_text, webvtt = words_to_vtt(
315	words,
316	start_trim,
317	duration,
318	is_first_caption,
319	text_caption,
320	start_caption,
321	last_word_added,
322	all_text,
323	webvtt,
324	)
325	"""
326	inference_end = timer() - inference_start	×
327
328	msg += "\nInference took %0.3fs." % inference_end	×
329	return msg, webvtt, all_text	×
330
331
332	def main_stt_transcript(norm_mp3_file, duration, transript_model):	×
333	"""STT transcription."""
334	msg = ""	×
335	inference_start = timer()	×
336	msg += "\nInference start %0.3fs." % inference_start	×
337	desired_sample_rate = transript_model.sampleRate()	×
338	webvtt = WebVTT()	×
339	last_word_added = ""	×
340	metadata = None	×
341	all_text = ""	×
342	for start_trim in range(0, duration, TRANSCRIPTION_AUDIO_SPLIT_TIME):	×
343	end_trim = (	×
344	duration
345	if start_trim + TRANSCRIPTION_AUDIO_SPLIT_TIME > duration
346	else (
347	start_trim
348	+ TRANSCRIPTION_AUDIO_SPLIT_TIME
349	+ TRANSCRIPTION_STT_SENTENCE_MAX_LENGTH
350	)
351	)
352
353	dur = (	×
354	(TRANSCRIPTION_AUDIO_SPLIT_TIME + TRANSCRIPTION_STT_SENTENCE_MAX_LENGTH)
355	if (
356	(
357	start_trim
358	+ TRANSCRIPTION_AUDIO_SPLIT_TIME
359	+ TRANSCRIPTION_STT_SENTENCE_MAX_LENGTH
360	)
361	< duration
362	)
363	else (duration - start_trim)
364	)
365
366	msg += "\ntake audio from %s to %s - %s" % (start_trim, end_trim, dur)	×
367
368	audio = convert_samplerate(norm_mp3_file, desired_sample_rate, start_trim, dur)	×
369	msg += "\nRunning inference."	×
370
371	metadata = transript_model.sttWithMetadata(audio)	×
372
373	for transcript in metadata.transcripts:	×
374	msg += "\nConfidence : %s" % transcript.confidence	×
375	words = words_from_candidate_transcript(transcript)	×
376	start_caption = start_trim + words[0]["start_time"]	×
377	text_caption = []	×
378	is_first_caption = True	×
379	all_text, webvtt = words_to_vtt(	×
380	words,
381	start_trim,
382	duration,
383	is_first_caption,
384	text_caption,
385	start_caption,
386	last_word_added,
387	all_text,
388	webvtt,
389	)
390	inference_end = timer() - inference_start	×
391
392	msg += "\nInference took %0.3fs." % inference_end	×
393	return msg, webvtt, all_text	×
394
395
396	def change_previous_end_caption(webvtt, start_caption):	×
397	"""Change the end time for caption."""
398	if len(webvtt.captions) > 0:	×
399	prev_end = dt.datetime.strptime(webvtt.captions[-1].end, "%H:%M:%S.%f")	×
400	td_prev_end = timedelta(	×
401	hours=prev_end.hour,
402	minutes=prev_end.minute,
403	seconds=prev_end.second,
404	microseconds=prev_end.microsecond,
405	).total_seconds()
406	if td_prev_end > start_caption:	×
407	webvtt.captions[-1].end = format_time_caption(start_caption)	×
408
409
410	def format_time_caption(time_caption):	×
411	"""Format time for webvtt caption."""
412	return (	×
413	dt.datetime.utcfromtimestamp(0) + timedelta(seconds=float(time_caption))
414	).strftime("%H:%M:%S.%f")[:-3]
415
416
417	def get_text_caption(text_caption, last_word_added):	×
418	"""get the text for a caption."""
419	try:	×
420	first_index = text_caption.index(last_word_added)	×
421	return text_caption[first_index + 1 :]	×
422	except ValueError:	×
423	return text_caption	×
424
425
426	def words_from_candidate_transcript(metadata):	×
427	"""Get words list from transcription."""
428	word = ""	×
429	word_list = []	×
430	word_start_time = 0	×
431	# Loop through each character
432	for i, token in enumerate(metadata.tokens):	×
433	# Append character to word if it's not a space
434	if token.text != " ":	×
435	if len(word) == 0:	×
436	# Log the start time of the new word
437	word_start_time = token.start_time	×
438
439	word = word + token.text	×
440	# Word boundary is either a space or the last character in the array
441	if token.text == " " or i == len(metadata.tokens) - 1:	×
442	word_duration = token.start_time - word_start_time	×
443
444	if word_duration < 0:	×
445	word_duration = 0	×
446
447	each_word = dict()	×
448	each_word["word"] = word	×
449	each_word["start_time"] = round(word_start_time, 4)	×
450	each_word["duration"] = round(word_duration, 4)	×
451
452	word_list.append(each_word)	×
453	# Reset
454	word = ""	×
455	word_start_time = 0	×
456
457	return word_list	×

EsupPortail / Esup-Pod / 6377635546

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