Changed: Moved webvtt tools from modules to views
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@ -1,17 +1,52 @@
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# -*- coding: utf-8 -*-
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# -------------------------------------------------------------------------
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# This is a sample controller
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# this file is released under public domain and you can use without limitations
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# REQUIREMENTS
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#
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# module srt
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# module vosk
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# language model
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#
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# INSTALL
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#
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# cd /usr/lib/
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# apt install ffmpeg
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# git clone --recursive https://github.com/web2py/web2py.git
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# cd web2py
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# cd web2py/applications/transcription
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# pip3 install -t modules srt
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# pip3 install -t modules vosk
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# pip3 install -t modules webvtt-py
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# cd private
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# wget https://alphacephei.com/vosk/models/vosk-model-de-0.21.zip
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# unzip vosk-model-de-0.21.zip
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# -------------------------------------------------------------------------
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import io
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#from transcription_tools import create_vtt
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transcription_tools = local_import('transcription_tools', reload=True)
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from vosk import KaldiRecognizer
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from webvtt import WebVTT, Caption
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import subprocess
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import srt
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import json
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import datetime
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import textwrap
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import transcription_tools
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# transcription_tools = local_import('transcription_tools', reload=True)
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model = 'private/model'
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# To let Eclipse know about predefined objects
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global db
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global request
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global session
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global reqponse
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global SQLFORM
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global redirect
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global auth
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global URL
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global response
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model_mod_path = 'private/model'
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# ---- example index page ----
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def index():
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media_files = db().select(db.media_file.ALL, orderby=db.media_file.title)
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return dict(media_files=media_files)
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@ -24,36 +59,155 @@ def manage():
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def webvtt_single_line():
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media_file = db.media_file(request.args(0, cast=int)) or redirect(URL('index'))
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# Get mediafile from request
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media_file = (db.media_file(request.args(0, cast=int)) or
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redirect(URL('index')))
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# Set vars
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media_path = '{}/{}/{}'.format(request.folder, 'uploads', media_file.file)
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model_path = '{}/{}'.format(request.folder, model)
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transkription = transcription_tools.vtt_single_line(model_path, media_path)
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db(db.media_file.id == media_file.id).update(vtt_single_line=transkription)
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model_path = '{}/{}'.format(request.folder, model_mod_path)
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# Trascribe to SubRip Subtitle file SRT
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sample_rate = 16000
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model = transcription_tools.get_model(model_path)
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rec = KaldiRecognizer(model, sample_rate)
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rec.SetWords(True)
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# 16bit mono with ffmpeg
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process = subprocess.Popen(
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['ffmpeg', '-loglevel', 'quiet', '-i', media_path, '-ar',
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str(sample_rate), '-ac', '1', '-f', 's16le', '-'],
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stdout=subprocess.PIPE
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)
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WORDS_PER_LINE = 7
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def transcribe():
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results = []
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subs = []
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while True:
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data = process.stdout.read(4000)
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if len(data) == 0:
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break
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if rec.AcceptWaveform(data):
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results.append(rec.Result())
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results.append(rec.FinalResult())
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for i, res in enumerate(results):
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jres = json.loads(res)
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if not 'result' in jres:
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continue
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words = jres['result']
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for j in range(0, len(words), WORDS_PER_LINE):
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line = words[j: j + WORDS_PER_LINE]
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s = srt.Subtitle(
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index=len(subs),
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content=" ".join([l['word'] for l in line]),
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start=datetime.timedelta(seconds=line[0]['start']),
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end=datetime.timedelta(seconds=line[-1]['end'])
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)
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subs.append(s)
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return subs
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srt_str = srt.compose(transcribe()) # create srt string
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# Create single line webvtt from srt with ffmepg
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process1 = subprocess.Popen(
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['ffmpeg', '-loglevel', 'quiet', '-i', '-', '-f', 'webvtt', '-'],
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stdin=subprocess.PIPE, stdout=subprocess.PIPE
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)
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# Send srt_str as input file to ffmpeg process
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webvtt = process1.communicate(input=bytes(srt_str, 'utf-8'))[0]
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# Add result to database
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db(db.media_file.id == media_file.id).update(vtt_single_line=webvtt)
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redirect(request.env.http_referer)
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def webvtt():
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media_file = db.media_file(request.args(0, cast=int)) or redirect(URL('index'))
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# Get mediafile from request
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media_file = (db.media_file(request.args(0, cast=int)) or
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redirect(URL('index')))
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# Set vars
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media_path = '{}/{}/{}'.format(request.folder, 'uploads', media_file.file)
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model_path = '{}/{}'.format(request.folder, model)
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transkription = transcription_tools.vtt(model_path, media_path)
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db(db.media_file.id == media_file.id).update(vtt=transkription)
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model_path = '{}/{}'.format(request.folder, model_mod_path)
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# Transcribe
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sample_rate = 16000
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model = transcription_tools.get_model(model_path) # cached model
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rec = KaldiRecognizer(model, sample_rate)
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rec.SetWords(True)
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def timeString(seconds):
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minutes = seconds / 60
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seconds = seconds % 60
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hours = int(minutes / 60)
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minutes = int(minutes % 60)
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return '%i:%02i:%06.3f' % (hours, minutes, seconds)
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def transcribe():
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command = ['ffmpeg', '-nostdin', '-loglevel', 'quiet', '-i',
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media_path, '-ar', str(sample_rate), '-ac', '1', '-f',
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's16le', '-']
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process = subprocess.Popen(command, stdout=subprocess.PIPE)
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results = []
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while True:
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data = process.stdout.read(4000)
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if len(data) == 0:
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break
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if rec.AcceptWaveform(data):
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results.append(rec.Result())
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results.append(rec.FinalResult())
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vtt = WebVTT()
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for i, res in enumerate(results):
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words = json.loads(res).get('result')
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if not words:
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continue
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start = timeString(words[0]['start'])
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end = timeString(words[-1]['end'])
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content = ' '.join([w['word'] for w in words])
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caption = Caption(start, end, textwrap.fill(content))
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vtt.captions.append(caption)
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return(vtt.content)
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# Write result to database
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db(db.media_file.id == media_file.id).update(vtt=transcribe())
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redirect(request.env.http_referer)
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def download_webvtt_single_line():
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media_file = db.media_file(request.args(0, cast=int)) or redirect(URL('index'))
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media_file = (db.media_file(request.args(0, cast=int)) or
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redirect(URL('index')))
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webvtt = media_file.vtt_single_line
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response.headers['Content-Type']='text/vtt'
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response.headers['Content-Disposition']='attachment; filename=transcript.vtt'
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response.headers['Content-Type'] = 'text/vtt'
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response.headers['Content-Disposition'] = ('attachment; '
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'filename=transcript.vtt')
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f = io.StringIO(webvtt)
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return(f)
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def download_webvtt():
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media_file = db.media_file(request.args(0, cast=int)) or redirect(URL('index'))
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media_file = (db.media_file(request.args(0, cast=int)) or
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redirect(URL('index')))
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webvtt = media_file.vtt
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response.headers['Content-Type']='text/vtt'
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response.headers['Content-Disposition']='attachment; filename=transcript.vtt'
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response.headers['Content-Type'] = 'text/vtt'
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response.headers['Content-Disposition'] = ('attachment; '
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'filename=transcript.vtt')
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f = io.StringIO(webvtt)
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return(f)
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@ -11,3 +11,8 @@
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/vosk/
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/vosk-0.3.31.dist-info/
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/vosk.libs/
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/docopt-0.6.2.dist-info/
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/tests/
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/webvtt/
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/webvtt_py-0.4.6.dist-info/
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/docopt.py
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@ -1,128 +1,8 @@
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# REQUIREMENTS
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#
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# module srt
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# module vosk
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# language model
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#
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# INSTALL
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#
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# cd web2py/applications/transcription
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# pip3 install -t modules srt
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# pip3 install -t modules vosk
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# cd private
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# wget https://alphacephei.com/vosk/models/vosk-model-de-0.21.zip
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# unzip vosk-model-de-0.21.zip
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from vosk import Model
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from gluon.cache import lazy_cache
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from vosk import Model, KaldiRecognizer, SetLogLevel
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from webvtt import WebVTT, Caption
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import sys
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import os
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import wave
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import subprocess
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import srt
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import json
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import datetime
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import textwrap
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def vtt_single_line(model_path, media_path):
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sample_rate = 16000
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@lazy_cache('get_model', time_expire=3600, cache_model='ram')
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def get_model(model_path):
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model = Model(model_path)
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rec = KaldiRecognizer(model, sample_rate)
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rec.SetWords(True)
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# 16bit mono with ffmpeg
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process = subprocess.Popen(['ffmpeg', '-loglevel', 'quiet', '-i',
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media_path,
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'-ar', str(sample_rate),
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'-ac', '1', '-f', 's16le', '-'],
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stdout=subprocess.PIPE)
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WORDS_PER_LINE = 7
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def transcribe():
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results = []
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subs = []
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while True:
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data = process.stdout.read(4000)
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if len(data) == 0:
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break
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if rec.AcceptWaveform(data):
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results.append(rec.Result())
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results.append(rec.FinalResult())
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for i, res in enumerate(results):
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jres = json.loads(res)
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if not 'result' in jres:
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continue
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words = jres['result']
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for j in range(0, len(words), WORDS_PER_LINE):
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line = words[j: j + WORDS_PER_LINE]
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s = srt.Subtitle(
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index=len(subs),
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content=" ".join([l['word'] for l in line]),
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start=datetime.timedelta(seconds=line[0]['start']),
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end=datetime.timedelta(seconds=line[-1]['end'])
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)
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subs.append(s)
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return subs
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srt_str = srt.compose(transcribe()) # create srt string
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# webvtt from srt with ffmepg
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process1 = subprocess.Popen(
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['ffmpeg', '-loglevel', 'quiet', '-i', '-', '-f', 'webvtt', '-'],
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stdin=subprocess.PIPE, stdout=subprocess.PIPE
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)
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webvtt = process1.communicate(input=bytes(srt_str, 'utf-8'))[0]
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return (webvtt)
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def vtt(model_path, media_path):
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sample_rate = 16000
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model = Model(model_path)
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rec = KaldiRecognizer(model, sample_rate)
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rec.SetWords(True)
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WORDS_PER_LINE = 7
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def timeString(seconds):
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minutes = seconds / 60
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seconds = seconds % 60
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hours = int(minutes / 60)
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minutes = int(minutes % 60)
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return '%i:%02i:%06.3f' % (hours, minutes, seconds)
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def transcribe():
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command = ['ffmpeg', '-nostdin', '-loglevel', 'quiet', '-i', media_path,
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'-ar', str(sample_rate), '-ac', '1', '-f', 's16le', '-']
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process = subprocess.Popen(command, stdout=subprocess.PIPE)
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results = []
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while True:
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data = process.stdout.read(4000)
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if len(data) == 0:
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break
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if rec.AcceptWaveform(data):
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results.append(rec.Result())
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results.append(rec.FinalResult())
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vtt = WebVTT()
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for i, res in enumerate(results):
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words = json.loads(res).get('result')
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if not words:
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continue
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start = timeString(words[0]['start'])
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end = timeString(words[-1]['end'])
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content = ' '.join([w['word'] for w in words])
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caption = Caption(start, end, textwrap.fill(content))
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vtt.captions.append(caption)
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return(vtt.content)
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return(transcribe())
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return model
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