Changed: Moved webvtt tools from modules to views
This commit is contained in:
parent
2946d6955d
commit
657bf6dfd4
@ -1,17 +1,52 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# -------------------------------------------------------------------------
|
||||
# This is a sample controller
|
||||
# this file is released under public domain and you can use without limitations
|
||||
# REQUIREMENTS
|
||||
#
|
||||
# module srt
|
||||
# module vosk
|
||||
# language model
|
||||
#
|
||||
# INSTALL
|
||||
#
|
||||
# cd /usr/lib/
|
||||
# apt install ffmpeg
|
||||
# git clone --recursive https://github.com/web2py/web2py.git
|
||||
# cd web2py
|
||||
# cd web2py/applications/transcription
|
||||
# pip3 install -t modules srt
|
||||
# pip3 install -t modules vosk
|
||||
# pip3 install -t modules webvtt-py
|
||||
# cd private
|
||||
# wget https://alphacephei.com/vosk/models/vosk-model-de-0.21.zip
|
||||
# unzip vosk-model-de-0.21.zip
|
||||
# -------------------------------------------------------------------------
|
||||
|
||||
import io
|
||||
#from transcription_tools import create_vtt
|
||||
transcription_tools = local_import('transcription_tools', reload=True)
|
||||
from vosk import KaldiRecognizer
|
||||
from webvtt import WebVTT, Caption
|
||||
import subprocess
|
||||
import srt
|
||||
import json
|
||||
import datetime
|
||||
import textwrap
|
||||
import transcription_tools
|
||||
# transcription_tools = local_import('transcription_tools', reload=True)
|
||||
|
||||
model = 'private/model'
|
||||
# To let Eclipse know about predefined objects
|
||||
global db
|
||||
global request
|
||||
global session
|
||||
global reqponse
|
||||
global SQLFORM
|
||||
global redirect
|
||||
global auth
|
||||
global URL
|
||||
global response
|
||||
|
||||
|
||||
model_mod_path = 'private/model'
|
||||
|
||||
|
||||
# ---- example index page ----
|
||||
def index():
|
||||
media_files = db().select(db.media_file.ALL, orderby=db.media_file.title)
|
||||
return dict(media_files=media_files)
|
||||
@ -24,36 +59,155 @@ def manage():
|
||||
|
||||
|
||||
def webvtt_single_line():
|
||||
media_file = db.media_file(request.args(0, cast=int)) or redirect(URL('index'))
|
||||
# Get mediafile from request
|
||||
media_file = (db.media_file(request.args(0, cast=int)) or
|
||||
redirect(URL('index')))
|
||||
# Set vars
|
||||
media_path = '{}/{}/{}'.format(request.folder, 'uploads', media_file.file)
|
||||
model_path = '{}/{}'.format(request.folder, model)
|
||||
transkription = transcription_tools.vtt_single_line(model_path, media_path)
|
||||
db(db.media_file.id == media_file.id).update(vtt_single_line=transkription)
|
||||
model_path = '{}/{}'.format(request.folder, model_mod_path)
|
||||
|
||||
# Trascribe to SubRip Subtitle file SRT
|
||||
sample_rate = 16000
|
||||
model = transcription_tools.get_model(model_path)
|
||||
rec = KaldiRecognizer(model, sample_rate)
|
||||
rec.SetWords(True)
|
||||
|
||||
# 16bit mono with ffmpeg
|
||||
process = subprocess.Popen(
|
||||
['ffmpeg', '-loglevel', 'quiet', '-i', media_path, '-ar',
|
||||
str(sample_rate), '-ac', '1', '-f', 's16le', '-'],
|
||||
stdout=subprocess.PIPE
|
||||
)
|
||||
|
||||
WORDS_PER_LINE = 7
|
||||
|
||||
def transcribe():
|
||||
results = []
|
||||
subs = []
|
||||
while True:
|
||||
data = process.stdout.read(4000)
|
||||
if len(data) == 0:
|
||||
break
|
||||
if rec.AcceptWaveform(data):
|
||||
results.append(rec.Result())
|
||||
results.append(rec.FinalResult())
|
||||
|
||||
for i, res in enumerate(results):
|
||||
jres = json.loads(res)
|
||||
if not 'result' in jres:
|
||||
continue
|
||||
words = jres['result']
|
||||
for j in range(0, len(words), WORDS_PER_LINE):
|
||||
line = words[j: j + WORDS_PER_LINE]
|
||||
s = srt.Subtitle(
|
||||
index=len(subs),
|
||||
content=" ".join([l['word'] for l in line]),
|
||||
start=datetime.timedelta(seconds=line[0]['start']),
|
||||
end=datetime.timedelta(seconds=line[-1]['end'])
|
||||
)
|
||||
subs.append(s)
|
||||
return subs
|
||||
|
||||
srt_str = srt.compose(transcribe()) # create srt string
|
||||
|
||||
# Create single line webvtt from srt with ffmepg
|
||||
process1 = subprocess.Popen(
|
||||
['ffmpeg', '-loglevel', 'quiet', '-i', '-', '-f', 'webvtt', '-'],
|
||||
stdin=subprocess.PIPE, stdout=subprocess.PIPE
|
||||
)
|
||||
# Send srt_str as input file to ffmpeg process
|
||||
webvtt = process1.communicate(input=bytes(srt_str, 'utf-8'))[0]
|
||||
|
||||
# Add result to database
|
||||
db(db.media_file.id == media_file.id).update(vtt_single_line=webvtt)
|
||||
redirect(request.env.http_referer)
|
||||
|
||||
|
||||
def webvtt():
|
||||
media_file = db.media_file(request.args(0, cast=int)) or redirect(URL('index'))
|
||||
# Get mediafile from request
|
||||
media_file = (db.media_file(request.args(0, cast=int)) or
|
||||
redirect(URL('index')))
|
||||
# Set vars
|
||||
media_path = '{}/{}/{}'.format(request.folder, 'uploads', media_file.file)
|
||||
model_path = '{}/{}'.format(request.folder, model)
|
||||
transkription = transcription_tools.vtt(model_path, media_path)
|
||||
db(db.media_file.id == media_file.id).update(vtt=transkription)
|
||||
model_path = '{}/{}'.format(request.folder, model_mod_path)
|
||||
|
||||
# Transcribe
|
||||
sample_rate = 16000
|
||||
model = transcription_tools.get_model(model_path) # cached model
|
||||
rec = KaldiRecognizer(model, sample_rate)
|
||||
rec.SetWords(True)
|
||||
|
||||
def timeString(seconds):
|
||||
minutes = seconds / 60
|
||||
seconds = seconds % 60
|
||||
hours = int(minutes / 60)
|
||||
minutes = int(minutes % 60)
|
||||
return '%i:%02i:%06.3f' % (hours, minutes, seconds)
|
||||
|
||||
def transcribe():
|
||||
command = ['ffmpeg', '-nostdin', '-loglevel', 'quiet', '-i',
|
||||
media_path, '-ar', str(sample_rate), '-ac', '1', '-f',
|
||||
's16le', '-']
|
||||
process = subprocess.Popen(command, stdout=subprocess.PIPE)
|
||||
|
||||
results = []
|
||||
while True:
|
||||
data = process.stdout.read(4000)
|
||||
if len(data) == 0:
|
||||
break
|
||||
if rec.AcceptWaveform(data):
|
||||
results.append(rec.Result())
|
||||
results.append(rec.FinalResult())
|
||||
|
||||
vtt = WebVTT()
|
||||
for i, res in enumerate(results):
|
||||
words = json.loads(res).get('result')
|
||||
if not words:
|
||||
continue
|
||||
|
||||
start = timeString(words[0]['start'])
|
||||
end = timeString(words[-1]['end'])
|
||||
content = ' '.join([w['word'] for w in words])
|
||||
|
||||
caption = Caption(start, end, textwrap.fill(content))
|
||||
vtt.captions.append(caption)
|
||||
|
||||
return(vtt.content)
|
||||
|
||||
# Write result to database
|
||||
db(db.media_file.id == media_file.id).update(vtt=transcribe())
|
||||
redirect(request.env.http_referer)
|
||||
|
||||
|
||||
def download_webvtt_single_line():
|
||||
media_file = db.media_file(request.args(0, cast=int)) or redirect(URL('index'))
|
||||
|
||||
media_file = (db.media_file(request.args(0, cast=int)) or
|
||||
redirect(URL('index')))
|
||||
|
||||
webvtt = media_file.vtt_single_line
|
||||
response.headers['Content-Type']='text/vtt'
|
||||
response.headers['Content-Disposition']='attachment; filename=transcript.vtt'
|
||||
|
||||
response.headers['Content-Type'] = 'text/vtt'
|
||||
response.headers['Content-Disposition'] = ('attachment; '
|
||||
'filename=transcript.vtt')
|
||||
|
||||
f = io.StringIO(webvtt)
|
||||
|
||||
return(f)
|
||||
|
||||
|
||||
def download_webvtt():
|
||||
media_file = db.media_file(request.args(0, cast=int)) or redirect(URL('index'))
|
||||
|
||||
media_file = (db.media_file(request.args(0, cast=int)) or
|
||||
redirect(URL('index')))
|
||||
|
||||
webvtt = media_file.vtt
|
||||
response.headers['Content-Type']='text/vtt'
|
||||
response.headers['Content-Disposition']='attachment; filename=transcript.vtt'
|
||||
|
||||
response.headers['Content-Type'] = 'text/vtt'
|
||||
response.headers['Content-Disposition'] = ('attachment; '
|
||||
'filename=transcript.vtt')
|
||||
|
||||
f = io.StringIO(webvtt)
|
||||
|
||||
return(f)
|
||||
|
||||
|
||||
|
||||
5
modules/.gitignore
vendored
5
modules/.gitignore
vendored
@ -11,3 +11,8 @@
|
||||
/vosk/
|
||||
/vosk-0.3.31.dist-info/
|
||||
/vosk.libs/
|
||||
/docopt-0.6.2.dist-info/
|
||||
/tests/
|
||||
/webvtt/
|
||||
/webvtt_py-0.4.6.dist-info/
|
||||
/docopt.py
|
||||
|
||||
@ -1,128 +1,8 @@
|
||||
# REQUIREMENTS
|
||||
#
|
||||
# module srt
|
||||
# module vosk
|
||||
# language model
|
||||
#
|
||||
# INSTALL
|
||||
#
|
||||
# cd web2py/applications/transcription
|
||||
# pip3 install -t modules srt
|
||||
# pip3 install -t modules vosk
|
||||
# cd private
|
||||
# wget https://alphacephei.com/vosk/models/vosk-model-de-0.21.zip
|
||||
# unzip vosk-model-de-0.21.zip
|
||||
from vosk import Model
|
||||
from gluon.cache import lazy_cache
|
||||
|
||||
|
||||
from vosk import Model, KaldiRecognizer, SetLogLevel
|
||||
from webvtt import WebVTT, Caption
|
||||
import sys
|
||||
import os
|
||||
import wave
|
||||
import subprocess
|
||||
import srt
|
||||
import json
|
||||
import datetime
|
||||
import textwrap
|
||||
|
||||
|
||||
def vtt_single_line(model_path, media_path):
|
||||
sample_rate = 16000
|
||||
@lazy_cache('get_model', time_expire=3600, cache_model='ram')
|
||||
def get_model(model_path):
|
||||
model = Model(model_path)
|
||||
rec = KaldiRecognizer(model, sample_rate)
|
||||
rec.SetWords(True)
|
||||
|
||||
# 16bit mono with ffmpeg
|
||||
process = subprocess.Popen(['ffmpeg', '-loglevel', 'quiet', '-i',
|
||||
media_path,
|
||||
'-ar', str(sample_rate),
|
||||
'-ac', '1', '-f', 's16le', '-'],
|
||||
stdout=subprocess.PIPE)
|
||||
|
||||
WORDS_PER_LINE = 7
|
||||
|
||||
def transcribe():
|
||||
results = []
|
||||
subs = []
|
||||
while True:
|
||||
data = process.stdout.read(4000)
|
||||
if len(data) == 0:
|
||||
break
|
||||
if rec.AcceptWaveform(data):
|
||||
results.append(rec.Result())
|
||||
results.append(rec.FinalResult())
|
||||
|
||||
for i, res in enumerate(results):
|
||||
jres = json.loads(res)
|
||||
if not 'result' in jres:
|
||||
continue
|
||||
words = jres['result']
|
||||
for j in range(0, len(words), WORDS_PER_LINE):
|
||||
line = words[j: j + WORDS_PER_LINE]
|
||||
s = srt.Subtitle(
|
||||
index=len(subs),
|
||||
content=" ".join([l['word'] for l in line]),
|
||||
start=datetime.timedelta(seconds=line[0]['start']),
|
||||
end=datetime.timedelta(seconds=line[-1]['end'])
|
||||
)
|
||||
subs.append(s)
|
||||
return subs
|
||||
|
||||
srt_str = srt.compose(transcribe()) # create srt string
|
||||
|
||||
# webvtt from srt with ffmepg
|
||||
process1 = subprocess.Popen(
|
||||
['ffmpeg', '-loglevel', 'quiet', '-i', '-', '-f', 'webvtt', '-'],
|
||||
stdin=subprocess.PIPE, stdout=subprocess.PIPE
|
||||
)
|
||||
|
||||
webvtt = process1.communicate(input=bytes(srt_str, 'utf-8'))[0]
|
||||
|
||||
return (webvtt)
|
||||
|
||||
|
||||
def vtt(model_path, media_path):
|
||||
sample_rate = 16000
|
||||
model = Model(model_path)
|
||||
rec = KaldiRecognizer(model, sample_rate)
|
||||
rec.SetWords(True)
|
||||
|
||||
WORDS_PER_LINE = 7
|
||||
|
||||
def timeString(seconds):
|
||||
minutes = seconds / 60
|
||||
seconds = seconds % 60
|
||||
hours = int(minutes / 60)
|
||||
minutes = int(minutes % 60)
|
||||
return '%i:%02i:%06.3f' % (hours, minutes, seconds)
|
||||
|
||||
def transcribe():
|
||||
command = ['ffmpeg', '-nostdin', '-loglevel', 'quiet', '-i', media_path,
|
||||
'-ar', str(sample_rate), '-ac', '1', '-f', 's16le', '-']
|
||||
process = subprocess.Popen(command, stdout=subprocess.PIPE)
|
||||
|
||||
results = []
|
||||
while True:
|
||||
data = process.stdout.read(4000)
|
||||
if len(data) == 0:
|
||||
break
|
||||
if rec.AcceptWaveform(data):
|
||||
results.append(rec.Result())
|
||||
results.append(rec.FinalResult())
|
||||
|
||||
vtt = WebVTT()
|
||||
for i, res in enumerate(results):
|
||||
words = json.loads(res).get('result')
|
||||
if not words:
|
||||
continue
|
||||
|
||||
start = timeString(words[0]['start'])
|
||||
end = timeString(words[-1]['end'])
|
||||
content = ' '.join([w['word'] for w in words])
|
||||
|
||||
caption = Caption(start, end, textwrap.fill(content))
|
||||
vtt.captions.append(caption)
|
||||
|
||||
return(vtt.content)
|
||||
|
||||
return(transcribe())
|
||||
return model
|
||||
|
||||
Loading…
Reference in New Issue
Block a user