Changed: Moved webvtt tools from modules to views

modules/.gitignore

@@ -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

modules/transcription_tools/__init__.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