51 lines
1.6 KiB
Python
51 lines
1.6 KiB
Python
|
from vosk import Model, KaldiRecognizer, SetLogLevel
|
||
|
|
import sys
|
||
|
|
import os
|
||
|
|
import wave
|
||
|
|
import subprocess
|
||
|
|
import srt
|
||
|
|
import json
|
||
|
|
import datetime
|
||
|
|
|
||
|
|
|
||
|
|
def create_vtt():
|
||
|
|
sample_rate = 16000
|
||
|
|
model = Model("applications/transcription/private/model")
|
||
|
|
rec = KaldiRecognizer(model, sample_rate)
|
||
|
|
rec.SetWords(True)
|
||
|
|
|
||
|
|
process = subprocess.Popen(['ffmpeg', '-loglevel', 'quiet', '-i',
|
||
|
|
'/home/mschmidt/Videos/100-Meinungen-Video-erstellen.mp4',
|
||
|
|
'-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
|
||
|
|
|
||
|
|
return (srt.compose(transcribe()))
|
||
|
|
|