67 lines
1.8 KiB
Python
67 lines
1.8 KiB
Python
# 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, KaldiRecognizer, SetLogLevel
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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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def create_vtt(model_path, video_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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process = subprocess.Popen(['ffmpeg', '-loglevel', 'quiet', '-i',
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video_path,
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'-ar', str(sample_rate) , '-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(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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subs.append(s)
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return subs
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return (srt.compose(transcribe()))
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