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• lavu/vulkan : add support for using libshaderc as a GLSL compiler

  19 novembre 2021, par Lynne

  lavu/vulkan : add support for using libshaderc as a GLSL compiler
  It's got a much better API that's actually maintained, it eliminates
  
  race conditions, it comes with a pkg-config file by default, and
  
  unfortunately isn't currently packaged by Debian or other large
  
  distributions.
  

  • [DH] configure
  • [DH] libavutil/vulkan.c
  • [DH] libavutil/vulkan_shaderc.c

• avcodec/ffv1dec : Remove redundant writes, fix races

  21 avril 2021, par Andreas Rheinhardt

  avcodec/ffv1dec : Remove redundant writes, fix races
  Every modification of the data that is copied in update_thread_context()
  
  is a data race if it happens after ff_thread_finish_setup. ffv1dec's
  
  update_thread_context() simply uses memcpy for updating the new context,
  
  so that every modification of the src's context is a race.
  
  Some of these modifications are unnecessary : picture_number is write-only
  
  for the decoder and cur will be reset when decoding the next frame anyway.
  
  So remove them. And while just at it, also don't set cur for the slice
  
  contexts as this variable is write-only.
  Reviewed-by : Anton Khirnov <anton@khirnov.net>
  
  Signed-off-by : Andreas Rheinhardt <andreas.rheinhardt@outlook.com>
  

  • [DH] libavcodec/ffv1dec.c

• Speech recognition with python-telegram-bot without downloading an audio file

  25 juin 2022, par linz

  I'm developing a telegram bot in which the user sends a voice message, the bot transcribes it and sends back what was said in text.
For that I am using the python-telegram-bot library and the speech_recognition library with the google engine.
My problem is, the voice messages sent by the users are .mp3, however in order to transcribe them i need to convert them to .wav. In order to do that I have to download the file sent to the bot.
Is there a way to avoid that ? I understand this is not an efficient and a safe way to do this since many active users at once will result in race conditions and takes a lot of space.

  


  
def voice_handler(update, context):
    bot = context.bot
    file = bot.getFile(update.message.voice.file_id)
    file.download('voice.mp3')
    filename = "voice.wav"
    
    # convert mp3 to wav file
    subprocess.call(['ffmpeg', '-i', 'voice.mp3',
                         'voice.wav', '-y'])

    # initialize the recognizer
    r = sr.Recognizer()
    
    # open the file
    with sr.AudioFile(filename) as source:
    
        # listen for the data (load audio to memory)
        audio_data = r.record(source)
        # recognize (convert from speech to text)
        text = r.recognize_google(audio_data, language='ar-AR')
        
        
def main() -> None:
    updater.dispatcher.add_handler(MessageHandler(Filters.voice, voice_handler))