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• Problems with Python's azure.cognitiveservices.speech when installing together with FFmpeg in a Linux web app

  15 mai 2024, par Kakobo kakobo

  I need some help.
I'm building an web app that takes any audio format, converts into a .wav file and then passes it to 'azure.cognitiveservices.speech' for transcription.I'm building the web app via a container Dockerfile as I need to install ffmpeg to be able to convert non ".wav" audio files to ".wav" (as azure speech services only process wav files). For some odd reason, the 'speechsdk' class of 'azure.cognitiveservices.speech' fails to work when I install ffmpeg in the web app. The class works perfectly fine when I install it without ffpmeg or when i build and run the container in my machine.

  


  I have placed debug print statements in the code. I can see the class initiating, for some reason it does not buffer in the same when when running it locally in my machine. The routine simply stops without any reason.

  


  Has anybody experienced a similar issue with azure.cognitiveservices.speech conflicting with ffmpeg ?

  


  Here's my Dockerfile :

  


  # Use an official Python runtime as a parent imageFROM python:3.11-slim

#Version RunRUN echo "Version Run 1..."

Install ffmpeg

RUN apt-get update && apt-get install -y ffmpeg && # Ensure ffmpeg is executablechmod a+rx /usr/bin/ffmpeg && # Clean up the apt cache by removing /var/lib/apt/lists saves spaceapt-get clean && rm -rf /var/lib/apt/lists/*

//Set the working directory in the container

WORKDIR /app

//Copy the current directory contents into the container at /app

COPY . /app

//Install any needed packages specified in requirements.txt

RUN pip install --no-cache-dir -r requirements.txt

//Make port 80 available to the world outside this container

EXPOSE 8000

//Define environment variable

ENV NAME World

//Run main.py when the container launches

CMD ["streamlit", "run", "main.py", "--server.port", "8000", "--server.address", "0.0.0.0"]`and here's my python code:


  


  def transcribe_audio_continuous_old(temp_dir, audio_file, language):
    speech_key = azure_speech_key
    service_region = azure_speech_region

    time.sleep(5)
    print(f"DEBUG TIME BEFORE speechconfig")

    ran = generate_random_string(length=5)
    temp_file = f"transcript_key_{ran}.txt"
    output_text_file = os.path.join(temp_dir, temp_file)
    speech_recognition_language = set_language_to_speech_code(language)
    
    speech_config = speechsdk.SpeechConfig(subscription=speech_key, region=service_region)
    speech_config.speech_recognition_language = speech_recognition_language
    audio_input = speechsdk.AudioConfig(filename=os.path.join(temp_dir, audio_file))
        
    speech_recognizer = speechsdk.SpeechRecognizer(speech_config=speech_config, audio_config=audio_input, language=speech_recognition_language)
    done = False
    transcript_contents = ""

    time.sleep(5)
    print(f"DEBUG TIME AFTER speechconfig")
    print(f"DEBUG FIle about to be passed {audio_file}")

    try:
        with open(output_text_file, "w", encoding=encoding) as file:
            def recognized_callback(evt):
                print("Start continuous recognition callback.")
                print(f"Recognized: {evt.result.text}")
                file.write(evt.result.text + "\n")
                nonlocal transcript_contents
                transcript_contents += evt.result.text + "\n"

            def stop_cb(evt):
                print("Stopping continuous recognition callback.")
                print(f"Event type: {evt}")
                speech_recognizer.stop_continuous_recognition()
                nonlocal done
                done = True
            
            def canceled_cb(evt):
                print(f"Recognition canceled: {evt.reason}")
                if evt.reason == speechsdk.CancellationReason.Error:
                    print(f"Cancellation error: {evt.error_details}")
                nonlocal done
                done = True

            speech_recognizer.recognized.connect(recognized_callback)
            speech_recognizer.session_stopped.connect(stop_cb)
            speech_recognizer.canceled.connect(canceled_cb)

            speech_recognizer.start_continuous_recognition()
            while not done:
                time.sleep(1)
                print("DEBUG LOOPING TRANSCRIPT")

    except Exception as e:
        print(f"An error occurred: {e}")

    print("DEBUG DONE TRANSCRIPT")

    return temp_file, transcript_contents


  


  The transcript this callback works fine locally, or when installed without ffmpeg in the linux web app. Not sure why it conflicts with ffmpeg when installed via container dockerfile. The code section that fails can me found on note #NOTE DEBUG"

  


• avcodec/mpeg2dec : Fix motion vector rounding for chroma components

  11 février 2018, par Nekopanda

  avcodec/mpeg2dec : Fix motion vector rounding for chroma components
  In 16x8 motion compensation, for lower 16x8 region, the input to mpeg_motion() for motion_y was "motion_y + 16", which causes wrong rounding. For 4:2:0, chroma scaling for y is dividing by two and rounding toward zero. When motion_y < 0 and motion_y + 16 > 0, the rounding direction of "motion_y" and "motion_y + 16" is different and rounding "motion_y + 16" would be incorrect.
  We should input "motion_y" as is to round correctly. I add "is_16x8" flag to do that.
  Signed-off-by : Michael Niedermayer <michael@niedermayer.cc>
  

  • [DH] libavcodec/mpegvideo_motion.c
  • [DH] tests/ref/fate/filter-mcdeint-fast
  • [DH] tests/ref/fate/filter-mcdeint-medium
  • [DH] tests/ref/fate/filter-w3fdif-complex
  • [DH] tests/ref/fate/filter-w3fdif-simple
  • [DH] tests/ref/fate/filter-yadif-mode0
  • [DH] tests/ref/fate/filter-yadif-mode1
  • [DH] tests/ref/fate/filter-yadif10
  • [DH] tests/ref/fate/filter-yadif16
  • [DH] tests/ref/fate/mpeg2-field-enc
  • [DH] tests/ref/fate/mpeg2-ticket6677

• Thinking about switching to GeoIP2 for better location detection ? Here’s what you should know

  5 juin 2018, par Matomo Core Team

  In Matomo 3.5.0 we added a new feature to improve the location detection (country, region, city) of your visitors. Especially when it comes to IPv6 addresses, you will see less “Unknown” locations and more accurate results in general. This feature is now enabled for all new installations but needs to be manually enabled for existing Matomo self-hosted users.

  

  Why is the Matomo plugin not enabled for existing users ?

  When you enable the GeoIP2 plugin, a database update will need to be executed on two datatable tables (“log_visit” and “log_conversion”) which stores some of the raw data. Please be aware that this update could take several hours depending on the size of your database.

  If you store many visits in your database, it is recommended to execute the update through the command line by executing the command ./console core:update to avoid the update from timing out. You may also have to put your Matomo into maintenance mode during this time and replay the missed traffic from logs afterwards as explained in the FAQ article.

  GeoIP2 may slow down your tracking

  In the past we have seen that a few Matomo databases with high traffic volumes struggle to handle all the tracking requests after enabling GeoIP2. The reason for this is the location database now contains many more entries because it has to store all the IPv6 addresses and the database itself has a different format. Hence, the location lookup takes longer.

  It is hard to say how much slower the location lookup gets, but we found GeoIP2-PHP is about 20 times slower than GeoIP1-PHP. On a fast CPU the lookup time for an IP with GeoIP2 takes about 1ms, but can also take much longer depending on the server.

  Making location lookups fast again

  There is a PHP extension available that makes lookups very fast, even faster than the old GeoIP1-PHP provider. If you can install additional PHP extensions on your server and have a high traffic website, you may want to install the GeoIP2 extension.

  There is also an Nginx module and an Apache module. Unfortunately, we don’t have any performance metrics for these providers.

  How do I activate the GeoIP2 location provider ?

  As a Super User, log in to your Matomo and go to “Administration => Plugins”. There you can activate the “GeoIP2” plugin. As mentioned, this will trigger a database update which can take a while and you may want to perform this update through the command line.

  Now you can go to “Administration => Geolocation”. Here you will first need to install the GeoIP2 databases at the bottom of the page before you can activate the GeoIP2-PHP provider. To activate any of the other GeoIP2 providers, you will need to install the required modules.

  You will be able to check if the detection works on the right next to location provider. Once you selected one of the available providers, you’re all good to go.

  The post Thinking about switching to GeoIP2 for better location detection ? Here’s what you should know appeared first on Analytics Platform - Matomo.