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• Start and end time of MoviePy's VideoClip not working

  21 mars 2024, par ernesto casco velazquez

  I'm trying to add captions to a video. The desired outcome is to show each word in the exact moment is being said.

  


  I have a method that gives me the accurate time start and end per each word :

  


  def get_words_per_time(audio_speech_file):
    model = whisper.load_model("base")
    transcribe = model.transcribe(
        audio=audio_speech_file, fp16=False, word_timestamps=True
    )
    segments = transcribe["segments"]
    words = []

    for seg in segments:
        for word in seg["words"]:
            words.append(
                {
                    "word": word["word"],
                    "start": word["start"],
                    "end": word["end"],
                    "prob": round(word["probability"], 4),
                }
            )
    return words


  


  Then I have a code that uses MoviePy to create TextClip and assing a given start and end time per pair of words (I know there are redundant statements, srry) :

  


  def generate_captions(
    words,
    font="Komika",
    fontsize=32,
    color="White",
    align="center",
    stroke_width=3,
    stroke_color="black",
):
    text_comp = []
    for i in track(range(0, len(words), 2), description="Creating captions..."):
        word1 = words[i]
        if i + 1 < len(words):
            word2 = words[i + 1]
        text_clip = TextClip(
            f"{word1['word']} {word2['word'] if i + 1 < len(words) else ''}",
            font=font,  # Change Font if not found
            fontsize=fontsize,
            color=color,
            align=align,
            method="caption",
            size=(660, None),
            stroke_width=stroke_width,
            stroke_color=stroke_color,
        )
        text_clip = text_clip.set_start(word1["start"])
        text_clip = text_clip.set_end(
            word2["end"] if i + 1 < len(words) else word1["end"]
        )
        text_comp.append(text_clip)
    return text_comp


  


  Finally, I concatenate the words into a single video :

  


  vid_clip = CompositeVideoClip(
    [vid_clip, concatenate_videoclips(text_comp).set_position(("center", 860))]
)


  


  The output is this, but you can clearly see the words are not flowing with the speech. They somehow move faster as if the start/end time did not matter. Here's the video

  


  The words with their respective start/end time, look like this :

  


  [
    {
        'word': 'This',
        'start': 0.0,
        'end': 0.22,
        'prob': 0.805
    },
    {
        'word': 'is',
        'start': 0.22,
        'end': 0.42,
        'prob': 0.9991
    },
    {
        'word': 'a',
        'start': 0.42,
        'end': 0.6,
        'prob': 0.999
    },
    {
        'word': 'test,
        ',
        'start': 0.6,
        'end': 1.04,
        'prob': 0.9939
    },
    {
        'word': 'to',
        'start': 1.18,
        'end': 1.3,
        'prob': 0.9847
    },
    {
        'word': 'show',
        'start': 1.3,
        'end': 1.54,
        'prob': 0.9971
    },
    {
        'word': 'words',
        'start': 1.54,
        'end': 1.9,
        'prob': 0.995
    },
    {
        'word': 'does',
        'start': 1.9,
        'end': 2.16,
        'prob': 0.997
    },
    {
        'word': 'not',
        'start': 2.16,
        'end': 2.4,
        'prob': 0.9978
    },
    {
        'word': 'appear.',
        'start': 2.4,
        'end': 2.82,
        'prob': 0.9984
    },
    {
        'word': 'At',
        'start': 3.46,
        'end': 3.6,
        'prob': 0.9793
    },
    {
        'word': 'their',
        'start': 3.6,
        'end': 3.8,
        'prob': 0.9984
    },
    {
        'word': 'proper',
        'start': 3.8,
        'end': 4.22,
        'prob': 0.9976
    },
    {
        'word': 'time.',
        'start': 4.22,
        'end': 4.72,
        'prob': 0.999
    },
    {
        'word': 'Thanks',
        'start': 5.04,
        'end': 5.4,
        'prob': 0.9662
    },
    {
        'word': 'for,
        ',
        'start': 5.4,
        'end': 5.66,
        'prob': 0.9941
    },
    {
        'word': 'watching.',
        'start': 5.94,
        'end': 6.36,
        'prob': 0.7701
    }
]


  


  What could be causing this ?

  


• Make better marketing decisions with attribution modeling

  19 décembre 2017, par InnoCraft — Community, Plugins

  Do you suspect some traffic sources are not getting the rewards they deserve ? Do you want to know how much credit each of your marketing channel actually gets ?

  When you look at which referrers contribute the most to your goal conversions or purchases, Piwik shows you only the referrer of the last visit. However, in reality, a visitor often visits a website multiple times from different referrers before they convert a goal. Giving all credit to the referrer of the last visit ignores all other referrers that contributed to a conversion as well.

  You can now push your marketing analysis to the next level with attribution modeling and finally discover the true value of all your marketing channels. As a result, you will be able to shift your marketing efforts and spending accordingly to maximize your success and stop wasting resources. In marketing, studying this data is called attribution modeling.

  Get the true value of your referrers

  Attribution is a premium feature that you can easily purchase from the Piwik marketplace.

  Once installed, you will be able to :

  • identify valuable referrers that you did not see before
  • invest in potential new partners
  • attribute a new level of conversion
  • make this work very easily by filling just a couple of form information

  Identify valuable referrers that you did not see before

  You probably have hundreds or even thousands of different sources listed within the referrer reports. We also guess that you have the feeling that it is always the same referrers which are credited of conversions.
  Guess what, those data are probably biased or at least are not telling you the whole story.
  Why ? Because by default, Piwik only attributes all credit to the last referrer.

  

  It is likely that many non credited sources played a role in the conversion process as well as people often visit your website several times before converting and they may come from different referrers.

  This is exactly where attribution modeling comes into play. With attribution modeling, you can decide which touchpoint you want to study. For example, you can choose to give credit to all the referrers a single visitor came from each time the user visits your website, and not only look at the last one. Without this feature, chances are, that you have spent too much money and / or efforts on the wrong referrer channels in the past because many referrers that contributed to conversions were ignored. Based on the insights you get by applying different attribution models, you can make better decisions on where to shift your marketing spending and efforts.

  Invest in potential new partners

  Once you apply different attribution models, you will find out that you need to consider a new list of referrers which you before either over- or under-estimated in terms of how much they contributed to your conversions. You probably did not identify those sources before because Piwik shows only the last referrer before a conversion. But you can now also look at what these newly discovered referrers are saying about your company, looking for any advertising programs they may offer, getting in contact with the owner of the website, and more.

  Apply up to 6 different attribution models

  By default, Piwik is attributing the conversion to the last referrer only. With attribution modeling you can analyze 6 different models :

  • Last Interaction : the conversion is attributed to the last referrer, even if it is a direct access.
  • Last Non-Direct : the conversion is attributed to the last referrer, but not in the case of a direct access.
  • First Interaction : the conversion is attributed to the first referrer which brought you the visit.
  • Linear : whatever the number of referrers which brought you the conversion, they will all get the same value.
  • Position Based : first and last referrer will be attributed 40% each the conversion value, the remaining 60% is divided between the rest of the referrers.
  • Time Decay : this attribution model means that the closer to the date of the conversion is, the more your last referrers will get credit.

  Those attribution models will enable you to analyze all your referrers deeply and increase your conversions.

  Let’s look at an example where we are comparing two models : “last interaction” and “first interaction”. Our goal is to identify whether some referrers that we are currently considering as less important, are finally playing a serious role in the total amount of conversions :

  

  Comparing Last Interaction model to First Interaction model

  Here it is interesting to observe that the website www.hongkiat.com is bringing almost 90% conversion more with the first interaction model rather than the last one.

  As a result we can look at this website and take the following actions :

  • have a look at the message on this website
  • look at opportunities to change the message
  • look at opportunities to display extra marketing messages
  • get in contact with the owner to identify any other communication opportunities

  The Multi Channel Attribution report

  Attribution modeling in Piwik does not require you to add any tracking code. The only thing you need is to install the plugin and let the magic happen.
  Simple as pie is the word you should keep in mind for this feature. Once installed, you will find the report within the goal section, just above the goals you created :

  

  The Multi Attribution menu

  There you can select the attribution model you would like to apply or compare.

  Attribution modeling is not just about playing with a new report. It is above all an opportunity to increase the number of conversions by identifying referrers that you may have not recognized as valuable in the past. To grow your business, it is crucial to identify the most (and least) successful channels correctly so you can spend your time and money wisely.

  Learn more about Multi Channel Conversion Attribution

• Unable to convert mov to mp4 using ffmpeg carrierwave in ruby

  9 avril 2021, par LearningROR

  I am unable to convert MOV files to MP4 using carrierwave and FFmpeg.

  


  It does upload mp4 file but does not convert it to mp4 and so on browser only sound gets played not the video.

  


  What I am missing here ? I tried most of the solutions.

  


  Code :

  


  class VideoUploader < CarrierWave::Uploader::Base
  include CarrierWave::MiniMagick
  include CarrierWave::Video
  include CarrierWave::Video::Thumbnailer
  include CarrierWave::FFmpeg
  # Choose what kind of storage to use for this uploader:
  storage :file
  # Override the directory where uploaded files will be stored.
  # This is a sensible default for uploaders that are meant to be mounted:
  def store_dir
    "uploads/#{model.class.to_s.underscore}/#{mounted_as}/#{model.id}"
  end
  version :video, :if => :video? do
    process :encode
  end
  version :thumb do
    process thumbnail: [{format: 'jpg', quality: 8, size: 360, logger: Rails.logger, square: false}]
    def full_filename for_file
      jpg_name for_file, version_name
    end
  end
  def encode
    tmp_path = File.join( File.dirname(current_path), "tmpfile.mp4" )
    movie = FFMPEG::Movie.new(current_path)
    movie.transcode(tmp_path, custom: %w(-c:v libx264 -c:a aac -vf format=yuv420p -movflags +faststart)) do |progress|
      puts progress
    end
    File.rename tmp_path, current_path
  end
  protected
  def video?(new_file)
    new_file.content_type.include? 'video'
  end
end


  


  Logs :

  


  I, [2021-04-09T15:33:24.783337 #9397]  INFO -- : Running transcoding...
["/usr/local/bin/ffmpeg", "-y", "-i", "/Users/osx/workspace_ror/xxx-react/tmp/1617964403-459168728101935-0021-9213/video/sample_iTunes__1_.mov", "-c:v", "libx264", "-c:a", "aac", "-vf", "format=yuv420p", "-movflags", "+faststart", "/Users/osx/workspace_ror/xxx-react/tmp/1617964403-459168728101935-0021-9213/video/tmpfile.mp4"]

0.0
0.08304093567251461
0.1672514619883041
0.2327485380116959
0.3229239766081871
0.4008187134502924
0.45426900584795327
0.5033918128654971
0.5403508771929825
0.6100584795321637
0.663859649122807
0.711812865497076
0.7657309941520468
0.8196491228070175
0.87953216374269
0.9394152046783625
1.0
1.0
I, [2021-04-09T15:33:33.555508 #9397]  INFO -- : Transcoding of /Users/osx/workspace_ror/xxx-react/tmp/1617964403-459168728101935-0021-9213/video/sample_iTunes__1_.mov to /Users/osx/workspace_ror/xxx-react/tmp/1617964403-459168728101935-0021-9213/video/tmpfile.mp4 succeeded

Running....ffmpegthumbnailer -i /Users/osx/workspace_ror/xxx-react/tmp/1617964403-459168728101935-0021-9213/thumb/sample_iTunes__1_.mov -o /Users/osx/workspace_ror/xxx-react/tmp/1617964403-459168728101935-0021-9213/thumb/tmpfile.jpg -c jpg -q 8 -s 360
Success!