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• Error : ffmpeg : Failed to download resource "sqlite"

  22 février 2023, par nour

  i am trying to download ffmpeg but I am getting this error ! could anyone help ? I am using paperclip gem to add images and videos. I am getting this error on heroku when i try to add a new video Av::UnableToDetect (Unable to detect any supported library so that's why im trying to download ffmpeg. could someone help please ! thanks in advance

  


  brew install ffmpeg

  


  curl: (60) SSL certificate problem: certificate has expired
More details here: https://curl.haxx.se/docs/sslcerts.html

curl performs SSL certificate verification by default, using a "bundle"
 of Certificate Authority (CA) public keys (CA certs). If the default
 bundle file isn't adequate, you can specify an alternate file
 using the --cacert option.
If this HTTPS server uses a certificate signed by a CA represented in
 the bundle, the certificate verification probably failed due to a
 problem with the certificate (it might be expired, or the name might
 not match the domain name in the URL).
If you'd like to turn off curl's verification of the certificate, use
 the -k (or --insecure) option.
HTTPS-proxy has similar options --proxy-cacert and --proxy-insecure.
Error: ffmpeg: Failed to download resource "sqlite"
Download failed: https://sqlite.org/2022/sqlite-autoconf-3400100.tar.gz


  


  when i add a new video to my website on heroku I got this error

  


  2023-02-22T06:23:03.207072+00:00 app[web.1]: User Load (0.8ms)  SELECT  "users".* FROM "users" WHERE "users"."id" = $1  ORDER BY "users"."id" ASC LIMIT 1  [["id", 1]]
2023-02-22T06:23:03.208471+00:00 app[web.1]: Steptation Load (0.7ms)  SELECT  "steptations".* FROM "steptations" WHERE "steptations"."id" = $1 LIMIT 1  [["id", 1802]]
2023-02-22T06:23:03.210118+00:00 app[web.1]: [paperclip] Trying to link /tmp/RackMultipart20230222-2-1i28xon.mp4 to /tmp/847606d3771b12aa2d91db7647fe3a4920230222-2-165fycl.mp4
2023-02-22T06:23:03.210923+00:00 app[web.1]: [paperclip] Trying to link /tmp/847606d3771b12aa2d91db7647fe3a4920230222-2-165fycl.mp4 to /tmp/847606d3771b12aa2d91db7647fe3a4920230222-2-p6icfm.mp4
2023-02-22T06:23:03.211217+00:00 app[web.1]: Command :: file -b --mime '/tmp/847606d3771b12aa2d91db7647fe3a4920230222-2-p6icfm.mp4'
2023-02-22T06:23:03.218799+00:00 app[web.1]: [AV] Running command: if command -v avprobe 2>/dev/null; then echo "true"; else echo "false"; fi
2023-02-22T06:23:03.221000+00:00 app[web.1]: [AV] Running command: if command -v ffmpeg 2>/dev/null; then echo "true"; else echo "false"; fi
2023-02-22T06:23:03.222208+00:00 heroku[router]: at=info method=POST path= "/step_images?id=1802" host=www.mobileimplantlab.ca request_id=39f2b001-ab36-4a59-ae94-1f300d386a40 fwd="207.216.102.221" dyno=web.1 connect=0ms service=900ms status=500 bytes=234 protocol=https
2023-02-22T06:23:03.222987+00:00 app[web.1]: Completed 500 Internal Server Error in 18ms (ActiveRecord: 1.6ms)
2023-02-22T06:23:03.223510+00:00 app[web.1]: 
2023-02-22T06:23:03.223511+00:00 app[web.1]: Av::UnableToDetect (Unable to detect any supported library):
2023-02-22T06:23:03.223511+00:00 app[web.1]: app/controllers/step_images_controller.rb:15:in `create'
2023-02-22T06:23:03.223512+00:00 app[web.1]: 
2023-02-22T06:23:03.223512+00:00 app[web.1]: 
2023-02-22T06:23:14.356278+00:00 app[web.1]: Started POST "/step_images?id=1802" for 207.216.102.221 at 2023-02-22 06:23:14 +0000
2023-02-22T06:23:14.357993+00:00 app[web.1]: Processing by StepImagesController#create as HTML
2023-02-22T06:23:14.358067+00:00 app[web.1]: Parameters: {"utf8"=>"✓", "authenticity_token"=>"PgRtPGgyOSPP8lQlbf9fAgOSZre/XlYcKBKJhe81oOz9Ge81GZZzEfc4Xcnz0zzZ35S7PpsfKOse3Q73mxhjEA==", "step_image"=>{"product_id"=>"4", "text"=>"", "text2"=>"", "text3"=>"", "text4"=>"", "text5"=>"", "video"=>#, @original_filename="IMG_2826.mp4", @content_type="video/mp4", @headers="Content-Disposition: form-data; name=\"step_image[video]\"; filename=\"IMG_2826.mp4\"\r\nContent-Type: video/mp4\r\n">, "text6"=>"test", "user_id"=>"73", "case_id"=>"311", "step_id"=>"3"}, "commit"=>"Submit", "id"=>"1802"}
2023-02-22T06:23:14.359735+00:00 app[web.1]: User Load (0.8ms)  SELECT  "users".* FROM "users" WHERE "users"."id" = $1  ORDER BY "users"."id" ASC LIMIT 1  [["id", 1]]
2023-02-22T06:23:14.360954+00:00 app[web.1]: Steptation Load (0.7ms)  SELECT  "steptations".* FROM "steptations" WHERE "steptations"."id" = $1 LIMIT 1  [["id", 1802]]
2023-02-22T06:23:14.362132+00:00 app[web.1]: [paperclip] Trying to link /tmp/RackMultipart20230222-2-1stq4d4.mp4 to /tmp/847606d3771b12aa2d91db7647fe3a4920230222-2-1jq0dqp.mp4
2023-02-22T06:23:14.362719+00:00 app[web.1]: [paperclip] Trying to link /tmp/847606d3771b12aa2d91db7647fe3a4920230222-2-1jq0dqp.mp4 to /tmp/847606d3771b12aa2d91db7647fe3a4920230222-2-9hfi7z.mp4
2023-02-22T06:23:14.362932+00:00 app[web.1]: Command :: file -b --mime '/tmp/847606d3771b12aa2d91db7647fe3a4920230222-2-9hfi7z.mp4'
2023-02-22T06:23:14.370045+00:00 app[web.1]: [AV] Running command: if command -v avprobe 2>/dev/null; then echo "true"; else echo "false"; fi
2023-02-22T06:23:14.372073+00:00 app[web.1]: [AV] Running command: if command -v ffmpeg 2>/dev/null; then echo "true"; else echo "false"; fi
2023-02-22T06:23:14.374070+00:00 app[web.1]: Completed 500 Internal Server Error in 16ms (ActiveRecord: 1.5ms)
2023-02-22T06:23:14.374254+00:00 heroku[router]: at=info method=POST path= "/step_images?id=1802" host=www.mobileimplantlab.ca request_id=4a0702a2-8d14-454a-9f1f-edf154e140fc fwd="207.216.102.221" dyno=web.1 connect=0ms service=267ms status=500 bytes=234 protocol=https
2023-02-22T06:23:14.374720+00:00 app[web.1]: 
2023-02-22T06:23:14.374721+00:00 app[web.1]: Av::UnableToDetect (Unable to detect any supported library):


  


  in my gem file

  


  source 'https://rubygems.org'


# Bundle edge Rails instead: gem 'rails', github: 'rails/rails'
gem 'rails', '4.2.11.1'
# Use postgresql as the database for Active Record
gem 'pg', '~> 0.20.0'
# Use SCSS for stylesheets
gem 'sass-rails', '~> 5.0'
# Use Uglifier as compressor for JavaScript assets
gem 'uglifier', '>= 1.3.0'
# Use CoffeeScript for .coffee assets and views
gem 'coffee-rails', '~> 4.1.0'
# See https://github.com/rails/execjs#readme for more supported runtimes
# gem 'therubyracer', platforms: :ruby
gem 'pry', '~> 0.13.1'
# Use jquery as the JavaScript library
gem 'jquery-rails'
# Turbolinks makes following links in your web application faster. Read more: https://github.com/rails/turbolinks
gem 'turbolinks'
# Build JSON APIs with ease. Read more: https://github.com/rails/jbuilder
gem 'jbuilder', '~> 2.0'
# bundle exec rake doc:rails generates the API under doc/api.
gem 'sdoc', '~> 0.4.0', group: :doc

# Use ActiveModel has_secure_password
# gem 'bcrypt', '~> 3.1.7'

# Use Unicorn as the app server
# gem 'unicorn'

# Use Capistrano for deployment
# gem 'capistrano-rails', group: :development


group :development do
  # Access an IRB console on exception pages or by using <%= console %> in views
  gem 'web-console', '~> 2.0'

  # Spring speeds up development by keeping your application running in the background. Read more: https://github.com/rails/spring
  gem 'spring'
end

gem 'devise'
group :development, :test do
  gem "interactive_editor"
  gem "hirb"
  gem "awesome_print"
  gem 'byebug'
end
group :production do
  gem 'rails_12factor'
  gem 'puma'
end
gem 'mail_form'
gem 'font-awesome-sass', '~> 5.12.0'

gem 'bootsnap', '>= 1.1.0', require: false
gem 'bootstrap-sass', '~>3.2.0'

gem 'paperclip'

gem 'aws-sdk-s3'
gem 'aws-sdk', '< 2.0'

gem 'thor', '0.19.1'
gem 'omniauth-google-oauth2'
gem "recaptcha", require: "recaptcha/rails"
gem 'friendly_id', '~> 5.4.0'

gem 'activeadmin'

gem 'cocoon'
gem 'bigdecimal', '1.3.5'
gem 'bundler', '1.17.3'
gem 'paperclip-av-transcoder'
gem "paperclip-ffmpeg", "~> 1.2.0"
gem "will_paginate", "~> 3.0.4" 
gem 'rails-erd'
gem 'rails_autolink'

gem 'rails-erd', group: :development


  


  in the model

  


    has_attached_file :video, :styles => {
     :medium => { :geometry => "500x500", :format => 'jpg' },
     :thumb => { :geometry => "100x100", :format => 'jpg' }
  }, :processors => [:transcoder]

  validates_attachment_content_type :video,
    :content_type => [
      "video/mp4", 
      "video/quicktime",
      "video/3gpp",
      "video/x-ms-wmv",
      "video/mov",
      "video/flv",
      
      ]


  


• What is Multi-Touch Attribution ? (And How To Get Started)

  2 février 2023, par Erin — Analytics Tips

  Good marketing thrives on data. Or more precisely — its interpretation. Using modern analytics software, we can determine which marketing actions steer prospects towards the desired action (a conversion event). 

  An attribution model in marketing is a set of rules that determine how various marketing tactics and channels impact the visitor’s progress towards a conversion. 

  Yet, as customer journeys become more complicated and involve multiple “touches”, standard marketing reports no longer tell the full picture. 

  That’s when multi-touch attribution analysis comes to the fore. 

  What is Multi-Touch Attribution ?

  Multi-touch attribution (also known as multi-channel attribution or cross-channel attribution) measures the impact of all touchpoints on the consumer journey on conversion. 

  Unlike single-touch reporting, multi-touch attribution models give credit to each marketing element — a social media ad, an on-site banner, an email link click, etc. By seeing impacts from every touchpoint and channel, marketers can avoid false assumptions or subpar budget allocations.

  To better understand the concept, let’s interpret the same customer journey using a standard single-touch report vs a multi-touch attribution model. 

  Picture this : Jammie is shopping around for a privacy-centred web analytics solution. She saw a recommendation on Twitter and ended up on the Matomo website. After browsing a few product pages and checking comparisons with other web analytics tools, she signs up for a webinar. One week after attending, Jammie is convinced that Matomo is the right tool for her business and goes directly to the Matomo website a starts a free trial. 

  • A standard single-touch report would attribute 100% of the conversion to direct traffic, which doesn’t give an accurate view of the multiple touchpoints that led Jammie to start a free trial. 
  • A multi-channel attribution report would showcase all the channels involved in the free trial conversion — social media, website content, the webinar, and then the direct traffic source.

  In other words : Multi-touch attribution helps you understand how prospects move through the sales funnel and which elements tinder them towards the desired outcome. 

  Types of Attribution Models

  As marketers, we know that multiple factors play into a conversion — channel type, timing, user’s stage on the buyer journey and so on. Various attribution models exist to reflect this variability. 

  

  First Interaction attribution model (otherwise known as first touch) gives all credit for the conversion to the first channel (for example — a referral link) and doesn’t report on all the other interactions a user had with your company (e.g., clicked a newsletter link, engaged with a landing page, or browsed the blog campaign).

  First-touch helps optimise the top of your funnel and establish which channels bring the best leads. However, it doesn’t offer any insight into other factors that persuaded a user to convert. 

  Last Interaction attribution model (also known as last touch) allocates 100% credit to the last channel before conversion — be it direct traffic, paid ad, or an internal product page.

  The data is useful for optimising the bottom-of-the-funnel (BoFU) elements. But you have no visibility into assisted conversions — interactions a user had prior to conversion. 

  Last Non-Direct attribution model model excludes direct traffic and assigns 100% credit for a conversion to the last channel a user interacted with before converting. For instance, a social media post will receive 100% of credit if a shopper buys a product three days later. 

  This model is more telling about the other channels, involved in the sales process. Yet, you’re seeing only one step backwards, which may not be sufficient for companies with longer sales cycles.

  Linear attribution model distributes an equal credit for a conversion between all tracked touchpoints.

  For instance, with a four touchpoint conversion (e.g., an organic visit, then a direct visit, then a social visit, then a visit and conversion from an ad campaign) each touchpoint would receive 25% credit for that single conversion.

  This is the simplest multi-channel attribution modelling technique many tools support. The nuance is that linear models don’t reflect the true impact of various events. After all, a paid ad that introduced your brand to the shopper and a time-sensitive discount code at the checkout page probably did more than the blog content a shopper browsed in between. 

  Position Based attribution model allocates a 40% credit to the first and the last touchpoints and then spreads the remaining 20% across the touchpoints between the first and last. 

  This attribution model comes in handy for optimising conversions across the top and the bottom of the funnel. But it doesn’t provide much insight into the middle, which can skew your decision-making. For instance, you may overlook cases when a shopper landed via a social media post, then was re-engaged via email, and proceeded to checkout after an organic visit. Without email marketing, that sale may not have happened.

  Time decay attribution model adjusts the credit, based on the timing of the interactions. Touchpoints that preceded the conversion get the highest score, while the first ones get less weight (e.g., 5%-5%-10%-15%-25%-30%).

  This multi-channel attribution model works great for tracking the bottom of the funnel, but it underestimates the impact of brand awareness campaigns or assisted conversions at mid-stage. 

  Why Use Multi-Touch Attribution Modelling

  Multi-touch attribution provides you with the full picture of your funnel. With accurate data across all touchpoints, you can employ targeted conversion rate optimisation (CRO) strategies to maximise the impact of each campaign. 

  Most marketers and analysts prefer using multi-touch attribution modelling — and for some good reasons.

  Issues multi-touch attribution solves 

  • Funnel visibility. Understand which tactics play an important role at the top, middle and bottom of your funnel, instead of second-guessing what’s working or not. 
  • Budget allocations. Spend money on channels and tactics that bring a positive return on investment (ROI). 
  • Assisted conversions. Learn how different elements and touchpoints cumulatively contribute to the ultimate goal — a conversion event — to optimise accordingly. 
  • Channel segmentation. Determine which assets drive the most qualified and engaged leads to replicate them at scale.
  • Campaign benchmarking. Compare how different marketing activities from affiliate marketing to social media perform against the same metrics.

  How To Get Started With Multi-Touch Attribution 

  To make multi-touch attribution part of your analytics setup, follow the next steps :

  1. Define Your Marketing Objectives 

  Multi-touch attribution helps you better understand what led people to convert on your site. But to capture that, you need to first map the standard purchase journeys, which include a series of touchpoints — instances, when a prospect forms an opinion about your business.

  Touchpoints include :

  • On-site interactions (e.g., reading a blog post, browsing product pages, using an on-site calculator, etc.)
  • Off-site interactions (e.g., reading a review, clicking a social media link, interacting with an ad, etc.)

  Combined these interactions make up your sales funnel — a designated path you’ve set up to lead people toward the desired action (aka a conversion). 

  Depending on your business model, you can count any of the following as a conversion :

  • Purchase 
  • Account registration 
  • Free trial request 
  • Contact form submission 
  • Online reservation 
  • Demo call request 
  • Newsletter subscription

  So your first task is to create a set of conversion objectives for your business and add them as Goals or Conversions in your web analytics solution. Then brainstorm how various touchpoints contribute to these objectives. 

  Web analytics tools with multi-channel attribution, like Matomo, allow you to obtain an extra dimension of data on touchpoints via Tracked Events. Using Event Tracking, you can analyse how many people started doing a desired action (e.g., typing details into the form) but never completed the task. This way you can quickly identify “leaking” touchpoints in your funnel and fix them. 

  2. Select an Attribution Model 

  Multi-attribution models have inherent tradeoffs. Linear attribution model doesn’t always represent the role and importance of each channel. Position-based attribution model emphasises the role of the last and first channel while diminishing the importance of assisted conversions. Time-decay model, on the contrary, downplays the role awareness-related campaigns played.

  To select the right attribution model for your business consider your objectives. Is it more important for you to understand your best top of funnel channels to optimise customer acquisition costs (CAC) ? Or would you rather maximise your on-site conversion rates ? 

  Your industry and the average cycle length should also guide your choice. Position-based models can work best for eCommerce and SaaS businesses where both CAC and on-site conversion rates play an important role. Manufacturing companies or educational services providers, on the contrary, will benefit more from a time-decay model as it better represents the lengthy sales cycles. 

  3. Collect and Organise Data From All Touchpoints 

  Multi-touch attribution models are based on available funnel data. So to get started, you will need to determine which data sources you have and how to best leverage them for attribution modelling. 

  Types of data you should collect : 

  • General web analytics data : Insights on visitors’ on-site actions — visited pages, clicked links, form submissions and more.
  • Goals (Conversions) : Reports on successful conversions across different types of assets. 
  • Behavioural user data : Some tools also offer advanced features such as heatmaps, session recording and A/B tests. These too provide ample data into user behaviours, which you can use to map and optimise various touchpoints.

  You can also implement extra tracking, for instance for contact form submissions, live chat contacts or email marketing campaigns to identify repeat users in your system. Just remember to stay on the good side of data protection laws and respect your visitors’ privacy. 

  Separately, you can obtain top-of-the-funnel data by analysing referral traffic sources (channel, campaign type, used keyword, etc). A Tag Manager comes in handy as it allows you to zoom in on particular assets (e.g., a newsletter, an affiliate, a social campaign, etc). 

  Combined, these data points can be parsed by an app, supporting multi-touch attribution (or a custom algorithm) and reported back to you as specific findings. 

  Sounds easy, right ? Well, the devil is in the details. Getting ample, accurate data for multi-touch attribution modelling isn’t easy. 

  Marketing analytics has an accuracy problem, mainly for two reasons :

  • Cookie consent banner rejection 
  • Data sampling application

  Please note that we are not able to provide legal advice, so it’s important that you consult with your own DPO to ensure compliance with all relevant laws and regulations.

  If you’re collecting web analytics in the EU, you know that showing a cookie consent banner is a GDPR must-do. But many consumers don’t often rush to accept cookie consent banners. The average consent rate for cookies in 2021 stood at 54% in Italy, 45% in France, and 44% in Germany. The consent rates are likely lower in 2023, as Google was forced to roll out a “reject all” button for cookie tracking in Europe, while privacy organisations lodge complaints against individual businesses for deceptive banners. 

  For marketers, cookie rejection means substantial gaps in analytics data. The good news is that you can fill in those gaps by using a privacy-centred web analytics tool like Matomo. 

  Matomo takes extra safeguards to protect user privacy and supports fully cookieless tracking. Because of that, Matomo is legally exempt from tracking consent in France. Plus, you can configure to use our analytics tool without consent banners in other markets outside of Germany and the UK. This way you get to retain the data you need for audience modelling without breaching any privacy regulations. 

  Data sampling application partially stems from the above. When a web analytics or multi-channel attribution tool cannot secure first-hand data, the “guessing game” begins. Google Analytics, as well as other tools, often rely on synthetic AI-generated data to fill in the reporting gaps. Respectively, your multi-attribution model doesn’t depict the real state of affairs. Instead, it shows AI-produced guesstimates of what transpired whenever not enough real-world evidence is available.

  4. Evaluate and Select an Attribution Tool 

  Google Analytics (GA) offers several multi-touch attribution models for free (linear, time-decay and position-based). The disadvantage of GA multi-touch attribution is its lower accuracy due to cookie rejection and data sampling application.

  At the same time, you cannot create custom credit allocations for the proposed models, unless you have the paid version of GA, Google Analytics 360. This version of GA comes with a custom Attribution Modeling Tool (AMT). The price tag, however, starts at USD $50,000 per year. 

  Matomo Cloud offers multi-channel conversion attribution as a feature and it is available as a plug-in on the marketplace for Matomo On-Premise. We support linear, position-based, first-interaction, last-interaction, last non-direct and time-decay modelling, based fully on first-hand data. You also get more precise insights because cookie consent isn’t an issue with us. 

  Most multi-channel attribution tools, like Google Analytics and Matomo, provide out-of-the-box multi-touch attribution models. But other tools, like Matomo On-Premise, also provide full access to raw data so you can develop your own multi-touch attribution models and do custom attribution analysis. The ability to create custom attribution analysis is particularly beneficial for data analysts or organisations with complex and unique buyer journeys. 

  Conclusion

  Ultimately, multi-channel attribution gives marketers greater visibility into the customer journey. By analysing multiple touchpoints, you can establish how various marketing efforts contribute to conversions. Then use this information to inform your promotional strategy, budget allocations and CRO efforts. 

  The key to benefiting the most from multi-touch attribution is accurate data. If your analytics solution isn’t telling you the full story, your multi-touch model won’t either. 

  Collect accurate visitor data for multi-touch attribution modelling with Matomo. Start your free 21-day trial now. 

• pyqt5 gui dependent on ffmpeg compiled with pyinstaller doesn't run on other machines ?

  19 octobre 2022, par Soren

  I am trying to create a simple Pyqt5 GUI for Windows 10 that uses OpenAI's model Whisper to transcribe a sound file and outputting the results in an Excel-file. It works on my own computer where I have installed the necessary dependencies for Whisper as stated on their github i.e. FFMEG. I provide a minimal example of my code below :

  


  # Import library
import whisper
import os
from PyQt5 import QtCore, QtGui, QtWidgets
import pandas as pd
import xlsxwriter


class Ui_Dialog(QtWidgets.QDialog):
    
    
    # Define functions to use in GUI
   
    # Define function for selecting input files
    def browsefiles(self, Dialog):
      
       
       # Make Dialog box and save files into tuple of paths
       files = QtWidgets.QFileDialog().getOpenFileNames(self, "Select soundfiles", os.getcwd(), "lyd(*mp2 *.mp3 *.mp4 *.m4a *wma *wav)")
       
       self.liste = []
       for url in range(len(files[0])):
           self.liste.append(files[0][url])   

    
    def model_load(self, Dialog):
               
        # Load picked model
        self.model = whisper.load_model(r'C:\Users\Søren\Downloads\Whisper_gui\models' + "\\" + self.combo_modelSize.currentText() + ".pt") ##the path is set to where the models are on the other machine
        
    
    def run(self, Dialog):
                
        # Make list for sound files
        liste_df = []
        
        
        # Running loop for interpreting and encoding sound files
        for url in range(len(self.liste)):
                          
            # Make dataframe
            df = pd.DataFrame(columns=["filename", "start", "end", "text"])
            
            # Run model
            result = self.model.transcribe(self.liste[url])
                            
            # Extract results
            for i in range(len(result["segments"])):
                start = result["segments"][i]["start"]
                end = result["segments"][i]["end"]
                text = result["segments"][i]["text"]
                
                df = df.append({"filename": self.liste[url].split("/")[-1],
                            "start": start, 
                            "end": end, 
                            "text": text}, ignore_index=True)
            
            # Add detected language to dataframe
            df["sprog"] = result["language"]
            
            
            liste_df.append(df)
        
        
        
        # Make excel output
        
        # Concatenate list of dfs
        dataframe = pd.concat(liste_df)
        
        
        # Create a Pandas Excel writer using XlsxWriter as the engine.
        writer = pd.ExcelWriter(self.liste[0].split(".")[0] + '_OUTPUT.xlsx', engine='xlsxwriter')
        writer_wrap_format = writer.book.add_format({"text_wrap": True, 'num_format': '@'})


        # Write the dataframe data to XlsxWriter. Turn off the default header and
        # index and skip one row to allow us to insert a user defined header.
        dataframe.to_excel(writer, sheet_name="Output", startrow=1, header=False, index=False)

        # Get the xlsxwriter workbook and worksheet objects.
        #workbook = writer.book
        worksheet = writer.sheets["Output"]

        # Get the dimensions of the dataframe.
        (max_row, max_col) = dataframe.shape

        # Create a list of column headers, to use in add_table().
        column_settings = [{'header': column} for column in dataframe.columns]

        # Add the Excel table structure. Pandas will add the data.
        worksheet.add_table(0, 0, max_row, max_col - 1, {'columns': column_settings})

        # Make the columns wider for clarity.
        worksheet.set_column(0, max_col - 1, 12)
        
        in_col_no = xlsxwriter.utility.xl_col_to_name(dataframe.columns.get_loc("text"))
        
        worksheet.set_column(in_col_no + ":" + in_col_no, 30, writer_wrap_format)

        # Close the Pandas Excel writer and output the Excel file.
        writer.save()
        writer.close()
    
    
    ## Design setup
    
    def setupUi(self, Dialog):
        Dialog.setObjectName("Dialog")
        Dialog.resize(730, 400)
        
        self.select_files = QtWidgets.QPushButton(Dialog)
        self.select_files.setGeometry(QtCore.QRect(40, 62, 81, 31))
        font = QtGui.QFont()
        font.setPointSize(6)
        self.select_files.setFont(font)
        self.select_files.setObjectName("select_files")
        
    
               
        
        self.combo_modelSize = QtWidgets.QComboBox(Dialog)
        self.combo_modelSize.setGeometry(QtCore.QRect(40, 131, 100, 21))
        font = QtGui.QFont()
        font.setPointSize(6)
        self.combo_modelSize.setFont(font)
        self.combo_modelSize.setObjectName("combo_modelSize")
               
        
        self.runButton = QtWidgets.QPushButton(Dialog)
        self.runButton.setGeometry(QtCore.QRect(40, 289, 71, 21))
        font = QtGui.QFont()
        font.setPointSize(6)
        self.runButton.setFont(font)
        self.runButton.setObjectName("runButton")
        
        
       

        self.retranslateUi(Dialog)
        QtCore.QMetaObject.connectSlotsByName(Dialog)
        
        
        
        modelSize_options = ['Chose model', 'tiny', 'base', 'small', 'medium', 'large']
        self.combo_modelSize.addItems(modelSize_options)
        
        # Do an action!
        self.select_files.clicked.connect(self.browsefiles)
        self.combo_modelSize.currentIndexChanged.connect(self.model_load)
        self.runButton.clicked.connect(self.run)
        
        
        
    

    def retranslateUi(self, Dialog):
        _translate = QtCore.QCoreApplication.translate
        Dialog.setWindowTitle(_translate("Dialog", "Dialog"))
        self.runButton.setText(_translate("Dialog", "Go!"))
        self.select_files.setText(_translate("Dialog", "Select"))


if __name__ == "__main__":
    import sys
    app = QtWidgets.QApplication(sys.argv)
    Dialog = QtWidgets.QDialog()
    ui = Ui_Dialog()
    ui.setupUi(Dialog)
    Dialog.show()
    sys.exit(app.exec_())


  


  I compile this app with pyinstaller using the following code. I had some issues to begin with so I found other with similar problems and ended up with this :

  


  pyinstaller --onedir --hidden-import=pytorch --collect-data torch --copy-metadata torch --copy-metadata tqdm --copy-metadata tokenizers --copy-metadata importlib_metadata --hidden-import="sklearn.utils._cython_blas" --hidden-import="sklearn.neighbors.typedefs" --hidden-import="sklearn.neighbors.quad_tree" --hidden-import="sklearn.tree" --hidden-import="sklearn.tree._utils" --copy-metadata regex --copy-metadata requests --copy-metadata packaging --copy-metadata filelock --copy-metadata numpy --add-data "./ffmpeg/*;./ffmpeg/" --hidden-import=whisper --copy-metadata whisper --collect-data whisper minimal_example_whisper.py

  


  When I take the outputtet dist directory and try to run the app on another Windows machine without FFMPEG installed (or Whisper or any other things), I get the following error from the terminal as I push the "run" button in the app (otherwise the app does run).

  


  C:\Users\Søren>"G:\minimal_example_whisper\minimal_example_whisper.exe"
whisper\transcribe.py:70: UserWarning: FP16 is not supported on CPU; using FP32 instead
Traceback (most recent call last):
  File "minimal_example_whisper.py", line 45, in run
  File "whisper\transcribe.py", line 76, in transcribe
  File "whisper\audio.py", line 111, in log_mel_spectrogram
  File "whisper\audio.py", line 42, in load_audio
  File "ffmpeg\_run.py", line 313, in run
  File "ffmpeg\_run.py", line 284, in run_async
  File "subprocess.py", line 951, in __init__
  File "subprocess.py", line 1420, in _execute_child
FileNotFoundError: [WinError 2] Den angivne fil blev ikke fundet


  


  I suspect this has something to do with FFMPEG not being installed on the other machines system ? Does anyone have an automatic solution for this when compiling the app or can it simply only run on machines that has FFMPEG installed ?

  


  Thanks in advance !