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• How to sell Piwik services without any confusion ?

  10 octobre 2017, par InnoCraft — Plugins

  As you may know, Piwik is a Free software under the GPL license which guarantees you :

  • The freedom to run the program for any purpose.
  • The freedom to study how it works and change it.
  • The freedom to improve the program, and release your changes.
  • The freedom to redistribute it under the GPL license, and to sell it if you wish.

  In this article we will focus on the Free aspect of Piwik, which is how to rebrand Piwik, how to offer your clients a better experience, and possibly how to make a profit from it ?

  How to sell Piwik services as an agency ?

  As a web analytics software, Piwik is often installed by web agencies when it comes to designing a brand new website for a given customer.
  Most of the time agencies are using Piwik for the following reasons :

  • free of charge
  • data ownership
  • user privacy compliance
  • feature rich while easy to use
  • open source

  Most of the agencies are charging their customers for the installation process, tracking code implementation, analysing reports to get insights about users… but do you know that you could also sell the software as your own brand ? This is where the “White Label” plugin, developed by the InnoCraft company, comes into play.

  White labelling for Piwik

  Creating a “white label” plugin came into the mind of InnoCraft founders when they realized that on any modern Piwik installation, the following components were visible :

  • Piwik branded widgets within the dashboards
  • Piwik marketplace plugin teasers on the admin page
  • Piwik help and support pages
  • the “Piwik” word in general
  • Piwik Mobile app banners

  

  Example of Piwik branded widgets

  In order to remove all those mentions of Piwik and to start selling this web analytics under your own name, you can either hack Piwik on your own (it is going to take you some precious time and money) or have a look at the White Label plugin on the marketplace where InnoCraft has solved all the challenges already for you.

  The White Label plugin is straightforward. Once downloaded and installed, you will have access to a dedicated interface where you will be able to change the Piwik name by a new custom brand of your choice :

  

  Piwik White Label settings

  Once you click Save, all “Piwik” mentions will be substituted by your company name/service :

  

  Here the Piwik version is changed by the name of the company

  How to make your installation even more customized ?

  Few Piwik users know about this trick, but since 2014 the Piwik templates can be customized through Themes. You are free to design your own template, installing existing ones, or even monetize them through the marketplace :

  

  A simple example of how Piwik can be easily customized, here fonts and colours are changed

  If you want to know how you can tweak your existing template and make it match your brand and image, just follow our theme documentation. A simple theme with your colors can be built in a few minutes simply by defining different color codes. You can also browse the public themes on the Marketplace.

  Get White Label for Piwik

  Tell us your story

  If you are an agency or any business related in selling Piwik services, we recommend having a look at our FAQ for rebranding, selling, reusing, re-licensing, and including Piwik in my offering. Are you interested or already re-selling Piwik services ? We would love to hear your story and write a blog post about it.

  Write for Piwik

  Do not hesitate to contact the Piwik core team, we’re looking forward to hearing from you.

• Computer crashing when using python tools in same script

  5 février 2023, par SL1997

  I am attempting to use the speech recognition toolkit VOSK and the speech diarization package Resemblyzer to transcibe audio and then identify the speakers in the audio.

  


  Tools :

  


  https://github.com/alphacep/vosk-api
  
https://github.com/resemble-ai/Resemblyzer

  


  I can do both things individually but run into issues when trying to do them when running the one python script.

  


  I used the following guide when setting up the diarization system :

  


  https://medium.com/saarthi-ai/who-spoke-when-build-your-own-speaker-diarization-module-from-scratch-e7d725ee279

  


  Computer specs are as follows :

  


  Intel(R) Core(TM) i3-7100 CPU @ 3.90GHz, 3912 Mhz, 2 Core(s), 4 Logical Processor(s)
  
32GB RAM

  


  The following is my code, I am not to sure if using threading is appropriate or if I even implemented it correctly, how can I best optimize this code as to achieve the results I am looking for and not crash.

  


  from vosk import Model, KaldiRecognizer
from pydub import AudioSegment
import json
import sys
import os
import subprocess
import datetime
from resemblyzer import preprocess_wav, VoiceEncoder
from pathlib import Path
from resemblyzer.hparams import sampling_rate
from spectralcluster import SpectralClusterer
import threading
import queue
import gc



def recognition(queue, audio, FRAME_RATE):

    model = Model("Vosk_Models/vosk-model-small-en-us-0.15")

    rec = KaldiRecognizer(model, FRAME_RATE)
    rec.SetWords(True)

    rec.AcceptWaveform(audio.raw_data)
    result = rec.Result()

    transcript = json.loads(result)#["text"]

    #return transcript
    queue.put(transcript)



def diarization(queue, audio):

    wav = preprocess_wav(audio)
    encoder = VoiceEncoder("cpu")
    _, cont_embeds, wav_splits = encoder.embed_utterance(wav, return_partials=True, rate=16)
    print(cont_embeds.shape)

    clusterer = SpectralClusterer(
        min_clusters=2,
        max_clusters=100,
        p_percentile=0.90,
        gaussian_blur_sigma=1)

    labels = clusterer.predict(cont_embeds)

    def create_labelling(labels, wav_splits):

        times = [((s.start + s.stop) / 2) / sampling_rate for s in wav_splits]
        labelling = []
        start_time = 0

        for i, time in enumerate(times):
            if i > 0 and labels[i] != labels[i - 1]:
                temp = [str(labels[i - 1]), start_time, time]
                labelling.append(tuple(temp))
                start_time = time
            if i == len(times) - 1:
                temp = [str(labels[i]), start_time, time]
                labelling.append(tuple(temp))

        return labelling

    #return
    labelling = create_labelling(labels, wav_splits)
    queue.put(labelling)



def identify_speaker(queue1, queue2):

    transcript = queue1.get()
    labelling = queue2.get()

    for speaker in labelling:

        speakerID = speaker[0]
        speakerStart = speaker[1]
        speakerEnd = speaker[2]

        result = transcript['result']
        words = [r['word'] for r in result if speakerStart < r['start'] < speakerEnd]
        #return
        print("Speaker",speakerID,":",' '.join(words), "\n")





def main():

    queue1 = queue.Queue()
    queue2 = queue.Queue()

    FRAME_RATE = 16000
    CHANNELS = 1

    podcast = AudioSegment.from_mp3("Podcast_Audio/Film-Release-Clip.mp3")
    podcast = podcast.set_channels(CHANNELS)
    podcast = podcast.set_frame_rate(FRAME_RATE)

    first_thread = threading.Thread(target=recognition, args=(queue1, podcast, FRAME_RATE))
    second_thread = threading.Thread(target=diarization, args=(queue2, podcast))
    third_thread = threading.Thread(target=identify_speaker, args=(queue1, queue2))

    first_thread.start()
    first_thread.join()
    gc.collect()

    second_thread.start()
    second_thread.join()
    gc.collect()

    third_thread.start()
    third_thread.join()
    gc.collect()

    # transcript = recognition(podcast,FRAME_RATE)
    #
    # labelling = diarization(podcast)
    #
    # print(identify_speaker(transcript, labelling))


if __name__ == '__main__':
    main()


  


  When I say crash I mean everything freezes, I have to hold down the power button on the desktop and turn it back on again. No blue/blank screen, just frozen in my IDE looking at my code. Any help in resolving this issue would be greatly appreciated.

  


• Trolls in trouble

  6 juin 2013, par Mans — Law and liberty

  Life as a patent troll is hopefully set to get more difficult. In a memo describing patent trolls as a “drain on the American economy,” the White House this week outlined a number of steps it is taking to stem this evil tide. Chiming in, the Chief Judge of the Court of Appeals for the Federal Circuit (where patent cases are heard) in a New York Times op-ed laments the toll patent trolling is taking on the industry, and urges judges to use powers already at their disposal to make the practice less attractive. However, while certainly a step in the right direction, these measures all fail to address the more fundamental properties of the patent system allowing trolls to exist in the first place.

  System and method for patent trolling

  Most patent trolling operations comprise the same basic elements :

  1. One or more patents with broad claims.
  2. The patents of (1) acquired by an otherwise non-practising entity (troll).
  3. The entity of (2) filing numerous lawsuits alleging infringement of the patents of (1).
  4. The lawsuits of (3) targeting end users or retailers.
  5. The lawsuits of (3) listing as plaintiffs difficult to trace shell companies.

  The recent legislative actions all take aim at the latter entries in this list. In so doing, they will no doubt cripple the trolls, but the trolls will remain alive, ready to resume their wicked ways once a new loophole is found in the system.

  To kill a patent troll

  As Judge Rader and his co-authors point out in the New York Times, “the problem stems largely from the fact that, [...] trolls have an important strategic advantage over their adversaries : they don’t make anything.” This is the heart of the troll, and this is where the blow should be struck. Our weapon shall be the mightiest judicial sword of all, the Constitution.

  The United States Constitution contains (in Article I, Section 8) the foundation for the patent system (emphasis mine) :

  The Congress shall have Power [...] To promote the Progress of Science and useful Arts, by securing for limited Times to Authors and Inventors the exclusive Right to their respective Writings and Discoveries.

  Patent trolls are typically not inventors. They are merely hoarders of other people’s discarded inventions, and that allowing others to reap the benefits of an inventor’s work would somehow promote progress should be a tough argument. Indeed, it is the dissociation between investment and reward which has allowed the patent trolls to rise and prosper.

  In light of the above, the solution to the troll menace is actually strikingly simple : make patents non-transferable.

  Having the inventor retain the rights to his or her inventions (works for hire still being recognised), would render the establishment of non-practising entities, which most trolls are, virtually impossible. The original purpose of patents, to protect the investment of inventors, would remain unaffected, if not strengthened, by such a change.

  Links

  • Taking on Patent Trolls to Protect American Innovation
  • Fact Sheet : White House Task Force on High-Tech Patent Issues
  • Make Patent Trolls Pay in Court
  • Constitution of the United States