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• How to make your plugin multilingual – Introducing the Piwik Platform

  29 octobre 2014, par Thomas Steur — Development

  This is the next post of our blog series where we introduce the capabilities of the Piwik platform (our previous post was Generating test data – Introducing the Piwik Platform). This time you’ll learn how to equip your plugin with translations. Users of your plugin will be very thankful that they can use and translate the plugin in their language !

  Getting started

  In this post, we assume that you have already set up your development environment and created a plugin. If not, visit the Piwik Developer Zone where you’ll find the tutorial Setting up Piwik and other Guides that help you to develop a plugin.

  Managing translations

  Piwik is available in over 50 languages and comes with many translations. The core itself provides some basic translations for words like “Visitor” and “Help”. They are stored in the directory /lang. In addition, each plugin can provide its own translations for wordings that are used in this plugin. They are located in /plugins/*/lang. In those directories you’ll find one JSON file for each language. Each language file consists in turn of tokens that belong to a group.

  {
  
      "MyPlugin":{
  
          "BlogPost": "Blog post",
  
          "MyToken": "My translation",
  
          "InteractionRate": "Interaction Rate"
  
      }
  
  }

  A group usually represents the name of a plugin, in this case “MyPlugin”. Within this group, all the tokens are listed on the left side and the related translations on the right side.

  Building a translation key

  As you will later see to actually translate a word or a sentence you’ll need to know the corresponding translation key. This key is built by combining a group and a token separated by an underscore. You can for instance use the key MyPlugin_BlogPost to get a translation of “Blog post”. Defining a new key is as easy as adding a new entry to the “MyPlugin” group.

  Providing default translations

  If a translation cannot be found then the English translation will be used as a default. Therefore, you should always provide a default translation in English for all keys in the file en.json (ie, /plugins/MyPlugin/lang/en.json).

  Adding translations for other languages

  This is as easy as creating new files in the lang subdirectory of your plugin. The filename consists of a 2 letter ISO 639-1 language code completed by the extension .json. This means German translations go into a file named de.json, French ones into a file named fr.json. To see a list of languages you can use have a look at the /lang directory.

  Reusing translations

  As mentioned Piwik comes with quite a lot of translations. You can and should reuse them but you are supposed to be aware that a translation key might be removed or renamed in the future. It is also possible that a translation key was added in a recent version and therefore is not available in older versions of Piwik. We do not currently announce any of such changes. Still, 99% of the translation keys do not change and it is therefore usually a good idea to reuse existing translations. Especially when you or your company would otherwise not be able to provide them. To find any existing translation keys go to Settings => Translation search in your Piwik installation. The menu item will only appear if the development mode is enabled.

  Translations in PHP

  Use the Piwik::translate() function to translate any text in PHP. Simply pass any existing translation key and you will get the translated text in the language of the current user in return. The English translation will be returned in case none for the current language exists.

  $translatedText = Piwik::translate('MyPlugin_BlogPost');

  Translations in Twig Templates

  To translate text in Twig templates, use the translate filter.

  {{ 'MyPlugin_BlogPost'|translate }}

  Contributing translations to Piwik

  Did you know you can contribute translations to Piwik ? In case you want to improve an existing translation, translate a missing one or add a new language go to Piwik Translations and sign up for an account. You won’t need any knowledge in development to do this.

  Advanced features

  Of course there are more useful things you can do with translations. For instance you can use placeholders like %s in your translations and you can use translations in JavaScript as well. In case you want to know more about those topics check out our Internationalization guide. Currently, this guide only covers translations but we will cover more topics like formatting numbers and handling currencies in the future.

  Congratulations, you have learnt how to make your plugin multilingual !

  If you have any feedback regarding our APIs or our guides in the Developer Zone feel free to send it to us.

• 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.

  


• Death of A Micro Center

  21 septembre 2012, par Multimedia Mike — History

  The Micro Center computer store located in Santa Clara, CA, USA closed recently :

  
  
  

  I liked Micro Center. I have liked Micro Center ever since I first visited their Denver, CO location 10 years ago. I would sometimes drive an hour in each direction just to visit that shop. I was excited to see that they had a location in the Bay Area when I moved here a few years ago (despite the preponderance of Fry’s stores).

  Now this location is gone. I wonder how much of the “we couldn’t come to favorable terms on a lease” was true (vs. an excuse to close a retail store at a time when more business is moving online, particularly in the heart of Silicon Valley). But that’s not what I wanted to discuss. I came here to discuss…

  The Micro Center Window Logos

  The craziest part about shopping the Santa Clara Micro Center location was the logos they displayed on the window outside. Every time I saw it, it made me sentimental for a time when some of these logos were current, or when some of these companies were still in business. Some of the logos on their front window were for companies I’ve never heard of. It reminds me of the nearby 7-11 convenience stores when I was growing up– their walls were decorated with people sporting embarrassingly 1970s styles long after the 1970s had transpired.

  I thought I would record what those front window logos were and try to pinpoint when the store launched exactly (assuming the logos have been their since the initial opening and never changed).

  
  
  Click for larger image
  

  Here we have Lotus, Hewlett Packard/HP, Corel, Fuji, Power Macintosh, NEC, and Fujitsu. Lotus was purchased by IBM in 1995 and still seems to be maintained as a separate brand. The Power Macintosh was introduced as a brand in 1994. Corel’s logo has seen a few mutations over the years but I don’t know when this one fell out of favor.

  Fuji (vs. Fujitsu) appears to refer to Fujifilm, though this logo is also obsolete.

  
  
  Click for larger image
  

  Hayes– I specifically remember reading the Slashdot post accouncing that Hayes is dead (followed by many comments reminiscing about the Hayes command set). Here is the post, from early 1999.

  From Googling, it doesn’t appear IBM still has a presence in the consumer computing space (though they do have something pertaining to software for consumer products). Then there’s the good old rainbow Apple logo, something that went away in 1997. I suspect 1997 was also the last hurrah of the name ‘Macintosh’ (though I remember mistakenly referring to Apple computer products as Macintoshes well into the mid-2000s and inadvertently angering some Apple enthusiasts).

  
  
  Click for larger image
  

  As for the next segment, obviously, both Sony and Toshiba are still very much alive. Iomega was acquired by EMC in 2008 but is still maintained as a separate brand. USRobotics is still around and making — what else ? — 56K modems (and their current logo is slightly different than the one seen here).

  Targus seems to be a case maker (“Leading Provider of Cases, Bags and Accessories for Laptops and Tablets”). I wonder if that’s just their current business or if they had more areas long ago ? It seems strange that they would get brand billing like this.

  Finally, searching for information about Practical Peripherals only produces sites about how they’re long dead (like this history lesson). It’s unclear when they died.

  The interior of this store was also decorated with more technology company logos near the ceiling (I didn’t really register that fact until I had visited many times). Regrettably, I now won’t be able to see how up to date those logos were.

  Based on the data points above, it’s safe to conclude that the store opened between 1995 or 1996 (again, assuming the logos were placed at opening and never changed).

  Epilogue

  Here’s one more curious item still visible from the outside :

  
  
  

  “See the world’s fastest PC !” Featuring an Intel Core 2 Extreme ? That CPU dates back to 2007 and was succeeded by Nehalem in late 2008. So even that sign, which is presumably easier and cleaner to replace than the window logos, was absurdly out of date.