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La file d’attente de SPIPmotion
28 novembre 2010, parUne file d’attente stockée dans la base de donnée
Lors de son installation, SPIPmotion crée une nouvelle table dans la base de donnée intitulée spip_spipmotion_attentes.
Cette nouvelle table est constituée des champs suivants : id_spipmotion_attente, l’identifiant numérique unique de la tâche à traiter ; id_document, l’identifiant numérique du document original à encoder ; id_objet l’identifiant unique de l’objet auquel le document encodé devra être attaché automatiquement ; objet, le type d’objet auquel (...) -
Les vidéos
21 avril 2011, parComme les documents de type "audio", Mediaspip affiche dans la mesure du possible les vidéos grâce à la balise html5 .
Un des inconvénients de cette balise est qu’elle n’est pas reconnue correctement par certains navigateurs (Internet Explorer pour ne pas le nommer) et que chaque navigateur ne gère en natif que certains formats de vidéos.
Son avantage principal quant à lui est de bénéficier de la prise en charge native de vidéos dans les navigateur et donc de se passer de l’utilisation de Flash et (...) -
Websites made with MediaSPIP
2 mai 2011, parThis page lists some websites based on MediaSPIP.
Sur d’autres sites (6832)
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How to add watermark with delay in FFmpeg
28 décembre 2022, par Мохамед РуслановичAm trying to put a gif file in all the edges of the video. So am using this command


ffmpeg -y -i film.mp4 -stream_loop -1 -i gif.gif -filter_complex \ 
"[1]colorchannelmixer=aa=0.8,scale=iw*1:-1[a];[0][a]overlay=
x='if(lt(mod(t\,16)\,8)\,W-w-W*10/200\,W*10/100)':
y='if(lt(mod(t+4\,16)\,8)\,H-h-H*5/200\,H*5/200)':
shortest=1" 
 -acodec copy output_task_3.mp4



But now am required to put the gif file on each edge with delay of 10 minutes


For exmaple :


1- render gif at top left for only 16 seconds.
Then wait 10 minutes


2- render gif at top right for 16 seconds.
Wait 10 minutes


3- rendder gif at right buttom for 16 seconds.
Wait 10 minutes


4- render gif at left buttom for 16 secnods.
Wait 10 minutes


This scenario should be repete till the movie ends.


How can i archive this ?


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How to do filter twice at different time ffmpeg
29 décembre 2022, par Мохамед РуслановичAm trying to implement overlay twice but at different position and different time
Here is what am trying to do :
i just duplicate the filter


ffmpeg -t 50 -y -i film.mp4 -stream_loop -1 -i gif.gif -filter_complex "


[1]colorchannelmixer=aa=1,scale=iw*2:-1[a];[0][a]overlay=x='200':y='300':shortest=1:enable='between(t,0,10)';

[1]colorchannelmixer=aa=1,scale=iw*2:-1[b];[0][b]overlay=x='200':y='300':shortest=1:enable='between(t,15,20)'" 

-acodec copy output_task_3.mp4



But only the first overlay is been implemented, the seconds is not !


how to archive this ?


Now i wrote a PHP script that dose this filter once each time, and repeat proccess then merge all videos, but this is taking so long.


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Computer crashing when using python tools in same script
5 février 2023, par SL1997I 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 :




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.