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Les tâches Cron régulières de la ferme
1er décembre 2010, parLa gestion de la ferme passe par l’exécution à intervalle régulier de plusieurs tâches répétitives dites Cron.
Le super Cron (gestion_mutu_super_cron)
Cette tâche, planifiée chaque minute, a pour simple effet d’appeler le Cron de l’ensemble des instances de la mutualisation régulièrement. Couplée avec un Cron système sur le site central de la mutualisation, cela permet de simplement générer des visites régulières sur les différents sites et éviter que les tâches des sites peu visités soient trop (...) -
Other interesting software
13 avril 2011, parWe don’t claim to be the only ones doing what we do ... and especially not to assert claims to be the best either ... What we do, we just try to do it well and getting better ...
The following list represents softwares that tend to be more or less as MediaSPIP or that MediaSPIP tries more or less to do the same, whatever ...
We don’t know them, we didn’t try them, but you can take a peek.
Videopress
Website : http://videopress.com/
License : GNU/GPL v2
Source code : (...) -
Supporting all media types
13 avril 2011, parUnlike most software and media-sharing platforms, MediaSPIP aims to manage as many different media types as possible. The following are just a few examples from an ever-expanding list of supported formats : images : png, gif, jpg, bmp and more audio : MP3, Ogg, Wav and more video : AVI, MP4, OGV, mpg, mov, wmv and more text, code and other data : OpenOffice, Microsoft Office (Word, PowerPoint, Excel), web (html, CSS), LaTeX, Google Earth and (...)
Sur d’autres sites (10971)
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Start and end time of MoviePy's VideoClip not working
21 mars 2024, par ernesto casco velazquezI'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 ?


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ffmpeg silenceremove - hear what bits are removed
7 avril 2020, par jimoffmpeg silenceremove is pretty cool. im loving it. i can trim 3 second silences to 2 seconds and reduce a 1.5 hour file of spoken audio down 3 or 4 minutes (depending on the speaker).



once in a while I do hear my choice for stop_threshold (ie-40dB on audio only analog file) does cause the end of a word to be clipped, just here and there when the speaker trails off softly at the end of the word.



is there any way to output what is trimmed to a file ? so I can listen to it and get an idea of just how often this word clipping happens ?



thanks !


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arm : fmtconvert : Split armv6 fmtconvert code off from vfp code
23 août 2013, par Diego Biurrun