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• FFmpeg encoding slow for 4K HDR content

  1er mars 2023, par Geno Diaz

  When processing 4K input with the following configuration it is taking upwards of 2 minutes to process a 35s, 60fps, 4K HDR clip recorded from an iPhone. Is this the expected performance or is there an inefficiency within the configuration that is causing this ?

  


  In comparison, running this configuration on a 35s, 30fps, 4K non-HDR clip, only takes about 20 seconds.

  


  ffmpeg 
-i "input path" 
-y 
-filter:v scale=w=2160:h=3840 
-threads 4 
-r 59.94 
-c:v libx264 
-preset veryfast 
-vsync 1 
-tune film 
-maxrate 6000k 
-bufsize 5400k 
-g 60 
-x264opts no-scenecut 
-c:a aac 
-af aresample=async=1:min_hard_comp=0.100000:first_pts=0 
-ac 2 
-b:a 128k 
-ar 44100 
-vf zscale=transfer=linear:npl=100,
  format=gbrpf32le,
  zscale=primaries=bt709,
  tonemap=tonemap=hable:desat=0,
  zscale=transfer=bt709:matrix=bt709:range=tv,
  format=yuv420p 
-sws_flags full_chroma_int+full_chroma_inp 
-pix_fmt yuv420p 
"outputfile".mp4


  


• 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.

  


• How to do filter twice at different time ffmpeg

  29 décembre 2022, par Мохамед Русланович

  Am 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.