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• FFMPEG, DrawText Issue in Live Stream

  7 décembre 2022, par Kenneth

  I am using the following command to create an H264 stream with text data from a text file. Example data is fake. I am sending this to an RTSP server that then allows clients to connect. I am connecting from VLC to view the stream.

  


  See update, this only happens for the live stream. If I output to file, it looks correct.

  


  OS : Windows 10

  


  ffmpeg -f lavfi -re -i color=size=1280x720:rate=1:color=black ^
  -vf drawtext="fontsize=16:fontfile=C\\:/Windows/fonts/consola.ttf:fontcolor=white:textfile='livetext.txt':x=50:y=50: reload=1" ^
  -c:v libx264 -preset ultrafast -tune zerolatency -x264-params keyint=10:min-keyint=10 ^
  -f rtsp rtsp://127.0.0.1:60000/sorting


  


  The issue I am having is that the text shown in the video seems to be limited to 10 rows. On a fresh restart, I get even less. I don't see anything mentioned in the documentation about a limitation on length.

  


  I have tried different -preset and -tune options. Nothing improves this issue.

  


  Are there settings I should adjust to help this ?

  


  

  


  Console Output :

  


  ..\ffmpeg\ffmpeg -f lavfi -re -i color=size=1280x720:rate=5:color=black -vf drawtext="fontsize=20:fontfile=C\\:/Windows/fonts/consola.ttf:fontcolor=white:textfile='livetext.txt':x=50:y=50: reload=5"  -c:v libx264 -preset ultrafast -tune zerolatency -x264-params keyint=10:min-keyint=10  -f rtsp rtsp://127.0.0.1:60000/sorting
ffmpeg version 2022-12-04-git-6c814093d8-essentials_build-www.gyan.dev Copyright (c) 2000-2022 the FFmpeg developers
  built with gcc 12.1.0 (Rev2, Built by MSYS2 project)
  configuration: --enable-gpl --enable-version3 --enable-static --disable-w32threads --disable-autodetect --enable-fontconfig --enable-iconv --enable-gnutls --enable-libxml2 --enable-gmp --enable-bzlib --enable-lzma --enable-zlib --enable-libsrt --enable-libssh --enable-libzmq --enable-avisynth --enable-sdl2 --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxvid --enable-libaom --enable-libopenjpeg --enable-libvpx --enable-mediafoundation --enable-libass --enable-libfreetype --enable-libfribidi --enable-libvidstab --enable-libvmaf --enable-libzimg --enable-amf --enable-cuda-llvm --enable-cuvid --enable-ffnvcodec --enable-nvdec --enable-nvenc --enable-d3d11va --enable-dxva2 --enable-libvpl --enable-libgme --enable-libopenmpt --enable-libopencore-amrwb --enable-libmp3lame --enable-libtheora --enable-libvo-amrwbenc --enable-libgsm --enable-libopencore-amrnb --enable-libopus --enable-libspeex --enable-libvorbis --enable-librubberband
  libavutil      57. 43.100 / 57. 43.100
  libavcodec     59. 54.100 / 59. 54.100
  libavformat    59. 34.102 / 59. 34.102
  libavdevice    59.  8.101 / 59.  8.101
  libavfilter     8. 51.100 /  8. 51.100
  libswscale      6.  8.112 /  6.  8.112
  libswresample   4.  9.100 /  4.  9.100
  libpostproc    56.  7.100 / 56.  7.100
Input #0, lavfi, from 'color=size=1280x720:rate=5:color=black':
  Duration: N/A, start: 0.000000, bitrate: N/A
  Stream #0:0: Video: wrapped_avframe, yuv420p, 1280x720 [SAR 1:1 DAR 16:9], 5 fps, 5 tbr, 5 tbn
Stream mapping:
  Stream #0:0 -> #0:0 (wrapped_avframe (native) -> h264 (libx264))
Press [q] to stop, [?] for help
[libx264 @ 000002402dcca140] using SAR=1/1
[libx264 @ 000002402dcca140] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2
[libx264 @ 000002402dcca140] profile Constrained Baseline, level 3.1, 4:2:0, 8-bit
[libx264 @ 000002402dcca140] 264 - core 164 r3101 b093bbe - H.264/MPEG-4 AVC codec - Copyleft 2003-2022 - http://www.videolan.org/x264.html - options: cabac=0 ref=1 deblock=0:0:0 analyse=0:0 me=dia subme=0 psy=1 psy_rd=1.00:0.00 mixed_ref=0 me_range=16 chroma_me=1 trellis=0 8x8dct=0 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=0 threads=11 lookahead_threads=11 sliced_threads=1 slices=11 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=0 weightp=0 keyint=10 keyint_min=6 scenecut=0 intra_refresh=0 rc=crf mbtree=0 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=0
Output #0, rtsp, to 'rtsp://127.0.0.1:60000/sorting':
  Metadata:
    encoder         : Lavf59.34.102
  Stream #0:0: Video: h264, yuv420p(progressive), 1280x720 [SAR 1:1 DAR 16:9], q=2-31, 5 fps, 90k tbn
    Metadata:
      encoder         : Lavc59.54.100 libx264
    Side data:
      cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A
frame=  485 fps=5.0 q=11.0 size=N/A time=00:01:36.80 bitrate=N/A speed=   1x     0x


  


  Update 1 :

  


  If I output to a file, the text is shown correctly (shown below). If I take out the -re argument for the output to match input rate, I get 150fps, so processing power does not seem to be the issue.

  


  

  


• android exoplayer add ffmpeg extension to my project how ?

  7 février 2023, par Malo

  I have an android application containing exoplayer instance, some udp video play without sounds , so i want to add Ffmpeg extension to my project, i am working on windows system and need to follow the instructions below :

  


  https://github.com/google/ExoPlayer/blob/release-v2/extensions/ffmpeg/README.md


  


  So first step is Set the following shell variable :
cd ""
FFMPEG_MODULE_PATH="$(pwd)/extensions/ffmpeg/src/main"

  


  i downloaded Git to use as power shell, so what is pwd ??

  


  PLus...
Set the host platform (use "darwin-x86_64" for Mac OS X) :
HOST_PLATFORM="linux-x86_64" what is this variable in windows ?

  


  Please i am confused how to build this library manually in windows and it is not straightforward at all....

  


• 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.