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• FileNotFoundError : [Errno 2] No such file or directory : 'ffprobe' heroku

  23 avril 2022, par yesha

  I download the application to heroku, it crashes with the error FileNotFoundError : [Errno 2] No such file or directory : 'ffprobe'. I've tried everything already, I can't figure out how to solve the problem

  


  import json
import os
import random
import requests
from pydub import AudioSegment
 
def get_audio(pathes):
    AudioSegment.converter = "ffmpeg/bin/ffmpeg.exe"
    AudioSegment.ffmpeg = "ffmpeg/bin/ffmpeg.exe"
    AudioSegment.ffprobe = "ffmpeg/bin/ffprobe.exe"
    result_sound = AudioSegment.empty()
    for path in pathes:
        little_sound = AudioSegment.from_mp3(path)
        result_sound += little_sound
        result_sound.export(os.getcwd() + "\\result_audio.mp3", format="mp3")


def get_audio_url():
    url = "http://httpbin.org/post"
    file_path = os.getcwd() + "\\result_audio.mp3"
    response = requests.post(url, files={"audio": open(file_path, 'rb')})
    json_response = response.json()
    audio = json_response['files']['audio']
    return audio


  


  requirements :

  


  anyio==3.5.0
asgiref==3.5.0
certifi==2021.10.8
charset-normalizer==2.0.12
click==8.0.3
colorama==0.4.4
coverage==6.3.2
Django==4.0.4
dnspython==2.2.1
email-validator==1.1.3
ffprobe-python==1.0.3
Flask==2.0.2
gunicorn==20.1.0
h11==0.12.0
httpcore==0.13.7
httpx==0.18.2
idna==3.3
itsdangerous==2.0.1
Jinja2==3.0.3
MarkupSafe==2.0.1
pydantic==1.9.0
pydub==0.25.1
python-dateutil==2.8.2
pytz==2021.3
pyuploadcare==3.0.0
requests==2.27.1
rfc3986==1.5.0
six==1.16.0
sniffio==1.2.0
sqlparse==0.4.2
typing-extensions==3.10.0.2
tzdata==2022.1
urllib3==1.26.9
Werkzeug==2.0.3

ffprobe~=0.5


  


  Procfile :
web : gunicorn app:app

  


• ffmpeg H.264 to VP9 always creates larger output files on Zoom Meeting Recordings

  22 avril 2022, par Sam

  ffmpeg -i input.mp4 -c:v libvpx-vp9 -b:v 0 -crf 30 -pass 1 -an -f null -row-mt 1 NUL && ^
ffmpeg -i input.mp4 -c:v libvpx-vp9 -b:v 0 -crf 30 -pass 2 -c:a libopus -row-mt 1 output.webm


  


  input.mp4 file size is 197,007 KiB and output.webm is 197,132 KiB.

  


  For context, we have approx. 9TB of Zoom Meeting Recordings stored in AWS S3. My goal is to optimize the storage consumption by converting to VP9. I'm willing to sacrifice a bit of quality to optimize storage.

  


  I am seeing better results on some other (non-Zoom) H.264 files (reduced from 88MiB in H.264 to 11MiB in VP9) with the exact same ffmpeg options.

  


  Here's the details on the input file :

  


  Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'input.mp4':
  Metadata:
    major_brand     : mp42
    minor_version   : 0
    compatible_brands: isommp42
    creation_time   : 2022-03-17T16:20:43.000000Z
  Duration: 01:14:31.74, start: 0.000000, bitrate: 360 kb/s
  Chapters:
    Chapter #0:0: start 0.000000, end 755.480000
      Metadata:
        title           : Recording Started
    Chapter #0:1: start 755.480000, end 4465.840000
      Metadata:
        title           : Sharing Started
    Chapter #0:2: start 4465.840000, end 4471.720000
      Metadata:
        title           : Sharing Stopped
  Stream #0:0[0x1](und): Audio: aac (LC) (mp4a / 0x6134706D), 32000 Hz, mono, fltp, 126 kb/s (default)
    Metadata:
      creation_time   : 2022-03-17T16:20:43.000000Z
      handler_name    : AAC audio
      vendor_id       : [0][0][0][0]
  Stream #0:1[0x2](und): Video: h264 (High) (avc1 / 0x31637661), yuv420p(progressive), 1920x1040, 232 kb/s, 25 fps, 25 tbr, 30k tbn (default)
    Metadata:
      creation_time   : 2022-03-17T16:20:43.000000Z
      handler_name    : H.264/AVC video
      vendor_id       : [0][0][0][0]
      encoder         : AVC Coding
  Stream #0:2[0x3](und): Data: bin_data (text / 0x74786574)
    Metadata:
      creation_time   : 2022-03-17T16:20:43.000000Z
      handler_name    : Text


  


  Output file :

  


  Input #0, matroska,webm, from 'output.webm':
  Metadata:
    COMPATIBLE_BRANDS: isommp42
    MAJOR_BRAND     : mp42
    MINOR_VERSION   : 0
    ENCODER         : Lavf59.22.100
  Duration: 01:14:31.75, start: -0.003000, bitrate: 361 kb/s
  Chapters:
    Chapter #0:0: start 0.000000, end 755.480000
      Metadata:
        title           : Recording Started
    Chapter #0:1: start 755.480000, end 4465.840000
      Metadata:
        title           : Sharing Started
    Chapter #0:2: start 4465.840000, end 4471.720000
      Metadata:
        title           : Sharing Stopped
  Stream #0:0: Video: vp9 (Profile 0), yuv420p(tv, progressive), 1920x1040, SAR 1:1 DAR 24:13, 25 fps, 25 tbr, 1k tbn (default)
    Metadata:
      HANDLER_NAME    : H.264/AVC video
      VENDOR_ID       : [0][0][0][0]
      ENCODER         : Lavc59.26.100 libvpx-vp9
      DURATION        : 01:14:31.727000000
  Stream #0:1: Audio: opus, 48000 Hz, mono, fltp (default)
    Metadata:
      HANDLER_NAME    : AAC audio
      VENDOR_ID       : [0][0][0][0]
      ENCODER         : Lavc59.26.100 libopus
      DURATION        : 01:14:31.752000000


  


  I'm noticing that H.264/AVC is still listed in the metadata for Stream #0:0 in the output file, but I don't see this same detail on my successfully-reduced VP9 files.

  


• How to fix error FFmpeg "Error during encoding : failed to allocate memory (-4)"

  22 avril 2022, par SCAR101

  I have a problem with FFmpeg encoding. It can write for an hour or two hours or 5 minutes, and then he gives out error : [h264_qsv @ 000001a47094f340] Error during encoding: failed to allocate memory (-4). What am I doing wrong ? Here is the FFmpeg string :

  


  -y -f dshow -video_size 1920x1080 -rtbufsize 702000k -framerate 25 
-i video="Decklink Video Capture":audio="Decklink Audio Capture
-vf "drawtext=fontfile=C\\:\\\\Windows\\\\Fonts\\\\arial.ttf:fontsize=36:fontcolor=red:text='%{localtime}'" 
-s 720x540 -pix_fmt yuv420p -c:v h264_qsv 
-preset veryfast -b:v 512k -acodec libmp3lame 
-b:a 96k -ac 2 -ar 44100 D:\Input_1\2022\4\20\Input_1_20.04.2022_08.16.31.mkv


  


  Computer Specifications : Core I5 11600, 16Gb.