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• [Turmux][ffmpeg] encounter "can't open stat file" when use "-pass"

  15 February 2020, by Jackal Zhao

  I installed ffmpeg in turmux, in my home directory, ran:

  ffmpeg -y -i /sdcard/FFMPEG/in.mp4 -c:v libx264 -b:v 2600k -pass 2 -c:a copy /sdcard/FFMPEG/out3.mp4

  the error bumps:

  ratecontrol_init: can't open stats file

  if I enter /sdcard/FFMEPG folder first, then run
  ffmpeg -y -i /sdcard/FFMPEG/in.mp4 -c:v libx264 -b:v 2600k -pass 2 -c:a copy /sdcard/FFMPEG/out3.mp4
  the task will run successfully.

  then I went back to turmux home directory, ran
  ffmpeg -y -i /sdcard/FFMPEG/in.mp4 -c:v libx264 -b:v 2600k -pass 2 -c:a copy /sdcard/FFMPEG/out3.mp4
  , error still happens.

  so I then added -strict -2 -passlogfile /tmp/mydummy and ran ran
  ffmpeg -y -i /sdcard/FFMPEG/in.mp4 -c:v libx264 -strict -2 -passlogfile /sdcard/FFMPEG/ -b:v 2600k -pass 2 -c:a copy /sdcard/FFMPEG/out3.mp4
  , and same error still happens.

  My question is, how to use ffmpeg "-pass" in termux successfully without especially entering the target folder?

• Unable to open audio file on Heroku using Librosa

  15 March 2020, by Rohan Bojja

  I have a feature extraction REST API written in Python using the Librosa library (Extracting audio features), it receives an audio file through HTTP POST and responds with a list of features(such as MFCC,etc).

  Since librosa depends on SoundFile (libsndfile1 / libsndfile-dev), it doesn’t support all the formats, I’m converting the audio file using ffmpeg-python wrapper (https://kkroening.github.io/ffmpeg-python/) .

  It works just fine on my Windows 10 machine with Conda, but when I deploy it on Heroku, the librosa.load() functions returns an unknown format error, no matter what format I convert it to. I have tried FLAC, AIFF and WAV.

  My first guess is that the converted format isn’t supported by libsndfile1, but it works on my local server (plus, their documentation says AIFF and WAV are supported), so I’m a little lost.

  I have attached all the relevant snippets of code below, I can provide anything extra if necessary. Any help is highly appreciated. Thanks.

  UPDATE1:

  I am using pipes instead of writing and reading from disk, worth a mention as the question could be misleading otherwise.

  The log:

  File "/app/app.py", line 31, in upload
  
  x , sr = librosa.load(audioFile,mono=True,duration=5)
  
  File "/app/.heroku/python/lib/python3.6/site-packages/librosa/core/audio.py", line 164, in load
  
  six.reraise(*sys.exc_info())
  
  File "/app/.heroku/python/lib/python3.6/site-packages/six.py", line 703, in reraise
  
  raise value
  
  File "/app/.heroku/python/lib/python3.6/site-packages/librosa/core/audio.py", line 129, in load
  
  with sf.SoundFile(path) as sf_desc:
  
  File "/app/.heroku/python/lib/python3.6/site-packages/soundfile.py", line 629, in __init__
  
  self._file = self._open(file, mode_int, closefd)
  
  File "/app/.heroku/python/lib/python3.6/site-packages/soundfile.py", line 1184, in _open
  
  "Error opening {0!r}: ".format(self.name))
  
  File "/app/.heroku/python/lib/python3.6/site-packages/soundfile.py", line 1357, in _error_check
  
  raise RuntimeError(prefix + _ffi.string(err_str).decode('utf-8', 'replace'))
  
  RuntimeError: Error opening <_io.BytesIO object at 0x7f46ad28beb8>: File contains data in an unknown format.
  
  10.69.244.94 - - [15/Mar/2020:12:37:28 +0000] "POST /receiveWav HTTP/1.1" 500 290 "-" "curl/7.55.1"

  Flask/Librosa code deployed on Heroku (app.py):

  from flask import Flask, jsonify, request
  
  import scipy.optimize
  
  import os,pickle
  
  import numpy as np
  
  from sklearn.preprocessing import StandardScaler
  
  import librosa
  
  import logging
  
  import soundfile as sf
  
  from pydub import AudioSegment
  
  import subprocess as sp
  
  import ffmpeg
  
  from io import BytesIO
  
  
  
  logging.basicConfig(level=logging.DEBUG)
  
  
  
  app = Flask(__name__) 
  
  
  
  @app.route('/receiveWav',methods = ['POST'])
  
  def upload():
  
      if(request.method == 'POST'):
  
          f = request.files['file']
  
          app.logger.info(f'AUDIO FORMAT\n\n\n\n\n\n\n\n\n\n: {f}')
  
          proc = (
  
              ffmpeg.input('pipe:')
  
              .output('pipe:', format='aiff')
  
              .run_async(pipe_stdin=True,pipe_stdout=True, pipe_stderr=True)
  
          )
  
          audioFile,err = proc.communicate(input=f.read())
  
          audioFile =  BytesIO(audioFile)
  
          scaler = pickle.load(open("scaler.ok","rb"))
  
          x , sr = librosa.load(audioFile,mono=True,duration=5)
  
          y=x
  
          #Extract the features
  
          chroma_stft = librosa.feature.chroma_stft(y=y, sr=sr)
  
          spec_cent = librosa.feature.spectral_centroid(y=y, sr=sr)
  
          spec_bw = librosa.feature.spectral_bandwidth(y=y, sr=sr)
  
          rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr)
  
          zcr = librosa.feature.zero_crossing_rate(y)
  
          rmse = librosa.feature.rms(y=y)
  
          mfcc = librosa.feature.mfcc(y=y, sr=sr)
  
          features = f'{np.mean(chroma_stft)} {np.mean(rmse)} {np.mean(spec_cent)} {np.mean(spec_bw)} {np.mean(rolloff)} {np.mean(zcr)}'    
  
          for e in mfcc:
  
              features += f' {np.mean(e)}'
  
          input_data2 = np.array([float(i) for i in features.split(" ")]).reshape(1,-1)
  
          input_data2 = scaler.transform(input_data2)
  
          return jsonify(input_data2.tolist())
  
  
  
  # driver function 
  
  if __name__ == '__main__':   
  
      app.run(debug = True) 

  Aptfile:

  libsndfile1
  
  libsndfile-dev
  
  libav-tools
  
  libavcodec-extra-53 
  
  libavcodec-extra-53
  
  ffmpeg 

  requirements.txt:

  aniso8601==8.0.0
  
  audioread==2.1.8
  
  certifi==2019.11.28
  
  cffi==1.14.0
  
  Click==7.0
  
  decorator==4.4.2
  
  ffmpeg-python==0.2.0
  
  Flask==1.1.1
  
  Flask-RESTful==0.3.8
  
  future==0.18.2
  
  gunicorn==20.0.4
  
  itsdangerous==1.1.0
  
  Jinja2==2.11.1
  
  joblib==0.14.1
  
  librosa==0.7.2
  
  llvmlite==0.31.0
  
  MarkupSafe==1.1.1
  
  marshmallow==3.2.2
  
  numba==0.48.0
  
  numpy==1.18.1
  
  pycparser==2.20
  
  pydub==0.23.1
  
  pytz==2019.3
  
  resampy==0.2.2
  
  scikit-learn==0.22.2.post1
  
  scipy==1.4.1
  
  six==1.14.0
  
  SoundFile==0.10.3.post1
  
  Werkzeug==1.0.0
  
  wincertstore==0.2

• Clarification on using FFmpeg in an open source app with —enable-gpl and —enable-nonfree [closed]

  24 April 2020, by Alex Hoffman

  Can someone please clarify the legality of bundling and using the full version of FFmpeg (with included libx264 and libx265 and other gpl codecs) in an open source app.

  



  I don't want to modify anything, all I want to do is to bundle compiled FFmpeg binaries downloaded from their website with my open source app which would use them for media files editing.

  



  I want to bundle the full version of FFmpeg since without --enable-gpl and --enable-nonfree, not being able to process h264, FFmpeg becomes effectivelly useless. So I want to know if I have to bundle the binaries compiled without --enable-gpl and --enable-nonfree or can I bundle the full version with shared linking (libs separated from the executable)?