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• swscale : Undeprecate sws_getContext()

  4 août 2014, par Diego Biurrun

  swscale : Undeprecate sws_getContext()
  sws_getCachedContext is not a full replacement for the function.
  

  • [DBH] doc/APIchanges
  • [DBH] libswscale/swscale.h
  • [DBH] libswscale/utils.c
  • [DBH] libswscale/version.h

• pyqt5 gui dependent on ffmpeg compiled with pyinstaller doesn't run on other machines ?

  19 octobre 2022, par Soren

  I am trying to create a simple Pyqt5 GUI for Windows 10 that uses OpenAI's model Whisper to transcribe a sound file and outputting the results in an Excel-file. It works on my own computer where I have installed the necessary dependencies for Whisper as stated on their github i.e. FFMEG. I provide a minimal example of my code below :

  


  # Import library
import whisper
import os
from PyQt5 import QtCore, QtGui, QtWidgets
import pandas as pd
import xlsxwriter


class Ui_Dialog(QtWidgets.QDialog):
    
    
    # Define functions to use in GUI
   
    # Define function for selecting input files
    def browsefiles(self, Dialog):
      
       
       # Make Dialog box and save files into tuple of paths
       files = QtWidgets.QFileDialog().getOpenFileNames(self, "Select soundfiles", os.getcwd(), "lyd(*mp2 *.mp3 *.mp4 *.m4a *wma *wav)")
       
       self.liste = []
       for url in range(len(files[0])):
           self.liste.append(files[0][url])   

    
    def model_load(self, Dialog):
               
        # Load picked model
        self.model = whisper.load_model(r'C:\Users\Søren\Downloads\Whisper_gui\models' + "\\" + self.combo_modelSize.currentText() + ".pt") ##the path is set to where the models are on the other machine
        
    
    def run(self, Dialog):
                
        # Make list for sound files
        liste_df = []
        
        
        # Running loop for interpreting and encoding sound files
        for url in range(len(self.liste)):
                          
            # Make dataframe
            df = pd.DataFrame(columns=["filename", "start", "end", "text"])
            
            # Run model
            result = self.model.transcribe(self.liste[url])
                            
            # Extract results
            for i in range(len(result["segments"])):
                start = result["segments"][i]["start"]
                end = result["segments"][i]["end"]
                text = result["segments"][i]["text"]
                
                df = df.append({"filename": self.liste[url].split("/")[-1],
                            "start": start, 
                            "end": end, 
                            "text": text}, ignore_index=True)
            
            # Add detected language to dataframe
            df["sprog"] = result["language"]
            
            
            liste_df.append(df)
        
        
        
        # Make excel output
        
        # Concatenate list of dfs
        dataframe = pd.concat(liste_df)
        
        
        # Create a Pandas Excel writer using XlsxWriter as the engine.
        writer = pd.ExcelWriter(self.liste[0].split(".")[0] + '_OUTPUT.xlsx', engine='xlsxwriter')
        writer_wrap_format = writer.book.add_format({"text_wrap": True, 'num_format': '@'})


        # Write the dataframe data to XlsxWriter. Turn off the default header and
        # index and skip one row to allow us to insert a user defined header.
        dataframe.to_excel(writer, sheet_name="Output", startrow=1, header=False, index=False)

        # Get the xlsxwriter workbook and worksheet objects.
        #workbook = writer.book
        worksheet = writer.sheets["Output"]

        # Get the dimensions of the dataframe.
        (max_row, max_col) = dataframe.shape

        # Create a list of column headers, to use in add_table().
        column_settings = [{'header': column} for column in dataframe.columns]

        # Add the Excel table structure. Pandas will add the data.
        worksheet.add_table(0, 0, max_row, max_col - 1, {'columns': column_settings})

        # Make the columns wider for clarity.
        worksheet.set_column(0, max_col - 1, 12)
        
        in_col_no = xlsxwriter.utility.xl_col_to_name(dataframe.columns.get_loc("text"))
        
        worksheet.set_column(in_col_no + ":" + in_col_no, 30, writer_wrap_format)

        # Close the Pandas Excel writer and output the Excel file.
        writer.save()
        writer.close()
    
    
    ## Design setup
    
    def setupUi(self, Dialog):
        Dialog.setObjectName("Dialog")
        Dialog.resize(730, 400)
        
        self.select_files = QtWidgets.QPushButton(Dialog)
        self.select_files.setGeometry(QtCore.QRect(40, 62, 81, 31))
        font = QtGui.QFont()
        font.setPointSize(6)
        self.select_files.setFont(font)
        self.select_files.setObjectName("select_files")
        
    
               
        
        self.combo_modelSize = QtWidgets.QComboBox(Dialog)
        self.combo_modelSize.setGeometry(QtCore.QRect(40, 131, 100, 21))
        font = QtGui.QFont()
        font.setPointSize(6)
        self.combo_modelSize.setFont(font)
        self.combo_modelSize.setObjectName("combo_modelSize")
               
        
        self.runButton = QtWidgets.QPushButton(Dialog)
        self.runButton.setGeometry(QtCore.QRect(40, 289, 71, 21))
        font = QtGui.QFont()
        font.setPointSize(6)
        self.runButton.setFont(font)
        self.runButton.setObjectName("runButton")
        
        
       

        self.retranslateUi(Dialog)
        QtCore.QMetaObject.connectSlotsByName(Dialog)
        
        
        
        modelSize_options = ['Chose model', 'tiny', 'base', 'small', 'medium', 'large']
        self.combo_modelSize.addItems(modelSize_options)
        
        # Do an action!
        self.select_files.clicked.connect(self.browsefiles)
        self.combo_modelSize.currentIndexChanged.connect(self.model_load)
        self.runButton.clicked.connect(self.run)
        
        
        
    

    def retranslateUi(self, Dialog):
        _translate = QtCore.QCoreApplication.translate
        Dialog.setWindowTitle(_translate("Dialog", "Dialog"))
        self.runButton.setText(_translate("Dialog", "Go!"))
        self.select_files.setText(_translate("Dialog", "Select"))


if __name__ == "__main__":
    import sys
    app = QtWidgets.QApplication(sys.argv)
    Dialog = QtWidgets.QDialog()
    ui = Ui_Dialog()
    ui.setupUi(Dialog)
    Dialog.show()
    sys.exit(app.exec_())


  


  I compile this app with pyinstaller using the following code. I had some issues to begin with so I found other with similar problems and ended up with this :

  


  pyinstaller --onedir --hidden-import=pytorch --collect-data torch --copy-metadata torch --copy-metadata tqdm --copy-metadata tokenizers --copy-metadata importlib_metadata --hidden-import="sklearn.utils._cython_blas" --hidden-import="sklearn.neighbors.typedefs" --hidden-import="sklearn.neighbors.quad_tree" --hidden-import="sklearn.tree" --hidden-import="sklearn.tree._utils" --copy-metadata regex --copy-metadata requests --copy-metadata packaging --copy-metadata filelock --copy-metadata numpy --add-data "./ffmpeg/*;./ffmpeg/" --hidden-import=whisper --copy-metadata whisper --collect-data whisper minimal_example_whisper.py

  


  When I take the outputtet dist directory and try to run the app on another Windows machine without FFMPEG installed (or Whisper or any other things), I get the following error from the terminal as I push the "run" button in the app (otherwise the app does run).

  


  C:\Users\Søren>"G:\minimal_example_whisper\minimal_example_whisper.exe"
whisper\transcribe.py:70: UserWarning: FP16 is not supported on CPU; using FP32 instead
Traceback (most recent call last):
  File "minimal_example_whisper.py", line 45, in run
  File "whisper\transcribe.py", line 76, in transcribe
  File "whisper\audio.py", line 111, in log_mel_spectrogram
  File "whisper\audio.py", line 42, in load_audio
  File "ffmpeg\_run.py", line 313, in run
  File "ffmpeg\_run.py", line 284, in run_async
  File "subprocess.py", line 951, in __init__
  File "subprocess.py", line 1420, in _execute_child
FileNotFoundError: [WinError 2] Den angivne fil blev ikke fundet


  


  I suspect this has something to do with FFMPEG not being installed on the other machines system ? Does anyone have an automatic solution for this when compiling the app or can it simply only run on machines that has FFMPEG installed ?

  


  Thanks in advance !

  


• Convert Webm to MP4 on the fly using ffmpeg for a Telegram bot using Typescript

  23 novembre 2022, par Hex

  I'm trying to make a very primitive telegram bot that get's a json and uploads the urls that are in the json to telegram.

  


  The problem is that there are urls that point to webm files I tried to see if there is a simple way to do this and I found this : https://www.npmjs.com/package/webm-to-mp4
but it doesn't seem to work sadly, it runs into this error : "

  


  


  


  throw new Error(`Conversion error: ${stderr}`)
                                ^

Error: Conversion error: ffmpeg version n4.2.2 Copyright (c) 2000-2019 the FFmpeg developers
  built with emcc (Emscripten gcc/clang-like replacement) 1.39.11
  configuration: --cc=emcc --ranlib=emranlib --enable-cross-compile --target-os=none --arch=x86 --disable-runtime-cpudetect --disable-asm --disable-fast-unaligned --disable-pthreads --disable-w32threads --disable-os2threads --disable-debug --disable-stripping --disable-safe-bitstream-reader --disable-all --enable-ffmpeg --enable-avcodec --enable-avformat --enable-avfilter --enable-swresample --enable-swscale --disable-network --disable-d3d11va --disable-dxva2 --disable-vaapi --disable-vdpau --enable-decoder=vp8 --enable-decoder=h264 --enable-decoder=vorbis --enable-decoder=opus --enable-decoder=mp3 --enable-decoder=aac --enable-decoder=pcm_s16le --enable-decoder=mjpeg --enable-decoder=png --enable-demuxer=matroska --enable-demuxer=ogg --enable-demuxer=mov --enable-demuxer=mp3 --enable-demuxer=wav --enable-demuxer=image2 --enable-demuxer=concat --enable-protocol=file --enable-filter=aresample --enable-filter=scale --enable-filter=crop --enable-filter=overlay --enable-filter=hstack --enable-filter=vstack --disable-bzlib --disable-iconv --disable-libxcb --disable-lzma --disable-sdl2 --disable-securetransport --disable-xlib --enable-zlib --enable-encoder=libx264 --enable-encoder=libmp3lame --enable-encoder=aac --enable-muxer=mp4 --enable-muxer=mp3 --enable-muxer=null --enable-gpl --enable-libmp3lame --enable-libx264 --extra-cflags='-s USE_ZLIB=1 -I../lame/dist/include' --extra-ldflags=-L../lame/dist/lib
  libavutil      56. 31.100 / 56. 31.100
  libavcodec     58. 54.100 / 58. 54.100
  libavformat    58. 29.100 / 58. 29.100
  libavfilter     7. 57.100 /  7. 57.100
  libswscale      5.  5.100 /  5.  5.100
  libswresample   3.  5.100 /  3.  5.100
input.webm: Invalid data found when processing input
exception thrown: Error: Conversion error: ffmpeg version n4.2.2 Copyright (c) 2000-2019 the FFmpeg developers
  built with emcc (Emscripten gcc/clang-like replacement) 1.39.11
  configuration: --cc=emcc --ranlib=emranlib --enable-cross-compile --target-os=none --arch=x86 --disable-runtime-cpudetect --disable-asm --disable-fast-unaligned --disable-pthreads --disable-w32threads --disable-os2threads --disable-debug --disable-stripping --disable-safe-bitstream-reader --disable-all --enable-ffmpeg --enable-avcodec --enable-avformat --enable-avfilter --enable-swresample --enable-swscale --disable-network --disable-d3d11va --disable-dxva2 --disable-vaapi --disable-vdpau --enable-decoder=vp8 --enable-decoder=h264 --enable-decoder=vorbis --enable-decoder=opus --enable-decoder=mp3 --enable-decoder=aac --enable-decoder=pcm_s16le --enable-decoder=mjpeg --enable-decoder=png --enable-demuxer=matroska --enable-demuxer=ogg --enable-demuxer=mov --enable-demuxer=mp3 --enable-demuxer=wav --enable-demuxer=image2 --enable-demuxer=concat --enable-protocol=file --enable-filter=aresample --enable-filter=scale --enable-filter=crop --enable-filter=overlay --enable-filter=hstack --enable-filter=vstack --disable-bzlib --disable-iconv --disable-libxcb --disable-lzma --disable-sdl2 --disable-securetransport --disable-xlib --enable-zlib --enable-encoder=libx264 --enable-encoder=libmp3lame --enable-encoder=aac --enable-muxer=mp4 --enable-muxer=mp3 --enable-muxer=null --enable-gpl --enable-libmp3lame --enable-libx264 --extra-cflags='-s USE_ZLIB=1 -I../lame/dist/include' --extra-ldflags=-L../lame/dist/lib
  libavutil      56. 31.100 / 56. 31.100
  libavcodec     58. 54.100 / 58. 54.100
  libavformat    58. 29.100 / 58. 29.100
  libavfilter     7. 57.100 /  7. 57.100
  libswscale      5.  5.100 /  5.  5.100
  libswresample   3.  5.100 /  3.  5.100
input.webm: Invalid data found when processing input
,Error: Conversion error: ffmpeg version n4.2.2 Copyright (c) 2000-2019 the FFmpeg developers
  built with emcc (Emscripten gcc/clang-like replacement) 1.39.11
  configuration: --cc=emcc --ranlib=emranlib --enable-cross-compile --target-os=none --arch=x86 --disable-runtime-cpudetect --disable-asm --disable-fast-unaligned --disable-pthreads --disable-w32threads --disable-os2threads --disable-debug --disable-stripping --disable-safe-bitstream-reader --disable-all --enable-ffmpeg --enable-avcodec --enable-avformat --enable-avfilter --enable-swresample --enable-swscale --disable-network --disable-d3d11va --disable-dxva2 --disable-vaapi --disable-vdpau --enable-decoder=vp8 --enable-decoder=h264 --enable-decoder=vorbis --enable-decoder=opus --enable-decoder=mp3 --enable-decoder=aac --enable-decoder=pcm_s16le --enable-decoder=mjpeg --enable-decoder=png --enable-demuxer=matroska --enable-demuxer=ogg --enable-demuxer=mov --enable-demuxer=mp3 --enable-demuxer=wav --enable-demuxer=image2 --enable-demuxer=concat --enable-protocol=file --enable-filter=aresample --enable-filter=scale --enable-filter=crop --enable-filter=overlay --enable-filter=hstack --enable-filter=vstack --disable-bzlib --disable-iconv --disable-libxcb --disable-lzma --disable-sdl2 --disable-securetransport --disable-xlib --enable-zlib --enable-encoder=libx264 --enable-encoder=libmp3lame --enable-encoder=aac --enable-muxer=mp4 --enable-muxer=mp3 --enable-muxer=null --enable-gpl --enable-libmp3lame --enable-libx264 --extra-cflags='-s USE_ZLIB=1 -I../lame/dist/include' --extra-ldflags=-L../lame/dist/lib
  libavutil      56. 31.100 / 56. 31.100
  libavcodec     58. 54.100 / 58. 54.100
  libavformat    58. 29.100 / 58. 29.100
  libavfilter     7. 57.100 /  7. 57.100
  libswscale      5.  5.100 /  5.  5.100
  libswresample   3.  5.100 /  3.  5.100
input.webm: Invalid data found when processing input


  


  I'm not sure what is causing it, my guess is that the webm-to.mp4 package was updated 2 years ago and something broke in the meantime.

  


  Is there a better way to do this then downloading the webm converting it and then sending it up to telegram ? If not how could I do the conversion using just ffmpeg ?

  


  Here is my current code :

  


  import { Telegram, MediaSource, HTML } from 'puregram'
import { HearManager } from '@puregram/hear'
import { createReadStream } from 'fs'
import postsJson from './posts.json';
const { promises: fs } = require("fs");
const webmToMp4 = require("webm-to-mp4");

const telegram = new Telegram({
  token: '********:AAEzriis6zNNjEQuw0BxF9M2RPA9V4lEqLA'
})
const hearManager = new HearManager()

telegram.updates.startPolling()
  .then(() => console.log(`started polling @${telegram.bot.username}`))
  .catch(console.error)

telegram.updates.on('message', hearManager.middleware)

var posts = postsJson;

telegram.updates.on('message', (context) => {
    posts.forEach( async data => {
      console.error(data.ext);
        if(data.ext == "jpg" || data.ext == "png"){
          context.sendPhoto(MediaSource.url(data.image), { caption: data.content  } );
          delay(1000);
        }
        if(data.ext == "gif"){
          context.sendAnimation(MediaSource.url(data.image), { caption: data.content  } );
          delay(1000);
        }
        if(data.ext == "webm"){
          //context.sendDocument(MediaSource.url(data.image), { caption: data.content  } );
          delay(1000);
        }
        delay(1000);
    })
})
fs.writeFile("file.mp4", Buffer.from(webmToMp4( fs.readFile("./file.webm"))));

function delay(ms: number) {
  return new Promise( resolve => setTimeout(resolve, ms) ); //This does not work either
}


  


  I wish everoyne a nice day !