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• Merge pull request #582 from Mr-Zer0/master

  26 mars 2014, par jackmoore

  Merge pull request #582 from Mr-Zer0/master
  Language translation for Myanmar.
  

• avformat/asfenc : write group_mutual_exclusion_objects for audio on multiple languages

  5 février 2016, par Marton Balint

  avformat/asfenc : write group_mutual_exclusion_objects for audio on multiple languages
  Improves streaming compatibility with Windows Media Services. Also tested for
  
  compatilbility in Windows Media Player, Windows Media ASF Viewer and VLC.
  This version of the patch only writes exclusion among audio streams, therefore
  
  choosing a subtitle language should be possible independently of audio language.
  Signed-off-by : Marton Balint <cus@passwd.hu>
  

  • [DH] libavformat/asf.c
  • [DH] libavformat/asf.h
  • [DH] libavformat/asfenc.c

• Improving Google Cloud Speech-to-Text accuracy

  6 juillet 2020, par lr_optim

  I'm working on a project where I need to perform these steps :

  


  

  1. Record a voice call (.webm -file)

  


  2. Split the webm -file into chunks with ffmpeg and convert the file into wav

  


  3. Transcribe the chunks using SpeechRecognition -library and Google Cloud API

  


  


  I've faced problems with the transcription accuracy and wondering if there is something I could do to improve it. At the time I'm splitting the original file into 30s chunks. I thought there might be one problem, that I might be missing words because of splitting so I've tried also with longer chunks under 60s but didn't notice any improve in accuracy.
Reading trough the speechRecognition docs I decided to set r.energy_threshold = 4000, I also tried to set the energy_treshold dynamically like this :

  


  with sr.AudioFile(name) as source:
    r.dynamic_energy_threshold = True
    r.adjust_for_ambient_noise(source, duration = 1)
    audio = r.record(source)


  


  I've also tested en-US and en-GB to see if there's some difference but there isn't as much as I'd want. The program is supposed to work with english language spoken by nordic people. If someone has experience about choosing a right language model for people speaking with accent, please let me know.

  


  This is the ffmpeg command is use to split the webm file into chunks : command = ['ffmpeg', '-i', filename, '-f', 'segment', '-segment_time', '30', parts_dir + outputname + '%09d.wav']

  


  Is there somethig I could do better ? I'm wondering if the quality is not good enough an Google is having hard time because of that ?

  


  The main problem is I'm getting bad results (lots of wrong words) from Google and wondering if there is something I could do about it.