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• 24kHz audio file problem : unsupported bitrate 64000

  28 novembre 2018, par R. Vait

  I use alexa audio tags a lot. I know that now audio tags support 24kHz audio files so tried converting my audio files from 16kHz. I used the provided command in the docs to do so :

  ffmpeg -i  -ac 2 -codec:a libmp3lame -b:a 48k -ar 24000 

  But when I try to play this file, I get an invalid response error, saying : Error: The audio is of an unsupported bitrate 64000. By looking into file details I clearly see, that bitrate is 48kbps and sample rate is 24kHz. I don’t see any value where it would say 64 or anything close to it.

  If I encode my file back to 16kHz it plays fine again.

  It seems that there is a problem with this command, because if I encode my files using audacity, they work with 24kHz. I still would prefer to use ffmpeg, because I need to encode a lot of files.

  I am asking, not about file format, format is correct. I need files in 24kHz sample rate and that is what causes issues. I saw another question about similar problem and others having the discussion there about sample rates, but no one was able to encode file to be 24kHz using ffmpeg.

  Did anyone had any luck on encoding files to 24kHz using ffmpeg ?

• FFMPEG failing in AWS Lambda

  18 février 2019, par Zaid Amir

  I am trying to create a transcoding function for short videos. The function is hosted on AWS Lambda. The problem is that AWS lambda seems to be missing something that FFMPEG requires, at least according to Amazon.

  I contacted Amazon earlier and this is their response to the issue :

  We found that the FFMPEG operations require at least libx264 and an
  acc library, both of which will have dependencies of their own. To
  troubleshoot the issue it will involve diving deeper into the full
  dependency chain. We can see that it works in the Amazon Linux
  environment however, the environment is similar but not identical to
  the lambda environment. There can be some dependencies that exist in
  Amazon Linux but not in lambda environment as Lambda runs on the
  container. Here, as FFmpeg is a third party software, diving deeper
  into the dependency chain and verifying the version compatibilities is
  very hard to do. Unfortunately going further, this is bound to go into
  architecture and code support which is out of AWS Support scope 1. I
  hope you understand our limitations. However should FFmpeg support
  have any questions specific to the Lambda platform, please do let us
  know and we will be happy to assist. We will be in better position to
  investigate further once you receive an update from the FFmpeg support
  suggesting an issue from Lambda end.

  Upon AWS suggestion, I contacted FFMPEG on the developers mailing list, my message was rejected with the reason being that its more suited to ffmpeg users mailing list than developers. I sent an email to ’ffmpeg-user@ffmpeg.org’ a week ago and did not get any response yet.

  I then went and built a dynamically linked ffmpeg version making sure to package all libraries, checked ddl on each one, then made a small lambda function that looped over all binaries and ddled each one of them, compared that to the output I got from Amazon Linux and the same dependencies/versions exists on both lambda and the AWS Linux instance yet ffmpeg still fails on lambda.

  You can find a detailed log file here : https://www.datafilehost.com/d/6e5e21bb

  And this is a sample of the errors I’m getting, repeated across the entire log file :

  2018-08-14T12:27:10.874Z [h264 @ 0x65c2fc0] concealing 2628 DC, 2628
  AC, 2628 MV errors in P frame

  2018-08-14T12:27:10.874Z [aac @ 0x65d2f00] channel element 2.11 is not
  allocated

  2018-08-14T12:27:10.874Z Error while decoding stream #0:1 : Invalid
  data found when processing input

  2018-08-14T12:27:10.874Z [h264 @ 0x67e86c0] Invalid NAL unit size
  (108085662 > 1649).

  2018-08-14T12:27:10.874Z [h264 @ 0x67e86c0] Error splitting the input
  into NAL units.

  2018-08-14T12:27:10.874Z [aac @ 0x65d2f00] channel element 2.0 is not
  allocated

  2018-08-14T12:27:10.874Z Error while decoding stream #0:1 : Invalid
  data found when processing input

  2018-08-14T12:27:10.874Z [h264 @ 0x68189c0] Invalid NAL unit size
  (71106974 > 1085).

  2018-08-14T12:27:10.874Z [h264 @ 0x68189c0] Error splitting the input
  into NAL units.

  2018-08-14T12:27:10.874Z [aac @ 0x65d2f00] Pulse tool not allowed in
  eight short sequence.

  This log is generated when trying to perform an HLS transcoding on this file : https://www.datafilehost.com/d/999a4492

  Note that the issue is not related to that file alone nor is it related to HLS, its general and happen on all videos and any ffmpeg command that tries to seek the stream, even tried extracting a single frame from a video using the simplest form possible for example : ffmpeg -ss 00:00:02 -I file.mp4 -vframes 1 -y output.jpg also fails with the same errors in the log file.

  Not sure how to debug this further. Tried enabling debug logs with ‘-loglevel debug’ but did not give me any extra info. Any help or suggestions

• How to generate video as fast as possible with subtitles and audio on node.js + ffmpeg ?

  12 septembre 2018, par DSeregin

  Intro :

  We receive from the site some pieces of text
  Pieces arrive to node.js-server

  At the output we need to get a video, merged from all the pieces of text, voiced by the machine voice, with the added subtitles and audio substrate. So that user could be share this video in the social networks. MKV format doesn`t supported by VK.com

  The options that we have tried :
  1. Get all the text at once, generate the entire speech, create a file with subtitles, burn subtitles in the video .mp4 (vk.com does not support the .mkv container). It took 12 seconds of operations for a 45-second video on the local computer.
  2. Generate audio and video files for each piece of text (with added subtitles). It took one second for one piece of text. At the final request, we merge all pieces together. The last request (merging) took 2-3 seconds, which is already bearable.

  The second variant looks acceptable in terms of speed, but if you run 50 clients at the same time, then the computer (tested on a MacBook PRO 2013, 2.4 GHz i7, 8gb 1600 Mhz DDR3, SSD 256gb) processed only 1 piece from 1 client in 60 seconds (60 times slower), then the computer hung tight.

  • Generation of audio is done through Amazon Polly
  • Generate subtitles via https://github.com/gsantiago/subtitle.js#readme
  • From node.js we call ffmpeg using https://github.com/fluent-ffmpeg/node-fluent-ffmpeg
  • Resolution for video - 1280x720

  The commands we used :

  • Burn video subtitles and trim up to conditional 6 seconds (in the code send unix timestamp)

  ffmpeg -i import / back.mov -i export_0 / tmp.srt -scodec mov_text -t 6 export_0 / output.mov

  • Merging all audio

  ffmpeg -i audio1.mp3 .... -i audio15.mp3 merged.mp3

  • Overlay audio-substrate on the text

  ffmpeg -i merged.mp3 -i back.mp3 -filter_complex amerge -ac 2-c: a libmp3lame -q: a 4 -shortest audio.mp3

  • Merging all videos

  ffmpeg -i video.txt -f concat -c copy video.mp4

  • Overlay audio on video

  ffmpeg -i audio.mp3 -i video.mp4 -i test.mp4 -i export / output.mp3 -c: v copy -c: a aac -map 0: v: 0 -map 1: a: 0 -shortest output .mp4

  Questions that torment :

  1. Is it faster ?

  2. Can I use other codecs or methods of gluing without re-encoding ?

  3. Try to call ffmpeg directly without a wrapper ? (in fact, it gives 50-100 ms of speed)

  4. Try not to save to disk, and write data to Stream and have them glue together in the end ?