Decode a base64 code into a audio file and save it

38 views (last 30 days)
Hello,
I'm trying to create a automated decoder for a large scale csv file containing bunch of audio base64 code.
When I copy/paste the base64 code into the online converter, it gives me the audio file nicely.
However, I tried to use matlab.net.base64decode()function in matlab, and after saving it to file as a wav with sample rate at 48000, the wav audio is just high pitch noise. Could someone help me out to make it work? Thank you!
My code is below:
wav_data = matlab.net.base64decode(char(audio_data));
audiowrite(filename,wav_data,48000);
audio_data is the base64 code, here is one of it:
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

Accepted Answer

Jan
Jan on 15 Feb 2023
Edited: Jan on 15 Feb 2023
Look into the output of the base64 decoding. You find these characters on the top:
.E...B...B...B...B...B..
webmB...B....S.g........
.I.f.*....B@M..ChromeWA.
Chrome.T.k......s......E
4+.....A_OPUSc..OpusHead
This is not a binary sound signal, but an encoded webm file. Saving as at WAV file does not work. Try:
datastream = matlab.net.base64decode(char(audio_data));
[fid, msg] = fopen('YouFile.webm', 'w');
assert(fid > 0, msg);
fwrite(fid, datastream, 'uint8');
fclose(fid);
Now play the webm file in a browser, VLC or the WIndows Media Player.
  2 Comments
Nick
Nick on 15 Feb 2023
Thank you Jan! It's working perfect now!
Is there anyway to convert the webm file into a wav or mp3 in the matlab before saving it to file? It's more convenient to play it with mac built-in media player, which does not support webm file.
Jan
Jan on 16 Feb 2023
@Nick: As far as I know, there is no Matlab code to parse WEBM files yet. I'd try using FFmpeg as external library.

Sign in to comment.

More Answers (0)

Products


Release

R2020b

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!