Comments (5)
Hi!
Glad you like it :)
About your requirement, you want each .wav
file to have just the audio content of each speaker? That is:
speaker_1/audio.wav # A wav file with the concatenation of every speaker_1 part of audio.wav
speaker_2/audio.wav # A wav file with the concatenation of every speaker_2 part of audio.wav
Is this it? In that case, there's no audio post-processing on this package as-is, so it won't do this automatically, but you could write a python
or awk
script that takes the output, parses each start-time=xxx
, end-time=xxx
and speaker=xxx
with a regex, and calls ffmpeg
to cut the appropriate segment of the original .wav
. You can later join all the speaker_x
segments together.
This answer is pretty close to what you want, you just need to modify the parsing part to this format: https://unix.stackexchange.com/a/400032/6301
from speaker-diarization.
Is the audio in Korean? The language model that is included is for English language. It would be possible to train a Korean language model with a lot of annotated data and using https://github.com/aalto-speech/AaltoASR but that's quite a big task and I can't guide you through it :(
from speaker-diarization.
Thank You !! < ^ o ^ > !!
I use python and now i'm making split file.
But, i meet a new problem.
I'm a korean, and i test one wav file.
But, performance is not good.
How can i solve this situation you think??
from speaker-diarization.
Thank you.
I will test other korean .wav file.
And I have to see that source and confirm that i can use it.
Thank you for your rapid answer.
from speaker-diarization.
Closing as it seems this was answered :)
from speaker-diarization.
Related Issues (15)
- Issue with Running Audio File HOT 3
- Dockerfile is missing the step to install ffmpeg HOT 1
- Questions about BIC distance calculation HOT 1
- DockerHub-hosted image not up to date HOT 1
- Speed is too slow. Is this docker can use gpu? HOT 1
- Building my own model HOT 3
- UnboundLocalError: local variable 'feas' referenced before assignment HOT 2
- Excessive CPU HOT 1
- Docker Image for Speech Diarization HOT 3
- separate `.wav` files per speaker HOT 2
- Can I recognize only one speaker? HOT 2
- Tunable Parameters HOT 4
- OSError: [Errno 2] No such file or directory HOT 1
- WAV files format HOT 4
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from speaker-diarization.