Comments (3)
steffiand
commented on June 23, 2024
Hi,
I am facing a similar problem. Due to trimming of silence, the mapping of the output prediction to the original audio sample becomes difficult because of the difference in time mapping. Is it possible to include silence in the final output? This will make the process of mapping easier.
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bishwaraj13
commented on June 23, 2024
Hi @steffi25 and @RawadMelhem , did you manage to find fix to this issue? I too am facing the same issue.
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RawadMelhem
commented on June 23, 2024
It is solved. You can take the object "wav" before diarization process, which is the audio file without silence.
wav object is the output of wav_preprocessing method.
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Related Issues (20)
- End-to-end trainable model HERE
- Async and parallel processing
- After diarization, The timestamps I got are irrelevants from original file HOT 3
- The Ability of Speaker Diarization with More Than 2 Speakers HOT 3
- Speaker Verification HOT 2
- Error while trying to plot speaker similarity in Resemblyzer
- Diarization Graph Issue HOT 1
- About pre-trained model HOT 2
- How to get time stamps for every speaker change?
- MATPLOTLIB FIGURES NOT DISPLAYING..How to solve this please help.. HOT 2
- New release plans to include latest changes? HOT 5
- Error while using Resemblyzer HOT 2
- Getting different speaker embeddings from same wav file on different machine HOT 2
- Speech Diarization
- Using VoiceEncoder as loss with time domain models
- AttributeError: module 'numpy' has no attribute 'bool'.
- What license does resemblyzer fall under? HOT 1
- RuntimeError: cuDNN error: CUDNN_STATUS_VERSION_MISMATCH
- is this speaker embedding better than X-vector?
- Demo2 not showing any result HOT 2
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