Comments (2)
This is certainly not by design.
I double checked that no_speech_threshold
and logprob_threshold
are correctly passed to whisper.transcribe()
.
And with the options that you give (beam search...), the transcription is first done by whisper and segments are identical in the end (except from start and end timestamp). I mean there are little chance of bugs there (contrarily to the more tricky efficient case).
So I don't understand what can happen.
Can you please double check, and give what python call you make and an extract of the output that you find suspicious?
(Also you can test what gives whisper alone on this)
from whisper-timestamped.
Yes, you are right, the issue was on my end, after thorough checking, it is working as intended.
from whisper-timestamped.
Related Issues (20)
- Broken link for plotting word alignment section HOT 7
- Loading finetuned model serialized with safetensors (and/or sharded models) HOT 10
- How to activate flash attention? HOT 2
- Could it be possible to apply the same technique to the whisper API? HOT 6
- ctranslate2 support HOT 1
- CPU only light install links are broken? HOT 3
- Issue with accented characters coming up as symbols in output json file
- Repetitive Phrase Looping HOT 3
- Bad timestamp prediction with some finetuned Whisper models HOT 9
- How to use cuda? HOT 2
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- There are plans to use ctranslate2 to speed up? similar to faster-whisper HOT 2
- Whether --max_line_width and --max_line_count are not supported? HOT 2
- When the transcription progress reaches 100%, it takes a long time to show the result, sometimes even up to 10 minutes. HOT 1
- What is it (very slowly) trying to download? HOT 4
- Output filenames aren't consistent with original openai-whisper implementation HOT 1
- Output syntaxically invalid vtt file (header appears twice) HOT 1
- Incorrect timestamps when using VAD with large model only
- Timestamps not provided per-word for Chinese/Japanese/Korean HOT 3
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from whisper-timestamped.