Closed PiotrEsse closed 1 month ago
Ad 1. Yes, Ive run same example, whithout any changes. I use diarize.py
~/speechlib/examples$ python3 transcribe.py
obama_zach_143156_en.txt Ad 2. I use medium Ad 3. Yes - it process the file. It takes time - 79sec to be precisely Ad 4. Sure, Ill have to prepare clean WSL VM.
This can happen due to a number of reasons because of an insane try/except
block in this function.
It literally says:
try:
trans = transcribe(file, language, modelSize, quantization)
# return -> [[start time, end time, transcript], [start time, end time, transcript], ..]
texts.append([segment[0], segment[1], trans])
except:
pass
I removed this via a monkeypatch and it revealed the actual issue:
ValueError: Requested float16 compute type, but the target device or backend do not support efficient float16 computation.
This is a common issue for faster-whisper
and is discussed here: https://github.com/SYSTRAN/faster-whisper/issues/42
There may be a different error in your case.
Im having the same problem and it could be solved partially with
https://github.com/NavodPeiris/speechlib/issues/37
In the meantime, i'll try to create a branch in my fork that doesn't use faster-whisper.
I am having an empty file at then end when I use sinhala language , I know in the codebase we are providing a different model for sinhala than normal whisper , Can you please help me with this
Hi, thank You for Your work but I am having issues. Theres no error but after run your example I am getting an almost empty file in logs: In the file theres only following string:
zach (206.8 : 206.8) :
In terminal theres no errors>
Content of the file:
I have python 3.9, clean conda env. Whisper works flawleslly