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start_time=time.time()
segments, info = model.transcribe(audio_file, beam_size=5)
end_time=time.time()
print("inferencetime : "+str(end_time-start_time))
print_wav_info(audio_file)
start2=time.time()
for segment in segments:
print(type(segment))
# print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
end2=time.time()
print("fetch segments time : "+str(end2-start2))
audio_file is 5 seconds and inference time is about 3 seconds ,whisch is normal;
BUT fetch time is too long,even i just print(type).i can't find any solutions.
The text was updated successfully, but these errors were encountered:
@Hestinorwu , hello. Maybe your audio has too much hallucination so the model takes longer to process. Ex:
Processing audio with duration 01:48.553
Processing segment at 00:00.000
Compression ratio threshold is not met with temperature 0.0 (4.642857 > 2.400000)
Compression ratio threshold is not met with temperature 0.2 (4.187500 > 2.400000)
Compression ratio threshold is not met with temperature 0.4 (18.111111 > 2.400000)
Compression ratio threshold is not met with temperature 0.6 (5.089552 > 2.400000)
Log probability threshold is not met with temperature 0.6 (-1.003701 < -1.000000)
Log probability threshold is not met with temperature 0.8 (-2.498282 < -1.000000)
Log probability threshold is not met with temperature 1.0 (-4.083577 < -1.000000)
Reset prompt. prompt_reset_on_temperature threshold is met 1.000000 > 0.500000
Processing segment at 00:10.420
[0.00s -> 8.00s] Qasid'aq Al-Maraham
[8.00s -> 10.42s] Iqul
Compression ratio threshold is not met with temperature 0.0 (14.322581 > 2.400000)
Compression ratio threshold is not met with temperature 0.2 (24.470588 > 2.400000)
...
Could you show your log and attach your example audio ?
audio_file is 5 seconds and inference time is about 3 seconds ,whisch is normal;
BUT fetch time is too long,even i just print(type).i can't find any solutions.
The text was updated successfully, but these errors were encountered: