some type of sound normalizer to increase quiet whispers

Then the models seems to somehow have a problem with disambiguation and starts 'seeing things'.Ī more elegant solution than just setting condition_on_previous_text to False would be something like this (not properly debugged yet):Īfter line 178 of whisper/transcribe.py exchange:ĭecode_options = all_tokens My working hypothesis is, that the problem arises if the last chunk is short (like a couple of seconds) compared to the text initializing the next chunks transcription. This is set to True by default and helps whisper (to my understanding) to keep the context going between chunks. The parameter: 'condition_on_previous_text' I looked into this a little and found a possible solution: These often consist of repeats and shuffles of the text of previous chunks and turned out to be quite detrimental to the overall quality of the transcript. It’s simply an automatic gain control for the whole song.I was having trouble with whisper creating 'ghost transcripts' at the end of a given sound file. That means that in and of itself, it’s not changing the sound of your master with any dynamic range compression. There’s an important feature in there though that could be easy to overlook, so I want to be sure to point it out: normalization just applies a static gain change. Normalization is just subtracting your measured value from your target value and applying the result as gain! Use the value you got from step 2 to apply gain to your file.You would subtract -10 from -14-remember, subtracting a negative value is like adding the corresponding positive value-to get -4.

For example, say your file measures an integrated loudness of -10 LUFS, and your target is -14 LUFS. Subtract your measured value from your target value.This could mean finding the maximum true peak level, the integrated loudness, etc. Scan the file to determine the value of the level you’re interested in.The process to achieve audio normalization-whether peak or loudness based-is essentially a three-step process using little more than elementary school arithmetic. They can all be effective and subtly different. These days, integrated LUFS measurements are the most common, but you can also use RMS, maximum short-term or momentary loudness, or even something like the top of the LRA value. Loudness normalization is effective-and useful-because it uses some form of loudness measurement to make disparate audio files have a similar perceived loudness. The problem is that peak levels have very little bearing on how loud we perceive a sound as being. This type of normalization used to be very common for sample libraries, and is still frequently available in many audio workstations. Practically, though, normalization is almost always applied to either peak level or some type of loudness measurement.įor peak normalization, you can use either sample peak or true peak measurements. As mentioned above, in principle, audio normalization could be applied to any aspect of an audio file that you can measure and adjust.

Some type of sound normalizer to increase quiet whispers