peruse 0.0.3

Explore a waveform with slang

peruse

Explore a waveform with slang

To install: pip install peruse

Example

from numpy import *
from hum import disp_wf, plot_wf
import soundfile as sf
from peruse.single_wf_snip_analysis import TaggedWaveformAnalysisExtended
import os
import pickle

try:
    from slang.util_data import displayable_unichr
    unichr_code_of_snip = array(displayable_unichr
                                + list(unique(list(set(range(33, 20000)).difference(displayable_unichr)))))
    snip_of_unichr_code = (nan * empty(unichr_code_of_snip.max() + 1)).astype(int)
    snip_of_unichr_code[unichr_code_of_snip] = arange(len(unichr_code_of_snip))

    def snip_to_str(snip):
        return chr(unichr_code_of_snip[snip])
except ImportError as e:
    def snip_to_str(snip):
        return chr(33 + snip)

def string_of_snips(snips):
    return "".join(map(snip_to_str, snips))

filepath = "Enter audio filepath here"
wf, sr = sf.read(filepath)
disp_wf(wf, sr)

Unsupervised

Perhaps you just want to get a perspective on your sound, without specifying annotations.

Perhaps you don't know what to annotate and you want snips to help you find patterns to annotate.

Fit the snipper

from peruse.single_wf_snip_analysis import TaggedWaveformAnalysisExtended

tw = TaggedWaveformAnalysisExtended(sr=sr, 
                                    tile_size_frm=2048, 
                                    chk_size_frm=43008, 
                                    prior_count=1)
tw.fit(wf)

Get the snips of a waveform (here the same as fit with, but could be another)

snips = tw.snips_of_wf(wf)
len(snips), len(unique(snips))

View them as characters

print(string_of_snips(snips))

annkncckkobbjhihjacjcjeneijaeofgibiecikjjkdnhajekachbobchoadkjjjjknkkkjofglmijaiinieajdccnkjnollil
hjhkokejkeacdkcofgaldikljnekdijekkjieoboocinchjadannadnnjofgiiielaekheiccnkkeejlbbllichckkinojaeoa
aofgllllnadkdkcneian

Plot (inverse of) snip probabilities (says how rare they are (outliers) from the perspective of the wf that was used to fit, and gives SOME view of the sound)

tw.plot_tiles(1 / array(list(map(tw.prob_of_snip.get, snips))));

tw.plot_tiles(log(1 / array(list(map(tw.prob_of_snip.get, snips)))));