vowel_length.py

import os
import numpy as np
from collections import defaultdict
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt


# return phones from the start to end time of one word
def getwps(start,end,phones):
 return [(p,s,e) for p,s,e in phones if (s>=start) & (e<=end)]

# read align data from mfa file
def read_mfa(apath):
 with open(apath,'r') as handle:
 f = handle.read().splitlines()
 f = [l.split(',') for l in f]
 wlines = [(w,float(s),float(e)) for s,e,w,t,_ in f if t=='words']
 plines = [(p,float(s),float(e)) for s,e,p,t,_ in f if t=='phones']
 aligns = [(w,s,e,getwps(s,e,plines)) for w,s,e in wlines]
 return aligns

# key specific to MFA pronunciation dictionary -
# which phones are relevant per word
def read_ph_key(fpath):
 def _winfo(l):
 def _f(n):
 return tuple([int(i) if i != 'X' else i for i in n.split(',')])
 return (_f(l[3]),_f(l[4]))
 
 def _d2d(dic):
 if isinstance(dic, dict):
 dic = {k: _d2d(v) for k, v in dic.items()}
 return dic
 
 with open(fpath,'r') as handle:
 f = handle.read().splitlines()
 f = [l.split('\t') for l in f[1:]]
 
 vcdict = {'w2v2': defaultdict(lambda: defaultdict(tuple)), \
 'mfa': defaultdict(lambda: defaultdict(tuple))}
 for wline in f:
 vcdict[wline[1]][wline[0]][wline[2]] = _winfo(wline)

 return _d2d(vcdict)


def get_vc_dur(kwd,atype,dat,vcd,adir):
 d = [l for l in dat if kwd in l[-1]]
 prlist = []
 
 if atype=='w2v2': # only one pronunciation-spelling for ctc
 pspel = kwd
 
 for l in d:
 apath = f'{adir}{l[2]}/{l[3].split(".")[0]}.csv'
 if os.path.exists(apath):
 aligns = read_mfa(apath)
 aligns = [a for a in aligns if a[0] == kwd]
 for al in aligns:
 if atype == 'mfa': # get this recording's phone spelling
 pspel = ' '.join([a for a,s,e in al[3]])
 
 vstart = al[3][vcd[pspel][0][0]][1]
 vend = al[3][vcd[pspel][0][-1]][2]
 cstart = al[3][vcd[pspel][1][0]][1]
 cend = al[3][vcd[pspel][1][-1]][2]
 vdur = vend-vstart
 cdur = cend-cstart
 prlist.append(tuple([vdur,cdur]))
 return prlist


# TODO:
# pass word, lang, aln info for fig title;
# pass r/l info for point colour
def displ(prinfo):
 rto = np.mean([v/c for v,c in prinfo])
 vs = [1000*v for v,c in prinfo]
 cs = [1000*c for v,c in prinfo]
 
 fig = plt.figure(figsize=(6,5))

 plt.xlim([0.0, max(500,min(max(vs),1000))])
 plt.ylim([0.0, max(500,min(max(cs),1000))])
 plt.scatter(vs,cs)
 plt.axline((0,0),slope=1,color="darkgray")
 
 plt.xlabel("Vowel length (ms)")
 plt.ylabel("Consonant length (ms)")
 plt.title(f'TITLE\nRatio: {round(rto,2)}')
 return(rto,fig)



#run analysis
# for keyword, speaker-background, align-source,
# vowel/consonant index key, and dataset
def runan(kwd,spl,aln,vck,dat,sources):
 kwd = kwd.lower()
 spl = spl.lower()
 aln = aln.lower()
 print(kwd,aln,spl)
 vcd = vck[aln][kwd]

 if any([ tuple('X') in el for el in vcd.values()]):
 #print("EXCLUDED WORD FOR THIS ALIGNMENT TYPE")
 return "EXCLUDED WORD FOR THIS ALIGNMENT TYPE"
 if spl == 'l1':
 d = [l for l in dat if l[8].lower() == 'icelandic']
 if spl == 'l2':
 d = [l for l in dat if l[8].lower() != 'icelandic']
 prinfo = get_vc_dur(kwd,aln,d,vcd,sources[aln])
 if len(prinfo) >5:
 r,f = displ(prinfo)
 return(f)
 else:
 return 0
 # TODO THIS CASE
 

def setup(meta,phkey):
 with open(meta,'r') as handle:
 dat = handle.read().splitlines()
 dat = [l.split('\t') for l in dat[1:]]
 dat = [l[:-1] + [tuple(l[-1].split(' '))] for l in dat]

 kws = [l[-1] for l in dat]
 kws = [i for t in kws for i in t]
 kws = sorted(list(set(kws)))

 return dat, read_ph_key(phkey), kws