Meas formants walkthrough
This page shows the old style of using audiolabel, which is not recommended. For a better approach see the notebooks on audiolabel's read_label function and automated formant measurements.
You can download audiolabel from its github repository.
This page walks you through many of the concepts used in meas_formants, a script that reads Praat TextGrids, searches for vowel tokens, and runs a formant analyzer. Formant measurements are extracted from the results and written to a text file, along with additional metadata retrieved from the TextGrids.
Let's take a look at this in pseudocode first for a conceptual overview of the program flow:
read in a textgrid
run the ifcformant system command
read the ifcformant output
search the textgrid for vowel tokens (V):
if V meets size condition:
get word and context of V from the textgrid
get regularly-sampled formant measurements for V:
output measurement with word and context
Our input TextGrids will have three interval tiers each, with tier names 'phone', 'word', and 'context'.
Getting started
First, import the necessary libraries:
import audiolabel # library for reading phonetic label files import os, subprocess # access to system commands import re # regular expressions import numpy as np # numeric processing routines reload(audiolabel)
Output:
<module 'audiolabel' from 'audiolabel.py'>
Read in a TextGrid
Our script assumes that TextGrids reside in the same directory as the
.wav files they annotate, and that they share the same name, except
for the extension.
tg = '../test/this_is_a_label_file.TextGrid' # name of Praat TextGrid fname = os.path.splitext(tg)[0] # get filename without extension fname
Output:
'../test/this_is_a_label_file'
Now we create a LabelManager by reading in the TextGrid with the
hint that the file type is 'praat'. The LabelManager
can distinguish 'short' and 'long' Praat formats. If
you want to be explicit, you can use 'praat_short' or
'praat_long' as the from_type.
pm = audiolabel.LabelManager(from_file=tg, from_type='praat')
lab = pm.tier('word').label_at(0.5736)
lab.center
Output:
0.621615744181556
Run the ifcformant system command
ifcformant is our formant analysis program. It is a separate
executable available on the system.
We will use Python's subprocess module to execute the command and
check the return code. To set this up we'll make a list of arguments to pass to
subprocess, with the ifcformant command itself
included as the first item in the list.
tempifc = '__temp.ifc' # output destination of ifcformant command
speaker = 'male' # gender of speaker
ifc_args = ['ifcformant', # list of arguments
'--speaker=' + speaker,
'-e', 'gain -n -3 sinc -t 10 60 contrast',
'--print-header',
'--output=' + tempifc,
fname + '.wav']
ifc_args
Output:
['ifcformant',
'--speaker=male',
'-e',
'gain -n -3 sinc -t 10 60 contrast',
'--print-header',
'--output=doc/__temp.ifc',
'../test/this_is_a_label_file.wav']
We use Popen to execute ifcformant and return a handle
to the running process. We need to wait() for the process to finish
before continuing on with our script. ifcformant can take a while
to finish, so this is important!
Any errors reported by ifcformant are connected to
subprocess.PIPE so that we can report the errors if
ifcformant fails.
proc = subprocess.Popen(ifc_args, stderr=subprocess.PIPE) proc.wait()
Output:
0
Finally, we check the process's return code to see whether
ifcformant succeeded, in which case the code is 0. If
ifcformant failed, we report the error and raise an exception.
if proc.returncode != 0:
for line in proc.stderr:
sys.stderr.write(line + '\n')
raise Exception("ifcformant exited with status: {0}".format(proc.returncode))
Read the ifcformant output
ifcformant produces a table of numbers. Since we included the --print-header argument the output contains a header row identifying the
fields. Here are the first 10 lines of output:
!head __temp.ifc
Output:
sec rms f1 f2 f3 f4 f0 0.0050 0.0 0.0 0.0 0.0 0.0 0.0 0.0150 0.0 0.0 0.0 0.0 0.0 0.0 0.0250 0.0 0.0 0.0 0.0 0.0 0.0 0.0350 75.0 354.4 1078.2 2368.6 3173.0 0.0 0.0450 75.0 386.1 1427.1 2427.6 3291.3 0.0 0.0550 82.2 473.6 1536.8 2525.5 3369.1 240.0 0.0650 82.2 450.1 1481.2 2584.4 3366.3 106.2 0.0750 240.8 526.8 1505.8 2572.2 3333.2 193.5 0.0850 766.0 500.5 1580.9 2337.8 3358.4 150.0
If you think about it, this output is a kind of label file. We have six columns
of output for each timepoint, and we can think of these as six label tiers.
LabelManager provides a read_table() method for reading
in tabular data, and we can invoke it automatically by specifying
from_type='table' when creating the LabelManager.
The t1_col='sec' argument tells read_table() that each
Label's t1 (see below) is identified by the column
labelled 'sec'.
ifc = audiolabel.LabelManager(from_file=tempifc, from_type='table', t1_col='sec')
ifc.tier('f1').label_at(1.620)
Output:
Label( t1=1.6250, text='445.7' )
Search the textgrid for vowel tokens
We want to extract formant measurements during sections of the audio file that
are identified as vowels. We set up a regular expression that matches vowel
tokens in the TextGrid's 'phone' tier. This regex contains two
named capture groups, 'vowel' and 'stress', that will
be returned when a matching label is found.
vre = re.compile(
"(?P<vowel>AA|AE|AH|AO|AW|AX|AXR|AY|EH|ER|EY|IH|IX|IY|OW|OY|UH|UW|UX)(?P<stress>\d)?"
)
vre
Output:
re.compile(r'(?P<vowel>AA|AE|AH|AO|AW|AX|AXR|AY|EH|ER|EY|IH|IX|IY|OW|OY|UH|UW|UX)(?P<stress>\d)?')
A LabelManager tier is a Sequence, which allows for easy access to
labels in a loop:
for lab in pm.tier('context'):
print lab.text
Output:
1 2
A LabelManager tier has a search() method that applies
a regex and returns every Label that matches.
tokens = pm.tier('phone').search(vre)
tokens
Output:
[Label( t1=0.1850, t2=0.3058, text='IH2' ),
Label( t1=0.4208, t2=0.5183, text='IH0' ),
Label( t1=0.5736, t2=0.6696, text='AH1' )]
In our script we also want to return the named captures from our match. We
change the return values of search() with the
return_match parameter. You can see that search() now returns
tuples of Label objects and associated Match objects. Notice
the parentheses surrounding each pair.
tokens = pm.tier('phone').search(vre, return_match=True)
tokens
Output:
[(Label( t1=0.1850, t2=0.3058, text='IH2' ), <_sre.SRE_Match at 0x5c00828>),
(Label( t1=0.4208, t2=0.5183, text='IH0' ), <_sre.SRE_Match at 0x5c00d78>),
(Label( t1=0.5736, t2=0.6696, text='AH1' ), <_sre.SRE_Match at 0x5f96140>)]
It is simple to set up a loop to access each matching Label and its
associated match data. This for loop iterates over every vowel and match in the list returned by search().
for v, m in pm.tier('phone').search(vre, return_match=True):
print v.t1, '-', v.t2
print v.text
print m.group('vowel')
print m.group('stress')
print
Output:
0.185027889005 - 0.305817195604 IH2 IH 2 0.4207853308 - 0.51828995179 IH0 IH 0 0.573591080112 - 0.669640408251 AH1 AH 1
A conditional expression restricts the results to other criteria:
for v, m in pm.tier('phone').search(vre, return_match=True):
if v.duration > 0.1:
print v.t1, '-', v.t2()
print v.text
print m.group('vowel')
print m.group('stress')
print
Output:
0.185027889005 - 0.305817195604 IH2 IH 2
The 'word' and 'context' tiers can be queried to
return the Label that occurs at the time of the vowel token with
the label_at method.
for v, m in pm.tier('phone').search(vre, return_match=True):
if v.duration > 0.1:
print v.text
word = pm.tier('word').label_at(v.center)
print word.text
context = pm.tier('context').label_at(v.center)
print context.text
Output:
IH2 This 1
Get regularly-sampled formant measurements
For this script we want formant measurments at the start and end of each vowel
token, plus five equally-spaced timepoints in between. The linspace
function from numpy simplifies the calculation of these timepoints:
for v, m in pm.tier('phone').search(vre, return_match=True):
if v.duration > 0.1:
points = np.linspace(v.t1, v.t2, num=7)
print points
Output:
[ 0.18502789 0.20515944 0.22529099 0.24542254 0.26555409 0.28568564
0.3058172 ]
The LabelManager has a labels_at method that calls the
label_at method on every tier managed by the
LabelManager and returns a tuple of the results. If the tiers are
named, the elements of the tuple can be accessed with the corresponding name.
for v, m in pm.tier('phone').search(vre, return_match=True):
if v.duration > 0.1:
for t in np.linspace(v.t1, v.t2, num=7):
meas = ifc.labels_at(t)
print meas.f1.text
Output:
237.8 425.9 416.8 476.5 503.4 510.5 474.9
Get word and context from the TextGrid
This is a format string that is used to output results. The field names are in curly brackets, and the portion after the colon specifies formatting details for numeric formats. Output is tab-separated.
fmt = '\t'.join(["{t1:0.4f}", "{t2:0.4f}", "{lintime:0.4f}", "{ifctime:0.4f}",
"{idx:d}", "{vowel}", "{stress}", "{rms}", "{f1}", "{f2}",
"{f3}", "{f4}", "{f0}", "{word}", "{context}\n"
])
fmt
Output:
'{t1:0.4f}\t{t2:0.4f}\t{lintime:0.4f}\t{ifctime:0.4f}\t{idx:d}\t{vowel}\t{stress}\t{rms}\t{f1}\t{f2}\t{f3}\t{f4}\t{f0}\t{word}\t{context}\n'
And this creates our header line. As with the fmt assignment, we
join the fields with a tab. Tabs could be inserted directly in to the string
instead of using join() and split(), but the extra
syntax makes the field names easier to pick out for a human reader.
head = '\t'.join(('t1 t2 lintime ifctime idx vowel stress \
rms f1 f2 f3 f4 f0 word context'
).split()) + '\n'
head
Output:
't1\tt2\tlintime\tifctime\tidx\tvowel\tstress\trms\tf1\tf2\tf3\tf4\tf0\tword\tcontext\n'
head2 = 't1\tt2\tlintime\tifctime\tidx\tvowel\tstress\trms\tf1\tf2\tf3\tf4\tf0\tword\tcontext\n' head2
Output:
't1\tt2\tlintime\tifctime\tidx\tvowel\tstress\trms\tf1\tf2\tf3\tf4\tf0\tword\tcontext\n'
