.directory

@@ -1,4 +0,0 @@
-[Dolphin]
-Timestamp=2021,8,16,9,0,3
-Version=4
-ViewMode=1

.gitignore

@@ -1 +1,6 @@
 *.swp
+__pycache__
+corpus-processed
+settings.json
+countries.mmdb
+.directory

LICENSE

@@ -0,0 +1,661 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
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+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU Affero General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU Affero General Public License for more details.
+
+    You should have received a copy of the GNU Affero General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If your software can interact with users remotely through a computer
+network, you should also make sure that it provides a way for users to
+get its source.  For example, if your program is a web application, its
+interface could display a "Source" link that leads users to an archive
+of the code.  There are many ways you could offer source, and different
+solutions will be better for different programs; see section 13 for the
+specific requirements.
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU AGPL, see
+<https://www.gnu.org/licenses/>.

acousticgender/.hidden

@@ -0,0 +1 @@
+__pycache__

acousticgender/__init__.py

@@ -0,0 +1 @@
+

acousticgender/corpusanalysis.py

@@ -0,0 +1,144 @@
+#!/usr/bin/env python3
+
+print('corpusanalysis.py: __package__ ->', __package__)
+
+import os, subprocess, json
+from collections import defaultdict 
+from statistics import mean, median, stdev
+
+from library import preprocessing, phones, resonance
+
+corpus_dir = './corpus'
+processed_corpus_dir = './corpus-processed'
+
+if not os.path.exists(processed_corpus_dir):
+	os.mkdir(processed_corpus_dir)
+
+	subprocess.run(['chmod', '777', '-R', processed_corpus_dir])
+
+	for directory in os.listdir(corpus_dir):
+		transcript = ''
+		recording = None
+		for filename in os.listdir(corpus_dir + '/' + directory):
+			if '.txt' in filename:
+				with open(corpus_dir + '/' + directory + '/' + filename) as f:
+					transcript = f.read()
+			elif len([
+				ftype for ftype in 
+				['.wav', '.mp3', '.ogg', '.opus'] 
+				if ftype in filename]
+			) == 1:
+				with open(corpus_dir + '/' + directory + '/' + filename, 'rb') as f:
+					recording = f.read()
+
+		out_dir = processed_corpus_dir + '/' + directory
+		if not os.path.exists(out_dir): os.mkdir(out_dir)
+		subprocess.run(['chmod', '777', '-R', out_dir])
+
+		preprocessing.process(recording, transcript, out_dir)
+				
+m_count = 0
+f_count = 0
+m_data = []
+f_data = []
+for directory in os.listdir(processed_corpus_dir):
+	tsv_file =  (processed_corpus_dir + '/' + directory + 
+	             '/output/recording.tsv')
+	if os.path.exists(tsv_file):
+		with open(tsv_file) as f:
+			tsv_text = f.read()
+
+		if directory[0] == 'm': 
+			m_data.append(phones.parse(tsv_text))
+			m_count += 1
+		if directory[0] == 'f': 
+			f_data.append(phones.parse(tsv_text))
+			f_count += 1
+
+if len(f_data) > len(m_data):
+	f_data = f_data[0:len(m_data)]
+if len(m_data) > len(f_data):
+	m_data = m_data[0:len(f_data)]
+
+print('m_count', m_count)
+print('f_count', f_count)
+print('len(f_data)', len(f_data))
+print('len(m_data)', len(m_data))
+
+	
+population_phones = defaultdict(list)
+for data in m_data + f_data:
+	for phone in data['phones']: 
+		if (phone['F'] and phone['F'][0] and phone['F'][1] and 
+		    phone['F'][2] and phone['F'][3]
+		):
+			population_phones[phone['phoneme']].append(phone)
+
+phone_stats = {}
+
+for phoneme in population_phones:
+	print(phoneme)
+	Fs = [[phone['F'][i] for phone in population_phones[phoneme] ]
+	       for i in range(4)]
+	for i in range(4):
+		print('\tf' + str(i), mean(Fs[i]), stdev(Fs[i]))
+
+	phone_stats[phoneme] = [
+		{	'mean' : mean(Fs[i]), 
+			'stdev': stdev(Fs[i]),
+			'median': median(Fs[i]),
+			'max': max(Fs[i]),
+			'min': min(Fs[i]),
+		} for i in range(4)
+	]
+
+with open('stats.json', 'w') as f:
+	f.write(json.dumps(phone_stats))
+
+
+# A little brute-forcing never hurt anyone.
+granularity = 56
+weights_candidates = []
+for i in range(granularity + 1):
+	for j in range(granularity + 1):
+		for k in range(granularity + 1):
+			if i + j + k == granularity:
+				weights_candidates.append([
+					i / granularity, 
+					j / granularity, 
+					k / granularity
+				])
+
+max_accuracy = 0
+winner = None
+for weights in weights_candidates:
+
+	for data in m_data + f_data:
+		resonance.compute_resonance(data, weights)
+
+	median_resonance = median([data['meanResonance'] for data in m_data + f_data])
+
+	correct_count = 0
+	total = 0
+
+	for data in m_data:
+		if data['meanResonance'] <= median_resonance:
+			correct_count += 1
+		total += 1
+
+	for data in f_data:
+		if data['meanResonance'] >= median_resonance:
+			correct_count += 1
+		total += 1
+
+	accuracy = correct_count / total
+	if accuracy >= max_accuracy:
+		max_accuracy = accuracy
+		winner = weights
+	print(weights, accuracy)
+
+print('Best weight:', winner)
+
+with open('weights.json', 'w') as f:
+	f.write(json.dumps(winner))
+

acousticgender/library/__init__.py

@@ -0,0 +1 @@
+

acousticgender/library/phones.py

@@ -0,0 +1,63 @@
+import re
+
+def parse(praat_output):
+	word_lines = []
+	phoneme_lines = []
+	active_list = None
+
+	for line in praat_output.split('\n'):
+		if line == "Words:":
+			active_list = word_lines
+			continue
+		if line == "Phonemes:":
+			active_list = phoneme_lines
+			continue
+
+		active_list.append(line)	
+
+	data = {}
+
+	pronunciation_dict = {}
+	with open('cmudict.txt', 'r', encoding='iso-8859-1') as f:
+		for line in f.readlines():
+			cols = line.split()
+			pronunciation_dict[cols[0]] = cols[1:]
+
+	data['words'] = []
+	for line in word_lines:
+		cols = line.split('\t')
+		data['words'].append({
+			'time' : float(cols[0]),
+			'word' : cols[1],
+			'expected' : (pronunciation_dict.get(cols[1].upper()) or [None]) + [None] * 10
+		})
+
+	data['phones'] = []
+	word_index = -1
+	phoneme_in_word_index = 0
+	for line in phoneme_lines:	
+		cols = line.split('\t')
+
+		if len(cols) < 3: continue
+
+		time = float(cols[0]) if re.match(r'^-?\d+(?:\.\d+)?$', cols[0]) else None
+		while (
+			type(time) == float and 
+			word_index < len(data['words']) - 1 and
+			time >= data['words'][word_index + 1]['time']
+		):
+			word_index += 1
+			phoneme_in_word_index = 0
+			
+		data['phones'].append({
+			'time': time,
+			'phoneme': cols[1],
+			'word_index': word_index,
+			'word': data['words'][word_index],
+			'word_time': data['words'][word_index]['time'],
+			'expected': data['words'][word_index]['expected'][phoneme_in_word_index],
+			'F': [None if '--' in f else float(f) for f in cols[2:]]
+		})
+		phoneme_in_word_index += 1
+
+	return data

acousticgender/library/preprocessing.py

@@ -0,0 +1,126 @@
+
+print('preprocessing.py: dir() -> ', dir())
+print('preprocessing.py: __name__ -> ', __name__)
+import subprocess, os, glob, shutil
+import magic
+
+from . import settings as settings_module
+
+settings = settings_module.settings
+
+def process(uploaded_file, transcript, tmp_dir):
+	################## Noise Removal ##################
+
+	os.mkdir(tmp_dir)
+	filetype = magic.from_buffer(uploaded_file)
+
+	input_file    = tmp_dir + '/orig'
+	format_file   = tmp_dir + '/format.wav'
+	silence_file  = tmp_dir + '/silence.wav'
+	clean_file    = tmp_dir + '/clean.wav'
+	noise_profile = tmp_dir + '/noise.prof'
+
+	with open(input_file, "wb") as f: 
+		f.write(uploaded_file)
+
+	assert(os.path.exists(input_file))
+
+	try:
+		subprocess.check_output(['ffmpeg', '-i', input_file, format_file])
+
+		ffmpeg_silence = subprocess.check_output([
+				'ffmpeg', '-i', input_file, 
+				'-af', 'silencedetect=n=-30dB:d=0.5', 
+				'-f', 'null', '-'
+		], stderr=subprocess.STDOUT).decode('utf-8').split('\n')
+
+		silence_ranges = list(zip(
+				[line.split(" ")[4] for line in ffmpeg_silence 
+						if 'silence_start' in line],
+				[line.split(" ")[4] for line in ffmpeg_silence
+						if 'silence_end' in line]
+		))
+
+		subprocess.check_output(['ffmpeg', '-i', input_file, 
+				'-af', "aselect='" + '+'.join(
+						['between(t,' + r[0] + ',' + r[1]+')' for r in silence_ranges]
+				) + "', asetpts=N/SR/TB", 
+				silence_file
+		])
+
+		assert(os.path.exists(silence_file))
+
+		subprocess.check_output([
+				'sox', silence_file, '-n', 'noiseprof', noise_profile
+		])
+		subprocess.check_output(['chmod', '777', noise_profile])
+		subprocess.check_output([
+				'sox', format_file, clean_file, 
+				'noisered', noise_profile, '0.2'
+		])
+
+		assert(os.path.exists(clean_file))
+	except e:
+		clean_file = input_file
+
+	################## Forced Alignment ##################
+	corpus_dir = tmp_dir + '/corpus'
+	output_dir = tmp_dir + '/output'
+	os.mkdir(corpus_dir)
+	os.mkdir(output_dir)
+
+	subprocess.check_output([
+		'ffmpeg' , 
+		'-i'     , clean_file,
+		'-acodec', 'pcm_s16le',
+		'-ac'    , '1',
+		'-ar'    , '16000',
+		corpus_dir + '/recording.wav'
+	])
+
+	subprocess.run(['chmod', '777', '-R', tmp_dir])
+
+	with open(corpus_dir + '/recording.txt', 'w') as f:
+		f.write(transcript)
+
+	with open(tmp_dir + '/align.sh', 'w') as f:
+		f.write("""#!/usr/bin/env bash
+		source /opt/conda/etc/profile.d/conda.sh
+		conda activate aligner
+		mfa align ./corpus/ english english ./output/ --clean
+		""")
+
+	assert(os.path.exists(tmp_dir + '/align.sh'))
+
+	subprocess.check_output(['chmod', '777', tmp_dir + '/align.sh'])
+	cwd = os.getcwd()
+	os.chdir(tmp_dir)
+
+	try:
+		# shell out so we can `source`
+		subprocess.check_output(['./align.sh'], stderr=subprocess.STDOUT) 
+	except subprocess.CalledProcessError as e:
+		print("CalledProcessError")
+		print(e)
+		print(str(e.output, 'utf-8'))
+	except Exception as e:
+		print("Error")
+		print(e)
+	
+	os.chdir(cwd)
+
+
+	################## Phonetic Processing ##################
+	for recording, grid in zip(
+		glob.glob(corpus_dir + '/*.wav'), 
+		glob.glob(output_dir + '/*.TextGrid')
+	):
+		praat_output = subprocess.check_output([
+			'./textgrid-formants.praat', recording, grid
+		]).decode('utf-8')
+
+		with open(grid.replace('.TextGrid', '.tsv'), 'w') as f:
+			f.write(praat_output)
+
+		shutil.rmtree(tmp_dir)
+		return praat_output