FA result evaluation and xsampa to ipa conversion is updated.
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yemaozi88
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5fb05ddab2
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d56ef7f075
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@ -22,11 +22,11 @@ dataset_list = ['devel', 'test', 'train']
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extract_features = 0
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make_feature_list = 0
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conv_lexicon = 0
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check_lexicon = 0
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check_lexicon = 1
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make_mlf = 0
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combine_files = 0
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flat_start = 0
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train_model = 1
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train_model = 0
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forced_alignment = 0
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@ -133,7 +133,11 @@ if check_lexicon:
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print("==== check if all the phones are successfully converted. ====\n")
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# the phones used in the lexicon.
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phonelist = am_func.get_phonelist(lex_htk)
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phonelist_asr = am_func.get_phonelist(lex_asr)
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phonelist_oov = am_func.get_phonelist(lex_oov)
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phonelist_htk = am_func.get_phonelist(lex_htk)
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phonelist = phonelist_asr.union(phonelist_oov)
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# the lines which include a specific phone.
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lines = am_func.find_phone(lex_asr, 'g')
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@ -3,19 +3,54 @@ import sys
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import csv
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import subprocess
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import configparser
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from collections import Counter
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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## ======================= user define =======================
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## ======================= functions =======================
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def read_fileFA(fileFA):
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"""
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read the result file of HTK forced alignment.
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this function only works when input is one word.
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"""
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with open(fileFA, 'r') as f:
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lines = f.read()
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lines = lines.split('\n')
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phones = []
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for line in lines:
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line_split = line.split()
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if len(line_split) > 1:
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phones.append(line_split[2])
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return ' '.join(phones)
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#####################
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## USER DEFINE ##
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#####################
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curr_dir = r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model'
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config_ini = curr_dir + '\\config.ini'
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forced_alignment_module = r'C:\Users\Aki\source\repos\forced_alignment'
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forced_alignment_module = r'C:\Users\Aki\source\repos\forced_alignment'
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forced_alignment_module_old = r'C:\OneDrive\Research\rug\code\forced_alignment\forced_alignment'
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ipa_xsampa_converter_dir = r'C:\Users\Aki\source\repos\ipa-xsama-converter'
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csvfile = r"C:\OneDrive\Research\rug\stimmen\Frisian Variants Picture Task Stimmen.csv"
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experiments_dir = r'C:\OneDrive\Research\rug\experiments'
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data_dir = experiments_dir + '\\stimmen\\data'
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cygwin_dir = r'C:\cygwin64\home\Aki\acoustic_model'
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# procedure
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convert_phones = 0
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make_dic_files = 0
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make_dic_files_short = 0
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do_forced_alignment = 0
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eval_forced_alignment = 1
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## ======================= add paths =======================
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@ -28,6 +63,10 @@ sys.path.append(curr_dir)
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import convert_xsampa2ipa
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import acoustic_model_functions as am_func
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# for forced-alignment
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sys.path.append(forced_alignment_module_old)
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import pyHTK
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## ======================= load variables =======================
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config = configparser.ConfigParser()
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@ -40,85 +79,179 @@ lex_asr = FAME_dir + '\\lexicon\\lex.asr'
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lex_asr_htk = FAME_dir + '\\lexicon\\lex.asr_htk'
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## ======================= check phones included in FAME! =======================
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# the phones used in the lexicon.
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#phonelist = am_func.get_phonelist(lex_htk)
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# the lines which include a specific phone.
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#lines = am_func.find_phone(lex_asr, 'x')
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## ======================= convert phones ======================
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mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', ipa_xsampa_converter_dir)
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if convert_phones:
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mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', ipa_xsampa_converter_dir)
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with open(csvfile, encoding="utf-8") as fin:
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lines = csv.reader(fin, delimiter=';', lineterminator="\n", skipinitialspace=True)
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next(lines, None) # skip the headers
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## check phones included in FAME!
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# the phones used in the lexicon.
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#phonelist = am_func.get_phonelist(lex_htk)
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filenames = []
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words = []
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pronunciations = []
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for line in lines:
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if line[1] is not '' and len(line) > 5:
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filenames.append(line[0])
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words.append(line[1])
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pron_xsampa = line[3]
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pron_ipa = convert_xsampa2ipa.conversion('xsampa', 'ipa', mapping, pron_xsampa)
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pron_ipa = pron_ipa.replace('ː', ':')
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pron_famehtk = convert_phone_set.ipa2famehtk(pron_ipa)
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# the lines which include a specific phone.
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#lines = am_func.find_phone(lex_asr, 'x')
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with open(csvfile, encoding="utf-8") as fin:
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lines = csv.reader(fin, delimiter=';', lineterminator="\n", skipinitialspace=True)
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next(lines, None) # skip the headers
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filenames = []
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words = []
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pronunciations = []
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for line in lines:
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if line[1] is not '' and len(line) > 5:
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filenames.append(line[0])
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words.append(line[1])
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pron_xsampa = line[3]
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pron_ipa = convert_xsampa2ipa.conversion('xsampa', 'ipa', mapping, pron_xsampa)
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pron_ipa = pron_ipa.replace('ː', ':')
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pron_famehtk = convert_phone_set.ipa2famehtk(pron_ipa)
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# adjust to phones used in the acoustic model.
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pron_famehtk = pron_famehtk.replace('sp', 'sil')
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pron_famehtk = pron_famehtk.replace('ce :', 'ce') # because ceh is ignored.
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pron_famehtk = pron_famehtk.replace('w :', 'wh')
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pron_famehtk = pron_famehtk.replace('e :', 'eh')
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pron_famehtk = pron_famehtk.replace('eh :', 'eh')
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pron_famehtk = pron_famehtk.replace('ih :', 'ih')
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# adjust to phones used in the acoustic model.
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pron_famehtk = pron_famehtk.replace('sp', 'sil')
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pron_famehtk = pron_famehtk.replace('ce :', 'ce') # because ceh is ignored.
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pron_famehtk = pron_famehtk.replace('w :', 'wh')
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pron_famehtk = pron_famehtk.replace('e :', 'eh')
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pron_famehtk = pron_famehtk.replace('eh :', 'eh')
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pron_famehtk = pron_famehtk.replace('ih :', 'ih')
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#translation_key = {'sp': 'sil', 'ce :': 'ceh', 'w :': 'wh'}
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#pron = []
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#for phoneme in pron_famehtk.split(' '):
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# pron.append(translation_key.get(phoneme, phoneme))
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#pronunciations.append(' '.join(pron_famehtk))
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pronunciations.append(pron_famehtk)
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#translation_key = {'sp': 'sil', 'ce :': 'ceh', 'w :': 'wh'}
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#pron = []
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#for phoneme in pron_famehtk.split(' '):
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# pron.append(translation_key.get(phoneme, phoneme))
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#pronunciations.append(' '.join(pron_famehtk))
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pronunciations.append(pron_famehtk)
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filenames = np.array(filenames)
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words = np.array(words)
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pronunciations = np.array(pronunciations)
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# check if all phones are in the phonelist of the acoustic model.
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#phonelist = ' '.join(pronunciations)
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#np.unique(phonelist.split(' '))
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#phonelist.find(':')
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del line, lines
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del pron_xsampa, pron_ipa, pron_famehtk
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filenames = np.array(filenames)
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words = np.array(words)
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pronunciations = np.array(pronunciations)
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# check if all phones are in the phonelist of the acoustic model.
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#phonelist = ' '.join(pronunciations)
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#np.unique(phonelist.split(' '))
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#phonelist.find(':')
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del line, lines
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del pron_xsampa, pron_ipa, pron_famehtk
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# make dict files.
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np.save(data_dir + '\\filenames.npy', filenames)
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np.save(data_dir + '\\words.npy', words)
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np.save(data_dir + '\\pronunciations.npy', pronunciations)
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else:
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filenames = np.load(data_dir + '\\filenames.npy')
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words = np.load(data_dir + '\\words.npy')
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pronunciations = np.load(data_dir + '\\pronunciations.npy')
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word_list = np.unique(words)
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word_id = 1
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word = word_list[word_id]
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## ======================= make dict files used for HTK. ======================
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if make_dic_files:
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output_dir = experiments_dir + r'\stimmen\dic'
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for word in word_list:
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WORD = word.upper()
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fileDic = output_dir + '\\' + word + '.dic'
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# make dic file.
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pronvar_ = pronunciations[words == word]
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pronvar = np.unique(pronvar_)
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with open(fileDic, 'w') as f:
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for pvar in pronvar:
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f.write('{0}\t{1}\n'.format(WORD, pvar))
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## ======================= make dict files for most popular words. ======================
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if make_dic_files_short:
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output_dir = experiments_dir + r'\stimmen\dic'
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#word = word_list[3]
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for word in word_list:
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WORD = word.upper()
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fileStat = output_dir + '\\' + word + '_stat.csv'
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pronvar = pronunciations[words == word]
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c = Counter(pronvar)
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total_num = sum(c.values())
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with open(fileStat, 'w') as f:
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for key, value in c.items():
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f.write('{0}\t{1:.2f}\t{2}\t{3}\n'.format(value, value/total_num*100, WORD, key))
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## ======================= forced alignment =======================
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#if forced_alignment:
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# try:
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# scripts.run_command([
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# 'HVite','-T', '1', '-a', '-C', configHVite,
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# '-H', AcousticModel, '-m', '-I',
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# mlf_file, '-i', fa_file, '-S',
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# script_file, htk_dict_file, filePhoneList
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# ])
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# except:
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# print("\033[91mHVite command failed with these input files:\033[0m")
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# print(_debug_show_file('HVite config', configHVite))
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# print(_debug_show_file('Accoustic model', AcousticModel))
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# print(_debug_show_file('Master Label file', mlf_file))
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# print(_debug_show_file('Output', fa_file))
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# print(_debug_show_file('Script file', script_file))
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# print(_debug_show_file('HTK dictionary', htk_dict_file))
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# print(_debug_show_file('Phoneme list', filePhoneList))
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# raise
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if do_forced_alignment:
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configHVite = cygwin_dir + r'\config\config.HVite'
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filePhoneList = experiments_dir + r'\friesian\acoustic_model\config\phonelist_friesian.txt'
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wav_dir = experiments_dir + r'\stimmen\wav'
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#for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128]:
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for hmm_num in [64]:
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hmm_num_str = str(hmm_num)
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AcousticModel = experiments_dir + r'\friesian\acoustic_model\model\hmm' + hmm_num_str + r'-3\hmmdefs'
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predictions = []
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file_num_max = len(filenames)
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for i in range(0, file_num_max):
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print('=== {0}/{1} ==='.format(i, file_num_max))
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filename = filenames[i]
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fileWav = wav_dir + '\\' + filename
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if os.path.exists(fileWav):
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word = words[i]
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WORD = word.upper()
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# make label file.
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fileLab = wav_dir + '\\' + filename.replace('.wav', '.lab')
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with open(fileLab, 'w') as f:
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lines = f.write(WORD)
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fileDic = experiments_dir + r'\stimmen\dic_short' + '\\' + word + '.dic'
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fileFA = experiments_dir + r'\stimmen\FA_short' + '\\' + filename.replace('.wav', '.txt') + hmm_num_str
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pyHTK.doHVite(fileWav, fileLab, fileDic, fileFA, configHVite, filePhoneList, AcousticModel)
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prediction = read_fileFA(fileFA)
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predictions.append(prediction)
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os.remove(fileLab)
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print('{0}: {1} -> {2}'.format(WORD, pronunciations[i], prediction))
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else:
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predictions.append('')
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print('!!!!! file not found.')
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predictions = np.array(predictions)
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match = np.c_[words[predictions != ''], pronunciations[predictions != ''], predictions[predictions != '']]
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np.save(data_dir + '\\match_hmm' + hmm_num_str + '.npy', match)
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##os.remove(hcopy_scp.name)
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## ======================= evaluate the result of forced alignment =======================
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if eval_forced_alignment:
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#for hmm_num in [1, 2, 4, 8, 16, 32, 64]:
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hmm_num = 64
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hmm_num_str = str(hmm_num)
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match = np.load(data_dir + '\\match_hmm' + hmm_num_str + '.npy')
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# use dic_short?
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if 1:
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pronunciation_variants = np.array(['WORD', 'pronunciation']).reshape(1, 2)
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for word in word_list:
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fileDic = experiments_dir + r'\stimmen\dic_short' + '\\' + word + '.dic'
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pronunciation_variants = np.r_[pronunciation_variants, pyHTK.loadHTKdic(fileDic)]
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match_short = []
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for line in match:
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word = line[0]
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WORD = word.upper()
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pronvar = pronunciation_variants[pronunciation_variants[:, 0] == word.upper(), 1]
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if line[1] in pronvar:
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match_short.append(line)
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match_short = np.array(match_short)
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match = np.copy(match_short)
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# number of match
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total_match = sum(match[:, 1] == match[:, 2])
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print("{}: {}/{}".format(hmm_num_str, total_match, match.shape[0]))
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