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@ -35,15 +35,9 @@
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<Compile Include="fa_test.py">
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<SubType>Code</SubType>
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</Compile>
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<Compile Include="novoapi_forced_alignment.py">
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<SubType>Code</SubType>
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</Compile>
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<Compile Include="htk_vs_kaldi.py">
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<SubType>Code</SubType>
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</Compile>
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<Compile Include="novoapi_functions.py">
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<SubType>Code</SubType>
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</Compile>
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</ItemGroup>
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<ItemGroup>
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<Content Include="config.ini" />
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@ -16,121 +16,47 @@ import acoustic_model_functions as am_func
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import convert_xsampa2ipa
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import defaultfiles as default
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from forced_alignment import pyhtk, convert_phone_set
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from forced_alignment import pyhtk
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import novoapi
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import novoapi_functions
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## ======================= novo phoneset ======================
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phoneset_ipa, phoneset_novo70, translation_key = novoapi_functions.load_phonset()
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translation_key = dict()
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# As per Nederlandse phoneset_aki.xlsx recieved from David
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# [ɔː] oh / ohr
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# [ɪː] ih / ihr
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# [iː] iy
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# [œː] uh
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# [ɛː] eh
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# [w] wv in IPA written as ʋ.
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david_suggestion = ['ɔː', 'ɪː', 'iː', 'œː', 'ɛː', 'w']
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#phonelist_novo70_ = pd.ExcelFile(default.phonelist_novo70_xlsx)
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#df = pd.read_excel(phonelist_novo70_, 'list')
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## *_simple includes columns which has only one phone in.
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#for ipa, novo70 in zip(df['IPA_simple'], df['novo70_simple']):
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# if not pd.isnull(ipa):
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# print('{0}:{1}'.format(ipa, novo70))
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# translation_key[ipa] = novo70
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#phonelist_novo70 = np.unique(list(df['novo70_simple']))
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phoneset_ipa = []
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phoneset_novo70 = []
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with open(default.cmu69_phoneset, "rt", encoding="utf-8") as fin:
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lines = fin.read()
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lines = lines.split('\n')
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for line in lines:
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words = line.split('\t')
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if len(words) > 1:
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novo70 = words[0]
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ipa = words[1]
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phoneset_ipa.append(ipa)
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phoneset_novo70.append(novo70)
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translation_key[ipa] = novo70
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phoneset_ipa = np.unique(phoneset_ipa)
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phoneset_novo70 = np.unique(phoneset_novo70)
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## ======================= extract words which is written only with novo70 ======================
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## ======================= convert phones ======================
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mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', default.ipa_xsampa_converter_dir)
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stimmen_transcription_ = pd.ExcelFile(default.stimmen_transcription_xlsx)
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df = pd.read_excel(stimmen_transcription_, 'frequency')
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df = pd.read_excel(stimmen_transcription_, 'check')
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#for xsampa, ipa in zip(df['X-SAMPA'], df['IPA']):
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# #ipa_converted = convert_xsampa2ipa.conversion('xsampa', 'ipa', mapping, xsampa_)
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# ipa_converted = convert_xsampa2ipa.xsampa2ipa(mapping, xsampa)
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# if not ipa_converted == ipa:
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# print('{0}: {1} - {2}'.format(xsampa, ipa_converted, ipa))
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transcription_ipa = list(df['IPA'])
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# transcription mistake?
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transcription_ipa = [ipa.replace(';', 'ː') for ipa in transcription_ipa if not ipa=='pypɪl' and not pd.isnull(ipa)]
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transcription_ipa = [ipa.replace('ˑ', '') for ipa in transcription_ipa] # only one case.
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not_in_novo70 = []
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all_in_novo70 = []
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for ipa in transcription_ipa:
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ipa = ipa.replace(':', 'ː')
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ipa = convert_phone_set.split_ipa(ipa)
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not_in_novo70_ = [phone for phone in ipa
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if not phone in phoneset_ipa and not phone in david_suggestion]
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not_in_novo70_ = [phone.replace('sp', '') for phone in not_in_novo70_]
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not_in_novo70_ = [phone.replace(':', '') for phone in not_in_novo70_]
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not_in_novo70_ = [phone.replace('ː', '') for phone in not_in_novo70_]
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if len(not_in_novo70_) == 0:
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all_in_novo70.append(''.join(ipa))
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#translation_key.get(phone, phone)
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not_in_novo70.extend(not_in_novo70_)
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not_in_novo70_list = list(set(not_in_novo70))
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## check which phone is used in stimmen but not in novo70
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# 'ʀ', 'ʁ',
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# 'ɒ', 'ɐ',
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# 'o', 'a' (o:, a:?)
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# [e] 'nyːver mɑntsjə' (1)
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# [ɾ] 'ɪːɾ'(1)
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# [ɹ] 'iːjəɹ' (1), 'ɪ:ɹ' (1)
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# [ø] 'gʀøtəpi:r'(1), 'grøtəpi:r'(1)
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# [æ] 'røːzəʀæt'(2), 'røːzəræt'(1)
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# [ʊ] 'ʊ'(1) --> can be ʏ (uh)??
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# [χ] --> can be x??
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def search_phone_ipa(x, phone_list):
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x_in_item = []
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for ipa in phone_list:
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ipa_original = ipa
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ipa = ipa.replace(':', 'ː')
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ipa = convert_phone_set.split_ipa(ipa)
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if x in ipa and not x+':' in ipa:
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x_in_item.append(ipa_original)
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return x_in_item
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#search_phone_ipa('ø', transcription_ipa)
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df = pd.read_excel(stimmen_transcription_, 'original')
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ipas = []
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famehtks = []
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for xsampa in df['Self Xsampa']:
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if not isinstance(xsampa, float): # 'NaN'
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# typo?
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xsampa = xsampa.replace('r2:z@rA:\\t', 'r2:z@rA:t')
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xsampa = xsampa.replace(';', ':')
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ipa = convert_xsampa2ipa.xsampa2ipa(mapping, xsampa)
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ipa = ipa.replace('ː', ':')
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ipa = ipa.replace(' ', '')
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ipas.append(ipa)
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else:
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ipas.append('')
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# extract interesting cols.
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df = pd.DataFrame({'filename': df['Filename'],
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'word': df['Word'],
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'xsampa': df['Self Xsampa'],
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'ipa': pd.Series(ipas)})
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# find options which all phones are in novo70.
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#word_list = list(set(df['word']))
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#word_list = [word for word in word_list if not pd.isnull(word)]
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#word = word_list[1]
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## pronunciation variants of 'word'
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#df_ = df[df['word'] == word]['xsampa']
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##pronunciation_variant = list(set(df_))
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cols = ['word', 'ipa', 'frequency']
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df_samples = pd.DataFrame(index=[], columns=cols)
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for ipa in all_in_novo70:
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ipa = ipa.replace('ː', ':')
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samples = df[df['ipa'] == ipa]
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word = list(set(samples['word']))[0]
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samples_Series = pd.Series([word, ipa, len(samples)], index=df_samples.columns)
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df_samples = df_samples.append(samples_Series, ignore_index=True)
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@ -42,4 +42,4 @@ phonelist_friesian_txt = os.path.join(experiments_dir, 'friesian', 'acoustic
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novo_api_dir = os.path.join(WSL_dir, 'python-novo-api', 'novoapi')
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#novo_api_dir = r'c:\Python36-32\Lib\site-packages\novoapi'
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novo70_phoneset = os.path.join(novo_api_dir, 'asr', 'phoneset', 'nl', 'novo70.phoneset')
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cmu69_phoneset = os.path.join(novo_api_dir, 'asr', 'phoneset', 'en', 'cmu69.phoneset')
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@ -1,118 +0,0 @@
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#
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# forced alignment using novo-api.
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#
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# *** IMPORTANT ***
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# This file should be treated as confidencial.
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# This file should not be copied or uploaded to public sites.
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#
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# NOTES:
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# The usage of novo api: https://bitbucket.org/novolanguage/python-novo-api
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# I couldn't make it work as I described in the mail to Martijn Bartelds on
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# 2018/12/03.
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# As per the advice from him, I modified testgrammer.py and made it a function.
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#
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# In order to run on Python 3.6, the following points are changed in novo-api.
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# (1) backend/__init__.py
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# - #import session
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# from . import session
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# (2) backend/session.py
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# - #except Exception, e:
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# except Exception as e:
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# - #print self.last_message
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# print(self.last_message)
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# (3) asr/segment/praat.py
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# - def print_tier(output, title, begin, end, segs, (format, formatter))
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# def print_tier(output, title, begin, end, segs, format, formatter):
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# (4) asr/spraaklab/__init.py
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# - #import session
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# from . import session
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# (5) asr/spraaklab/schema.py
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# - #print data, "validated not OK", e.message
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# print("{0} validated not OK {1}".format(data, e.message))
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# - #print data, "validated OK"
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# print("{} validated OK".format(data))
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# - #if isinstance(object, basestring):
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# if isinstance(object, str)
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#
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# Aki Kunikoshi
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# 428968@gmail.com
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#
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import argparse
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import json
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from novoapi.backend import session
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import novoapi_functions
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# username / password cannot be passed as artuments...
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p = argparse.ArgumentParser()
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#p.add_argument("--user", default=None)
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#p.add_argument("--password", default=None)
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p.add_argument("--user", default='martijn.wieling')
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p.add_argument("--password", default='fa0Thaic')
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args = p.parse_args()
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wav_file = 'c:\\OneDrive\\WSL\\test\\onetwothree.wav'
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rec = session.Recognizer(grammar_version="1.0", lang="nl", snodeid=101, user=args.user, password=args.password, keepopen=True) # , modeldir=modeldir)
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grammar = {
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"type": "confusion_network",
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"version": "1.0",
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"data": {
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"kind": "sequence",
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"elements": [{
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"kind": "word",
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"pronunciation": [{
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"phones": ["wv",
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"a1",
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"n"],
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"id": 0
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},
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{
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"phones": ["wv",
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"uh1",
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"n"],
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"id": 1
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}],
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"label": "one"
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},
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{
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"kind": "word",
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"pronunciation": [{
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"phones": ["t",
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"uw1"],
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"id": 0
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}],
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"label": "two"
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},
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{
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"kind": "word",
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"pronunciation": [{
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"phones": ["t",
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"r",
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"iy1"],
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"id": 0
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},
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{
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"phones": ["s",
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"r",
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"iy1"],
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"id": 1
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}],
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"label": "three"
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}]
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},
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"return_objects": ["grammar"],
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"phoneset": "novo70"
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}
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res = rec.setgrammar(grammar)
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#print "Set grammar result", res
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#res = rec.recognize_wav("test/onetwothree.wav")
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res = rec.recognize_wav(wav_file)
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#print "Recognition result:", json.dumps(res.export(), indent=4)
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# list of the pronunciation for each words
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word = 'pauw'
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pronunciation_ipa = ['pau', 'pɑu']
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grammar = novoapi_functions.make_grammar(word, pronunciation_ipa)
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@ -1,138 +0,0 @@
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import numpy as np
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import defaultfiles as default
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def load_phonset():
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translation_key_ipa2novo70 = dict()
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translation_key_novo702ipa = dict()
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#phonelist_novo70_ = pd.ExcelFile(default.phonelist_novo70_xlsx)
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#df = pd.read_excel(phonelist_novo70_, 'list')
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## *_simple includes columns which has only one phone in.
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#for ipa, novo70 in zip(df['IPA_simple'], df['novo70_simple']):
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# if not pd.isnull(ipa):
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# print('{0}:{1}'.format(ipa, novo70))
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# translation_key[ipa] = novo70
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#phonelist_novo70 = np.unique(list(df['novo70_simple']))
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phoneset_ipa = []
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phoneset_novo70 = []
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with open(default.novo70_phoneset, "rt", encoding="utf-8") as fin:
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lines = fin.read()
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lines = lines.split('\n')
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for line in lines:
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words = line.split('\t')
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if len(words) > 1:
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novo70 = words[0]
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ipa = words[1]
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phoneset_ipa.append(ipa)
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phoneset_novo70.append(novo70)
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translation_key_ipa2novo70[ipa] = novo70
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translation_key_novo702ipa[novo70] = ipa
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phoneset_ipa = np.unique(phoneset_ipa)
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phoneset_novo70 = np.unique(phoneset_novo70)
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return phoneset_ipa, phoneset_novo70, translation_key_ipa2novo70, translation_key_novo702ipa
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def multi_character_tokenize(line, multi_character_tokens):
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"""
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Tries to match one of the tokens in multi_character_tokens at each position of line,
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starting at position 0,
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if so tokenizes and eats that token. Otherwise tokenizes a single character.
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Copied from forced_alignment.convert_phone_set.py
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"""
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while line != '':
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for token in multi_character_tokens:
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if line.startswith(token) and len(token) > 0:
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yield token
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line = line[len(token):]
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break
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else:
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yield line[:1]
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line = line[1:]
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def split_ipa(line):
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"""
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Split a line by IPA phones.
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If nasalized sound (such as ɛ̃ː) is included, it will give error.
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:param string line: one line written in IPA.
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:return string lineSeperated: the line splitted in IPA phone.
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"""
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multi_character_phones = [
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# IPAs in CGN.
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u'ʌu', u'ɛi', u'œy', u'aː', u'eː', u'iː', u'oː', u'øː', u'ɛː', u'œː', u'ɔː', u'ɛ̃ː', u'ɑ̃ː', u'ɔ̃ː', u'œ̃', u'ɪː'
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]
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return [phone for phone in multi_character_tokenize(line.strip(), multi_character_phones)]
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def split_novo70(line):
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"""
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Split a line by novo70 phones.
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:param string line: one line written in novo70.
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:return string lineSeperated: the line splitted by novo70 phones.
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"""
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_, phoneset_novo70, _, _ = load_phonset()
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multi_character_phones = [p for p in phoneset_novo70 if len(p) > 1]
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multi_character_phones = sorted(multi_character_phones, key=len, reverse=True)
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return ['sp' if phone == ' ' else phone
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for phone in multi_character_tokenize(line.strip(), multi_character_phones)]
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def novo702ipa(tokens):
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pronunciation = []
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_, _, _, translation_key = load_phonset()
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for phone in split_novo70(tokens):
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pronunciation.append(translation_key.get(phone, phone))
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return ' '.join(pronunciation)
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# numbering of novo70 should be checked.
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def ipa2novo70(tokens):
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pronunciation = []
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_, _, translation_key, _ = load_phonset()
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for phone in split_ipa(tokens):
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pronunciation.append(translation_key.get(phone, phone))
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return ' '.join(pronunciation)
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def make_grammar(word, pronunciation_ipa):
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"""
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Args:
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words
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pronunciation_ipa: list of pronunciation variants.
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"""
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#word = 'pauw'
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#pronunciation_ipa = ['pau', 'pɑu']
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grammer_data_elements0_pronunciation = []
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for id, ipa in enumerate(pronunciation_ipa):
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novo70 = novoapi_functions.ipa2novo70(ipa)
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grammer_data_elements0_pronunciation.append({
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"phones": novo70.split(),
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"id": id
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})
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grammar_data = {
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"kind": 'sequence',
|
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"elements": [{
|
||||
"kind": "word",
|
||||
"pronunciation": grammer_data_elements0_pronunciation,
|
||||
"label": word
|
||||
}]
|
||||
}
|
||||
|
||||
grammar = {
|
||||
"type": "confusion_network",
|
||||
"version": "1.0",
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||||
"data": grammar_data,
|
||||
"return_objects": ["grammar"],
|
||||
"phoneset": "novo70"
|
||||
}
|
||||
|
||||
return grammar
|
||||
Loading…
Reference in New Issue
Block a user