Speech synthesis on a shoe string
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1 Speech synthesis on a shoe string Francis M. Tyers ftyers@hse.ru Национальный исследовательский университет «Высшая школа экономики» (Москва) ngton and Marinyé Andreeva
2 Introductions Project Motivation Methdology
3 Introductions Main research interest: Language technology for maintenance and revivial I have worked with: Machine translation [Assimilation, Dissemination] Modelling morphology [Spellchecking] Syntactically-annotated corpora [Involve MLers] And now: Speech synthesis
4 Motivation
5 Why speech synthesis? Interesting results with little data Lay groundwork for further speech-related work Real «application»
6 Data requirements Synthesis (TTS) Recognition (STT) Hours of audio 100s of hours of audio Studio quality Telephone quality Single speaker Many speakers Not processor intensive Processor intensive
7 Virtual assistants Talk to your computer Information retrieval «Smart» home stuff 3x faster than typing People really use them! in English (or Russian)
8 How they work STT Intent parser TTS script script script
9
10 Project description
11 Project The idea: Collect a speech corpus of Chuvash At least enough data to build a prototype Train a speech synthesis system Let s get the computer to speak Chuvash! Develop an end-to-end recipe Budget: Recordings: 7000 RUB (approx. 100 ) 10 /hour Estimated 1 min of audio = 2 mins recording 10 minutes/day: 5 hours audio (10h recording) in 1 month Train ticket (MSK SpB): 3000 RUB (approx. 50 )
12 Let s make a Chuvash voice! Speech Synthesis Workshop, Higher School of Economics 9th December, well spent! F. M. Tyers Высшая школа экономики
13 Participants Follow Josh on F. M. Tyers Высшая школа экономики
14 Chuvash Turkic core Turkic Oghur non-ne Yeniseian Turkic Sayan Turkic Lena Turkic Chuvash SE Western core Turkic SW NW Altay Turkic language, Oghur sub-group Spoken in Chuvashia, Volga region, Russia Approximately one million speakers (c. 2010) Intergenerational transmission breaking down
15 Chuvash Turkic language, Oghur sub-group Spoken in Chuvashia, Volga region, Russia Approximately one million speakers (c. 2010) Intergenerational transmission breaking down
16 Chuvash Turkic language, Oghur sub-group Spoken in Chuvashia, Volga region, Russia Approximately one million speakers (c. 2010) Intergenerational transmission breaking down F. M. Tyers Высшая школа экономики
17 Chuvash Turkic language, Oghur sub-group Spoken in Chuvashia, Volga region, Russia Approximately one million speakers (c. 2010) Intergenerational transmission breaking down
18 «Most respondents, even Chuvash-speakers, think that Chuvash is not modern.» 1 Sociolinguistic study of pupils and teachers First in Chuvashia 1 Alòs i Font, H. (2016) The Chuvash language in the Chuvash Republic: An example of the rapid decline of one of Russia s major languages
19 Methdology
20 Collecting data chuvash.org: Online community Daily news in Chuvash Short articles toks. CC-BY-SA licence collection wget BeautifulSoup
21 Collecting data chuvash.org: Online community Daily news in Chuvash Short articles toks. CC-BY-SA licence collection wget BeautifulSoup
22 Processing Bespoke sentence segmenter in Python Fixing minor encoding errors (Latin vs. Cyrillic)
23 Audio recording materials Recording device Noise insulation
24 Jonathan: «It s amazing that the hardest part is managing to get the cushions to stand up straight»
25 Software Recording: Auphonic (for iphone 4S) Processing: Google sheets sox pyaudioanalysis Modelling: Ossian Merlin
26 Recording Our instructions: 5 seconds of silence at the beginning For the noise profile 2 seconds between each sentence To facilitate splitting What Marinyé did: Produce a transcript Practice three times Read out numerals and abbreviations Correct errors in text and note them down
27 Data storage and organisation uploadfiles.io Files last 30 days Easy to use
28 Data storage and organisation Links files with text Some metadata Comments about quality, edits
29 Processing Use sox to make a silence profile Clean the audio using the profile Trim the first three seconds
30 Processing Detect sentence spans using pyaudioanalysis Split sentences using sox.
31 Processing Result: Ҫӗмӗрлесем хула тӑрӑх пӗр каяччӑ хӑйне евӗр транспортпа ҫӳренине асӑрханӑ. Вӑл гироскутера аса илтернӗ. Иртен-ҫӳрен ӑна видо ӳкерсе халӑх тетелӗсене вырнаҫтарнӑ. Text and sound sentence aligned corpus
32 Data Files Sentences Words Minutes Total: (6h24m) Postproc: (3h20m) Loss: 5 seconds per file (approx. 24m) 2 seconds per sentence (approx. 1h40m) = 2h04m. Max: 4h20m minus approx. 25% of files 3h20m
33 Modelling
34 Modelling
35 Modelling
36 Modelling
37 Modelling
38 Modelling
39 Modelling
40 Modelling
41 Modelling
42 Modelling
43 Modelling
44 Frontend: Text features Шупашкартан Питӗре вӗҫес килет-и? Letters ш у п а ш к а р т а н п и т ӗ р е в ӗ ҫ е с к и л е т - и? Phonemes ʂ u b a ʂ k a r d a n p i t ɘ r ɛ ʋ ɘ ʑ ɛ s k i l ɛ t _ i _ Words [ʂ u b a ʂ k a r d a n] [p i t ɘ r ɛ] [ʋ ɘ ʑ ɛ s] [k i l ɛ t] [_ i _] Parts of speech [PROPN ʂ u b a ʂ k a r d a n] [PROPN p i t ɘ r ɛ] [VERB ʋ ɘ ʑ ɛ s] [VERB k i l ɛ t] [AUX _ i _] Syllables [PROPN [ʂ u] [b a ʂ] [k a r] [d a n]] [PROPN [p i] [t ɘ] [r ɛ]] [VERB [ʋ ɘ ʑ] [ɛ s]] [VERB [k i l] [ɛ t]] [AUX [_ i _]] etc.
45 Frontend: Features to segments Шупашкартан Питӗре вӗҫес килет -и? ʂ u b a ʂ k a r d a n p i t ɘ r ɛ ʋ ɘ ʑ ɛ s k i l ɛ t i
46 Frontend: Features to segments Шупашкартан Питӗре вӗҫес килет -и? ʂ u b a ʂ k a r d a n p i t ɘ r ɛ ʋ ɘ ʑ ɛ s k i l ɛ t i c-1:n,c0:p,c+1:i,w0:питĕре,w-1:шупашкартан,w+1:вĕçес,s-1:dan,s+1:t,
47 Frontend: Features to segments Шупашкартан Питӗре вӗҫес килет -и? ʂ u b a ʂ k a r d a n p i t ɘ r ɛ ʋ ɘ ʑ ɛ s k i l ɛ t i c-1:n,c0:p,c+1:i,w0:питĕре,w-1:шупашкартан,w+1:вĕçес,s-1:dan,s+1:t, [ ]
48 Backend: Audio features Each feature vector is a frame of audio (10ms) Features are amplitudes at frequency ranges
49 Alignment Unequal number of vectors for text and audio so just repeat them until they align
50 Training
51 Training
52 Training
53 Training
54 Evaluation 2 evaluators Scores 1 (very bad) 5 (very good) Russian* Chuvash Intelligibility 5, 5 5, 3 Pronunciation 5, 5 5, 3 Stress 5, 5 4, 3 Intonation 2, 5 2, 3 Intelligible Pronunciation and stress mostly ok Flat intonation * Google TTS
55 Discussion
56 Future directions Speech synthesis Better linguistic features [POS, morphs, etc.] Fix segmentation of remaining 25% Numeral and abbreviation processing Speech recognition Systems Kaldi Mozilla DeepSpeech Collecting more data More volume (100s of hours) How? More varied (different recording conditions)
57 Common Voice Web platform for collecting voice data Requires CC-0 mostly impossible But Backend software is free/open-source
58 Ман ҫине вӑхӑт уйӑрнишӗн тавах! Merci beaucoup pour votre attention!
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