🇺🇦 Speech Recognition & Synthesis for Ukrainian

Overview

This repository collects links to models, datasets, and tools for Ukrainian Speech-to-Text and Text-to-Speech projects.

Community

• Discord: https://bit.ly/discord-uds
• Speech Recognition: https://t.me/speech_recognition_uk
• Speech Synthesis: https://t.me/speech_synthesis_uk

🎤 Speech-to-Text

📦 Implementations

wav2vec2-bert

• 600M params: https://huggingface.co/Yehor/w2v-bert-2.0-uk-v2 (demo: https://huggingface.co/spaces/Yehor/w2v-bert-2.0-uk-v2-demo)

wav2vec2

• 1B params (with language model based on small portion of data): https://huggingface.co/Yehor/wav2vec2-xls-r-1b-uk-with-lm
• 1B params (with language model based on News texts): https://huggingface.co/Yehor/wav2vec2-xls-r-1b-uk-with-news-lm
• 1B params (with binary language model based on News texts): https://huggingface.co/Yehor/wav2vec2-xls-r-1b-uk-with-binary-news-lm
• 1B params (with language model: OSCAR): https://huggingface.co/arampacha/wav2vec2-xls-r-1b-uk
• 1B params (with language model: OSCAR): https://huggingface.co/arampacha/wav2vec2-xls-r-1b-uk-cv
• 300M params (with language model based on small portion of data): https://huggingface.co/Yehor/wav2vec2-xls-r-300m-uk-with-lm
• 300M params (but without language model): https://huggingface.co/robinhad/wav2vec2-xls-r-300m-uk
• 300M params (with language model based on small portion of data): https://huggingface.co/Yehor/wav2vec2-xls-r-300m-uk-with-small-lm
• 300M params (with language model based on small portion of data) and noised data: https://huggingface.co/Yehor/wav2vec2-xls-r-300m-uk-with-small-lm-noisy
• 300M params (with language model based on News texts): https://huggingface.co/Yehor/wav2vec2-xls-r-300m-uk-with-news-lm
• 300M params (with language model based on Wikipedia texts): https://huggingface.co/Yehor/wav2vec2-xls-r-300m-uk-with-wiki-lm
• 90M params (with language model based on small portion of data): https://huggingface.co/Yehor/wav2vec2-xls-r-base-uk-with-small-lm
• 90M params (with language model based on small portion of data): https://huggingface.co/Yehor/wav2vec2-xls-r-base-uk-with-cv-lm
• ONNX model (1B and 300M models): https://github.com/egorsmkv/ukrainian-onnx-model

You can check demos out here: https://github.com/egorsmkv/wav2vec2-uk-demo

data2vec

• data2vec-large: https://huggingface.co/robinhad/data2vec-large-uk

Citrinet

• NVIDIA Streaming Citrinet 1024 (uk): https://huggingface.co/nvidia/stt_uk_citrinet_1024_gamma_0_25
• NVIDIA Streaming Citrinet 512 (uk): https://huggingface.co/neongeckocom/stt_uk_citrinet_512_gamma_0_25

ContextNet

• NVIDIA Streaming ContextNet 512 (uk): https://huggingface.co/theodotus/stt_uk_contextnet_512

FastConformer

• FastConformer Hybrid Transducer-CTC Large P&C: https://huggingface.co/theodotus/stt_ua_fastconformer_hybrid_large_pc

  • Demo: https://huggingface.co/spaces/theodotus/asr-uk-punctuation-capitalization

Squeezeformer

• Squeezeformer-CTC ML: https://huggingface.co/theodotus/stt_uk_squeezeformer_ctc_ml

  • Demo 1: https://huggingface.co/spaces/theodotus/streaming-asr-uk
  • Demo 2: https://huggingface.co/spaces/theodotus/buffered-asr-uk

• Squeezeformer-CTC SM: https://huggingface.co/theodotus/stt_uk_squeezeformer_ctc_sm

• Squeezeformer-CTC XS: https://huggingface.co/theodotus/stt_uk_squeezeformer_ctc_xs

Conformer-CTC

• https://huggingface.co/taras-sereda/uk-pods-conformer

VOSK

• VOSK v3 nano (with dynamic graph): https://drive.google.com/file/d/1Pwlxmtz7SPPm1DThBPM3u66nH6-Dsb1n/view?usp=sharing (73 mb)
• VOSK v3 small (with dynamic graph): https://drive.google.com/file/d/1Zkambkw2hfpLbMmpq2AR04-I7nhyjqtd/view?usp=sharing (133 mb)
• VOSK v3 (with dynamic graph): https://drive.google.com/file/d/12AdVn-EWFwEJXLzNvM0OB-utSNf7nJ4Q/view?usp=sharing (345 mb)
• VOSK v3: https://drive.google.com/file/d/17umTgQuvvWyUiCJXET1OZ3kWNfywPjW2/view?usp=sharing (343 mb)
• VOSK v2: https://drive.google.com/file/d/1MdlN3JWUe8bpCR9A0irEr-Icc1WiPgZs/view?usp=sharing (339 mb, demo code: https://github.com/egorsmkv/vosk-ukrainian-demo)
• VOSK v1: https://drive.google.com/file/d/1nzpXRd4Gtdi0YVxCFYzqtKKtw_tPZQfK/view?usp=sharing (87 mb, an old model with less trained data)

Note: VOSK models are licensed under Apache License 2.0.

DeepSpeech

• DeepSpeech using transfer learning from English model: https://github.com/robinhad/voice-recognition-ua
  • v0.5: https://github.com/robinhad/voice-recognition-ua/releases/tag/v0.5 (1230+ hours)
  • v0.4: https://github.com/robinhad/voice-recognition-ua/releases/tag/v0.4 (1230 hours)
  • v0.3: https://github.com/robinhad/voice-recognition-ua/releases/tag/v0.3 (751 hours)

M-CTC-T

• m-ctc-t-large: https://huggingface.co/speechbrain/m-ctc-t-large

whisper

• official whisper: https://github.com/openai/whisper
• whisper (small, fine-tuned for Ukrainian): https://github.com/egorsmkv/whisper-ukrainian
• whisper (large, fine-tuned for Ukrainian): https://huggingface.co/arampacha/whisper-large-uk-2
• https://huggingface.co/mitchelldehaven/whisper-medium-uk
• https://huggingface.co/mitchelldehaven/whisper-large-v2-uk

Flashlight

• Flashlight Conformer: https://github.com/egorsmkv/flashlight-ukrainian

📊 Benchmarks

This benchmark uses Common Voice 10 test split.

wav2vec2-bert

Model	WER	CER	Accuracy, %	WER+LM	CER+LM	Accuracy+LM, %
Yehor/w2v-bert-2.0-uk	0.0727	0.0151	92.73%	0.0655	0.0139	93.45%

wav2vec2

Model	WER	CER	Accuracy, %	WER+LM	CER+LM	Accuracy+LM, %
Yehor/wav2vec2-xls-r-1b-uk-with-lm	0.1807	0.0317	81.93%	0.1193	0.0218	88.07%
Yehor/wav2vec2-xls-r-1b-uk-with-binary-news-lm	0.1807	0.0317	81.93%	0.0997	0.0191	90.03%
Yehor/wav2vec2-xls-r-300m-uk-with-lm	0.2906	0.0548	70.94%	0.172	0.0355	82.8%
Yehor/wav2vec2-xls-r-300m-uk-with-news-lm	0.2027	0.0365	79.73%	0.0929	0.019	90.71%
Yehor/wav2vec2-xls-r-300m-uk-with-wiki-lm	0.2027	0.0365	79.73%	0.1045	0.0208	89.55%
Yehor/wav2vec2-xls-r-base-uk-with-small-lm	0.4441	0.0975	55.59%	0.2878	0.0711	71.22%
robinhad/wav2vec2-xls-r-300m-uk	0.2736	0.0537	72.64%	-	-	-
arampacha/wav2vec2-xls-r-1b-uk	0.1652	0.0293	83.48%	0.0945	0.0175	90.55%

Citrinet

lm-4gram-500k is used as the LM

Model	WER	CER	Accuracy, %	WER+LM	CER+LM	Accuracy+LM, %
nvidia/stt_uk_citrinet_1024_gamma_0_25	0.0432	0.0094	95.68%	0.0352	0.0079	96.48%
neongeckocom/stt_uk_citrinet_512_gamma_0_25	0.0746	0.016	92.54%	0.0563	0.0128	94.37%

ContextNet

Model	WER	CER	Accuracy, %
theodotus/stt_uk_contextnet_512	0.0669	0.0145	93.31%

FastConformer P&C

This model supports text punctuation and capitalization

Model	WER	CER	Accuracy, %	WER+P&C	CER+P&C	Accuracy+P&C, %
theodotus/stt_ua_fastconformer_hybrid_large_pc	0.0400	0.0102	96.00%	0.0710	0.0167	92.90%

Squeezeformer

lm-4gram-500k is used as the LM

Model	WER	CER	Accuracy, %	WER+LM	CER+LM	Accuracy+LM, %
theodotus/stt_uk_squeezeformer_ctc_xs	0.1078	0.0229	89.22%	0.0777	0.0174	92.23%
theodotus/stt_uk_squeezeformer_ctc_sm	0.082	0.0175	91.8%	0.0605	0.0142	93.95%
theodotus/stt_uk_squeezeformer_ctc_ml	0.0591	0.0126	94.09%	0.0451	0.0105	95.49%

Flashlight

lm-4gram-500k is used as the LM

Model	WER	CER	Accuracy, %	WER+LM	CER+LM	Accuracy+LM, %
Flashlight Conformer	0.1915	0.0244	80.85%	0.0907	0.0198	90.93%

data2vec

Model	WER	CER	Accuracy, %
robinhad/data2vec-large-uk	0.3117	0.0731	68.83%

VOSK

Model	WER	CER	Accuracy, %
v3	0.5325	0.3878	46.75%

m-ctc-t

Model	WER	CER	Accuracy, %
speechbrain/m-ctc-t-large	0.57	0.1094	43%

whisper

Model	WER	CER	Accuracy, %
tiny	0.6308	0.1859	36.92%
base	0.521	0.1408	47.9%
small	0.3057	0.0764	69.43%
medium	0.1873	0.044	81.27%
large (v1)	0.1642	0.0393	83.58%
large (v2)	0.1372	0.0318	86.28%

Fine-tuned version for Ukrainian:

Model	WER	CER	Accuracy, %
small	0.2704	0.0565	72.96%
large	0.2482	0.055	75.18%

If you want to fine-tune a Whisper model on own data, then use this repository: https://github.com/egorsmkv/whisper-ukrainian

DeepSpeech

Model	WER	CER	Accuracy, %
v0.5	0.7025	0.2009	29.75%

📖 Development

• How to train own model using Kaldi (in Russian): https://github.com/egorsmkv/speech-recognition-uk/blob/master/vosk-model-creation/INSTRUCTION.md
• How to train a KenLM model based on Ukrainian Wikipedia data: https://github.com/egorsmkv/ukwiki-kenlm
• Export a traced JIT version of wav2vec2 models: https://github.com/egorsmkv/wav2vec2-jit

📚 Datasets

Compiled dataset from different open sources + Companies + Community = 188.31GB / ~1200 hours 💪

• Storage Share powered by Nextcloud: https://nx16725.your-storageshare.de/s/cAbcBeXtdz7znDN (use Wget to download, downloading in a browser has speed limitations)
• Torrent file: https://academictorrents.com/details/fcf8bb60c59e9eb583df003d54ed61776650beb8 (188.31 GB)

Voice of America (398 hours)

• Storage Share powered by Nextcloud: https://nx16725.your-storageshare.de/s/f4NYHXdEw2ykZKa

FLEURS

• Ukrainian subset: https://huggingface.co/datasets/google/fleurs/viewer/uk_ua/train

YODAS2

• Ukrainian subsets:
  • https://huggingface.co/datasets/espnet/yodas2/tree/main/data/uk000
  • https://huggingface.co/datasets/espnet/yodas2/tree/main/data/uk100

Companies

• Mozilla Common Voice has the Ukrainian dataset: https://commonvoice.mozilla.org/uk/datasets
• M-AILABS Ukrainian Corpus Ukrainian: http://www.caito.de/data/Training/stt_tts/uk_UK.tgz
• Espreso TV subset: https://blog.gdeltproject.org/visual-explorer-quick-workflow-for-downloading-belarusian-russian-ukrainian-transcripts-translations/

Ukrainian podcasts

• https://huggingface.co/datasets/taras-sereda/uk-pods

Cleaned Common Voice 10 (test set)

• Repository: https://github.com/egorsmkv/cv10-uk-testset-clean

Noised Common Voice 10

• Transcriptions: https://www.dropbox.com/s/ohj3y2cq8f4207a/transcriptions.zip?dl=0
• Audio files: https://www.dropbox.com/s/v8crgclt9opbrv1/data.zip?dl=0

Community

• VoxForge Repository: http://www.repository.voxforge1.org/downloads/uk/Trunk/

Other

• ASR Corpus created using a Telegram bot for Ukrainian: https://github.com/egorsmkv/asr-tg-bot-corpus
• Speech Dataset with Ukrainian: https://www.caito.de/2019/01/the-m-ailabs-speech-dataset/

⭐ Related works

Language models

• Ukrainian LMs: https://huggingface.co/Yehor/kenlm-ukrainian

Inverse Text Normalization:

• WFST for Ukrainian Inverse Text Normalization: https://github.com/lociko/ukraine_itn_wfst

Text Enhancement

• Punctuation and capitalization model: https://huggingface.co/dchaplinsky/punctuation_uk_bert (demo: https://huggingface.co/spaces/Yehor/punctuation-uk)

Aligners

• Aligner for wav2vec2-bert models: https://github.com/egorsmkv/w2v2-bert-aligner
• Aligner based on FasterWhisper (mostly for TTS): https://github.com/patriotyk/narizaka
• Aligner based on Kaldi: https://github.com/proger/uk

📢 Text-to-Speech

Test sentence with stresses:

К+ам'ян+ець-Под+ільський - м+істо в Хмельн+ицькій +області Укра+їни, ц+ентр Кам'ян+ець-Под+ільської міськ+ої об'+єднаної територі+альної гром+ади +і Кам'ян+ець-Под+ільського рай+ону.

Without stresses:

Кам'янець-Подільський - місто в Хмельницькій області України, центр Кам'янець-Подільської міської об'єднаної територіальної громади і Кам'янець-Подільського району.

📦 Implementations

StyleTTS2

• StyleTTS2 demo & the code

P-Flow TTS

• P-Flow TTS

audio.mp4

RAD-TTS

• RAD-TTS, the voice "Lada"
• RAD-TTS with three voices, voices of Lada, Tetiana, and Mykyta

demo.mp4

Coqui TTS

• v1.0.0 using M-AILABS dataset: https://github.com/robinhad/ukrainian-tts/releases/tag/v1.0.0 (200,000 steps)

• v2.0.0 using Mykyta/Olena dataset: https://github.com/robinhad/ukrainian-tts/releases/tag/v2.0.0 (140,000 steps)

tts_output.mp4

Neon TTS

• Coqui TTS model implemented in the Neon Coqui TTS Python Plugin. An interactive demo is available on huggingface. This model and others can be downloaded from huggingface and more information can be found at neon.ai

neon_tts.mp4

FastPitch

• NVIDIA FastPitch: https://huggingface.co/theodotus/tts_uk_fastpitch

Balacoon TTS

• Balacoon TTS, voices of Lada, Tetiana and Mykyta. Blog post on model release.

balacoon_tts.mp4

📚 Datasets

• Open Text-to-Speech voices for 🇺🇦 Ukrainian: https://huggingface.co/datasets/Yehor/opentts-uk
  • Voice "LADA", female
  • Voice "TETIANA", female
  • Voice "KATERYNA", female
  • Voice "MYKYTA", male
  • Voice "OLEKSA", male

⭐ Related works

Accentors

• https://github.com/NeonBohdan/ukrainian-accentor-transformer
• https://github.com/lang-uk/ukrainian-word-stress
• https://github.com/egorsmkv/ukrainian-accentor

Misc

• Tool to make high quality text to speech (TTS) corpus from audio + text books: https://github.com/patriotyk/narizaka
• A model to do Text Normalization: https://huggingface.co/skypro1111/mbart-large-50-verbalization