🇺🇦 Speech Recognition & Synthesis for Ukrainian

Overview

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

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-uk (demo: https://huggingface.co/spaces/Yehor/w2v-bert-uk-demo)
• 600M params: https://huggingface.co/Yehor/w2v-bert-uk-v2.1 (demo: https://huggingface.co/spaces/Yehor/w2v-bert-uk-v2.1-demo)

wav2vec2

• 300M params (with language model based on Wikipedia texts): https://huggingface.co/Yehor/w2v-xls-r-uk
• 300M params: https://huggingface.co/robinhad/wav2vec2-xls-r-300m-uk
• 1B params: https://huggingface.co/arampacha/wav2vec2-xls-r-1b-uk

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

HuBERT

• hubert-uk: https://huggingface.co/Yehor/hubert-uk

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

Models: https://huggingface.co/Yehor/vosk-uk

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://huggingface.co/Yehor/flashlight-uk

📊 Benchmarks

This benchmark uses Common Voice 10 test split.

• WER: Word Error Rate
• CER: Character Error Rate

wav2vec2-bert

Model	WER	CER	Accuracy (words)
Yehor/w2v-bert-uk (F16)	6.6%	1.34%	93.4%
Yehor/w2v-bert-uk-v2.1 (F16)	17.34%	3.33%	82.66%

wav2vec2

Model	WER	CER	Accuracy (words)
Yehor/w2v-xls-r-uk	20.24%	3.64%	79.76%
robinhad/wav2vec2-xls-r-300m-uk	27.36%	5.37%	72.64%
arampacha/wav2vec2-xls-r-1b-uk	16.52%	2.93%	83.48%

HuBERT

Model	WER	CER	Accuracy (words)
Yehor/hubert-uk (F16)	37.07%	6.87%	62.93%

Citrinet

Model	WER	CER	Accuracy (words)
nvidia/stt_uk_citrinet_1024_gamma_0_25	4.32%	0.94%	95.68%
neongeckocom/stt_uk_citrinet_512_gamma_0_25	7.46%	1.6%	92.54%

ContextNet

Model	WER	CER	Accuracy (words)
theodotus/stt_uk_contextnet_512	6.69%	1.45%	93.31%

FastConformer P&C

This model supports text punctuation and capitalization

Model	WER	CER	Accuracy (words)
theodotus/stt_ua_fastconformer_hybrid_large_pc	4%	1.02%	96%

Squeezeformer

Model	WER	CER	Accuracy (words)
theodotus/stt_uk_squeezeformer_ctc_xs	10.78%	2.29%	89.22%
theodotus/stt_uk_squeezeformer_ctc_sm	8.2%	1.75%	91.8%
theodotus/stt_uk_squeezeformer_ctc_ml	5.91%	1.26%	94.09%

Conformer-CTC

Model	WER	CER	Accuracy (words)
taras-sereda/uk-pods-conformer	6.75%	1.41%	93.25%

Flashlight

Model	WER	CER	Accuracy (words)
Flashlight Conformer	19.15%	2.44%	80.85%

data2vec

Model	WER	CER	Accuracy (words)
robinhad/data2vec-large-uk	31.17%	7.31%	68.83%

VOSK

Model	WER	CER	Accuracy (words)
v3	53.25%	38.78%	46.75%

m-ctc-t

Model	WER	CER	Accuracy (words)
speechbrain/m-ctc-t-large	57%	10.94%	43%

whisper

Model	WER	CER	Accuracy (words)
tiny	63.08%	18.59%	36.92%
base	52.1%	14.08%	47.9%
small	30.57%	7.64%	69.43%
medium	18.73%	4.4%	81.27%
large (v1)	16.42	3.93%	83.58%
large (v2)	13.72%	3.18%	86.28%
large (v3)	20.53%	5.28%	79.478%
turbo	22.83%	7.05%	77.17%

Fine-tuned version for Ukrainian:

Model	WER	CER	Accuracy (words)
small	27.04%	5.65%	72.96%
large	24.82%	5.5%	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 (words)
v0.5	70.25%	20.09%	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

Ukrainian broadcast

• Ukrainian broadcast speech: https://huggingface.co/datasets/Yehor/broadcast-speech-uk

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://huggingface.co/datasets/Yehor/tg-voices-uk
• 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-uk

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
• Audio Aesthetics for opentts-uk: https://huggingface.co/datasets/Yehor/opentts-uk-aesthetics