Silero Models

May 6, 2026 · View on GitHub

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Silero Models

Silero Models

Our TTS models satisfy the following criteria:

Fully end-to-end;
Large library of voices;
Natural-sounding speech;
One-line usage, minimal, portable;
Impressively fast on CPU and GPU;
For the Russian language - automated stress and homographs;

Installation and Basics

You can basically use our models in 3 flavours:

Via PyTorch Hub: torch.hub.load();
Via pip: pip install silero and then from silero import silero_tts;
Via caching the required models and utils manually and modifying if necessary;

Models are downloaded on demand both by pip and PyTorch Hub. If you need caching, do it manually or via invoking a necessary model once (it will be downloaded to a cache folder). Please see these docs for more information.

PyTorch Hub and pip package are based on the same code. All of the torch.hub.load examples can be used with the pip package via this basic change:

from silero import silero_tts
model, example_text = silero_tts(language='ru',
                                 speaker='v5_ru')
audio = model.apply_tts(text=example_text)

Text-To-Speech

Models and Speakers

All of the provided models are listed in the models.yml file. Any metadata and newer versions will be added there.

V5

V5 models support SSML. Also see Colab examples for main SSML tag usage.

Russian-only models support automated stress and homographs. v5_2_ru cointains minor fixes and removes numpy and scipy dependencies.

v5_3_ru cointains minor fixes. v5_4_ru also supports questions.

ID	Speakers	Auto-stress / Homographs / Questions	Language	SR
`v5_5_ru`	`aidar`, `baya`, `kseniya`, `xenia`, `eugene`	✅ / ✅ / ✅	`ru` (Russian)	`8000`, `24000`, `48000`
`v5_4_ru`	`aidar`, `baya`, `kseniya`, `xenia`	✅ / ✅ / ✅	`ru` (Russian)	`8000`, `24000`, `48000`
`v5_3_ru`	`aidar`, `baya`, `kseniya`, `xenia`, `eugene`	✅ / ✅ / ❌	`ru` (Russian)	`8000`, `24000`, `48000`
`v5_2_ru`	`aidar`, `baya`, `kseniya`, `xenia`, `eugene`	✅ / ✅ / ❌	`ru` (Russian)	`8000`, `24000`, `48000`
`v5_ru`	`aidar`, `baya`, `kseniya`, `xenia`, `eugene`	✅ / ✅ / ❌	`ru` (Russian)	`8000`, `24000`, `48000`

V5 CIS Base Models

All of the below models support 8000, 24000, 48000 sampling rates and contain no auto-stress or homographs;
v5_cis_base models assume that proper stress should be added for each word for all languages, i.e. к+ошка;
v5_cis_base_nostress models assume that proper stress should be added for each word ONLY for slavic languages (i.e. ru, bel, ukr);
All of the below models are published under MIT licence;
V5 UTMOS and throughput metrics;
V5 models support SSML. Also see Colab examples for main SSML tag usage;
Use cases for the model;
Minimal system requirements: a PyTorch-compatible system, a modern processor with AVX2 instruction set for x86/64 platform.

ID	Speakers	Language
`v5_cis_base`, `v5_cis_base_nostress`	`aze_gamat`	`aze` (Azerbaijani)
`v5_cis_base`, `v5_cis_base_nostress`	`hye_zara`	`hye` (Armenian)
`v5_cis_base`, `v5_cis_base_nostress`	`bak_aigul`, `bak_alfia`, `bak_alfia2`	`bak` (Bashkir)
`v5_cis_base`, `v5_cis_base_nostress`	`bak_miyau`, `bak_ramilia`	`bak` (Bashkir)
`v5_cis_base`, `v5_cis_base_nostress`	`bel_anatoliy`, `bel_dmitriy`, `bel_larisa`	`bel` (Belarus)
`v5_cis_base`, `v5_cis_base_nostress`	`kat_vika`	`kat` (Georgian)
`v5_cis_base`, `v5_cis_base_nostress`	`kbd_eduard`	`kbd` (Kab.-Cherkes)
`v5_cis_base`, `v5_cis_base_nostress`	`kaz_zhadyra`, `kaz_zhazira`	`kaz` (Kazakh)
`v5_cis_base`, `v5_cis_base_nostress`	`xal_kejilgan`, `xal_kermen`	`xal` (Kalmyk)
`v5_cis_base`, `v5_cis_base_nostress`	`kir_nurgul`	`kir` (Kyrgyz)
`v5_cis_base`, `v5_cis_base_nostress`	`mdf_oksana`	`mdf` (Moksha)
`v5_cis_base`, `v5_cis_base_nostress`	all of these speakers, but with `ru_` prefix	`ru` (Russian)
`v5_cis_base`, `v5_cis_base_nostress`	`tgk_onaoy`, `tgk_safarhuja`	`tgk` (Tajik)
`v5_cis_base`, `v5_cis_base_nostress`	`tat_albina`, `tat_marat`	`tat` (Tatar)
`v5_cis_base`, `v5_cis_base_nostress`	`udm_bogdan`	`udm` (Udmurt)
`v5_cis_base`, `v5_cis_base_nostress`	`uzb_saida`	`uzb` (Uzbek)
`v5_cis_base`, `v5_cis_base_nostress`	`ukr_igor`, `ukr_roman`	`ukr` (Ukrainian)
`v5_cis_base`, `v5_cis_base_nostress`	`kjh_karina`, `kjh_sibday`	`kjh` (Khakas)
`v5_cis_base`, `v5_cis_base_nostress`	`chv_ekaterina`	`chv` (Chuvash)
`v5_cis_base`, `v5_cis_base_nostress`	`erz_alexandr`	`erz` (Erzya)
`v5_cis_base`, `v5_cis_base_nostress`	`sah_zinaida`	`sah` (Yakut)

Supported alphabets

Please note that Georgian and Armenian are in fact internally supported via direct translation into cyrillic script inside of the package. Azerbaijani and Uzbek support both alphabets (Cyrillic and Latin).

ID	Название	Алфавит(ы)
aze	`aze` (Azerbaijani)	abcçdeәfgğhxıijkqlmnoöprsştuüvyz
aze	`aze` (Azerbaijani)	абвгғдеәжзиыјкҝлмноөпрстуүфхһчҹш
hye	`hye` (Armenian)	աբգդեզէըթժիլխծկհձղճմյնշոչպջռսվտրցւփքօֆև
bak	`bak` (Bashkir)	абвгдежзийклмнопрстуфхцчшщъыьэюяёғҙҡңҫүһәө
bel	`bel` (Belarus)	абвгдежзйклмнопрстуфхцчшыьэюяёіў
kat	`kat` (Georgian)	აბგდევზთიკლმნოპჟრსტუფქღყშჩცძწჭხჯჰ
kbd	`kbd` (Kab.-Cherkes)	абвгдежзийклмнопрстуфхцчшщъыьэюяёӏ
kaz	`kaz` (Kazakh)	абвгдежзийклмнопрстуфхцчшщыьэюяіғқңүұһәө
xal	`xal` (Kalmyk)	абвгдежзийклмнопрстуфхцчшщъыьэюяҗңүһәө
kir	`kir` (Kyrgyz)	абвгдежзийклмнопрстуфхцчшыьэюяёңүө
mdf	`mdf` (Moksha)	абвгдежзийклмнопрстуфхцчшщъыьэюяё
ru	`ru` (Russian)	абвгдеёжзийклмнопрстуфхцчшщъыьэюя
tgk	`tgk` (Tajik)	абвгдежзийклмнопрстуфхчшъэюяёғқҳҷӣӯ
tat	`tat` (Tatar)	абвгдежзийклмнопрстуфхцчшъыьэюяҗңүһәө
udm	`udm` (Udmurt)	абвгдежзийклмнопрстуфхцчшщъыьэюяёӝӟӥӧӵ
uzb	`uzb` (Uzbek)	абвгдежзийклмнопрстуфхцчшъьэюяёўғқҳ
uzb	`uzb` (Uzbek)	abcdefghijklmnopqrstuvxyz
ukr	`ukr` (Ukrainian)	абвгґдеєжзиіїйклмнопрстуфхцчшщьюя
kjh	`kjh` (Khakas)	абвгдежзийклмнопрстуфхцчшщъыьэюяёіғңҷӧӱ
chv	`chv` (Chuvash)	абвгдежзийклмнопрстуфхцчшщъыьэюяёҫӑӗӳ
erz	`erz` (Erzya)	абвгдежзийклмнопрстуфхцчшщъыьэюяё
sah	`sah` (Yakut)	абвгдежзийклмнопрстуфхцчшщъыьэюяёҕҥүһө

V5 CIS Ext Models

All of the below models support 8000, 24000, 48000 sampling rates and contain no auto-stress or homographs;
v5_cis_ext models assume that proper stress should be added for each word for all languages, i.e. к+ошка;
v5_cis_ext_nostress are coming soon;
All of the below models are published under CC-NC-BY licence;
V5 models support SSML. Also see Colab examples for main SSML tag usage.

ID	Speakers	Language
`v5_cis_ext`	`kaz_abai`, `kaz_aidana`, `kaz_aisha`, `kaz_bakir`, `kaz_danara`	`kaz` (Kazakh)
`v5_cis_ext`	`xal_delghir`, `xal_erdni`	`xal` (Kalmyk)
`v5_cis_ext`	`tat_adiba`, `tat_alsou`, `tat_amir`, `tat_azat`, `tat_batir`	`tat` (Tatar)
`v5_cis_ext`	`tat_bulat`, `tat_damir`, `tat_guzel`, `tat_ildar`, `tat_ilgiz`	`tat` (Tatar)
`v5_cis_ext`	`tat_karim`, `tat_mansur`, `tat_murat`, `tat_rasima`, `tat_rustem`	`tat` (Tatar)
`v5_cis_ext`	`tat_timur`, `tat_zifa`, `tat_zufar`, `tat_zulfiya`	`tat` (Tatar)
`v5_cis_ext`	`uzb_anora`, `uzb_dilnavoz`	`uzb` (Uzbek)
`v5_cis_ext`	`ukr_kateryna`, `ukr_lada`, `ukr_mykyta`, `ukr_oleksa`, `ukr_tetiana`	`ukr` (Ukrainian)
`v5_cis_ext`	`chv_aihwa`, `chv_alima`	`chv` (Chuvash)

V4

V4 models support SSML. Also see Colab examples for main SSML tag usage.

V4 models: v4_ru, v4_cyrillic, v4_ua, v4_uz, v4_indic

ID	Speakers	Auto-stress	Language	SR
`v4_ru`	`aidar`, `baya`, `kseniya`, `xenia`, `eugene`, `random`	yes	`ru` (Russian)	`8000`, `24000`, `48000`
`v4_cyrillic`	`b_ava`, `marat_tt`, `kalmyk_erdni`...	no	`cyrillic` (Avar, Tatar, Kalmyk, ...)	`8000`, `24000`, `48000`
`v4_ua`	`mykyta`, `random`	no	`ua` (Ukrainian)	`8000`, `24000`, `48000`
`v4_uz`	`dilnavoz`	no	`uz` (Uzbek)	`8000`, `24000`, `48000`
`v4_indic`	`hindi_male`, `hindi_female`, ..., `random`	no	`indic` (Hindi, Telugu, ...)	`8000`, `24000`, `48000`

V3

V3 models support SSML. Also see Colab examples for main SSML tag usage.

V3 models: v3_en, v3_en_indic, v3_de, v3_es, v3_fr, v3_indic

ID	Speakers	Auto-stress	Language	SR
`v3_en`	`en_0`, `en_1`, ..., `en_117`, `random`	no	`en` (English)	`8000`, `24000`, `48000`
`v3_en_indic`	`tamil_female`, ..., `assamese_male`, `random`	no	`en` (English)	`8000`, `24000`, `48000`
`v3_de`	`eva_k`, ..., `karlsson`, `random`	no	`de` (German)	`8000`, `24000`, `48000`
`v3_es`	`es_0`, `es_1`, `es_2`, `random`	no	`es` (Spanish)	`8000`, `24000`, `48000`
`v3_fr`	`fr_0`, ..., `fr_5`, `random`	no	`fr` (French)	`8000`, `24000`, `48000`
`v3_indic`	`hindi_male`, `hindi_female`, ..., `random`	no	`indic` (Hindi, Telugu, ...)	`8000`, `24000`, `48000`

Dependencies

Basic dependencies for Colab examples:

torch, 1.10+ for v3 models/ 2.0+ for v4 and v5 models;
torchaudio, latest version bound to PyTorch should work (required only because models are hosted together with STT, not required for work);
omegaconf, latest (can be removed as well, if you do not load all of the configs);

PyTorch

# V5
import torch

language = 'ru'
model_id = 'v5_ru'
sample_rate = 48000
speaker = 'xenia'
device = torch.device('cpu')

model, example_text = torch.hub.load(repo_or_dir='snakers4/silero-models',
                                     model='silero_tts',
                                     language=language,
                                     speaker=model_id)
model.to(device)  # gpu or cpu

audio = model.apply_tts(text=example_text,
                        speaker=speaker,
                        sample_rate=sample_rate)

Standalone Use

Standalone usage only requires PyTorch 1.12+ and the Python Standard Library;
Please see the detailed examples in Colab;

# V5
import os
import torch

device = torch.device('cpu')
torch.set_num_threads(4)
local_file = 'model.pt'

if not os.path.isfile(local_file):
    torch.hub.download_url_to_file('https://models.silero.ai/models/tts/ru/v5_ru.pt',
                                   local_file)  

model = torch.package.PackageImporter(local_file).load_pickle("tts_models", "model")
model.to(device)

example_text = 'Меня зовут Лева Королев. Я из готов. И я уже готов открыть все ваши замки любой сложности!'
sample_rate = 48000
speaker='baya'

audio_paths = model.save_wav(text=example_text,
                             speaker=speaker,
                             sample_rate=sample_rate)

SSML

Check out our TTS Wiki page.

Cyrillic languages v4

To be superseded with v5 model(s) soon.

Supported tokenset: !,-.:?iµöабвгдежзийклмнопрстуфхцчшщъыьэюяёђѓєіјњћќўѳғҕҗҙқҡңҥҫүұҳҷһӏӑӓӕӗәӝӟӥӧөӱӳӵӹ

Speaker_ID	Language	Gender
b_ava	Avar	F
b_bashkir	Bashkir	M
b_bulb	Bulgarian	M
b_bulc	Bulgarian	M
b_che	Chechen	M
b_cv	Chuvash	M
cv_ekaterina	Chuvash	F
b_myv	Erzya	M
b_kalmyk	Kalmyk	M
b_krc	Karachay-Balkar	M
kz_M1	Kazakh	M
kz_M2	Kazakh	M
kz_F3	Kazakh	F
kz_F1	Kazakh	F
kz_F2	Kazakh	F
b_kjh	Khakas	F
b_kpv	Komi-Ziryan	M
b_lez	Lezghian	M
b_mhr	Mari	F
b_mrj	Mari High	M
b_nog	Nogai	F
b_oss	Ossetic	M
b_ru	Russian	M
b_tat	Tatar	M
marat_tt	Tatar	M
b_tyv	Tuvinian	M
b_udm	Udmurt	M
b_uzb	Uzbek	M
b_sah	Yakut	M
kalmyk_erdni	Kalmyk	M
kalmyk_delghir	Kalmyk	F

Indic languages v4

Example

(!!!) All input sentences should be romanized to ISO format using aksharamukha. An example for hindi:

# V3
import torch
from aksharamukha import transliterate

# Loading model
model, example_text = torch.hub.load(repo_or_dir='snakers4/silero-models',
                                     model='silero_tts',
                                     language='indic',
                                     speaker='v4_indic')

orig_text = "प्रसिद्द कबीर अध्येता, पुरुषोत्तम अग्रवाल का यह शोध आलेख, उस रामानंद की खोज करता है"
roman_text = transliterate.process('Devanagari', 'ISO', orig_text)
print(roman_text)

audio = model.apply_tts(roman_text,
                        speaker='hindi_male')

Supported languages

Language	Speakers	Romanization function
hindi	`hindi_female`, `hindi_male`	`transliterate.process('Devanagari', 'ISO', orig_text)`
malayalam	`malayalam_female`, `malayalam_male`	`transliterate.process('Malayalam', 'ISO', orig_text)`
manipuri	`manipuri_female`	`transliterate.process('Bengali', 'ISO', orig_text)`
bengali	`bengali_female`, `bengali_male`	`transliterate.process('Bengali', 'ISO', orig_text)`
rajasthani	`rajasthani_female`, `rajasthani_female`	`transliterate.process('Devanagari', 'ISO', orig_text)`
tamil	`tamil_female`, `tamil_male`	`transliterate.process('Tamil', 'ISO', orig_text, pre_options=['TamilTranscribe'])`
telugu	`telugu_female`, `telugu_male`	`transliterate.process('Telugu', 'ISO', orig_text)`
gujarati	`gujarati_female`, `gujarati_male`	`transliterate.process('Gujarati', 'ISO', orig_text)`
kannada	`kannada_female`, `kannada_male`	`transliterate.process('Kannada', 'ISO', orig_text)`

@misc{Silero Models,
  author = {Silero Team},
  title = {Silero Models: pre-trained text-to-speech models made embarrassingly simple},
  year = {2025},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/snakers4/silero-models}},
  commit = {insert_some_commit_here},
  email = {hello@silero.ai}
}

Silero Models

Silero Models

Installation and Basics

Text-To-Speech

Models and Speakers

V5

V5 CIS Base Models

V5 CIS Ext Models

V4

V3

Dependencies

PyTorch

Standalone Use

SSML

Cyrillic languages v4

Indic languages v4

Example

Supported languages

Contact

Licence

Citations

Further reading

English

Chinese

Russian