https://github.com/snakers4/silero-models
Silero Models: pre-trained text-to-speech models made embarrassingly simple
https://github.com/snakers4/silero-models
armenian azerbaijani belarus colab georgian kazakh kyrgyz pretrained-models pytorch russian speech speech-synthesis speech-to-text tajik text-to-speech torch-hub tts tts-models ukrainian uzbek
Last synced: 5 months ago
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Silero Models: pre-trained text-to-speech models made embarrassingly simple
- Host: GitHub
- URL: https://github.com/snakers4/silero-models
- Owner: snakers4
- License: other
- Created: 2020-09-11T12:10:23.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2026-01-29T15:55:18.000Z (5 months ago)
- Last Synced: 2026-01-30T04:36:58.099Z (5 months ago)
- Topics: armenian, azerbaijani, belarus, colab, georgian, kazakh, kyrgyz, pretrained-models, pytorch, russian, speech, speech-synthesis, speech-to-text, tajik, text-to-speech, torch-hub, tts, tts-models, ukrainian, uzbek
- Language: Jupyter Notebook
- Homepage:
- Size: 1.25 MB
- Stars: 5,743
- Watchers: 88
- Forks: 358
- Open Issues: 14
-
Metadata Files:
- Readme: README.md
- Changelog: changelog.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
- awesomeLibrary - silero-models - Silero Models: pre-trained speech-to-text, text-to-speech and text-enhancement models made embarrassingly simple (语言资源库 / python)
- StarryDivineSky - snakers4/silero-models
- awesome-tts-stt - Silero Models
- awesome-voice-agents - Silero Models - models) | Pre-trained text-to-speech models made embarrassingly simple. Multi-language support. | 简单易用，高质量 | (TTS (Text-to-Speech) | 文本转语音 / Open Source TTS Models | 开源 TTS 模型)
- awesome-local-ai - Silero Models - trained TTS/STT models (Voice & Multimodal (local) (16))
README
[](mailto:hello@silero.ai) [](https://t.me/silero_speech) [](https://github.com/snakers4/silero-models/blob/master/LICENSE)
[](https://badge.fury.io/py/silero)
- [Silero Models](#silero-models)
- [Installation and Basics](#installation-and-basics)
- [Text-To-Speech](#text-to-speech)
- [Models and Speakers](#models-and-speakers)
- [V5](#v5)
- [V5 CIS Base Models](#v5-cis-base-models)
- [V5 CIS Ext Models](#v5-cis-ext-models)
- [V4](#v4)
- [V3](#v3)
- [Dependencies](#dependencies)
- [PyTorch](#pytorch)
- [Standalone Use](#standalone-use)
- [SSML](#ssml)
- [Cyrillic languages v4](#cyrillic-languages-v4)
- [Indic languages v4](#indic-languages-v4)
- [Example](#example)
- [Supported languages](#supported-languages)
- [Contact](#contact)
- [Licence](#licence)
- [Citations](#citations)
- [Further reading](#further-reading)
- [English](#english)
- [Chinese](#chinese)
- [Russian](#russian)
# 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](https://pytorch.org/docs/stable/hub.html#loading-models-from-hub) 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:
```python
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](https://github.com/snakers4/silero-models/blob/master/models.yml) file. Any metadata and newer versions will be added there.
#### V5
V5 models support [SSML](https://github.com/snakers4/silero-models/wiki/SSML). Also see Colab examples for main SSML tag usage.
Russian-only models support automated stress and homographs.
| ID | Speakers | Auto-stress / Homographs | Language | SR | Colab |
| ------- | --------------------------------------------- | ----------- | -------------- | ------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `v5_ru` | `aidar`, `baya`, `kseniya`, `xenia`, `eugene` | yes / yes | `ru` (Russian) | `8000`, `24000`, `48000` | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts.ipynb) |
#### 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](https://github.com/snakers4/silero-models/blob/master/LICENSE_CIS);
- V5 UTMOS and throughput [metrics](https://github.com/snakers4/silero-models/wiki/%D0%9F%D1%83%D0%B1%D0%BB%D0%B8%D1%87%D0%BD%D0%B0%D1%8F-%D0%B4%D0%BE%D0%BA%D1%83%D0%BC%D0%B5%D0%BD%D1%82%D0%B0%D1%86%D0%B8%D1%8F-%D0%BF%D0%BE-%D1%81%D0%BA%D0%BE%D1%80%D0%BE%D1%81%D1%82%D0%B8-%D0%B8-%D0%BA%D0%B0%D1%87%D0%B5%D1%81%D1%82%D0%B2%D1%83-%D1%80%D0%B0%D0%B1%D0%BE%D1%82%D1%8B);
- V5 models support [SSML](https://github.com/snakers4/silero-models/wiki/SSML). Also see Colab examples for main SSML tag usage;
- Use [cases](https://github.com/snakers4/silero-models/wiki/%D0%9D%D0%B0%D0%BF%D1%80%D0%B0%D0%B2%D0%BB%D0%B5%D0%BD%D0%B8%D1%8F-%D0%BF%D1%80%D0%B8%D0%BA%D0%BB%D0%B0%D0%B4%D0%BD%D0%BE%D0%B3%D0%BE-%D0%B8%D1%81%D0%BF%D0%BE%D0%BB%D1%8C%D0%B7%D0%BE%D0%B2%D0%B0%D0%BD%D0%B8%D1%8F-%D0%BC%D0%BE%D0%B4%D0%B5%D0%BB%D0%B5%D0%B9-%D1%81%D0%B8%D0%BD%D1%82%D0%B5%D0%B7%D0%B0) for the model;
- Minimal system requirements: a PyTorch-compatible system, a modern processor with AVX2 instruction set for x86/64 platform.
| ID | Speakers | Language | Colab |
| ------------------------------------- | -------------------------------------------- | -------------------- | -------------------- |
| `v5_cis_base`, `v5_cis_base_nostress` | `aze_gamat` | `aze` (Azerbaijani) | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts_cis.ipynb) |
| `v5_cis_base`, `v5_cis_base_nostress` | `hye_zara` | `hye` (Armenian) | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts_cis.ipynb) |
| `v5_cis_base`, `v5_cis_base_nostress` | `bak_aigul`, `bak_alfia`, `bak_alfia2` | `bak` (Bashkir) | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts_cis.ipynb) |
| `v5_cis_base`, `v5_cis_base_nostress` | `bak_miyau`, `bak_ramilia` | `bak` (Bashkir) | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts_cis.ipynb) |
| `v5_cis_base`, `v5_cis_base_nostress` | `bel_anatoliy`, `bel_dmitriy`, `bel_larisa` | `bel` (Belarus) | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts_cis.ipynb) |
| `v5_cis_base`, `v5_cis_base_nostress` | `kat_vika` | `kat` (Georgian) | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts_cis.ipynb) |
| `v5_cis_base`, `v5_cis_base_nostress` | `kbd_eduard` | `kbd` (Kab.-Cherkes) | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts_cis.ipynb) |
| `v5_cis_base`, `v5_cis_base_nostress` | `kaz_zhadyra`, `kaz_zhazira` | `kaz` (Kazakh) | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts_cis.ipynb) |
| `v5_cis_base`, `v5_cis_base_nostress` | `xal_kejilgan`, `xal_kermen` | `xal` (Kalmyk) | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts_cis.ipynb) |
| `v5_cis_base`, `v5_cis_base_nostress` | `kir_nurgul` | `kir` (Kyrgyz) | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts_cis.ipynb) |
| `v5_cis_base`, `v5_cis_base_nostress` | `mdf_oksana` | `mdf` (Moksha) | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts_cis.ipynb) |
| `v5_cis_base`, `v5_cis_base_nostress` | all of these speakers, but with `ru_` prefix | `ru` (Russian) | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts_cis.ipynb) |
| `v5_cis_base`, `v5_cis_base_nostress` | `tgk_onaoy`, `tgk_safarhuja` | `tgk` (Tajik) | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts_cis.ipynb) |
| `v5_cis_base`, `v5_cis_base_nostress` | `tat_albina`, `tat_marat` | `tat` (Tatar) | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts_cis.ipynb) |
| `v5_cis_base`, `v5_cis_base_nostress` | `udm_bogdan` | `udm` (Udmurt) | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts_cis.ipynb) |
| `v5_cis_base`, `v5_cis_base_nostress` | `uzb_saida` | `uzb` (Uzbek) | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts_cis.ipynb) |
| `v5_cis_base`, `v5_cis_base_nostress` | `ukr_igor`, `ukr_roman` | `ukr` (Ukrainian) | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts_cis.ipynb) |
| `v5_cis_base`, `v5_cis_base_nostress` | `kjh_karina`, `kjh_sibday` | `kjh` (Khakas) | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts_cis.ipynb) |
| `v5_cis_base`, `v5_cis_base_nostress` | `chv_ekaterina` | `chv` (Chuvash) | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts_cis.ipynb) |
| `v5_cis_base`, `v5_cis_base_nostress` | `erz_alexandr` | `erz` (Erzya) | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts_cis.ipynb) |
| `v5_cis_base`, `v5_cis_base_nostress` | `sah_zinaida` | `sah` (Yakut) | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts_cis.ipynb)
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](https://github.com/snakers4/silero-models/wiki/SSML). Also see Colab examples for main SSML tag usage.
| ID | Speakers | Language | Colab |
| ------------ | --------------------------------------------------------------------- | ----------------- | -------------------- |
| `v5_cis_ext` | `kaz_abai`, `kaz_aidana`, `kaz_aisha`, `kaz_bakir`, `kaz_danara` | `kaz` (Kazakh) | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts_cis.ipynb) |
| `v5_cis_ext` | `xal_delghir`, `xal_erdni` | `xal` (Kalmyk) | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts_cis.ipynb) |
| `v5_cis_ext` | `tat_adiba`, `tat_alsou`, `tat_amir`, `tat_azat`, `tat_batir` | `tat` (Tatar) | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts_cis.ipynb) |
| `v5_cis_ext` | `tat_bulat`, `tat_damir`, `tat_guzel`, `tat_ildar`, `tat_ilgiz` | `tat` (Tatar) | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts_cis.ipynb) |
| `v5_cis_ext` | `tat_karim`, `tat_mansur`, `tat_murat`, `tat_rasima`, `tat_rustem` | `tat` (Tatar) | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts_cis.ipynb) |
| `v5_cis_ext` | `tat_timur`, `tat_zifa`, `tat_zufar`, `tat_zulfiya` | `tat` (Tatar) | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts_cis.ipynb) |
| `v5_cis_ext` | `uzb_anora`, `uzb_dilnavoz` | `uzb` (Uzbek) | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts_cis.ipynb) |
| `v5_cis_ext` | `ukr_kateryna`, `ukr_lada`, `ukr_mykyta`, `ukr_oleksa`, `ukr_tetiana` | `ukr` (Ukrainian) | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts_cis.ipynb) |
| `v5_cis_ext` | `chv_aihwa`, `chv_alima` | `chv` (Chuvash) | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts_cis.ipynb) |
#### V4
V4 models support [SSML](https://github.com/snakers4/silero-models/wiki/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 | Colab |
| ------------- | ----------- | ----------- |---------------------------------- | --------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `v4_ru` | `aidar`, `baya`, `kseniya`, `xenia`, `eugene`, `random` | yes | `ru` (Russian) | `8000`, `24000`, `48000` | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts.ipynb) |
| [`v4_cyrillic`](#cyrillic-languages) | `b_ava`, `marat_tt`, `kalmyk_erdni`... | no | `cyrillic` [(Avar, Tatar, Kalmyk, ...)](#cyrillic-languages) | `8000`, `24000`, `48000` | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts.ipynb) |
| `v4_ua` | `mykyta`, `random` | no | `ua` (Ukrainian) | `8000`, `24000`, `48000` | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts.ipynb) |
| `v4_uz` | `dilnavoz` | no | `uz` (Uzbek) | `8000`, `24000`, `48000` | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts.ipynb) |
| [`v4_indic`](#indic-languages) | `hindi_male`, `hindi_female`, ..., `random` | no | `indic` [(Hindi, Telugu, ...)](#indic-languages) | `8000`, `24000`, `48000` | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts.ipynb) |
#### V3
V3 models support [SSML](https://github.com/snakers4/silero-models/wiki/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 | Colab |
| ------------- | ----------- | ----------- |---------------------------------- | --------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `v3_en` | `en_0`, `en_1`, ..., `en_117`, `random` | no | `en` (English) | `8000`, `24000`, `48000` | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts.ipynb) |
| `v3_en_indic` | `tamil_female`, ..., `assamese_male`, `random` | no | `en` (English) | `8000`, `24000`, `48000` | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts.ipynb) |
| `v3_de` | `eva_k`, ..., `karlsson`, `random` | no | `de` (German) | `8000`, `24000`, `48000` | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts.ipynb) |
| `v3_es` | `es_0`, `es_1`, `es_2`, `random` | no | `es` (Spanish) | `8000`, `24000`, `48000` | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts.ipynb) |
| `v3_fr` | `fr_0`, ..., `fr_5`, `random` | no | `fr` (French) | `8000`, `24000`, `48000` | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts.ipynb) |
| [`v3_indic`](#indic-languages) | `hindi_male`, `hindi_female`, ..., `random` | no | `indic` [(Hindi, Telugu, ...)](#indic-languages) | `8000`, `24000`, `48000` | [](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts.ipynb) |
### 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
[](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_tts.ipynb)
[](https://pytorch.org/hub/snakers4_silero-models_tts/)
```python
# 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;
```python
# 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.](https://github.com/snakers4/silero-models/wiki/SSML)
### 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`](https://aksharamukha.appspot.com/python). An example for `hindi`:
```python
# 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)`
## Contact
Try our models, create an [issue](https://github.com/snakers4/silero-models/issues/new), join our [chat](https://t.me/silero_speech), [email](mailto:hello@silero.ai) us, and read the latest [news](https://t.me/silero_news).
## Licence
All of the models are published under the main repo license (i.e. CC-NC-BY) except for the `base` cis-tts models, which are under MIT.
## Citations
```bibtex
@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}
}
```
## Further reading
### English
- STT:
- Towards an Imagenet Moment For Speech-To-Text - [link](https://thegradient.pub/towards-an-imagenet-moment-for-speech-to-text/)
- A Speech-To-Text Practitioners Criticisms of Industry and Academia - [link](https://thegradient.pub/a-speech-to-text-practitioners-criticisms-of-industry-and-academia/)
- Modern Google-level STT Models Released - [link](https://habr.com/ru/post/519562/)
- TTS:
- Multilingual Text-to-Speech Models for Indic Languages - [link](https://www.analyticsvidhya.com/blog/2022/06/multilingual-text-to-speech-models-for-indic-languages/)
- Our new public speech synthesis in super-high quality, 10x faster and more stable - [link](https://habr.com/ru/post/660571/)
- High-Quality Text-to-Speech Made Accessible, Simple and Fast - [link](https://habr.com/ru/post/549482/)
- VAD:
- One Voice Detector to Rule Them All - [link](https://thegradient.pub/one-voice-detector-to-rule-them-all/)
- Modern Portable Voice Activity Detector Released - [link](https://habr.com/ru/post/537276/)
- Text Enhancement:
- We have published a model for text repunctuation and recapitalization for four languages - [link](https://habr.com/ru/post/581960/)
### Chinese
- STT:
- 迈向语音识别领域的 ImageNet 时刻 - [link](https://www.infoq.cn/article/4u58WcFCs0RdpoXev1E2)
- 语音领域学术界和工业界的七宗罪 - [link](https://www.infoq.cn/article/lEe6GCRjF1CNToVITvNw)
### Russian
- STT
- OpenAI решили распознавание речи! Разбираемся так ли это … - [link](https://habr.com/ru/post/689572/)
- Наши сервисы для бесплатного распознавания речи стали лучше и удобнее - [link](https://habr.com/ru/post/654227/)
- Telegram-бот Silero бесплатно переводит речь в текст - [link](https://habr.com/ru/post/591563/)
- Бесплатное распознавание речи для всех желающих - [link](https://habr.com/ru/post/587512/)
- Последние обновления моделей распознавания речи из Silero Models - [link](https://habr.com/ru/post/577630/)
- Сжимаем трансформеры: простые, универсальные и прикладные способы cделать их компактными и быстрыми - [link](https://habr.com/ru/post/563778/)
- Ультимативное сравнение систем распознавания речи: Ashmanov, Google, Sber, Silero, Tinkoff, Yandex - [link](https://habr.com/ru/post/559640/)
- Мы опубликовали современные STT модели сравнимые по качеству с Google - [link](https://habr.com/ru/post/519564/)
- Понижаем барьеры на вход в распознавание речи - [link](https://habr.com/ru/post/494006/)
- Огромный открытый датасет русской речи версия 1.0 - [link](https://habr.com/ru/post/474462/)
- Насколько Быстрой Можно Сделать Систему STT? - [link](https://habr.com/ru/post/531524/)
- Наша система Speech-To-Text - [link](https://www.silero.ai/tag/our-speech-to-text/)
- Speech-To-Text - [link](https://www.silero.ai/tag/speech-to-text/)
- TTS:
- Делаем быстрый, качественный и доступный синтез на языках России — нужно ваше участие - [link](https://habr.com/ru/articles/872474/)
- Мы решили задачу омографов и ударений в русском языке - [link](https://habr.com/ru/articles/955130/)
- Теперь наш синтез также доступен в виде бота в Телеграме - [link](https://habr.com/ru/post/682188/)
- Может ли синтез речи обмануть систему биометрической идентификации? - [link](https://habr.com/ru/post/673996/)
- Теперь наш синтез на 20 языках - [link](https://habr.com/ru/post/669910/)
- Теперь наш публичный синтез в супер-высоком качестве, в 10 раз быстрее и без детских болячек - [link](https://habr.com/ru/post/660565/)
- Синтезируем голос бабушки, дедушки и Ленина + новости нашего публичного синтеза - [link](https://habr.com/ru/post/584750/)
- Мы сделали наш публичный синтез речи еще лучше - [link](https://habr.com/ru/post/563484/)
- Мы Опубликовали Качественный, Простой, Доступный и Быстрый Синтез Речи - [link](https://habr.com/ru/post/549480/)
- VAD:
- Новый релиз публичного детектора голоса Silero VAD v6 - [link](https://habr.com/ru/articles/940750/)
- Наш публичный детектор голоса стал лучше - [link](https://habr.com/ru/post/695738/)
- А ты используешь VAD? Что это такое и зачем он нужен - [link](https://habr.com/ru/post/594745/)
- Модели для Детекции Речи, Чисел и Распознавания Языков - [link](https://www.silero.ai/vad-lang-classifier-number-detector/)
- Мы опубликовали современный Voice Activity Detector и не только -[link](https://habr.com/ru/post/537274/)
- Text Enhancement:
- Восстановление знаков пунктуации и заглавных букв — теперь и на длинных текстах - [link](https://habr.com/ru/post/594565/)
- Мы опубликовали модель, расставляющую знаки препинания и заглавные буквы в тексте на четырех языках - [link](https://habr.com/ru/post/581946/)