snakers4/silero-models

snakers4/silero-models hiện có 6.1k sao trên GitHub, viết chủ yếu bằng Jupyter Notebook. Silero Models: pre-trained text-to-speech models made embarrassingly simple
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Snapshot
Cộng tác viên hàng đầu
Xem cộng tác viên hàng đầu

- Silero Models
- Installation and Basics
- Text-To-Speech
- Models and Speakers
- V5 Turkic
- V5 Caucasian
- 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
- Models and Speakers
- Contact
- Licence
- Citations
- Further reading
- English
- Chinese
- 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 sileroand thenfrom 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 Turkic
- This model does not support stress at all;
- This model supports SSML;
- This model supports
8000,24000,48000sampling rates;
| ID | Speakers | Language | Colab |
|---|---|---|---|
v5_turkic | chv_0,chv_1 | chv (Chuvash) | |
v5_turkic | crh_0, crh_1, crh_2 | crh (Crimean Tatar) | |
v5_turkic | gag_0, gag_1, gag_2 | gag (Gagauz) | |
v5_turkic | kaa_0, kaa_1 | kaa (Karakalpak) | |
v5_turkic | kir_0 | kir (Kyrgyz) | |
v5_turkic | kjh_0 | kjh (Khakas) | |
v5_turkic | sah_0, sah_1, sah_2 | sah (Yakut ) | |
v5_turkic | sty_0 | sty (Siberian Tatar) | |
v5_turkic | tat_0 | tat (Tatar) | |
v5_turkic | tgk_0 | tgk (Tajik) | |
v5_turkic | tuk_0, tuk_1, tuk_2 | tuk (Turkmen) | |
v5_turkic | tyv_0 | tyv (Tuvan) | |
v5_turkic | uzb_0 | uzb (Uzbek) | |
v5_turkic | xal_0, xal_1, xal_2 | xal (Kalmyk) |
V5 Caucasian
- This model does not support stress at all;
- This model supports SSML;
- This model supports
8000,24000,48000sampling rates;
| ID | Speakers | Language | Colab |
|---|---|---|---|
v5_caucasian | abq_0, abq_1, abq_2 | abq (Abaza) | |
v5_caucasian | ady_0, ady_1, ady_2, ady_3 | ady (Adygean) | |
v5_caucasian | agx_0 | agx (Aghul) | |
v5_caucasian | ava_0 | ava (Avar) | |
v5_caucasian | che_0, che_1, che_2, che_3, che_4 | che (Chechen) | |
v5_caucasian | darg_0, darg_1 | darg (Dargin) | |
v5_caucasian | inh_0, inh_1 | inh (Ingush) | |
v5_caucasian | kbd_0, kbd_1, kbd_2 | kbd (K.-Ciscassian) | |
v5_caucasian | krc_0, krc_1, krc_2 | krc (K.-Balkar) | |
v5_caucasian | kum_0, kum_1, kum_2 | kum (Kumyk) | |
v5_caucasian | lbe_0 | lbe (Lak) | |
v5_caucasian | lez_0 | lez (Lezgin) | |
v5_caucasian | oss_0 | oss (Ossetian) | |
v5_caucasian | tab_0, tab_1 | tab (Tabasaran) | |
v5_caucasian | tkr_0 | tkr (Tsakhur) |
examples_tts_turkic_caucasian.ipynb
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 | Colab |
|---|---|---|---|---|---|
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
8000,24000,48000sampling rates and contain no auto-stress or homographs; v5_cis_basemodels assume that proper stress should be added for each word for all languages, i.e.к+ошка;v5_cis_base_nostressmodels 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
MITlicence; - 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 | Colab |
|---|---|---|---|
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
8000,24000,48000sampling rates and contain no auto-stress or homographs; v5_cis_extmodels assume that proper stress should be added for each word for all languages, i.e.к+ошка;v5_cis_ext_nostressare coming soon;- All of the below models are published under
CC-NC-BYlicence; - V5 models support 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) | |
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 | Colab |
|---|---|---|---|---|---|
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 | Colab |
|---|---|---|---|---|---|
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) |
Contact
Try our models, create an issue, join our chat, email us, and read the latest 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
@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 = {[email protected]}
}
Further reading
English
-
STT:
- Towards an Imagenet Moment For Speech-To-Text - link
- A Speech-To-Text Practitioners Criticisms of Industry and Academia - link
- Modern Google-level STT Models Released - link
-
TTS:
- Multilingual Text-to-Speech Models for Indic Languages - link
- Our new public speech synthesis in super-high quality, 10x faster and more stable - link
- High-Quality Text-to-Speech Made Accessible, Simple and Fast - link
-
VAD:
- One Voice Detector to Rule Them All - link
- Modern Portable Voice Activity Detector Released - link
-
Text Enhancement:
- We have published a model for text repunctuation and recapitalization for four languages - link
Chinese
- STT:
- 迈向语音识别领域的 ImageNet 时刻 - link
- 语音领域学术界和工业界的七宗罪 - link
Russian
-
STT
- OpenAI решили распознавание речи! Разбираемся так ли это … - link
- Наши сервисы для бесплатного распознавания речи стали лучше и удобнее - link
- Telegram-бот Silero бесплатно переводит речь в текст - link
- Бесплатное распознавание речи для всех желающих - link
- Последние обновления моделей распознавания речи из Silero Models - link
- Сжимаем трансформеры: простые, универсальные и прикладные способы cделать их компактными и быстрыми - link
- Ультимативное сравнение систем распознавания речи: Ashmanov, Google, Sber, Silero, Tinkoff, Yandex - link
- Мы опубликовали современные STT модели сравнимые по качеству с Google - link
- Понижаем барьеры на вход в распознавание речи - link
- Огромный открытый датасет русской речи версия 1.0 - link
- Насколько Быстрой Можно Сделать Систему STT? - link
- Наша система Speech-To-Text - link
- Speech-To-Text - link
-
TTS:
- Теперь silero-tts v5 на русском языке умеет задавать вопросы - link
- Наш синтез для 20 языков теперь работает локально под Windows как экранная читалка (SAPI5) и в Балаболке - link
- Мы добавили поддержку ещё 19 языков России и СНГ в проект silero-stress - link
- Мы опубликовали стабильный, быстрый, качественный и доступный синтез для 20 языков России - link
- Мы опубликовали silero-tts v5 на русском языке - link
- Мы решили задачу омографов и ударений в русском языке - link
- Делаем быстрый, качественный и доступный синтез на языках России — нужно ваше участие - link
- Теперь наш синтез также доступен в виде бота в Телеграме - link
- Может ли синтез речи обмануть систему биометрической идентификации? - link
- Теперь наш синтез на 20 языках - link
- Теперь наш публичный синтез в супер-высоком качестве, в 10 раз быстрее и без детских болячек - link
- Синтезируем голос бабушки, дедушки и Ленина + новости нашего публичного синтеза - link
- Мы сделали наш публичный синтез речи еще лучше - link
- Мы Опубликовали Качественный, Простой, Доступный и Быстрый Синтез Речи - link
-
VAD:
- Новый релиз публичного детектора голоса Silero VAD v6 - link
- Наш публичный детектор голоса стал лучше - link
- А ты используешь VAD? Что это такое и зачем он нужен - link
- Модели для Детекции Речи, Чисел и Распознавания Языков - link
- Мы опубликовали современный Voice Activity Detector и не только -link
-
Text Enhancement:
- Восстановление знаков пунктуации и заглавных букв — теперь и на длинных текстах - link
- Мы опубликовали модель, расставляющую знаки препинания и заглавные буквы в тексте на четырех языках - link
Repo liên quan
Hugging Face Transformers (huggingface/transformers) is a Python library that centralizes model definitions for text, computer vision, audio, video, and multimodal machine learning, covering both inference and training. The README describes it as a pivot point compatible with training frameworks such as Axolotl, DeepSpeed, and PyTorch-Lightning, and inference engines such as vLLM, SGLang, and TGI, with more than 1M+ model checkpoints listed on the Hugging Face Hub.
利用 AI 大模型和自动化工作流,根据主题或关键词一键生成高清短视频。Generate HD short videos from a topic or keyword with an automated AI workflow.
LLMs-from-scratch is Sebastian Raschka's GitHub companion to the Manning book Build a Large Language Model (From Scratch), ISBN 9781633437166, with 102585 stars and 15720 forks. It walks through building, pretraining, and finetuning a GPT-style model in PyTorch across seven chapters and five appendices, plus a growing set of bonus notebooks covering newer open architectures.
A high-throughput and memory-efficient inference and serving engine for LLMs
Trả lời nhanh
Cùng nhóm AI Tools còn repo nào?
snakers4/silero-models thuộc nhóm AI Tools trên TopGit, cùng 20 topic GitHub. Trang Trending và Topics liệt kê các repo cùng số sao và cùng ngôn ngữ để so sánh.
Đọc thêm về snakers4/silero-models ở đâu?
Trang TopGit này là một snapshot — tab "Readme" hiển thị nguyên văn README của repo (đã bỏ link, giữ ảnh). Repo GitHub ở github.com/snakers4/silero-models là nguồn chính thức.
snakers4/silero-models có bao nhiêu sao?
snakers4/silero-models có 6.1k sao GitHub — tải lại trang để xem số mới nhất, hoặc xem trực tiếp github.com/snakers4/silero-models. TopGit phản chiếu số sao của GitHub nhưng không cam kết đến từng phút.
snakers4/silero-models có những chủ đề gì?
GitHub topics của 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". TopGit xếp repo vào nhóm AI Tools.
snakers4/silero-models còn đang phát triển không?
Commit gần nhất trên snakers4/silero-models là 2 tháng trước (theo timestamp GitHub). Repo có 370 fork — một chỉ báo về mức độ quan tâm của cộng đồng.
snakers4/silero-models viết bằng ngôn ngữ gì?
snakers4/silero-models chủ yếu viết bằng Jupyter Notebook. Trường "language" của GitHub dựa trên phần lớn byte ở nhánh mặc định.
Vì sao snakers4/silero-models được xếp vào nhóm AI Tools?
TopGit xếp snakers4/silero-models vào nhóm AI Tools dựa trên GitHub topics và mô tả của repo (gắn thẻ: "armenian", "azerbaijani", "belarus"). Việc phân loại dựa trên metadata thật của repo, không phải đoán theo cảm tính biên tập.
Đọc đầy đủ README ở tab phía trên.
silero-models có đáng để bạn bỏ thời gian?
ChatGPT, Claude và Perplexity đều đọc được trang này. Hỏi thử xem họ nghĩ gì về silero-models.