🎙️ Piper Neural TTS Voices Mirror (Sherpa-ONNX Bundles)
High-speed global CDN mirror of 129 curated single-speaker Piper VITS neural TTS models packaged for zero-config, offline, on-device text-to-speech synthesis in sherpa-onnx and the Anuwad cross-platform application.
📌 Features & Highlights
- 100% Single-Speaker Studio Models: Every model in this repository is configured for
sid: 0out-of-the-box — no speaker ID lookup, multi-speaker indexing, or complex configuration required. - Self-Contained Model Bundles: Each
.tar.bz2archive contains everything needed for fully offline text-to-speech inference:<model>.onnx— The optimized neural acoustic model (VITS architecture)tokens.txt— Phoneme vocabulary and symbol mappingsespeak-ng-data/— Complete phonemization directory for offline text processing
- High-Speed CDN Acceleration: Hosted directly on Hugging Face's global edge infrastructure powered by Cloudflare, enabling fast, resilient model downloads worldwide.
- Broad Language Coverage: 129 voices spanning 42+ languages and regional accents across Europe, Asia, and the Americas.
🔗 Download URL Format
Any voice archive can be downloaded directly from the Cloudflare CDN edge via:
https://huggingface.co/4-alokk/piper-voices/resolve/main/<voice_id>.tar.bz2
Example (cURL):
curl -L -O https://huggingface.co/4-alokk/piper-voices/resolve/main/vits-piper-en_US-lessac-medium.tar.bz2
Example (Python with huggingface_hub):
from huggingface_hub import hf_hub_download
# Download any Piper bundle with local caching
bundle_path = hf_hub_download(
repo_id="4-alokk/piper-voices",
filename="vits-piper-hi_IN-pratham-medium.tar.bz2",
repo_type="model",
)
print(f"Model downloaded to: {bundle_path}")
Example (Flutter / Dart with dio):
final url = 'https://huggingface.co/4-alokk/piper-voices/resolve/main/$voiceId.tar.bz2';
await dio.download(url, savePath);
👥 Credits, Contributors & Acknowledgments
This repository stands on the shoulders of remarkable open-source speech synthesis pioneers and dataset creators. Deepest gratitude to:
- Michael Hansen (@synesthesiam) and the Piper TTS Community:
- For designing and training the fast, high-quality VITS-based neural text-to-speech engine.
- Fangjun Kuang (@csukuangfj) and the k2-fsa / sherpa-onnx project:
- For converting, quantizing, packaging, and optimizing Piper models for embedded and cross-platform runtime execution.
- Voice Dataset Creators & Community Contributors:
- Thorsten Müller (Thorsten Voice) for top-tier open German voice datasets.
- Keith Ito and the LJSpeech corpus team.
- LibriTTS, M-AILABS, and OpenSLR speech contributors across all 42+ languages.
- Community voice actors who generously contributed high-fidelity audio recordings.
- Curated, Mirrored & Maintained by:
📋 Catalog of Available Voices (129 Models)
| Language | Voice ID | Speaker / Name | Gender | Size |
|---|---|---|---|---|
| Arabic (JO) | vits-piper-ar_JO-SA_miro_V2-high |
Sa Miro V2 [high] | Neutral | 64 MB |
| Arabic (JO) | vits-piper-ar_JO-kareem-medium |
Kareem | Male | 64 MB |
| Spanish (Spain) | vits-piper-ca_ES-upc_ona-medium |
Upc Ona | Female | 64 MB |
| Spanish (Spain) | vits-piper-ca_ES-upc_pau-x_low |
Upc Pau | Male | 25 MB |
| Czech (CZ) | vits-piper-cs_CZ-jirka-medium |
Jirka | Male | 64 MB |
| English (UK) | vits-piper-cy_GB-bu_tts-medium |
Bu Tts | Male | 77 MB |
| English (UK) | vits-piper-cy_GB-gwryw_gogleddol-medium |
Gwryw Gogleddol | Male | 64 MB |
| Danish (DK) | vits-piper-da_DK-talesyntese-medium |
Talesyntese | Male | 64 MB |
| German (DE) | vits-piper-de_DE-eva_k-x_low |
Eva K | Female | 25 MB |
| German (DE) | vits-piper-de_DE-karlsson-low |
Karlsson | Male | 64 MB |
| German (DE) | vits-piper-de_DE-kerstin-low |
Kerstin | Female | 64 MB |
| German (DE) | vits-piper-de_DE-pavoque-low |
Pavoque | Male | 64 MB |
| German (DE) | vits-piper-de_DE-ramona-low |
Ramona | Female | 64 MB |
| German (DE) | vits-piper-de_DE-thorsten-medium |
Thorsten | Male | 64 MB |
| German (DE) | vits-piper-de_DE-thorsten-high |
Thorsten [high] | Male | 110 MB |
| German (DE) | vits-piper-de_DE-thorsten_emotional-medium |
Thorsten Emotional | Male | 76 MB |
| Greek (GR) | vits-piper-el_GR-rapunzelina-low |
Rapunzelina | Female | 64 MB |
| English (UK) | vits-piper-en_GB-alan-medium |
Alan | Male | 64 MB |
| English (UK) | vits-piper-en_GB-alba-medium |
Alba | Female | 64 MB |
| English (UK) | vits-piper-en_GB-aru-medium |
Aru | Female | 77 MB |
| English (UK) | vits-piper-en_GB-cori-medium |
Cori | Female | 64 MB |
| English (UK) | vits-piper-en_GB-cori-high |
Cori [high] | Female | 110 MB |
| English (UK) | vits-piper-en_GB-jenny_dioco-medium |
Jenny Dioco | Female | 64 MB |
| English (UK) | vits-piper-en_GB-northern_english_male-medium |
Northern English Male | Male | 64 MB |
| English (UK) | vits-piper-en_GB-southern_english_female-medium |
Southern English Female | Female | 77 MB |
| English (UK) | vits-piper-en_GB-southern_english_female_medium |
Southern English Female Medium | Female | 77 MB |
| English (UK) | vits-piper-en_GB-southern_english_male-medium |
Southern English Male | Male | 77 MB |
| English (US) | vits-piper-en_US-amy-medium |
Amy | Female | 64 MB |
| English (US) | vits-piper-en_US-bryce-medium |
Bryce | Male | 64 MB |
| English (US) | vits-piper-en_US-danny-low |
Danny | Male | 64 MB |
| English (US) | vits-piper-en_US-hfc_female-medium |
Hfc Female | Female | 64 MB |
| English (US) | vits-piper-en_US-hfc_male-medium |
Hfc Male | Male | 64 MB |
| English (US) | vits-piper-en_US-joe-medium |
Joe | Male | 64 MB |
| English (US) | vits-piper-en_US-john-medium |
John | Male | 64 MB |
| English (US) | vits-piper-en_US-kathleen-low |
Kathleen | Female | 64 MB |
| English (US) | vits-piper-en_US-kristin-medium |
Kristin | Female | 64 MB |
| English (US) | vits-piper-en_US-kusal-medium |
Kusal | Male | 64 MB |
| English (US) | vits-piper-en_US-lessac-medium |
Lessac | Female | 64 MB |
| English (US) | vits-piper-en_US-lessac-high |
Lessac [high] | Female | 110 MB |
| English (US) | vits-piper-en_US-ljspeech-medium |
Ljspeech | Female | 64 MB |
| English (US) | vits-piper-en_US-norman-medium |
Norman | Male | 64 MB |
| English (US) | vits-piper-en_US-reza_ibrahim-medium |
Reza Ibrahim | Male | 64 MB |
| English (US) | vits-piper-en_US-ryan-medium |
Ryan | Male | 64 MB |
| English (US) | vits-piper-en_US-ryan-high |
Ryan [high] | Male | 110 MB |
| English (US) | vits-piper-en_US-sam-medium |
Sam | Male | 64 MB |
| Spanish (Argentina) | vits-piper-es_AR-daniela-high |
Daniela [high] | Female | 110 MB |
| Spanish (Spain) | vits-piper-es_ES-carlfm-x_low |
Carlfm | Male | 25 MB |
| Spanish (Spain) | vits-piper-es_ES-davefx-medium |
Davefx | Male | 64 MB |
| Spanish (Mexico) | vits-piper-es_MX-ald-medium |
Ald | Male | 64 MB |
| Spanish (Mexico) | vits-piper-es_MX-claude-high |
Claude [high] | Male | 64 MB |
| Spanish (Spain) | vits-piper-eu_ES-antton-medium |
Antton | Male | 64 MB |
| Spanish (Spain) | vits-piper-eu_ES-maider-medium |
Maider | Female | 64 MB |
| Persian (IR) | vits-piper-fa_IR-amir-medium |
Amir | Male | 64 MB |
| Persian (IR) | vits-piper-fa_IR-ganji-medium |
Ganji | Male | 64 MB |
| Persian (IR) | vits-piper-fa_IR-ganji_adabi-medium |
Ganji Adabi | Male | 64 MB |
| Persian (IR) | vits-piper-fa_IR-gyro-medium |
Gyro | Male | 64 MB |
| Persian (IR) | vits-piper-fa_IR-reza_ibrahim-medium |
Reza Ibrahim | Male | 64 MB |
| Persian (en) | vits-piper-fa_en-rezahedayatfar-ibrahimwalk-medium |
Rezahedayatfar-Ibrahimwalk | Neutral | 64 MB |
| Finnish (FI) | vits-piper-fi_FI-harri-medium |
Harri | Male | 64 MB |
| French (FR) | vits-piper-fr_FR-gilles-low |
Gilles | Male | 64 MB |
| French (FR) | vits-piper-fr_FR-siwis-medium |
Siwis | Female | 64 MB |
| French (FR) | vits-piper-fr_FR-tom-medium |
Tom | Male | 64 MB |
| Hindi (IN) | vits-piper-hi_IN-pratham-medium |
Pratham | Male | 64 MB |
| Hindi (IN) | vits-piper-hi_IN-priyamvada-medium |
Priyamvada | Female | 64 MB |
| Hindi (IN) | vits-piper-hi_IN-rohan-medium |
Rohan | Male | 64 MB |
| Hungarian (HU) | vits-piper-hu_HU-anna-medium |
Anna | Female | 64 MB |
| Hungarian (HU) | vits-piper-hu_HU-berta-medium |
Berta | Female | 64 MB |
| Hungarian (HU) | vits-piper-hu_HU-imre-medium |
Imre | Male | 64 MB |
| Indonesian (ID) | vits-piper-id_ID-news_tts-medium |
News Tts | Male | 64 MB |
| Icelandic (IS) | vits-piper-is_IS-bui-medium |
Bui | Male | 64 MB |
| Icelandic (IS) | vits-piper-is_IS-salka-medium |
Salka | Female | 64 MB |
| Icelandic (IS) | vits-piper-is_IS-steinn-medium |
Steinn | Male | 64 MB |
| Icelandic (IS) | vits-piper-is_IS-ugla-medium |
Ugla | Female | 64 MB |
| Italian (IT) | vits-piper-it_IT-paola-medium |
Paola | Female | 64 MB |
| Italian (IT) | vits-piper-it_IT-riccardo-x_low |
Riccardo | Male | 25 MB |
| Georgian (GE) | vits-piper-ka_GE-natia-medium |
Natia | Female | 64 MB |
| Kazakh (KZ) | vits-piper-kk_KZ-iseke-x_low |
Iseke | Male | 25 MB |
| Kazakh (KZ) | vits-piper-kk_KZ-issai-high |
Issai [high] | Male | 123 MB |
| Kazakh (KZ) | vits-piper-kk_KZ-raya-x_low |
Raya | Female | 25 MB |
| Kurdish (TR) | vits-piper-ku_TR-berfin_renas-medium |
Berfin Renas | Female | 77 MB |
| Luxembourgish (LU) | vits-piper-lb_LU-marylux-medium |
Marylux | Female | 64 MB |
| Latvian (LV) | vits-piper-lv_LV-aivars-medium |
Aivars | Male | 64 MB |
| Malayalam (IN) | vits-piper-ml_IN-arjun-medium |
Arjun | Male | 64 MB |
| Malayalam (IN) | vits-piper-ml_IN-meera-medium |
Meera | Female | 64 MB |
| Nepali (NP) | vits-piper-ne_NP-chitwan-medium |
Chitwan | Male | 64 MB |
| Nepali (NP) | vits-piper-ne_NP-google-medium |
Female | 77 MB | |
| Dutch (BE) | vits-piper-nl_BE-nathalie-medium |
Nathalie | Female | 64 MB |
| Dutch (BE) | vits-piper-nl_BE-rdh-medium |
Rdh | Male | 64 MB |
| Dutch (NL) | vits-piper-nl_NL-alex-medium |
Alex | Male | 64 MB |
| Dutch (NL) | vits-piper-nl_NL-pim-medium |
Pim | Male | 64 MB |
| Dutch (NL) | vits-piper-nl_NL-ronnie-medium |
Ronnie | Male | 64 MB |
| Norwegian (NO) | vits-piper-no_NO-talesyntese-medium |
Talesyntese | Male | 64 MB |
| Polish (PL) | vits-piper-pl_PL-bass-high |
Bass [high] | Male | 110 MB |
| Polish (PL) | vits-piper-pl_PL-darkman-medium |
Darkman | Male | 64 MB |
| Polish (PL) | vits-piper-pl_PL-gosia-medium |
Gosia | Female | 64 MB |
| Polish (PL) | vits-piper-pl_PL-justyna_wg_glos-medium |
Justyna Wg Glos | Female | 64 MB |
| Polish (PL) | vits-piper-pl_PL-mc_speech-medium |
Mc Speech | Male | 64 MB |
| Polish (PL) | vits-piper-pl_PL-meski_wg_glos-medium |
Meski Wg Glos | Male | 64 MB |
| Polish (PL) | vits-piper-pl_PL-zenski_wg_glos-medium |
Zenski Wg Glos | Female | 64 MB |
| Portuguese (Brazil) | vits-piper-pt_BR-cadu-medium |
Cadu | Male | 64 MB |
| Portuguese (Brazil) | vits-piper-pt_BR-edresson-low |
Edresson | Male | 64 MB |
| Portuguese (Brazil) | vits-piper-pt_BR-faber-medium |
Faber | Male | 64 MB |
| Portuguese (Brazil) | vits-piper-pt_BR-jeff-medium |
Jeff | Male | 64 MB |
| Portuguese (Portugal) | vits-piper-pt_PT-tugao-medium |
Tugao | Male | 64 MB |
| Romanian (RO) | vits-piper-ro_RO-mihai-medium |
Mihai | Male | 64 MB |
| Russian (RU) | vits-piper-ru_RU-denis-medium |
Denis | Male | 64 MB |
| Russian (RU) | vits-piper-ru_RU-dmitri-medium |
Dmitri | Male | 64 MB |
| Russian (RU) | vits-piper-ru_RU-irina-medium |
Irina | Female | 64 MB |
| Russian (RU) | vits-piper-ru_RU-ruslan-medium |
Ruslan | Male | 64 MB |
| Slovak (SK) | vits-piper-sk_SK-lili-medium |
Lili | Female | 64 MB |
| Slovenian (SI) | vits-piper-sl_SI-artur-medium |
Artur | Male | 64 MB |
| Albanian (AL) | vits-piper-sq_AL-edon-medium |
Edon | Male | 64 MB |
| Serbian (RS) | vits-piper-sr_RS-serbski_institut-medium |
Serbski Institut | Female | 77 MB |
| Swedish (SE) | vits-piper-sv_SE-alma-medium |
Alma | Female | 64 MB |
| Swedish (SE) | vits-piper-sv_SE-lisa-medium |
Lisa | Female | 64 MB |
| Swedish (SE) | vits-piper-sv_SE-nst-medium |
Nst | Male | 64 MB |
| Swahili (CD) | vits-piper-sw_CD-lanfrica-medium |
Lanfrica | Male | 64 MB |
| Turkish (TR) | vits-piper-tr_TR-dfki-medium |
Dfki | Male | 64 MB |
| Turkish (TR) | vits-piper-tr_TR-fahrettin-medium |
Fahrettin | Male | 64 MB |
| Turkish (TR) | vits-piper-tr_TR-fettah-medium |
Fettah | Male | 64 MB |
| Ukrainian (UA) | vits-piper-uk_UA-lada-x_low |
Lada | Female | 25 MB |
| Ukrainian (UA) | vits-piper-uk_UA-ukrainian_tts-medium |
Ukrainian Tts | Male | 77 MB |
| Urdu (PK) | vits-piper-ur_PK-fasih-medium |
Fasih | Male | 64 MB |
| Vietnamese (VN) | vits-piper-vi_VN-25hours_single-low |
25Hours Single | Male | 64 MB |
| Vietnamese (VN) | vits-piper-vi_VN-vais1000-medium |
Vais1000 | Female | 64 MB |
| Vietnamese (VN) | vits-piper-vi_VN-vivos-x_low |
Vivos | Female | 32 MB |
| Chinese (CN) | vits-piper-zh_CN-chaowen-medium |
Chaowen | Male | 58 MB |
| Chinese (CN) | vits-piper-zh_CN-huayan-medium |
Huayan | Female | 64 MB |
| Chinese (CN) | vits-piper-zh_CN-xiao_ya-medium |
Xiao Ya | Female | 58 MB |
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