Skip to page content
SAVRN
Search Plan a project

Open-weight model · Text to speech

AIPod-AuK

by Andy Stewart manateelazycat/AIPod-AuK

AIPod-AuK is an open-weight model for text to speech from Andy Stewart, released under other. Its published files total 32.1 GB.

Built with Qwen. Non-commercial research and evaluation only. AuK code and speech weights are MIT. Required Qwen2.5-Omni-3B encoder is under the Qwen Research License.

Parameters—
Context—
Weights18.7 GB
Licenseother
AccessOpen weights
Monthly Downloads—

Model Card

Built with Qwen. Non-commercial research and evaluation only. AuK code and speech weights are MIT. Required Qwen2.5-Omni-3B encoder is under the Qwen Research License. This release includes fixed model snapshots, model-free Orin/Thor runtime archives, controller, sources, licenses and minimal LPK. See licenses/NOTICE and all agreements before using or redistributing.

Excerpt from the card by Andy Stewart, licensed other.

Identity and Version

Repository
manateelazycat/AIPod-AuK
Publisher
Andy Stewart
Task
Text to speech
Modality
Audio
Library
Not stated by the source
Parameters
Not stated by the source
Languages
Not stated by the source
Revision
90d3f53142a8ce07cc8ea9f05bcfb8c41c54a1b7
First published
2026-10-10
Last updated
2026-10-11

Files and Weights

99 files, 32.1 GB in total. The weights are 5 files totalling 18.7 GB in safetensors.

Weights5 files · 18.7 GB
Configuration41 files · 975.8 KB
Tokenizer4 files · 15.9 MB
Documentation12 files · 45.4 KB
Other36 files · 13.4 GB
Repository1 file · 2.2 KB
Every file
FileTypeSizeSHA-256
auk/auk_base.safetensorsWeights6.1 GB 29c65c0c6045
auk/vae.safetensorsWeights637.3 MB 0f5857fd0d6b
encoder/model-00001-of-00003.safetensorsWeights5.0 GB 349972cebff4
encoder/model-00002-of-00003.safetensorsWeights5.0 GB b29a76dfefb3
encoder/model-00003-of-00003.safetensorsWeights2.0 GB 8f2138170a87
auk/config.yamlConfiguration1.3 KB —
control/identity.jsonConfiguration191 B —
encoder/added_tokens.jsonConfiguration579 B —
encoder/chat_template.jsonConfiguration1.3 KB —
encoder/config.jsonConfiguration13.2 KB —
encoder/generation_config.jsonConfiguration74 B —
encoder/model.safetensors.index.jsonConfiguration241.8 KB —
encoder/preprocessor_config.jsonConfiguration667 B —
encoder/special_tokens_map.jsonConfiguration832 B —
evidence/acceptance-test-support.jsonConfiguration340 B —
evidence/acceptance-transport.jsonConfiguration1.9 KB —
evidence/audio-quality-orin.jsonConfiguration4.0 KB —
evidence/audio-quality-thor.jsonConfiguration3.8 KB —
evidence/audit-orin.jsonConfiguration64.4 KB —
evidence/audit-thor.jsonConfiguration67.9 KB —
evidence/bootstrap-verification.jsonConfiguration4.0 KB —
evidence/browser-verification.jsonConfiguration1.3 KB —
evidence/editing-quality-orin.jsonConfiguration1.6 KB —
evidence/editing-quality-thor.jsonConfiguration1.5 KB —
evidence/final-image-output-consistency.jsonConfiguration4.1 KB —
evidence/install-matrix.jsonConfiguration115.4 KB —
evidence/inventory.jsonConfiguration4.6 KB —
evidence/performance-optimization/experiment-plan.jsonConfiguration1.3 KB —
evidence/performance-optimization/results.jsonConfiguration82.3 KB —
evidence/performance-optimization/round-0-quality.jsonConfiguration7.8 KB —
evidence/performance-optimization/round-1-quality.jsonConfiguration8.0 KB —
evidence/performance-optimization/round-2-quality.jsonConfiguration8.0 KB —
evidence/performance-optimization/round-3-quality.jsonConfiguration8.0 KB —
evidence/performance-optimization/round-4-quality.jsonConfiguration8.0 KB —
evidence/performance-optimization/round-5-quality.jsonConfiguration8.0 KB —
evidence/performance-optimization/round-6-quality.jsonConfiguration8.0 KB —
evidence/performance-optimization/verification-correction.jsonConfiguration607 B —
evidence/profile-orin.jsonConfiguration40.0 KB —
evidence/profile-thor.jsonConfiguration39.4 KB —
metadata-release-accepted.jsonConfiguration6.1 KB —
release-accepted.jsonConfiguration151.8 KB —
release-summary.jsonConfiguration38.6 KB —
source/runtime/optimizations.pyConfiguration9.9 KB —
source/runtime/probe.pyConfiguration957 B —
source/runtime/server.pyConfiguration6.8 KB —
upstream.jsonConfiguration7.5 KB —
README.mdDocumentation580 B —
auk/LICENSEDocumentation1.7 KB —
encoder/LICENSEDocumentation7.4 KB —
evidence/performance-optimization.mdDocumentation2.7 KB —
licenses/AuK-LICENSEDocumentation1.7 KB —
licenses/NOTICEDocumentation110 B —
licenses/QWEN-RESEARCH-LICENSEDocumentation7.4 KB —
licenses/Qwen-LICENSEDocumentation7.4 KB —
source/docs/licenses/AuK-LICENSEDocumentation1.7 KB —
source/docs/licenses/NOTICEDocumentation110 B —
source/docs/licenses/QWEN-RESEARCH-LICENSEDocumentation7.4 KB —
source/docs/licenses/Qwen-LICENSEDocumentation7.4 KB —
cloud.lazycat.aipod.auk-v0.1.0.lpkOther17.4 KB —
cloud.lazycat.aipod.auk-v0.1.1.lpkOther17.4 KB —
cloud.lazycat.aipod.auk-v0.1.2.lpkOther16.9 KB —
cloud.lazycat.aipod.auk-v0.1.3.lpkOther114.2 KB 7578759dcd5b
cloud.lazycat.aipod.auk-v0.1.4.lpkOther114.2 KB 4eee8d4802ab
control/auk-control-0.1.0.tar.gzOther17.4 MB 7909334e1307
control/auk-control-0.1.1.tar.gzOther17.4 MB 848ae5001095
control/auk-control-image-0.1.0.tar.gzOther21.1 MB 418e79485a9e
control/auk-control-image-0.1.1.tar.gzOther21.1 MB 6cd73ffff62c
evidence/auk-performance-0.1.1.tar.gzOther309.0 MB bfcf035db051
evidence/browser/browser-en-US-1280.pngOther70.1 KB —
evidence/browser/browser-en-US-390.pngOther60.3 KB —
evidence/browser/browser-zh-CN-1280.pngOther65.2 KB —
evidence/browser/browser-zh-CN-390.pngOther59.7 KB —
evidence/samples/orin/chineseLong.wavOther384.0 KB f1b13093261c
evidence/samples/orin/chineseShort.wavOther144.0 KB bfa6486c496c
evidence/samples/orin/edit.wavOther96.0 KB —
evidence/samples/orin/englishLong.wavOther384.0 KB 3212aaa9beb0
evidence/samples/orin/englishShort.wavOther96.0 KB —
evidence/samples/thor/chineseLong.wavOther384.0 KB 90d4668f02f6
evidence/samples/thor/chineseShort.wavOther144.0 KB 6b88696699c9
evidence/samples/thor/edit.wavOther96.0 KB —
evidence/samples/thor/englishLong.wavOther384.0 KB fe40febc1af6
evidence/samples/thor/englishShort.wavOther96.0 KB —
runtime/auk-orin-0.1.0.tar.gzOther2.7 GB 8710fdcc8a08
runtime/auk-orin-0.1.1.tar.gzOther2.7 GB 596ade514f10
runtime/auk-thor-0.1.0.tar.gzOther3.8 GB 77aa34ba528d
runtime/auk-thor-0.1.1.tar.gzOther3.8 GB 83645effd54e
source/auk-0.1.0.tar.gzOther1.6 MB c7ce167374b6
source/auk-0.1.1.tar.gzOther1.6 MB 49dd844f924e
source/auk-acceptance-scripts-0.1.0.tar.gzOther43.5 KB 87baddba1d84
source/auk-acceptance-scripts-0.1.1.tar.gzOther55.9 KB 9eea144aa693
source/auk-final-source-0.1.0.tar.gzOther1.6 MB d7c78e9c2813
source/auk-final-source-0.1.1.tar.gzOther1.6 MB 64c3e95e72a0
source/auk-reviewed-source.tar.gzOther95.9 KB 106ace4744f3
source/runtime/requirements-reference.txtOther199 B —
.gitattributesRepository2.2 KB —
encoder/merges.txtTokenizer1.7 MB —
encoder/tokenizer.jsonTokenizer11.4 MB 8441917e39ae
encoder/tokenizer_config.jsonTokenizer6.5 KB —
encoder/vocab.jsonTokenizer2.8 MB —

License and Download

License
other
Access
Open weights, no gate
Download size
18.7 GB
Download from Andy Stewart

Released by Andy Stewart through its official repository on Hugging Face. Read the license.

Memory Requirements

PrecisionWeights in memory
As published18.7 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About AIPod-AuK

What license is AIPod-AuK released under?

other, as its publisher declares it. Read the license text before commercial use.

Similar Models

Model · Text to speech

Kokoro-82M

Hexgrad

Kokoro is an open-weight TTS model with 82 million parameters. Despite its lightweight architecture, it delivers comparable quality to larger models while being significantly faster and more cost-efficient. With Apache-licensed weights, Kokoro can be deployed anywhere from production environments to personal projects. You can run this basic cell on Google Colab. Listen to samples. For more languages and details, see Advanced Usage. Under the hood, kokoro uses misaki, a G2P library at https://github.com/hexgrad/misaki Model SHA256 Hash: 496dba118d1a58f5f3db2efc88dbdc216e0483fc89fe6e47ee1f2c53f18ad1e4 Data: Kokoro was trained exclusively on permissive/non-copyrighted audio data and IPA…

Open weights apache-2.0

Model · Text to speech

audio.cpp-gguf

Audio.cpp

This directory contains audio.cpp-native GGUF conversions of multiple speech models. These files are intended for use with audio.cpp. If you enjoy the project, please star audio.cpp on GitHub and this Hugging Face repository. For conversion details, supported layouts, direct-file loading, sidecar embedding, and the latest compatibility notes, see the audio.cpp GGUF guide: - https://github.com/0xShug0/audio.cpp/blob/main/docs/gguf.md!!! Converted and quantized packages are checked with automated metrics, but perceived quality can still differ for human listeners. Please validate the exact package, backend, and route to confirm the output is acceptable for your use case. The table lists the…

Open weights other audio.cpp

Model · Text to speech

XTTS-v2

Coqui.ai

ⓍTTS is a Voice generation model that lets you clone voices into different languages by using just a quick 6-second audio clip. There is no need for an excessive amount of training data that spans countless hours. This is the same or similar model to what powers Coqui Studio and Coqui API. - Supports 17 languages. - Voice cloning with just a 6-second audio clip. - Emotion and style transfer by cloning. - Cross-language voice cloning. - Multi-lingual speech generation. - 24khz sampling rate. - 2 new languages; Hungarian and Korean - Architectural improvements for speaker conditioning. - Enables the use of multiple speaker references and interpolation between speakers. - Stability…

Open weights other coqui

Model · Text to speech

chatterbox

Resemble AI

Chatterbox Multilingual V3 is the latest general-purpose multilingual TTS model in the Chatterbox family. It keeps the same 0.5B model size while improving speaker similarity, reducing hallucinations, and producing more natural, conversational speech across languages. V3 is designed for broad language coverage like V2, but with stronger stability and more expressive generation. It is the recommended multilingual model for users who want one voice cloning model that works across many languages. Try it in the Chatterbox Multilingual TTS V3 Space. Alongside V3, we are releasing the Single Language Pack: dedicated finetunes for priority languages where tighter quality control, stronger…

Open weights mit chatterbox

Model · Text to speech

F5-TTS

Yushen CHEN

Download F5-TTS or E2 TTS and place under ckpts/ Paper: F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching

Open weights cc-by-nc-4.0 f5-tts

Model · Text to speech

Kokoro-82M-v1.0-ONNX

ONNX Community

Kokoro is a frontier TTS model for its size of 82 million parameters (text in/audio out). First, install the kokoro-js library from NPM using: You can then generate speech as follows: Optionally, save the audio to a file: The model is resilient to quantization, enabling efficient high-quality speech synthesis at a fraction of the original model size.

Open weights apache-2.0 transformers.js