Skip to page content
SAVRN
Search Plan a project

Open-weight model · Text to speech

OmniVoice-ONNX-CPU-INT8

by G gaber/OmniVoice-ONNX-CPU-INT8

OmniVoice-ONNX-CPU-INT8 is an open-weight model for text to speech from G. It has 40,960-token context. Its published files total 1.7 GB.

Self-contained ONNX export of k2-fsa/OmniVoice for CPU inference, including voice cloning: a dynamic-INT8 Qwen3-0.6B diffusion backbone, the Higgs audio-v2 encoder and vocoder in FP32, and the tokenizer/config files.

Parameters—
Context40,960
Weights1.6 GB
License—
AccessOpen weights
Monthly Downloads—

Model Card

Self-contained ONNX export of k2-fsa/OmniVoice for CPU inference, including voice cloning: a dynamic-INT8 Qwen3-0.6B diffusion backbone, the Higgs audio-v2 encoder and vocoder in FP32, and the tokenizer/config files. Nothing is downloaded from any other model repo. Backbone: inputids[B,8,S] int64, audiomask[B,S] bool, attentionmask[B,1,S,S] bool → logits[B,8,S,1025]. Encoder: wav[1,1,N] float32 (24 kHz, N a multiple of 960) → audiocodes[1,8,N/960] int64. Vocoder: audiocodes[B,8,T] int64 → audiovalues[B,1,960T] (24 kHz). B, S, N and T are dynamic. --threads should be the physical core count. The omnivoice package supplies only the diffusion loop and text front-end code; every weight comes…

Excerpt from the card by G.

Configuration

Architecture
OmniVoice
Context length (tokens)
40,960
Layers
28
Hidden size
1,024
Feed-forward size
3,072
Attention heads
16
Key/value heads
8
Head dimension
128
Vocabulary size
151,676
Model type
omnivoice

Identity and Version

Repository
gaber/OmniVoice-ONNX-CPU-INT8
Publisher
G
Task
Text to speech
Modality
Audio
Library
onnxruntime
Parameters
Not stated by the source
Languages
cpu
Revision
f19d3a0490067c1935ee791a4b1dce6935da50ad
First published
2026-10-01
Last updated
2026-10-01

Files and Weights

50 files, 1.7 GB in total. The weights are 4 files totalling 1.6 GB in npz, onnx.

Weights4 files · 1.6 GB
Configuration35 files · 204.0 KB
Tokenizer2 files · 11.4 MB
Documentation5 files · 28.8 KB
Other3 files · 86.3 MB
Repository1 file · 1.8 KB
Every file
FileTypeSizeSHA-256
backbone.int8.onnxWeights993.3 MB 241610742675
encoder.fp32.onnxWeights654.4 MB 8e9697cf64ad
scripts/smoothquant_calibration.npzWeights593.8 KB fc992f9b55df
vocoder.fp32.onnxWeights803.9 KB cfea73af6cf8
config.jsonConfiguration2.2 KB —
eval/alpha_tuned.jsonConfiguration17.2 KB —
eval/audio_quality2.jsonConfiguration27.7 KB —
eval/edge_cases.jsonConfiguration4.2 KB —
eval/policy.jsonConfiguration1.4 KB —
eval/rerank2.jsonConfiguration24.5 KB —
eval/scores_d12.jsonConfiguration1.2 KB —
preprocessor_config.jsonConfiguration206 B —
scripts/audio_quality.pyConfiguration6.2 KB —
scripts/audio_quality2.pyConfiguration8.9 KB —
scripts/bench_perf.pyConfiguration3.6 KB —
scripts/build_candidates.pyConfiguration1.4 KB —
scripts/build_graph.pyConfiguration5.3 KB —
scripts/build_int8.pyConfiguration6.4 KB —
scripts/calib2.pyConfiguration6.2 KB —
scripts/calib_dump.pyConfiguration1.0 KB —
scripts/capture_traj.pyConfiguration2.7 KB —
scripts/diagnose.pyConfiguration3.0 KB —
scripts/e2e_corpus.pyConfiguration8.8 KB —
scripts/edge_cases.pyConfiguration6.1 KB —
scripts/export_encoder.pyConfiguration2.7 KB —
scripts/export_omnivoice_onnx.pyConfiguration16.1 KB —
scripts/export_onnx.pyConfiguration4.2 KB —
scripts/fq_sim.pyConfiguration5.1 KB —
scripts/make_listen.pyConfiguration3.9 KB —
scripts/make_samples.pyConfiguration3.2 KB —
scripts/qa_lib.pyConfiguration8.1 KB —
scripts/rerank2.pyConfiguration5.0 KB —
scripts/score_graph.pyConfiguration1.6 KB —
scripts/smooth_all.pyConfiguration3.5 KB —
scripts/smooth_sim.pyConfiguration2.5 KB —
scripts/test_encoder_parity.pyConfiguration2.0 KB —
scripts/verify_map.pyConfiguration845 B —
scripts/xplat.pyConfiguration2.3 KB —
use_onnx.pyConfiguration4.6 KB —
LICENSE-HIGGS-AUDIO-2.txtDocumentation9.2 KB —
LICENSE-LLAMA3.txtDocumentation12.4 KB —
NOTICEDocumentation343 B —
README.mdDocumentation4.2 KB —
scripts/README.mdDocumentation2.6 KB —
SHA256SUMSOther844 B —
chat_template.jinjaOther4.2 KB —
vocoder.fp32.onnx.dataOther86.3 MB ebbdf1cf7ddb
.gitattributesRepository1.8 KB —
tokenizer.jsonTokenizer11.4 MB 408f669b7e2b
tokenizer_config.jsonTokenizer533 B —

License and Download

License
Not stated by the source
Access
Open weights, no gate
Download size
1.6 GB
Download from G

Released by G through its official repository on Hugging Face.

Built From

Memory Requirements

PrecisionWeights in memory
As published1.6 GB

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

Questions About OmniVoice-ONNX-CPU-INT8

What is OmniVoice-ONNX-CPU-INT8's context length?

40,960 tokens, from the maximum position embeddings in its published configuration.

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