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Open-weight model · Text to speech

duplexdataengine

by Cheng Kuang Chang penguinfish1688/duplexdataengine

duplexdataengine is an open-weight model for text to speech from Cheng Kuang Chang, released under Creative Commons Attribution-NonCommercial 4.0. It has 1.9B parameters. At 16-bit it needs about 4.6 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.

DuDE is a full-duplex conversation TTS (text-to-speech) model that generates two speakers together from an XML dialogue. It supports turn-taking, backchannels, and overlapping speech, with a separate voice for each speaker.

Parameters1.9B
Context—
Weights8.4 GB
Licensecc-by-nc-4.0
AccessOpen weights
Monthly Downloads—

Runs On

What it takes to serve duplexdataengine (1.9B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 3.8 GB 4.6 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 1.9 GB 2.3 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 1.0 GB 1.2 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 18, 2026.

duplexdataengine on every accelerator the SAVRN Index prices, at every precision

Model Card

DuDE is a full-duplex conversation TTS (text-to-speech) model that generates two speakers together from an XML dialogue. It supports turn-taking, backchannels, and overlapping speech, with a separate voice for each speaker. The output is a 24 kHz stereo WAV: speaker A on the left, speaker B on the right. DuDE first self-distills Qwen3-TTS on independent utterances to adapt a shared backbone to two interleaved channels. It is then fine-tuned on approximately 1,922 hours of synchronized Seamless Interaction conversations to learn turn-taking, backchannels, and overlapping speech. The XML frontend resolves turn dependencies into interleaved text inputs. Reference-speaker embeddings condition…

Excerpt from the card by Cheng Kuang Chang, licensed cc-by-nc-4.0.

Configuration

Architecture
Qwen3TTSForConditionalGeneration
Model type
qwen3_tts

Identity and Version

Repository
penguinfish1688/duplexdataengine
Publisher
Cheng Kuang Chang
Task
Text to speech
Modality
Audio
Library
Not stated by the source
Parameters
1.9B parameters
Languages
en
Revision
f063927ea2f8bd03e3e87ed9ef5c53ed46d7f9a4
First published
2026-09-27
Last updated
2026-09-27

Files and Weights

43 files, 8.4 GB in total. The weights are 3 files totalling 8.4 GB in safetensors.

Weights3 files · 8.4 GB
Configuration22 files · 222.7 KB
Tokenizer3 files · 4.5 MB
Documentation4 files · 32.0 KB
Other10 files · 11.5 MB
Repository1 file · 2.0 KB
Every file
FileTypeSizeSHA-256
model.safetensorsWeights7.7 GB e0145a34d1a7
speaker_encoder/model.safetensorsWeights24.0 MB df60a638e7f4
speech_tokenizer/model.safetensorsWeights682.3 MB 836b7b357f5e
config.jsonConfiguration4.9 KB —
dude_tts/__init__.pyConfiguration72 B —
dude_tts/audio.pyConfiguration1.9 KB —
dude_tts/frontend.pyConfiguration3.4 KB —
dude_tts/interleave.pyConfiguration10.4 KB —
dude_tts/layout.pyConfiguration1.3 KB —
dude_tts/model.pyConfiguration7.3 KB —
dude_tts/runtime.pyConfiguration7.6 KB —
dude_tts/voice.pyConfiguration2.8 KB —
event-token-map.jsonConfiguration134 B —
examples/examples.jsonConfiguration561 B —
generation_config.jsonConfiguration245 B —
infer.pyConfiguration1.2 KB —
preprocessor_config.jsonConfiguration127 B —
provenance.jsonConfiguration2.7 KB —
release.jsonConfiguration1.5 KB —
speaker_encoder/config.jsonConfiguration46 B —
speech_tokenizer/config.jsonConfiguration2.3 KB —
speech_tokenizer/configuration.jsonConfiguration76 B —
speech_tokenizer/preprocessor_config.jsonConfiguration234 B —
verification.jsonConfiguration1.5 KB —
voices/presets.jsonConfiguration172.3 KB —
LICENSEDocumentation204 B —
LICENSE-CODEDocumentation11.3 KB —
LICENSE-QWENDocumentation11.3 KB —
README.mdDocumentation9.1 KB —
CITATION.cffOther304 B —
examples/sample_1.wavOther4.6 MB 2e136bffa181
examples/sample_1.xmlOther1.3 KB —
examples/sample_2.wavOther5.2 MB 3bd3a1a23248
examples/sample_2.xmlOther1.4 KB —
requirements.txtOther249 B —
voices/voice_1.wavOther426.3 KB 4ca8356864cc
voices/voice_2.wavOther576.0 KB dba4d901c29f
voices/voice_3.wavOther357.2 KB 0ca0f1e96907
voices/voice_4.wavOther311.1 KB 168f569c6674
.gitattributesRepository2.0 KB —
merges.txtTokenizer1.7 MB —
tokenizer_config.jsonTokenizer7.3 KB —
vocab.jsonTokenizer2.8 MB —

License and Download

License
cc-by-nc-4.0
Access
Open weights, no gate
Download size
8.4 GB
Download from Cheng Kuang Chang

Released by Cheng Kuang Chang through its official repository on Hugging Face. Read the license.

Built From

Memory Requirements

PrecisionWeights in memory
As published8.4 GB
16-bit3.8 GB
8-bit1.9 GB
4-bit1.0 GB

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

Questions About duplexdataengine

How much GPU memory does duplexdataengine need?

About 4.6 GB at 16-bit and 1.2 GB at 4-bit: the weights (1.9B parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run duplexdataengine on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use duplexdataengine commercially?

Not without separate permission. duplexdataengine is released under Creative Commons Attribution-NonCommercial 4.0. CC BY-NC 4.0 permits sharing and adapting with credit for non-commercial purposes only. Commercial use needs separate permission from the rights holder.

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