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Model Detail

Qwen logo

Qwen3-TTS-12Hz-1.7B-CustomVoice

—
Provider: QwenCategory: audioPipeline: text-to-speechParameters: 1.7B
DB Score
13.1
Downloads
2.6M
Likes
2K
Day
+0.0%
Week
+12.5%
Month
+4.5%
Overview

Qwen3-TTS-12Hz-1.7B-CustomVoice is an audio model with 1.7B parameters released by Qwen. The model is registered under the text-to-speech pipeline tag on Hugging Face, distributed under the permissive apache-2.0 license.

Technical

Qwen3-TTS-12Hz-1.7B-CustomVoice ships with 1.7B parameters. Total weight footprint is approximately 1.9 GB, which is the relevant figure when planning local-inference VRAM. The apache-2.0 license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.

Trending Signal

Downloads of Qwen3-TTS-12Hz-1.7B-CustomVoice have moved +12.5% over the trailing seven days, +4.5% over the trailing thirty days. The trend is mildly positive, consistent with a model that is being picked up incrementally rather than going viral. These numbers are signal, not guarantee — week-over-week download counts on Hugging Face also reflect mirror traffic, CI scrapes, and one-off benchmarking runs.

Read about databubble_score →
Use Cases

Qwen3-TTS-12Hz-1.7B-CustomVoice is best fit for speech recognition, transcription, or speech synthesis depending on the task head. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.

Download History
Research Paper
arXiv: 2601.15621→
Model Info
Licenseapache-2.0
Citations3,938 (424 influential)
Recent newsView all news →
Related News
arxivneutral2d ago

Post-Training Ternarization of Qwen3-4B Capability, Effective Bit Budget, Storage Compression, and Deployment

arXiv:2609.01962v1 Announce Type: new Abstract: Ultra-low-bit language models can reduce storage and memory bandwidth, but a nominal "1.58-bit" label does not fully describe the stored representation, retained capability, or runtime behavior. We study an end-to-end post-training conversion of Qwen,

arxiv4d ago

Fine-Tuning Qwen3-27B for C-to-Rust Code Translation: A Three-Stage Curriculum of Pretraining, Debugging-Aware SFT, and Task-Specific SFT

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arxiv4d ago

On the Design of Qwen3.8-Next Architecture: Evaluation, Efficiency, and Training Stability

arXiv:2608.30320v1 Announce Type: new Abstract: We describe the architecture and ablations of Qwen3.8-Flash-Next, a sparse mixture-of-experts model with 125B parameters, 6B activated per token, and additional 51B parameters of n-gram embedding tables held off the accelerator. On fourteen pre-trainin

arxiv64d ago

TUDUM: A Turkish-Thinking Reasoning Pipeline for Qwen3.5-27B

arXiv:2607.01927v1 Announce Type: cross Abstract: This paper presents TUDUM (T\"urk\c{c}e D\"u\c{s}\"unen \"Uretken Model), a project pipeline for adapting a Qwen-family 27B thinking model toward Turkish reasoning. The central problem is not only to answer Turkish prompts in Turkish, but to make the

arxiv67d ago

Fine-Tuning General-Purpose Large Language Models for Agricultural Applications:A Reproducible Framework and Evaluation Protocol Based on Qwen3-8B

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arxiv95d ago

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