·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Startup Battlefield 200 applications officially close in 3 days2h◆Google will pay SpaceX $920M per month for compute3h◆The most interesting startups right now want to get you off your phone4h◆This is your laptop… on AI5h◆New York lawmakers pass one-year ban on new data centers6h◆The token bill comes due: Inside the industry scramble to manage AI’s runaway costs7h◆The latest AI news we announced in May 20267h◆The ‘together tech’ wave might be the most intriguing startup bet of 20268h◆This AI startup says it can tell if a script will make a hit film8h◆AirTrunk commits $30B to build 5GW of AI data centers in India8h◆The Meta hack shows there’s more to AI security than Mythos13h◆Mira Murati steps back into the spotlight, carefully16h◆SFMambaNet: Spectral-Frequency Enhanced Selective State Space Model for Correspondence Pruning18h◆Optical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning18h◆Dynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models18h◆Temporal Order Matters for Agentic Memory: Segment Trees for Long-Horizon Agents18h◆Why Muon Outperforms Adam: A Curvature Perspective18h◆Vision Hopfield Memory Networks18h◆Provably Auditable and Safe LLM Agents from Human-Authored Ontologies18h◆FlexRank: Nested Low-Rank Knowledge Decomposition for Adaptive Model Deployment18h◆Startup Battlefield 200 applications officially close in 3 days2h◆Google will pay SpaceX $920M per month for compute3h◆The most interesting startups right now want to get you off your phone4h◆This is your laptop… on AI5h◆New York lawmakers pass one-year ban on new data centers6h◆The token bill comes due: Inside the industry scramble to manage AI’s runaway costs7h◆The latest AI news we announced in May 20267h◆The ‘together tech’ wave might be the most intriguing startup bet of 20268h◆This AI startup says it can tell if a script will make a hit film8h◆AirTrunk commits $30B to build 5GW of AI data centers in India8h◆The Meta hack shows there’s more to AI security than Mythos13h◆Mira Murati steps back into the spotlight, carefully16h◆SFMambaNet: Spectral-Frequency Enhanced Selective State Space Model for Correspondence Pruning18h◆Optical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning18h◆Dynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models18h◆Temporal Order Matters for Agentic Memory: Segment Trees for Long-Horizon Agents18h◆Why Muon Outperforms Adam: A Curvature Perspective18h◆Vision Hopfield Memory Networks18h◆Provably Auditable and Safe LLM Agents from Human-Authored Ontologies18h◆FlexRank: Nested Low-Rank Knowledge Decomposition for Adaptive Model Deployment18h◆
DataBubble·

Model Detail

Qwen logo

Qwen3-TTS-12Hz-1.7B-CustomVoice

—
Provider: QwenCategory: audioPipeline: text-to-speechParameters: 1.7B
DB Score
13.1
Downloads
2.0M
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
Citations3,775 (409 influential)
Recent newsView all news →
Related News
arxiv3d ago

LinguIUTics at PsyDefDetect: Iterative Imbalance-Aware Fine-tuning of Qwen3-8B for Psychological Defense Mechanism Classification

arXiv:2606.00647v1 Announce Type: cross Abstract: Detecting psychological defense mechanisms in conversational text remains a challenging clinical NLP problem. For the PsyDefDetect 2026 shared task (nine-class utterance classification evaluated via macro F1), our team LinguIUTics achieves a macro F1

arxivneutral21d ago

Procedural-skill SFT across capacity tiers: A W-Shaped pre-SFT Trajectory and Regime-Asymmetric Mechanism on 0.8B-4B Qwen3.5 Models

arXiv:2605.11907v2 Announce Type: replace Abstract: We measure procedural-skill SFT contribution across three Qwen3.5 dense scales (0.8B, 2B, 4B) on a 200-task / 40-skill holdout, with Claude Haiku 4.5 as a frontier reference. The corpus is 353 rows of (task + procedural-skill block, Opus chain-of-t

arxiv25d ago

Qwen3-VL-Seg: Unlocking Open-World Referring Segmentation with Vision-Language Grounding

arXiv:2605.07141v1 Announce Type: cross Abstract: Open-world referring segmentation requires grounding unconstrained language expressions to precise pixel-level regions. Existing multimodal large language models (MLLMs) exhibit strong open-world visual grounding, but their outputs remain limited to

arxiv44d ago

Qwen3.5-Omni Technical Report

arXiv:2604.15804v2 Announce Type: replace Abstract: In this work, we present Qwen3.5-Omni, the latest advancement in the Qwen-Omni model family. Representing a significant evolution over its predecessor, Qwen3.5-Omni scales to hundreds of billions of parameters and supports a 256k context length. By

arxivbullish11d ago

AGZO: Activation-Guided Zeroth-Order Optimization for LLM Fine-Tuning

arXiv:2601.17261v4 Announce Type: replace Abstract: Zeroth-Order (ZO) optimization has emerged as a promising solution for fine-tuning LLMs under strict memory constraints, as it avoids the prohibitive memory cost of storing activations for backpropagation. However, existing ZO methods typically emp

Related Models
Qwen logo
Qwen3-VL-2B-Instruct
Qwen · 22.5M downloads
Qwen logo
Qwen3-0.6B
Qwen · 22.2M downloads
hexgrad logo
Kokoro-82M
hexgrad · 13.8M downloads
coqui logo
XTTS-v2
coqui · 10.0M downloads
HomeModelsNews