SoulX-Podcast-1.7B GGUF Models
Model Generation Details
This model was generated using llama.cpp at commit 16724b5b6.
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SoulX-Podcast
Official inference code for
SoulX-Podcast: Towards Realistic Long-form Podcasts with Dialectal and Paralinguistic Diversity
SoulX-Podcast: Towards Realistic Long-form Podcasts with Dialectal and Paralinguistic Diversity
Overview
SoulX-Podcast is designed for podcast-style multi-turn, multi-speaker dialogic speech generation, while also achieving superior performance in the conventional monologue TTS task.
To meet the higher naturalness demands of multi-turn spoken dialogue, SoulX-Podcast integrates a range of paralinguistic controls and supports both Mandarin and English, as well as several Chinese dialects, including Sichuanese, Henanese, and Cantonese, enabling more personalized podcast-style speech generation.
Key Features 🔥
Long-form, multi-turn, multi-speaker dialogic speech generation: SoulX-Podcast excels in generating high-quality, natural-sounding dialogic speech for multi-turn, multi-speaker scenarios.
Cross-dialectal, zero-shot voice cloning: SoulX-Podcast supports zero-shot voice cloning across different Chinese dialects, enabling the generation of high-quality, personalized speech in any of the supported dialects.
Paralinguistic controls: SoulX-Podcast supports a variety of paralinguistic events, as as laugher and sighs to enhance the realism of synthesized results.
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Install
Clone and Install
Here are instructions for installing on Linux.
- Clone the repo
git clone git@github.com:Soul-AILab/SoulX-Podcast.git
cd SoulX-Podcast
- Install Conda: please see https://docs.conda.io/en/latest/miniconda.html
- Create Conda env:
conda create -n soulxpodcast -y python=3.11
conda activate soulxpodcast
pip install -r requirements.txt
# If you are in mainland China, you can set the mirror as follows:
pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host=mirrors.aliyun.com
Model Download
pip install -U huggingface_hub
# base model
huggingface-cli download --resume-download Soul-AILab/SoulX-Podcast-1.7B --local-dir pretrained_models/SoulX-Podcast-1.7B
# dialectal model
huggingface-cli download --resume-download Soul-AILab/SoulX-Podcast-1.7B-dialect --local-dir pretrained_models/SoulX-Podcast-1.7B-dialect
Download via python:
from huggingface_hub import snapshot_download
# base model
snapshot_download("Soul-AILab/SoulX-Podcast-1.7B", local_dir="pretrained_models/SoulX-Podcast-1.7B")
# dialectal model
snapshot_download("Soul-AILab/SoulX-Podcast-1.7B-dialect", local_dir="pretrained_models/SoulX-Podcast-1.7B-dialect")
Download via git clone:
mkdir -p pretrained_models
# Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install
# base model
git clone https://huggingface.co/Soul-AILab/SoulX-Podcast-1.7B pretrained_models/SoulX-Podcast-1.7B
# dialectal model
git clone https://huggingface.co/Soul-AILab/SoulX-Podcast-1.7B-dialect pretrained_models/SoulX-Podcast-1.7B-dialect
Basic Usage
You can simply run the demo with the following commands:
# dialectal inference
bash example/infer_dialogue.sh
TODOs
- Add example scripts for monologue TTS.
- Publish the technical report.
- Develop a WebUI for easy inference.
- Deploy an online demo on Hugging Face Spaces.
- Dockerize the project with vLLM support.
- Add support for streaming inference.
Citation
@misc{SoulXPodcast,
title = {SoulX-Podcast: Towards Realistic Long-form Podcasts with Dialectal and Paralinguistic Diversity},
author = {Hanke Xie and Haopeng Lin and Wenxiao Cao and Dake Guo and Wenjie Tian and Jun Wu and Hanlin Wen and Ruixuan Shang and Hongmei Liu and Zhiqi Jiang and Yuepeng Jiang and Wenxi Chen and Ruiqi Yan and Jiale Qian and Yichao Yan and Shunshun Yin and Ming Tao and Xie Chen and Lei Xie and Xinsheng Wang},
year = {2025},
archivePrefix={arXiv},
url = {https://arxiv.org/abs/2510.23541}
}
License
We use the Apache 2.0 license. Researchers and developers are free to use the codes and model weights of our SoulX-Podcast. Check the license at LICENSE for more details.
Acknowledge
- This repo benefits from FlashCosyVoice
Usage Disclaimer
This project provides a speech synthesis model for podcast generation capable of zero-shot voice cloning, intended for academic research, educational purposes, and legitimate applications, such as personalized speech synthesis, assistive technologies, and linguistic research.
Please note:
Do not use this model for unauthorized voice cloning, impersonation, fraud, scams, deepfakes, or any illegal activities.
Ensure compliance with local laws and regulations when using this model and uphold ethical standards.
The developers assume no liability for any misuse of this model.
We advocate for the responsible development and use of AI and encourage the community to uphold safety and ethical principles in AI research and applications. If you have any concerns regarding ethics or misuse, please contact us.
Contact Us
If you are interested in leaving a message to our work, feel free to email hkxie@mail.nwpu.edu.cn or linhaopeng@soulapp.cn or lxie@nwpu.edu.cn or wangxinsheng@soulapp.cn
You’re welcome to join our WeChat group for technical discussions, updates.
Due to group limits, if you can't scan the QR code, please add my WeChat for group access
🚀 If you find these models useful
Help me test my AI-Powered Quantum Network Monitor Assistant with quantum-ready security checks:
The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : Source Code Quantum Network Monitor. You will also find the code I use to quantize the models if you want to do it yourself GGUFModelBuilder
💬 How to test:
Choose an AI assistant type:
TurboLLM(GPT-4.1-mini)HugLLM(Hugginface Open-source models)TestLLM(Experimental CPU-only)
What I’m Testing
I’m pushing the limits of small open-source models for AI network monitoring, specifically:
- Function calling against live network services
- How small can a model go while still handling:
- Automated Nmap security scans
- Quantum-readiness checks
- Network Monitoring tasks
🟡 TestLLM – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):
- ✅ Zero-configuration setup
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Other Assistants
🟢 TurboLLM – Uses gpt-4.1-mini :
- **It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited.
- Create custom cmd processors to run .net code on Quantum Network Monitor Agents
- Real-time network diagnostics and monitoring
- Security Audits
- Penetration testing (Nmap/Metasploit)
🔵 HugLLM – Latest Open-source models:
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💡 Example commands you could test:
"Give me info on my websites SSL certificate""Check if my server is using quantum safe encyption for communication""Run a comprehensive security audit on my server"- '"Create a cmd processor to .. (what ever you want)" Note you need to install a Quantum Network Monitor Agent to run the .net code on. This is a very flexible and powerful feature. Use with caution!
Final Word
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