Examples of Voice cloning using Qwen3 TTS
You can access its demo at Modelscope Demo, HuggingFace Demo
Have you ever wanted to clone a voice with just 3 seconds of audio? Qwen 3 TTS actually makes this possible with its groundbreaking voice AI technology. I’ve been exploring various text-to-speech solutions, but this open-source series developed by the Qwen team at Alibaba Cloud has genuinely impressed me with its capabilities.
What’s more, Qwen 3 TTS isn’t just about voice cloning. It supports 10 languages and 9 dialects while offering 49 different voice styles. You can find the entire Qwen model series on GitHub and Huggingface, available in two sizes: a powerful 1.7B version for peak performance and a more efficient 0.6B option. When it comes to performance, I was surprised to discover it outperforms mainstream commercial models in Word Error Rate tests. If you’re interested in free-form voice design or stable, expressive speech generation, this might be the best voice AI solution you’ll try this year.
In this comprehensive guide, we’ll dive into everything you need to know about Qwen3 TTS, from its impressive capabilities to how you can implement it in your projects. Whether you’re curious about the ComfyUI integration or looking to access the Qwen3 TTS API, we’ve got you covered.
Qwen3-TTS: Intro
The Qwen3-TTS family represents a major step forward in AI voice technology. Let’s look under the hood at what makes these models tick.
Model sizes: 1.7B vs 0.6B
Qwen3-TTS comes in two distinct sizes to accommodate different needs and hardware constraints:
The 1.7B parameter models deliver maximum performance with powerful control capabilities. These larger models include three variants:
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Qwen3-TTS-12Hz-1.7B-VoiceDesign: Creates custom voices from text descriptions
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Qwen3-TTS-12Hz-1.7B-CustomVoice: Provides style control over 9 premium timbres
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Qwen3-TTS-12Hz-1.7B-Base: Enables rapid voice cloning from short samples
For those with more limited computing resources, the 0.6B parameter models offer an excellent balance between quality and efficiency. These models require less VRAM (around 4GB vs 6GB for the 1.7B versions) yet still maintain impressive capabilities across the same language set.
Notably, both model sizes support streaming generation for real-time applications, though only the 1.7B versions offer full instruction control capabilities for advanced voice customization.
Supported languages and dialects
One of the most impressive aspects of Qwen3-TTS is its extensive language support. The models handle 10 major languages: Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, and Italian.
Beyond just basic language support, these models understand dialectal nuances. For instance, the Chinese language support includes Mandarin, Hokkien, Wu, Cantonese, Sichuanese, Beijing, Nanjing, Tianjin, and Shaanxi dialects. This means you’re not limited to generic accents – I can generate speech that authentically captures regional speaking patterns.
Furthermore, the CustomVoice variants offer 9 premium timbres covering various combinations of gender, age, language, and dialect profiles. This helps create more natural and contextually appropriate speech for specific use cases.
Open-source availability on GitHub and HuggingFace
You can access it freely at Github, HuggingFace
Unlike many commercial speech solutions, the entire Qwen3-TTS family is fully open-source under the Apache 2.0 license. This generous licensing means you can freely use, modify, and distribute these models for your projects.
All models are readily available on both GitHub and HuggingFace, with additional access through ModelScope. For those in mainland China, ModelScope downloads may offer better performance.
Additionally, you can access the models programmatically through the Qwen API or by installing the qwen-tts Python package from PyPI. This package handles the required dependencies and allows you to load any released Qwen3-TTS model with minimal configuration.
Features That Set Qwen3-TTS Apart
What truly makes Qwen 3 TTS special are its groundbreaking features that set it apart from other voice AI solutions. These capabilities transform how we interact with voice technology in both creative and practical applications.
Voice cloning from 3-second samples
The ability to clone voices with minimal input is perhaps the most impressive feature of Qwen3-TTS. You only need a 3-second audio sample to create remarkably accurate voice replications. This works across all model variants, including both the 1.7B and 0.6B versions. I’ve found this especially useful for quickly creating personalized content without extensive recording sessions.
Free-form voice design with natural language
Rather than tweaking technical parameters, Qwen3-TTS lets you design voices through simple descriptions. The VoiceDesign model interprets natural language instructions to create entirely new voice profiles. For instance, you might request “a deep male voice with slight rasp” or “an elderly woman speaking softly.” Moreover, once you’ve designed a voice you like, you can save it as a reference clip and reuse it for future content without extracting features again.
Streaming generation with 97ms latency
Qwen3-TTS features an innovative dual-track hybrid architecture that achieves remarkably low latency - just 97ms end-to-end. In practice, this means the model starts generating audio almost immediately after receiving the first character of text. Consequently, real-time conversations with AI voices feel natural and responsive rather than awkwardly delayed.
Instruction-based voice control
Beyond basic text-to-speech, Qwen3-TTS responds to natural language instructions that control various speech aspects:
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Timbre and voice characteristics
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Emotional expression and tone
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Speaking rate and rhythm
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Prosody and intonation
This creates a “what you imagine is what you hear” experience, making it simpler to get exactly the voice performance you want without complex adjustments.
Multilingual and cross-lingual synthesis
In addition to supporting 10 languages (Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, and Italian), Qwen3-TTS offers impressive cross-lingual capabilities. You can clone a voice in one language and generate speech in another, particularly useful for content localization and global applications.
Performance Benchmarks and Comparisons
Let’s check the numbers! When comparing voice AI systems, performance metrics tell us exactly how well they work in real-world scenarios. The benchmark results for qwen 3 tts show why it’s generating so much excitement.
Word Error Rate (WER) vs MiniMax and ElevenLabs
First and foremost, Qwen3-TTS-12Hz-1.7B achieves a remarkable Word Error Rate of just 1.24 on the Seed-TTS test-en set. This means the AI correctly reproduces almost every word in the source text. In direct comparisons, Qwen3-TTS delivers superior intelligibility in 6 out of 10 languages compared to commercial options. Specifically:
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Average WER across 10 languages: 1.835%
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Speaker similarity score: 0.89 (compared to 0.81 for ElevenLabs)
You’ll notice the difference immediately when testing complex passages or technical content.
Speaker similarity and PESQ scores
In terms of voice fidelity, Qwen3-TTS achieves the highest speaker similarity scores across all 10 evaluated languages. This explains why the cloned voices sound so authentic - the model captures subtle vocal characteristics others miss. Indeed, the system reaches a 0.789 similarity score across languages, outperforming both MiniMax and ElevenLabs.
Tokenizer performance on LibriSpeech
The underlying tokenizer is what makes qwen3 tts so effective. On the LibriSpeech test-clean benchmark, it achieves impressive scores:
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PESQ (Wideband): 3.21
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PESQ (Narrowband): 3.68
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STOI: 0.96
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UTMOS: 4.16
These numbers represent near-lossless speech reconstruction, preserving almost all original audio qualities.
Long-form generation and stability
Above all, Qwen3-TTS excels at creating extended audio content. The system generates up to 10 minutes of continuous speech without the quality degradation or boundary artifacts common in other systems. The Qwen3-TTS-25Hz-1.7B-CustomVoice variant achieves the lowest WERs for long-form content (1.517 for Chinese and 1.225 for English), making it perfect for audiobooks or podcasts.
How Developers Can Use Qwen3-TTS
Getting hands-on with Qwen3-TTS is surprisingly straightforward, whether you’re a beginner or seasoned developer. I’ve explored multiple implementation paths, each offering unique advantages.
Accessing the Qwen3-TTS API
The simplest approach is using the official Qwen API through dashscope. You’ll need an API key, then just a few lines of Python code to generate speech. For instance:
import dashscope
response = dashscope.MultiModalConversation.call(
model="qwen3-tts-flash",
api_key=YOUR_API_KEY,
text="Your text here",
voice="Ryan",
language_type="English"
)
Using Qwen3-TTS with ComfyUI
For visual workflow enthusiasts, the ComfyUI integration is excellent. The custom node provides high-quality speech generation directly inside ComfyUI workflows – perfect for storytelling, agents, or video generation projects. You can place models in ComfyUI/models/TTS/Qwen3-TTS/<MODEL_NAME>/
Deploying models via HuggingFace
Initially, try the browser-based demos on HuggingFace Spaces or ModelScope. For local deployment:
pip install -U qwen-tts
qwen-tts-demo Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice --ip 0.0.0.0 --port 8000
Fine-tuning and custom voice creation
Yes, you can fine-tune these models! The base variants (1.7B-Base and 0.6B-Base) support single-speaker fine-tuning, with multi-speaker capabilities coming soon. Furthermore, you can save designed voices as reference clips for consistent, reusable voice identities across projects.
Conclusion
Qwen3-TTS truly stands out as a remarkable advancement in voice AI technology. After exploring its capabilities, I’m convinced it deserves serious consideration for anyone interested in text-to-speech solutions. You can start with just three seconds of audio to clone voices across ten languages and numerous dialects – something previously unimaginable in this space.
The flexibility of having both 1.7B and 0.6B model sizes means you don’t need cutting-edge hardware to benefit from this technology. Whether you’re creating audiobooks, developing conversational AI, or localizing content, Qwen3-TTS delivers exceptional results with minimal effort.
Most impressively, the benchmarks speak for themselves. Qwen3-TTS outperforms many commercial alternatives in Word Error Rate tests while maintaining superior speaker similarity scores. This means the voices you create will sound natural and accurately represent your intended speech patterns.
Developers will appreciate the multiple implementation options available. You can access Qwen3-TTS through the official API, integrate it with ComfyUI, or deploy models via HuggingFace. The open-source nature under the Apache 2.0 license additionally gives you freedom to modify and distribute these models for your projects.
The next time you need to generate realistic speech with precise control over voice characteristics, emotional expression, or speaking rate, remember Qwen3-TTS offers a “what you imagine is what you hear” experience. This powerful tool has fundamentally changed what we can expect from voice AI technology. Whether you’re a hobbyist or professional developer, Qwen3-TTS might just be the best voice AI solution you’ll try in 2026.
All this information is Open-Source and also avaliable at Qwen AI’s official site, you can also get more deeper information from here.
Frequently Asked Questions
How does Qwen3-TTS compare to other voice AI solutions?
Qwen3-TTS offers impressive capabilities, including voice cloning from 3-second samples and support for 10 languages and 9 dialects. While it outperforms some commercial models in Word Error Rate tests, opinions vary on how it compares to other open-source options like VibeVoice for specific use cases.
What are the key features of Qwen3-TTS?
Qwen3-TTS offers voice cloning from short samples, free-form voice design using natural language, streaming generation with low latency, instruction-based voice control, and multilingual synthesis. It comes in two model sizes (1.7B and 0.6B) to accommodate different hardware capabilities.
How can developers implement Qwen3-TTS in their projects?
Developers can access Qwen3-TTS through its API, integrate it with ComfyUI, or deploy models via HuggingFace. The models are open-source and available on GitHub, with additional access through ModelScope. Fine-tuning options are also available for customization.
What languages does Qwen3-TTS support?
Qwen3-TTS supports 10 major languages: Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, and Italian. It also includes support for various Chinese dialects, enhancing its versatility for different linguistic needs.
How does Qwen3-TTS handle emotional expression in speech?
Qwen3-TTS allows for control over multi-dimensional acoustic attributes such as timbre, emotion, and prosody through natural language instructions. The model can adaptively adjust tone, rhythm, and emotional expression based on text semantics and user prompts, aiming to achieve lifelike output.