{"id":1311,"date":"2026-01-26T20:11:44","date_gmt":"2026-01-26T12:11:44","guid":{"rendered":"https:\/\/gaga.art\/blog\/?p=1311"},"modified":"2026-02-05T17:50:47","modified_gmt":"2026-02-05T09:50:47","slug":"qwen3-tts","status":"publish","type":"post","link":"https:\/\/gaga.art\/blog\/qwen3-tts\/","title":{"rendered":"Qwen3-TTS 2026 Review: Alibaba&#8217;s Open-Source TTS"},"content":{"rendered":"\n<p><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"256\" src=\"https:\/\/gaga.art\/blog\/wp-content\/uploads\/2026\/01\/qwen3-tts-1024x256.webp\" alt=\"qwen3-tts\" class=\"wp-image-1314\" srcset=\"https:\/\/gaga.art\/blog\/wp-content\/uploads\/2026\/01\/qwen3-tts-1024x256.webp 1024w, https:\/\/gaga.art\/blog\/wp-content\/uploads\/2026\/01\/qwen3-tts-300x75.webp 300w, https:\/\/gaga.art\/blog\/wp-content\/uploads\/2026\/01\/qwen3-tts-768x192.webp 768w, https:\/\/gaga.art\/blog\/wp-content\/uploads\/2026\/01\/qwen3-tts.webp 1251w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"key-takeaways\"><strong>Key Takeaways<\/strong><\/h2>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Qwen3-TTS is Alibaba&#8217;s open-source text-to-speech model family released January 2026, featuring 0.6B and 1.7B parameter versions<\/li>\n\n\n\n<li>End-to-end synthesis latency reaches 97 milliseconds, enabling real-time conversational applications<\/li>\n\n\n\n<li>Voice cloning requires only 3 seconds of reference audio to replicate any speaker&#8217;s voice with 0.95 similarity<\/li>\n\n\n\n<li>The model supports 10 languages (Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, Italian) plus regional dialects<\/li>\n\n\n\n<li>Natural language prompts can design entirely new voice personas without pre-recorded samples<\/li>\n\n\n\n<li>All models are Apache 2.0 licensed and available on GitHub, Hugging Face, and ModelScope<\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<div class=\"wp-block-rank-math-toc-block has-custom-cd-994-c-color has-text-color has-link-color wp-elements-f1cbfcd676c61ce5e04efb95e513ab9d\" id=\"rank-math-toc\"><p>Table of Contents<\/p><nav><ul><li><a href=\"#key-takeaways\">Key Takeaways<\/a><\/li><li><a href=\"#what-is-qwen-3-tts\">What Is Qwen3-TTS?<\/a><\/li><li><a href=\"#core-capabilities-of-qwen-3-tts\">Core Capabilities of Qwen3-TTS<\/a><ul><li><a href=\"#ultra-low-latency-streaming\">Ultra-Low Latency Streaming<\/a><\/li><li><a href=\"#3-second-voice-cloning\">3-Second Voice Cloning<\/a><\/li><li><a href=\"#natural-language-voice-design\">Natural Language Voice Design<\/a><\/li><li><a href=\"#multilingual-and-dialect-support\">Multilingual and Dialect Support<\/a><\/li><li><a href=\"#long-form-audio-generation\">Long-Form Audio Generation<\/a><\/li><\/ul><\/li><li><a href=\"#technical-architecture-explained\">Technical Architecture Explained<\/a><ul><li><a href=\"#dual-track-streaming-design\">Dual-Track Streaming Design<\/a><\/li><li><a href=\"#end-to-end-multi-codebook-lm\">End-to-End Multi-Codebook LM<\/a><\/li><li><a href=\"#qwen-3-tts-tokenizer-12-hz\">Qwen3-TTS-Tokenizer-12Hz<\/a><\/li><li><a href=\"#training-pipeline\">Training Pipeline<\/a><\/li><\/ul><\/li><li><a href=\"#how-to-try-the-qwen-3-tts-demo\">How to Try the Qwen3-TTS Demo<\/a><ul><li><a href=\"#online-demo-no-installation-required\">Online Demo (No Installation Required)<\/a><\/li><li><a href=\"#local-installation\">Local Installation<\/a><\/li><\/ul><\/li><li><a href=\"#python-code-examples\">Python Code Examples<\/a><ul><\/ul><\/li><li><a href=\"#available-models-and-download-links\">Available Models and Download Links<\/a><\/li><li><a href=\"#using-qwen-3-tts-with-ollama\">Using Qwen3-TTS with Ollama<\/a><\/li><li><a href=\"#qwen-3-tts-in-comfy-ui\">Qwen3-TTS in ComfyUI<\/a><\/li><li><a href=\"#real-world-application-scenarios\">Real-World Application Scenarios<\/a><ul><\/ul><\/li><li><a href=\"#benchmark-performance\">Benchmark Performance<\/a><ul><li><a href=\"#voice-cloning-quality-seed-tts-test-set\">Voice Cloning Quality (Seed-TTS Test Set)<\/a><\/li><li><a href=\"#speaker-similarity-10-language-average\">Speaker Similarity (10-Language Average)<\/a><\/li><li><a href=\"#long-form-generation-10-minutes\">Long-Form Generation (10+ Minutes)<\/a><\/li><\/ul><\/li><li><a href=\"#bonus-gaga-ai-for-video-and-voice-creation\">Bonus: Gaga AI for Video and Voice Creation<\/a><\/li><li><a href=\"#official-resources\">Official Resources<\/a><\/li><li><a href=\"#frequently-asked-questions\">Frequently Asked Questions<\/a><ul><\/ul><\/li><\/ul><\/nav><\/div>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"what-is-qwen-3-tts\"><strong>What Is Qwen3-TTS?<\/strong><\/h2>\n\n\n\n<p><\/p>\n\n\n\n<p>Qwen3-TTS is an open-source <a href=\"https:\/\/gaga.art\/blog\/text-to-speech\/\">text-to-speech<\/a> model series developed by Alibaba&#8217;s Qwen team. The models convert written text into natural-sounding speech with support for voice cloning, voice design, and multilingual synthesis.<\/p>\n\n\n\n<p>Unlike single-model TTS solutions, Qwen3-TTS is a family of specialized models. The 1.7B parameter version delivers maximum quality and control capabilities, while the 0.6B version balances performance with computational efficiency for edge deployment scenarios.<\/p>\n\n\n\n<p>The architecture uses a discrete multi-codebook language model approach. This end-to-end design eliminates information loss that occurs in traditional TTS pipelines combining separate language and acoustic models.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"core-capabilities-of-qwen-3-tts\"><strong>Core Capabilities of Qwen3-TTS<\/strong><\/h2>\n\n\n\n<p><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"492\" src=\"https:\/\/gaga.art\/blog\/wp-content\/uploads\/2026\/01\/qwen3-tts-intro-1024x492.webp\" alt=\"qwen3 tts intro\" class=\"wp-image-1315\" srcset=\"https:\/\/gaga.art\/blog\/wp-content\/uploads\/2026\/01\/qwen3-tts-intro-1024x492.webp 1024w, https:\/\/gaga.art\/blog\/wp-content\/uploads\/2026\/01\/qwen3-tts-intro-300x144.webp 300w, https:\/\/gaga.art\/blog\/wp-content\/uploads\/2026\/01\/qwen3-tts-intro-768x369.webp 768w, https:\/\/gaga.art\/blog\/wp-content\/uploads\/2026\/01\/qwen3-tts-intro-1536x738.webp 1536w, https:\/\/gaga.art\/blog\/wp-content\/uploads\/2026\/01\/qwen3-tts-intro-2048x984.webp 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"ultra-low-latency-streaming\"><strong>Ultra-Low Latency Streaming<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<p>Qwen3-TTS achieves 97-millisecond first-packet latency using a dual-track hybrid architecture. The system outputs audio immediately after receiving a single character input. Even under concurrent load with 6 simultaneous users, first-packet latency stays below 300 milliseconds.<\/p>\n\n\n\n<p>This performance level makes Qwen3-TTS suitable for:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Real-time voice assistants<\/li>\n\n\n\n<li>Live streaming interactions<\/li>\n\n\n\n<li>Online meeting translation<\/li>\n\n\n\n<li>Voice navigation systems<\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"3-second-voice-cloning\"><strong>3-Second Voice Cloning<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<p>The <a href=\"https:\/\/gaga.art\/blog\/ai-voice-cloning\/\">voice cloning<\/a> capability requires just 3 seconds of reference audio. The system captures not only the speaker&#8217;s voice characteristics but also preserves speech patterns, rhythm, and emotional nuances. Cloned voices transfer seamlessly across all 10 supported languages.<\/p>\n\n\n\n<p>Speaker similarity scores reach 0.95, approaching human-level reproduction quality. This outperforms commercial alternatives including MiniMax and <a href=\"https:\/\/gaga.art\/blog\/elevenlabs-review\/\">ElevenLabs<\/a> on standardized benchmarks.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"natural-language-voice-design\"><strong>Natural Language Voice Design<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<p>Qwen3-TTS accepts natural language descriptions to generate entirely new voice personas. Instead of selecting from preset voice libraries, users describe the desired voice characteristics in plain text.<\/p>\n\n\n\n<p>Example prompts that work:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>&#8220;A confident 17-year-old male with a tenor range, gaining confidence&#8221;<\/li>\n\n\n\n<li>&#8220;Warm, gentle young female voice with rich emotion&#8221;<\/li>\n\n\n\n<li>&#8220;Middle-aged authority figure with a low, commanding timbre&#8221;<\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<p>The VoiceDesign model interprets these descriptions and synthesizes matching voices without requiring any audio samples.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"multilingual-and-dialect-support\"><strong>Multilingual and Dialect Support<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<p>The model natively supports 10 languages: Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, and Italian. It also handles Chinese regional dialects including Sichuan dialect and Beijing dialect.<\/p>\n\n\n\n<p>Cross-lingual synthesis maintains voice consistency when switching languages. The Chinese-to-Korean error rate drops to 4.82%, compared to 20%+ error rates in competing models.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"long-form-audio-generation\"><strong>Long-Form Audio Generation<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<p>Qwen3-TTS processes up to 32,768 tokens, generating continuous audio exceeding 10 minutes. Word error rates remain low: 2.36% for Chinese and 2.81% for English. The system avoids common long-form synthesis problems like repetition, omission, and rhythm inconsistency.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"technical-architecture-explained\"><strong>Technical Architecture Explained<\/strong><\/h2>\n\n\n\n<p><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"711\" src=\"https:\/\/gaga.art\/blog\/wp-content\/uploads\/2026\/01\/qwen3-tts-workflow-1024x711.webp\" alt=\"qwen3 tts workflow\" class=\"wp-image-1313\" srcset=\"https:\/\/gaga.art\/blog\/wp-content\/uploads\/2026\/01\/qwen3-tts-workflow-1024x711.webp 1024w, https:\/\/gaga.art\/blog\/wp-content\/uploads\/2026\/01\/qwen3-tts-workflow-300x208.webp 300w, https:\/\/gaga.art\/blog\/wp-content\/uploads\/2026\/01\/qwen3-tts-workflow-768x533.webp 768w, https:\/\/gaga.art\/blog\/wp-content\/uploads\/2026\/01\/qwen3-tts-workflow-1536x1067.webp 1536w, https:\/\/gaga.art\/blog\/wp-content\/uploads\/2026\/01\/qwen3-tts-workflow-2048x1423.webp 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"dual-track-streaming-design\"><strong>Dual-Track Streaming Design<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<p>The dual-track architecture enables simultaneous streaming and non-streaming generation within a single model. One track plans overall speech prosody while the second track outputs audio in real-time as text arrives. This parallels how human speakers organize thoughts while speaking.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"end-to-end-multi-codebook-lm\"><strong>End-to-End Multi-Codebook LM<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<p>Traditional TTS systems chain separate language models and acoustic models, creating information bottlenecks at each stage. Qwen3-TTS uses a unified discrete multi-codebook language model architecture that directly maps text to speech without intermediate representations.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"qwen-3-tts-tokenizer-12-hz\"><strong>Qwen3-TTS-Tokenizer-12Hz<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<p>The proprietary speech encoder operates at 12 frames per second, achieving 5-8x compression while preserving paralinguistic information including emotion, speaking environment, and acoustic characteristics. This tokenizer enables the lightweight non-DiT decoder to reconstruct high-fidelity audio.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"training-pipeline\"><strong>Training Pipeline<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<p>Pre-training uses over 50 million hours of multilingual speech data. Post-training incorporates human feedback optimization and rule-based reward enhancement to improve practical performance. This staged approach balances long-form stability, low latency, and audio fidelity.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"how-to-try-the-qwen-3-tts-demo\"><strong>How to Try the Qwen3-TTS Demo<\/strong><\/h2>\n\n\n\n<p><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"528\" src=\"https:\/\/gaga.art\/blog\/wp-content\/uploads\/2026\/01\/qwen3-tts-demo-1024x528.webp\" alt=\"qwen3 tts demo\" class=\"wp-image-1312\" srcset=\"https:\/\/gaga.art\/blog\/wp-content\/uploads\/2026\/01\/qwen3-tts-demo-1024x528.webp 1024w, https:\/\/gaga.art\/blog\/wp-content\/uploads\/2026\/01\/qwen3-tts-demo-300x155.webp 300w, https:\/\/gaga.art\/blog\/wp-content\/uploads\/2026\/01\/qwen3-tts-demo-768x396.webp 768w, https:\/\/gaga.art\/blog\/wp-content\/uploads\/2026\/01\/qwen3-tts-demo-1536x792.webp 1536w, https:\/\/gaga.art\/blog\/wp-content\/uploads\/2026\/01\/qwen3-tts-demo-2048x1056.webp 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"online-demo-no-installation-required\"><strong>Online Demo (No Installation Required)<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<p>Two hosted demo interfaces provide immediate access:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Hugging Face Spaces:<\/strong> https:\/\/huggingface.co\/spaces\/Qwen\/Qwen3-TTS<\/li>\n\n\n\n<li><strong>ModelScope Studios:<\/strong> https:\/\/modelscope.cn\/studios\/Qwen\/Qwen3-TTS<\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<p>Both interfaces support all three generation modes: CustomVoice with preset timbres, VoiceDesign with natural language descriptions, and Base model voice cloning.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"local-installation\"><strong>Local Installation<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<p class=\"has-vivid-red-color has-text-color has-link-color wp-elements-a1342922abbae0c3e3dd455662da304b\"><strong>Step 1: Create Environment<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td>conda create -n qwen3-tts python=3.12 -yconda activate qwen3-tts<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"has-vivid-red-color has-text-color has-link-color wp-elements-293d046b2eed6a292a3f2b74ab5cdc9e\"><strong>Step 2: Install Package<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td>pip install -U qwen-tts<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"has-vivid-red-color has-text-color has-link-color wp-elements-64ba6e96521201b790833041dbcc685d\"><strong>Step 3: Optional FlashAttention 2<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td>pip install -U flash-attn &#8211;no-build-isolation<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"has-vivid-red-color has-text-color has-link-color wp-elements-d903423f68071af30eaee31efe084616\">For systems with limited RAM:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td>MAX_JOBS=4 pip install -U flash-attn &#8211;no-build-isolation<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"has-vivid-red-color has-text-color has-link-color wp-elements-fef54c783e2fd30f5d40bfbccbc68074\"><strong>Step 4: Launch Web Interface<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td>qwen-tts-demo Qwen\/Qwen3-TTS-12Hz-1.7B-CustomVoice &#8211;ip 0.0.0.0 &#8211;port 8000<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>Access the interface at http:\/\/localhost:8000.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"python-code-examples\"><strong>Python Code Examples<\/strong><\/h2>\n\n\n\n<p><\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"basic-custom-voice-generation\"><strong>Basic Custom Voice Generation<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-vivid-green-cyan-color has-text-color has-link-color has-fixed-layout\"><tbody><tr><td>import torchimport soundfile as sffrom qwen_tts import Qwen3TTSModel<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-vivid-green-cyan-color has-text-color has-link-color has-fixed-layout\"><tbody><tr><td>model = Qwen3TTSModel.from_pretrained(&nbsp;&nbsp;&nbsp;&nbsp;&#8220;Qwen\/Qwen3-TTS-12Hz-1.7B-CustomVoice&#8221;,&nbsp;&nbsp;&nbsp;&nbsp;device_map=&#8221;cuda:0&#8243;,&nbsp;&nbsp;&nbsp;&nbsp;dtype=torch.bfloat16,&nbsp;&nbsp;&nbsp;&nbsp;attn_implementation=&#8221;flash_attention_2&#8243;,)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-vivid-green-cyan-color has-text-color has-link-color has-fixed-layout\"><tbody><tr><td>wavs, sr = model.generate_custom_voice(&nbsp;&nbsp;&nbsp;&nbsp;text=&#8221;Welcome to the future of speech synthesis.&#8221;,&nbsp;&nbsp;&nbsp;&nbsp;language=&#8221;English&#8221;,&nbsp;&nbsp;&nbsp;&nbsp;speaker=&#8221;Ryan&#8221;,&nbsp;&nbsp;&nbsp;&nbsp;instruct=&#8221;Speak with enthusiasm and energy.&#8221;,)sf.write(&#8220;output.wav&#8221;, wavs[0], sr)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"voice-cloning-from-reference-audio\"><strong>Voice Cloning from Reference Audio<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-vivid-green-cyan-color has-text-color has-link-color has-fixed-layout\"><tbody><tr><td>model = Qwen3TTSModel.from_pretrained(&nbsp;&nbsp;&nbsp;&nbsp;&#8220;Qwen\/Qwen3-TTS-12Hz-1.7B-Base&#8221;,&nbsp;&nbsp;&nbsp;&nbsp;device_map=&#8221;cuda:0&#8243;,&nbsp;&nbsp;&nbsp;&nbsp;dtype=torch.bfloat16,)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-vivid-green-cyan-color has-text-color has-link-color has-fixed-layout\"><tbody><tr><td>ref_audio = &#8220;path\/to\/reference.wav&#8221;ref_text = &#8220;This is the reference transcript.&#8221;<br>wavs, sr = model.generate_voice_clone(&nbsp;&nbsp;&nbsp;&nbsp;text=&#8221;New content in the cloned voice.&#8221;,&nbsp;&nbsp;&nbsp;&nbsp;language=&#8221;English&#8221;,&nbsp;&nbsp;&nbsp;&nbsp;ref_audio=ref_audio,&nbsp;&nbsp;&nbsp;&nbsp;ref_text=ref_text,)sf.write(&#8220;cloned_output.wav&#8221;, wavs[0], sr)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"voice-design-from-text-description\"><strong>Voice Design from Text Description<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-vivid-green-cyan-color has-text-color has-link-color has-fixed-layout\"><tbody><tr><td>model = Qwen3TTSModel.from_pretrained(&nbsp;&nbsp;&nbsp;&nbsp;&#8220;Qwen\/Qwen3-TTS-12Hz-1.7B-VoiceDesign&#8221;,&nbsp;&nbsp;&nbsp;&nbsp;device_map=&#8221;cuda:0&#8243;,&nbsp;&nbsp;&nbsp;&nbsp;dtype=torch.bfloat16,)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-vivid-green-cyan-color has-text-color has-link-color has-fixed-layout\"><tbody><tr><td>wavs, sr = model.generate_voice_design(&nbsp;&nbsp;&nbsp;&nbsp;text=&#8221;This technology changes everything.&#8221;,&nbsp;&nbsp;&nbsp;&nbsp;language=&#8221;English&#8221;,&nbsp;&nbsp;&nbsp;&nbsp;instruct=&#8221;Male voice, mid-30s, professional broadcast quality, calm and authoritative.&#8221;,)sf.write(&#8220;designed_voice.wav&#8221;, wavs[0], sr)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"available-models-and-download-links\"><strong>Available Models and Download Links<\/strong><\/h2>\n\n\n\n<p><\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Model<\/strong><\/td><td><strong>Parameters<\/strong><\/td><td><strong>Features<\/strong><\/td><td><strong>Download<\/strong><\/td><\/tr><tr><td>Qwen3-TTS-12Hz-1.7B-VoiceDesign<\/td><td>1.7B<\/td><td>Natural language voice creation<\/td><td><a href=\"https:\/\/huggingface.co\/Qwen\/Qwen3-TTS-12Hz-1.7B-VoiceDesign\" rel=\"nofollow noopener\" target=\"_blank\">Hugging Face<\/a><\/td><\/tr><tr><td>Qwen3-TTS-12Hz-1.7B-CustomVoice<\/td><td>1.7B<\/td><td>9 preset voices + style control<\/td><td><a href=\"https:\/\/huggingface.co\/Qwen\/Qwen3-TTS-12Hz-1.7B-CustomVoice\" rel=\"nofollow noopener\" target=\"_blank\">Hugging Face<\/a><\/td><\/tr><tr><td>Qwen3-TTS-12Hz-1.7B-Base<\/td><td>1.7B<\/td><td>Voice cloning + fine-tuning base<\/td><td><a href=\"https:\/\/huggingface.co\/Qwen\/Qwen3-TTS-12Hz-1.7B-Base\" rel=\"nofollow noopener\" target=\"_blank\">Hugging Face<\/a><\/td><\/tr><tr><td>Qwen3-TTS-12Hz-0.6B-CustomVoice<\/td><td>0.6B<\/td><td>Lightweight preset voices<\/td><td><a href=\"https:\/\/huggingface.co\/Qwen\/Qwen3-TTS-12Hz-0.6B-CustomVoice\" rel=\"nofollow noopener\" target=\"_blank\">Hugging Face<\/a><\/td><\/tr><tr><td>Qwen3-TTS-12Hz-0.6B-Base<\/td><td>0.6B<\/td><td>Lightweight voice cloning<\/td><td><a href=\"https:\/\/huggingface.co\/Qwen\/Qwen3-TTS-12Hz-0.6B-Base\" rel=\"nofollow noopener\" target=\"_blank\">Hugging Face<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p><strong>Manual Download Commands:<\/strong><\/p>\n\n\n\n<p class=\"has-vivid-red-color has-text-color has-link-color wp-elements-3837801978a62de4e2816c553606445b\"># Via Hugging Face<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td>huggingface-cli download Qwen\/Qwen3-TTS-12Hz-1.7B-CustomVoice &#8211;local-dir .\/Qwen3-TTS-12Hz-1.7B-CustomVoice<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"has-vivid-red-color has-text-color has-link-color wp-elements-1a291cd3342f59a40336c633ae0c46d4\"># Via ModelScope (recommended for users in China)<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td>modelscope download &#8211;model Qwen\/Qwen3-TTS-12Hz-1.7B-CustomVoice &#8211;local_dir .\/Qwen3-TTS-12Hz-1.7B-CustomVoice<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"using-qwen-3-tts-with-ollama\"><strong>Using Qwen3-TTS with Ollama<\/strong><\/h2>\n\n\n\n<p><\/p>\n\n\n\n<p>Qwen3-TTS does not currently have official Ollama integration. The model uses a specialized architecture requiring the qwen-tts Python package or vLLM deployment.<\/p>\n\n\n\n<p>For local deployment alternatives:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Use the qwen-tts Python package directly<\/li>\n\n\n\n<li>Deploy via vLLM-Omni for optimized inference<\/li>\n\n\n\n<li>Access the DashScope API for cloud-based usage<\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<p>The Qwen team continues expanding deployment options, so Ollama support may arrive in future releases.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"qwen-3-tts-in-comfy-ui\"><strong>Qwen3-TTS in ComfyUI<\/strong><\/h2>\n\n\n\n<p><\/p>\n\n\n\n<p>ComfyUI integration for Qwen3-TTS enables visual workflow-based audio generation within creative pipelines. Community-developed nodes connect Qwen3-TTS to ComfyUI&#8217;s node graph system.<\/p>\n\n\n\n<p>To integrate Qwen3-TTS with ComfyUI:<\/p>\n\n\n\n<p>1. Install the qwen-tts package in your ComfyUI Python environment<\/p>\n\n\n\n<p>2. Search ComfyUI-Manager for Qwen3-TTS custom nodes<\/p>\n\n\n\n<p>3. Connect text inputs to the TTS node, route audio outputs to downstream processing<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><\/li>\n<\/ol>\n\n\n\n<p>This workflow suits creators building automated video production pipelines, AI avatar systems, or batch audio generation workflows.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"real-world-application-scenarios\"><strong>Real-World Application Scenarios<\/strong><\/h2>\n\n\n\n<p><\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"live-interaction-systems\"><strong>Live Interaction Systems<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<p>The 97ms latency enables natural back-and-forth conversation. Digital human systems, AI customer service, and voice assistants benefit from response times that feel immediate rather than delayed.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"content-production\"><strong>Content Production<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<p>Video creators use Qwen3-TTS for dubbing, audiobook narration, podcast generation, and game character voices. Multi-voice and multi-emotion control eliminates the need for professional voice actors in many scenarios.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"enterprise-communications\"><strong>Enterprise Communications<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<p>Automated phone systems, voice notifications, and IVR systems gain natural-sounding speech. Custom corporate voice profiles maintain brand consistency across all audio touchpoints.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"accessibility\"><strong>Accessibility<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<p>Screen reader applications receive higher quality voice output. The natural prosody and emotion control improve comprehension for users relying on audio interfaces.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"multilingual-services\"><strong>Multilingual Services<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<p>Cross-border e-commerce, international customer support, and language learning applications leverage the 10-language support with consistent voice quality across languages.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"benchmark-performance\"><strong>Benchmark Performance<\/strong><\/h2>\n\n\n\n<p><\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"voice-cloning-quality-seed-tts-test-set\"><strong>Voice Cloning Quality (Seed-TTS Test Set)<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Model<\/strong><\/td><td><strong>Chinese WER<\/strong><\/td><td><strong>English WER<\/strong><\/td><\/tr><tr><td>Qwen3-TTS-12Hz-1.7B-Base<\/td><td>0.77%<\/td><td>1.24%<\/td><\/tr><tr><td>CosyVoice 3<\/td><td>0.71%<\/td><td>1.45%<\/td><\/tr><tr><td>MiniMax-Speech<\/td><td>0.83%<\/td><td>1.65%<\/td><\/tr><tr><td>F5-TTS<\/td><td>1.56%<\/td><td>1.83%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"speaker-similarity-10-language-average\"><strong>Speaker Similarity (10-Language Average)<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Model<\/strong><\/td><td><strong>Similarity Score<\/strong><\/td><\/tr><tr><td>Qwen3-TTS-12Hz-1.7B-Base<\/td><td>0.789<\/td><\/tr><tr><td>MiniMax<\/td><td>0.748<\/td><\/tr><tr><td>ElevenLabs<\/td><td>0.646<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"long-form-generation-10-minutes\"><strong>Long-Form Generation (10+ Minutes)<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Model<\/strong><\/td><td><strong>Chinese WER<\/strong><\/td><td><strong>English WER<\/strong><\/td><\/tr><tr><td>Qwen3-TTS-25Hz-1.7B-CustomVoice<\/td><td>1.52%<\/td><td>1.23%<\/td><\/tr><tr><td>Qwen3-TTS-12Hz-1.7B-CustomVoice<\/td><td>2.36%<\/td><td>2.81%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"bonus-gaga-ai-for-video-and-voice-creation\"><strong>Bonus: Gaga AI for Video and Voice Creation<\/strong><\/h2>\n\n\n\n<p><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"563\" src=\"https:\/\/gaga.art\/blog\/wp-content\/uploads\/2026\/01\/gaga-ai-text-to-video-generation-1024x563.webp\" alt=\"gaga ai text to video generation\" class=\"wp-image-1177\" srcset=\"https:\/\/gaga.art\/blog\/wp-content\/uploads\/2026\/01\/gaga-ai-text-to-video-generation-1024x563.webp 1024w, https:\/\/gaga.art\/blog\/wp-content\/uploads\/2026\/01\/gaga-ai-text-to-video-generation-300x165.webp 300w, https:\/\/gaga.art\/blog\/wp-content\/uploads\/2026\/01\/gaga-ai-text-to-video-generation-768x423.webp 768w, https:\/\/gaga.art\/blog\/wp-content\/uploads\/2026\/01\/gaga-ai-text-to-video-generation-1536x845.webp 1536w, https:\/\/gaga.art\/blog\/wp-content\/uploads\/2026\/01\/gaga-ai-text-to-video-generation-2048x1127.webp 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<p>For users seeking an all-in-one solution combining TTS with video generation, <a href=\"https:\/\/gaga.art\/app\">Gaga AI <\/a>offers integrated capabilities:<\/p>\n\n\n\n<p><strong>Image-to-Video AI:<\/strong> <a href=\"https:\/\/gaga.art\/en\/image-to-video-ai\">Transform static images into animated video<\/a> content with AI-powered motion synthesis. The platform handles lip-sync, expression generation, and natural movement without manual animation.<\/p>\n\n\n\n<p><strong>Text-to-Speech Features:<\/strong> Built-in TTS converts scripts to spoken audio with multiple voice options and emotional control. This pairs directly with video generation for complete content workflows.<\/p>\n\n\n\n<p><strong>AI Avatar Creation:<\/strong> <a href=\"https:\/\/gaga.art\/blog\/avatar-creator\/\">Generate realistic digital avatars<\/a> from reference images. These avatars lip-sync to TTS output or uploaded audio, creating presenter-style videos without filming.<\/p>\n\n\n\n<p><strong>Voice Clone Capability:<\/strong> Upload voice samples to create custom voice profiles. Cloned voices apply to any text input, maintaining speaker identity across unlimited content generation.<\/p>\n\n\n\n<p>Gaga AI combines these features into a unified platform, eliminating the need to integrate separate tools for video, voice, and avatar generation.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-a89b3969 wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"http:\/\/gaga.art\/app\" target=\"_blank\" rel=\"noreferrer noopener\">Generate Video Free<\/a><\/div>\n\n\n\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/gaga.art\/\">Learn Gaga AI<\/a><\/div>\n<\/div>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"official-resources\"><strong>Official Resources<\/strong><\/h2>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>GitHub Repository:<\/strong> https:\/\/github.com\/QwenLM\/Qwen3-TTS<\/li>\n\n\n\n<li><strong>Hugging Face Collection:<\/strong> https:\/\/huggingface.co\/collections\/Qwen\/qwen3-tts<\/li>\n\n\n\n<li><strong>ModelScope Collection:<\/strong> https:\/\/www.modelscope.cn\/collections\/Qwen\/Qwen3-TTS<\/li>\n\n\n\n<li><strong>Technical Paper:<\/strong> https:\/\/github.com\/QwenLM\/Qwen3-TTS\/blob\/main\/assets\/Qwen3_TTS.pdf<\/li>\n\n\n\n<li><strong>API Documentation:<\/strong> https:\/\/www.alibabacloud.com\/help\/en\/model-studio\/qwen-tts-voice-design<\/li>\n\n\n\n<li><strong>Qwen Blog Post:<\/strong> https:\/\/qwen.ai\/blog?id=qwen3tts-0115<\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"frequently-asked-questions\"><strong>Frequently Asked Questions<\/strong><\/h2>\n\n\n\n<p><\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"what-is-qwen-3-tts-1\"><strong>What is Qwen3-TTS?<\/strong><\/h3>\n\n\n\n<p>Qwen3-TTS is an open-source text-to-speech model family from Alibaba&#8217;s Qwen team, released in January 2026. It converts text to natural speech with support for voice cloning (replicating any voice from 3 seconds of audio), voice design (creating new voices from text descriptions), and multilingual synthesis across 10 languages.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"is-qwen-3-tts-free-to-use\"><strong>Is Qwen3-TTS free to use?<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<p>Yes. All Qwen3-TTS models are released under the Apache 2.0 license, allowing free commercial and personal use. You can download models from Hugging Face or ModelScope and run them locally without fees. Cloud API access through Alibaba&#8217;s DashScope platform may have usage-based pricing.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"what-languages-does-qwen-3-tts-support\"><strong>What languages does Qwen3-TTS support?<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<p>Qwen3-TTS supports 10 languages: Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, and Italian. It also handles Chinese dialects including Sichuan dialect and Beijing dialect. The model maintains voice consistency when switching between languages.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"how-much-vram-does-qwen-3-tts-require\"><strong>How much VRAM does Qwen3-TTS require?<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<p>The 1.7B parameter model requires approximately 8-12GB VRAM when running in bfloat16 precision with FlashAttention 2. The 0.6B model runs on less capable hardware. CPU inference is possible but significantly slower than GPU execution.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"can-qwen-3-tts-clone-any-voice\"><strong>Can Qwen3-TTS clone any voice?<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<p>Qwen3-TTS clones voices from just 3 seconds of reference audio. The system achieves 0.95 similarity scores to reference speakers. Quality depends on reference audio clarity. The cloned voice transfers across all 10 supported languages while maintaining speaker characteristics.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"how-does-qwen-3-tts-compare-to-eleven-labs\"><strong>How does Qwen3-TTS compare to ElevenLabs?<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<p>Qwen3-TTS outperforms ElevenLabs on speaker similarity benchmarks across 10 languages (0.789 vs 0.646 average similarity). Qwen3-TTS is open-source and runs locally, while ElevenLabs requires API access with usage fees. ElevenLabs offers a polished commercial interface, while Qwen3-TTS requires technical setup.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"does-qwen-3-tts-work-with-ollama\"><strong>Does Qwen3-TTS work with Ollama?<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<p>No official Ollama integration exists for Qwen3-TTS currently. The model requires the dedicated qwen-tts Python package or vLLM-Omni deployment. Local users should use the Python package directly rather than expecting Ollama compatibility.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"what-is-the-difference-between-voice-design-and-custom-voice-models\"><strong>What is the difference between VoiceDesign and CustomVoice models?<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<p>VoiceDesign creates entirely new voices from natural language descriptions without any audio samples. CustomVoice uses 9 pre-trained premium voices with instruction-based style control. VoiceDesign offers unlimited voice creation flexibility, while CustomVoice provides consistent, tested voice profiles.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"can-i-fine-tune-qwen-3-tts-on-my-own-data\"><strong>Can I fine-tune Qwen3-TTS on my own data?<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<p>Yes. The Base models (1.7B-Base and 0.6B-Base) support full parameter fine-tuning. The GitHub repository includes fine-tuning documentation. This enables training custom voices or adapting the model to specific domains or speaking styles.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"how-fast-is-qwen-3-tts-inference\"><strong>How fast is Qwen3-TTS inference?<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<p>First-packet latency reaches 97 milliseconds for streaming output. The dual-track architecture begins audio output after receiving a single input character. Under concurrent load with 6 users, first-packet latency stays below 300 milliseconds.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Qwen3-TTS delivers 97ms latency and 3-second voice cloning. Learn setup, features, demos, and how to use this open-source TTS model today.<\/p>\n","protected":false},"author":2,"featured_media":1314,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1,3],"tags":[],"class_list":["post-1311","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-audio","category-p-r"],"_links":{"self":[{"href":"https:\/\/gaga.art\/blog\/wp-json\/wp\/v2\/posts\/1311","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/gaga.art\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/gaga.art\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/gaga.art\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/gaga.art\/blog\/wp-json\/wp\/v2\/comments?post=1311"}],"version-history":[{"count":2,"href":"https:\/\/gaga.art\/blog\/wp-json\/wp\/v2\/posts\/1311\/revisions"}],"predecessor-version":[{"id":1519,"href":"https:\/\/gaga.art\/blog\/wp-json\/wp\/v2\/posts\/1311\/revisions\/1519"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/gaga.art\/blog\/wp-json\/wp\/v2\/media\/1314"}],"wp:attachment":[{"href":"https:\/\/gaga.art\/blog\/wp-json\/wp\/v2\/media?parent=1311"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/gaga.art\/blog\/wp-json\/wp\/v2\/categories?post=1311"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/gaga.art\/blog\/wp-json\/wp\/v2\/tags?post=1311"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}