来源：Qwen-Audio-3.0-TTS 发布博客：https://funaudiollm.github.io/qwen-audio-3.0-tts/ · 同艺实验室公告：https://tongyilab.substack.com/p/qwen-audio-30-tts-more-multilingual · 阿里云 Model Studio 实时 TTS 文档：https://www.alibabacloud.com/help/en/model-studio/realtime-tts-user-guide · Artificial Analysis 排行榜：https://artificialanalysis.ai/text-to-speech/models/qwen-audio-3-0-tts-plus · 同艺实验室在 X：https://x.com/Ali_TongyiLab/status/2079154078517772739
Asif Razzaq 是 Marktechpost Media Inc. 的首席执行官。作为一名有远见的企业家和工程师，Asif 致力于利用人工智能的潜力创造社会价值。他最近的努力是推出人工智能媒体平台 Marktechpost，该平台以对机器学习和深度学习新闻的深入报道而闻名，既技术可靠，又便于广大观众理解。该平台每月浏览量超过 200 万次，显示了其受观众欢迎的程度。
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Alibaba’s Tongyi Lab：https://tongyilab.substack.com/p/qwen-audio-30-tts-more-multilingual has released Qwen-Audio-3.0-TTS：https://funaudiollm.github.io/qwen-audio-3.0-tts/ , a production-oriented text-to-speech (TTS) system. The model ships in two variants from the same lineage. Flash targets real-time interaction. Plus targets high-quality generation. Both are delivered as hosted models through Alibaba Cloud Model Studio：https://www.alibabacloud.com/help/en/model-studio/realtime-tts-user-guide, not as downloadable weights.
The release focuses on four things developers hit in production: broader language coverage, natural-language style control, fine-grained tag control, and robustness when the reference audio is not clean. Qwen-Audio-3.0-TTS-Plus also ranks first on the independent Artificial Analysis：https://artificialanalysis.ai/text-to-speech/models/qwen-audio-3-0-tts-plus Text-to-Speech leaderboard.
The two tiers map to different jobs. Flash is tuned for real-time interaction, with first-packet latency at the 300 ms level. Plus is tuned for high-quality generation, where naturalness and timbre fidelity matter more than speed.
The model IDs are qwen-audio-3.0-tts-flash and qwen-audio-3.0-tts-plus . Both are called over a bidirectional WebSocket streaming protocol. The API supports PCM, WAV, MP3, and Opus, with sample-rate output up to 48 kHz. It exposes streaming input and output, voice cloning, Voice Design：https://www.alibabacloud.com/help/en/model-studio/voice-design-user-guide, and instruction control. Alibaba provides the DashScope SDK plus raw WebSocket examples in Python, Java, Go, C#, PHP, and Node.js, across its Singapore and Beijing regions.
Two design choices anchor the system.
The model also handles one-pass long-form synthesis up to 3 minutes, hard text-normalization cases, and vocoder super-resolution for 48 kHz output.
Qwen-Audio-3.0-TTS supports 16 languages : Arabic, Chinese, English, French, German, Indonesian, Italian, Japanese, Korean, Malay, Portuguese, Russian, Spanish, Tagalog, Thai, and Vietnamese. Seven of these are newly added versus the prior line. It also covers 20 Chinese dialect regions.
On multilingual intelligibility, the model family posts the best word/character error rate (WER/CER) in 10 of the 16 languages. Flash delivers the lowest average WER/CER at 3.87 ; Plus is close at 3.96 . Lower is better on this metric.
On speaker similarity, Plus ranks first across all 16 languages with an average of 82.75 , and Flash follows at 80.44 . The release also adds a curated preset voice library spanning the 16 supported languages, so teams can ship a voice without cloning one first.
For precise control, the research team embed inline tags directly in the target text. The release adds 86 fine-grained inline tags for localized control at the phrase and word level. These cover expressive transitions and non-verbal events such as laughter, breathing, coughing, and sighing.
The Model Studio documentation：https://www.alibabacloud.com/help/en/model-studio/realtime-tts-user-guide splits these into two groups. Control tags such as [excited] , [sad] , [whispers] , and [asmr] set an emotion or style until the next tag. Rich-language tags such as [laughing] , [gasp] , and [clears throat] insert a single vocal effect without changing surrounding tone. A worked example: [excited]What a beautiful day today![laughing]Let's go out and have fun together! One limitation is worth noting here: these emotion and rich-language tags are supported only in unidirectional streaming mode.
Qwen-Audio-3.0-TTS-Plus took the top quality spot on the Artificial Analysis：https://x.com/ArtificialAnlys/status/2077140161335631957 Speech Arena for Provider Voices. It posts an Elo near 1,236, narrowly ahead of Simba 3.2 at 1,234, and clear of Gemini 3.1 Flash TTS (1,214) and Sonic 3.5 (1,207). The lead over Simba 3.2 sits inside overlapping confidence intervals, so it is a statistical tie at the very top.
Two trade-offs are worth stating plainly. Throughput is modest: Plus generates about 16 characters per second, below Simba 3.2 (30.2), Gemini 3.1 Flash TTS (27), and Sonic 3.5 (120). Price is competitive: the listed rate is $27.59 per 1M characters , roughly a third of what ElevenLabs and MiniMax charge for the tiers it outranks. Rank and price move often, so confirm both before planning around them.
The early reception is cautiously enthusiastic. The most-shared story is that a non-Western TTS topped the arena at a fraction of incumbent pricing. The most common reservations are that the model is hosted-only, that its throughput trails rivals, and that its name overlaps with the open Qwen3-TTS line. The dashboard below aggregates that early signal across X, Reddit, and Hacker News.
Sources: Qwen-Audio-3.0-TTS release blog：https://funaudiollm.github.io/qwen-audio-3.0-tts/ · Tongyi Lab announcement：https://tongyilab.substack.com/p/qwen-audio-30-tts-more-multilingual · Alibaba Cloud Model Studio real-time TTS docs：https://www.alibabacloud.com/help/en/model-studio/realtime-tts-user-guide · Artificial Analysis leaderboard：https://artificialanalysis.ai/text-to-speech/models/qwen-audio-3-0-tts-plus · Tongyi Lab on X：https://x.com/Ali_TongyiLab/status/2079154078517772739
Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.
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