HiAPI
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Alibaba

Qwen Audio 3.0 TTS

qwen-audio-3.0-tts-flash

Alibaba Tongyi's managed multilingual speech synthesis series, with two tiers—low-latency Flash and high-quality Plus—supporting natural language instructions to control emotion and expressive style

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Pricing

qwen-audio-3.0-tts-flash per 1,000 characters

Unit Price $0.030

Positioning Overview

qwen-audio-3.0-tts-flash is a text-to-speech model in the Qwen Audio 3.0 TTS series aimed at real-time interaction, invoked through the unified HiAPI.ai interface. It synthesizes input text into natural speech, supports prompts in 16 languages including Chinese, English, Japanese, and Korean, and allows emotion and expressive style to be adjusted via natural-language instructions. It is suited to developers building voice assistants, conversational applications, customer-service announcements, and multilingual dubbing, especially real-time scenarios that are sensitive to first-packet latency and need a balance between response speed and audio quality. The maximum text length per task is 20,000 Unicode code points.

Key Highlights

  • Low-latency tier for real-time interaction — Flash is oriented toward real-time response, with first-packet latency in the 300-millisecond range, suitable for synchronous dialogue and voice announcement pipelines.
  • Instruction-driven control of emotion and expressive style — An optional instruction field can describe tone and delivery, reducing post-processing of the audio.
  • Broad multilingual synthesis coverage — Supports prompts in 16 languages, so a single model can serve multilingual content without switching models by language.
  • Selectable output format and sample rate — Supports pcm, wav, mp3, and opus output, with sample rates from 8000 to 48000 Hz selectable for the playback device.

Capabilities

Multilingual text-to-speech: Given text and a voice, speech can be synthesized; language_hints can specify the target language, so one integration covers multilingual content production.

Fine-grained speech parameter adjustment: Speed and pitch can both be adjusted in the 0.5x to 2x range, and volume from 0 to 100, making it easy to fit different playback devices and content pacing.

Output format and sample rate control: Output can be selected as mp3, wav, pcm, or opus, with sample rates from 8000 to 48000 Hz; for opus, a bitrate from 6 to 510 kbps can also be specified.

Instruction and seed control: The instruction field describes the synthesis style, and seed can fix the random seed, making it easy to reproduce the same synthesis result and to run stable regression tests.

Long-text synthesis in a single task: A single task accepts up to 20,000 Unicode code points of text, so long passages need not be split on the caller side first.

Use Cases

  • Real-time voice assistant developers: dialogue reply text — Synthesize conversational-system replies into speech in real time and feed them directly into the announcement stage of a voice assistant or conversational application.
  • Customer service and call centers: announcement script text — Convert standard scripts and notification content into speech in batches for outbound calls, IVR prompts, and ticket-status announcements.
  • Online education and audiobooks: course manuscripts — Synthesize courseware and lecture scripts into audio with adjustable speed for course dubbing and audiobook production.
  • Multilingual content teams: multilingual copy — Synthesize different language versions of the same copy according to language hints, for product localization and multilingual video dubbing.

Limitations and Selection Advice

This model is in the Flash tier and prioritizes response speed; its voice naturalness and fidelity are below the Plus variant in the same series, so scenarios with high requirements for final audio quality should use Plus instead. Only four system voices are available, and designing a voice from a text description is not supported; voice cloning capability is outside this model's parameter scope. Synthesis is billed by output characters, with CJK characters counted as two characters, so usage should be estimated for long-text batch generation. Self-hosted deployment or open-source weights are outside this model's scope.

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