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Description
Utilize a browser equipped with in-app prompts alongside a recording tool to gather audio samples. You can access a curated collection of phonetically balanced sentences designed to help build a 30-minute dataset aimed at training a TTS model that captures the nuances of your distinct voice. Tailor the model's sound by selecting the pitch range that aligns best with your vocal characteristics, as a suggested typical voice pitch range setting is already included, along with a preconfigured optimal recipe for personalizing the TTS model to reflect your voice. To further enhance functionality, create an API that allows seamless integration of your customized TTS model into various applications. You’ll also have the option to download a deployable package that includes a helm chart, facilitating deployment on any cloud platform or an on-premises Kubernetes cluster. Following that, you can effortlessly host your voice microservice using NVIDIA or implement it with a simple line of code, ensuring smooth operation. Additionally, the Riva TTS model can be set up, customized, and deployed through user-friendly no-code, end-to-end graphical workflows, eliminating the need for intricate infrastructure configuration, and making the process accessible for everyone. This approach not only streamlines the deployment process but also empowers users to create high-quality TTS solutions with minimal technical barriers.
Description
Qwen3-TTS represents an innovative collection of advanced text-to-speech models created by the Qwen team at Alibaba Cloud, released under the Apache-2.0 license, which delivers stable, expressive, and real-time speech output with functionalities like voice cloning, voice design, and precise control over prosody and acoustic features. This suite supports ten prominent languages—Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, and Italian—along with various dialect-specific voice profiles, enabling adaptive management of tone, speech rate, and emotional delivery tailored to text semantics and user instructions. The architecture of Qwen3-TTS incorporates efficient tokenization and a dual-track design, facilitating ultra-low-latency streaming synthesis, with the first audio packet generated in approximately 97 milliseconds, making it ideal for interactive and real-time applications. Additionally, the range of models available offers diverse capabilities, such as rapid three-second voice cloning, customization of voice timbres, and voice design based on given instructions, ensuring versatility for users in many different scenarios. This flexibility in design and performance highlights the model's potential for a wide array of applications in both commercial and personal contexts.
API Access
Has API
API Access
Has API
Integrations
Alibaba Cloud
Azure Marketplace
Kubernetes
NVIDIA TensorRT
Qwen
Integrations
Alibaba Cloud
Azure Marketplace
Kubernetes
NVIDIA TensorRT
Qwen
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
Free
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
NVIDIA
Founded
1993
Country
United States
Website
www.nvidia.com/en-us/gpu-cloud/riva-studio/
Vendor Details
Company Name
Alibaba
Founded
1999
Country
China
Website
github.com/QwenLM/Qwen3-TTS
Product Features
Text to Speech
API
Adjust Speaking Rate / Pitch
Audio Optimization
Custom Lexicons
Different Voice Choices
Multi-Language Support
Synchronize Speech
Product Features
Text to Speech
API
Adjust Speaking Rate / Pitch
Audio Optimization
Custom Lexicons
Different Voice Choices
Multi-Language Support
Synchronize Speech