Three new models for speech recognition and text-to-speech are now available in Amazon SageMaker JumpStart

Today, AWS announced the availability of Qwen3-TTS-12Hz-1.7B-CustomVoice, Qwen3-TTS-12Hz-1.7B-Base, and Qwen3-ASR-1.7B in Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These three models from Qwen bring advanced speech synthesis and recognition capabilities across 10+ languages, enabling customers to build intelligent voice-powered applications on AWS infrastructure.

These models address different enterprise speech and audio challenges with specialized capabilities:

Qwen3-TTS-12Hz-1.7B-CustomVoice excels at multilingual text-to-speech with customizable voice styles, supporting 10 languages with instruction-driven control over timbre, emotion, and prosody. It is ideal for building real-time interactive voice applications, customer-facing virtual assistants, and content creation workflows that require natural, expressive speech output.

Qwen3-TTS-12Hz-1.7B-Base excels at multilingual text-to-speech with 3-second rapid voice cloning from audio input. It is ideal for building custom voice applications, fine-tuning domain-specific speech synthesis, and scenarios where developers need a flexible foundation model for voice generation.

Qwen3-ASR-1.7B excels at automatic speech recognition supporting 52 languages and dialects with state-of-the-art accuracy in complex acoustic environments. It is ideal for transcription services, multilingual customer support, real-time captioning, and applications that require robust streaming and offline speech-to-text.

With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases.

To get started with these models, navigate to the Models section of SageMaker Studio or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.