Tencent Hunyuan Launches Hy ASR 3.0 Preview: Speech Recognition No Longer Just Converts Words, but Truly Understands the Context

Tencent Hunyuan has officially released the new generation speech recognition model Hy ASR3.0 preview. It leverages the latest Hy3 large language model's language understanding capabilities, combining high-precision speech recognition with deep semantic understanding. It has significantly improved in core dimensions such as general recognition, context awareness, multi-scenario robustness, and dialect coverage, providing more accurate, coherent, and user-intent-aligned transcription results in more complex real-world inputs — as the official description says, it has evolved from "word-by-word transcription and single-point optimization" to "context understanding, scenario compatibility, and one-click output."

The performance is impressive. In overseas open-source evaluation sets, Hy ASR3.0 preview has reduced the word error rate (WER) of multiple languages to around 3%: 3.34% for Mandarin Chinese, 2.62% for English, and 3.12% for Cantonese. On Tencent's own built evaluation set, WER remains low in various scenarios including general recognition, dialect recognition, context understanding, specialized term understanding, and complex acoustic situations like high noise or whispering. It achieved the lowest error rate on the comprehensive evaluation set.

Tencent Hunyuan Launches Hy ASR3.0 Preview: Speech Recognition Enters a New Era of Understanding Context

Tencent Hunyuan launches Hy ASR3.0preview, integrating Hy3 LLM's semantic understanding into speech recognition for context-aware, direct transcription. On open benchmarks, word error rates reach 3.34% for Mandarin, 2.62% for English, and 3.12% for Cantonese, achieving around 3% overall with impressive multi-dimensional performance.....

xAI releases Grok Voice Think Fast 2.0 with comprehensive upgrades in speech recognition and AI agent capabilities

xAI releases the Grok Voice Think Fast 2.0 speech recognition model for developers, achieving improvements in intelligent level, transcription accuracy, conversation ability, and tool calling efficiency. The pricing is $0.08 per minute. This model enhances performance without requiring adjustments to existing prompts. It achieved a total score of 82.9% in the Artificial Analysis benchmark test, surpassing the previous version's 75.7%.

High Demand Causes Strain on Computing Power WorkBuddy Completes Emergency Upgrade of Tencent Hunyuan Hy3 Model

WorkBuddy and Tencent Hunyuan joint project announced that due to the popularity of the initial launch of the Hunyuan Hy3 large model, computing resources experienced peak congestion. Since 10:00 a.m. on July 8th, a queue phenomenon occurred. The project team has completed an emergency computing power expansion and scheduling to ensure stable service operations.

AI Audio Editing Enters a New Era: Tencent Hunyuan Collaborates with Leading Institutions to Release the MMAE Benchmark. Current Model Precision in Audio Editing is Less Than 5%

Tencent Hunyuan, in collaboration with Shanghai Jiao Tong University, Nanyang Technological University of Singapore, Tianjin University, Peking University, and Fudan University, has launched the first general instruction-driven audio editing benchmark dataset, MMAE. This benchmark addresses the current limitations in AI's ability to edit audio, filling a gap in the field of audio generation and providing an important evaluation standard for multi-task audio editing research.

Tsinghua University and Tencent Hunyuan Win the MLSys2026 MoE Inference Challenge with a 4.1x Speedup on NPU

The Tsinghua University Storage Lab and the Tencent Hunyuan AI Infra team won the global championship in the MLSys2026 MoE Model Inference Optimization Challenge. To address the inference bottlenecks of the trillion-parameter mixture-of-experts (MoE) architecture on heterogeneous NPUs, the joint team designed a full-chain optimization solution, including the E-Shard strategy, PSUM three-dimensional tensor batch reading, and GEMV path, significantly improving performance.