开发者Owen Song独立完成并开源了Inflect-Micro-v2，这是一款参数量低于1000万的完整文本转语音（TTS）合成模型。[1]该模型包含9,356,513个可部署参数，FP32格式下占用37.53 MB的存储空间。[1]
Inflect-Micro-v2在性能和易用性上具有多项优势。[1]模型支持确定性种子设置、长文本处理以及CPU和CUDA两种推理方式，采用英文音素前端和端到端架构设计。[1]在盲测评估中，该模型获得66.2%的人类偏好率（21胜10负3平），UTMOS22评分达到4.395分。[1]在自动语音识别（ASR）智能度测试中，经Qwen3-ASR、Nemotron 3.5和Whisper分别检测的错误率为2.52%、5.45%和2.73%。[1]模型输出规格为24 kHz单声道音频，采用Apache-2.0开源许可证，已发布在Hugging Face平台。[1]
Owen Song has independently developed and open-sourced Inflect-Micro-v2, a text-to-speech synthesis model featuring fewer than 10 million parameters [1]. The model achieves end-to-end voice generation while maintaining a compact footprint of 9,356,513 deployable parameters, consuming 37.53 MB in FP32 format [1]. Released under the Apache-2.0 license on Hugging Face, Inflect-Micro-v2 supports deterministic seeding, long-form text processing, and inference across both CPU and CUDA environments [1].
The model produces 24 kHz mono audio output and incorporates an English phoneme frontend architecture [1]. In human blind preference testing, Inflect-Micro-v2 achieved a 66.2% preference rate, with 21 wins, 10 losses, and 3 draws compared to alternatives [1]. It scored 4.395 on the UTMOS22 evaluation benchmark [1]. Speech recognition intelligibility testing showed error rates of 2.52% with Qwen3-ASR, 5.45% with Nemotron 3.5, and 2.73% with Whisper [1].