ep-hindi-asr is a Hindi speech recognition model built on the audio encoder of EmbeddingGemma 2. A CTC head reads a learned mix of the encoder's layers and predicts Gemma tokenizer units. Only the top two encoder layers, the layer mix and the head are trained; the rest of the encoder is used as released by Google DeepMind. The model was trained in two stages on a single NVIDIA A10: 300 hours of IndicVoices Hindi, then continued fine-tuning on IndicVoices, Vaani, Kathbath and Gram Vaani with noise augmentation. It decodes in one pass with no autoregressive decoder, and transcribes about 800 times faster than real time with batched input. All results use the released checkpoint (stage 2, step…