Memory efficient transducer loss computation
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Updated
Jun 10, 2022 - CMake
Memory efficient transducer loss computation
Streaming on-device speech recognition for Android — NEON-accelerated FastConformer (32M params), cache-aware streaming with 80 ms look-ahead, no cloud. Powered by the VoxRT runtime.
Streaming on-device speech recognition for iOS — NEON-accelerated, encrypted FastConformer (32M params), RTF 0.08–0.10 on iPhone 13 Pro Max. Built on the VoxRT custom Rust inference runtime. SwiftPM distribution.
A tutorial on how to train RNN-T from scratch with Whisper encoder
Streaming 가능한 RNN Transducer 모델을 PyTorch Lightning으로 구현해본다.
PyTorch Implementation of RNN-Transducer
Streaming, offline-first speech-recognition toolkit — fbank frontend, VAD, chunked encoder, CTC/RNN-T decoding with LM fusion, WER/RTF scoring. NumPy/SciPy reference kernels, optional PyTorch backend.
Unofficial, From-scratch PyTorch replication of the Conformer paper (Gulati et al., 2020) — encoder, RNN-T decoder, training loop, and NeMo weight validation. Built block by block with documented maths.
ASR (Speech to Text) in Bengali
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