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Speech recognition systems that transcribe audio to text using Transformer models with optimized inference for speed and memory efficiency.
Distinct from On-Device Speech Recognizers: Distinct from On-Device Speech Recognizers: focuses on accelerated inference via quantization and runtime optimizations, not specifically on local-only processing.
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CTranslate2 is a C++ inference engine and runtime for Transformer models, designed to execute models on both CPU and GPU with optimizations for speed and memory efficiency. It functions as a model format converter, quantization tool, and REST API server, enabling deployment of neural machine translation, automatic speech recognition, and text generation models. The engine distinguishes itself through a suite of runtime optimizations including layer fusion, weight-matrix quantization, batch-by-length grouping, and a caching allocator that reuses GPU memory. It supports tensor-parallel model di
CTranslate2 transcribes audio to text using Transformer-based speech recognition models with accelerated inference.
PocketSphinx is an offline speech recognition engine that converts raw audio from files or live microphone streams into written text without requiring a network connection. It functions as a speech-to-text library, a real-time transcription engine, and a voice command processor, capable of detecting and transcribing spoken commands from continuous audio streams with configurable acoustic and language models. The engine uses weighted finite-state transducers to represent acoustic, phonetic, and language models as a single search graph for efficient decoding. It employs fixed-point acoustic mod
Reads single-channel 16-bit PCM audio from files or standard input and outputs recognized text as line-delimited JSON.