k2-fsa/sherpa-ncnn
Sherpa Ncnn
Sherpa-ncnn is an edge-based speech recognition and synthesis engine designed to run neural network models locally on mobile, embedded, and desktop hardware. It provides a cross-platform framework for offline speech-to-text transcription and text-to-speech synthesis, ensuring that all audio processing occurs on-device without requiring an internet connection or external cloud services.
The project distinguishes itself through its use of the ncnn inference engine, which is optimized for low-latency execution on resource-constrained devices. It incorporates on-device model quantization to reduce memory usage and supports static model compilation, allowing pre-trained weights to be bundled directly into application binaries. These capabilities enable real-time voice processing, including live microphone transcription and automated voice activity detection, which identifies speech segments while filtering out background noise.
The library offers a comprehensive set of tools for audio analysis and transcription, supporting both live streaming input and the processing of pre-recorded audio files. It includes native language bindings that expose these core speech recognition and detection functions to Python development environments, facilitating integration across multiple operating systems.
Features
- Real-Time Speech Transcription - Provides real-time, low-latency speech-to-text transcription by processing live microphone input through optimized local neural network models.
- Mobile and Edge AI - Provides a high-performance toolkit for deploying speech recognition and synthesis models on edge hardware.
- On-Device Speech Recognizers - Performs private, offline speech-to-text transcription using on-device neural network inference.
- Inference Engines - Uses a high-performance inference engine optimized for low-latency execution on mobile and embedded hardware.
- Speech Transcription Engines - Implements a high-performance, offline speech-to-text engine that runs neural network inference locally on edge devices without cloud connectivity.
- Text-to-Speech Synthesis - Converts text to natural-sounding audio using local models that operate entirely offline.
- Audio and Video File Transcription - Processes local audio files to perform accurate, offline speech-to-text conversion.
- Speech Boundary Detection - Identifies precise speech boundaries within audio streams to separate spoken content from background noise.
- Edge AI Model Deployment - Supports deployment of speech recognition models on mobile and embedded platforms using optimized local inference.
- On-Load Quantizers - Reduces neural network memory footprint through on-device model quantization for resource-constrained hardware.
- Real-Time Speech Processing - Processes live microphone input for immediate speech recognition and voice activity detection.
- Speech Activity Detection - Automatically identifies speech segments in continuous audio to filter out silence and background noise.
- End-of-Speech Detectors - Implements signal processing logic to detect the start and end of speech segments for automated transcription.
- Voice Activity Detection - Analyzes audio streams to distinguish between speech and silence for efficient task triggering.
- Static Binary Compilers - Bundles pre-trained neural network weights directly into application binaries for offline, dependency-free execution.
- Audio Buffers - Utilizes circular memory buffers to capture and process continuous microphone input in real-time.
Star history
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