# k2-fsa/sherpa-ncnn **Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/k2-fsa-sherpa-ncnn).** _How this analysis was created: the description and tags below were written by an AI model that read this project's README and public documentation pages; stars, license and language come straight from the GitHub API. The model does not read the source code._ 1,743 stars · 217 forks · C++ · Apache-2.0 ## Links - GitHub: https://github.com/k2-fsa/sherpa-ncnn - Homepage: https://k2-fsa.github.io/sherpa/ncnn/index.html - awesome-repositories: https://awesome-repositories.com/repository/k2-fsa-sherpa-ncnn.md ## Topics `asr` `c` `cpp` `csharp` `go` `kotlin` `python` `speech-recognition` `vad` `voice-activity-detection` ## Description 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. ## Tags ### Artificial Intelligence & ML - [Real-Time Speech Transcription](https://awesome-repositories.com/f/artificial-intelligence-ml/real-time-speech-processing/real-time-speech-transcription.md) — Provides real-time, low-latency speech-to-text transcription by processing live microphone input through optimized local neural network models. ([source](https://github.com/k2-fsa/sherpa-ncnn/tree/master/python-api-examples)) - [On-Device Speech Recognizers](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/speech-processing/automatic-speech-recognition/on-device-speech-recognizers.md) — Performs private, offline speech-to-text transcription using on-device neural network inference. - [Inference Engines](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-network-architectures/inference-engines.md) — Uses a high-performance inference engine optimized for low-latency execution on mobile and embedded hardware. - [Speech Transcription Engines](https://awesome-repositories.com/f/artificial-intelligence-ml/speech-transcription-engines.md) — Implements a high-performance, offline speech-to-text engine that runs neural network inference locally on edge devices without cloud connectivity. ([source](https://k2-fsa.github.io/sherpa/ncnn/index.html)) - [Text-to-Speech Synthesis](https://awesome-repositories.com/f/artificial-intelligence-ml/text-to-speech-synthesis.md) — Converts text to natural-sounding audio using local models that operate entirely offline. ([source](https://github.com/k2-fsa/sherpa-ncnn#readme)) - [Audio and Video File Transcription](https://awesome-repositories.com/f/artificial-intelligence-ml/audio-and-video-file-transcription.md) — Processes local audio files to perform accurate, offline speech-to-text conversion. ([source](https://github.com/k2-fsa/sherpa-ncnn/tree/master/ffmpeg-examples)) - [Speech Boundary Detection](https://awesome-repositories.com/f/artificial-intelligence-ml/audio-transcription/transcription-buffering/audio-segmenting/speech-boundary-detection.md) — Identifies precise speech boundaries within audio streams to separate spoken content from background noise. ([source](https://github.com/k2-fsa/sherpa-ncnn/tree/master/python-api-examples)) - [Edge AI Model Deployment](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-deployment-and-serving/local-and-on-device-inference/edge-ai-model-deployment.md) — Supports deployment of speech recognition models on mobile and embedded platforms using optimized local inference. ([source](https://k2-fsa.github.io/sherpa/ncnn/index.html)) - [On-Load Quantizers](https://awesome-repositories.com/f/artificial-intelligence-ml/quantized-inference-runtimes/weight-quantization/quantized-model-implementations/on-load-quantizers.md) — Reduces neural network memory footprint through on-device model quantization for resource-constrained hardware. - [Real-Time Speech Processing](https://awesome-repositories.com/f/artificial-intelligence-ml/real-time-speech-processing.md) — Processes live microphone input for immediate speech recognition and voice activity detection. - [Speech Activity Detection](https://awesome-repositories.com/f/artificial-intelligence-ml/speech-activity-detection.md) — Automatically identifies speech segments in continuous audio to filter out silence and background noise. - [End-of-Speech Detectors](https://awesome-repositories.com/f/artificial-intelligence-ml/speech-activity-detection/end-of-speech-detectors.md) — Implements signal processing logic to detect the start and end of speech segments for automated transcription. - [Voice Activity Detection](https://awesome-repositories.com/f/artificial-intelligence-ml/voice-activity-detection.md) — Analyzes audio streams to distinguish between speech and silence for efficient task triggering. ([source](https://github.com/k2-fsa/sherpa-ncnn#readme)) ### Part of an Awesome List - [Mobile and Edge AI](https://awesome-repositories.com/f/awesome-lists/devtools/mobile-and-edge-ai.md) — Provides a high-performance toolkit for deploying speech recognition and synthesis models on edge hardware. ### Development Tools & Productivity - [Static Binary Compilers](https://awesome-repositories.com/f/development-tools-productivity/static-binaries/static-binary-compilers.md) — Bundles pre-trained neural network weights directly into application binaries for offline, dependency-free execution. ### Operating Systems & Systems Programming - [Audio Buffers](https://awesome-repositories.com/f/operating-systems-systems-programming/kernel-core-internals/process-and-memory-management/memory-management/buffer-and-cache-management/binary-buffer-managers/trace-buffer-managers/audio-buffers.md) — Utilizes circular memory buffers to capture and process continuous microphone input in real-time.