Vosk is an offline speech-to-text engine and API that converts spoken audio into text locally on a device. It provides a cross-platform speech toolkit with language bindings for integrating voice recognition into server environments, Android, iOS, and Raspberry Pi. The project includes a speaker identification tool to distinguish between different voices and an acoustic model trainer for building custom neural network models. These training tools enable speech feature extraction and model accuracy evaluation to improve recognition for specialized domains. The system supports real-time audio
Sherpa-ONNX is an ONNX-based speech processing toolkit that provides a local speech recognition engine, an on-device voice synthesis tool, and a speaker identification framework. It is designed as a cross-platform speech API that enables speech-to-text, text-to-speech, and speaker verification tasks to be executed locally on a device without requiring network access. The project is distinguished by its ability to perform zero-shot voice cloning and speaker diarization on-device. It supports a wide range of hardware accelerations, including GPU and various NPU architectures, and provides a Web
Pipecat is a framework and software development kit for building real-time multimodal AI agents and speech-to-speech systems. It utilizes a frame-based data pipeline to route audio, video, and text through a modular sequence of processors, enabling the orchestration of low-latency conversational AI. The project is distinguished by its ability to coordinate complex multimodal services, including speech-to-text, language models, and text-to-speech, within a single pipeline. It features semantic voice activity detection for natural turn-taking, state-machine conversation flows for dialogue manag
wav2letter is an automatic speech recognition toolkit and deep learning framework designed to convert audio speech signals into written text. It functions as a distributed training system and an inference engine for building and deploying neural network architectures. The system enables the training of large-scale speech models across multiple compute nodes using custom architecture files and structured recipes. It includes an inference engine that allows these trained models to be executed within Python workflows to transform audio sequences into text. The framework covers the full speech r
DeepSpeech 是一个开源的语音转文字框架和机器学习引擎,旨在本地设备上将口语音频转换为书面文本。它提供无需连接外部服务器即可运行的设备端语音识别。
mozilla/deepspeech 的主要功能包括:Local Speech-to-Text, Real-Time Transcription, On-Device Inference Engines, Speech Recognition, Speech-to-Text Engines, Speech-to-Text Modeling Toolkits, Speech-to-Text Frameworks, Embedded Voice Processing。
mozilla/deepspeech 的开源替代品包括: alphacep/vosk-api — Vosk is an offline speech-to-text engine and API that converts spoken audio into text locally on a device. It provides… k2-fsa/sherpa-onnx — Sherpa-ONNX is an ONNX-based speech processing toolkit that provides a local speech recognition engine, an on-device… pipecat-ai/pipecat — Pipecat is a framework and software development kit for building real-time multimodal AI agents and speech-to-speech… facebookresearch/wav2letter — wav2letter is an automatic speech recognition toolkit and deep learning framework designed to convert audio speech… sevask/ecoute — Ecoute is a live transcription tool that provides real-time transcripts for both the user's microphone input (You) and… koljab/realtimestt — RealtimeSTT is a local speech-to-text engine and real-time automatic speech recognition server. It utilizes…