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This project is a Python speech recognition library that serves as a unified interface for converting spoken audio into text. It functions as a bridge between Python applications and a variety of speech-to-text engines, providing a consistent way to interact with both local and cloud-based recognition services. The library distinguishes itself as a multi-engine transcription tool, wrapping diverse online APIs and offline recognition backends into a standardized format. This allows for interchangeable recognition engines and supports multilingual audio transcription through various language pa
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 reduc
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
This project is a machine learning toolkit designed for the development, training, and deployment of automatic speech recognition engines. It provides a comprehensive framework for converting spoken audio into written text, specifically supporting models trained on Mandarin and English datasets. The library utilizes an end-to-end neural architecture that processes raw audio input directly into character sequences, bypassing the need for intermediate linguistic alignment. It incorporates signal processing techniques to transform sound waves into numerical spectrograms and feature vectors, whic
Expo Speech Recognition is a cross-platform mobile module that converts live microphone audio and pre-recorded files into text using native speech engines. It provides offline speech recognition capabilities by downloading and verifying local speech models to enable on-device processing without an active network connection.
The main features of jamsch/expo-speech-recognition are: Mobile Speech Recognition, On-Device Speech Recognizers, Speech Recognition Engines, Speech Recognition Libraries, Real-Time Microphone Captures, Audio and Video File Transcription, Audio Transcriptions, Spoken Language Detection.
Projects with overlapping indexed features include: uberi/speech_recognition — This project is a Python speech recognition library that serves as a unified interface for converting spoken audio… k2-fsa/sherpa-ncnn — Sherpa-ncnn is an edge-based speech recognition and synthesis engine designed to run neural network models locally on… cmusphinx/pocketsphinx — PocketSphinx is an offline speech recognition engine that converts raw audio from files or live microphone streams… zzw922cn/automatic_speech_recognition — This project is a machine learning toolkit designed for the development, training, and deployment of automatic speech… pluja/whishper — Whishper is a graphical user interface for transcribing audio and video files into text using the Whisper model. It… soniqo/speech-swift — This project is a comprehensive toolkit for on-device speech recognition, synthesis, and audio processing,…