alphacep/vosk-api

Features

• Local Speech-to-Text - Provides an on-device transcription system that converts spoken audio to text without an internet connection.
• Speech Transcription - Converts spoken audio, including mono 16-bit PCM WAV files, into written text using local models.
• Real-Time Transcription - Processes raw 16-bit mono PCM audio buffers in real-time for continuous, low-latency transcription.
• Local Model Execution - Enables the execution of acoustic and language models directly on local hardware for offline inference.
• Real-Time Speech Processing - Processes live speech input in real-time to provide continuous text output with minimal latency.
• Speech Processing Toolkits - Provides a comprehensive toolkit for integrating speech-to-text and speaker identification across Android, iOS, and Raspberry Pi.
• Speech Recognition - Performs speech-to-text transcription locally on the device without requiring an internet connection.
• Speech Recognition APIs - Provides native C bindings for integrating offline audio-to-text transcription into applications.
• Speech-to-Text Integrations - Offers interfaces for integrating offline voice-to-text capabilities across mobile apps and server environments.
• Speech-to-Text Pipelines - Implements a complete local speech-to-text workflow to ensure data privacy and offline functionality.
• Keyword Spotting - Restricts transcription to a specific list of predefined phrases to increase recognition accuracy.
• Acoustic Model Trainers - Provides a pipeline for preparing audio data and training custom neural network acoustic models.
• Speech Model Training - Enables building and evaluating specialized acoustic and language models to improve recognition for specific domains.
• Recognition Accuracy Evaluation - Calculates the word error rate by decoding test audio to measure the precision of trained speech models.
• Speaker Identification Frameworks - Analyzes audio streams to distinguish between different voices and attribute transcribed text to specific individuals.
• Custom Vocabularies - Allows customization of the recognized word set to improve accuracy for specialized terminology and domains.
• Speech Decoding Transducers - Uses weighted finite state transducers via the Kaldi engine to map audio features to words.
• Command Line Interfaces - Ships a command line interface for converting spoken audio files into text directly from the terminal.
• Audio Feature Extraction - Provides utilities for converting raw audio into normalized coefficients to prepare data for model training.
• C Interoperability Layers - Implements a stable C-API binary interface to enable interoperability between the core engine and multiple programming languages.
• Native Bindings - Uses native bindings to expose low-level C++ speech recognition logic to high-level languages like Python and Java.
• Speech Recognition - Offline speech recognition toolkit for low-resource devices.

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