nl8590687/ASRT_SpeechRecognition
ASRT SpeechRecognition
This project is a Chinese automatic speech recognition framework and deep learning system designed to convert spoken Chinese audio into written text. It functions as a toolkit for training, evaluating, and deploying speech-to-text models, utilizing a specialized pinyin-to-text converter that transforms phonetic sequences into Chinese characters using a probability graph model.
The system is distinguished by its deployment flexibility, offering a dockerized recognition server that provides transcription capabilities as a remote API. It supports high-performance streaming through a gRPC speech-to-text interface, enabling bidirectional data transmission for real-time transcriptions and asynchronous audio streaming.
The framework covers a full machine learning workflow, including custom acoustic and language model training, n-gram language modeling, and accuracy evaluation via word error rate calculations. It handles the entire audio pipeline from raw WAVE file parsing and feature extraction to the hosting of recognition services via RESTful API gateways.
Features
- Chinese Language Recognition - Provides a complete system for converting spoken Chinese audio into written text using deep learning.
- Speech-to-Text Conversions - Provides a full system for transforming spoken Chinese audio into machine-processable text.
- Real-Time Transcription - Provides instantaneous conversion of live audio streams into text transcripts with low latency.
- Audio Transcriptions - Converts individual audio recordings into written text transcriptions.
- Chinese ASR Frameworks - Provides a full toolkit for training, evaluating, and deploying automatic speech recognition models specifically for Chinese.
- Custom Model Training - Builds custom acoustic and language models using specialized datasets to optimize recognition accuracy.
- Audio Dataset Preprocessing - Implements tools for cleaning and standardizing raw audio datasets specifically for machine learning training.
- Acoustic Modeling Architectures - Utilizes deep neural networks to convert raw audio signals into pinyin phonetic sequences.
- Speech Model Training - Provides specialized training infrastructure for the acoustic and language models used in speech recognition.
- Speech Recognition Systems - Implements a deep learning system that converts spoken audio into written text.
- Phonetic Sequence Extraction - Processes raw audio data to produce pinyin sequences using a deep learning acoustic model.
- Phonetic-to-Text Graph Mappings - Converts phonetic pinyin sequences into Chinese characters using a specialized probability graph model.
- Real-Time Speech Transcription - Processes live audio streams via gRPC to provide immediate text output as a person speaks.
- Real-Time Speech-to-Text Servers - Ships a backend service that converts live audio streams into text using bidirectional gRPC protocols.
- Speech Recognition APIs - Provides programmatic interfaces for integrating audio-to-text transcription via HTTP requests.
- Continuous - Captures long-duration audio and manages asynchronous requests to maintain a continuous sequential text stream.
- Pinyin-to-Text Mapping - Implements a specialized probability graph model to transform phonetic pinyin sequences into written Chinese characters.
- Speech-to-Pinyin Conversion - Uses deep learning models to transform audio input into a sequence of Chinese pinyin.
- Training Dataset Preparation - Standardizes the format of audio files, labels, and dictionaries to ensure compatibility with training models.
- Pinyin-to-Text Converters - Uses a probability graph model to transform phonetic pinyin sequences into corresponding Chinese characters.
- Audio Feature Extraction - Transforms raw audio waveforms into Mel-frequency cepstral coefficients (MFCC) and spectrograms.
- Bidirectional Speech-to-Text Streams - Enables real-time recognition through persistent duplex gRPC connections for audio and text.
- Asynchronous Speech-to-Text Streams - Implements asynchronous audio streaming using generators and callbacks for real-time recognition.
- Long Audio Chunk Transcribers - Processes extended audio sequences by automatically segmenting them into smaller chunks for stable transcription.
- N-Gram Language Models - Generates statistical probability distributions for word sequences to refine speech-to-text accuracy.
- Speech Recognition Accuracy Evaluators - Calculates word error rates to measure the performance of speech recognition models against test sets.
- Speech Recognition Services - Provides containerized infrastructure for processing audio files and live streams into text.
- Language Model Rescoring - Refines transcription accuracy using probability distributions of word sequences from n-gram language models.
- Docker Container Deployments - Packages the system into Docker images to simplify installation and provide transcription as an API service.
- Containerized Service Deployments - Packages the recognition server and dependencies into Docker images for consistent cross-environment deployment.
- Recognition Server Deployments - Provides the ability to host a speech-to-text service on local or cloud machines to accept HTTP requests.
- Application REST API Gateways - Exposes recognition capabilities as a web service allowing audio submission via RESTful HTTP endpoints.
Star history
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