tensorflow-ctc-speech-recognition and kaggle_speech_recognition

These are ecosystem siblings—both are independent implementations of the CTC-based speech recognition architecture using TensorFlow, serving as reference implementations or learning resources for the same algorithmic approach rather than tools meant to be used together or as alternatives to each other.

Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 22/25
Maintenance 0/25
Adoption 9/25
Maturity 16/25
Community 19/25
Stars: 131
Forks: 47
Downloads:
Commits (30d): 0
Language: Python
License: Apache-2.0
Stars: 72
Forks: 20
Downloads:
Commits (30d): 0
Language: Python
License: MIT
Stale 6m No Package No Dependents
Stale 6m No Package No Dependents

About tensorflow-ctc-speech-recognition

philipperemy/tensorflow-ctc-speech-recognition

Application of Connectionist Temporal Classification (CTC) for Speech Recognition (Tensorflow 1.0 but compatible with 2.0).

This project helps speech technologists and researchers convert spoken audio into written text using a neural network approach. You provide audio files of someone speaking, and it attempts to transcribe those words into a written transcript. It's designed for those exploring or implementing speech recognition systems, particularly with Connectionist Temporal Classification (CTC).

speech-to-text audio-transcription natural-language-processing acoustic-modeling voice-technology

About kaggle_speech_recognition

huschen/kaggle_speech_recognition

Conv-LSTM-CTC speech recognition network (end-to-end), written in TensorFlow.

This project helps you automatically detect simple spoken commands, like "yes" or "no", from audio recordings. It takes sound wave files as input and outputs the recognized command as text. This tool is designed for anyone needing to build or evaluate a system that understands specific voice commands.

speech-recognition voice-commands audio-processing natural-language-processing human-computer-interaction

Scores updated daily from GitHub, PyPI, and npm data. How scores work