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.
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).
Uses LSTM networks with CTC loss to decode speech directly to text, trained and evaluated on the VCTK Corpus with configurable batch sizes and network architectures. Extracts audio features via librosa and python_speech_features, then feeds spectrograms through recurrent layers followed by CTC decoding to handle variable-length audio-text alignment without explicit frame-level annotations. Demonstrates end-to-end training on single-speaker subsets, showing reasonable generalization despite limited data through techniques like random silence truncation for realistic validation.
About kaggle_speech_recognition
huschen/kaggle_speech_recognition
Conv-LSTM-CTC speech recognition network (end-to-end), written in TensorFlow.
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