mdangschat/ctc-asr

End-to-end trained speech recognition system, based on RNNs and the connectionist temporal classification (CTC) cost function.

47
/ 100
Emerging

Implements bidirectional RNN layers with dense layers trained on 900+ hours of multi-corpus audio data (LibriSpeech, Common Voice, TEDLIUM, Tatoeba), achieving 12.6% WER without external language models. Built on TensorFlow with configurable architecture parameters, supporting GPU acceleration and modular training/evaluation workflows via CSV-based corpus definitions. Includes utilities for multi-corpus preparation, checkpoint management, and real-time training visualization through TensorBoard.

123 stars. No commits in the last 6 months.

Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 10 / 25
Maturity 16 / 25
Community 21 / 25

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Stars

123

Forks

36

Language

Python

License

MIT

Last pushed

Apr 15, 2020

Commits (30d)

0

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