What it does This repo houses Python experiments in speech-to-text using TensorFlow’s now-deprecated seq2seq APIs. It includes toy classifiers for numbers and speakers, plus denser architectures, all wired up to spectrograms and live audio via pyaudio. The stated goal was a standalone Linux speech recognizer built on plentiful public training data.

The interesting bit The README is unusually honest: the authors have twice updated it to tell you to go elsewhere—first to Mozilla DeepSpeech in 2020, then to OpenAI’s Whisper in 2024. That makes it a rare self-annotated graveyard, useful for tracing how quickly the SOTA treadmill can obsolete a project.

Key highlights

• Built on TensorFlow 1.0 seq2seq, now incompatible with current releases
• Includes toy examples (number_classifier_tflearn.py, speaker_classifier_tflearn.py) and a densenet variant
• Ships with spectrogram visualizations and live recording via record.py
• Proposes extensions that now look prescient: GPU WarpCTC, modular graphs, and “P2P learning” snapshots
• Explicitly maintained “only for educational purposes” since 2020

Caveats

• The installation instructions require building portaudio from source and hand-tweaking LD_LIBRARY_PATH; the README even misspells LIBRARY_PATH as LIDRARY_PATH
• Dependencies (layer, tensorpeers) are separate repos by the same author, so the project is somewhat glued together
• No training scripts beyond train.sh with no documented contents; getting to a working model is left as an exercise

Verdict Worth a quick browse if you’re writing a history-of-STT talk or want to see how seq2seq was wielded in 2016. Anyone building something today should follow the authors’ own advice and use Whisper instead.