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Tensorflow Speech Recognition

Tensorflow Speech Recognition is a public Python research repository for educational speech-recognition and speech-to-text experiments using TensorFlow 1.0 and sequence-to-sequence neural networks.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Tensorflow Speech Recognition — OpenViST project profile in Audio & Voice AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it as an educational starting point when studying legacy TensorFlow speech-model experiments, not as a ready-to-use current speech-recognition backend.

Who it is for

  • Learners examining older speech-recognition model code.
  • Developers exploring classifier, spectrogram, recording, or data-augmentation experiments.

Documented uses

  • Inspect example number- and speaker-classifier scripts.
  • Study sequence-to-sequence and LSTM-based speech-model approaches.
  • Explore audio recording and spectrogram examples.
  • Consider data augmentation experiments involving frequency, background noise, or pitch.
Before you choose

Requirements & limits

The repository is public and includes source-code clone instructions.

  • The implementation uses TensorFlow 1.0, which the maintainers say is no longer compatible with current TensorFlow.
  • The README lists PortAudio and PyAudio for recording-related setup.
  • The project is maintained for educational purposes; the README recommends Whisper for current speech-recognition use but documents no migration.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Clone and prepare the project’s documented dependencies

Set up the repository and its PortAudio and PyAudio prerequisites for exploring this educational project.

Before you start

  • A Linux environment is suggested by the project’s stated goal of standalone speech recognition for Linux.
  • Git, a shell, and the build tools used by the documented PortAudio steps.
  • Python with pip for the documented PyAudio installation.

Steps

  1. Clone the tensorflow-speech-recognition repository, enter its directory, and clone the layer and tensorpeers repositories as shown in the README.

  2. Clone PortAudio, configure it with a local installation prefix, then run make and make install.

  3. Add the local PortAudio library and include directories to the documented environment variables, then source ~/.bashrc.

  4. Install PyAudio with pip install pyaudio.

Expected outcome

The project and its documented audio-related dependencies are prepared for further exploration.

Guide limitations

  • The project uses TensorFlow 1.0, which its maintainers say is no longer compatible with current TensorFlow; it is maintained for educational purposes.
  • The README recommends considering Whisper for current use.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
17 October 2026
Original website title
pannous/tensorflow-speech-recognition · Official homepage title · retained in its source language
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Related options

Whisper — The repository maintainers recommend it for current speech-recognition use; they do not provide migration instructions.

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