Practical overview
When it fits
Use it when you want a TensorFlow implementation to study or adapt end-to-end ASR architectures and have a separate approach to corpus preprocessing.
Who it is for
- Researchers comparing CTC and attention-based speech recognition methods.
- Developers prototyping speech-to-text models with recurrent or VGG-based encoders.
- Users working with the documented TIMIT, LibriSpeech or CSJ corpus configurations.
Documented uses
- Train end-to-end speech recognition models.
- Compare greedy and beam-search CTC decoding.
- Experiment with attention mechanisms and joint CTC-attention training.
- Configure synchronous multi-GPU training.
Requirements & limits
The repository is public and lists an MIT license.
- The implementation requires TensorFlow 1.3 or later, plus tqdm, python-Levenshtein, setproctitle and seaborn.
- Preprocessing is handled separately; the repository refers users to another project for that stage.
- Some corpus configurations and decoder options are marked as under implementation, including attention beam search and CTC beam search with a character language model.
- The codebase lists synchronous multi-GPU training, frame stacking and splicing as options.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Prepare dependencies for the TensorFlow speech-recognition implementation
Set up the listed Python package prerequisites before following a corpus-specific usage guide.
Before you start
- Python environment capable of running TensorFlow.
- TensorFlow 1.3.0 or later, tqdm 4.14.0 or later, python-Levenshtein 0.12.0 or later, setproctitle 1.1.10 or later, and seaborn 0.7.1 or later.
Steps
Review the repository README’s requirements and identify the listed package versions.
Prepare an environment with those prerequisites.
For a later corpus workflow, consult the repository documentation for TIMIT, LibriSpeech, or CSJ.
Expected outcome
The listed dependencies are identified and an environment can be prepared for the corpus-specific workflow.
Guide limitations
- The README does not provide installation commands or a step-by-step training or inference procedure.
- Data preprocessing is not included; the README points to a separate preprocessing repository.
- The README marks several corpus labels, decoder options, and model features as under implementation.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 15 October 2026
- Original website title
- hirofumi0810/tensorflow_end2end_speech_recognition · Official homepage title · retained in its source language