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Athena — athena-team

Athena is an open-source TensorFlow speech-processing codebase with end-to-end models and example dataset recipes for speech recognition, speech synthesis, voice activity detection and wake-word spotting. It also documents training, decoding and local C++ deployment capabilities.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Athena — athena-team — 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 Athena when you need an inspectable codebase and example recipes for developing speech-processing models, rather than a hosted speech service.

Who it is for

  • Researchers developing or evaluating end-to-end speech models.
  • Engineering teams adapting speech-recognition, synthesis, voice-activity or keyword-spotting recipes.

Documented uses

  • Develop speech-recognition systems using Attention/CTC or streaming methods.
  • Explore text-to-speech models including FastSpeech, FastSpeech2 and Transformer.
  • Prototype voice activity detection or wake-word spotting.
  • Train with multiple GPUs using Horovod, or use documented WFST and C++ decoding components.
Before you choose

Requirements & limits

The repository identifies Athena as open source and lists the Apache-2.0 license.

  • Models are implemented in TensorFlow 2 or later; the README gives specific Athena v2.0 installation paths for TensorFlow GPU 2.3.0 and 2.8.0.
  • The documented Python feature extractor is described as Kaldi-free.
  • Multi-GPU training with Horovod is listed for one or multiple machines.
  • The project describes local-server deployment with TensorFlow C++, distinct from a hosted service.

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

Install Athena v2.0 using TensorFlow 2.8

Build and install the Athena v2.0 package using the README’s TensorFlow 2.8 path.

Before you start

  • Python and pip, as used by the documented commands.
  • TensorFlow GPU 2.8.0.
  • Athena repository files, including requirements.txt and setup.tf2.8.py.

Steps

  1. Install tensorflow-gpu==2.8.0 with pip.

  2. Install the packages listed in requirements.txt.

  3. Build the wheel and source distribution with python setup.tf2.8.py bdist_wheel sdist.

  4. Install the generated Athena 2.0 wheel from dist using the documented pip command.

Expected outcome

An Athena 2.0 wheel is built and installed in the Python environment.

Guide limitations

  • This recipe covers package installation only, not running a model or deploying a service.
  • The README documents this path with TensorFlow GPU 2.8.0; it does not establish compatibility with other versions or environments.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
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Sources checked
15 October 2026
Original website title
athena-team/athena · Official homepage title · retained in its source language
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