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Tensorflowasr

TensorFlowASR is an open-source Python library for implementing automatic speech recognition models, training and testing them, and transcribing audio with checkpoints or exported TensorFlow Lite models. It supports CTC and transducer architectures, including DeepSpeech2, Jasper, RNN Transducer, ContextNet and Conformer.

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Independent OpenViST project profile, based on linked sources.

Tensorflowasr — OpenViST project profile in Audio & Voice AI.
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Understand the project

Practical overview

When it fits

Use TensorFlowASR when you need a code-based workflow for developing ASR models or adding checkpoint-based and streaming transcription to an application, rather than a hosted transcription interface.

Who it is for

  • Developers building speech-to-text applications with TensorFlow.
  • Teams training or evaluating custom ASR models.
  • Engineers implementing one-pass or streaming transcription in their own systems.

Documented uses

  • Train and test speech-recognition models based on supported CTC or transducer architectures.
  • Transcribe an audio stream in one pass or in streaming chunks using a checkpoint or TensorFlow Lite model.
  • Convert speech-recognition models to TensorFlow Lite for deployment with reduced memory and computation.
  • Decode CTC and transducer outputs with greedy or beam-search methods.
Before you choose

Requirements & limits

The project is published as a public repository under the Apache-2.0 license.

  • The current installation instructions specify Python 3.12 or 3.13 and uv.
  • The standard dependency setup targets CPU and Apple Silicon; NVIDIA GPU dependencies use the cuda extra.
  • Cloud TPU setup is separate and replaces the standard TensorFlow distribution; a later uv sync restores the standard build.
  • Training and testing instructions call for additional packages from other authors, including ctc_decoders and rnnt_loss.
  • Inference can share one loaded ASREngine across multiple sessions.

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

Set up TensorFlowASR for CPU or Apple Silicon

Install the repository dependencies for a CPU or Apple Silicon environment.

Before you start

  • Python 3.12 or 3.13
  • uv package manager
  • Git

Steps

  1. Clone the TensorSpeech/TensorFlowASR repository.

  2. Change into the TensorFlowASR directory.

  3. Run uv sync to install TensorFlow and TensorFlow Text.

Expected outcome

The standard environment is installed for CPU or Apple Silicon use.

Guide limitations

  • Training and testing require additional packages from other authors, including ctc_decoders and rnnt_loss.
  • NVIDIA GPU support is configured separately with the cuda extra.
Documentation 1 ↗
Source-based practical guide

Install the NVIDIA GPU dependencies

Set up TensorFlowASR's documented CUDA environment.

Before you start

  • Python 3.12 or 3.13
  • uv package manager
  • Git
  • An NVIDIA GPU environment

Steps

  1. Clone the TensorSpeech/TensorFlowASR repository.

  2. Change into the TensorFlowASR directory.

  3. Run uv sync --extra cuda.

Expected outcome

The cuda extra installs TensorFlow with its NVIDIA CUDA dependencies.

Guide limitations

  • The instructions describe dependency setup; they do not by themselves run training or inference.
  • Training and testing require additional packages from other authors, including ctc_decoders and rnnt_loss.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
13 October 2026
Original website title
TensorSpeech/TensorFlowASR · Official homepage title · retained in its source language
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