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Conformer

Conformer is an unofficial Python/PyTorch implementation of a convolution-augmented Transformer neural network for speech recognition. The repository supplies model code and demonstrates a forward pass and CTC loss calculation.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Conformer — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use this repository when you need Conformer model code for a PyTorch speech-recognition experiment or model component. For a documented training path, follow its reference to the separate OpenSpeech project.

Who it is for

  • Researchers and developers prototyping speech-recognition models in PyTorch.
  • Teams looking for a Conformer model component to use with a separate training workflow.

Documented uses

  • Instantiate the Conformer model with class, input-dimension, encoder-dimension and layer-count settings.
  • Pass feature sequences and input lengths through the model to obtain outputs and output lengths.
  • Calculate CTC loss from model outputs and target sequences using PyTorch.
Before you choose

Requirements & limits

The repository is public and identifies its license as Apache-2.0.

  • This repository contains model code rather than a complete training platform.
  • The README recommends Python 3.7 or later, NumPy, PyTorch and installation from source using setuptools.
  • The example uses randomly created feature and target tensors, so it demonstrates model and loss wiring rather than a complete audio-data workflow.

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 the Conformer model package from source

Prepare the repository as an editable Python package.

Before you start

  • Python 3.7 or later is recommended.
  • NumPy and a PyTorch installation appropriate to the environment.
  • A virtual environment using virtualenv or conda is recommended.

Steps

  1. Check out the repository source code.

  2. From the source directory, run the command below.

    Documented command
    pip install -e .

Expected outcome

The repository is installed from source in editable mode.

Guide limitations

  • The repository provides model code only; its README points to OpenSpeech for training.
  • The guide documents installation steps but does not verify a successful installation.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
14 October 2026
Original website title
sooftware/conformer · Official homepage title · retained in its source language
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