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Paddlepaddle Deepspeech

Paddlepaddle Deepspeech is an open-source Python project implementing end-to-end automatic speech recognition with PaddlePaddle. It includes materials for preparing datasets, augmenting audio, training and evaluating models, exporting models, and running speech-to-text inference.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Paddlepaddle Deepspeech — 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 when you need code and documentation to train or run PaddlePaddle speech-recognition models in an environment you manage, rather than a hosted transcription endpoint.

Who it is for

  • Developers building local speech-to-text workflows.
  • Researchers or practitioners preparing data and training or evaluating recognition models.
  • Teams adapting documented inference for web, GUI, or supported embedded-board deployments.

Documented uses

  • Transcribe WAV audio to text using an exported model.
  • Prepare speech datasets and apply audio augmentation.
  • Train and evaluate speech-recognition models.
  • Export models and follow documented web, GUI, or Jetson inference paths.
Before you choose

Requirements & limits

The public GitHub repository identifies its license as Apache-2.0. The README lists Python 3.11, PaddlePaddle 2.6.1, and Windows or Ubuntu as its project environment.

  • The project is built on PaddlePaddle and implemented in Python.
  • The README documents training and prediction on Windows and Linux, plus inference on Nvidia Jetson boards.
  • Long-audio recognition uses a separate documented path involving webrtcvad.
  • The repository includes local inference examples and web and GUI deployment documentation; it is not presented as a hosted transcription 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

Transcribe a WAV file with a local model

Run the documented inference script on a WAV file to produce recognized text.

Before you start

  • Python 3.11 and PaddlePaddle 2.6.1 are listed as the project environment.
  • A downloaded author-provided model or a trained model, exported for inference.
  • A WAV file to recognize.

Steps

  1. Download an author-provided model or train a model.

  2. Export the model using the project's export-model instructions.

  3. Run the command below, replacing the example path with the WAV file path.

    Documented command
    python infer_path.py --wav_path=./dataset/test.wav

Expected outcome

The script performs speech recognition and prints a recognition result.

Guide limitations

  • The README's example uses a specific test WAV and model configuration; it does not establish results for other audio files or configurations.
  • The README links to the inference documentation for model deployment details.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
yeyupiaoling/PaddlePaddle-DeepSpeech · Official homepage title · retained in its source language
How we review sourcesSuggest a correction

Related options

PPASR — A related PaddlePaddle dynamic-graph speech-recognition project identified by the repository; its README says it supports DeepSpeech2, Conformer, and Squeezeformer models.