ASR module using Mozilla DeepSpeech for German speech
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This repository provides a comprehensive pipeline for training and deploying German Automatic Speech Recognition (ASR) models using Mozilla's DeepSpeech toolkit. It is targeted at researchers and developers needing a robust, end-to-end solution for German speech-to-text conversion, offering a detailed guide from data preparation to model training and optimization.
How It Works
The project leverages Mozilla DeepSpeech, an open-source ASR toolkit based on Baidu's research, utilizing TensorFlow for implementation. It focuses on end-to-end training, meaning the model learns to map acoustic features directly to character sequences without intermediate steps like phoneme alignment. This approach simplifies the pipeline and can lead to improved accuracy. The project also incorporates KenLM for language modeling, enhancing the ASR output by considering linguistic context.
Quick Start & Requirements
git pull --recurse-submodules
). A specific Python environment (3.7.9) is recommended using pyenv
and virtualenv
. Install dependencies via pip3 install -r python_requirements.txt
.pyenv
, virtualenv
, git
, cmake
, make
, and wget
. CUDA is recommended for training acceleration.Highlighted Details
Maintenance & Community
The project is based on Mozilla DeepSpeech v0.9.3, with a note to refer to Mozilla's repository for latest updates. No specific community channels (like Discord/Slack) or active maintenance signals are mentioned in the README.
Licensing & Compatibility
The README does not explicitly state a license for this specific repository's code or scripts. It relies on Mozilla DeepSpeech, which is typically Apache 2.0 licensed. Compatibility for commercial use would depend on the licenses of all included components and datasets.
Limitations & Caveats
The project specifies TensorFlow 1.15, which is outdated and only supports Python up to 3.7. Some dependencies like audiomate
might require patched GitHub versions. The training process is resource-intensive, and while CUDA is recommended, setup can be complex. The provided Word Error Rate (WER) results are from 2019 and may not reflect current state-of-the-art performance.
2 years ago
Inactive