Practical overview
When it fits
Use Kospeech when working on ASR research or model development and when its existing code and architectures fit the experiment. For a quick trial of a trained Korean model, the repository points readers to Pororo ASR or Whisper instead.
Who it is for
- Researchers studying Korean speech recognition and end-to-end ASR.
- Python developers who want to inspect or adapt implementations of supported ASR architectures.
- Users with a pretrained Kospeech checkpoint who want to follow its documented single-audio inference workflow.
Documented uses
- Study and develop end-to-end Korean ASR models.
- Prepare the documented KsponSpeech dataset workflow; LibriSpeech is also listed as supported.
- Configure training experiments with Hydra.
- Run documented inference on one audio file with a pretrained model.
- Load or resume from documented experiment checkpoints.
Requirements & limits
The repository identifies the project as open source under the Apache-2.0 license. Its README states that the repository is archived.
- The project is archived, so its status matters for ongoing model-development work.
- The documentation recommends Python 3.7 or higher and lists PyTorch and other audio, data and configuration dependencies.
- The README says not all cases were tested and specifically notes that subword and grapheme units were untested.
- The documented inference command assumes a pretrained model checkpoint is already available.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install Kospeech from source
Prepare the documented Python environment and install the library from source.
Before you start
- Python 3.7 or higher.
- A virtual environment is recommended.
- Dependencies listed in the repository documentation, including PyTorch, NumPy, Pandas, Matplotlib, librosa, torchaudio, tqdm, SentencePiece, warp-rnnt and Hydra.
Steps
Create a virtual environment using virtualenv or conda.
Install the documented prerequisites, choosing a PyTorch installation appropriate to the environment.
Check out the Kospeech source code.
From the source directory, run the command below.
pip install -e .
Expected outcome
Kospeech is installed from source in editable mode.
Guide limitations
- The documentation does not provide a single dependency command covering all environments.
- The repository is archived, and its README warns that not all cases were tested.
Run documented inference on one audio file
Use a pretrained Kospeech model to produce a transcription for one audio file.
Before you start
- Kospeech installed from source with its prerequisites.
- A pretrained model checkpoint and an audio file.
- A device value for the documented inference command.
Steps
Set the model checkpoint path, audio path and device value.
Run the command below.
python3 ./bin/inference.py --model_path $MODEL_PATH --audio_path $AUDIO_PATH --device $DEVICERead the recognition result printed by the command.
Expected outcome
The documented inference workflow returns a speech-recognition result for the supplied audio.
Guide limitations
- The instructions require an existing pretrained checkpoint and do not describe obtaining one in this workflow.
- The README notes that not all cases were tested.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 15 October 2026
- Original website title
- sooftware/kospeech · Official homepage title · retained in its source language
Related options
OpenSpeech — The Kospeech README recommends it for people who want to train their own voice-recognition model or study model code.
Explore project →Pororo ASR — The Kospeech README recommends it for people who want to try a trained Korean speech-recognition model right away.
Whisper — The Kospeech README also recommends it for people who want to try a trained Korean speech-recognition model right away.
Explore project →