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ESPnet
ESPnet · founded 2018
What it is
ESPnet is an open-source, end-to-end toolkit for speech processing. It covers speech recognition, text-to-speech, speech translation, speech enhancement, speaker diarization and spoken-language understanding, with listed support for real-time transcription, speaker identification and speaker labeling. The project supplies complete recipes for many tasks, plus tutorials, notebooks and demos for existing models. Users can install ESPnet and espnet-model-zoo with pip to run inference, or use published models in CPU or GPU inference containers without checking out the repository. Editable pip installation and optional dependency groups support ASR, TTS and speech enhancement. Training requires a separate extra containing Lightning, TensorBoard, W&B, torch_optimizer, matplotlib and Dask; some task-specific packages require separate installation. ESPnet 1 is no longer supported, while ESPnet2 users can skip Kaldi. The toolkit uses Apache License 2.0, permits commercial use, offers API access and supports Linux, macOS, Windows and self-hosted deployment. WAV is the listed export format.
For and against
For
- Covers many speech-processing tasks
- Recipes and tutorials included
- Pretrained-model inference available
- Commercial use permitted
- CPU and GPU containers supported
Against
- Voice cloning is not supported
- Some task dependencies require separate installation
- ESPnet 1 is no longer supported
- Training needs an additional dependency extra
The verdict
ESPnet offers a broad open-source toolkit with recipes, pretrained-model inference and training paths. Its optional dependencies and unsupported ESPnet 1 branch are important setup considerations.
Who it suits
It suits developers and researchers building speech applications or learning through recipes, tutorials and notebooks. Commercial projects can use the Apache-licensed toolkit on listed platforms.
Plans and prices
ESPnet open-source toolkit
self-hosted research toolkit · optional task and training dependencies · no hosted SaaS plan stated
Read 15 Oct 2026 · espnet.github.io
ESPnet
Apache License 2.0 · open-source toolkit · pretrained-model inference and training via optional task extras
Read 12 Oct 2026 · espnet.github.io
On the record
- Additional task coverage
- The documentation lists singing-voice synthesis, speech codecs, language modeling, spoken dialogue systems and self-supervised learning.espnet.github.io · read 15 Oct 2026
- API access
- Yesgithub.com · read 20 Sep 2026
- Automatic speech recognition
- The project provides automatic speech-recognition recipes, including discrete-unit and enhancement-assisted variants.espnet.github.io · read 15 Oct 2026
- Commercial use
- Yesgithub.com · read 20 Sep 2026
- Company identity
- The independent project profile describes espnet/espnet as an open-source project with Apache-2.0 licensing, about 9,984 stars and 2,444 forks at its October 2026 snapshot.hysenlabs.com · read 15 Oct 2026
- Containers
- The Docker guide says published models can run in CPU or GPU inference images without checking out the repository.espnet.github.io · read 12 Oct 2026
- Current PyPI package
- PyPI lists ESPnet release 202610.post2 as a Python 3 universal wheel published on September 24, 2026.pypi.org · read 15 Oct 2026
- Dependency groups
- The package defines optional groups for training, ASR, TTS, enhancement, speech translation, speaker recognition, speech language models, MCP, development, testing and documentation.espnet.github.io · read 15 Oct 2026
- Distributed training
- The project documents multi-GPU and distributed training and provides job-scheduling tutorials.espnet.github.io · read 15 Oct 2026
- Docker
- Official Docker instructions provide CPU and GPU images, recipe execution, local builds and configurable CUDA environments.espnet.github.io · read 15 Oct 2026
- Experiment integrations
- Training documentation describes optional Weights & Biases logging and multi-GPU execution through CUDA device selection.espnet.github.io · read 15 Oct 2026
- Export formats
- WAVgithub.com · read 20 Sep 2026
- Hugging Face availability
- Hugging Face's ESPnet library filter shows 1,054 compatible models.huggingface.co · read 15 Oct 2026
- Inference
- The homepage says users can install espnet and espnet-model-zoo with pip to run inference on existing models.espnet.github.io · read 12 Oct 2026
- Installation
- The installation guide documents editable pip installation and optional dependency groups for tasks such as ASR, TTS, and speech enhancement.espnet.github.io · read 12 Oct 2026
- Installation prerequisites
- Full installation may require CMake, SoX, FLAC, Python environment setup and optional Kaldi or task-specific installers.espnet.github.io · read 15 Oct 2026
- Integrations
- The installation guide lists W&B and TensorBoard among the training dependencies.espnet.github.io · read 12 Oct 2026
- Legacy status
- ESPnet1 is marked unsupported, while ESPnet2 recipes can be used without compiling Kaldi.espnet.github.io · read 15 Oct 2026
