espnet | End-to-End Speech Processing Toolkit | Speech library

 by   espnet Python Version: 202402 License: Apache-2.0

kandi X-RAY | espnet Summary

kandi X-RAY | espnet Summary

espnet is a Python library typically used in Artificial Intelligence, Speech, Deep Learning, Pytorch applications. espnet has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install espnet' or download it from GitHub, PyPI.

Docs | Example | Example (ESPnet2) | Docker | Notebook | Tutorial (2019). ESPnet is an end-to-end speech processing toolkit covering end-to-end speech recognition, text-to-speech, speech translation, speech enhancement, speaker diarization, spoken language understanding, and so on. ESPnet uses pytorch as a deep learning engine and also follows Kaldi style data processing, feature extraction/format, and recipes to provide a complete setup for various speech processing experiments.

            kandi-support Support

              espnet has a medium active ecosystem.
              It has 6684 star(s) with 1936 fork(s). There are 179 watchers for this library.
              There were 2 major release(s) in the last 6 months.
              There are 404 open issues and 1714 have been closed. On average issues are closed in 44 days. There are 70 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of espnet is 202402

            kandi-Quality Quality

              espnet has no bugs reported.

            kandi-Security Security

              espnet has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              espnet is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              espnet releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed espnet and discovered the below as its top functions. This is intended to give you an instant insight into espnet implemented functionality, and help decide if they suit your requirements.
            • Train the model
            • Decrement gradable eps
            • A decorator for adadelta
            • Creates a function that restores the given snapshot
            • Perform a forward computation
            • Pad a list
            • Create argument parser
            • Adds command line arguments
            • Return a config parser
            • Returns None if value is None or None
            • Performs the forward transformation
            • Traverse the data
            • Enhance model
            • Argument parser
            • Argument specific to feed - forward transformer
            • Adds task arguments to argparse ArgumentParser
            • Modified adaptive expansion search
            • NSC beam search
            • Prepare audio files
            • Decode a trained model
            • Train one epoch
            • Process the metadata
            • Calculate speech features
            • Recogenerate a model
            • Adds the command line arguments to the parser
            • Wrapper for inference
            Get all kandi verified functions for this library.

            espnet Key Features

            No Key Features are available at this moment for espnet.

            espnet Examples and Code Snippets

            3D ESPNet for segmenting brain images [,Citation
            Pythondot img1Lines of Code : 22dot img1License : Permissive (MIT)
            copy iconCopy
            author="Nuechterlein, Nicholas and Mehta, Sachin",
            title="3D-ESPNet with Pyramidal Refinement for Volumetric Brain Tumor Image Segmentation",
            booktitle="Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic B  
            2. Dependencies,Kaldi
            Shelldot img2Lines of Code : 13dot img2License : Permissive (Apache-2.0)
            copy iconCopy
            cd $WORK
            git clone
            cd $WORK/kaldi/tools
            bash extras/
            touch python/.use_default_python
            make -j$(nproc)
            cd $WORK/kaldi/src
            ./configure --shared \
                --use-cuda=no \
            copy iconCopy
            git clone
            cd jets; ./
            cd jets/espnet/tools
            ./setup_venv $(which python3)
            # LJSPEECH training
            cd jets/espnet/egs2/ljspeech/tts1
            ./ --stage 1 --stop_stage 6 --ngpu 4
            # KSS training
            How to train a deep learning model on a GPU server with laptop closed?
            Pythondot img4Lines of Code : 4dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            [myhost ~]$ screen
            [myhost ~]$ screen -r
            Reading a training progress log file, but binaries are written in it
            Pythondot img5Lines of Code : 2dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy

            Community Discussions


            How to train a deep learning model on a GPU server with laptop closed?
            Asked 2021-Aug-20 at 13:40

            I'm about to train my own ASR model using ESPNet on a GPU server. If my calculations are right, it's going to take about 4 consecutive days (using about 100G of audio data).

            I'm mainly using VScode to remotely connect to the SSH server, and will run the shell file with the VScode terminal.

            My question is that will I have to leave my laptop open for four days in order to train my model?

            not sure if this is any useful info, but this is my nvcc --version:



            Answered 2021-Aug-20 at 07:40

            Many Linux versions include the GNU Screen program, which - amongst other things - allow you to keep processes running after you've logged off.

            Once connected, simply run the screen command:


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install espnet

            If you intend to do full experiments including DNN training, then see Installation.
            If you intend to do full experiments including DNN training, then see Installation.
            If you just need the Python module only: # We recommend you installing pytorch before installing espnet following pip install espnet # To install latest # pip install git+ # To install additional packages # pip install "espnet[all]" If you'll use ESPnet1, please install chainer and cupy. pip install chainer==6.0.0 cupy==6.0.0 # [Option] You might need to install some packages depending on each task. We prepared various installation scripts at tools/installers.
            (ESPnet2) Once installed, run wandb login and set --use_wandb true to enable tracking runs using W&B.


            Thank you for taking times for ESPnet! Any contributions to ESPnet are welcome and feel free to ask any questions or requests to issues. If it's the first contribution to ESPnet for you, please follow the contribution guide.
            Find more information at:

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          • PyPI

            pip install espnet

          • CLONE
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          • CLI

            gh repo clone espnet/espnet

          • sshUrl


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