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

by espnet Python Version: 202402 License: Apache-2.0

X-Ray Key Features Code Snippets(5)Community Discussions(1)Vulnerabilities Install Support

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.

Support

Quality

Security

License

Reuse

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 1 major release(s) in the last 12 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

Quality

espnet has no bugs reported.

Security

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

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.

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

Python

Lines of Code : 22

License : Permissive (MIT)

Copy

@inproceedings{mehta20193despnet,
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

Shell

Lines of Code : 13

License : Permissive (Apache-2.0)

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cd $WORK
git clone https://github.com/kaldi-asr/kaldi.git

cd $WORK/kaldi/tools
bash extras/check_dependencies.sh
touch python/.use_default_python
make -j$(nproc)

cd $WORK/kaldi/src
./configure --shared \
    --use-cuda=no \
    --mkl-root=/some/pat

JETS: Jointly Training FastSpeech2 and HiFi-GAN for End to End Text to Speech,How to use

Python

Lines of Code : 11

License : Permissive (Apache-2.0)

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git clone https://github.com/imdanboy/jets.git

cd jets; ./patch_to_espnet.sh

cd jets/espnet/tools
./setup_venv $(which python3)
make

# LJSPEECH training
cd jets/espnet/egs2/ljspeech/tts1
./run.sh --stage 1 --stop_stage 6 --ngpu 4
# KSS training
cd

How to train a deep learning model on a GPU server with laptop closed?

Python

Lines of Code : 4

License : Strong Copyleft (CC BY-SA 4.0)

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[myhost ~]$ screen

[myhost ~]$ screen -r

Reading a training progress log file, but binaries are written in it

Python

Lines of Code : 2

License : Strong Copyleft (CC BY-SA 4.0)

Copy

app.py 2>train.log

Community Discussions

Trending Discussions on espnet

How to train a deep learning model on a GPU server with laptop closed?

QUESTION

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:

...

ANSWER

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:

Source https://stackoverflow.com/questions/68858284

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

Vulnerabilities

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 https://pytorch.org/get-started/locally/ pip install espnet # To install latest # pip install git+https://github.com/espnet/espnet # 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.

Support

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: