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

by espnet Python Version: 202211 License: Apache-2.0

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

kandi-support Support

espnet has a medium active ecosystem.
It has 6019 star(s) with 1814 fork(s). There are 181 watchers for this library.
There were 2 major release(s) in the last 6 months.
There are 353 open issues and 1661 have been closed. On average issues are closed in 57 days. There are 56 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of espnet is 202211

espnet Support

Best in #Speech

Average in #Speech

espnet Support

Best in #Speech

Average in #Speech

quality kandi Quality

espnet has no bugs reported.

espnet Quality

Best in #Speech

Average in #Speech

espnet Quality

Best in #Speech

Average in #Speech

security Security

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

espnet Security

Best in #Speech

Average in #Speech

espnet Security

Best in #Speech

Average in #Speech

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.

espnet License

Best in #Speech

Average in #Speech

espnet License

Best in #Speech

Average in #Speech

build 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.

espnet Reuse

Best in #Speech

Average in #Speech

espnet Reuse

Best in #Speech

Average in #Speech

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

End-to-End Speech Processing Toolkit

espnet Examples and Code Snippets

See all related Code Snippets

Community Discussions

Trending Discussions on espnet

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

Trending Discussions on espnet

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:

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Wed_Oct_23_19:24:38_PDT_2019
Cuda compilation tools, release 10.2, V10.2.89

and my nvidia-smi:

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.33.01    Driver Version: 440.33.01    CUDA Version: 10.2     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  Quadro RTX 6000     Off  | 00000000:00:06.0 Off |                    0 |
| N/A   32C    P0    41W / 250W |      0MiB / 22698MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

Once my data is all prepared, I'll execute the run.sh file. Espnet github: https://github.com/espnet/espnet

The model I'm using is located in espnet/egs2/zeroth_korean/asr1.

I'm fairly new to linux servers and models this heavy and large, so any type of feedback would be much appreciated.

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:

[myhost ~]$ screen

Start your long running process inside this screen terminal.

You can now close the terminal. Power off, restart your computer, whatever.

When you want to check up on your process, just re-connect and run the following command to re-attach:

[myhost ~]$ screen -r

I hope this works for you.

screen has lots of other nice tricks. Just google "Linux Screen" for an abundance of articles on this.

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: