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https://github.com/awni/speech
A PyTorch Implementation of End-to-End Models for Speech-to-Text
https://github.com/awni/speech
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A PyTorch Implementation of End-to-End Models for Speech-to-Text
- Host: GitHub
- URL: https://github.com/awni/speech
- Owner: awni
- License: apache-2.0
- Created: 2017-06-07T22:23:03.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2023-07-06T21:09:27.000Z (over 1 year ago)
- Last Synced: 2024-12-25T14:04:22.325Z (8 days ago)
- Language: Python
- Homepage:
- Size: 222 KB
- Stars: 757
- Watchers: 31
- Forks: 176
- Open Issues: 24
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# speech
Speech is an open-source package to build end-to-end models for automatic
speech recognition. Sequence-to-sequence models with attention,
Connectionist Temporal Classification and the RNN Sequence Transducer
are currently supported.
The goal of this software is to facilitate research in end-to-end models for
speech recognition. The models are implemented in PyTorch.
The software has only been tested in Python3.6.
**We will not be providing backward compatability for Python2.7.**
## Install
We recommend creating a virtual environment and installing the python
requirements there.
```
virtualenv
source /bin/activate
pip install -r requirements.txt
```
Then follow the installation instructions for a version of
[PyTorch](http://pytorch.org/) which works for your machine.
After all the python requirements are installed, from the top level directory,
run:
```
make
```
The build process requires CMake as well as Make.
After that, source the `setup.sh` from the repo root.
```
source setup.sh
```
Consider adding this to your `bashrc`.
You can verify the install was successful by running the
tests from the `tests` directory.
```
cd tests
pytest
```
## Run
To train a model run
```
python train.py
```
After the model is done training you can evaluate it with
```
python eval.py
```
To see the available options for each script use `-h`:
```
python {train, eval}.py -h
```
## Examples
For examples of model configurations and datasets, visit the examples
directory. Each example dataset should have instructions and/or scripts for
downloading and preparing the data. There should also be one or more model
configurations available. The results for each configuration will documented in
each examples corresponding `README.md`.