Command Line Interface for Behavioral Signals Emotion and Behavior Recognition Engine in the Cloud
Project description
# Behavioral Signals CLI
Command Line Interface for Behavioral Signals' Emotion and Behavior Recognition in the Cloud
* Free software: MIT license
## Getting Started
1. First request your account id and token for the Behavioral Signals Web API by sending an email to nassos@behavioralsignals.com
2. Export the following as environmental variables:
```
export BEST_ID=your_id_on_service_api
export BEST_TOKEN=your_token_for_service_api
```
3. Run the CLI to submit your audio files:
```
behaviorals_signals_cli send_audio [csv_file] [pids_log]
```
The .csv file must have the following form (order matters):
path/to/file, number of channels, call direction, agentId, agentTeam, campaign Id, calltype, calltime, timezone, ANI. The [pids_log] file
is an empty file where the process ids of the created jobs will be written.
4. Run the CLI to get the emotion/behavior recognition, diarization and other results:
```
behaviorals_signals_cli get_results [pids_log] [results_dir]
```
The results will be written as .json files inside [results_dir] (polling may be performed if results
are not readily available).
5. Run the CLI to get ASR results:
```
behaviorals_signals_cli get_results_asr [pids_log] [results_dir]
```
The results will be written as "[filename]_[pid]_words.json" files inside [results_dir] (polling may be performed if results
are not readily available).
Type:
```
behavioral_signals_cli --help
```
for more info.
Features
--------
The CLI allows you to easily:
- Submit multiple audio files to API,
- Get behavior and emotion recognition results
- Get speech recognition results
* TODO
Credits
---------
This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.
.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
=======
History
=======
0.1.0 (2017-11-17)
------------------
* First release on PyPI.
Command Line Interface for Behavioral Signals' Emotion and Behavior Recognition in the Cloud
* Free software: MIT license
## Getting Started
1. First request your account id and token for the Behavioral Signals Web API by sending an email to nassos@behavioralsignals.com
2. Export the following as environmental variables:
```
export BEST_ID=your_id_on_service_api
export BEST_TOKEN=your_token_for_service_api
```
3. Run the CLI to submit your audio files:
```
behaviorals_signals_cli send_audio [csv_file] [pids_log]
```
The .csv file must have the following form (order matters):
path/to/file, number of channels, call direction, agentId, agentTeam, campaign Id, calltype, calltime, timezone, ANI. The [pids_log] file
is an empty file where the process ids of the created jobs will be written.
4. Run the CLI to get the emotion/behavior recognition, diarization and other results:
```
behaviorals_signals_cli get_results [pids_log] [results_dir]
```
The results will be written as .json files inside [results_dir] (polling may be performed if results
are not readily available).
5. Run the CLI to get ASR results:
```
behaviorals_signals_cli get_results_asr [pids_log] [results_dir]
```
The results will be written as "[filename]_[pid]_words.json" files inside [results_dir] (polling may be performed if results
are not readily available).
Type:
```
behavioral_signals_cli --help
```
for more info.
Features
--------
The CLI allows you to easily:
- Submit multiple audio files to API,
- Get behavior and emotion recognition results
- Get speech recognition results
* TODO
Credits
---------
This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.
.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
=======
History
=======
0.1.0 (2017-11-17)
------------------
* First release on PyPI.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distributions
Close
Hashes for behavioral_signals_cli-0.1.0.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | e3fef594e71490b98ba33b310f9affe102787d21f7315d2d21d23240fec02947 |
|
MD5 | a474b7b94d3797bb20d62f3534562d13 |
|
BLAKE2b-256 | 245cb7baecab47f42f980599772d9eb4d7df13ce15d4aa6829f1d58912048934 |
Close
Hashes for behavioral_signals_cli-0.1.0-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 856244a06d628e120fc295217364c8b57e1f637d896072376eb631c355051ca4 |
|
MD5 | 578741f2417c89d1e866ce059a060f8d |
|
BLAKE2b-256 | d0df0f7a312992a18d2ff5f023e2fd7ac8f2aa6085efa4b675729ccac32a8238 |
Close
Hashes for behavioral_signals_cli-0.1.0-py2.7.egg
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9578152d4ac6953e5d29672d1f6e231e284b4a5475d8ac67f6a7c7e6e5f9e7c2 |
|
MD5 | 7bec3ac50e9e795c396f3f31b2ecaa0c |
|
BLAKE2b-256 | b2c77cccfbb9eabb66579343305b7a39c28aecce0676590d6f73c260c751b61f |