Metadata-Version: 2.1
Name: speech-features-kit
Version: 1.0.0.dev6
Summary: This is a kit for simple speech feature extraction.
Home-page: https://github.com/dhchenx/speech-feature-kit
Author: Donghua Chen
Author-email: douglaschan@126.com
License: UNKNOWN
Project-URL: Bug Reports, https://github.com/dhchenx/speech-feature-kit/issues
Project-URL: Say Thanks!, https://github.com/aishoot/Speech_Feature_Extraction
Project-URL: Source, https://github.com/dhchenx/speech-feature-kit
Keywords: speech,feature extraction,machine learning
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3 :: Only
Requires-Python: >=3.6, <4
Description-Content-Type: text/markdown
Provides-Extra: dev
Provides-Extra: test
License-File: LICENSE.txt

# Speech Feature Kit

A Python wrapper for convenient speech feature extraction

## Installation
```python
pip install speech-features-kit
```

## Functions
1. MFCC feature analysis
2. Volume analysis
3. Emotion analysis

## Example of emotion analysis
```python
from speech_features_kit.Emotion.speech_toolkit import SpeechEmotionToolkit

# set the path of pre-trained model for speech emotion model
# the used model here is optimized for Chinese speech; however, it is possible you can train your own model. 
speech_kit = SpeechEmotionToolkit()

# load the model
speech_kit.load()

# obtain emotion list with timestamp given an audio file
list_emo, list_timestamp = speech_kit.get_emotion_list_by_blocks(audio_file="../data/english.wav",
                                                                     num_sec_each_file=1)

# print the list of emotion over timestamp
print("Time interval\tEmotion")
for idx, e in enumerate(list_emo):
    print(list_timestamp[idx], "\t", e)

```

## Note
Other functions please see the examples folder!



