<commit_before>python_speech_features
======================

This library provides common speech features for ASR including MFCCs and filterbank energies.

You will need numpy and scipy to run these files.

To use MFCC features,

```python
from mfcc import mfcc
from mfcc import fbank
import scipy.io.wavfile as wav

(rate,sig) = wav.read("/file.wav")<commit_msg>added link to readme<commit_after>python_speech_features
======================

This library provides common speech features for ASR including MFCCs and filterbank energies.
If you are not sure what MFCCs are, and would like to know more have a look at this [MFCC tutorial](http://www.practicalcryptography.com/miscellaneous/machine-learning/guide-mel-frequency-cepstral-coefficients-mfccs/).


You will need numpy and scipy to run these files.

To use MFCC features,

```python
from mfcc import mfcc
from mfcc import fbank
import scipy.io.wavfile as wav

(rate,sig) = wav.read("/file.wav")
{'fraction_non_alphanumeric': 0.11413612565445026, 'fraction_numerical': 0.0, 'mean_word_length': 5.248366013071895, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 3, 'https://': 0, 'lorem ipsum': 0, 'www.': 1, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '3415163', 'n_tokens_mistral': 311, 'n_tokens_neox': 290, 'n_words': 109}