Comments (8)
Hi,
first make a virtual environment (http://docs.python-guide.org/en/latest/dev/virtualenvs/) in your project folder using:
virtualenv venv
source venv/bin/activate
Then clone or copy this repo in your project folder and run
python setup.py install
After that, the command pip list
should return speech-features.
After that, you have t install numpy and scipy:
pip install numpy
pip install scipy
I also made a pull request for this: #18
from python_speech_features.
I need only that mfcc implementation code python is already installled in
my system
On Wed, Apr 20, 2016 at 5:56 PM, Josef Hölzl [email protected]
wrote:
Hi,
first make a virtual environment (
http://docs.python-guide.org/en/latest/dev/virtualenvs/) in your project
folder using:virtualenv venv
source venv/bin/activateThen clone or copy this repo in your project folder and run
python setup.py installl
After that, the command pip list should return speech-features.
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#17 (comment)
from python_speech_features.
When you have performed the seps above, this module is already installed and you can run python example.py
(file.wav has to be in the same folder).
from python_speech_features.
In example.py it seems you have used inbuilt functions ....
On Thu, Apr 21, 2016 at 1:12 PM, Josef Hölzl [email protected]
wrote:
When you have performed the seps above, this module is already installed
and you can run python example.py (file.wav has to be in the same folder).—
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#17 (comment)
from python_speech_features.
Please post your error message otherwise we can not help you.
from python_speech_features.
I dont have a program to run I need that code which you have explained in
http://practicalcryptography.com/miscellaneous/machine-learning/guide-mel-frequency-cepstral-coefficients-mfccs/
On Thu, Apr 21, 2016 at 1:39 PM, Josef Hölzl [email protected]
wrote:
Please post your error message otherwise we can not help you.
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#17 (comment)
from python_speech_features.
the python_speech_features library is the algorithm explained on that page. You have python installed, you now need to install the python_speech_features library so that you can run example.py. To install the library, download this zip file: https://github.com/jameslyons/python_speech_features/archive/master.zip
extract it somewhere. Open a terminal, cd into the directory (there should be a setup.py file in the directory). Run 'python setup.py install'.
You should then be able to run example.py
from python_speech_features.
thanks its working but if I want to do all this manually
manually in the sense ....want to implement that what u have explained
On Thu, Apr 21, 2016 at 2:10 PM, James Lyons [email protected]
wrote:
the python_speech_features library is the algorithm explained on that
page. You have python installed, you now need to install the
python_speech_features library so that you can run example.py. To install
the library, download this zip file:
https://github.com/jameslyons/python_speech_features/archive/master.zipextract it somewhere. Open a terminal, cd into the directory (there should
be a setup.py file in the directory). Run 'python setup.py install'.You should then be able to run example.py
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#17 (comment)
from python_speech_features.
Related Issues (20)
- Error in fbank HOT 1
- logfbank functionstrange winstep size HOT 2
- I do not found code about inverse DFT
- logfbank interface exist error, lack of winfunc HOT 1
- Reading Spectogram
- missing frequencies in Mel FillterBank HOT 2
- Std of log mel-filterbank will be close to zero in some dimension when nfilt == 80. HOT 1
- sample audio link is invalid HOT 1
- Cannot read audio file sklearn.
- How to ignore the NFFT warning HOT 5
- Pypi not updating release?
- Reason for not windowing by default?
- Frame length is greater than FFT size HOT 1
- Can I use this MFCC function on edf file
- Minor issue on round vs. floor
- ValueError: File format b'OggS'... not understood. HOT 2
- [Question:] inverse fbank back to wav
- viseme generation
- High CPU Utilization HOT 2
- Use another augmented assignment statement
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from python_speech_features.