Comments (3)
@Nikhilbharu08, you can used the trained model directly or use your own model.
Just create your server code and front end which you can find easy tutorials online. Include your trained model location and supply it your input. Care should be taken are regarding the audio clippings with the desired sampling rate. If they don't match the model wouldn't make any predictions.
from speech-emotion-analyzer.
@Nikhilbharu08, you can used the trained model directly or use your own model.
Just create your server code and front end which you can find easy tutorials online. Include your trained model location and supply it your input. Care should be taken are regarding the audio clippings with the desired sampling rate. If they don't match the model wouldn't make any predictions.
yeah i have created a front end to choose a audio file of .wav format file,
can you please give more details/info regarding the sampling rate which you mentioned !!!?
from speech-emotion-analyzer.
Hey @Nikhilbharu08, sampling rate is what you change according to the number of features you want to extract from the audio files. Higher sampling rates means more features.
from speech-emotion-analyzer.
Related Issues (20)
- Recommendations for Replicability HOT 3
- Error on JupiterLab HOT 2
- getting RawData missing Error HOT 7
- getting an error in this state Getting the features of audio files using librosa
- ValueError: Incomplete wav chunk
- Inference code? HOT 2
- AttributeError:'list' object has no attribute'items'
- ValueError: Shapes (None, 4) and (None, 10) are incompatible HOT 2
- ERRROR : 'feeling_list' is not defined
- Libra Not working?
- Can you share the paper you published with reference to this project
- data
- requirements.txt HOT 2
- Mfcc
- wrong extraction of features HOT 1
- Please Share the Data
- Dataset HOT 3
- list index out of range
- ValueError: Shapes (None, 11) and (None, 10) are incompatible HOT 1
- dataset HOT 4
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