Structure analysis
Segmentation
Dhrupad Bandish Segmentation
Note
REQUIRES: torch
- class compiam.structure.segmentation.dhrupad_bandish_segmentation.DhrupadBandishSegmentation(mode='net', fold=0, model_path=None, splits_path=None, annotations_path=None, features_path=None, original_audios_path=None, processed_audios_path=None, download_link=None, download_checksum=None, device=None)[source]
Dhrupad Bandish Segmentation
- load_model(model_path)[source]
Loading weights for model, given self.mode and self.fold
- Parameters:
model_path – path to model weights
- predict_stm(input_data, input_sr=44100, save_output=False, output_path=None)[source]
Predict Dhrupad Bandish Segmentation
- Parameters:
input_data – path to audio file or numpy array like audio signal.
input_sr – sampling rate of the input array of data (if any). This variable is only relevant if the input is an array of data instead of a filepath.
save_output – boolean indicating whether the output figure for the estimation is stored.
output_path – if the input is an array, and the user wants to save the estimation, the output_path must be provided, path/to/picture.png.
- train(verbose=True)[source]
Train the Dhrupad Bandish Segmentation model
- Parameters:
verbose – showing details of the model
- update_fold(fold)[source]
Update data fold for the training and sampling
- Parameters:
fold – new fold to use
- update_mode(mode)[source]
Update mode for the training and sampling. Mode is one of net, voc, pakh, indicating the source for s.t.m. estimation. Use the net mode if audio is a mixture signal, else use voc or pakh for clean/source-separated vocals or pakhawaj tracks.
- Parameters:
mode – new mode to use