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• speech-music-detection-master
  • hyper_params_opt.py
  • smd
    • config.py
    • models
      • model_loader.py
      • cldnn.py
      • lstm.py
      • __init__.py
      • tcn.py
    • evaluation
      • eval.py
      • __init__.py
    • data
      • data_augmentation
        • loudness.py
        • mixing.py
        • noise.py
        • filter.py
        • __init__.py
        • pitch_time.py
      • postprocessing
        • threshold.py
        • smoothing.py
        • __init__.py
      • display
        • events.py
        • audio.py
        • __init__.py
      • data_generator.py
      • __init__.py
      • preprocessing
        • labels.py
        • audio.py
        • __init__.py
      • dataset_loader.py
    • __init__.py
    • utils.py
  • evaluate.py
  • LICENSE
  • get_validation_loss.py
  • .gitattributes
  • predict.py
  • notebooks
    • test-label-processing.ipynb
    • example_annotation.txt
    • speech.txt
    • music.txt
    • example_annotation_processed.txt
  • vizualize_data.py
  • train.py
  • experiments.json
  • README.md
  • requirements.txt
  • .gitignore
  • prepare_dataset
    • statistics.py
    • extract_annotations
      • ofai.py
      • esc-50.py
      • muspeak.py
      • klaus_seyerlehner.py
      • gtzan.py
      • scheirer_slaney.py
      • musan.py
      • sveriges_radio.py
    • prepare_audio.py
    • mp2_to_wav.py
  • datasets.json

from smd import config
from smd.models.lstm import create_lstm
from smd.models.tcn import create_tcn
from smd.models.cldnn import create_cldnn
from keras import optimizers


def load_model(cfg):
    if cfg["type"] == "lstm":
        model = create_lstm(hidden_units=cfg["hidden_units"],
                            dropout=cfg["dropout"],
                            bidirectional=cfg["bidirectional"])
    elif cfg["type"] == "cldnn":
        model = create_cldnn(filters_list=cfg["filters_list"],
                             lstm_units=cfg["lstm_units"],
                             fc_units=cfg["fc_units"],
                             kernel_sizes=cfg["kernel_sizes"],
                             dropout=cfg["dropout"])
    elif cfg["type"] == "tcn":
        model = create_tcn(list_n_filters=cfg["list_n_filters"],
                           kernel_size=cfg["kernel_size"],
                           dilations=cfg["dilations"],
                           nb_stacks=cfg["nb_stacks"],
                           activation=cfg["activation"],
                           n_layers=cfg["n_layers"],
                           dropout_rate=cfg["dropout_rate"],
                           use_skip_connections=cfg["use_skip_connections"],
                           bidirectional=cfg["bidirectional"])
    else:
        raise ValueError(
            "Configuration error: the specified model is not yet implemented.")

    if cfg["optimizer"]["name"] == "SGD":
        optimizer = optimizers.SGD(
            lr=cfg["optimizer"]["lr"], momentum=cfg["optimizer"]["momentum"], decay=cfg["optimizer"]["decay"])
    elif cfg["optimizer"]["name"] == "adam":
        optimizer = optimizers.adam(lr=cfg["optimizer"]["lr"],
                                    beta_1=cfg["optimizer"]["beta_1"],
                                    beta_2=cfg["optimizer"]["beta_2"],
                                    epsilon=cfg["optimizer"]["epsilon"],
                                    decay=cfg["optimizer"]["decay"],
                                    clipnorm=cfg["optimizer"]["clipnorm"])
    else:
        raise ValueError(
            "Configuration error: the specified optimizer is not yet implemented.")

    model.compile(optimizer, loss=config.LOSS, metrics=config.METRICS)

    model.summary()
    return model


def compile_model(model, cfg):
    if cfg["optimizer"]["name"] == "SGD":
        optimizer = optimizers.SGD(
            lr=cfg["optimizer"]["lr"], momentum=cfg["optimizer"]["momentum"], decay=cfg["optimizer"]["decay"])
    elif cfg["optimizer"]["name"] == "adam":
        optimizer = optimizers.adam(lr=cfg["optimizer"]["lr"],
                                    beta_1=cfg["optimizer"]["beta_1"],
                                    beta_2=cfg["optimizer"]["beta_2"],
                                    epsilon=cfg["optimizer"]["epsilon"],
                                    decay=cfg["optimizer"]["decay"],
                                    clipnorm=cfg["optimizer"]["clipnorm"])
    else:
        raise ValueError(
            "Configuration error: the specified optimizer is not yet implemented.")

    model.compile(optimizer, loss=config.LOSS, metrics=config.METRICS)

    model.summary()
    return model