def yamnet_frames_model_transfer(params, last_layers):
"""Defines the YAMNet waveform-to-class-scores model.
Args:
params: An instance of Params containing hyperparameters;
last_layers: Path to the classifier model.
Returns:
A model accepting (num_samples,) waveform input and emitting:
- predictions: (num_patches, num_classes) matrix of class scores per time frame
- embeddings: (num_patches, embedding size) matrix of embeddings per time frame
"""
waveform = layers.Input(batch_shape=(None, ), dtype=tf.float32)
waveform_padded = features_lib.pad_waveform(waveform, params)
log_mel_spectrogram, features = features_lib.waveform_to_log_mel_spectrogram_patches(
waveform_padded, params)
embeddings = yamnet_transfer(features, params)
prediction = embeddings
last_layers = load_model(last_layers)
for layer in last_layers.layers[1:]:
prediction = layer(prediction)
frames_model = Model(name='yamnet_frames',
inputs=waveform,
outputs=[prediction, embeddings])
return frames_model
def yamnet_frames_model(params):
"""Defines the YAMNet waveform-to-class-scores model.
Args:
params: An instance of Params containing hyperparameters.
Returns:
A model accepting (num_samples,) waveform input and emitting:
- predictions: (num_patches, num_classes) matrix of class scores per time frame
- embeddings: (num_patches, embedding size) matrix of embeddings per time frame
- log_mel_spectrogram: (num_spectrogram_frames, num_mel_bins) spectrogram feature matrix
"""
waveform = layers.Input(batch_shape=(None,), dtype=tf.float32)
waveform_padded = features_lib.pad_waveform(waveform, params)
log_mel_spectrogram, features = features_lib.waveform_to_log_mel_spectrogram_patches(
waveform_padded, params)
predictions, embeddings = yamnet(features, params)
frames_model = Model(
name='yamnet_frames', inputs=waveform,
outputs=[predictions, embeddings, log_mel_spectrogram])
return frames_model