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models.py
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from tensorflow.python.keras.models import Model, load_model
from tensorflow.python.keras.layers import Input
from tensorflow.python.keras.optimizers import Adam
from layers import Encoder, Decoder, PostProcessing, Conditioning, InferenceSpeakerEmbedding, custom_layers, \
TestSpeakerEmbedding, TestSpeakerSimilarity
import hparams
def get_full_model(vocab_size=len(hparams.VOCAB),
char_embed_size=hparams.CHAR_EMBED_SIZE,
sliding_window_size=hparams.SLIDING_WINDOW_SIZE,
spk_embed_lstm_units=hparams.SPK_EMBED_LSTM_UNITS,
spk_embed_size=hparams.SPK_EMBED_SIZE,
spk_embed_num_layers=hparams.SPK_EMBED_NUM_LAYERS,
enc_conv1_bank_depth=hparams.ENC_CONV1_BANK_DEPTH,
enc_convprojec_filters1=hparams.ENC_CONVPROJEC_FILTERS1,
enc_convprojec_filters2=hparams.ENC_CONVPROJEC_FILTERS2,
enc_highway_depth=hparams.ENC_HIGHWAY_DEPTH,
hidden_size=hparams.HIDDEN_SIZE,
post_conv1_bank_depth=hparams.POST_CONV1_BANK_DEPTH,
post_convprojec_filters1=hparams.POST_CONVPROJEC_FILTERS1,
post_convprojec_filters2=hparams.POST_CONVPROJEC_FILTERS2,
post_highway_depth=hparams.POST_HIGHWAY_DEPTH,
attention_dim=hparams.ATTENTION_DIM,
target_size=hparams.TARGET_MAG_FRAME_SIZE,
n_mels=hparams.SYNTHESIZER_N_MELS,
output_per_step=hparams.OUTPUT_PER_STEP,
embed_mels=hparams.SPK_EMBED_N_MELS,
enc_seq_len=None,
dec_seq_len=None
):
char_inputs = Input(shape=(enc_seq_len,), name='char_inputs')
decoder_inputs = Input(shape=(dec_seq_len, n_mels), name='decoder_inputs')
spk_inputs = Input(shape=(None, sliding_window_size, embed_mels), name='spk_embed_inputs')
char_encoder = Encoder(hidden_size=hidden_size // 2,
vocab_size=vocab_size,
embedding_size=char_embed_size,
conv1d_bank_depth=enc_conv1_bank_depth,
convprojec_filters1=enc_convprojec_filters1,
convprojec_filters2=enc_convprojec_filters2,
highway_depth=enc_highway_depth,
name='char_encoder')
speaker_encoder = InferenceSpeakerEmbedding(lstm_units=spk_embed_lstm_units,
proj_size=spk_embed_size,
num_layers=spk_embed_num_layers,
trainable=False,
name='embeddings')
condition = Conditioning()
decoder = Decoder(hidden_size=hidden_size,
attention_dim=attention_dim,
n_mels=n_mels,
output_per_step=output_per_step,
name='decoder')
post_processing = PostProcessing(hidden_size=hidden_size // 2,
conv1d_bank_depth=post_conv1_bank_depth,
convprojec_filters1=post_convprojec_filters1,
convprojec_filters2=post_convprojec_filters2,
highway_depth=post_highway_depth,
n_fft=target_size,
name='postprocessing')
char_enc = char_encoder(char_inputs)
spk_embed = speaker_encoder(spk_inputs)
conditioned_char_enc = condition([char_enc, spk_embed])
decoder_pred, alignments = decoder([conditioned_char_enc, decoder_inputs], initial_state=None)
postnet_out = post_processing(decoder_pred)
full_model = Model(inputs=[char_inputs, spk_inputs, decoder_inputs],
outputs=[decoder_pred, postnet_out, alignments, spk_embed])
return full_model
def get_speaker_embedding_model(sliding_window_size=hparams.SLIDING_WINDOW_SIZE,
embed_mels=hparams.SPK_EMBED_N_MELS,
spk_embed_lstm_units=hparams.SPK_EMBED_LSTM_UNITS,
spk_embed_size=hparams.SPK_EMBED_SIZE,
spk_embed_num_layers=hparams.SPK_EMBED_NUM_LAYERS):
spk_inputs = Input(shape=(None, sliding_window_size, embed_mels), name='spk_embed_inputs')
speaker_encoder = InferenceSpeakerEmbedding(lstm_units=spk_embed_lstm_units,
proj_size=spk_embed_size,
num_layers=spk_embed_num_layers,
trainable=False,
name='embeddings')
spk_embed = speaker_encoder(spk_inputs)
speaker_embedding_model = Model(inputs=[spk_inputs],
outputs=[spk_embed])
return speaker_embedding_model
def get_synthesizer_model(vocab_size=len(hparams.VOCAB),
char_embed_size=hparams.CHAR_EMBED_SIZE,
spk_embed_size=hparams.SPK_EMBED_SIZE,
enc_conv1_bank_depth=hparams.ENC_CONV1_BANK_DEPTH,
enc_convprojec_filters1=hparams.ENC_CONVPROJEC_FILTERS1,
enc_convprojec_filters2=hparams.ENC_CONVPROJEC_FILTERS2,
enc_highway_depth=hparams.ENC_HIGHWAY_DEPTH,
hidden_size=hparams.HIDDEN_SIZE,
post_conv1_bank_depth=hparams.POST_CONV1_BANK_DEPTH,
post_convprojec_filters1=hparams.POST_CONVPROJEC_FILTERS1,
post_convprojec_filters2=hparams.POST_CONVPROJEC_FILTERS2,
post_highway_depth=hparams.POST_HIGHWAY_DEPTH,
attention_dim=hparams.ATTENTION_DIM,
target_size=hparams.TARGET_MAG_FRAME_SIZE,
n_mels=hparams.SYNTHESIZER_N_MELS,
output_per_step=hparams.OUTPUT_PER_STEP,
learning_rate=hparams.LEARNING_RATE,
clipnorm=hparams.CLIPNORM,
enc_seq_len=None,
dec_seq_len=None):
char_inputs = Input(shape=(enc_seq_len,), name='char_inputs')
decoder_inputs = Input(shape=(dec_seq_len, n_mels), name='decoder_inputs')
spk_embed_inputs = Input(shape=(spk_embed_size,), name='spk_embed_inputs')
char_encoder = Encoder(hidden_size=hidden_size // 2,
vocab_size=vocab_size,
embedding_size=char_embed_size,
conv1d_bank_depth=enc_conv1_bank_depth,
convprojec_filters1=enc_convprojec_filters1,
convprojec_filters2=enc_convprojec_filters2,
highway_depth=enc_highway_depth,
name='char_encoder')
condition = Conditioning()
decoder = Decoder(hidden_size=hidden_size,
attention_dim=attention_dim,
n_mels=n_mels,
output_per_step=output_per_step,
name='decoder')
post_processing = PostProcessing(hidden_size=hidden_size // 2,
conv1d_bank_depth=post_conv1_bank_depth,
convprojec_filters1=post_convprojec_filters1,
convprojec_filters2=post_convprojec_filters2,
highway_depth=post_highway_depth,
n_fft=target_size,
name='postprocessing')
char_enc = char_encoder(char_inputs)
conditioned_char_enc = condition([char_enc, spk_embed_inputs])
decoder_pred, alignments = decoder([conditioned_char_enc, decoder_inputs], initial_state=None)
postnet_out = post_processing(decoder_pred)
synthesizer_model = Model(inputs=[char_inputs, spk_embed_inputs, decoder_inputs],
outputs=[decoder_pred, postnet_out, alignments])
optimizer = Adam(lr=learning_rate, clipnorm=clipnorm)
synthesizer_model.compile(optimizer=optimizer, loss=['mae', 'mae', None], loss_weights=[1., 1., None])
return synthesizer_model
def get_SV_test_model(embedded_input=True,
pretrained_model=None,
n_mels=hparams.SPK_EMBED_N_MELS,
lstm_units=hparams.SPK_EMBED_LSTM_UNITS,
proj_size=hparams.SPK_EMBED_SIZE,
num_layers=hparams.SPK_EMBED_NUM_LAYERS,
sliding_window_size=hparams.SLIDING_WINDOW_SIZE):
pair1 = Input(shape=(proj_size,) if embedded_input else (None, sliding_window_size, n_mels))
pair2 = Input(shape=(proj_size,) if embedded_input else (None, sliding_window_size, n_mels))
if not embedded_input:
pair1_embed, pair2_embed = TestSpeakerEmbedding(lstm_units, proj_size,
num_layers, name='embeddings')([pair1, pair2])
else:
pair1_embed, pair2_embed = pair1, pair2
sim = TestSpeakerSimilarity(name='similarity')([pair1_embed, pair2_embed])
model = Model([pair1, pair2], sim)
if not embedded_input:
model.load_weights(pretrained_model, by_name=True)
return model
def load_saved_model(model_path):
return load_model(model_path, custom_objects=custom_layers)