def __init__(self, num_chars, num_speakers, r=5, linear_dim=1025, mel_dim=80, memory_size=5, attn_win=False, attn_norm="sigmoid", prenet_type="original", prenet_dropout=True, forward_attn=False, trans_agent=False, forward_attn_mask=False, location_attn=True, separate_stopnet=True): super(Tacotron, self).__init__() self.r = r self.mel_dim = mel_dim self.linear_dim = linear_dim self.embedding = nn.Embedding(num_chars, 256) self.embedding.weight.data.normal_(0, 0.3) if num_speakers > 1: self.speaker_embedding = nn.Embedding(num_speakers, 256) self.speaker_embedding.weight.data.normal_(0, 0.3) self.encoder = Encoder(256) self.decoder = Decoder(256, mel_dim, r, memory_size, attn_win, attn_norm, prenet_type, prenet_dropout, forward_attn, trans_agent, forward_attn_mask, location_attn, separate_stopnet) self.postnet = PostCBHG(mel_dim) self.last_linear = nn.Linear(self.postnet.cbhg.gru_features * 2, linear_dim)
def __init__(self, num_chars, embedding_dim=256, linear_dim=1025, mel_dim=80, r=5, padding_idx=None, memory_size=5, attn_windowing=False, forward_attention=False): super(Tacotron, self).__init__() self.r = r self.mel_dim = mel_dim self.linear_dim = linear_dim self.embedding = nn.Embedding(num_chars, embedding_dim, padding_idx=padding_idx) self.embedding.weight.data.normal_(0, 0.3) self.encoder = Encoder(embedding_dim) self.decoder = Decoder(256, mel_dim, r, memory_size, attn_windowing, forward_attention) self.postnet = PostCBHG(mel_dim) self.last_linear = nn.Sequential( nn.Linear(self.postnet.cbhg.gru_features * 2, linear_dim), nn.Sigmoid())
def test_in_out(): layer = Decoder( in_features=256, memory_dim=80, r=2, memory_size=4, attn_windowing=False, attn_norm="sigmoid", prenet_type='original', prenet_dropout=True, forward_attn=True, trans_agent=True, forward_attn_mask=True, location_attn=True, separate_stopnet=True) dummy_input = T.rand(4, 8, 256) dummy_memory = T.rand(4, 2, 80) output, alignment, stop_tokens = layer( dummy_input, dummy_memory, mask=None) assert output.shape[0] == 4 assert output.shape[1] == 1, "size not {}".format(output.shape[1]) assert output.shape[2] == 80 * 2, "size not {}".format(output.shape[2]) assert stop_tokens.shape[0] == 4
def test_in_out(self): layer = Decoder(in_features=256, memory_dim=80, r=2, memory_size=4, attn_windowing=False, attn_norm="sigmoid") dummy_input = T.rand(4, 8, 256) dummy_memory = T.rand(4, 2, 80) output, alignment, stop_tokens = layer(dummy_input, dummy_memory, mask=None) assert output.shape[0] == 4 assert output.shape[1] == 1, "size not {}".format(output.shape[1]) assert output.shape[2] == 80 * 2, "size not {}".format(output.shape[2]) assert stop_tokens.shape[0] == 4
class Tacotron(nn.Module): def __init__(self, num_chars, r=5, linear_dim=1025, mel_dim=80, memory_size=5, attn_win=False, attn_norm="sigmoid", prenet_type="original", prenet_dropout=True, forward_attn=False, trans_agent=False, location_attn=True, separate_stopnet=True): super(Tacotron, self).__init__() self.r = r self.mel_dim = mel_dim self.linear_dim = linear_dim self.embedding = nn.Embedding(num_chars, 256) self.embedding.weight.data.normal_(0, 0.3) self.encoder = Encoder(256) self.decoder = Decoder(256, mel_dim, r, memory_size, attn_win, attn_norm, prenet_type, prenet_dropout, forward_attn, trans_agent, location_attn, separate_stopnet) self.postnet = PostCBHG(mel_dim) self.last_linear = nn.Sequential( nn.Linear(self.postnet.cbhg.gru_features * 2, linear_dim), nn.Sigmoid()) def forward(self, characters, text_lengths, mel_specs): B = characters.size(0) mask = sequence_mask(text_lengths).to(characters.device) inputs = self.embedding(characters) encoder_outputs = self.encoder(inputs) mel_outputs, alignments, stop_tokens = self.decoder( encoder_outputs, mel_specs, mask) mel_outputs = mel_outputs.view(B, -1, self.mel_dim) linear_outputs = self.postnet(mel_outputs) linear_outputs = self.last_linear(linear_outputs) return mel_outputs, linear_outputs, alignments, stop_tokens def inference(self, characters): B = characters.size(0) inputs = self.embedding(characters) encoder_outputs = self.encoder(inputs) mel_outputs, alignments, stop_tokens = self.decoder.inference( encoder_outputs) mel_outputs = mel_outputs.view(B, -1, self.mel_dim) linear_outputs = self.postnet(mel_outputs) linear_outputs = self.last_linear(linear_outputs) return mel_outputs, linear_outputs, alignments, stop_tokens
def test_in_out(self): layer = Decoder(in_features=256, memory_dim=80, r=2) dummy_input = T.rand(4, 8, 256) dummy_memory = T.rand(4, 2, 80) output, alignment, stop_tokens = layer(dummy_input, dummy_memory) assert output.shape[0] == 4 assert output.shape[1] == 1, "size not {}".format(output.shape[1]) assert output.shape[2] == 80 * 2, "size not {}".format(output.shape[2]) assert stop_tokens.shape[0] == 4 assert stop_tokens.max() <= 1.0 assert stop_tokens.min() >= 0
def __init__(self, embedding_dim=256, linear_dim=1025, mel_dim=80, r=5, padding_idx=None): super(Tacotron, self).__init__() self.r = r self.mel_dim = mel_dim self.linear_dim = linear_dim self.embedding = nn.Embedding(len(symbols), embedding_dim, padding_idx=padding_idx) print(" | > Number of characters : {}".format(len(symbols))) self.embedding.weight.data.normal_(0, 0.3) self.encoder = Encoder(embedding_dim) self.decoder = Decoder(256, mel_dim, r) self.postnet = CBHG(mel_dim, K=8, projections=[256, mel_dim]) self.last_linear = nn.Linear(mel_dim * 2, linear_dim)
def __init__(self, embedding_dim=256, linear_dim=1025, mel_dim=80, r=5, padding_idx=None): super(Tacotron, self).__init__() self.r = r self.mel_dim = mel_dim self.linear_dim = linear_dim self.embedding = nn.Embedding( len(symbols), embedding_dim, padding_idx=padding_idx) print(" | > Number of characters : {}".format(len(symbols))) self.embedding.weight.data.normal_(0, 0.3) self.encoder = Encoder(embedding_dim) self.decoder = Decoder(256, mel_dim, r) self.postnet = PostCBHG(mel_dim) self.last_linear = nn.Sequential( nn.Linear(self.postnet.cbhg.gru_features * 2, linear_dim), nn.Sigmoid())
class TacotronGST(nn.Module): def __init__(self, num_chars, num_speakers, r=5, linear_dim=1025, mel_dim=80, memory_size=5, attn_win=False, attn_norm="sigmoid", prenet_type="original", prenet_dropout=True, forward_attn=False, trans_agent=False, forward_attn_mask=False, location_attn=True, separate_stopnet=True): super(TacotronGST, self).__init__() self.r = r self.mel_dim = mel_dim self.linear_dim = linear_dim self.embedding = nn.Embedding(num_chars, 256) self.embedding.weight.data.normal_(0, 0.3) if num_speakers > 1: self.speaker_embedding = nn.Embedding(num_speakers, 256) self.speaker_embedding.weight.data.normal_(0, 0.3) self.encoder = Encoder(256) self.gst = GST(num_mel=80, num_heads=4, num_style_tokens=10, embedding_dim=256) self.decoder = Decoder(256, mel_dim, r, memory_size, attn_win, attn_norm, prenet_type, prenet_dropout, forward_attn, trans_agent, forward_attn_mask, location_attn, separate_stopnet) self.postnet = PostCBHG(mel_dim) self.last_linear = nn.Sequential( nn.Linear(self.postnet.cbhg.gru_features * 2, linear_dim), nn.Sigmoid()) def forward(self, characters, text_lengths, mel_specs, speaker_ids=None): B = characters.size(0) mask = sequence_mask(text_lengths).to(characters.device) inputs = self.embedding(characters) encoder_outputs = self.encoder(inputs) encoder_outputs = self._add_speaker_embedding(encoder_outputs, speaker_ids) gst_outputs = self.gst(mel_specs) gst_outputs = gst_outputs.expand(-1, encoder_outputs.size(1), -1) encoder_outputs = encoder_outputs + gst_outputs mel_outputs, alignments, stop_tokens = self.decoder( encoder_outputs, mel_specs, mask) mel_outputs = mel_outputs.view(B, -1, self.mel_dim) linear_outputs = self.postnet(mel_outputs) linear_outputs = self.last_linear(linear_outputs) return mel_outputs, linear_outputs, alignments, stop_tokens def inference(self, characters, speaker_ids=None, style_mel=None): B = characters.size(0) inputs = self.embedding(characters) encoder_outputs = self.encoder(inputs) encoder_outputs = self._add_speaker_embedding(encoder_outputs, speaker_ids) if style_mel is not None: gst_outputs = self.gst(style_mel) gst_outputs = gst_outputs.expand(-1, encoder_outputs.size(1), -1) encoder_outputs = encoder_outputs + gst_outputs mel_outputs, alignments, stop_tokens = self.decoder.inference( encoder_outputs) mel_outputs = mel_outputs.view(B, -1, self.mel_dim) linear_outputs = self.postnet(mel_outputs) linear_outputs = self.last_linear(linear_outputs) return mel_outputs, linear_outputs, alignments, stop_tokens def _add_speaker_embedding(self, encoder_outputs, speaker_ids): if hasattr(self, "speaker_embedding") and speaker_ids is not None: speaker_embeddings = self.speaker_embedding(speaker_ids) speaker_embeddings.unsqueeze_(1) speaker_embeddings = speaker_embeddings.expand( encoder_outputs.size(0), encoder_outputs.size(1), -1) encoder_outputs = encoder_outputs + speaker_embeddings return encoder_outputs