60 lines
2.4 KiB
Python
60 lines
2.4 KiB
Python
import paddle.fluid.dygraph as dg
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import paddle.fluid as fluid
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from parakeet.modules.utils import *
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from parakeet.models.fastspeech.FFTBlock import FFTBlock
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class Decoder(dg.Layer):
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def __init__(self,
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len_max_seq,
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n_layers,
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n_head,
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d_k,
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d_v,
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d_model,
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d_inner,
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fft_conv1d_kernel,
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fft_conv1d_padding,
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dropout=0.1):
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super(Decoder, self).__init__()
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n_position = len_max_seq + 1
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self.pos_inp = get_sinusoid_encoding_table(n_position, d_model, padding_idx=0)
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self.position_enc = dg.Embedding(size=[n_position, d_model],
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padding_idx=0,
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param_attr=fluid.ParamAttr(
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initializer=fluid.initializer.NumpyArrayInitializer(self.pos_inp),
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trainable=False))
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self.layer_stack = [FFTBlock(d_model, d_inner, n_head, d_k, d_v, fft_conv1d_kernel, fft_conv1d_padding, dropout=dropout) for _ in range(n_layers)]
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for i, layer in enumerate(self.layer_stack):
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self.add_sublayer('fft_{}'.format(i), layer)
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def forward(self, enc_seq, enc_pos):
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"""
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Decoder layer of FastSpeech.
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Args:
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enc_seq (Variable), Shape(B, text_T, C), dtype: float32.
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The output of length regulator.
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enc_pos (Variable, optional): Shape(B, T_mel),
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dtype: int64. The spectrum position. T_mel means the timesteps of input spectrum.
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Returns:
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dec_output (Variable), Shape(B, mel_T, C), the decoder output.
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dec_slf_attn_list (Variable), Shape(B, mel_T, mel_T), the decoder self attention list.
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"""
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dec_slf_attn_list = []
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# -- Prepare masks
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slf_attn_mask = get_attn_key_pad_mask(seq_k=enc_pos, seq_q=enc_pos)
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non_pad_mask = get_non_pad_mask(enc_pos)
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# -- Forward
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dec_output = enc_seq + self.position_enc(enc_pos)
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for dec_layer in self.layer_stack:
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dec_output, dec_slf_attn = dec_layer(
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dec_output,
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non_pad_mask=non_pad_mask,
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slf_attn_mask=slf_attn_mask)
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dec_slf_attn_list += [dec_slf_attn]
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return dec_output, dec_slf_attn_list |