add docstring for LocationSensitiveAttention
This commit is contained in:
lfchener
parent
dd2c5cc6c6
commit
1af9127ee6
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@ -32,16 +32,16 @@ class DecoderPreNet(nn.Layer):
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Parameters
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----------
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d_input: int
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input feature size
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The input feature size.
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d_hidden: int
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hidden size
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The hidden size.
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d_output: int
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output feature size
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The output feature size.
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dropout_rate: float
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droput probability
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The droput probability.
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"""
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@ -49,7 +49,7 @@ class DecoderPreNet(nn.Layer):
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d_input: int,
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d_hidden: int,
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d_output: int,
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dropout_rate: float=0.2):
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dropout_rate: float):
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super().__init__()
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self.dropout_rate = dropout_rate
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@ -62,12 +62,12 @@ class DecoderPreNet(nn.Layer):
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Parameters
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----------
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x: Tensor [shape=(B, T_mel, C)]
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batch of the sequences of padded mel spectrogram
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Batch of the sequences of padded mel spectrogram.
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Returns
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-------
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output: Tensor [shape=(B, T_mel, C)]
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batch of the sequences of padded hidden state
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Batch of the sequences of padded hidden state.
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"""
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@ -82,28 +82,28 @@ class DecoderPostNet(nn.Layer):
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Parameters
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----------
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d_mels: int
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number of mel bands
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The number of mel bands.
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d_hidden: int
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hidden size of postnet
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The hidden size of postnet.
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kernel_size: int
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kernel size of the conv layer in postnet
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The kernel size of the conv layer in postnet.
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num_layers: int
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number of conv layers in postnet
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The number of conv layers in postnet.
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dropout: float
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droput probability
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The droput probability.
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"""
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def __init__(self,
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d_mels: int=80,
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d_hidden: int=512,
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kernel_size: int=5,
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num_layers: int=5,
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dropout: float=0.1):
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d_mels: int,
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d_hidden: int,
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kernel_size: int,
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num_layers: int,
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dropout: float):
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super().__init__()
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self.dropout = dropout
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self.num_layers = num_layers
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@ -150,12 +150,12 @@ class DecoderPostNet(nn.Layer):
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Parameters
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----------
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input: Tensor [shape=(B, T_mel, C)]
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output sequence of features from decoder
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Output sequence of features from decoder.
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Returns
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-------
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output: Tensor [shape=(B, T_mel, C)]
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output sequence of features after postnet
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Output sequence of features after postnet.
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"""
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@ -173,16 +173,16 @@ class Tacotron2Encoder(nn.Layer):
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Parameters
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----------
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d_hidden: int
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hidden size in encoder module
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The hidden size in encoder module.
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conv_layers: int
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number of conv layers
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The number of conv layers.
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kernel_size: int
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kernel size of conv layers
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The kernel size of conv layers.
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p_dropout: float
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droput probability
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The droput probability.
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"""
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def __init__(self,
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@ -216,15 +216,15 @@ class Tacotron2Encoder(nn.Layer):
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Parameters
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----------
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x: Tensor [shape=(B, T)]
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batch of the sequencees of padded character ids
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Batch of the sequencees of padded character ids.
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text_lens: Tensor [shape=(B,)]
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batch of lengths of each text input batch.
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text_lens: Tensor [shape=(B,)], optional
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Batch of lengths of each text input batch. Defaults to None.
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Returns
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-------
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output : Tensor [shape=(B, T, C)]
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batch of the sequences of padded hidden states
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Batch of the sequences of padded hidden states.
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"""
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for conv_batchnorm in self.conv_batchnorms:
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@ -241,40 +241,40 @@ class Tacotron2Decoder(nn.Layer):
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Parameters
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----------
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d_mels: int
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number of mel bands
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The number of mel bands.
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reduction_factor: int
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reduction factor of tacotron
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The reduction factor of tacotron.
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d_encoder: int
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hidden size of encoder
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The hidden size of encoder.
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d_prenet: int
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hidden size in decoder prenet
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The hidden size in decoder prenet.
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d_attention_rnn: int
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attention rnn layer hidden size
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The attention rnn layer hidden size.
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d_decoder_rnn: int
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decoder rnn layer hidden size
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The decoder rnn layer hidden size.
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d_attention: int
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hidden size of the linear layer in location sensitive attention
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The hidden size of the linear layer in location sensitive attention.
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attention_filters: int
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filter size of the conv layer in location sensitive attention
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The filter size of the conv layer in location sensitive attention.
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attention_kernel_size: int
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kernel size of the conv layer in location sensitive attention
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The kernel size of the conv layer in location sensitive attention.
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p_prenet_dropout: float
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droput probability in decoder prenet
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The droput probability in decoder prenet.
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p_attention_dropout: float
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droput probability in location sensitive attention
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The droput probability in location sensitive attention.
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p_decoder_dropout: float
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droput probability in decoder
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The droput probability in decoder.
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"""
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def __init__(self,
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@ -382,25 +382,25 @@ class Tacotron2Decoder(nn.Layer):
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Parameters
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----------
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keys: Tensor[shape=(B, T_text, C)]
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batch of the sequences of padded output from encoder
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keys: Tensor[shape=(B, T_key, C)]
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Batch of the sequences of padded output from encoder.
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querys: Tensor[shape(B, T_mel, C)]
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batch of the sequences of padded mel spectrogram
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querys: Tensor[shape(B, T_query, C)]
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Batch of the sequences of padded mel spectrogram.
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mask: Tensor[shape=(B, T_text, 1)]
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mask generated with text length
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mask: Tensor
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Mask generated with text length. Shape should be (B, T_key, T_query) or broadcastable shape.
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Returns
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-------
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mel_output: Tensor [shape=(B, T_mel, C)]
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output sequence of features
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mel_output: Tensor [shape=(B, T_query, C)]
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Output sequence of features.
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stop_logits: Tensor [shape=(B, T_mel)]
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output sequence of stop logits
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stop_logits: Tensor [shape=(B, T_query)]
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Output sequence of stop logits.
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alignments: Tensor [shape=(B, T_mel, T_text)]
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attention weights
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alignments: Tensor [shape=(B, T_query, T_key)]
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Attention weights.
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"""
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querys = paddle.reshape(
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querys,
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@ -437,25 +437,25 @@ class Tacotron2Decoder(nn.Layer):
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Parameters
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----------
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keys: Tensor [shape=(B, T_text, C)]
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batch of the sequences of padded output from encoder
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keys: Tensor [shape=(B, T_key, C)]
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Batch of the sequences of padded output from encoder.
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stop_threshold: float
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stop synthesize when stop logit is greater than this stop threshold
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stop_threshold: float, optional
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Stop synthesize when stop logit is greater than this stop threshold. Defaults to 0.5.
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max_decoder_steps: int
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number of max step when synthesize
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max_decoder_steps: int, optional
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Number of max step when synthesize. Defaults to 1000.
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Returns
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-------
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mel_output: Tensor [shape=(B, T_mel, C)]
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output sequence of features
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Output sequence of features.
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stop_logits: Tensor [shape=(B, T_mel)]
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output sequence of stop logits
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Output sequence of stop logits.
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alignments: Tensor [shape=(B, T_mel, T_text)]
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attention weights
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alignments: Tensor [shape=(B, T_mel, T_key)]
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Attention weights.
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"""
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query = paddle.zeros(
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@ -493,75 +493,72 @@ class Tacotron2(nn.Layer):
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"""Tacotron2 model for end-to-end text-to-speech (E2E-TTS).
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This is a model of Spectrogram prediction network in Tacotron2 described
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in ``Natural TTS Synthesis
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by Conditioning WaveNet on Mel Spectrogram Predictions``,
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in `Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
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<https://arxiv.org/abs/1712.05884>`_,
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which converts the sequence of characters
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into the sequence of mel spectrogram.
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`Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
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<https://arxiv.org/abs/1712.05884>`_.
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Parameters
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----------
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frontend : parakeet.frontend.Phonetics
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frontend used to preprocess text
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Frontend used to preprocess text.
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d_mels: int
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number of mel bands
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Number of mel bands.
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d_encoder: int
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hidden size in encoder module
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Hidden size in encoder module.
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encoder_conv_layers: int
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number of conv layers in encoder
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Number of conv layers in encoder.
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encoder_kernel_size: int
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kernel size of conv layers in encoder
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Kernel size of conv layers in encoder.
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d_prenet: int
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hidden size in decoder prenet
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Hidden size in decoder prenet.
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d_attention_rnn: int
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attention rnn layer hidden size in decoder
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Attention rnn layer hidden size in decoder.
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d_decoder_rnn: int
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decoder rnn layer hidden size in decoder
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Decoder rnn layer hidden size in decoder.
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attention_filters: int
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filter size of the conv layer in location sensitive attention
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Filter size of the conv layer in location sensitive attention.
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attention_kernel_size: int
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kernel size of the conv layer in location sensitive attention
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Kernel size of the conv layer in location sensitive attention.
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d_attention: int
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hidden size of the linear layer in location sensitive attention
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Hidden size of the linear layer in location sensitive attention.
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d_postnet: int
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hidden size of postnet
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Hidden size of postnet.
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postnet_kernel_size: int
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kernel size of the conv layer in postnet
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Kernel size of the conv layer in postnet.
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postnet_conv_layers: int
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number of conv layers in postnet
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Number of conv layers in postnet.
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reduction_factor: int
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reduction factor of tacotron
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Reduction factor of tacotron2.
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p_encoder_dropout: float
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droput probability in encoder
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Droput probability in encoder.
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p_prenet_dropout: float
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droput probability in decoder prenet
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Droput probability in decoder prenet.
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p_attention_dropout: float
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droput probability in location sensitive attention
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Droput probability in location sensitive attention.
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p_decoder_dropout: float
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droput probability in decoder
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Droput probability in decoder.
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p_postnet_dropout: float
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droput probability in postnet
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Droput probability in postnet.
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"""
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@ -616,28 +613,28 @@ class Tacotron2(nn.Layer):
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Parameters
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----------
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text_inputs: Tensor [shape=(B, T_text)]
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batch of the sequencees of padded character ids
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Batch of the sequencees of padded character ids.
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mels: Tensor [shape(B, T_mel, C)]
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batch of the sequences of padded mel spectrogram
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Batch of the sequences of padded mel spectrogram.
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text_lens: Tensor [shape=(B,)]
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batch of lengths of each text input batch.
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Batch of lengths of each text input batch.
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output_lens: Tensor [shape=(B,)]
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batch of lengths of each mels batch.
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output_lens: Tensor [shape=(B,)], optional
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Batch of lengths of each mels batch. Defaults to None.
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Returns
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-------
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outputs : Dict[str, Tensor]
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mel_output: output sequence of features (B, T_mel, C)
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mel_output: output sequence of features (B, T_mel, C);
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mel_outputs_postnet: output sequence of features after postnet (B, T_mel, C)
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mel_outputs_postnet: output sequence of features after postnet (B, T_mel, C);
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stop_logits: output sequence of stop logits (B, T_mel)
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stop_logits: output sequence of stop logits (B, T_mel);
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alignments: attention weights (B, T_mel, T_text)
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alignments: attention weights (B, T_mel, T_text).
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"""
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embedded_inputs = self.embedding(text_inputs)
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encoder_outputs = self.encoder(embedded_inputs, text_lens)
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@ -675,25 +672,25 @@ class Tacotron2(nn.Layer):
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Parameters
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----------
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text_inputs: Tensor [shape=(B, T_text)]
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batch of the sequencees of padded character ids
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Batch of the sequencees of padded character ids.
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stop_threshold: float
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stop synthesize when stop logit is greater than this stop threshold
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stop_threshold: float, optional
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Stop synthesize when stop logit is greater than this stop threshold. Defaults to 0.5.
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max_decoder_steps: int
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number of max step when synthesize
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max_decoder_steps: int, optional
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Number of max step when synthesize. Defaults to 1000.
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Returns
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-------
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outputs : Dict[str, Tensor]
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mel_output: output sequence of sepctrogram (B, T_mel, C)
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mel_output: output sequence of sepctrogram (B, T_mel, C);
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mel_outputs_postnet: output sequence of sepctrogram after postnet (B, T_mel, C)
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mel_outputs_postnet: output sequence of sepctrogram after postnet (B, T_mel, C);
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stop_logits: output sequence of stop logits (B, T_mel)
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stop_logits: output sequence of stop logits (B, T_mel);
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alignments: attention weights (B, T_mel, T_text)
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alignments: attention weights (B, T_mel, T_text).
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"""
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embedded_inputs = self.embedding(text_inputs)
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encoder_outputs = self.encoder(embedded_inputs)
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@ -721,21 +718,21 @@ class Tacotron2(nn.Layer):
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Parameters
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----------
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text: str
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sequence of characters
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Sequence of characters.
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stop_threshold: float
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stop synthesize when stop logit is greater than this stop threshold
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stop_threshold: float, optional
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Stop synthesize when stop logit is greater than this stop threshold. Defaults to 0.5.
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max_decoder_steps: int
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number of max step when synthesize
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max_decoder_steps: int, optional
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Number of max step when synthesize. Defaults to 1000.
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Returns
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-------
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outputs : Dict[str, Tensor]
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mel_outputs_postnet: output sequence of sepctrogram after postnet (T_mel, C)
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mel_outputs_postnet: output sequence of sepctrogram after postnet (T_mel, C);
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alignments: attention weights (T_mel, T_text)
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alignments: attention weights (T_mel, T_text).
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"""
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ids = np.asarray(self.frontend(text))
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ids = paddle.unsqueeze(paddle.to_tensor(ids, dtype='int64'), [0])
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@ -750,21 +747,21 @@ class Tacotron2(nn.Layer):
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Parameters
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----------
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frontend: parakeet.frontend.Phonetics
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frontend used to preprocess text
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Frontend used to preprocess text.
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config: yacs.config.CfgNode
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model configs
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Model configs.
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checkpoint_path: Path
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the path of pretrained model checkpoint
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The path of pretrained model checkpoint.
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Returns
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-------
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mel_outputs_postnet: Tensor [shape=(T_mel, C)]
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output sequence of sepctrogram after postnet
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Output sequence of sepctrogram after postnet.
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alignments: Tensor [shape=(T_mel, T_text)]
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attention weights
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Attention weights.
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"""
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model = cls(frontend,
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d_mels=config.data.d_mels,
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@ -805,31 +802,31 @@ class Tacotron2Loss(nn.Layer):
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Parameters
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----------
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mel_outputs: Tensor [shape=(B, T_mel, C)]
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output mel spectrogram sequence
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Output mel spectrogram sequence.
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mel_outputs_postnet: Tensor [shape(B, T_mel, C)]
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output mel spectrogram sequence after postnet
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Output mel spectrogram sequence after postnet.
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stop_logits: Tensor [shape=(B, T_mel)]
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output sequence of stop logits befor sigmoid
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Output sequence of stop logits befor sigmoid.
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mel_targets: Tensor [shape=(B, T_mel, C)]
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target mel spectrogram sequence
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Target mel spectrogram sequence.
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stop_tokens: Tensor [shape=(B,)]
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target stop token
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Target stop token.
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Returns
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-------
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losses : Dict[str, Tensor]
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loss: the sum of the other three losses
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loss: the sum of the other three losses;
|
||||
|
||||
mel_loss: MSE loss compute by mel_targets and mel_outputs
|
||||
mel_loss: MSE loss compute by mel_targets and mel_outputs;
|
||||
|
||||
post_mel_loss: MSE loss compute by mel_targets and mel_outputs_postnet
|
||||
post_mel_loss: MSE loss compute by mel_targets and mel_outputs_postnet;
|
||||
|
||||
stop_loss: stop loss computed by stop_logits and stop token
|
||||
stop_loss: stop loss computed by stop_logits and stop token.
|
||||
"""
|
||||
mel_loss = paddle.nn.MSELoss()(mel_outputs, mel_targets)
|
||||
post_mel_loss = paddle.nn.MSELoss()(mel_outputs_postnet, mel_targets)
|
||||
|
|
|
|||
|
|
@ -18,6 +18,7 @@ import paddle
|
|||
from paddle import nn
|
||||
from paddle.nn import functional as F
|
||||
|
||||
|
||||
def scaled_dot_product_attention(q,
|
||||
k,
|
||||
v,
|
||||
|
|
@ -139,10 +140,11 @@ class MonoheadAttention(nn.Layer):
|
|||
Feature size of the key of each scaled dot product attention. If not
|
||||
provided, it is set to `model_dim / num_heads`. Defaults to None.
|
||||
"""
|
||||
def __init__(self,
|
||||
model_dim: int,
|
||||
dropout: float=0.0,
|
||||
k_dim: int=None,
|
||||
|
||||
def __init__(self,
|
||||
model_dim: int,
|
||||
dropout: float=0.0,
|
||||
k_dim: int=None,
|
||||
v_dim: int=None):
|
||||
super(MonoheadAttention, self).__init__()
|
||||
k_dim = k_dim or model_dim
|
||||
|
|
@ -219,6 +221,7 @@ class MultiheadAttention(nn.Layer):
|
|||
ValueError
|
||||
If ``model_dim`` is not divisible by ``num_heads``.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
model_dim: int,
|
||||
num_heads: int,
|
||||
|
|
@ -279,6 +282,28 @@ class MultiheadAttention(nn.Layer):
|
|||
|
||||
|
||||
class LocationSensitiveAttention(nn.Layer):
|
||||
"""Location Sensitive Attention module.
|
||||
|
||||
Reference: `Attention-Based Models for Speech Recognition <https://arxiv.org/pdf/1506.07503.pdf>`_
|
||||
|
||||
Parameters
|
||||
-----------
|
||||
d_query: int
|
||||
The feature size of query.
|
||||
|
||||
d_key : int
|
||||
The feature size of key.
|
||||
|
||||
d_attention : int
|
||||
The feature size of dimension.
|
||||
|
||||
location_filters : int
|
||||
Filter size of attention convolution.
|
||||
|
||||
location_kernel_size : int
|
||||
Kernel size of attention convolution.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
d_query: int,
|
||||
d_key: int,
|
||||
|
|
@ -310,6 +335,34 @@ class LocationSensitiveAttention(nn.Layer):
|
|||
value,
|
||||
attention_weights_cat,
|
||||
mask=None):
|
||||
"""Compute context vector and attention weights.
|
||||
|
||||
Parameters
|
||||
-----------
|
||||
query : Tensor [shape=(batch_size, d_query)]
|
||||
The queries.
|
||||
|
||||
processed_key : Tensor [shape=(batch_size, time_steps_k, d_attention)]
|
||||
The keys after linear layer.
|
||||
|
||||
value : Tensor [shape=(batch_size, time_steps_k, d_key)]
|
||||
The values.
|
||||
|
||||
attention_weights_cat : Tensor [shape=(batch_size, time_step_k, 2)]
|
||||
Attention weights concat.
|
||||
|
||||
mask : Tensor, optional
|
||||
The mask. Shape should be (batch_size, times_steps_q, time_steps_k) or broadcastable shape.
|
||||
Defaults to None.
|
||||
|
||||
Returns
|
||||
----------
|
||||
attention_context : Tensor [shape=(batch_size, time_steps_q, d_attention)]
|
||||
The context vector.
|
||||
|
||||
attention_weights : Tensor [shape=(batch_size, times_steps_q, time_steps_k)]
|
||||
The attention weights.
|
||||
"""
|
||||
|
||||
processed_query = self.query_layer(paddle.unsqueeze(query, axis=[1]))
|
||||
processed_attention_weights = self.location_layer(
|
||||
|
|
|
|||
Loading…
Reference in New Issue