update docstring of tacotron2
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lfchener
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ecdeb14a40
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@ -27,19 +27,18 @@ __all__ = ["Tacotron2", "Tacotron2Loss"]
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class DecoderPreNet(nn.Layer):
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"""
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Decoder prenet module for Tacotron2.
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"""Decoder prenet module for Tacotron2.
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Parameters
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----------
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d_input: int
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input dimension
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input feature size
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d_hidden: int
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hidden size
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d_output: int
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output Dimension
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output feature size
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dropout_rate: float
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droput probability
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@ -62,12 +61,12 @@ 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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x: Tensor [shape=(B, T_mel, C)]
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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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output: Tensor [shape=(B, T_mel, C)]
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batch of the sequences of padded hidden state
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"""
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@ -78,8 +77,7 @@ class DecoderPreNet(nn.Layer):
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class DecoderPostNet(nn.Layer):
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"""
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Decoder postnet module for Tacotron2.
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"""Decoder postnet module for Tacotron2.
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Parameters
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----------
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@ -151,12 +149,12 @@ 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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input: Tensor [shape=(B, T_mel, C)]
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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: Tensor [shape=(B, T_mel, C)]
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output sequence of features after postnet
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"""
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@ -170,8 +168,7 @@ class DecoderPostNet(nn.Layer):
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class Tacotron2Encoder(nn.Layer):
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"""
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Tacotron2 encoder module for Tacotron2.
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"""Tacotron2 encoder module for Tacotron2.
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Parameters
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----------
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@ -218,15 +215,15 @@ 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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x: Tensor [shape=(B, T)]
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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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text_lens: Tensor [shape=(B,)]
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batch of lengths of each text input batch.
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Returns
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-------
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output : Tensor[shape=(B, T, C)]
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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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"""
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@ -239,8 +236,7 @@ class Tacotron2Encoder(nn.Layer):
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class Tacotron2Decoder(nn.Layer):
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"""
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Tacotron2 decoder module for Tacotron2.
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"""Tacotron2 decoder module for Tacotron2.
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Parameters
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----------
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@ -278,7 +274,8 @@ class Tacotron2Decoder(nn.Layer):
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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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"""
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def __init__(self,
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d_mels: int,
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@ -396,15 +393,14 @@ class Tacotron2Decoder(nn.Layer):
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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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mel_output: Tensor [shape=(B, T_mel, C)]
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output sequence of features
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stop_logits: Tensor[shape=(B, T_mel)]
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stop_logits: Tensor [shape=(B, T_mel)]
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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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alignments: Tensor [shape=(B, T_mel, T_text)]
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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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@ -441,7 +437,7 @@ 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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keys: Tensor [shape=(B, T_text, 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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@ -452,13 +448,13 @@ class Tacotron2Decoder(nn.Layer):
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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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mel_output: Tensor [shape=(B, T_mel, C)]
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output sequence of features
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stop_logits: Tensor[shape=(B, T_mel)]
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stop_logits: Tensor [shape=(B, T_mel)]
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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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alignments: Tensor [shape=(B, T_mel, T_text)]
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attention weights
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"""
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@ -494,12 +490,11 @@ class Tacotron2Decoder(nn.Layer):
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class Tacotron2(nn.Layer):
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"""
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Tacotron2 model for end-to-end text-to-speech (E2E-TTS).
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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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by Conditioning WaveNet on Mel Spectrogram Predictions`_,
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which converts the sequence of characters
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into the sequence of mel spectrogram.
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@ -620,16 +615,16 @@ 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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text_inputs: Tensor [shape=(B, T_text)]
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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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mels: Tensor [shape(B, T_mel, C)]
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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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text_lens: Tensor [shape=(B,)]
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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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output_lens: Tensor [shape=(B,)]
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batch of lengths of each mels batch.
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Returns
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@ -679,7 +674,7 @@ 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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text_inputs: Tensor [shape=(B, T_text)]
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batch of the sequencees of padded character ids
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stop_threshold: float
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@ -765,10 +760,10 @@ class Tacotron2(nn.Layer):
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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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mel_outputs_postnet: Tensor [shape=(T_mel, C)]
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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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alignments: Tensor [shape=(T_mel, T_text)]
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attention weights
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"""
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model = cls(frontend,
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@ -809,24 +804,24 @@ 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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mel_outputs: Tensor [shape=(B, T_mel, C)]
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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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mel_outputs_postnet: Tensor [shape(B, T_mel, C)]
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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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stop_logits: Tensor [shape=(B, T_mel)]
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output sequence of stop logits befor sigmoid
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mel_targets: Tensor[shape=(B,)]
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mel_targets: Tensor [shape=(B, T_mel, C)]
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target mel spectrogram sequence
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stop_tokens:
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stop_tokens: Tensor [shape=(B,)]
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target stop token
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Returns
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-------
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losses : Dict[str, float]
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losses : Dict[str, Tensor]
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loss: the sum of the other three losses
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