Merge pull request #45 from lfchener/reborn

add TTS model tacotron2

parakeet/frontend/normalizer/normalizer.py

@@ -0,0 +1,32 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import re
+import unicodedata
+from builtins import str as unicode
+from parakeet.frontend.normalizer.numbers import normalize_numbers
+
+
+def normalize(sentence):
+    # preprocessing
+    sentence = unicode(sentence)
+    sentence = normalize_numbers(sentence)
+    sentence = ''.join(
+        char for char in unicodedata.normalize('NFD', sentence)
+        if unicodedata.category(char) != 'Mn')  # Strip accents
+    sentence = sentence.lower()
+    sentence = re.sub(r"[^ a-z'.,?!\-]", "", sentence)
+    sentence = sentence.replace("i.e.", "that is")
+    sentence = sentence.replace("e.g.", "for example")
+    return sentence.split()

parakeet/frontend/normalizer/numbers.py

@@ -1,3 +1,84 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 # number expansion is not that easy
-import num2words
-import inflect
+import inflect
+import re
+
+_inflect = inflect.engine()
+_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
+_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)')
+_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)')
+_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)')
+_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)')
+_number_re = re.compile(r'[0-9]+')
+
+
+def _remove_commas(m):
+    return m.group(1).replace(',', '')
+
+
+def _expand_decimal_point(m):
+    return m.group(1).replace('.', ' point ')
+
+
+def _expand_dollars(m):
+    match = m.group(1)
+    parts = match.split('.')
+    if len(parts) > 2:
+        return match + ' dollars'  # Unexpected format
+    dollars = int(parts[0]) if parts[0] else 0
+    cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
+    if dollars and cents:
+        dollar_unit = 'dollar' if dollars == 1 else 'dollars'
+        cent_unit = 'cent' if cents == 1 else 'cents'
+        return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit)
+    elif dollars:
+        dollar_unit = 'dollar' if dollars == 1 else 'dollars'
+        return '%s %s' % (dollars, dollar_unit)
+    elif cents:
+        cent_unit = 'cent' if cents == 1 else 'cents'
+        return '%s %s' % (cents, cent_unit)
+    else:
+        return 'zero dollars'
+
+
+def _expand_ordinal(m):
+    return _inflect.number_to_words(m.group(0))
+
+
+def _expand_number(m):
+    num = int(m.group(0))
+    if num > 1000 and num < 3000:
+        if num == 2000:
+            return 'two thousand'
+        elif num > 2000 and num < 2010:
+            return 'two thousand ' + _inflect.number_to_words(num % 100)
+        elif num % 100 == 0:
+            return _inflect.number_to_words(num // 100) + ' hundred'
+        else:
+            return _inflect.number_to_words(
+                num, andword='', zero='oh', group=2).replace(', ', ' ')
+    else:
+        return _inflect.number_to_words(num, andword='')
+
+
+def normalize_numbers(text):
+    text = re.sub(_comma_number_re, _remove_commas, text)
+    text = re.sub(_pounds_re, r'\1 pounds', text)
+    text = re.sub(_dollars_re, _expand_dollars, text)
+    text = re.sub(_decimal_number_re, _expand_decimal_point, text)
+    text = re.sub(_ordinal_re, _expand_ordinal, text)
+    text = re.sub(_number_re, _expand_number, text)
+    return text

parakeet/frontend/phonectic.py

@@ -1,3 +1,17 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from abc import ABC, abstractmethod
 from typing import Union
 from g2p_en import G2p
@@ -5,30 +19,32 @@ from g2pM import G2pM
 from parakeet.frontend import Vocab
 from opencc import OpenCC
 from parakeet.frontend.punctuation import get_punctuations
+from parakeet.frontend.normalizer.normalizer import normalize
 
-__all__ = ["Phonetics", "English", "Chinese"]
+__all__ = ["Phonetics", "English", "EnglishCharacter", "Chinese"]
 
 
 class Phonetics(ABC):
     @abstractmethod
     def __call__(self, sentence):
         pass
-    
+
     @abstractmethod
     def phoneticize(self, sentence):
         pass
-    
+
     @abstractmethod
     def numericalize(self, phonemes):
         pass
 
+
 class English(Phonetics):
     def __init__(self):
         self.backend = G2p()
         self.phonemes = list(self.backend.phonemes)
         self.punctuations = get_punctuations("en")
         self.vocab = Vocab(self.phonemes + self.punctuations)
-    
+
     def phoneticize(self, sentence):
         start = self.vocab.start_symbol
         end = self.vocab.end_symbol
@@ -36,17 +52,58 @@ class English(Phonetics):
                  + self.backend(sentence) \
                  + ([] if end is None else [end])
         return phonemes
-    
+
     def numericalize(self, phonemes):
-        ids = [self.vocab.lookup(item) for item in phonemes if item in self.vocab.stoi]
+        ids = [
+            self.vocab.lookup(item) for item in phonemes
+            if item in self.vocab.stoi
+        ]
         return ids
-    
+
     def reverse(self, ids):
         return [self.vocab.reverse(i) for i in ids]
-    
+
     def __call__(self, sentence):
         return self.numericalize(self.phoneticize(sentence))
-    
+
+    @property
+    def vocab_size(self):
+        return len(self.vocab)
+
+
+class EnglishCharacter(Phonetics):
+    def __init__(self):
+        self.backend = G2p()
+        self.graphemes = list(self.backend.graphemes)
+        self.punctuations = get_punctuations("en")
+        self.vocab = Vocab(self.graphemes + self.punctuations)
+
+    def phoneticize(self, sentence):
+        start = self.vocab.start_symbol
+        end = self.vocab.end_symbol
+
+        words = ([] if start is None else [start]) \
+                 + normalize(sentence) \
+                 + ([] if end is None else [end])
+        return words
+
+    def numericalize(self, words):
+        ids = []
+        for word in words:
+            if word in self.vocab.stoi:
+                ids.append(self.vocab.lookup(word))
+                continue
+            for char in word:
+                if char in self.vocab.stoi:
+                    ids.append(self.vocab.lookup(char))
+        return ids
+
+    def reverse(self, ids):
+        return [self.vocab.reverse(i) for i in ids]
+
+    def __call__(self, sentence):
+        return self.numericalize(self.phoneticize(sentence))
+
     @property
     def vocab_size(self):
         return len(self.vocab)
@@ -59,9 +116,11 @@ class Chinese(Phonetics):
         self.phonemes = self._get_all_syllables()
         self.punctuations = get_punctuations("cn")
         self.vocab = Vocab(self.phonemes + self.punctuations)
-    
+
     def _get_all_syllables(self):
-        all_syllables = set([syllable for k, v in self.backend.cedict.items() for syllable in v])
+        all_syllables = set([
+            syllable for k, v in self.backend.cedict.items() for syllable in v
+        ])
         return list(all_syllables)
 
     def phoneticize(self, sentence):
@@ -73,7 +132,7 @@ class Chinese(Phonetics):
                  + phonemes \
                  + ([] if end is None else [end])
         return self._filter_symbols(phonemes)
-        
+
     def _filter_symbols(self, phonemes):
         cleaned_phonemes = []
         for item in phonemes:
@@ -84,17 +143,17 @@ class Chinese(Phonetics):
                     if char in self.vocab.stoi:
                         cleaned_phonemes.append(char)
         return cleaned_phonemes
-    
+
     def numericalize(self, phonemes):
         ids = [self.vocab.lookup(item) for item in phonemes]
         return ids
-    
+
     def __call__(self, sentence):
         return self.numericalize(self.phoneticize(sentence))
-    
+
     @property
     def vocab_size(self):
         return len(self.vocab)
-    
+
     def reverse(self, ids):
         return [self.vocab.reverse(i) for i in ids]

parakeet/models/tacotron2.py

@@ -0,0 +1,427 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+import paddle
+from paddle import nn
+from paddle.nn import functional as F
+import parakeet
+from parakeet.modules.conv import Conv1dBatchNorm
+from parakeet.modules.attention import LocationSensitiveAttention
+from parakeet.modules import masking
+
+__all__ = ["Tacotron2", "Tacotron2Loss"]
+
+
+class DecoderPreNet(nn.Layer):
+    def __init__(self,
+                 d_input: int,
+                 d_hidden: int,
+                 d_output: int,
+                 dropout_rate: int=0.2):
+        super().__init__()
+
+        self.dropout_rate = dropout_rate
+        self.linear1 = nn.Linear(d_input, d_hidden, bias_attr=False)
+        self.linear2 = nn.Linear(d_hidden, d_output, bias_attr=False)
+
+    def forward(self, x):
+        x = F.dropout(F.relu(self.linear1(x)), self.dropout_rate)
+        output = F.dropout(F.relu(self.linear2(x)), self.dropout_rate)
+        return output
+
+
+class DecoderPostNet(nn.Layer):
+    def __init__(self,
+                 d_mels: int=80,
+                 d_hidden: int=512,
+                 kernel_size: int=5,
+                 padding: int=0,
+                 num_layers: int=5,
+                 dropout=0.1):
+        super().__init__()
+        self.dropout = dropout
+        self.num_layers = num_layers
+
+        self.conv_batchnorms = nn.LayerList()
+        k = math.sqrt(1.0 / (d_mels * kernel_size))
+        self.conv_batchnorms.append(
+            Conv1dBatchNorm(
+                d_mels,
+                d_hidden,
+                kernel_size=kernel_size,
+                padding=padding,
+                bias_attr=paddle.ParamAttr(initializer=nn.initializer.Uniform(
+                    low=-k, high=k)),
+                data_format='NLC'))
+
+        k = math.sqrt(1.0 / (d_hidden * kernel_size))
+        self.conv_batchnorms.extend([
+            Conv1dBatchNorm(
+                d_hidden,
+                d_hidden,
+                kernel_size=kernel_size,
+                padding=padding,
+                bias_attr=paddle.ParamAttr(initializer=nn.initializer.Uniform(
+                    low=-k, high=k)),
+                data_format='NLC') for i in range(1, num_layers - 1)
+        ])
+
+        self.conv_batchnorms.append(
+            Conv1dBatchNorm(
+                d_hidden,
+                d_mels,
+                kernel_size=kernel_size,
+                padding=padding,
+                bias_attr=paddle.ParamAttr(initializer=nn.initializer.Uniform(
+                    low=-k, high=k)),
+                data_format='NLC'))
+
+    def forward(self, input):
+        for i in range(len(self.conv_batchnorms) - 1):
+            input = F.dropout(
+                F.tanh(self.conv_batchnorms[i](input), self.dropout))
+        input = F.dropout(self.conv_batchnorms[self.num_layers - 1](input),
+                          self.dropout)
+        return input
+
+
+class Tacotron2Encoder(nn.Layer):
+    def __init__(self,
+                 d_hidden: int,
+                 conv_layers: int,
+                 kernel_size: int,
+                 p_dropout: float):
+        super().__init__()
+
+        k = math.sqrt(1.0 / (d_hidden * kernel_size))
+        self.conv_batchnorms = paddle.nn.LayerList([
+            Conv1dBatchNorm(
+                d_hidden,
+                d_hidden,
+                kernel_size,
+                stride=1,
+                padding=int((kernel_size - 1) / 2),
+                bias_attr=paddle.ParamAttr(initializer=nn.initializer.Uniform(
+                    low=-k, high=k)),
+                data_format='NLC') for i in range(conv_layers)
+        ])
+        self.p_dropout = p_dropout
+
+        self.hidden_size = int(d_hidden / 2)
+        self.lstm = nn.LSTM(
+            d_hidden, self.hidden_size, direction="bidirectional")
+
+    def forward(self, x, input_lens=None):
+        for conv_batchnorm in self.conv_batchnorms:
+            x = F.dropout(F.relu(conv_batchnorm(x)),
+                          self.p_dropout)  #(B, T, C)
+
+        output, _ = self.lstm(inputs=x, sequence_length=input_lens)
+        return output
+
+
+class Tacotron2Decoder(nn.Layer):
+    def __init__(self,
+                 d_mels: int,
+                 reduction_factor: int,
+                 d_encoder: int,
+                 d_prenet: int,
+                 d_attention_rnn: int,
+                 d_decoder_rnn: int,
+                 d_attention: int,
+                 attention_filters: int,
+                 attention_kernel_size: int,
+                 p_prenet_dropout: float,
+                 p_attention_dropout: float,
+                 p_decoder_dropout: float):
+        super().__init__()
+        self.d_mels = d_mels
+        self.reduction_factor = reduction_factor
+        self.d_encoder = d_encoder
+        self.d_attention_rnn = d_attention_rnn
+        self.d_decoder_rnn = d_decoder_rnn
+        self.p_attention_dropout = p_attention_dropout
+        self.p_decoder_dropout = p_decoder_dropout
+
+        self.prenet = DecoderPreNet(
+            d_mels * reduction_factor,
+            d_prenet,
+            d_prenet,
+            dropout_rate=p_prenet_dropout)
+
+        self.attention_rnn = nn.LSTMCell(d_prenet + d_encoder, d_attention_rnn)
+
+        self.attention_layer = LocationSensitiveAttention(
+            d_attention_rnn, d_encoder, d_attention, attention_filters,
+            attention_kernel_size)
+        self.decoder_rnn = nn.LSTMCell(d_attention_rnn + d_encoder,
+                                       d_decoder_rnn)
+        self.linear_projection = nn.Linear(d_decoder_rnn + d_encoder,
+                                           d_mels * reduction_factor)
+        self.stop_layer = nn.Linear(d_decoder_rnn + d_encoder, 1)
+
+    def _initialize_decoder_states(self, key):
+        batch_size = key.shape[0]
+        MAX_TIME = key.shape[1]
+
+        self.attention_hidden = paddle.zeros(
+            shape=[batch_size, self.d_attention_rnn], dtype=key.dtype)
+        self.attention_cell = paddle.zeros(
+            shape=[batch_size, self.d_attention_rnn], dtype=key.dtype)
+
+        self.decoder_hidden = paddle.zeros(
+            shape=[batch_size, self.d_decoder_rnn], dtype=key.dtype)
+        self.decoder_cell = paddle.zeros(
+            shape=[batch_size, self.d_decoder_rnn], dtype=key.dtype)
+
+        self.attention_weights = paddle.zeros(
+            shape=[batch_size, MAX_TIME], dtype=key.dtype)
+        self.attention_weights_cum = paddle.zeros(
+            shape=[batch_size, MAX_TIME], dtype=key.dtype)
+        self.attention_context = paddle.zeros(
+            shape=[batch_size, self.d_encoder], dtype=key.dtype)
+
+        self.key = key  #[B, T, C]
+        self.processed_key = self.attention_layer.key_layer(key)  #[B, T, C]
+
+    def _decode(self, query):
+        cell_input = paddle.concat([query, self.attention_context], axis=-1)
+
+        # The first lstm layer
+        _, (self.attention_hidden, self.attention_cell) = self.attention_rnn(
+            cell_input, (self.attention_hidden, self.attention_cell))
+        self.attention_hidden = F.dropout(self.attention_hidden,
+                                          self.p_attention_dropout)
+
+        # Loaction sensitive attention
+        attention_weights_cat = paddle.stack(
+            [self.attention_weights, self.attention_weights_cum], axis=-1)
+        self.attention_context, self.attention_weights = self.attention_layer(
+            self.attention_hidden, self.processed_key, self.key,
+            attention_weights_cat, self.mask)
+        self.attention_weights_cum += self.attention_weights
+
+        # The second lstm layer
+        decoder_input = paddle.concat(
+            [self.attention_hidden, self.attention_context], axis=-1)
+        _, (self.decoder_hidden, self.decoder_cell) = self.decoder_rnn(
+            decoder_input, (self.decoder_hidden, self.decoder_cell))
+        self.decoder_hidden = F.dropout(
+            self.decoder_hidden, p=self.p_decoder_dropout)
+
+        # decode output one step
+        decoder_hidden_attention_context = paddle.concat(
+            [self.decoder_hidden, self.attention_context], axis=-1)
+        decoder_output = self.linear_projection(
+            decoder_hidden_attention_context)
+        stop_logit = self.stop_layer(decoder_hidden_attention_context)
+        return decoder_output, stop_logit, self.attention_weights
+
+    def forward(self, keys, querys, mask):
+        querys = paddle.reshape(
+            querys,
+            [querys.shape[0], querys.shape[1] // self.reduction_factor, -1])
+        querys = paddle.concat(
+            [
+                paddle.zeros(
+                    shape=[
+                        querys.shape[0], 1,
+                        querys.shape[-1] * self.reduction_factor
+                    ],
+                    dtype=querys.dtype), querys
+            ],
+            axis=1)
+        querys = self.prenet(querys)
+
+        self._initialize_decoder_states(keys)
+        self.mask = mask
+
+        mel_outputs, stop_logits, alignments = [], [], []
+        while len(mel_outputs) < querys.shape[
+                1] - 1:  # Ignore the last time step
+            query = querys[:, len(mel_outputs), :]
+            mel_output, stop_logit, attention_weights = self._decode(query)
+            mel_outputs += [mel_output]
+            stop_logits += [stop_logit]
+            alignments += [attention_weights]
+
+        alignments = paddle.stack(alignments, axis=1)
+        stop_logits = paddle.concat(stop_logits, axis=1)
+        mel_outputs = paddle.stack(mel_outputs, axis=1)
+
+        return mel_outputs, stop_logits, alignments
+
+    def infer(self, key, stop_threshold=0.5, max_decoder_steps=1000):
+        decoder_input = paddle.zeros(
+            shape=[key.shape[0], self.d_mels * self.reduction_factor],
+            dtype=key.dtype)  #[B, C]
+
+        self.initialize_decoder_states(key)
+        self.mask = None
+
+        mel_outputs, stop_logits, alignments = [], [], []
+        while True:
+            decoder_input = self.prenet(decoder_input)
+            mel_output, stop_logit, alignment = self.decode(decoder_input)
+
+            mel_outputs += [mel_output]
+            stop_logits += [stop_logit]
+            alignments += [alignment]
+
+            if F.sigmoid(stop_logit) > stop_threshold:
+                break
+            elif len(mel_outputs) == max_decoder_steps:
+                print("Warning! Reached max decoder steps!!!")
+                break
+
+            decoder_input = mel_output
+
+        alignments = paddle.stack(alignments, axis=1)
+        stop_logits = paddle.concat(stop_logits, axis=1)
+        mel_outputs = paddle.stack(mel_outputs, axis=1)
+
+        return mel_outputs, stop_logits, alignments
+
+
+class Tacotron2(nn.Layer):
+    """
+    Tacotron2 module for end-to-end text-to-speech (E2E-TTS).
+
+    This is a module of Spectrogram prediction network in Tacotron2 described
+    in `Natural TTS Synthesis
+    by Conditioning WaveNet on Mel Spectrogram Predictions`_,
+    which converts the sequence of characters
+    into the sequence of mel spectrogram.
+
+    .. _`Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions`:
+       https://arxiv.org/abs/1712.05884
+    """
+
+    def __init__(self,
+                 frontend: parakeet.frontend.Phonetics,
+                 d_mels: int=80,
+                 d_encoder: int=512,
+                 encoder_conv_layers: int=3,
+                 encoder_kernel_size: int=5,
+                 d_prenet: int=256,
+                 d_attention_rnn: int=1024,
+                 d_decoder_rnn: int=1024,
+                 attention_filters: int=32,
+                 attention_kernel_size: int=31,
+                 d_attention: int=128,
+                 d_postnet: int=512,
+                 postnet_kernel_size: int=5,
+                 postnet_conv_layers: int=5,
+                 reduction_factor: int=1,
+                 p_encoder_dropout: float=0.5,
+                 p_prenet_dropout: float=0.5,
+                 p_attention_dropout: float=0.1,
+                 p_decoder_dropout: float=0.1,
+                 p_postnet_dropout: float=0.5):
+        super().__init__()
+
+        std = math.sqrt(2.0 / (frontend.vocab_size + d_encoder))
+        val = math.sqrt(3.0) * std  # uniform bounds for std
+        self.embedding = nn.Embedding(
+            frontend.vocab_size,
+            d_encoder,
+            weight_attr=paddle.ParamAttr(initializer=nn.initializer.Uniform(
+                low=-val, high=val)))
+        self.encoder = Tacotron2Encoder(d_encoder, encoder_conv_layers,
+                                        encoder_kernel_size, p_encoder_dropout)
+        self.decoder = Tacotron2Decoder(
+            d_mels, reduction_factor, d_encoder, d_prenet, d_attention_rnn,
+            d_decoder_rnn, d_attention, attention_filters,
+            attention_kernel_size, p_prenet_dropout, p_attention_dropout,
+            p_decoder_dropout)
+        self.postnet = DecoderPostNet(
+            d_mels=d_mels,
+            d_hidden=d_postnet,
+            kernel_size=postnet_kernel_size,
+            padding=int((postnet_kernel_size - 1) / 2),
+            num_layers=postnet_conv_layers,
+            dropout=p_postnet_dropout)
+
+    def forward(self, text_inputs, mels, text_lens, output_lens=None):
+        embedded_inputs = self.embedding(text_inputs)
+        encoder_outputs = self.encoder(embedded_inputs, text_lens)
+
+        mask = paddle.tensor.unsqueeze(
+            paddle.fluid.layers.sequence_mask(
+                x=text_lens, dtype=encoder_outputs.dtype), [-1])
+        mel_outputs, stop_logits, alignments = self.decoder(
+            encoder_outputs, mels, mask=mask)
+
+        mel_outputs_postnet = self.postnet(mel_outputs)
+        mel_outputs_postnet = mel_outputs + mel_outputs_postnet
+
+        if output_lens is not None:
+            mask = paddle.tensor.unsqueeze(
+                paddle.fluid.layers.sequence_mask(x=output_lens),
+                [-1])  #[B, T, 1]
+            mel_outputs = mel_outputs * mask  #[B, T, C]
+            mel_outputs_postnet = mel_outputs_postnet * mask  #[B, T, C]
+            stop_logits = stop_logits * mask[:, :, 0] + (1 - mask[:, :, 0]
+                                                         ) * 1e3  #[B, T]
+        outputs = {
+            "mel_output": mel_outputs,
+            "mel_outputs_postnet": mel_outputs_postnet,
+            "stop_logits": stop_logits,
+            "alignments": alignments
+        }
+
+        return outputs
+
+    def infer(self, text_inputs, stop_threshold=0.5, max_decoder_steps=1000):
+        embedded_inputs = self.embedding(text_inputs)
+        encoder_outputs = self.encoder(embedded_inputs)
+        mel_outputs, stop_logits, alignments = self.decoder.inference(
+            encoder_outputs,
+            stop_threshold=stop_threshold,
+            max_decoder_steps=max_decoder_steps)
+
+        mel_outputs_postnet = self.postnet(mel_outputs)
+        mel_outputs_postnet = mel_outputs + mel_outputs_postnet
+
+        outputs = {
+            "mel_output": mel_outputs,
+            "mel_outputs_postnet": mel_outputs_postnet,
+            "stop_logits": stop_logits,
+            "alignments": alignments
+        }
+
+        return outputs
+
+    def predict(self, text):
+        # TODO(lifuchen): implement predict function to product mel from texts
+        pass
+
+
+class Tacotron2Loss(nn.Layer):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, mel_outputs, mel_outputs_postnet, stop_logits,
+                mel_targets, stop_tokens):
+        mel_loss = paddle.nn.MSELoss()(mel_outputs, mel_targets)
+        post_mel_loss = paddle.nn.MSELoss()(mel_outputs_postnet, mel_targets)
+        stop_loss = paddle.nn.BCEWithLogitsLoss()(stop_logits, stop_tokens)
+        total_loss = mel_loss + post_mel_loss + stop_loss
+        losses = dict(
+            loss=total_loss,
+            mel_loss=mel_loss,
+            post_mel_loss=post_mel_loss,
+            stop_loss=stop_loss)
+        return losses

parakeet/models/transformer_tts.py

@@ -1,3 +1,17 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import math
 from tqdm import trange
 import paddle
@@ -15,6 +29,7 @@ from parakeet.modules import losses as L
 
 __all__ = ["TransformerTTS", "TransformerTTSLoss"]
 
+
 # Transformer TTS's own implementation of transformer
 class MultiheadAttention(nn.Layer):
     """
@@ -25,7 +40,14 @@ class MultiheadAttention(nn.Layer):
     Another deviation is that it concats the input query and context vector before
     applying the output projection.
     """
-    def __init__(self, model_dim, num_heads, k_dim=None, v_dim=None, k_input_dim=None, v_input_dim=None):
+
+    def __init__(self,
+                 model_dim,
+                 num_heads,
+                 k_dim=None,
+                 v_dim=None,
+                 k_input_dim=None,
+                 v_input_dim=None):
         """
         Args:
             model_dim (int): the feature size of query.
@@ -41,7 +63,7 @@ class MultiheadAttention(nn.Layer):
             ValueError: if model_dim is not divisible by num_heads
         """
         super(MultiheadAttention, self).__init__()
-        if model_dim % num_heads !=0:
+        if model_dim % num_heads != 0:
             raise ValueError("model_dim must be divisible by num_heads")
         depth = model_dim // num_heads
         k_dim = k_dim or depth
@@ -52,10 +74,10 @@ class MultiheadAttention(nn.Layer):
         self.affine_k = nn.Linear(k_input_dim, num_heads * k_dim)
         self.affine_v = nn.Linear(v_input_dim, num_heads * v_dim)
         self.affine_o = nn.Linear(model_dim + num_heads * v_dim, model_dim)
-        
+
         self.num_heads = num_heads
         self.model_dim = model_dim
-    
+
     def forward(self, q, k, v, mask, drop_n_heads=0):
         """
         Compute context vector and attention weights.
@@ -72,17 +94,18 @@ class MultiheadAttention(nn.Layer):
             attention_weights (Tensor): shape(batch_size, times_steps_q, time_steps_k), the attention weights.
         """
         q_in = q
-        q = _split_heads(self.affine_q(q), self.num_heads) # (B, h, T, C)
+        q = _split_heads(self.affine_q(q), self.num_heads)  # (B, h, T, C)
         k = _split_heads(self.affine_k(k), self.num_heads)
         v = _split_heads(self.affine_v(v), self.num_heads)
         if mask is not None:
-            mask = paddle.unsqueeze(mask, 1) # unsqueeze for the h dim
-        
+            mask = paddle.unsqueeze(mask, 1)  # unsqueeze for the h dim
+
         context_vectors, attention_weights = scaled_dot_product_attention(
             q, k, v, mask, training=self.training)
-        context_vectors = drop_head(context_vectors, drop_n_heads, self.training)
-        context_vectors = _concat_heads(context_vectors) # (B, T, h*C)
-        
+        context_vectors = drop_head(context_vectors, drop_n_heads,
+                                    self.training)
+        context_vectors = _concat_heads(context_vectors)  # (B, T, h*C)
+
         concat_feature = paddle.concat([q_in, context_vectors], -1)
         out = self.affine_o(concat_feature)
         return out, attention_weights
@@ -92,6 +115,7 @@ class TransformerEncoderLayer(nn.Layer):
     """
     Transformer encoder layer.
     """
+
     def __init__(self, d_model, n_heads, d_ffn, dropout=0.):
         """
         Args:
@@ -114,8 +138,10 @@ class TransformerEncoderLayer(nn.Layer):
         # PreLN scheme: Norm -> SubLayer -> Dropout -> Residual
         x_in = x
         x = self.layer_norm1(x)
-        context_vector, attn_weights = self.self_mha(x, x, x, mask, drop_n_heads)
-        context_vector = x_in + F.dropout(context_vector, self.dropout, training=self.training)
+        context_vector, attn_weights = self.self_mha(x, x, x, mask,
+                                                     drop_n_heads)
+        context_vector = x_in + F.dropout(
+            context_vector, self.dropout, training=self.training)
         return context_vector, attn_weights
 
     def _forward_ffn(self, x):
@@ -123,9 +149,9 @@ class TransformerEncoderLayer(nn.Layer):
         x_in = x
         x = self.layer_norm2(x)
         x = self.ffn(x)
-        out= x_in + F.dropout(x, self.dropout, training=self.training)
+        out = x_in + F.dropout(x, self.dropout, training=self.training)
         return out
-    
+
     def forward(self, x, mask, drop_n_heads=0):
         """
         Args:
@@ -145,6 +171,7 @@ class TransformerDecoderLayer(nn.Layer):
     """
     Transformer decoder layer.
     """
+
     def __init__(self, d_model, n_heads, d_ffn, dropout=0., d_encoder=None):
         """
         Args:
@@ -157,37 +184,42 @@ class TransformerDecoderLayer(nn.Layer):
         super(TransformerDecoderLayer, self).__init__()
         self.self_mha = MultiheadAttention(d_model, n_heads)
         self.layer_norm1 = nn.LayerNorm([d_model], epsilon=1e-6)
-        
-        self.cross_mha = MultiheadAttention(d_model, n_heads, k_input_dim=d_encoder, v_input_dim=d_encoder)
+
+        self.cross_mha = MultiheadAttention(
+            d_model, n_heads, k_input_dim=d_encoder, v_input_dim=d_encoder)
         self.layer_norm2 = nn.LayerNorm([d_model], epsilon=1e-6)
-        
+
         self.ffn = PositionwiseFFN(d_model, d_ffn, dropout)
         self.layer_norm3 = nn.LayerNorm([d_model], epsilon=1e-6)
 
         self.dropout = dropout
-    
+
     def _forward_self_mha(self, x, mask, drop_n_heads):
         # PreLN scheme: Norm -> SubLayer -> Dropout -> Residual
         x_in = x
         x = self.layer_norm1(x)
-        context_vector, attn_weights = self.self_mha(x, x, x, mask, drop_n_heads)
-        context_vector = x_in + F.dropout(context_vector, self.dropout, training=self.training)
+        context_vector, attn_weights = self.self_mha(x, x, x, mask,
+                                                     drop_n_heads)
+        context_vector = x_in + F.dropout(
+            context_vector, self.dropout, training=self.training)
         return context_vector, attn_weights
 
     def _forward_cross_mha(self, q, k, v, mask, drop_n_heads):
         # PreLN scheme: Norm -> SubLayer -> Dropout -> Residual
         q_in = q
         q = self.layer_norm2(q)
-        context_vector, attn_weights = self.cross_mha(q, k, v, mask, drop_n_heads)
-        context_vector = q_in + F.dropout(context_vector, self.dropout, training=self.training)
+        context_vector, attn_weights = self.cross_mha(q, k, v, mask,
+                                                      drop_n_heads)
+        context_vector = q_in + F.dropout(
+            context_vector, self.dropout, training=self.training)
         return context_vector, attn_weights
-    
+
     def _forward_ffn(self, x):
         # PreLN scheme: Norm -> SubLayer -> Dropout -> Residual
         x_in = x
         x = self.layer_norm3(x)
         x = self.ffn(x)
-        out= x_in + F.dropout(x, self.dropout, training=self.training)
+        out = x_in + F.dropout(x, self.dropout, training=self.training)
         return out
 
     def forward(self, q, k, v, encoder_mask, decoder_mask, drop_n_heads=0):
@@ -204,8 +236,10 @@ class TransformerDecoderLayer(nn.Layer):
             self_attn_weights (Tensor), shape(batch_size, n_heads, time_steps_q, time_steps_q), decoder self attention.
             cross_attn_weights (Tensor), shape(batch_size, n_heads, time_steps_q, time_steps_k), decoder-encoder cross attention.
         """
-        q, self_attn_weights = self._forward_self_mha(q, decoder_mask, drop_n_heads)
-        q, cross_attn_weights = self._forward_cross_mha(q, k, v, encoder_mask, drop_n_heads)
+        q, self_attn_weights = self._forward_self_mha(q, decoder_mask,
+                                                      drop_n_heads)
+        q, cross_attn_weights = self._forward_cross_mha(q, k, v, encoder_mask,
+                                                        drop_n_heads)
         q = self._forward_ffn(q)
         return q, self_attn_weights, cross_attn_weights
 
@@ -214,7 +248,8 @@ class TransformerEncoder(nn.LayerList):
     def __init__(self, d_model, n_heads, d_ffn, n_layers, dropout=0.):
         super(TransformerEncoder, self).__init__()
         for _ in range(n_layers):
-            self.append(TransformerEncoderLayer(d_model, n_heads, d_ffn, dropout))
+            self.append(
+                TransformerEncoderLayer(d_model, n_heads, d_ffn, dropout))
 
     def forward(self, x, mask, drop_n_heads=0):
         """
@@ -236,10 +271,18 @@ class TransformerEncoder(nn.LayerList):
 
 
 class TransformerDecoder(nn.LayerList):
-    def __init__(self, d_model, n_heads, d_ffn, n_layers, dropout=0., d_encoder=None):
+    def __init__(self,
+                 d_model,
+                 n_heads,
+                 d_ffn,
+                 n_layers,
+                 dropout=0.,
+                 d_encoder=None):
         super(TransformerDecoder, self).__init__()
         for _ in range(n_layers):
-            self.append(TransformerDecoderLayer(d_model, n_heads, d_ffn, dropout, d_encoder=d_encoder))
+            self.append(
+                TransformerDecoderLayer(
+                    d_model, n_heads, d_ffn, dropout, d_encoder=d_encoder))
 
     def forward(self, q, k, v, encoder_mask, decoder_mask, drop_n_heads=0):
         """[summary]
@@ -260,7 +303,8 @@ class TransformerDecoder(nn.LayerList):
         self_attention_weights = []
         cross_attention_weights = []
         for layer in self:
-            q, self_attention_weights_i, cross_attention_weights_i = layer(q, k, v, encoder_mask, decoder_mask, drop_n_heads)
+            q, self_attention_weights_i, cross_attention_weights_i = layer(
+                q, k, v, encoder_mask, decoder_mask, drop_n_heads)
             self_attention_weights.append(self_attention_weights_i)
             cross_attention_weights.append(cross_attention_weights_i)
         return q, self_attention_weights, cross_attention_weights
@@ -268,6 +312,7 @@ class TransformerDecoder(nn.LayerList):
 
 class MLPPreNet(nn.Layer):
     """Decoder's prenet."""
+
     def __init__(self, d_input, d_hidden, d_output, dropout):
         # (lin + relu + dropout) * n + last projection
         super(MLPPreNet, self).__init__()
@@ -275,16 +320,24 @@ class MLPPreNet(nn.Layer):
         self.lin2 = nn.Linear(d_hidden, d_hidden)
         self.lin3 = nn.Linear(d_hidden, d_hidden)
         self.dropout = dropout
-        
+
     def forward(self, x, dropout):
-        l1 = F.dropout(F.relu(self.lin1(x)), self.dropout, training=self.training)
-        l2 = F.dropout(F.relu(self.lin2(l1)), self.dropout, training=self.training)
+        l1 = F.dropout(
+            F.relu(self.lin1(x)), self.dropout, training=self.training)
+        l2 = F.dropout(
+            F.relu(self.lin2(l1)), self.dropout, training=self.training)
         l3 = self.lin3(l2)
         return l3
 
+
 # NOTE: not used in 
 class CNNPreNet(nn.Layer):
-    def __init__(self, d_input, d_hidden, d_output, kernel_size, n_layers, 
+    def __init__(self,
+                 d_input,
+                 d_hidden,
+                 d_output,
+                 kernel_size,
+                 n_layers,
                  dropout=0.):
         # (conv + bn + relu + dropout) * n + last projection
         super(CNNPreNet, self).__init__()
@@ -292,16 +345,21 @@ class CNNPreNet(nn.Layer):
         c_in = d_input
         for _ in range(n_layers):
             self.convs.append(
-                Conv1dBatchNorm(c_in, d_hidden, kernel_size, 
-                                weight_attr=I.XavierUniform(),
-                                padding="same", data_format="NLC"))
+                Conv1dBatchNorm(
+                    c_in,
+                    d_hidden,
+                    kernel_size,
+                    weight_attr=I.XavierUniform(),
+                    padding="same",
+                    data_format="NLC"))
             c_in = d_hidden
         self.affine_out = nn.Linear(d_hidden, d_output)
         self.dropout = dropout
-    
+
     def forward(self, x):
         for layer in self.convs:
-            x = F.dropout(F.relu(layer(x)), self.dropout, training=self.training)
+            x = F.dropout(
+                F.relu(layer(x)), self.dropout, training=self.training)
         x = self.affine_out(x)
         return x
 
@@ -310,21 +368,25 @@ class CNNPostNet(nn.Layer):
     def __init__(self, d_input, d_hidden, d_output, kernel_size, n_layers):
         super(CNNPostNet, self).__init__()
         self.convs = nn.LayerList()
-        kernel_size = kernel_size if isinstance(kernel_size, (tuple, list)) else (kernel_size, ) 
+        kernel_size = kernel_size if isinstance(kernel_size, (
+            tuple, list)) else (kernel_size, )
         padding = (kernel_size[0] - 1, 0)
         for i in range(n_layers):
             c_in = d_input if i == 0 else d_hidden
             c_out = d_output if i == n_layers - 1 else d_hidden
             self.convs.append(
-                Conv1dBatchNorm(c_in, c_out, kernel_size, 
-                                weight_attr=I.XavierUniform(), 
-                                padding=padding))
+                Conv1dBatchNorm(
+                    c_in,
+                    c_out,
+                    kernel_size,
+                    weight_attr=I.XavierUniform(),
+                    padding=padding))
         self.last_bn = nn.BatchNorm1D(d_output)
         # for a layer that ends with a normalization layer that is targeted to
         # output a non zero-central output, it may take a long time to 
         # train the scale and bias
         # NOTE: it can also be a non-causal conv
-    
+
     def forward(self, x):
         x_in = x
         for i, layer in enumerate(self.convs):
@@ -336,19 +398,19 @@ class CNNPostNet(nn.Layer):
 
 
 class TransformerTTS(nn.Layer):
-    def __init__(self, 
-                 frontend: parakeet.frontend.Phonetics, 
-                 d_encoder: int, 
-                 d_decoder: int, 
-                 d_mel: int, 
+    def __init__(self,
+                 frontend: parakeet.frontend.Phonetics,
+                 d_encoder: int,
+                 d_decoder: int,
+                 d_mel: int,
                  n_heads: int,
                  d_ffn: int,
-                 encoder_layers: int, 
-                 decoder_layers: int, 
-                 d_prenet: int, 
-                 d_postnet: int, 
-                 postnet_layers: int, 
-                 postnet_kernel_size: int, 
+                 encoder_layers: int,
+                 decoder_layers: int,
+                 d_prenet: int,
+                 d_postnet: int,
+                 postnet_layers: int,
+                 postnet_kernel_size: int,
                  max_reduction_factor: int,
                  decoder_prenet_dropout: float,
                  dropout: float):
@@ -359,29 +421,34 @@ class TransformerTTS(nn.Layer):
 
         # encoder
         self.encoder_prenet = nn.Embedding(
-            frontend.vocab_size, d_encoder, 
-            padding_idx=frontend.vocab.padding_index, 
+            frontend.vocab_size,
+            d_encoder,
+            padding_idx=frontend.vocab.padding_index,
             weight_attr=I.Uniform(-0.05, 0.05))
         # position encoding matrix may be extended later
-        self.encoder_pe = pe.positional_encoding(0, 1000, d_encoder) 
+        self.encoder_pe = pe.positional_encoding(0, 1000, d_encoder)
         self.encoder_pe_scalar = self.create_parameter(
             [1], attr=I.Constant(1.))
-        self.encoder = TransformerEncoder(
-            d_encoder, n_heads, d_ffn, encoder_layers, dropout)
-        
+        self.encoder = TransformerEncoder(d_encoder, n_heads, d_ffn,
+                                          encoder_layers, dropout)
+
         # decoder
         self.decoder_prenet = MLPPreNet(d_mel, d_prenet, d_decoder, dropout)
         self.decoder_pe = pe.positional_encoding(0, 1000, d_decoder)
         self.decoder_pe_scalar = self.create_parameter(
             [1], attr=I.Constant(1.))
         self.decoder = TransformerDecoder(
-            d_decoder, n_heads, d_ffn, decoder_layers, dropout, 
+            d_decoder,
+            n_heads,
+            d_ffn,
+            decoder_layers,
+            dropout,
             d_encoder=d_encoder)
         self.final_proj = nn.Linear(d_decoder, max_reduction_factor * d_mel)
-        self.decoder_postnet = CNNPostNet(
-            d_mel, d_postnet, d_mel, postnet_kernel_size, postnet_layers)
+        self.decoder_postnet = CNNPostNet(d_mel, d_postnet, d_mel,
+                                          postnet_kernel_size, postnet_layers)
         self.stop_conditioner = nn.Linear(d_mel, 3)
-        
+
         # specs
         self.padding_idx = frontend.vocab.padding_index
         self.d_encoder = d_encoder
@@ -390,21 +457,22 @@ class TransformerTTS(nn.Layer):
         self.max_r = max_reduction_factor
         self.dropout = dropout
         self.decoder_prenet_dropout = decoder_prenet_dropout
-        
+
         # start and end: though it is only used in predict 
         # it can also be used in training
         dtype = paddle.get_default_dtype()
         self.start_vec = paddle.full([1, d_mel], 0.5, dtype=dtype)
         self.end_vec = paddle.full([1, d_mel], -0.5, dtype=dtype)
         self.stop_prob_index = 2
-        
+
         # mutables
-        self.r = max_reduction_factor # set it every call
+        self.r = max_reduction_factor  # set it every call
         self.drop_n_heads = 0
-        
+
     def forward(self, text, mel):
         encoded, encoder_attention_weights, encoder_mask = self.encode(text)
-        mel_output, mel_intermediate, cross_attention_weights, stop_logits = self.decode(encoded, mel, encoder_mask)
+        mel_output, mel_intermediate, cross_attention_weights, stop_logits = self.decode(
+            encoded, mel, encoder_mask)
         outputs = {
             "mel_output": mel_output,
             "mel_intermediate": mel_intermediate,
@@ -420,51 +488,54 @@ class TransformerTTS(nn.Layer):
         if embed.shape[1] > self.encoder_pe.shape[0]:
             new_T = max(embed.shape[1], self.encoder_pe.shape[0] * 2)
             self.encoder_pe = pe.positional_encoding(0, new_T, self.d_encoder)
-        pos_enc = self.encoder_pe[:T_enc, :] # (T, C)
-        x = embed.scale(math.sqrt(self.d_encoder)) + pos_enc * self.encoder_pe_scalar
+        pos_enc = self.encoder_pe[:T_enc, :]  # (T, C)
+        x = embed.scale(math.sqrt(
+            self.d_encoder)) + pos_enc * self.encoder_pe_scalar
         x = F.dropout(x, self.dropout, training=self.training)
 
         # TODO(chenfeiyu): unsqueeze a decoder_time_steps=1 for the mask
         encoder_padding_mask = paddle.unsqueeze(
-            masking.id_mask(text, self.padding_idx, dtype=x.dtype), 1)
-        x, attention_weights = self.encoder(x, encoder_padding_mask, self.drop_n_heads)
+            masking.id_mask(
+                text, self.padding_idx, dtype=x.dtype), 1)
+        x, attention_weights = self.encoder(x, encoder_padding_mask,
+                                            self.drop_n_heads)
         return x, attention_weights, encoder_padding_mask
-    
+
     def decode(self, encoder_output, input, encoder_padding_mask):
         batch_size, T_dec, mel_dim = input.shape
-        
+
         x = self.decoder_prenet(input, self.decoder_prenet_dropout)
         # twice its length if needed
         if x.shape[1] * self.r > self.decoder_pe.shape[0]:
             new_T = max(x.shape[1] * self.r, self.decoder_pe.shape[0] * 2)
             self.decoder_pe = pe.positional_encoding(0, new_T, self.d_decoder)
-        pos_enc = self.decoder_pe[:T_dec*self.r:self.r, :]  
-        x = x.scale(math.sqrt(self.d_decoder)) + pos_enc * self.decoder_pe_scalar
+        pos_enc = self.decoder_pe[:T_dec * self.r:self.r, :]
+        x = x.scale(math.sqrt(
+            self.d_decoder)) + pos_enc * self.decoder_pe_scalar
         x = F.dropout(x, self.dropout, training=self.training)
 
         no_future_mask = masking.future_mask(T_dec, dtype=input.dtype)
-        decoder_padding_mask = masking.feature_mask(input, axis=-1, dtype=input.dtype)
-        decoder_mask = masking.combine_mask(decoder_padding_mask.unsqueeze(-1), no_future_mask)
+        decoder_padding_mask = masking.feature_mask(
+            input, axis=-1, dtype=input.dtype)
+        decoder_mask = masking.combine_mask(
+            decoder_padding_mask.unsqueeze(-1), no_future_mask)
         decoder_output, _, cross_attention_weights = self.decoder(
-            x, 
-            encoder_output, 
-            encoder_output, 
-            encoder_padding_mask, 
-            decoder_mask,
-            self.drop_n_heads)
+            x, encoder_output, encoder_output, encoder_padding_mask,
+            decoder_mask, self.drop_n_heads)
 
         # use only parts of it
-        output_proj = self.final_proj(decoder_output)[:, :, : self.r * mel_dim]
-        mel_intermediate = paddle.reshape(output_proj, [batch_size, -1, mel_dim])
+        output_proj = self.final_proj(decoder_output)[:, :, :self.r * mel_dim]
+        mel_intermediate = paddle.reshape(output_proj,
+                                          [batch_size, -1, mel_dim])
         stop_logits = self.stop_conditioner(mel_intermediate)
-        
+
         # cnn postnet
         mel_channel_first = paddle.transpose(mel_intermediate, [0, 2, 1])
         mel_output = self.decoder_postnet(mel_channel_first)
         mel_output = paddle.transpose(mel_output, [0, 2, 1])
 
         return mel_output, mel_intermediate, cross_attention_weights, stop_logits
-    
+
     def predict(self, input, raw_input=True, max_length=1000, verbose=True):
         """Predict log scale magnitude mel spectrogram from text input.
 
@@ -475,26 +546,32 @@ class TransformerTTS(nn.Layer):
         """
         if raw_input:
             text_ids = paddle.to_tensor(self.frontend(input))
-            text_input = paddle.unsqueeze(text_ids, 0) # (1, T)
+            text_input = paddle.unsqueeze(text_ids, 0)  # (1, T)
         else:
             text_input = input
-        
-        decoder_input = paddle.unsqueeze(self.start_vec, 0) # (B=1, T, C)
-        decoder_output = paddle.unsqueeze(self.start_vec, 0) # (B=1, T, C)
-        
+
+        decoder_input = paddle.unsqueeze(self.start_vec, 0)  # (B=1, T, C)
+        decoder_output = paddle.unsqueeze(self.start_vec, 0)  # (B=1, T, C)
+
         # encoder the text sequence
-        encoder_output, encoder_attentions, encoder_padding_mask = self.encode(text_input)
-        for _ in trange(int(max_length // self.r) + 1):
+        encoder_output, encoder_attentions, encoder_padding_mask = self.encode(
+            text_input)
+        for _ in range(int(max_length // self.r) + 1):
             mel_output, _, cross_attention_weights, stop_logits = self.decode(
                 encoder_output, decoder_input, encoder_padding_mask)
-            
+
             # extract last step and append it to decoder input
-            decoder_input = paddle.concat([decoder_input, mel_output[:, -1:, :]], 1)
+            decoder_input = paddle.concat(
+                [decoder_input, mel_output[:, -1:, :]], 1)
             # extract last r steps and append it to decoder output
-            decoder_output = paddle.concat([decoder_output, mel_output[:, -self.r:, :]], 1)
-            
+            decoder_output = paddle.concat(
+                [decoder_output, mel_output[:, -self.r:, :]], 1)
+
             # stop condition: (if any ouput frame of the output multiframes hits the stop condition)
-            if paddle.any(paddle.argmax(stop_logits[0, -self.r:, :], axis=-1) == self.stop_prob_index):
+            if paddle.any(
+                    paddle.argmax(
+                        stop_logits[0, -self.r:, :], axis=-1) ==
+                    self.stop_prob_index):
                 if verbose:
                     print("Hits stop condition.")
                 break
@@ -516,24 +593,28 @@ class TransformerTTSLoss(nn.Layer):
     def __init__(self, stop_loss_scale):
         super(TransformerTTSLoss, self).__init__()
         self.stop_loss_scale = stop_loss_scale
-    
-    def forward(self, mel_output, mel_intermediate, mel_target, stop_logits, stop_probs):
-        mask = masking.feature_mask(mel_target, axis=-1, dtype=mel_target.dtype)
+
+    def forward(self, mel_output, mel_intermediate, mel_target, stop_logits,
+                stop_probs):
+        mask = masking.feature_mask(
+            mel_target, axis=-1, dtype=mel_target.dtype)
         mask1 = paddle.unsqueeze(mask, -1)
         mel_loss1 = L.masked_l1_loss(mel_output, mel_target, mask1)
         mel_loss2 = L.masked_l1_loss(mel_intermediate, mel_target, mask1)
-        
+
         mel_len = mask.shape[-1]
-        last_position = F.one_hot(mask.sum(-1).astype("int64") - 1, num_classes=mel_len)
-        mask2 = mask + last_position.scale(self.stop_loss_scale - 1).astype(mask.dtype)
+        last_position = F.one_hot(
+            mask.sum(-1).astype("int64") - 1, num_classes=mel_len)
+        mask2 = mask + last_position.scale(self.stop_loss_scale - 1).astype(
+            mask.dtype)
         stop_loss = L.masked_softmax_with_cross_entropy(
             stop_logits, stop_probs.unsqueeze(-1), mask2.unsqueeze(-1))
-        
-        loss =  mel_loss1 + mel_loss2 + stop_loss
+
+        loss = mel_loss1 + mel_loss2 + stop_loss
         losses = dict(
-            loss=loss,           # total loss
-            mel_loss1=mel_loss1, # ouput mel loss
-            mel_loss2=mel_loss2, # intermediate mel loss
+            loss=loss,  # total loss
+            mel_loss1=mel_loss1,  # ouput mel loss
+            mel_loss2=mel_loss2,  # intermediate mel loss
             stop_loss=stop_loss  # stop prob loss
         )
         return losses
@@ -542,26 +623,29 @@ class TransformerTTSLoss(nn.Layer):
 class AdaptiveTransformerTTSLoss(nn.Layer):
     def __init__(self):
         super(AdaptiveTransformerTTSLoss, self).__init__()
-    
-    def forward(self, mel_output, mel_intermediate, mel_target, stop_logits, stop_probs):
-        mask = masking.feature_mask(mel_target, axis=-1, dtype=mel_target.dtype)
+
+    def forward(self, mel_output, mel_intermediate, mel_target, stop_logits,
+                stop_probs):
+        mask = masking.feature_mask(
+            mel_target, axis=-1, dtype=mel_target.dtype)
         mask1 = paddle.unsqueeze(mask, -1)
         mel_loss1 = L.masked_l1_loss(mel_output, mel_target, mask1)
         mel_loss2 = L.masked_l1_loss(mel_intermediate, mel_target, mask1)
-        
+
         batch_size, mel_len = mask.shape
         valid_lengths = mask.sum(-1).astype("int64")
         last_position = F.one_hot(valid_lengths - 1, num_classes=mel_len)
         stop_loss_scale = valid_lengths.sum() / batch_size - 1
-        mask2 = mask + last_position.scale(stop_loss_scale - 1).astype(mask.dtype)
+        mask2 = mask + last_position.scale(stop_loss_scale - 1).astype(
+            mask.dtype)
         stop_loss = L.masked_softmax_with_cross_entropy(
             stop_logits, stop_probs.unsqueeze(-1), mask2.unsqueeze(-1))
-        
-        loss =  mel_loss1 + mel_loss2 + stop_loss
+
+        loss = mel_loss1 + mel_loss2 + stop_loss
         losses = dict(
-            loss=loss,           # total loss
-            mel_loss1=mel_loss1, # ouput mel loss
-            mel_loss2=mel_loss2, # intermediate mel loss
+            loss=loss,  # total loss
+            mel_loss1=mel_loss1,  # ouput mel loss
+            mel_loss2=mel_loss2,  # intermediate mel loss
             stop_loss=stop_loss  # stop prob loss
         )
-        return losses
+        return losses

parakeet/modules/attention.py

@@ -1,10 +1,30 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import math
 import numpy as np
 import paddle
 from paddle import nn
 from paddle.nn import functional as F
 
-def scaled_dot_product_attention(q, k, v, mask=None, dropout=0.0, training=True):
+
+def scaled_dot_product_attention(q,
+                                 k,
+                                 v,
+                                 mask=None,
+                                 dropout=0.0,
+                                 training=True):
     """
     scaled dot product attention with mask. Assume q, k, v all have the same 
     leader dimensions(denoted as * in descriptions below). Dropout is applied to 
@@ -22,18 +42,19 @@ def scaled_dot_product_attention(q, k, v, mask=None, dropout=0.0, training=True)
         out (Tensor): shape(*, T_q, d_v), the context vector.
         attn_weights (Tensor): shape(*, T_q, T_k), the attention weights.
     """
-    d = q.shape[-1] # we only support imperative execution
+    d = q.shape[-1]  # we only support imperative execution
     qk = paddle.matmul(q, k, transpose_y=True)
     scaled_logit = paddle.scale(qk, 1.0 / math.sqrt(d))
-    
+
     if mask is not None:
-        scaled_logit += paddle.scale((1.0 - mask), -1e9) # hard coded here
-    
+        scaled_logit += paddle.scale((1.0 - mask), -1e9)  # hard coded here
+
     attn_weights = F.softmax(scaled_logit, axis=-1)
     attn_weights = F.dropout(attn_weights, dropout, training=training)
     out = paddle.matmul(attn_weights, v)
     return out, attn_weights
 
+
 def drop_head(x, drop_n_heads, training):
     """
     Drop n heads from multiple context vectors.
@@ -48,12 +69,12 @@ def drop_head(x, drop_n_heads, training):
     """
     if not training or (drop_n_heads == 0):
         return x
-    
+
     batch_size, num_heads, _, _ = x.shape
     # drop all heads
     if num_heads == drop_n_heads:
         return paddle.zeros_like(x)
-    
+
     mask = np.ones([batch_size, num_heads])
     mask[:, :drop_n_heads] = 0
     for subarray in mask:
@@ -63,18 +84,21 @@ def drop_head(x, drop_n_heads, training):
     out = x * paddle.to_tensor(mask)
     return out
 
+
 def _split_heads(x, num_heads):
     batch_size, time_steps, _ = x.shape
     x = paddle.reshape(x, [batch_size, time_steps, num_heads, -1])
     x = paddle.transpose(x, [0, 2, 1, 3])
     return x
 
+
 def _concat_heads(x):
     batch_size, _, time_steps, _ = x.shape
     x = paddle.transpose(x, [0, 2, 1, 3])
     x = paddle.reshape(x, [batch_size, time_steps, -1])
     return x
 
+
 # Standard implementations of Monohead Attention & Multihead Attention
 class MonoheadAttention(nn.Layer):
     def __init__(self, model_dim, dropout=0.0, k_dim=None, v_dim=None):
@@ -99,10 +123,10 @@ class MonoheadAttention(nn.Layer):
         self.affine_k = nn.Linear(model_dim, k_dim)
         self.affine_v = nn.Linear(model_dim, v_dim)
         self.affine_o = nn.Linear(v_dim, model_dim)
-        
+
         self.model_dim = model_dim
         self.dropout = dropout
-    
+
     def forward(self, q, k, v, mask):
         """
         Compute context vector and attention weights.
@@ -119,22 +143,28 @@ class MonoheadAttention(nn.Layer):
             out (Tensor), shape(batch_size, time_steps_q, model_dim), the context vector.
             attention_weights (Tensor): shape(batch_size, times_steps_q, time_steps_k), the attention weights.
         """
-        q = self.affine_q(q) # (B, T, C)
+        q = self.affine_q(q)  # (B, T, C)
         k = self.affine_k(k)
         v = self.affine_v(v)
-        
+
         context_vectors, attention_weights = scaled_dot_product_attention(
             q, k, v, mask, self.dropout, self.training)
-        
+
         out = self.affine_o(context_vectors)
         return out, attention_weights
 
-        
+
 class MultiheadAttention(nn.Layer):
     """
     Multihead scaled dot product attention.
     """
-    def __init__(self, model_dim, num_heads, dropout=0.0, k_dim=None, v_dim=None):
+
+    def __init__(self,
+                 model_dim,
+                 num_heads,
+                 dropout=0.0,
+                 k_dim=None,
+                 v_dim=None):
         """
         Multihead Attention module.
 
@@ -154,7 +184,7 @@ class MultiheadAttention(nn.Layer):
             ValueError: if model_dim is not divisible by num_heads
         """
         super(MultiheadAttention, self).__init__()
-        if model_dim % num_heads !=0:
+        if model_dim % num_heads != 0:
             raise ValueError("model_dim must be divisible by num_heads")
         depth = model_dim // num_heads
         k_dim = k_dim or depth
@@ -163,11 +193,11 @@ class MultiheadAttention(nn.Layer):
         self.affine_k = nn.Linear(model_dim, num_heads * k_dim)
         self.affine_v = nn.Linear(model_dim, num_heads * v_dim)
         self.affine_o = nn.Linear(num_heads * v_dim, model_dim)
-        
+
         self.num_heads = num_heads
         self.model_dim = model_dim
         self.dropout = dropout
-    
+
     def forward(self, q, k, v, mask):
         """
         Compute context vector and attention weights.
@@ -184,14 +214,67 @@ class MultiheadAttention(nn.Layer):
             out (Tensor), shape(batch_size, time_steps_q, model_dim), the context vector.
             attention_weights (Tensor): shape(batch_size, times_steps_q, time_steps_k), the attention weights.
         """
-        q = _split_heads(self.affine_q(q), self.num_heads) # (B, h, T, C)
+        q = _split_heads(self.affine_q(q), self.num_heads)  # (B, h, T, C)
         k = _split_heads(self.affine_k(k), self.num_heads)
         v = _split_heads(self.affine_v(v), self.num_heads)
-        mask = paddle.unsqueeze(mask, 1) # unsqueeze for the h dim
-        
+        mask = paddle.unsqueeze(mask, 1)  # unsqueeze for the h dim
+
         context_vectors, attention_weights = scaled_dot_product_attention(
             q, k, v, mask, self.dropout, self.training)
         # NOTE: there is more sophisticated implementation: Scheduled DropHead
-        context_vectors = _concat_heads(context_vectors) # (B, T, h*C)
+        context_vectors = _concat_heads(context_vectors)  # (B, T, h*C)
         out = self.affine_o(context_vectors)
         return out, attention_weights
+
+
+class LocationSensitiveAttention(nn.Layer):
+    def __init__(self,
+                 d_query: int,
+                 d_key: int,
+                 d_attention: int,
+                 location_filters: int,
+                 location_kernel_size: int):
+        super().__init__()
+
+        self.query_layer = nn.Linear(d_query, d_attention, bias_attr=False)
+        self.key_layer = nn.Linear(d_key, d_attention, bias_attr=False)
+        self.value = nn.Linear(d_attention, 1, bias_attr=False)
+
+        #Location Layer
+        self.location_conv = nn.Conv1D(
+            2,
+            location_filters,
+            location_kernel_size,
+            1,
+            int((location_kernel_size - 1) / 2),
+            1,
+            bias_attr=False,
+            data_format='NLC')
+        self.location_layer = nn.Linear(
+            location_filters, d_attention, bias_attr=False)
+
+    def forward(self,
+                query,
+                processed_key,
+                value,
+                attention_weights_cat,
+                mask=None):
+
+        processed_query = self.query_layer(paddle.unsqueeze(query, axis=[1]))
+        processed_attention_weights = self.location_layer(
+            self.location_conv(attention_weights_cat))
+        alignment = self.value(
+            paddle.tanh(processed_attention_weights + processed_key +
+                        processed_query))
+
+        if mask is not None:
+            alignment = alignment + (1.0 - mask) * -1e9
+
+        attention_weights = F.softmax(alignment, axis=1)
+        attention_context = paddle.matmul(
+            attention_weights, value, transpose_x=True)
+
+        attention_weights = paddle.squeeze(attention_weights, axis=[-1])
+        attention_context = paddle.squeeze(attention_context, axis=[1])
+
+        return attention_context, attention_weights

parakeet/modules/conv.py

@@ -1,6 +1,21 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import paddle
 from paddle import nn
 
+
 class Conv1dCell(nn.Conv1D):
     """
     A subclass of Conv1d layer, which can be used like an RNN cell. It can take 
@@ -14,30 +29,33 @@ class Conv1dCell(nn.Conv1D):
     
     As a result, these arguments are removed form the initializer.
     """
-    def __init__(self, 
+
+    def __init__(self,
                  in_channels,
                  out_channels,
                  kernel_size,
                  dilation=1,
                  weight_attr=None,
                  bias_attr=None):
-        _dilation = dilation[0] if isinstance(dilation, (tuple, list)) else dilation
-        _kernel_size = kernel_size[0] if isinstance(kernel_size, (tuple, list)) else kernel_size
+        _dilation = dilation[0] if isinstance(dilation,
+                                              (tuple, list)) else dilation
+        _kernel_size = kernel_size[0] if isinstance(kernel_size, (
+            tuple, list)) else kernel_size
         self._r = 1 + (_kernel_size - 1) * _dilation
         super(Conv1dCell, self).__init__(
-            in_channels, 
-            out_channels, 
-            kernel_size, 
-            padding=(self._r - 1, 0), 
-            dilation=dilation, 
-            weight_attr=weight_attr, 
-            bias_attr=bias_attr, 
+            in_channels,
+            out_channels,
+            kernel_size,
+            padding=(self._r - 1, 0),
+            dilation=dilation,
+            weight_attr=weight_attr,
+            bias_attr=bias_attr,
             data_format="NCL")
 
     @property
     def receptive_field(self):
         return self._r
-    
+
     def start_sequence(self):
         if self.training:
             raise Exception("only use start_sequence in evaluation")
@@ -50,15 +68,15 @@ class Conv1dCell(nn.Conv1D):
         # see also: https://github.com/pytorch/pytorch/issues/47588
         for hook in self._forward_pre_hooks.values():
             hook(self, None)
-        self._reshaped_weight = paddle.reshape(
-            self.weight, (self._out_channels, -1))
-    
+        self._reshaped_weight = paddle.reshape(self.weight,
+                                               (self._out_channels, -1))
+
     def initialize_buffer(self, x_t):
         batch_size, _ = x_t.shape
         self._buffer = paddle.zeros(
-            (batch_size, self._in_channels, self.receptive_field), 
+            (batch_size, self._in_channels, self.receptive_field),
             dtype=x_t.dtype)
-    
+
     def update_buffer(self, x_t):
         self._buffer = paddle.concat(
             [self._buffer[:, :, 1:], paddle.unsqueeze(x_t, -1)], -1)
@@ -74,7 +92,7 @@ class Conv1dCell(nn.Conv1D):
         if self.receptive_field > 1:
             if self._buffer is None:
                 self.initialize_buffer(x_t)
-            
+
             # update buffer
             self.update_buffer(x_t)
             if self._dilation[0] > 1:
@@ -90,20 +108,34 @@ class Conv1dCell(nn.Conv1D):
 
 
 class Conv1dBatchNorm(nn.Layer):
-    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0,
-                 weight_attr=None, bias_attr=None, data_format="NCL"):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride=1,
+                 padding=0,
+                 weight_attr=None,
+                 bias_attr=None,
+                 data_format="NCL",
+                 momentum=0.9,
+                 epsilon=1e-05):
         super(Conv1dBatchNorm, self).__init__()
-        # TODO(chenfeiyu): carefully initialize Conv1d's weight
-        self.conv = nn.Conv1D(in_channels, out_channels, kernel_size, stride,
-                              padding=padding,
-                              weight_attr=weight_attr,
-                              bias_attr=bias_attr,
-                              data_format=data_format)
-        # TODO: channel last, but BatchNorm1d does not support channel last layout
-        self.bn = nn.BatchNorm1D(out_channels, momentum=0.99, epsilon=1e-3, data_format=data_format)
+        self.conv = nn.Conv1D(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding=padding,
+            weight_attr=weight_attr,
+            bias_attr=bias_attr,
+            data_format=data_format)
+        self.bn = nn.BatchNorm1D(
+            out_channels,
+            momentum=momentum,
+            epsilon=epsilon,
+            data_format=data_format)
 
     def forward(self, x):
         x = self.conv(x)
         x = self.bn(x)
         return x
-    

parakeet/training/experiment.py

@@ -1,3 +1,17 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import time
 import logging
 from pathlib import Path
@@ -11,6 +25,7 @@ from collections import defaultdict
 import parakeet
 from parakeet.utils import checkpoint, mp_tools
 
+
 class ExperimentBase(object):
     """
     An experiment template in order to structure the training code and take care of saving, loading, logging, visualization stuffs. It's intended to be flexible and simple. 
@@ -22,7 +37,7 @@ class ExperimentBase(object):
     We have some conventions to follow.
     1. Experiment should have `.model`, `.optimizer`, `.train_loader` and `.valid_loader`, `.config`, `.args` attributes.
     2. The config should have a `.training` field, which has `valid_interval`, `save_interval` and `max_iteration` keys. It is used as the trigger to invoke validation, checkpointing and stop of the experiment.
-    3. There are three method, namely `train_batch`, `valid`, `setup_model` and `setup_dataloader` that should be implemented.
+    3. There are four method, namely `train_batch`, `valid`, `setup_model` and `setup_dataloader` that should be implemented.
 
     Feel free to add/overwrite other methods and standalone functions if you need.
 
@@ -54,6 +69,7 @@ class ExperimentBase(object):
         main(config, args)
 
     """
+
     def __init__(self, config, args):
         self.config = config
         self.args = args
@@ -67,7 +83,7 @@ class ExperimentBase(object):
         self.setup_visualizer()
         self.setup_logger()
         self.setup_checkpointer()
-        
+
         self.setup_dataloader()
         self.setup_model()
 
@@ -82,13 +98,13 @@ class ExperimentBase(object):
         dist.init_parallel_env()
 
     def save(self):
-        checkpoint.save_parameters(
-            self.checkpoint_dir, self.iteration, self.model, self.optimizer)
+        checkpoint.save_parameters(self.checkpoint_dir, self.iteration,
+                                   self.model, self.optimizer)
 
     def resume_or_load(self):
         iteration = checkpoint.load_parameters(
-            self.model, 
-            self.optimizer, 
+            self.model,
+            self.optimizer,
             checkpoint_dir=self.checkpoint_dir,
             checkpoint_path=self.args.checkpoint_path)
         self.iteration = iteration
@@ -115,10 +131,10 @@ class ExperimentBase(object):
 
             if self.iteration % self.config.training.valid_interval == 0:
                 self.valid()
-        
+
             if self.iteration % self.config.training.save_interval == 0:
                 self.save()
-    
+
     def run(self):
         self.resume_or_load()
         try:
@@ -126,7 +142,7 @@ class ExperimentBase(object):
         except KeyboardInterrupt:
             self.save()
             exit(-1)
-    
+
     @mp_tools.rank_zero_only
     def setup_output_dir(self):
         # output dir
@@ -134,7 +150,7 @@ class ExperimentBase(object):
         output_dir.mkdir(parents=True, exist_ok=True)
 
         self.output_dir = output_dir
-    
+
     @mp_tools.rank_zero_only
     def setup_checkpointer(self):
         # checkpoint dir
@@ -161,7 +177,7 @@ class ExperimentBase(object):
 
     @mp_tools.rank_zero_only
     def dump_config(self):
-        with open(self.output_dir / "config.yaml", 'wt') as f: 
+        with open(self.output_dir / "config.yaml", 'wt') as f:
             print(self.config, file=f)
 
     def train_batch(self):
@@ -177,4 +193,3 @@ class ExperimentBase(object):
 
     def setup_dataloader(self):
         raise NotImplementedError("setup_dataloader should be implemented.")
-