add FastSpeech

parakeet/models/dataloader/jlspeech.py

@@ -0,0 +1,148 @@
+from pathlib import Path
+import numpy as np
+import pandas as pd
+import librosa
+
+from paddle import fluid
+from parakeet import g2p
+from parakeet import audio
+from parakeet.data.sampler import *
+from parakeet.data.datacargo import DataCargo
+from parakeet.data.dataset import Dataset
+from parakeet.data.batch import TextIDBatcher, SpecBatcher
+
+class LJSpeechLoader:
+    def __init__(self, config, nranks, rank, is_vocoder=False):
+        place = fluid.CUDAPlace(rank) if config.use_gpu else fluid.CPUPlace()
+
+        LJSPEECH_ROOT = Path(config.data_path)
+        dataset = LJSpeech(LJSPEECH_ROOT, config)
+        sampler = DistributedSampler(len(dataset), nranks, rank)
+
+        assert config.batch_size % nranks == 0
+        each_bs = config.batch_size // nranks
+        if is_vocoder:
+            dataloader = DataCargo(dataset, sampler=sampler, batch_size=each_bs, shuffle=True, collate_fn=batch_examples_vocoder, drop_last=True)
+        else:
+            dataloader = DataCargo(dataset, sampler=sampler, batch_size=each_bs, shuffle=True, collate_fn=batch_examples, drop_last=True)
+        
+        self.reader = fluid.io.DataLoader.from_generator(
+            capacity=32,
+            iterable=True,
+            use_double_buffer=True,
+            return_list=True)
+        self.reader.set_batch_generator(dataloader, place)
+
+
+class LJSpeech(Dataset):
+    def __init__(self, root, config):
+        super(LJSpeech, self).__init__()
+        assert isinstance(root, (str, Path)), "root should be a string or Path object"
+        self.root = root if isinstance(root, Path) else Path(root)
+        self.metadata = self._prepare_metadata()
+        self.config = config
+        
+    def _prepare_metadata(self):
+        csv_path = self.root.joinpath("metadata.csv")
+        metadata = pd.read_csv(csv_path, sep="|", header=None, quoting=3,
+                               names=["fname", "raw_text", "normalized_text"])
+        return metadata
+            
+    def _get_example(self, metadatum):
+        """All the code for generating an Example from a metadatum. If you want a 
+        different preprocessing pipeline, you can override this method. 
+        This method may require several processor, each of which has a lot of options.
+        In this case, you'd better pass a composed transform and pass it to the init
+        method.
+        """
+        
+        fname, raw_text, normalized_text = metadatum
+        wav_path = self.root.joinpath("wavs", fname + ".wav")
+        
+        _ljspeech_processor = audio.AudioProcessor(
+            sample_rate=22050, 
+            num_mels=80, 
+            min_level_db=-100, 
+            ref_level_db=20, 
+            n_fft=2048, 
+            win_length= int(22050 * 0.05), 
+            hop_length= int(22050 * 0.0125),
+            power=1.2,
+            preemphasis=0.97,
+            signal_norm=True,
+            symmetric_norm=False,
+            max_norm=1.,
+            mel_fmin=0,
+            mel_fmax=None,
+            clip_norm=True,
+            griffin_lim_iters=60,
+            do_trim_silence=False,
+            sound_norm=False)
+        # load -> trim -> preemphasis -> stft -> magnitude -> mel_scale -> logscale -> normalize
+        wav = _ljspeech_processor.load_wav(str(wav_path))
+        mag = _ljspeech_processor.spectrogram(wav).astype(np.float32)
+        mel = _ljspeech_processor.melspectrogram(wav).astype(np.float32)
+        phonemes = np.array(g2p.en.text_to_sequence(normalized_text), dtype=np.int64)
+        return (mag, mel, phonemes) # maybe we need to implement it as a map in the future
+
+    def __getitem__(self, index):
+        metadatum = self.metadata.iloc[index]
+        example = self._get_example(metadatum)
+        return example
+    
+    def __iter__(self):
+        for i in range(len(self)):
+            yield self[i]
+    
+    def __len__(self):
+        return len(self.metadata)
+
+
+def batch_examples(batch):
+    texts = []
+    mels = []
+    mel_inputs = []
+    text_lens = []
+    pos_texts = []
+    pos_mels = []
+    for data in batch:
+        _, mel, text = data
+        mel_inputs.append(np.concatenate([np.zeros([mel.shape[0], 1], np.float32), mel[:,:-1]], axis=-1))
+        text_lens.append(len(text))
+        pos_texts.append(np.arange(1, len(text) + 1))
+        pos_mels.append(np.arange(1, mel.shape[1] + 1))
+        mels.append(mel)
+        texts.append(text)
+    
+    # Sort by text_len in descending order
+    texts = [i for i,_ in sorted(zip(texts, text_lens), key=lambda x: x[1], reverse=True)]
+    mels = [i for i,_ in sorted(zip(mels, text_lens), key=lambda x: x[1], reverse=True)]
+    mel_inputs = [i for i,_ in sorted(zip(mel_inputs, text_lens), key=lambda x: x[1], reverse=True)]
+    pos_texts = [i for i,_ in sorted(zip(pos_texts, text_lens), key=lambda x: x[1], reverse=True)]
+    pos_mels = [i for i,_ in sorted(zip(pos_mels, text_lens), key=lambda x: x[1], reverse=True)]
+    text_lens = sorted(text_lens, reverse=True)
+
+    # Pad sequence with largest len of the batch
+    texts = TextIDBatcher(pad_id=0)(texts)
+    pos_texts = TextIDBatcher(pad_id=0)(pos_texts)
+    pos_mels = TextIDBatcher(pad_id=0)(pos_mels)
+    mels = np.transpose(SpecBatcher(pad_value=0.)(mels), axes=(0,2,1))
+    mel_inputs = np.transpose(SpecBatcher(pad_value=0.)(mel_inputs), axes=(0,2,1))
+    return (texts, mels, mel_inputs, pos_texts, pos_mels, np.array(text_lens))
+
+def batch_examples_vocoder(batch):
+    mels=[]
+    mags=[]
+    for data in batch:
+        mag, mel, _ = data
+        mels.append(mel)
+        mags.append(mag)
+
+    mels = np.transpose(SpecBatcher(pad_value=0.)(mels), axes=(0,2,1))
+    mags = np.transpose(SpecBatcher(pad_value=0.)(mags), axes=(0,2,1))
+
+    return (mels, mags)
+
+
+
+        

parakeet/models/fastspeech/config/fastapeech.yaml

@@ -0,0 +1,41 @@
+audio:
+  num_mels: 80
+  n_fft: 2048
+  sr: 22050
+  preemphasis: 0.97
+  hop_length: 275
+  win_length: 1102
+  power: 1.2
+  min_level_db: -100
+  ref_level_db: 20
+  outputs_per_step: 1
+
+encoder_n_layer: 6
+encoder_head: 2
+encoder_conv1d_filter_size: 1536
+max_sep_len: 2048
+encoder_output_size: 384
+word_vec_dim: 384
+decoder_n_layer: 6
+decoder_head: 2
+decoder_conv1d_filter_size: 1536
+decoder_output_size: 384
+d_model: 384
+duration_predictor_output_size: 256
+duration_predictor_filter_size: 3
+fft_conv1d_filter: 3
+fft_conv1d_padding: 1
+
+
+batch_size: 32
+epochs: 10000
+lr: 0.001
+save_step: 500
+image_step: 2000
+use_gpu: False
+use_data_parallel: False
+
+data_path: ../../../dataset/LJSpeech-1.1
+transtts_path: ./checkpoint
+transformer_step: 70000
+log_dir: ./log

parakeet/models/fastspeech/config/fastspeech.yaml

@@ -0,0 +1,43 @@
+audio:
+  num_mels: 80
+  n_fft: 2048
+  sr: 22050
+  preemphasis: 0.97
+  hop_length: 275
+  win_length: 1102
+  power: 1.2
+  min_level_db: -100
+  ref_level_db: 20
+  outputs_per_step: 1
+
+encoder_n_layer: 6
+encoder_head: 2
+encoder_conv1d_filter_size: 1536
+max_sep_len: 2048
+encoder_output_size: 384
+embedding_size: 384
+decoder_n_layer: 6
+decoder_head: 2
+decoder_conv1d_filter_size: 1536
+decoder_output_size: 384
+hidden_size: 384
+duration_predictor_output_size: 256
+duration_predictor_filter_size: 3
+fft_conv1d_filter: 3
+fft_conv1d_padding: 1
+dropout: 0.1
+transformer_head: 4
+
+warm_up_step: 4000
+grad_clip_thresh: 0.1
+batch_size: 32
+epochs: 10000
+lr: 0.001
+save_step: 500
+use_gpu: True
+use_data_parallel: False
+
+data_path: ../../../dataset/LJSpeech-1.1
+transtts_path: ../transformerTTS/checkpoint
+transformer_step: 20
+log_dir: ./log

parakeet/models/fastspeech/dataset.py

@@ -0,0 +1,124 @@
+import torch
+from torch.nn import functional as F
+from torch.utils.data import Dataset, DataLoader
+
+import numpy as np
+import math
+import os
+
+import hparams
+import Audio
+from text import text_to_sequence
+from utils import process_text, pad_1D, pad_2D
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+
+class FastSpeechDataset(Dataset):
+    """ LJSpeech """
+
+    def __init__(self):
+        self.text = process_text(os.path.join("data", "train.txt"))
+
+    def __len__(self):
+        return len(self.text)
+
+    def __getitem__(self, idx):
+        mel_gt_name = os.path.join(
+            hparams.mel_ground_truth, "ljspeech-mel-%05d.npy" % (idx+1))
+        mel_gt_target = np.load(mel_gt_name)
+        D = np.load(os.path.join(hparams.alignment_path, str(idx)+".npy"))
+
+        character = self.text[idx][0:len(self.text[idx])-1]
+        character = np.array(text_to_sequence(
+            character, hparams.text_cleaners))
+
+        sample = {"text": character,
+                  "mel_target": mel_gt_target,
+                  "D": D}
+
+        return sample
+
+
+def reprocess(batch, cut_list):
+    texts = [batch[ind]["text"] for ind in cut_list]
+    mel_targets = [batch[ind]["mel_target"] for ind in cut_list]
+    Ds = [batch[ind]["D"] for ind in cut_list]
+
+    length_text = np.array([])
+    for text in texts:
+        length_text = np.append(length_text, text.shape[0])
+
+    src_pos = list()
+    max_len = int(max(length_text))
+    for length_src_row in length_text:
+        src_pos.append(np.pad([i+1 for i in range(int(length_src_row))],
+                              (0, max_len-int(length_src_row)), 'constant'))
+    src_pos = np.array(src_pos)
+
+    length_mel = np.array(list())
+    for mel in mel_targets:
+        length_mel = np.append(length_mel, mel.shape[0])
+
+    mel_pos = list()
+    max_mel_len = int(max(length_mel))
+    for length_mel_row in length_mel:
+        mel_pos.append(np.pad([i+1 for i in range(int(length_mel_row))],
+                              (0, max_mel_len-int(length_mel_row)), 'constant'))
+    mel_pos = np.array(mel_pos)
+
+    texts = pad_1D(texts)
+    Ds = pad_1D(Ds)
+    mel_targets = pad_2D(mel_targets)
+
+    out = {"text": texts,
+           "mel_target": mel_targets,
+           "D": Ds,
+           "mel_pos": mel_pos,
+           "src_pos": src_pos,
+           "mel_max_len": max_mel_len}
+
+    return out
+
+
+def collate_fn(batch):
+    len_arr = np.array([d["text"].shape[0] for d in batch])
+    index_arr = np.argsort(-len_arr)
+    batchsize = len(batch)
+    real_batchsize = int(math.sqrt(batchsize))
+
+    cut_list = list()
+    for i in range(real_batchsize):
+        cut_list.append(index_arr[i*real_batchsize:(i+1)*real_batchsize])
+
+    output = list()
+    for i in range(real_batchsize):
+        output.append(reprocess(batch, cut_list[i]))
+
+    return output
+
+
+if __name__ == "__main__":
+    # Test
+    dataset = FastSpeechDataset()
+    training_loader = DataLoader(dataset,
+                                 batch_size=1,
+                                 shuffle=False,
+                                 collate_fn=collate_fn,
+                                 drop_last=True,
+                                 num_workers=0)
+    total_step = hparams.epochs * len(training_loader) * hparams.batch_size
+
+    cnt = 0
+    for i, batchs in enumerate(training_loader):
+        for j, data_of_batch in enumerate(batchs):
+            mel_target = torch.from_numpy(
+                data_of_batch["mel_target"]).float().to(device)
+            D = torch.from_numpy(data_of_batch["D"]).int().to(device)
+            # print(mel_target.size())
+            # print(D.sum())
+            print(cnt)
+            if mel_target.size(1) == D.sum().item():
+                cnt += 1
+
+    print(cnt)

parakeet/models/fastspeech/modules.py

@@ -0,0 +1,117 @@
+import numpy as np
+import math
+import utils
+import paddle.fluid.dygraph as dg
+import paddle.fluid.layers as layers
+import paddle.fluid as fluid
+from parakeet.modules.layers import Conv1D
+from parakeet.modules.multihead_attention import MultiheadAttention
+from parakeet.modules.feed_forward import PositionwiseFeedForward
+
+
+
+class FFTBlock(dg.Layer):
+    """FFT Block"""
+    def __init__(self, d_model, d_inner, n_head, d_k, d_v, filter_size, padding, dropout=0.2):
+        super(FFTBlock, self).__init__()
+        self.slf_attn = MultiheadAttention(d_model, d_k, d_v, num_head=n_head, dropout=dropout)
+        self.pos_ffn = PositionwiseFeedForward(d_model, d_inner, filter_size =filter_size, padding =padding, dropout=dropout)
+
+    def forward(self, enc_input, non_pad_mask=None, slf_attn_mask=None):
+        enc_output, enc_slf_attn = self.slf_attn(enc_input, enc_input, enc_input, mask=slf_attn_mask)
+        enc_output *= non_pad_mask
+
+        enc_output = self.pos_ffn(enc_output)
+        enc_output *= non_pad_mask
+
+        return enc_output, enc_slf_attn
+
+
+class LengthRegulator(dg.Layer):
+    def __init__(self, input_size, out_channels, filter_size, dropout=0.1):
+        super(LengthRegulator, self).__init__()
+        self.duration_predictor = DurationPredictor(input_size=input_size, 
+                                                    out_channels=out_channels, 
+                                                    filter_size=filter_size, 
+                                                    dropout=dropout)
+
+    def LR(self, x, duration_predictor_output, alpha=1.0):
+        output = []
+        batch_size = x.shape[0]
+        for i in range(batch_size):
+            output.append(self.expand(x[i:i+1], duration_predictor_output[i:i+1], alpha))
+        output = self.pad(output)
+        return output
+    
+    def pad(self, input_ele):
+        max_len = max([input_ele[i].shape[0] for i in range(len(input_ele))])
+        out_list = []
+        for i in range(len(input_ele)):
+            pad_len = max_len - input_ele[i].shape[0]
+            one_batch_padded = layers.pad(
+                input_ele[i], [0, pad_len, 0, 0], pad_value=0.0)
+            out_list.append(one_batch_padded)
+        out_padded = layers.stack(out_list)
+        return out_padded
+    
+    def expand(self, batch, predicted, alpha):
+        out = []
+        time_steps = batch.shape[1]
+        fertilities = predicted.numpy()
+        batch = layers.squeeze(batch,[0]) 
+        
+        
+        for i in range(time_steps):
+            if fertilities[0,i]==0:
+                continue
+            out.append(layers.expand(batch[i: i + 1, :], [int(fertilities[0,i]), 1]))
+        out = layers.concat(out, axis=0)
+        return out
+    
+
+    def forward(self, x, alpha=1.0, target=None):
+        duration_predictor_output = self.duration_predictor(x)
+        if fluid.framework._dygraph_tracer()._train_mode:
+            output = self.LR(x, target)
+            return output, duration_predictor_output
+        else:
+            duration_predictor_output = layers.round(duration_predictor_output)
+            output = self.LR(x, duration_predictor_output, alpha)
+            mel_pos = dg.to_variable([i+1 for i in range(output.shape[1])])
+            return output, mel_pos
+
+class DurationPredictor(dg.Layer):
+    """ Duration Predictor """
+    def __init__(self, input_size, out_channels, filter_size, dropout=0.1):
+        super(DurationPredictor, self).__init__()
+        self.input_size = input_size
+        self.out_channels = out_channels
+        self.filter_size = filter_size
+        self.dropout = dropout
+
+        self.conv1 = Conv1D(in_channels = self.input_size, 
+                        out_channels = self.out_channels, 
+                        filter_size = self.filter_size,
+                        padding=1,
+                        data_format='NTC')
+        self.conv2 = Conv1D(in_channels = self.out_channels, 
+                        out_channels = self.out_channels, 
+                        filter_size = self.filter_size,
+                        padding=1,
+                        data_format='NTC')
+        self.layer_norm1 = dg.LayerNorm(self.out_channels)
+        self.layer_norm2 = dg.LayerNorm(self.out_channels)
+
+        self.linear =dg.Linear(self.out_channels, 1)
+
+    def forward(self, encoder_output):
+        
+        # encoder_output.shape(N, T, C)
+        out = layers.dropout(layers.relu(self.layer_norm1(self.conv1(encoder_output))), self.dropout)
+        out = layers.dropout(layers.relu(self.layer_norm2(self.conv2(out))), self.dropout)
+        out = layers.relu(self.linear(out))
+        out = layers.squeeze(out, axes=[-1])
+            
+        return out
+
+        

parakeet/models/fastspeech/network.py

@@ -0,0 +1,163 @@
+from utils import *
+from modules import *
+import paddle.fluid.dygraph as dg
+import paddle.fluid as fluid
+from parakeet.g2p.text.symbols import symbols
+from parakeet.modules.utils import *
+from parakeet.modules.post_convnet import PostConvNet
+
+class Encoder(dg.Layer):
+    def __init__(self,
+                 n_src_vocab,
+                 len_max_seq,
+                 d_word_vec,
+                 n_layers,
+                 n_head,
+                 d_k,
+                 d_v,
+                 d_model,
+                 d_inner,
+                 fft_conv1d_kernel,
+                 fft_conv1d_padding,
+                 dropout=0.1):
+        super(Encoder, self).__init__()
+        n_position = len_max_seq + 1
+
+        self.src_word_emb = dg.Embedding(size=[n_src_vocab, d_word_vec], padding_idx=0)
+        self.pos_inp = get_sinusoid_encoding_table(n_position, d_word_vec, padding_idx=0)
+        self.position_enc = dg.Embedding(size=[n_position, d_word_vec],
+                                 padding_idx=0,
+                                 param_attr=fluid.ParamAttr(
+                                     initializer=fluid.initializer.NumpyArrayInitializer(self.pos_inp),
+                                     trainable=False))
+        self.layer_stack = [FFTBlock(d_model, d_inner, n_head, d_k, d_v, fft_conv1d_kernel, fft_conv1d_padding, dropout=dropout) for _ in range(n_layers)]
+        for i, layer in enumerate(self.layer_stack):
+            self.add_sublayer('fft_{}'.format(i), layer)
+
+    def forward(self, character, text_pos):
+        enc_slf_attn_list = []
+        # -- prepare masks
+        # shape character (N, T)
+        slf_attn_mask = get_attn_key_pad_mask(seq_k=character, seq_q=character)
+        non_pad_mask = get_non_pad_mask(character)
+
+        # -- Forward
+        enc_output = self.src_word_emb(character) + self.position_enc(text_pos) #(N, T, C)
+
+        for enc_layer in self.layer_stack:
+            enc_output, enc_slf_attn = enc_layer(
+                enc_output,
+                non_pad_mask=non_pad_mask,
+                slf_attn_mask=slf_attn_mask)
+            enc_slf_attn_list += [enc_slf_attn]
+        
+        return enc_output, non_pad_mask, enc_slf_attn_list
+
+class Decoder(dg.Layer):
+    def __init__(self,
+                 len_max_seq,
+                 d_word_vec,
+                 n_layers,
+                 n_head,
+                 d_k,
+                 d_v,
+                 d_model,
+                 d_inner,
+                 fft_conv1d_kernel,
+                 fft_conv1d_padding,
+                 dropout=0.1):
+        super(Decoder, self).__init__()
+
+        n_position = len_max_seq + 1
+        self.pos_inp = get_sinusoid_encoding_table(n_position, d_word_vec, padding_idx=0)
+        self.position_enc = dg.Embedding(size=[n_position, d_word_vec],
+                                 padding_idx=0,
+                                 param_attr=fluid.ParamAttr(
+                                     initializer=fluid.initializer.NumpyArrayInitializer(self.pos_inp),
+                                     trainable=False))
+        self.layer_stack = [FFTBlock(d_model, d_inner, n_head, d_k, d_v, fft_conv1d_kernel, fft_conv1d_padding, dropout=dropout) for _ in range(n_layers)] 
+        for i, layer in enumerate(self.layer_stack):
+            self.add_sublayer('fft_{}'.format(i), layer)
+    
+    def forward(self, enc_seq, enc_pos):
+        dec_slf_attn_list = []
+
+        # -- Prepare masks
+        slf_attn_mask = get_attn_key_pad_mask(seq_k=enc_pos, seq_q=enc_pos)
+        non_pad_mask = get_non_pad_mask(enc_pos)
+
+        # -- Forward
+        dec_output = enc_seq + self.position_enc(enc_pos)
+
+        for dec_layer in self.layer_stack:
+            dec_output, dec_slf_attn = dec_layer(
+                dec_output,
+                non_pad_mask=non_pad_mask,
+                slf_attn_mask=slf_attn_mask)
+            dec_slf_attn_list += [dec_slf_attn]
+
+        return dec_output, dec_slf_attn_list
+
+class FastSpeech(dg.Layer):
+    def __init__(self, cfg):
+        " FastSpeech"
+        super(FastSpeech, self).__init__()
+
+        self.encoder = Encoder(n_src_vocab=len(symbols)+1,
+                               len_max_seq=cfg.max_sep_len,
+                               d_word_vec=cfg.embedding_size,
+                               n_layers=cfg.encoder_n_layer,
+                               n_head=cfg.encoder_head,
+                               d_k=64,
+                               d_v=64,
+                               d_model=cfg.hidden_size,
+                               d_inner=cfg.encoder_conv1d_filter_size,
+                               fft_conv1d_kernel=cfg.fft_conv1d_filter, 
+                               fft_conv1d_padding=cfg.fft_conv1d_padding,
+                               dropout=0.1)
+        self.length_regulator = LengthRegulator(input_size=cfg.hidden_size, 
+                                                out_channels=cfg.duration_predictor_output_size, 
+                                                filter_size=cfg.duration_predictor_filter_size, 
+                                                dropout=cfg.dropout)
+        self.decoder = Decoder(len_max_seq=cfg.max_sep_len,
+                                d_word_vec=cfg.embedding_size,
+                                n_layers=cfg.decoder_n_layer,
+                                n_head=cfg.decoder_head,
+                                d_k=64,
+                                d_v=64,
+                                d_model=cfg.hidden_size,
+                                d_inner=cfg.decoder_conv1d_filter_size,
+                                fft_conv1d_kernel=cfg.fft_conv1d_filter, 
+                                fft_conv1d_padding=cfg.fft_conv1d_padding,
+                                dropout=0.1)
+        self.mel_linear = dg.Linear(cfg.decoder_output_size, cfg.audio.num_mels)
+        self.postnet = PostConvNet(n_mels=80,
+                 num_hidden=512,
+                 filter_size=5,
+                 padding=int(5 / 2),
+                 num_conv=5,
+                 outputs_per_step=1,
+                 use_cudnn=True,
+                 dropout=0.1)
+
+    def forward(self, character, text_pos, mel_pos=None, length_target=None, alpha=1.0):
+        encoder_output, non_pad_mask, enc_slf_attn_list = self.encoder(character, text_pos)
+        if fluid.framework._dygraph_tracer()._train_mode:
+            
+            length_regulator_output, duration_predictor_output = self.length_regulator(encoder_output,
+                                                                                       target=length_target,
+                                                                                       alpha=alpha)
+            decoder_output, dec_slf_attn_list = self.decoder(length_regulator_output, mel_pos)
+
+            mel_output = self.mel_linear(decoder_output)
+            mel_output_postnet = self.postnet(mel_output) + mel_output
+
+            return mel_output, mel_output_postnet, duration_predictor_output, enc_slf_attn_list, dec_slf_attn_list
+        else:
+            length_regulator_output, decoder_pos = self.length_regulator(encoder_output, alpha=alpha)
+            decoder_output = self.decoder(length_regulator_output, decoder_pos)
+
+            mel_output = self.mel_linear(decoder_output)
+            mel_output_postnet = self.postnet(mel_output) + mel_output
+
+            return mel_output, mel_output_postnet

parakeet/models/fastspeech/parse.py

@@ -0,0 +1,93 @@
+import jsonargparse
+
+def add_config_options_to_parser(parser):
+    parser.add_argument('--audio.num_mels', type=int, default=80,
+        help="the number of mel bands when calculating mel spectrograms.")
+    parser.add_argument('--audio.n_fft', type=int, default=2048,
+        help="the number of fft components.")
+    parser.add_argument('--audio.sr', type=int, default=22050,
+        help="the sampling rate of audio data file.")
+    parser.add_argument('--audio.preemphasis', type=float, default=0.97,
+        help="the preemphasis coefficient.")
+    parser.add_argument('--audio.hop_length', type=float, default=128,
+        help="the number of samples to advance between frames.")
+    parser.add_argument('--audio.win_length', type=float, default=1024,
+        help="the length (width) of the window function.")
+    parser.add_argument('--audio.power', type=float, default=1.4,
+        help="the power to raise before griffin-lim.")
+    parser.add_argument('--audio.min_level_db', type=int, default=-100,
+        help="the minimum level db.")
+    parser.add_argument('--audio.ref_level_db', type=int, default=20,
+        help="the reference level db.")
+    parser.add_argument('--audio.outputs_per_step', type=int, default=1,
+        help="the outputs per step.")
+    
+    parser.add_argument('--embedding_size', type=int, default=256,
+        help="the dim size of embedding.")
+    parser.add_argument('--encoder_n_layer', type=int, default=6,
+        help="the number of FFT Block in encoder.")
+    parser.add_argument('--encoder_head', type=int, default=2,
+        help="the attention head number in encoder.")
+    parser.add_argument('--encoder_conv1d_filter_size', type=int, default=1024,
+        help="the filter size of conv1d in encoder.")
+    parser.add_argument('--max_sep_len', type=int, default=2048,
+        help="the max length of sequence.")
+    parser.add_argument('--encoder_output_size', type=int, default=256,
+        help="the output channel size of encoder.")
+    parser.add_argument('--decoder_n_layer', type=int, default=6,
+        help="the number of FFT Block in decoder.")
+    parser.add_argument('--decoder_head', type=int, default=2,
+        help="the attention head number in decoder.")
+    parser.add_argument('--decoder_conv1d_filter_size', type=int, default=1024,
+        help="the filter size of conv1d in decoder.")
+    parser.add_argument('--decoder_output_size', type=int, default=256,
+        help="the output channel size of decoder.")
+    parser.add_argument('--hidden_size', type=int, default=256,
+        help="the hidden size in model.")
+    parser.add_argument('--duration_predictor_output_size', type=int, default=256,
+        help="the output size of duration predictior.")
+    parser.add_argument('--duration_predictor_filter_size', type=int, default=3,
+        help="the filter size of conv1d in duration prediction.")
+    parser.add_argument('--fft_conv1d_filter', type=int, default=3,
+        help="the filter size of conv1d in fft.")
+    parser.add_argument('--fft_conv1d_padding', type=int, default=1,
+        help="the padding size of conv1d in fft.")
+    parser.add_argument('--dropout', type=float, default=0.1,
+        help="the dropout in network.")
+    parser.add_argument('--transformer_head', type=int, default=4,
+        help="the attention head num of transformerTTS.")
+
+    parser.add_argument('--warm_up_step', type=int, default=4000,
+        help="the warm up step of learning rate.")
+    parser.add_argument('--grad_clip_thresh', type=float, default=1.0,
+        help="the threshold of grad clip.")
+    parser.add_argument('--batch_size', type=int, default=32,
+        help="batch size for training.")
+    parser.add_argument('--epochs', type=int, default=10000,
+        help="the number of epoch for training.")
+    parser.add_argument('--lr', type=float, default=0.001,
+        help="the learning rate for training.")
+    parser.add_argument('--save_step', type=int, default=500,
+        help="checkpointing interval during training.")
+    parser.add_argument('--use_gpu', type=bool, default=True,
+        help="use gpu or not during training.")
+    parser.add_argument('--use_data_parallel', type=bool, default=False,
+        help="use data parallel or not during training.")
+
+    parser.add_argument('--data_path', type=str, default='./dataset/LJSpeech-1.1',
+        help="the path of dataset.")
+    parser.add_argument('--checkpoint_path', type=str, default=None,
+        help="the path to load checkpoint or pretrain model.")
+    parser.add_argument('--save_path', type=str, default='./checkpoint',
+        help="the path to save checkpoint.")
+    parser.add_argument('--log_dir', type=str, default='./log',
+        help="the directory to save tensorboard log.")
+    parser.add_argument('--sample_path', type=str, default='./sample',
+        help="the directory to save audio sample in synthesis.")
+    parser.add_argument('--transtts_path', type=str, default='./log',
+        help="the directory to load pretrain transformerTTS model.")
+    parser.add_argument('--transformer_step', type=int, default=70000,
+        help="the step to load transformerTTS model.")
+    
+
+    parser.add_argument('-c', '--config', action=jsonargparse.ActionConfigFile)

parakeet/models/fastspeech/train.py

@@ -0,0 +1,139 @@
+import numpy as np
+import argparse
+import os
+import time
+import math
+import jsonargparse
+from pathlib import Path
+from tqdm import tqdm
+from tensorboardX import SummaryWriter
+import paddle.fluid.dygraph as dg
+import paddle.fluid.layers as layers
+import paddle.fluid as fluid
+from parse import add_config_options_to_parser
+from pprint import pprint
+from network import FastSpeech
+from utils import get_alignment
+from parakeet.models.dataloader.jlspeech import LJSpeechLoader
+from parakeet.models.transformerTTS.network import TransformerTTS
+
+class MyDataParallel(dg.parallel.DataParallel):
+    """
+    A data parallel proxy for model.
+    """
+
+    def __init__(self, layers, strategy):
+        super(MyDataParallel, self).__init__(layers, strategy)
+
+    def __getattr__(self, key):
+        if key in self.__dict__:
+            return object.__getattribute__(self, key)
+        elif key is "_layers":
+            return object.__getattribute__(self, "_sub_layers")["_layers"]
+        else:
+            return getattr(
+                object.__getattribute__(self, "_sub_layers")["_layers"], key)
+
+def main(cfg):
+
+    local_rank = dg.parallel.Env().local_rank if cfg.use_data_parallel else 0
+    nranks = dg.parallel.Env().nranks if cfg.use_data_parallel else 1
+
+    if local_rank == 0:
+        # Print the whole config setting.
+        pprint(jsonargparse.namespace_to_dict(cfg))
+
+    global_step = 0
+    place = (fluid.CUDAPlace(dg.parallel.Env().dev_id)
+             if cfg.use_data_parallel else fluid.CUDAPlace(0)
+             if cfg.use_gpu else fluid.CPUPlace())
+
+    if not os.path.exists(cfg.log_dir):
+            os.mkdir(cfg.log_dir)
+    path = os.path.join(cfg.log_dir,'fastspeech')
+
+    writer = SummaryWriter(path) if local_rank == 0 else None
+
+    with dg.guard(place):
+        transformerTTS = TransformerTTS(cfg)
+        model_path = os.path.join(cfg.transtts_path, "transformer")
+        model_dict, _ = fluid.dygraph.load_dygraph(os.path.join(model_path, str(cfg.transformer_step)))
+        #for param in transformerTTS.state_dict():
+        #   print(param)
+        
+        transformerTTS.set_dict(model_dict)
+        transformerTTS.eval()
+
+        model = FastSpeech(cfg)
+        model.train()
+        optimizer = fluid.optimizer.AdamOptimizer(learning_rate=dg.NoamDecay(1/(cfg.warm_up_step *( cfg.lr ** 2)), cfg.warm_up_step))
+
+        reader = LJSpeechLoader(cfg, nranks, local_rank).reader()
+        
+        if cfg.checkpoint_path is not None:
+            model_dict, opti_dict = fluid.dygraph.load_dygraph(cfg.checkpoint_path)
+            model.set_dict(model_dict)
+            optimizer.set_dict(opti_dict)
+            print("load checkpoint!!!")
+
+        if cfg.use_data_parallel:
+            strategy = dg.parallel.prepare_context()
+            model = MyDataParallel(model, strategy)
+
+        for epoch in range(cfg.epochs):
+            pbar = tqdm(reader)
+
+            for i, data in enumerate(pbar):
+                pbar.set_description('Processing at epoch %d'%epoch)
+                character, mel, mel_input, pos_text, pos_mel, text_length = data
+
+                _, _, attn_probs, _, _, _ = transformerTTS(character, mel_input, pos_text, pos_mel)
+                alignment = dg.to_variable(get_alignment(attn_probs, cfg.transformer_head)).astype(np.float32)
+
+                global_step += 1
+                    
+                #Forward
+                result= model(character, 
+                              pos_text, 
+                              mel_pos=pos_mel,  
+                              length_target=alignment)
+                mel_output, mel_output_postnet, duration_predictor_output, _, _ = result
+                mel_loss = layers.mse_loss(mel_output, mel)
+                mel_postnet_loss = layers.mse_loss(mel_output_postnet, mel)
+                duration_loss = layers.mean(layers.abs(layers.elementwise_sub(duration_predictor_output, alignment)))
+                total_loss = mel_loss + mel_postnet_loss + duration_loss
+
+                if local_rank==0:
+                    print('epoch:{}, step:{}, mel_loss:{}, mel_postnet_loss:{}, duration_loss:{}'.format(epoch, global_step, mel_loss.numpy(), mel_postnet_loss.numpy(), duration_loss.numpy()))
+
+                    writer.add_scalar('mel_loss', mel_loss.numpy(), global_step)
+                    writer.add_scalar('post_mel_loss', mel_postnet_loss.numpy(), global_step)
+                    writer.add_scalar('duration_loss', duration_loss.numpy(), global_step)
+                    writer.add_scalar('learning_rate', optimizer._learning_rate.step().numpy(), global_step)
+
+
+                if cfg.use_data_parallel:
+                    total_loss = model.scale_loss(total_loss)
+                    total_loss.backward()
+                    model.apply_collective_grads()
+                else:
+                    total_loss.backward()
+                optimizer.minimize(total_loss, grad_clip = fluid.dygraph_grad_clip.GradClipByGlobalNorm(cfg.grad_clip_thresh))
+                model.clear_gradients()
+
+                 # save checkpoint
+                if local_rank==0 and global_step % cfg.save_step == 0:
+                    if not os.path.exists(cfg.save_path):
+                        os.mkdir(cfg.save_path)
+                    save_path = os.path.join(cfg.save_path,'fastspeech/%d' % global_step)
+                    dg.save_dygraph(model.state_dict(), save_path)
+                    dg.save_dygraph(optimizer.state_dict(), save_path)
+        if local_rank==0:
+            writer.close()
+
+
+if __name__ =='__main__':
+    parser = jsonargparse.ArgumentParser(description="Train Fastspeech model", formatter_class='default_argparse')
+    add_config_options_to_parser(parser)
+    cfg = parser.parse_args('-c config/fastspeech.yaml'.split())
+    main(cfg)

parakeet/models/fastspeech/utils.py

@@ -0,0 +1,32 @@
+import numpy as np
+
+def get_alignment(attn_probs, n_head):
+    max_F = 0
+    assert attn_probs[0].shape[0] % n_head == 0
+    batch_size = int(attn_probs[0].shape[0] // n_head)
+    for i in range(len(attn_probs)):
+        multi_attn = attn_probs[i].numpy()
+        for j in range(n_head):
+            attn = multi_attn[j*batch_size:(j+1)*batch_size]
+            F = score_F(attn)
+            if max_F < F:
+                max_F = F
+                max_attn = attn
+    alignment = compute_duration(max_attn)
+    return alignment
+    
+def score_F(attn):
+    max = np.max(attn, axis=-1)
+    mean = np.mean(max)
+    return mean
+
+def compute_duration(attn):
+    alignment = np.zeros([attn.shape[0],attn.shape[2]])
+    for i in range(attn.shape[0]):
+        for j in range(attn.shape[1]):
+            max_index = attn[i,j].tolist().index(attn[i,j].max())
+            alignment[i,max_index] += 1
+
+    return alignment
+
+

parakeet/models/transformerTTS/config/train_postnet.yaml

@@ -10,9 +10,8 @@ audio:
   ref_level_db: 20
   outputs_per_step: 1
 
-network:
-  hidden_size: 256
-  embedding_size: 512
+hidden_size: 256
+embedding_size: 512
 
 
 batch_size: 32

parakeet/models/transformerTTS/config/train_transformer.yaml

@@ -10,15 +10,15 @@ audio:
   ref_level_db: 20
   outputs_per_step: 1
 
-network:
-  hidden_size: 256
-  embedding_size: 512
+
+hidden_size: 384 #256
+embedding_size: 384 #512
 
 
 batch_size: 32
 epochs: 10000
 lr: 0.001
-save_step: 500
+save_step: 10
 image_step: 2000
 use_gpu: True
 use_data_parallel: True

parakeet/models/transformerTTS/data.py

@@ -3,10 +3,10 @@ import numpy as np
 from paddle import fluid
 from parakeet.data.sampler import DistributedSampler
 from parakeet.data.datacargo import DataCargo
-from preprocess import batch_examples, LJSpeech, batch_examples_postnet
+from preprocess import batch_examples, LJSpeech, batch_examples_vocoder
 
 class LJSpeechLoader:
-    def __init__(self, config, nranks, rank, is_postnet=False):
+    def __init__(self, config, nranks, rank, is_vocoder=False):
         place = fluid.CUDAPlace(rank) if config.use_gpu else fluid.CPUPlace()
 
         LJSPEECH_ROOT = Path(config.data_path)
@@ -15,8 +15,8 @@ class LJSpeechLoader:
 
         assert config.batch_size % nranks == 0
         each_bs = config.batch_size // nranks
-        if is_postnet:
-            dataloader = DataCargo(dataset, sampler=sampler, batch_size=each_bs, shuffle=True, collate_fn=batch_examples_postnet, drop_last=True)
+        if is_vocoder:
+            dataloader = DataCargo(dataset, sampler=sampler, batch_size=each_bs, shuffle=True, collate_fn=batch_examples_vocoder, drop_last=True)
         else:
             dataloader = DataCargo(dataset, sampler=sampler, batch_size=each_bs, shuffle=True, collate_fn=batch_examples, drop_last=True)
         

parakeet/models/transformerTTS/layers.py

@@ -14,7 +14,6 @@ class Conv1D(dg.Layer):
     """
 
     def __init__(self,
-                 name_scope,
                  in_channels,
                  num_filters,
                  filter_size=3,
@@ -28,7 +27,7 @@ class Conv1D(dg.Layer):
                  act=None,
                  data_format='NCT',
                  dtype="float32"):
-        super(Conv1D, self).__init__(name_scope, dtype=dtype)
+        super(Conv1D, self).__init__(dtype=dtype)
 
         self.padding = padding
         self.in_channels = in_channels
@@ -41,7 +40,7 @@ class Conv1D(dg.Layer):
         self.data_format = data_format
 
         self.conv = dg.Conv2D(
-            self.full_name(),
+            in_channels=in_channels,
             num_filters=num_filters,
             filter_size=(1, filter_size),
             stride=(1, stride),
@@ -77,7 +76,6 @@ class Pool1D(dg.Layer):
     A Pool 1D block implemented with Pool2D.
     """
     def __init__(self,
-                 name_scope,
                  pool_size=-1, 
                  pool_type='max', 
                  pool_stride=1, 
@@ -88,7 +86,7 @@ class Pool1D(dg.Layer):
                  exclusive=True,
                  data_format='NCT', 
                  dtype='float32'):
-        super(Pool1D, self).__init__(name_scope, dtype=dtype)
+        super(Pool1D, self).__init__(dtype=dtype)
         self.pool_size = pool_size
         self.pool_type = pool_type
         self.pool_stride = pool_stride
@@ -101,7 +99,7 @@ class Pool1D(dg.Layer):
         self.dtype = dtype
 
 
-        self.pool2d = dg.Pool2D(self.full_name(), [1,pool_size], pool_type = pool_type,
+        self.pool2d = dg.Pool2D([1,pool_size], pool_type = pool_type,
                                 pool_stride = [1,pool_stride], pool_padding = [0, pool_padding],
                                 global_pooling = global_pooling, use_cudnn = use_cudnn,
                                 ceil_mode = ceil_mode, exclusive = exclusive, dtype = dtype)
@@ -127,7 +125,6 @@ class Pool1D(dg.Layer):
 
 class DynamicGRU(dg.Layer):
     def __init__(self,
-                 scope_name,
                  size,
                  param_attr=None,
                  bias_attr=None,
@@ -137,9 +134,8 @@ class DynamicGRU(dg.Layer):
                  h_0=None,
                  origin_mode=False,
                  init_size=None):
-        super(DynamicGRU, self).__init__(scope_name)
+        super(DynamicGRU, self).__init__()
         self.gru_unit = dg.GRUUnit(
-            self.full_name(),
             size * 3,
             param_attr=param_attr,
             bias_attr=bias_attr,

parakeet/models/transformerTTS/module.py

@@ -3,339 +3,63 @@ from parakeet.g2p.text.symbols import symbols
 import paddle.fluid.dygraph as dg
 import paddle.fluid as fluid
 import paddle.fluid.layers as layers
-from layers import Conv1D, Pool1D, DynamicGRU
+from parakeet.modules.layers import Conv1D, Pool1D
+from parakeet.modules.dynamicGRU import DynamicGRU
 import numpy as np
 
-class FC(dg.Layer):
-    def __init__(self, name_scope, in_features, out_features, is_bias=True, dtype="float32", gain=1):
-        super(FC, self).__init__(name_scope)
-        self.in_features = in_features
-        self.out_features = out_features
-        self.is_bias = is_bias
-        self.dtype = dtype
-        self.gain = gain
-
-        self.weight = self.create_parameter(fluid.ParamAttr(name='weight'), shape=(in_features, out_features), 
-                                            dtype=dtype,
-                                            default_initializer = fluid.initializer.XavierInitializer())
-        #self.weight = gain * self.weight
-        # mind the implicit conversion to ParamAttr for many cases                                    
-        if is_bias is not False:
-            k = math.sqrt(1 / in_features)
-            self.bias = self.create_parameter(fluid.ParamAttr(name='bias'), shape=(out_features, ),
-                                              is_bias=True,
-                                              dtype=dtype,
-                                              default_initializer = fluid.initializer.Uniform(low=-k, high=k))
-
-        # 默认初始化权重使用 Xavier 的方法，偏置使用均匀分布，范围是(-\sqrt{k},/sqrt{k}),k=1/infeature
-    
-    def forward(self, x):
-        x = fluid.layers.matmul(x, self.weight)
-        if hasattr(self, "bias"):
-            x = fluid.layers.elementwise_add(x, self.bias)
-        return x
-
-class Conv(dg.Layer):
-    def __init__(self, name_scope, in_channels, out_channels, filter_size=1,
-                padding=0, dilation=1, stride=1, use_cudnn=True, 
-                data_format="NCT", is_bias=True, gain=1):
-        super(Conv, self).__init__(name_scope)
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.filter_size = filter_size
-        self.padding = padding
-        self.dilation = dilation
-        self.stride = stride
-        self.use_cudnn = use_cudnn
-        self.data_format = data_format
-        self.is_bias = is_bias
-        self.gain = gain
-
-        self.weight_attr = fluid.ParamAttr(name='weight', initializer=fluid.initializer.XavierInitializer())
-        self.bias_attr = None
-        if is_bias is not False:
-            k = math.sqrt(1 / in_channels)
-            self.bias_attr = fluid.ParamAttr(name='bias', initializer=fluid.initializer.Uniform(low=-k, high=k)) 
-        
-        self.conv = Conv1D( self.full_name(),
-                            in_channels = in_channels,
-                            num_filters = out_channels,
-                            filter_size = filter_size,
-                            padding = padding,
-                            dilation = dilation,
-                            stride = stride,
-                            param_attr = self.weight_attr,
-                            bias_attr = self.bias_attr,
-                            use_cudnn = use_cudnn,
-                            data_format = data_format)
-
-    def forward(self, x):
-        x = self.conv(x)
-        return x
 
 class EncoderPrenet(dg.Layer):
-    def __init__(self, name_scope, embedding_size, num_hidden, use_cudnn=True):
-        super(EncoderPrenet, self).__init__(name_scope)
+    def __init__(self, embedding_size, num_hidden, use_cudnn=True):
+        super(EncoderPrenet, self).__init__()
         self.embedding_size = embedding_size
         self.num_hidden = num_hidden
         self.use_cudnn = use_cudnn
-        self.embedding = dg.Embedding(self.full_name(), 
-                                        size = [len(symbols), embedding_size], 
+        self.embedding = dg.Embedding( size = [len(symbols), embedding_size], 
                                         param_attr = fluid.ParamAttr(name='weight'),
                                         padding_idx = None)
-        self.conv1 = Conv(self.full_name(),
-                            in_channels = embedding_size, 
+        self.conv_list = []
+        self.conv_list.append(Conv1D(in_channels = embedding_size, 
                             out_channels = num_hidden, 
                             filter_size = 5,
                             padding = int(np.floor(5/2)),
                             use_cudnn = use_cudnn,
-                            data_format = "NCT",
-                            gain = math.sqrt(2))
-        self.conv2 = Conv(self.full_name(),
-                            in_channels = num_hidden, 
-                            out_channels = num_hidden, 
-                            filter_size = 5,
-                            padding = int(np.floor(5/2)),
-                            use_cudnn = use_cudnn,
-                            data_format = "NCT",
-                            gain = math.sqrt(2))
-        self.conv3 = Conv(self.full_name(),
-                            in_channels = num_hidden, 
-                            out_channels = num_hidden, 
-                            filter_size = 5,
-                            padding = int(np.floor(5/2)),
-                            use_cudnn = use_cudnn,
-                            data_format = "NCT",
-                            gain = math.sqrt(2))
-        
-        self.batch_norm1 = dg.BatchNorm(self.full_name(), num_hidden, 
-                            param_attr = fluid.ParamAttr(name='weight'), 
-                            bias_attr = fluid.ParamAttr(name='bias'),
-                            moving_mean_name =  'moving_mean',
-                            moving_variance_name = 'moving_var',
-                            data_layout='NCHW')
-        self.batch_norm2 = dg.BatchNorm(self.full_name(), num_hidden, 
-                            param_attr = fluid.ParamAttr(name='weight'), 
-                            bias_attr = fluid.ParamAttr(name='bias'),
-                            moving_mean_name =  'moving_mean',
-                            moving_variance_name = 'moving_var',
-                            data_layout='NCHW')
-        self.batch_norm3 = dg.BatchNorm(self.full_name(), num_hidden, 
-                            param_attr = fluid.ParamAttr(name='weight'), 
-                            bias_attr = fluid.ParamAttr(name='bias'),
-                            moving_mean_name =  'moving_mean',
-                            moving_variance_name = 'moving_var',
-                            data_layout='NCHW')
+                            data_format = "NCT"))
+        for _ in range(2):
+            self.conv_list = Conv1D(in_channels = num_hidden, 
+                                out_channels = num_hidden, 
+                                filter_size = 5,
+                                padding = int(np.floor(5/2)),
+                                use_cudnn = use_cudnn,
+                                data_format = "NCT")
 
-        self.projection = FC(self.full_name(), num_hidden, num_hidden)
-
-    def forward(self, x):
-        x = self.embedding(x) #(batch_size, seq_len, embending_size)
-        x = layers.transpose(x,[0,2,1])
-        x = layers.dropout(layers.relu(self.batch_norm1(self.conv1(x))), 0.2)
-        x = layers.dropout(layers.relu(self.batch_norm2(self.conv2(x))), 0.2)
-        x = layers.dropout(layers.relu(self.batch_norm3(self.conv3(x))), 0.2)
-        x = layers.transpose(x,[0,2,1]) #(N,T,C)
-        x = self.projection(x)
-        return x
-
-class FFN(dg.Layer):
-    def __init__(self, name_scope, num_hidden, use_cudnn=True):
-        super(FFN, self).__init__(name_scope)
-        self.num_hidden = num_hidden
-        self.use_cudnn = use_cudnn
-        self.w_1 = Conv(self.full_name(),
-                          in_channels = num_hidden, 
-                          out_channels = num_hidden * 4, 
-                          filter_size = 1,
-                          use_cudnn = use_cudnn,
-                          data_format = "NCT",
-                          gain = math.sqrt(2))
-        self.w_2 = Conv(self.full_name(),
-                          in_channels = num_hidden * 4,
-                          out_channels = num_hidden,
-                          filter_size = 1,
-                          use_cudnn = use_cudnn,
-                          data_format = "NCT",
-                          gain = math.sqrt(2))
-        self.layer_norm = dg.LayerNorm(self.full_name(), begin_norm_axis=2)
-
-    def forward(self, input):
-        #FFN Networt
-        x = layers.transpose(input, [0,2,1])
-        x = self.w_2(layers.relu(self.w_1(x)))
-        x = layers.transpose(x,[0,2,1])
-        
-        # dropout
-        # x = layers.dropout(x, 0.1)
-        # not sure where dropout should be placed, in paper should before residual, 
-        # but the diagonal alignment did not appear correctly in the attention plot.
-
-        # residual connection
-        x = x + input
-        
-
-        #layer normalization
-        x = self.layer_norm(x)
-
-        return x
-
-class DecoderPrenet(dg.Layer):
-    def __init__(self, name_scope, input_size, hidden_size, output_size, dropout_rate=0.5):
-        super(DecoderPrenet, self).__init__(name_scope)
-        self.input_size = input_size
-        self.hidden_size = hidden_size
-        self.output_size = output_size
-        self.dropout_rate = dropout_rate
-        
-        self.fc1 = FC(self.full_name(), input_size, hidden_size) #in pytorch this gian=1
-        self.fc2 = FC(self.full_name(), hidden_size, output_size)
-
-    def forward(self, x):
-        x = layers.dropout(layers.relu(self.fc1(x)), self.dropout_rate)
-        x = layers.dropout(layers.relu(self.fc2(x)), self.dropout_rate)
-        return x
-
-class ScaledDotProductAttention(dg.Layer):
-    def __init__(self, name_scope, d_key):
-        super(ScaledDotProductAttention, self).__init__(name_scope)
-
-        self.d_key = d_key
-    
-    # please attention this mask is diff from pytorch
-    def forward(self, key, value, query, mask=None, query_mask=None):
-        # Compute attention score
-        attention = layers.matmul(query, key, transpose_y=True) #transpose the last dim in y
-        attention = attention / math.sqrt(self.d_key)
-
-        # Mask key to ignore padding
-        if mask is not None:
-            attention = attention * mask
-            mask = (mask == 0).astype(np.float32) * (-2 ** 32 + 1)
-            attention = attention + mask
-            
-
-        attention = layers.softmax(attention)
-        # Mask query to ignore padding
-        # Not sure how to work
-        if query_mask is not None:
-            attention = attention * query_mask
-        
-        result = layers.matmul(attention, value)
-        return result, attention
-
-class MultiheadAttention(dg.Layer):
-    def __init__(self, name_scope, num_hidden, num_head=4):
-        super(MultiheadAttention, self).__init__(name_scope)
-        self.num_hidden = num_hidden
-        self.num_hidden_per_attn = num_hidden // num_head
-        self.num_head = num_head
-
-        self.key = FC(self.full_name(), num_hidden, num_hidden, is_bias=False)
-        self.value = FC(self.full_name(), num_hidden, num_hidden, is_bias=False)
-        self.query = FC(self.full_name(), num_hidden, num_hidden, is_bias=False)
-
-        self.scal_attn = ScaledDotProductAttention(self.full_name(), self.num_hidden_per_attn)
-
-        self.fc = FC(self.full_name(), num_hidden * 2, num_hidden)
-
-        self.layer_norm = dg.LayerNorm(self.full_name(), begin_norm_axis=2)
-
-    def forward(self, key, value, query_input, mask=None, query_mask=None):
-        batch_size = key.shape[0]
-        seq_len_key = key.shape[1]
-        seq_len_query = query_input.shape[1]
-
-        # repeat masks h times
-        if query_mask is not None:
-            query_mask = layers.unsqueeze(query_mask, axes=[-1])
-            query_mask = layers.expand(query_mask, [self.num_head, 1, seq_len_key])
-        if mask is not None:
-            mask = layers.expand(mask, (self.num_head, 1, 1))
-        
-        # Make multihead attention
-        # key & value.shape = (batch_size, seq_len, feature)(feature = num_head * num_hidden_per_attn)
-        key = layers.reshape(self.key(key), [batch_size, seq_len_key, self.num_head, self.num_hidden_per_attn])
-        value = layers.reshape(self.value(value), [batch_size, seq_len_key, self.num_head, self.num_hidden_per_attn])
-        query = layers.reshape(self.query(query_input), [batch_size, seq_len_query, self.num_head, self.num_hidden_per_attn])
-
-        key = layers.reshape(layers.transpose(key, [2, 0, 1, 3]), [-1, seq_len_key, self.num_hidden_per_attn])
-        value = layers.reshape(layers.transpose(value, [2, 0, 1, 3]), [-1, seq_len_key, self.num_hidden_per_attn])
-        query = layers.reshape(layers.transpose(query, [2, 0, 1, 3]), [-1, seq_len_query, self.num_hidden_per_attn])
-        
-        result, attention = self.scal_attn(key, value, query, mask=mask, query_mask=query_mask)
-        
-        # concat all multihead result
-        result = layers.reshape(result, [self.num_head, batch_size, seq_len_query, self.num_hidden_per_attn])
-        result = layers.reshape(layers.transpose(result, [1,2,0,3]),[batch_size, seq_len_query, -1])
-        #print(result.().shape)
-        # concat result with input
-        result = layers.concat([query_input, result], axis=-1)
-        
-        result = self.fc(result)
-        result = result + query_input
-        
-        result = self.layer_norm(result)
-        return result, attention
-
-class PostConvNet(dg.Layer):
-    def __init__(self, name_scope, config):
-        super(PostConvNet, self).__init__(name_scope)
-        
-        num_hidden = config.network.hidden_size
-        self.num_hidden = num_hidden
-        self.conv1 = Conv(self.full_name(),
-                            in_channels = config.audio.num_mels * config.audio.outputs_per_step,
-                            out_channels = num_hidden,
-                            filter_size = 5,
-                            padding = 4,
-                            use_cudnn = config.use_gpu,
-                            data_format = "NCT",
-                            gain = 5 / 3)
-        self.conv_list = [Conv(self.full_name(),
-                            in_channels = num_hidden,
-                            out_channels = num_hidden,
-                            filter_size = 5,
-                            padding = 4,
-                            use_cudnn = config.use_gpu,
-                            data_format = "NCT",
-                            gain = 5 / 3) for _ in range(3)]
         for i, layer in enumerate(self.conv_list):
             self.add_sublayer("conv_list_{}".format(i), layer)
-        self.conv5 = Conv(self.full_name(),
-                            in_channels = num_hidden,
-                            out_channels = config.audio.num_mels * config.audio.outputs_per_step,
-                            filter_size = 5,
-                            padding = 4,
-                            use_cudnn = config.use_gpu,
-                            data_format = "NCT")
 
-        self.batch_norm_list = [dg.BatchNorm(self.full_name(), num_hidden, 
+        self.batch_norm_list = [dg.BatchNorm(num_hidden, 
                             param_attr = fluid.ParamAttr(name='weight'), 
                             bias_attr = fluid.ParamAttr(name='bias'),
                             moving_mean_name =  'moving_mean',
                             moving_variance_name = 'moving_var',
                             data_layout='NCHW') for _ in range(3)]
+
         for i, layer in enumerate(self.batch_norm_list):
             self.add_sublayer("batch_norm_list_{}".format(i), layer)
-        self.batch_norm1 = dg.BatchNorm(self.full_name(), num_hidden, 
-                            param_attr = fluid.ParamAttr(name='weight'), 
-                            bias_attr = fluid.ParamAttr(name='bias'),
-                            moving_mean_name =  'moving_mean',
-                            moving_variance_name = 'moving_var',
-                            data_layout='NCHW')
 
-    def forward(self, input):
-        input = layers.dropout(layers.tanh(self.batch_norm1(self.conv1(input)[:, :, :-4])),0.1)
+        self.projection = dg.Linear(num_hidden, num_hidden)
+
+    def forward(self, x):
+        x = self.embedding(x) #(batch_size, seq_len, embending_size)
+        x = layers.transpose(x,[0,2,1])
         for batch_norm, conv in zip(self.batch_norm_list, self.conv_list):
-            input = layers.dropout(layers.tanh(batch_norm(conv(input)[:, :, :-4])),0.1)
-        input = self.conv5(input)[:, :, :-4]
-        return input
+            x = layers.dropout(layers.relu(batch_norm(conv(x))), 0.2)
+        x = layers.transpose(x,[0,2,1]) #(N,T,C)
+        x = self.projection(x)
+        return x
 
 class CBHG(dg.Layer):
-    def __init__(self, name_scope, config, K=16, projection_size = 256, num_gru_layers=2, 
+    def __init__(self, hidden_size, batch_size, K=16, projection_size = 256, num_gru_layers=2, 
                  max_pool_kernel_size=2, is_post=False):
-        super(CBHG, self).__init__(name_scope)
+        super(CBHG, self).__init__()
         """
         :param hidden_size: dimension of hidden unit
         :param K: # of convolution banks
@@ -344,19 +68,16 @@ class CBHG(dg.Layer):
         :param max_pool_kernel_size: max pooling kernel size
         :param is_post: whether post processing or not
         """
-        hidden_size = config.network.hidden_size
         self.hidden_size = hidden_size
         self.projection_size = projection_size
         self.conv_list = []
-        self.conv_list.append(Conv(self.full_name(),
-                            in_channels = projection_size,
+        self.conv_list.append(Conv1D(in_channels = projection_size,
                             out_channels = hidden_size,
                             filter_size = 1,
                             padding = int(np.floor(1/2)),
                             data_format = "NCT"))
         for i in range(2,K+1):
-            self.conv_list.append(Conv(self.full_name(),
-                            in_channels = hidden_size,
+            self.conv_list.append(Conv1D(in_channels = hidden_size,
                             out_channels = hidden_size,
                             filter_size = i,
                             padding = int(np.floor(i/2)),
@@ -367,7 +88,7 @@ class CBHG(dg.Layer):
 
         self.batchnorm_list = []
         for i in range(K):
-            self.batchnorm_list.append(dg.BatchNorm(self.full_name(), hidden_size, 
+            self.batchnorm_list.append(dg.BatchNorm(hidden_size, 
                             param_attr = fluid.ParamAttr(name='weight'), 
                             bias_attr = fluid.ParamAttr(name='bias'),
                             moving_mean_name =  'moving_mean',
@@ -379,69 +100,63 @@ class CBHG(dg.Layer):
 
         conv_outdim = hidden_size * K
 
-        self.conv_projection_1 = Conv(self.full_name(),
-                            in_channels = conv_outdim,
+        self.conv_projection_1 = Conv1D(in_channels = conv_outdim,
                             out_channels = hidden_size,
                             filter_size = 3,
                             padding = int(np.floor(3/2)),
                             data_format = "NCT")
 
-        self.conv_projection_2 = Conv(self.full_name(),
-                            in_channels = hidden_size,
+        self.conv_projection_2 = Conv1D(in_channels = hidden_size,
                             out_channels = projection_size,
                             filter_size = 3,
                             padding = int(np.floor(3/2)),
                             data_format = "NCT")
 
-        self.batchnorm_proj_1 = dg.BatchNorm(self.full_name(), hidden_size, 
+        self.batchnorm_proj_1 = dg.BatchNorm(hidden_size, 
                             param_attr = fluid.ParamAttr(name='weight'), 
                             bias_attr = fluid.ParamAttr(name='bias'),
                             moving_mean_name =  'moving_mean',
                             moving_variance_name = 'moving_var',
                             data_layout='NCHW')
-        self.batchnorm_proj_2 = dg.BatchNorm(self.full_name(), projection_size, 
+        self.batchnorm_proj_2 = dg.BatchNorm(projection_size, 
                             param_attr = fluid.ParamAttr(name='weight'), 
                             bias_attr = fluid.ParamAttr(name='bias'),
                             moving_mean_name =  'moving_mean',
                             moving_variance_name = 'moving_var',
                             data_layout='NCHW')
-        self.max_pool = Pool1D(self.full_name(), pool_size = max_pool_kernel_size, 
+        self.max_pool = Pool1D(pool_size = max_pool_kernel_size, 
                     pool_type='max', 
                     pool_stride=1, 
                     pool_padding=1,
                     data_format = "NCT")
-        self.highway = Highwaynet(self.full_name(), self.projection_size)
+        self.highway = Highwaynet(self.projection_size)
 
-        h_0 = np.zeros((config.batch_size, hidden_size // 2), dtype="float32")
+        h_0 = np.zeros((batch_size, hidden_size // 2), dtype="float32")
         h_0 = dg.to_variable(h_0)
-        self.fc_forward1 = FC(self.full_name(), hidden_size, hidden_size // 2 * 3)
-        self.fc_reverse1 = FC(self.full_name(), hidden_size, hidden_size // 2 * 3)
-        self.gru_forward1 = DynamicGRU(self.full_name(),
-                              size = self.hidden_size // 2,
+        self.fc_forward1 = dg.Linear(hidden_size, hidden_size // 2 * 3)
+        self.fc_reverse1 = dg.Linear(hidden_size, hidden_size // 2 * 3)
+        self.gru_forward1 = DynamicGRU(size = self.hidden_size // 2,
                               param_attr = fluid.ParamAttr(name='weight'), 
                               bias_attr = fluid.ParamAttr(name='bias'),
                               is_reverse = False,
                               origin_mode = True,
                               h_0 = h_0)
-        self.gru_reverse1 = DynamicGRU(self.full_name(),
-                              size = self.hidden_size // 2,
+        self.gru_reverse1 = DynamicGRU(size = self.hidden_size // 2,
                               param_attr = fluid.ParamAttr(name='weight'), 
                               bias_attr = fluid.ParamAttr(name='bias'),
                               is_reverse=True,
                               origin_mode=True,
                               h_0 = h_0)
 
-        self.fc_forward2 = FC(self.full_name(), hidden_size, hidden_size // 2 * 3)
-        self.fc_reverse2 = FC(self.full_name(), hidden_size, hidden_size // 2 * 3)
-        self.gru_forward2 = DynamicGRU(self.full_name(),
-                              size = self.hidden_size // 2,
+        self.fc_forward2 = dg.Linear(hidden_size, hidden_size // 2 * 3)
+        self.fc_reverse2 = dg.Linear(hidden_size, hidden_size // 2 * 3)
+        self.gru_forward2 = DynamicGRU(size = self.hidden_size // 2,
                               param_attr = fluid.ParamAttr(name='weight'), 
                               bias_attr = fluid.ParamAttr(name='bias'),
                               is_reverse = False,
                               origin_mode = True,
                               h_0 = h_0)
-        self.gru_reverse2 = DynamicGRU(self.full_name(),
-                              size = self.hidden_size // 2,
+        self.gru_reverse2 = DynamicGRU(size = self.hidden_size // 2,
                               param_attr = fluid.ParamAttr(name='weight'), 
                               bias_attr = fluid.ParamAttr(name='bias'),
                               is_reverse=True,
@@ -491,8 +206,8 @@ class CBHG(dg.Layer):
         return out
 
 class Highwaynet(dg.Layer):
-    def __init__(self, name_scope, num_units, num_layers=4):
-        super(Highwaynet, self).__init__(name_scope)
+    def __init__(self, num_units, num_layers=4):
+        super(Highwaynet, self).__init__()
         self.num_units = num_units
         self.num_layers = num_layers
 
@@ -500,8 +215,8 @@ class Highwaynet(dg.Layer):
         self.linears = []
 
         for i in range(num_layers):
-            self.linears.append(FC(self.full_name(), num_units, num_units))
-            self.gates.append(FC(self.full_name(), num_units, num_units))
+            self.linears.append(dg.Linear(num_units, num_units))
+            self.gates.append(dg.Linear(num_units, num_units))
         
         for i, (linear, gate) in enumerate(zip(self.linears,self.gates)):
             self.add_sublayer("linears_{}".format(i), linear)

parakeet/models/transformerTTS/network.py

@@ -1,39 +1,42 @@
-from module import *
-from utils import get_positional_table, get_sinusoid_encoding_table
+from parakeet.models.transformerTTS.module import *
 import paddle.fluid.dygraph as dg
 import paddle.fluid as fluid
+from parakeet.modules.layers import Conv1D
+from parakeet.modules.utils import *
+from parakeet.modules.multihead_attention import MultiheadAttention
+from parakeet.modules.feed_forward import PositionwiseFeedForward
+from parakeet.modules.prenet import PreNet
+from parakeet.modules.post_convnet import PostConvNet
+
 
 class Encoder(dg.Layer):
-    def __init__(self, name_scope, embedding_size, num_hidden, config):
-        super(Encoder, self).__init__(name_scope)
+    def __init__(self, embedding_size, num_hidden, config):
+        super(Encoder, self).__init__()
         self.num_hidden = num_hidden
         param = fluid.ParamAttr(name='alpha',  
                                 initializer=fluid.initializer.Constant(value=1.0))
-        self.alpha = self.create_parameter(param, shape=(1, ), dtype='float32')
+        self.alpha = self.create_parameter(shape=(1, ), attr=param, dtype='float32')
         self.pos_inp = get_sinusoid_encoding_table(1024, self.num_hidden, padding_idx=0)
-        self.pos_emb = dg.Embedding(name_scope=self.full_name(),
-                                 size=[1024, num_hidden],
+        self.pos_emb = dg.Embedding(size=[1024, num_hidden],
                                  padding_idx=0,
                                  param_attr=fluid.ParamAttr(
                                      name='weight',
                                      initializer=fluid.initializer.NumpyArrayInitializer(self.pos_inp),
                                      trainable=False))
-        self.encoder_prenet = EncoderPrenet(name_scope = self.full_name(), 
-                                            embedding_size = embedding_size, 
+        self.encoder_prenet = EncoderPrenet(embedding_size = embedding_size, 
                                             num_hidden = num_hidden, 
                                             use_cudnn=config.use_gpu)
-        self.layers = [MultiheadAttention(self.full_name(), num_hidden) for _ in range(3)]
+        self.layers = [MultiheadAttention(num_hidden, num_hidden, num_hidden) for _ in range(3)]
         for i, layer in enumerate(self.layers):
             self.add_sublayer("self_attn_{}".format(i), layer)
-        self.ffns = [FFN(self.full_name(), num_hidden, use_cudnn = config.use_gpu) for _ in range(3)]
+        self.ffns = [PositionwiseFeedForward(num_hidden, num_hidden*4, filter_size=1, use_cudnn = config.use_gpu) for _ in range(3)]
         for i, layer in enumerate(self.ffns):
             self.add_sublayer("ffns_{}".format(i), layer)
 
     def forward(self, x, positional):
         if fluid.framework._dygraph_tracer()._train_mode:
-            query_mask = (positional != 0).astype(np.float32)
-            mask = (positional != 0).astype(np.float32)
-            mask = fluid.layers.expand(fluid.layers.unsqueeze(mask,[1]), [1,x.shape[1], 1])  
+            query_mask = get_non_pad_mask(positional)
+            mask = get_attn_key_pad_mask(positional, x)
         else:
             query_mask, mask = None, None
         
@@ -59,65 +62,60 @@ class Encoder(dg.Layer):
         return x, query_mask, attentions
 
 class Decoder(dg.Layer):
-    def __init__(self, name_scope, num_hidden, config):
-        super(Decoder, self).__init__(name_scope)
+    def __init__(self, num_hidden, config):
+        super(Decoder, self).__init__()
         self.num_hidden = num_hidden
         param = fluid.ParamAttr(name='alpha')
-        self.alpha = self.create_parameter(param, shape=(1,), dtype='float32',
+        self.alpha = self.create_parameter(shape=(1,), attr=param, dtype='float32',
                         default_initializer = fluid.initializer.ConstantInitializer(value=1.0))
         self.pos_inp = get_sinusoid_encoding_table(1024, self.num_hidden, padding_idx=0)
-        self.pos_emb = dg.Embedding(name_scope=self.full_name(),
-                                 size=[1024, num_hidden],
+        self.pos_emb = dg.Embedding(size=[1024, num_hidden],
                                  padding_idx=0,
                                  param_attr=fluid.ParamAttr(
                                      name='weight',
                                      initializer=fluid.initializer.NumpyArrayInitializer(self.pos_inp),
                                      trainable=False))
-        self.decoder_prenet = DecoderPrenet(self.full_name(),
-                                            input_size = config.audio.num_mels, 
+        self.decoder_prenet = PreNet(input_size = config.audio.num_mels, 
                                             hidden_size = num_hidden * 2, 
                                             output_size = num_hidden, 
                                             dropout_rate=0.2)
-        self.linear = FC(self.full_name(), num_hidden, num_hidden)
+        self.linear = dg.Linear(num_hidden, num_hidden)
 
-        self.selfattn_layers = [MultiheadAttention(self.full_name(), num_hidden) for _ in range(3)]
+        self.selfattn_layers = [MultiheadAttention(num_hidden, num_hidden, num_hidden) for _ in range(3)]
         for i, layer in enumerate(self.selfattn_layers):
             self.add_sublayer("self_attn_{}".format(i), layer)
-        self.attn_layers = [MultiheadAttention(self.full_name(), num_hidden) for _ in range(3)]
+        self.attn_layers = [MultiheadAttention(num_hidden, num_hidden, num_hidden) for _ in range(3)]
         for i, layer in enumerate(self.attn_layers):
             self.add_sublayer("attn_{}".format(i), layer)
-        self.ffns = [FFN(self.full_name(), num_hidden) for _ in range(3)]
+        self.ffns = [PositionwiseFeedForward(num_hidden, num_hidden*4, filter_size=1) for _ in range(3)]
         for i, layer in enumerate(self.ffns):
             self.add_sublayer("ffns_{}".format(i), layer)
-        self.mel_linear = FC(self.full_name(), num_hidden, config.audio.num_mels * config.audio.outputs_per_step)
-        self.stop_linear = FC(self.full_name(), num_hidden, 1, gain = 1)
+        self.mel_linear = dg.Linear(num_hidden, config.audio.num_mels * config.audio.outputs_per_step)
+        self.stop_linear = dg.Linear(num_hidden, 1)
 
-        self.postconvnet = PostConvNet(self.full_name(), config)
+        self.postconvnet = PostConvNet(config.audio.num_mels, config.hidden_size, 
+                                       filter_size = 5, padding = 4, num_conv=5, 
+                                       outputs_per_step=config.audio.outputs_per_step, 
+                                       use_cudnn = config.use_gpu)
 
     def forward(self, key, value, query, c_mask, positional):
-        batch_size = key.shape[0]
-        decoder_len = query.shape[1]
 
         # get decoder mask with triangular matrix
         
         if fluid.framework._dygraph_tracer()._train_mode:
-            #zeros = np.zeros(positional.shape, dtype=np.float32)
-            m_mask = (positional != 0).astype(np.float32)
-            mask = np.repeat(np.expand_dims(m_mask.numpy() == 0, axis=1), decoder_len, axis=1)
-            mask = mask + np.repeat(np.expand_dims(np.triu(np.ones([decoder_len, decoder_len]), 1), axis=0) ,batch_size, axis=0)
-            mask = fluid.layers.cast(dg.to_variable(mask == 0), np.float32)
+            m_mask = get_non_pad_mask(positional)
+            mask = get_attn_key_pad_mask(positional, query)
+            triu_tensor = dg.to_variable(get_triu_tensor(query.numpy(), query.numpy())).astype(np.float32)
+            mask = mask + triu_tensor
+            mask = fluid.layers.cast(mask != 0, np.float32)
             
 
-            # (batch_size, decoder_len, decoder_len)
-            zero_mask = fluid.layers.expand(fluid.layers.unsqueeze((c_mask != 0).astype(np.float32), axes=2), [1,1,decoder_len])
-            # (batch_size, decoder_len, seq_len)
-            zero_mask = fluid.layers.transpose(zero_mask, [0,2,1])
-        
+            # (batch_size, decoder_len, encoder_len)
+            zero_mask = get_attn_key_pad_mask(layers.squeeze(c_mask,[-1]), query)
         else:
-            mask = np.repeat(np.expand_dims(np.triu(np.ones([decoder_len, decoder_len]), 1), axis=0) ,batch_size, axis=0)
-            mask = fluid.layers.cast(dg.to_variable(mask == 0), np.float32)
+            mask = get_triu_tensor(query.numpy(), query.numpy()).astype(np.float32)
+            mask = fluid.layers.cast(dg.to_variable(mask != 0), np.float32)
             m_mask, zero_mask = None, None
-        #import pdb; pdb.set_trace()
         # Decoder pre-network
         query = self.decoder_prenet(query)
 
@@ -145,21 +143,21 @@ class Decoder(dg.Layer):
         # Mel linear projection
         mel_out = self.mel_linear(query)
         # Post Mel Network
-        postnet_input = layers.transpose(mel_out, [0,2,1])
-        out = self.postconvnet(postnet_input)
-        out = postnet_input + out
-        out = layers.transpose(out, [0,2,1])
+        out = self.postconvnet(mel_out)
+        out = mel_out + out
         
         # Stop tokens
         stop_tokens = self.stop_linear(query)
+        stop_tokens = layers.squeeze(stop_tokens, [-1])
+        stop_tokens = layers.sigmoid(stop_tokens)
 
         return mel_out, out, attn_list, stop_tokens, selfattn_list
 
-class Model(dg.Layer):
-    def __init__(self, name_scope, config):
-        super(Model, self).__init__(name_scope)
-        self.encoder = Encoder(self.full_name(), config.network.embedding_size, config.network.hidden_size, config)
-        self.decoder = Decoder(self.full_name(), config.network.hidden_size, config)
+class TransformerTTS(dg.Layer):
+    def __init__(self, config):
+        super(TransformerTTS, self).__init__()
+        self.encoder = Encoder(config.embedding_size, config.hidden_size, config)
+        self.decoder = Decoder(config.hidden_size, config)
         self.config = config
 
     def forward(self, characters, mel_input, pos_text, pos_mel):
@@ -180,16 +178,16 @@ class ModelPostNet(dg.Layer):
     """
     CBHG Network (mel -> linear)
     """
-    def __init__(self, name_scope, config):
-        super(ModelPostNet, self).__init__(name_scope)
-        self.pre_proj = Conv(self.full_name(), 
-                             in_channels = config.audio.num_mels, 
-                             out_channels = config.network.hidden_size,
+    def __init__(self, config):
+        super(ModelPostNet, self).__init__()
+        self.pre_proj = Conv1D(in_channels = config.audio.num_mels, 
+                             out_channels = config.hidden_size,
+                             filter_size=1,
                              data_format = "NCT")
-        self.cbhg = CBHG(self.full_name(), config)
-        self.post_proj = Conv(self.full_name(), 
-                             in_channels = config.audio.num_mels, 
+        self.cbhg = CBHG(config.hidden_size, config.batch_size)
+        self.post_proj = Conv1D(in_channels = config.hidden_size, 
                              out_channels = (config.audio.n_fft // 2) + 1,
+                             filter_size=1,
                              data_format = "NCT")
 
     def forward(self, mel):

parakeet/models/transformerTTS/parse.py

@@ -22,9 +22,9 @@ def add_config_options_to_parser(parser):
     parser.add_argument('--audio.outputs_per_step', type=int, default=1,
         help="the outputs per step.")
     
-    parser.add_argument('--network.hidden_size', type=int, default=256,
+    parser.add_argument('--hidden_size', type=int, default=256,
         help="the hidden size in network.")
-    parser.add_argument('--network.embedding_size', type=int, default=512,
+    parser.add_argument('--embedding_size', type=int, default=512,
         help="the embedding vector size.")
 
     parser.add_argument('--batch_size', type=int, default=32,

parakeet/models/transformerTTS/preprocess.py

@@ -62,20 +62,6 @@ class LJSpeech(Dataset):
         phonemes = np.array(g2p.en.text_to_sequence(normalized_text), dtype=np.int64)
         return (mag, mel, phonemes) # maybe we need to implement it as a map in the future
 
-    def _batch_examples(self, minibatch):
-        mag_batch = []
-        mel_batch = []
-        phoneme_batch = []
-        for example in minibatch:
-            mag, mel, phoneme = example
-            mag_batch.append(mag)
-            mel_batch.append(mel)
-            phoneme_batch.append(phoneme)
-        mag_batch = SpecBatcher(pad_value=0.)(mag_batch)
-        mel_batch = SpecBatcher(pad_value=0.)(mel_batch)
-        phoneme_batch = TextIDBatcher(pad_id=0)(phoneme_batch)
-        return (mag_batch, mel_batch, phoneme_batch)
-
     def __getitem__(self, index):
         metadatum = self.metadata.iloc[index]
         example = self._get_example(metadatum)
@@ -121,7 +107,7 @@ def batch_examples(batch):
     mel_inputs = np.transpose(SpecBatcher(pad_value=0.)(mel_inputs), axes=(0,2,1))
     return (texts, mels, mel_inputs, pos_texts, pos_mels, np.array(text_lens))
 
-def batch_examples_postnet(batch):
+def batch_examples_vocoder(batch):
     mels=[]
     mags=[]
     for data in batch:

parakeet/models/transformerTTS/synthesis.py

@@ -28,8 +28,8 @@ def synthesis(text_input, cfg):
     writer = SummaryWriter(path)
 
     with dg.guard(place):
-        model = Model('transtts', cfg)
-        model_postnet = ModelPostNet('postnet', cfg)
+        model = Model(cfg)
+        model_postnet = ModelPostNet(cfg)
 
         model.set_dict(load_checkpoint(str(cfg.transformer_step), os.path.join(cfg.checkpoint_path, "transformer")))
         model_postnet.set_dict(load_checkpoint(str(cfg.postnet_step), os.path.join(cfg.checkpoint_path, "postnet")))

parakeet/models/transformerTTS/train_postnet.py

@@ -47,7 +47,7 @@ def main(cfg):
     writer = SummaryWriter(path) if local_rank == 0 else None
 
     with dg.guard(place):   
-        model = ModelPostNet('postnet', cfg)
+        model = ModelPostNet(cfg)
 
         model.train()
         optimizer = fluid.optimizer.AdamOptimizer(learning_rate=dg.NoamDecay(1/(4000 *( cfg.lr ** 2)), 4000))
@@ -62,7 +62,7 @@ def main(cfg):
             strategy = dg.parallel.prepare_context()
             model = MyDataParallel(model, strategy)
 
-        reader = LJSpeechLoader(cfg, nranks, local_rank, is_postnet=True).reader()
+        reader = LJSpeechLoader(cfg, nranks, local_rank, is_vocoder=True).reader()
 
         for epoch in range(cfg.epochs):
             pbar = tqdm(reader)
@@ -74,7 +74,6 @@ def main(cfg):
                 global_step += 1
 
                 mag_pred = model(mel)
-                
                 loss = layers.mean(layers.abs(layers.elementwise_sub(mag_pred, mag)))
                 if cfg.use_data_parallel:
                     loss = model.scale_loss(loss)

parakeet/models/transformerTTS/train_transformer.py

@@ -9,7 +9,8 @@ import jsonargparse
 from parse import add_config_options_to_parser
 from pprint import pprint
 from matplotlib import cm
-from data import LJSpeechLoader
+from parakeet.modules.utils import cross_entropy
+from parakeet.models.dataloader.jlspeech import LJSpeechLoader
 
 class MyDataParallel(dg.parallel.DataParallel):
     """
@@ -49,7 +50,7 @@ def main(cfg):
     writer = SummaryWriter(path) if local_rank == 0 else None
     
     with dg.guard(place):
-        model = Model('transtts', cfg)
+        model = TransformerTTS(cfg)
 
         model.train()
         optimizer = fluid.optimizer.AdamOptimizer(learning_rate=dg.NoamDecay(1/(4000 *( cfg.lr ** 2)), 4000))
@@ -75,15 +76,22 @@ def main(cfg):
                 global_step += 1
                 
                 mel_pred, postnet_pred, attn_probs, stop_preds, attn_enc, attn_dec = model(character, mel_input, pos_text, pos_mel)
-        
+
+                label = np.zeros(stop_preds.shape).astype(np.float32)
+                text_length = text_length.numpy()
+                for i in range(label.shape[0]):
+                    label[i][text_length[i] - 1] = 1
+                
                 mel_loss = layers.mean(layers.abs(layers.elementwise_sub(mel_pred, mel)))
                 post_mel_loss = layers.mean(layers.abs(layers.elementwise_sub(postnet_pred, mel)))
-                loss = mel_loss + post_mel_loss
+                stop_loss = cross_entropy(stop_preds, dg.to_variable(label))
+                loss = mel_loss + post_mel_loss + stop_loss
 
                 if local_rank==0:
                     writer.add_scalars('training_loss', {
                         'mel_loss':mel_loss.numpy(),
                         'post_mel_loss':post_mel_loss.numpy(),
+                        'stop_loss':stop_loss.numpy()
                     }, global_step)
 
                     writer.add_scalars('alphas', {
@@ -97,7 +105,7 @@ def main(cfg):
                         for i, prob in enumerate(attn_probs):
                             for j in range(4):
                                     x = np.uint8(cm.viridis(prob.numpy()[j*16]) * 255)
-                                    writer.add_image('Attention_enc_%d_0'%global_step, x, i*4+j, dataformats="HWC")
+                                    writer.add_image('Attention_%d_0'%global_step, x, i*4+j, dataformats="HWC")
 
                         for i, prob in enumerate(attn_enc):
                             for j in range(4):

parakeet/modules/dynamicGRU.py

@@ -0,0 +1,44 @@
+import paddle.fluid.dygraph as dg
+import paddle.fluid.layers as layers
+
+class DynamicGRU(dg.Layer):
+    def __init__(self,
+                 size,
+                 param_attr=None,
+                 bias_attr=None,
+                 is_reverse=False,
+                 gate_activation='sigmoid',
+                 candidate_activation='tanh',
+                 h_0=None,
+                 origin_mode=False,
+                 init_size=None):
+        super(DynamicGRU, self).__init__()
+        self.gru_unit = dg.GRUUnit(
+            size * 3,
+            param_attr=param_attr,
+            bias_attr=bias_attr,
+            activation=candidate_activation,
+            gate_activation=gate_activation,
+            origin_mode=origin_mode)
+        self.size = size
+        self.h_0 = h_0
+        self.is_reverse = is_reverse
+
+    def forward(self, inputs):
+        hidden = self.h_0
+        res = []
+        for i in range(inputs.shape[1]):
+            if self.is_reverse:
+                i = inputs.shape[1] - 1 - i
+            input_ = inputs[:, i:i + 1, :]
+            input_ = layers.reshape(
+                input_, [-1, input_.shape[2]], inplace=False)
+            hidden, reset, gate = self.gru_unit(input_, hidden)
+            hidden_ = layers.reshape(
+                hidden, [-1, 1, hidden.shape[1]], inplace=False)
+            res.append(hidden_)
+        if self.is_reverse:
+            res = res[::-1]
+        res = layers.concat(res, axis=1)
+        return res
+

parakeet/modules/feed_forward.py

@@ -0,0 +1,40 @@
+import paddle.fluid.dygraph as dg
+import paddle.fluid.layers as layers
+from parakeet.modules.layers import Conv1D
+
+class PositionwiseFeedForward(dg.Layer):
+    ''' A two-feed-forward-layer module '''
+    def __init__(self, d_in, num_hidden, filter_size, padding=0, use_cudnn=True, dropout=0.1):
+        super(PositionwiseFeedForward, self).__init__()
+        self.num_hidden = num_hidden
+        self.use_cudnn = use_cudnn
+        self.dropout = dropout
+        
+        self.w_1 = Conv1D(in_channels = d_in, 
+                        out_channels = num_hidden, 
+                        filter_size = filter_size,
+                        padding=padding,
+                        use_cudnn = use_cudnn,
+                        data_format = "NTC")
+        self.w_2 = Conv1D(in_channels = num_hidden,
+                        out_channels = d_in,
+                        filter_size = filter_size,
+                        padding=padding,
+                        use_cudnn = use_cudnn,
+                        data_format = "NTC")
+        self.layer_norm = dg.LayerNorm(d_in)
+
+    def forward(self, input):
+        #FFN Networt
+        x = self.w_2(layers.relu(self.w_1(input)))
+        
+        # dropout
+        x = layers.dropout(x, self.dropout)
+
+        # residual connection
+        x = x + input
+        
+        #layer normalization
+        x = self.layer_norm(x)
+
+        return x

parakeet/modules/layers.py

@@ -0,0 +1,122 @@
+import math
+import numpy as np
+
+import paddle
+from paddle import fluid
+import paddle.fluid.dygraph as dg
+
+
+class Conv1D(dg.Layer):
+    """
+    A convolution 1D block implemented with Conv2D. Form simplicity and 
+    ensuring the output has the same length as the input, it does not allow 
+    stride > 1.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 filter_size=3,
+                 padding=0,
+                 dilation=1,
+                 stride=1,
+                 groups=None,
+                 param_attr=None,
+                 bias_attr=None,
+                 use_cudnn=True,
+                 act=None,
+                 data_format='NCT',
+                 dtype="float32"):
+        super(Conv1D, self).__init__(dtype=dtype)
+
+        self.padding = padding
+        self.in_channels = in_channels
+        self.num_filters = out_channels
+        self.filter_size = filter_size
+        self.stride = stride
+        self.dilation = dilation
+        self.padding = padding
+        self.act = act
+        self.data_format = data_format
+
+        self.conv = dg.Conv2D(
+            num_channels=in_channels,
+            num_filters=out_channels,
+            filter_size=(1, filter_size),
+            stride=(1, stride),
+            dilation=(1, dilation),
+            padding=(0, padding),
+            groups=groups,
+            param_attr=param_attr,
+            bias_attr=bias_attr,
+            use_cudnn=use_cudnn,
+            act=act,
+            dtype=dtype)
+
+    def forward(self, x):
+        """
+        Args:
+            x (Variable): Shape(B, C_in, 1, T), the input, where C_in means
+                input channels.
+        Returns:
+            x (Variable): Shape(B, C_out, 1, T), the outputs, where C_out means
+                output channels (num_filters).
+        """
+        if self.data_format == 'NTC':
+            x = fluid.layers.transpose(x, [0, 2, 1])
+        x = fluid.layers.unsqueeze(x, [2])
+        x = self.conv(x)
+        x = fluid.layers.squeeze(x, [2])
+        if self.data_format == 'NTC':
+            x = fluid.layers.transpose(x, [0, 2, 1])
+        return x
+
+class Pool1D(dg.Layer):
+    """
+    A Pool 1D block implemented with Pool2D.
+    """
+    def __init__(self,
+                 pool_size=-1, 
+                 pool_type='max', 
+                 pool_stride=1, 
+                 pool_padding=0, 
+                 global_pooling=False, 
+                 use_cudnn=True, 
+                 ceil_mode=False, 
+                 exclusive=True,
+                 data_format='NCT'):
+        super(Pool1D, self).__init__()
+        self.pool_size = pool_size
+        self.pool_type = pool_type
+        self.pool_stride = pool_stride
+        self.pool_padding = pool_padding
+        self.global_pooling = global_pooling
+        self.use_cudnn = use_cudnn
+        self.ceil_mode = ceil_mode
+        self.exclusive = exclusive
+        self.data_format = data_format
+
+
+        self.pool2d = dg.Pool2D([1,pool_size], pool_type = pool_type,
+                                pool_stride = [1,pool_stride], pool_padding = [0, pool_padding],
+                                global_pooling = global_pooling, use_cudnn = use_cudnn,
+                                ceil_mode = ceil_mode, exclusive = exclusive)
+
+    
+    def forward(self, x):
+        """
+        Args:
+            x (Variable): Shape(B, C_in, 1, T), the input, where C_in means
+                input channels.
+        Returns:
+            x (Variable): Shape(B, C_out, 1, T), the outputs, where C_out means
+                output channels (num_filters).
+        """
+        if self.data_format == 'NTC':
+            x = fluid.layers.transpose(x, [0, 2, 1])
+        x = fluid.layers.unsqueeze(x, [2])
+        x = self.pool2d(x)
+        x = fluid.layers.squeeze(x, [2])
+        if self.data_format == 'NTC':
+            x = fluid.layers.transpose(x, [0, 2, 1])
+        return x

parakeet/modules/multihead_attention.py

@@ -0,0 +1,84 @@
+import math
+import numpy as np
+import paddle.fluid.dygraph as dg
+import paddle.fluid.layers as layers
+
+class ScaledDotProductAttention(dg.Layer):
+    def __init__(self, d_key):
+        super(ScaledDotProductAttention, self).__init__()
+
+        self.d_key = d_key
+    
+    # please attention this mask is diff from pytorch
+    def forward(self, key, value, query, mask=None, query_mask=None):
+        # Compute attention score
+        attention = layers.matmul(query, key, transpose_y=True) #transpose the last dim in y
+        attention = attention / math.sqrt(self.d_key)
+
+        # Mask key to ignore padding
+        if mask is not None:
+            attention = attention * (mask == 0).astype(np.float32)
+            mask = mask * (-2 ** 32 + 1)
+            attention = attention + mask
+            
+
+        attention = layers.softmax(attention)
+        attention = layers.dropout(attention, 0.0)
+        # Mask query to ignore padding
+        # Not sure how to work
+        if query_mask is not None:
+            attention = attention * query_mask
+        
+        result = layers.matmul(attention, value)
+        return result, attention
+
+class MultiheadAttention(dg.Layer):
+    def __init__(self, num_hidden, d_k, d_q, num_head=4, dropout=0.1):
+        super(MultiheadAttention, self).__init__()
+        self.num_hidden = num_hidden
+        self.num_head = num_head
+        self.d_k = d_k
+        self.d_q = d_q
+        self.dropout = dropout
+
+        self.key = dg.Linear(num_hidden, num_head * d_k)
+        self.value = dg.Linear(num_hidden, num_head * d_k)
+        self.query = dg.Linear(num_hidden, num_head * d_q)
+
+        self.scal_attn = ScaledDotProductAttention(d_k)
+
+        self.fc = dg.Linear(num_head * d_q, num_hidden)
+
+        self.layer_norm = dg.LayerNorm(num_hidden)
+
+    def forward(self, key, value, query_input, mask=None, query_mask=None):
+        batch_size = key.shape[0]
+        seq_len_key = key.shape[1]
+        seq_len_query = query_input.shape[1]
+
+        # repeat masks h times
+        if query_mask is not None:
+            query_mask = layers.expand(query_mask, [self.num_head, 1, seq_len_key])
+        if mask is not None:
+            mask = layers.expand(mask, (self.num_head, 1, 1))
+        
+        # Make multihead attention
+        # key & value.shape = (batch_size, seq_len, feature)(feature = num_head * num_hidden_per_attn)
+        key = layers.reshape(self.key(key), [batch_size, seq_len_key, self.num_head, self.d_k])
+        value = layers.reshape(self.value(value), [batch_size, seq_len_key, self.num_head, self.d_k])
+        query = layers.reshape(self.query(query_input), [batch_size, seq_len_query, self.num_head, self.d_q])
+
+        key = layers.reshape(layers.transpose(key, [2, 0, 1, 3]), [-1, seq_len_key, self.d_k])
+        value = layers.reshape(layers.transpose(value, [2, 0, 1, 3]), [-1, seq_len_key, self.d_k])
+        query = layers.reshape(layers.transpose(query, [2, 0, 1, 3]), [-1, seq_len_query, self.d_q])
+        result, attention = self.scal_attn(key, value, query, mask=mask, query_mask=query_mask)
+        
+        # concat all multihead result
+        result = layers.reshape(result, [self.num_head, batch_size, seq_len_query, self.d_q])
+        result = layers.reshape(layers.transpose(result, [1,2,0,3]),[batch_size, seq_len_query, -1])
+        
+        result = layers.dropout(self.fc(result), self.dropout)
+        result = result + query_input
+        
+        result = self.layer_norm(result)
+        return result, attention

parakeet/modules/post_convnet.py

@@ -0,0 +1,67 @@
+import paddle.fluid.dygraph as dg
+import paddle.fluid as fluid
+import paddle.fluid.layers as layers
+from parakeet.modules.layers import Conv1D
+
+class PostConvNet(dg.Layer):
+    def __init__(self, 
+                 n_mels=80,
+                 num_hidden=512,
+                 filter_size=5,
+                 padding=0,
+                 num_conv=5,
+                 outputs_per_step=1,
+                 use_cudnn=True,
+                 dropout=0.1):
+        super(PostConvNet, self).__init__()
+        
+        self.dropout = dropout
+        self.conv_list = []
+        self.conv_list.append(Conv1D(in_channels = n_mels * outputs_per_step,
+                            out_channels = num_hidden,
+                            filter_size = filter_size,
+                            padding = padding,
+                            use_cudnn = use_cudnn,
+                            data_format = "NCT"))
+
+        for _ in range(1, num_conv-1):
+            self.conv_list.append(Conv1D(in_channels = num_hidden,
+                                out_channels = num_hidden,
+                                filter_size = filter_size,
+                                padding = padding,
+                                use_cudnn = use_cudnn,
+                                data_format = "NCT") )
+        
+        self.conv_list.append(Conv1D(in_channels = num_hidden,
+                            out_channels = n_mels * outputs_per_step,
+                            filter_size = filter_size,
+                            padding = padding,
+                            use_cudnn = use_cudnn,
+                            data_format = "NCT"))
+
+        for i, layer in enumerate(self.conv_list):
+            self.add_sublayer("conv_list_{}".format(i), layer)
+
+        self.batch_norm_list = [dg.BatchNorm(num_hidden, 
+                            param_attr = fluid.ParamAttr(name='weight'), 
+                            bias_attr = fluid.ParamAttr(name='bias'),
+                            moving_mean_name =  'moving_mean',
+                            moving_variance_name = 'moving_var',
+                            data_layout='NCHW') for _ in range(num_conv-1)]
+        self.batch_norm_list.append(dg.BatchNorm(n_mels * outputs_per_step, 
+                            param_attr = fluid.ParamAttr(name='weight'), 
+                            bias_attr = fluid.ParamAttr(name='bias'),
+                            moving_mean_name =  'moving_mean',
+                            moving_variance_name = 'moving_var',
+                            data_layout='NCHW'))
+        for i, layer in enumerate(self.batch_norm_list):
+            self.add_sublayer("batch_norm_list_{}".format(i), layer)
+        
+
+    def forward(self, input):
+        input = layers.transpose(input, [0,2,1])
+        len = input.shape[-1]
+        for batch_norm, conv in zip(self.batch_norm_list, self.conv_list):
+            input = layers.dropout(layers.tanh(batch_norm(conv(input)[:,:,:len])), self.dropout)
+        input = layers.transpose(input, [0,2,1])
+        return input

parakeet/modules/prenet.py

@@ -0,0 +1,26 @@
+import paddle.fluid.dygraph as dg
+import paddle.fluid.layers as layers
+
+class PreNet(dg.Layer):
+    """
+    Pre Net before passing through the network
+    """
+    def __init__(self, input_size, hidden_size, output_size, dropout_rate=0.2):
+        """
+        :param input_size: dimension of input
+        :param hidden_size: dimension of hidden unit
+        :param output_size: dimension of output
+        """
+        super(PreNet, self).__init__()
+        self.input_size = input_size
+        self.hidden_size = hidden_size
+        self.output_size = output_size
+        self.dropout_rate = dropout_rate
+        
+        self.linear1 = dg.Linear(input_size, hidden_size)
+        self.linear2 = dg.Linear(hidden_size, output_size)
+
+    def forward(self, x):
+        x = layers.dropout(layers.relu(self.linear1(x)), self.dropout_rate)
+        x = layers.dropout(layers.relu(self.linear2(x)), self.dropout_rate)
+        return x

parakeet/modules/utils.py

@@ -2,6 +2,7 @@ import numpy as np
 import librosa
 import os, copy
 from scipy import signal
+import paddle.fluid.layers as layers
 
 
 def get_positional_table(d_pos_vec, n_position=1024):
@@ -33,6 +34,28 @@ def get_sinusoid_encoding_table(n_position, d_hid, padding_idx=None):
 
     return sinusoid_table
 
+def get_non_pad_mask(seq):
+    return layers.unsqueeze((seq != 0).astype(np.float32),[-1])
+
+def get_attn_key_pad_mask(seq_k, seq_q):
+    ''' For masking out the padding part of key sequence. '''
+
+    # Expand to fit the shape of key query attention matrix.
+    len_q = seq_q.shape[1]
+    padding_mask = (seq_k != 0).astype(np.float32)
+    padding_mask = layers.expand(layers.unsqueeze(padding_mask,[1]), [1, len_q, 1]) 
+    return padding_mask
+
+def get_triu_tensor(seq_k, seq_q):
+    ''' For make a triu tensor '''
+    len_k = seq_k.shape[1]
+    len_q = seq_q.shape[1]
+    batch_size = seq_k.shape[0]
+    triu_tensor = np.triu(np.ones([len_k, len_q]), 1)
+    triu_tensor = np.repeat(np.expand_dims(triu_tensor, axis=0) ,batch_size, axis=0)
+    
+    return triu_tensor
+
 def guided_attention(N, T, g=0.2):
     '''Guided attention. Refer to page 3 on the paper.'''
     W = np.zeros((N, T), dtype=np.float32)
@@ -40,3 +63,11 @@ def guided_attention(N, T, g=0.2):
         for t_pos in range(W.shape[1]):
             W[n_pos, t_pos] = 1 - np.exp(-(t_pos / float(T) - n_pos / float(N)) ** 2 / (2 * g * g))
     return W
+
+
+def cross_entropy(input, label, position_weight=5.0, epsilon=0.0001):
+    input = -1 * label * layers.log(input + epsilon) - (1-label) * layers.log(1 - input + epsilon)
+    label = input * (label * (position_weight - 1) + 1)
+    return layers.reduce_sum(label, dim=[0, 1])
+        
+