Merge branch 'master' into 'master'

modified the process of generating masks to speed up batching

See merge request !46

examples/fastspeech/parse.py

@@ -18,7 +18,7 @@ def add_config_options_to_parser(parser):
     parser.add_argument(
         '--config_path',
         type=str,
-        default='config/fastspeech.yaml',
+        default='configs/fastspeech.yaml',
         help="the yaml config file path.")
     parser.add_argument(
         '--batch_size', type=int, default=32, help="batch size for training.")
@@ -87,7 +87,7 @@ def add_config_options_to_parser(parser):
     parser.add_argument(
         '--transtts_path',
         type=str,
-        default='./log',
+        default='../transformer_tts/checkpoint',
         help="the directory to load pretrain transformerTTS model.")
     parser.add_argument(
         '--transformer_step',

examples/fastspeech/train.sh

@@ -10,7 +10,7 @@ python -u train.py \
 --use_data_parallel=0 \
 --data_path='../../dataset/LJSpeech-1.1' \
 --transtts_path='../transformer_tts/checkpoint' \
---transformer_step=160000 \
+--transformer_step=120000 \
 --save_path='./checkpoint' \
 --log_dir='./log' \
 --config_path='configs/fastspeech.yaml' \

examples/transformer_tts/README.md

@@ -55,6 +55,8 @@ python -m paddle.distributed.launch --selected_gpus=0,1,2,3 --log_dir ./mylog tr
 
 If you wish to resume from an existing model, please set ``--checkpoint_path`` and ``--transformer_step``.
 
+**Note: In order to ensure the training effect, we recommend using multi-GPU training to enlarge the batch size, and at least 16 samples in single batch per GPU.**
+
 For more help on arguments:
 ``python train_transformer.py --help``.
 

examples/transformer_tts/data.py

@@ -23,7 +23,7 @@ from parakeet import audio
 from parakeet.data.sampler import *
 from parakeet.data.datacargo import DataCargo
 from parakeet.data.batch import TextIDBatcher, SpecBatcher
-from parakeet.data.dataset import DatasetMixin, TransformDataset, CacheDataset
+from parakeet.data.dataset import DatasetMixin, TransformDataset, CacheDataset, SliceDataset
 from parakeet.models.transformer_tts.utils import *
 
 
@@ -44,7 +44,7 @@ class LJSpeechLoader:
         dataset = CacheDataset(dataset)
 
         sampler = DistributedSampler(
-            len(metadata), nranks, rank, shuffle=shuffle)
+            len(dataset), nranks, rank, shuffle=shuffle)
 
         assert args.batch_size % nranks == 0
         each_bs = args.batch_size // nranks
@@ -64,7 +64,6 @@ class LJSpeechLoader:
                 shuffle=shuffle,
                 batch_fn=batch_examples,
                 drop_last=True)
-
         self.reader = fluid.io.DataLoader.from_generator(
             capacity=32,
             iterable=True,
@@ -199,12 +198,13 @@ def batch_examples(batch):
         SpecBatcher(pad_value=0.)(mels), axes=(0, 2, 1))  #(B,T,num_mels)
     mel_inputs = np.transpose(
         SpecBatcher(pad_value=0.)(mel_inputs), axes=(0, 2, 1))  #(B,T,num_mels)
-    enc_slf_mask = get_attn_key_pad_mask(pos_texts, texts).astype(np.float32)
+
+    enc_slf_mask = get_attn_key_pad_mask(pos_texts).astype(np.float32)
     enc_query_mask = get_non_pad_mask(pos_texts).astype(np.float32)
     dec_slf_mask = get_dec_attn_key_pad_mask(pos_mels,
                                              mel_inputs).astype(np.float32)
-    enc_dec_mask = get_attn_key_pad_mask(enc_query_mask[:, :, 0],
-                                         mel_inputs).astype(np.float32)
+    enc_dec_mask = get_attn_key_pad_mask(enc_query_mask[:, :, 0]).astype(
+        np.float32)
     dec_query_slf_mask = get_non_pad_mask(pos_mels).astype(np.float32)
     dec_query_mask = get_non_pad_mask(pos_mels).astype(np.float32)
 

examples/transformer_tts/parse.py

@@ -18,7 +18,7 @@ def add_config_options_to_parser(parser):
     parser.add_argument(
         '--config_path',
         type=str,
-        default='config/train_transformer.yaml',
+        default='configs/train_transformer.yaml',
         help="the yaml config file path.")
     parser.add_argument(
         '--batch_size', type=int, default=32, help="batch size for training.")

examples/transformer_tts/train_transformer.py

@@ -14,7 +14,6 @@
 import os
 from tqdm import tqdm
 from tensorboardX import SummaryWriter
-#from pathlib import Path
 from collections import OrderedDict
 import argparse
 from parse import add_config_options_to_parser
@@ -69,9 +68,6 @@ def main(args):
                 cfg['warm_up_step'] * (args.lr**2)), cfg['warm_up_step']),
             parameter_list=model.parameters())
 
-        reader = LJSpeechLoader(
-            cfg, args, nranks, local_rank, shuffle=True).reader()
-
         if args.checkpoint_path is not None:
             model_dict, opti_dict = load_checkpoint(
                 str(args.transformer_step),
@@ -85,6 +81,9 @@ def main(args):
             strategy = dg.parallel.prepare_context()
             model = fluid.dygraph.parallel.DataParallel(model, strategy)
 
+        reader = LJSpeechLoader(
+            cfg, args, nranks, local_rank, shuffle=True).reader()
+
         for epoch in range(args.epochs):
             pbar = tqdm(reader)
             for i, data in enumerate(pbar):
@@ -148,7 +147,8 @@ def main(args):
                         for i, prob in enumerate(attn_probs):
                             for j in range(4):
                                 x = np.uint8(
-                                    cm.viridis(prob.numpy()[j * 16]) * 255)
+                                    cm.viridis(prob.numpy()[j * args.batch_size
+                                                            // 2]) * 255)
                                 writer.add_image(
                                     'Attention_%d_0' % global_step,
                                     x,
@@ -158,7 +158,8 @@ def main(args):
                         for i, prob in enumerate(attn_enc):
                             for j in range(4):
                                 x = np.uint8(
-                                    cm.viridis(prob.numpy()[j * 16]) * 255)
+                                    cm.viridis(prob.numpy()[j * args.batch_size
+                                                            // 2]) * 255)
                                 writer.add_image(
                                     'Attention_enc_%d_0' % global_step,
                                     x,
@@ -168,7 +169,8 @@ def main(args):
                         for i, prob in enumerate(attn_dec):
                             for j in range(4):
                                 x = np.uint8(
-                                    cm.viridis(prob.numpy()[j * 16]) * 255)
+                                    cm.viridis(prob.numpy()[j * args.batch_size
+                                                            // 2]) * 255)
                                 writer.add_image(
                                     'Attention_dec_%d_0' % global_step,
                                     x,

parakeet/models/transformer_tts/utils.py

@@ -56,15 +56,13 @@ def get_non_pad_mask(seq):
     return mask
 
 
-def get_attn_key_pad_mask(seq_k, seq_q):
+def get_attn_key_pad_mask(seq_k):
     ''' 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 = np.expand_dims(padding_mask, axis=1)
-    padding_mask = padding_mask.repeat([len_q], axis=1)
-    padding_mask = (padding_mask == 0).astype(np.float32) * (-2**32 + 1)
+    padding_mask = (
+        padding_mask == 0).astype(np.float32) * -1e30  #* (-2**32 + 1)
     return padding_mask
 
 
@@ -72,12 +70,12 @@ def get_dec_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 = np.expand_dims(padding_mask, axis=1)
     triu_tensor = get_triu_tensor(seq_q, seq_q)
-    padding_mask = padding_mask.repeat([len_q], axis=1) + triu_tensor
-    padding_mask = (padding_mask != 0).astype(np.float32) * (-2**32 + 1)
+    padding_mask = padding_mask + triu_tensor
+    padding_mask = (
+        padding_mask != 0).astype(np.float32) * -1e30  #* (-2**32 + 1)
     return padding_mask
 
 
@@ -85,12 +83,7 @@ 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
 
 

parakeet/modules/multihead_attention.py

@@ -89,7 +89,7 @@ class ScaledDotProductAttention(dg.Layer):
         # Mask key to ignore padding
         if mask is not None:
             attention = attention + mask
-        attention = layers.softmax(attention)
+        attention = layers.softmax(attention, use_cudnn=True)
         attention = layers.dropout(
             attention, dropout, dropout_implementation='upscale_in_train')