Merge pull request #62 from lfchener/develop

add example for tacotron2

examples/tacotron2/config.py

@@ -0,0 +1,70 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from yacs.config import CfgNode as CN
+
+_C = CN()
+_C.data = CN(
+    dict(
+        batch_size=32,  # batch size
+        valid_size=64,  # the first N examples are reserved for validation
+        sample_rate=22050,  # Hz, sample rate
+        n_fft=1024,  # fft frame size
+        win_length=1024,  # window size
+        hop_length=256,  # hop size between ajacent frame
+        f_max=8000,  # Hz, max frequency when converting to mel
+        f_min=0,  # Hz, min frequency when converting to mel
+        d_mels=80,  # mel bands
+        padding_idx=0,  # text embedding's padding index
+    ))
+
+_C.model = CN(
+    dict(
+        reduction_factor=1,  # reduction factor
+        d_encoder=512,  # embedding & encoder's internal size
+        encoder_conv_layers=3,  # number of conv layer in tacotron2 encoder
+        encoder_kernel_size=5,  # kernel size of conv layers in tacotron2 encoder
+        d_prenet=256,  # hidden size of decoder prenet
+        d_attention_rnn=1024,  # hidden size of the first rnn layer in tacotron2 decoder
+        d_decoder_rnn=1024,  #hidden size of the second rnn layer in tacotron2 decoder
+        d_attention=128,  # hidden size of  decoder location linear layer
+        attention_filters=32,  # number of filter in decoder location conv layer 
+        attention_kernel_size=31,  # kernel size of decoder location conv layer
+        d_postnet=512,  # hidden size of decoder postnet
+        postnet_kernel_size=5,  # kernel size of conv layers in postnet
+        postnet_conv_layers=5,  # number of conv layer in decoder postnet
+        p_encoder_dropout=0.5,  # droput probability in encoder
+        p_prenet_dropout=0.5,  # droput probability in decoder prenet
+        p_attention_dropout=0.1,  # droput probability of first rnn layer in decoder
+        p_decoder_dropout=0.1,  # droput probability of second rnn layer in decoder
+        p_postnet_dropout=0.5,  #droput probability in decoder postnet
+    ))
+
+_C.training = CN(
+    dict(
+        lr=1e-3,  # learning rate
+        weight_decay=1e-6,  # the coeff of weight decay
+        grad_clip_thresh=1.0,  # the clip norm of grad clip.
+        plot_interval=1000,  # plot attention and spectrogram
+        valid_interval=1000,  # validation
+        save_interval=1000,  # checkpoint
+        max_iteration=500000,  # max iteration to train
+    ))
+
+
+def get_cfg_defaults():
+    """Get a yacs CfgNode object with default values for my_project."""
+    # Return a clone so that the defaults will not be altered
+    # This is for the "local variable" use pattern
+    return _C.clone()

examples/tacotron2/ljspeech.py

@@ -0,0 +1,106 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+from pathlib import Path
+import pickle
+import numpy as np
+from paddle.io import Dataset, DataLoader
+
+from parakeet.data.batch import batch_spec, batch_text_id
+from parakeet.data import dataset
+
+
+class LJSpeech(Dataset):
+    """A simple dataset adaptor for the processed ljspeech dataset."""
+
+    def __init__(self, root):
+        self.root = Path(root).expanduser()
+        records = []
+        with open(self.root / "metadata.pkl", 'rb') as f:
+            metadata = pickle.load(f)
+        for mel_name, text, ids in metadata:
+            mel_name = self.root / "mel" / (mel_name + ".npy")
+            records.append((mel_name, text, ids))
+        self.records = records
+
+    def __getitem__(self, i):
+        mel_name, _, ids = self.records[i]
+        mel = np.load(mel_name)
+        return ids, mel
+
+    def __len__(self):
+        return len(self.records)
+
+
+class LJSpeechCollector(object):
+    """A simple callable to batch LJSpeech examples."""
+
+    def __init__(self, padding_idx=0, padding_value=0.,
+                 padding_stop_token=1.0):
+        self.padding_idx = padding_idx
+        self.padding_value = padding_value
+        self.padding_stop_token = padding_stop_token
+
+    def __call__(self, examples):
+        texts = []
+        mels = []
+        text_lens = []
+        mel_lens = []
+        stop_tokens = []
+        for data in examples:
+            text, mel = data
+            text = np.array(text, dtype=np.int64)
+            text_lens.append(len(text))
+            mels.append(mel)
+            texts.append(text)
+            mel_lens.append(mel.shape[1])
+            stop_token = np.zeros([mel.shape[1] - 1], dtype=np.float32)
+            stop_tokens.append(np.append(stop_token, 1.0))
+
+        # 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_lens = [
+            i
+            for i, _ in sorted(
+                zip(mel_lens, text_lens), key=lambda x: x[1], reverse=True)
+        ]
+
+        stop_tokens = [
+            i
+            for i, _ in sorted(
+                zip(stop_tokens, 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 = batch_text_id(texts, pad_id=self.padding_idx)
+        mels = np.transpose(
+            batch_spec(
+                mels, pad_value=self.padding_value), axes=(0, 2, 1))
+        stop_tokens = batch_text_id(
+            stop_tokens, pad_id=self.padding_stop_token, dtype=mels[0].dtype)
+
+        return (texts, mels, text_lens, mel_lens, stop_tokens)

examples/tacotron2/preprocess.py

@@ -0,0 +1,99 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import tqdm
+import pickle
+import argparse
+import numpy as np
+from pathlib import Path
+
+from parakeet.datasets import LJSpeechMetaData
+from parakeet.audio import AudioProcessor, LogMagnitude
+from parakeet.frontend import EnglishCharacter
+
+from config import get_cfg_defaults
+
+
+def create_dataset(config, source_path, target_path, verbose=False):
+    # create output dir
+    target_path = Path(target_path).expanduser()
+    mel_path = target_path / "mel"
+    os.makedirs(mel_path, exist_ok=True)
+
+    meta_data = LJSpeechMetaData(source_path)
+    frontend = EnglishCharacter()
+    processor = AudioProcessor(
+        sample_rate=config.data.sample_rate,
+        n_fft=config.data.n_fft,
+        n_mels=config.data.d_mels,
+        win_length=config.data.win_length,
+        hop_length=config.data.hop_length,
+        f_max=config.data.f_max,
+        f_min=config.data.f_min)
+    normalizer = LogMagnitude()
+
+    records = []
+    for (fname, text, _) in tqdm.tqdm(meta_data):
+        wav = processor.read_wav(fname)
+        mel = processor.mel_spectrogram(wav)
+        mel = normalizer.transform(mel)
+        ids = frontend(text)
+        mel_name = os.path.splitext(os.path.basename(fname))[0]
+
+        # save mel spectrogram
+        records.append((mel_name, text, ids))
+        np.save(mel_path / mel_name, mel)
+    if verbose:
+        print("save mel spectrograms into {}".format(mel_path))
+
+    # save meta data as pickle archive
+    with open(target_path / "metadata.pkl", 'wb') as f:
+        pickle.dump(records, f)
+        if verbose:
+            print("saved metadata into {}".format(target_path /
+                                                  "metadata.pkl"))
+
+    print("Done.")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="create dataset")
+    parser.add_argument(
+        "--config",
+        type=str,
+        metavar="FILE",
+        help="extra config to overwrite the default config")
+    parser.add_argument(
+        "--input", type=str, help="path of the ljspeech dataset")
+    parser.add_argument(
+        "--output", type=str, help="path to save output dataset")
+    parser.add_argument(
+        "--opts",
+        nargs=argparse.REMAINDER,
+        help="options to overwrite --config file and the default config, passing in KEY VALUE pairs"
+    )
+    parser.add_argument(
+        "-v", "--verbose", action="store_true", help="print msg")
+
+    config = get_cfg_defaults()
+    args = parser.parse_args()
+    if args.config:
+        config.merge_from_file(args.config)
+    if args.opts:
+        config.merge_from_list(args.opts)
+    config.freeze()
+    print(config.data)
+
+    create_dataset(config, args.input, args.output, args.verbose)

examples/tacotron2/synthesize.py

@@ -0,0 +1,89 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+from pathlib import Path
+import numpy as np
+
+import paddle
+import parakeet
+from parakeet.frontend import EnglishCharacter
+from parakeet.models.tacotron2 import Tacotron2
+
+from config import get_cfg_defaults
+
+
+def main(config, args):
+    paddle.set_device(args.device)
+
+    # model
+    frontend = EnglishCharacter()
+    model = Tacotron2.from_pretrained(frontend, config, args.checkpoint_path)
+    model.eval()
+
+    # inputs
+    input_path = Path(args.input).expanduser()
+    with open(input_path, "rt") as f:
+        sentences = f.readlines()
+
+    if args.output is None:
+        output_dir = input_path.parent / "synthesis"
+    else:
+        output_dir = Path(args.output).expanduser()
+    output_dir.mkdir(exist_ok=True)
+
+    for i, sentence in enumerate(sentences):
+        mel_output, _ = model.predict(sentence)
+        mel_output = mel_output.T
+
+        np.save(str(output_dir / f"sentence_{i}"), mel_output)
+        if args.verbose:
+            print("spectrogram saved at {}".format(output_dir /
+                                                   f"sentence_{i}.npy"))
+
+
+if __name__ == "__main__":
+    config = get_cfg_defaults()
+
+    parser = argparse.ArgumentParser(
+        description="generate mel spectrogram with TransformerTTS.")
+    parser.add_argument(
+        "--config",
+        type=str,
+        metavar="FILE",
+        help="extra config to overwrite the default config")
+    parser.add_argument(
+        "--checkpoint_path", type=str, help="path of the checkpoint to load.")
+    parser.add_argument("--input", type=str, help="path of the text sentences")
+    parser.add_argument("--output", type=str, help="path to save outputs")
+    parser.add_argument(
+        "--device", type=str, default="cpu", help="device type to use.")
+    parser.add_argument(
+        "--opts",
+        nargs=argparse.REMAINDER,
+        help="options to overwrite --config file and the default config, passing in KEY VALUE pairs"
+    )
+    parser.add_argument(
+        "-v", "--verbose", action="store_true", help="print msg")
+
+    args = parser.parse_args()
+    if args.config:
+        config.merge_from_file(args.config)
+    if args.opts:
+        config.merge_from_list(args.opts)
+    config.freeze()
+    print(config)
+    print(args)
+
+    main(config, args)

examples/tacotron2/train.py

@@ -0,0 +1,211 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import time
+from collections import defaultdict
+import numpy as np
+
+import paddle
+from paddle import distributed as dist
+from paddle.io import DataLoader, DistributedBatchSampler
+
+import parakeet
+from parakeet.data import dataset
+from parakeet.frontend import EnglishCharacter
+from parakeet.training.cli import default_argument_parser
+from parakeet.training.experiment import ExperimentBase
+from parakeet.utils import display, mp_tools
+from parakeet.models.tacotron2 import Tacotron2, Tacotron2Loss
+
+from config import get_cfg_defaults
+from ljspeech import LJSpeech, LJSpeechCollector
+
+
+class Experiment(ExperimentBase):
+    def compute_losses(self, inputs, outputs):
+        _, mel_targets, _, _, stop_tokens = inputs
+
+        mel_outputs = outputs["mel_output"]
+        mel_outputs_postnet = outputs["mel_outputs_postnet"]
+        stop_logits = outputs["stop_logits"]
+
+        losses = self.criterion(mel_outputs, mel_outputs_postnet, stop_logits,
+                                mel_targets, stop_tokens)
+        return losses
+
+    def train_batch(self):
+        start = time.time()
+        batch = self.read_batch()
+        data_loader_time = time.time() - start
+
+        self.optimizer.clear_grad()
+        self.model.train()
+        texts, mels, text_lens, output_lens, stop_tokens = batch
+        outputs = self.model(texts, mels, text_lens, output_lens)
+        losses = self.compute_losses(batch, outputs)
+        loss = losses["loss"]
+        loss.backward()
+        self.optimizer.step()
+        iteration_time = time.time() - start
+
+        losses_np = {k: float(v) for k, v in losses.items()}
+        # logging
+        msg = "Rank: {}, ".format(dist.get_rank())
+        msg += "step: {}, ".format(self.iteration)
+        msg += "time: {:>.3f}s/{:>.3f}s, ".format(data_loader_time,
+                                                  iteration_time)
+        msg += ', '.join('{}: {:>.6f}'.format(k, v)
+                         for k, v in losses_np.items())
+        self.logger.info(msg)
+
+        if dist.get_rank() == 0:
+            for k, v in losses_np.items():
+                self.visualizer.add_scalar(f"train_loss/{k}", v,
+                                           self.iteration)
+
+    @mp_tools.rank_zero_only
+    @paddle.no_grad()
+    def valid(self):
+        valid_losses = defaultdict(list)
+        for i, batch in enumerate(self.valid_loader):
+            texts, mels, text_lens, output_lens, stop_tokens = batch
+            outputs = self.model(texts, mels, text_lens, output_lens)
+            losses = self.compute_losses(batch, outputs)
+            for k, v in losses.items():
+                valid_losses[k].append(float(v))
+
+            attention_weights = outputs["alignments"]
+            display.add_attention_plots(self.visualizer,
+                                        f"valid_sentence_{i}_alignments",
+                                        attention_weights[0], self.iteration)
+            display.add_spectrogram_plots(
+                self.visualizer, f"valid_sentence_{i}_target_spectrogram",
+                mels[0], self.iteration)
+            display.add_spectrogram_plots(
+                self.visualizer, f"valid_sentence_{i}_predicted_spectrogram",
+                outputs['mel_outputs_postnet'][0], self.iteration)
+
+        # write visual log
+        valid_losses = {k: np.mean(v) for k, v in valid_losses.items()}
+
+        # logging
+        msg = "Valid: "
+        msg += "step: {}, ".format(self.iteration)
+        msg += ', '.join('{}: {:>.6f}'.format(k, v)
+                         for k, v in valid_losses.items())
+        self.logger.info(msg)
+
+        for k, v in valid_losses.items():
+            self.visualizer.add_scalar(f"valid/{k}", v, self.iteration)
+
+    def setup_model(self):
+        config = self.config
+        frontend = EnglishCharacter()
+        model = Tacotron2(
+            frontend,
+            d_mels=config.data.d_mels,
+            d_encoder=config.model.d_encoder,
+            encoder_conv_layers=config.model.encoder_conv_layers,
+            encoder_kernel_size=config.model.encoder_kernel_size,
+            d_prenet=config.model.d_prenet,
+            d_attention_rnn=config.model.d_attention_rnn,
+            d_decoder_rnn=config.model.d_decoder_rnn,
+            attention_filters=config.model.attention_filters,
+            attention_kernel_size=config.model.attention_kernel_size,
+            d_attention=config.model.d_attention,
+            d_postnet=config.model.d_postnet,
+            postnet_kernel_size=config.model.postnet_kernel_size,
+            postnet_conv_layers=config.model.postnet_conv_layers,
+            reduction_factor=config.model.reduction_factor,
+            p_encoder_dropout=config.model.p_encoder_dropout,
+            p_prenet_dropout=config.model.p_prenet_dropout,
+            p_attention_dropout=config.model.p_attention_dropout,
+            p_decoder_dropout=config.model.p_decoder_dropout,
+            p_postnet_dropout=config.model.p_postnet_dropout)
+
+        if self.parallel:
+            model = paddle.DataParallel(model)
+
+        grad_clip = paddle.nn.ClipGradByGlobalNorm(
+            config.training.grad_clip_thresh)
+        optimizer = paddle.optimizer.Adam(
+            learning_rate=config.training.lr,
+            parameters=model.parameters(),
+            weight_decay=paddle.regularizer.L2Decay(
+                config.training.weight_decay),
+            grad_clip=grad_clip)
+        criterion = Tacotron2Loss()
+        self.model = model
+        self.optimizer = optimizer
+        self.criterion = criterion
+
+    def setup_dataloader(self):
+        args = self.args
+        config = self.config
+        ljspeech_dataset = LJSpeech(args.data)
+
+        valid_set, train_set = dataset.split(ljspeech_dataset,
+                                             config.data.valid_size)
+        batch_fn = LJSpeechCollector(padding_idx=config.data.padding_idx)
+
+        if not self.parallel:
+            self.train_loader = DataLoader(
+                train_set,
+                batch_size=config.data.batch_size,
+                shuffle=True,
+                drop_last=True,
+                collate_fn=batch_fn)
+        else:
+            sampler = DistributedBatchSampler(
+                train_set,
+                batch_size=config.data.batch_size,
+                shuffle=True,
+                drop_last=True)
+            self.train_loader = DataLoader(
+                train_set, batch_sampler=sampler, collate_fn=batch_fn)
+
+        self.valid_loader = DataLoader(
+            valid_set,
+            batch_size=config.data.batch_size,
+            shuffle=False,
+            drop_last=False,
+            collate_fn=batch_fn)
+
+
+def main_sp(config, args):
+    exp = Experiment(config, args)
+    exp.setup()
+    exp.run()
+
+
+def main(config, args):
+    if args.nprocs > 1 and args.device == "gpu":
+        dist.spawn(main_sp, args=(config, args), nprocs=args.nprocs)
+    else:
+        main_sp(config, args)
+
+
+if __name__ == "__main__":
+    config = get_cfg_defaults()
+    parser = default_argument_parser()
+    args = parser.parse_args()
+    if args.config:
+        config.merge_from_file(args.config)
+    if args.opts:
+        config.merge_from_list(args.opts)
+    config.freeze()
+    print(config)
+    print(args)
+
+    main(config, args)