fix conflict

README.md

@@ -45,8 +45,10 @@ nltk.download("cmudict")
 
 - [Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning](https://arxiv.org/abs/1710.07654)
 - [Neural Speech Synthesis with Transformer Network](https://arxiv.org/abs/1809.08895)
-- [FastSpeech: Fast, Robust and Controllable Text to Speech](https://arxiv.org/abs/1905.09263).
+- [FastSpeech: Fast, Robust and Controllable Text to Speech](https://arxiv.org/abs/1905.09263)
 - [WaveFlow: A Compact Flow-based Model for Raw Audio](https://arxiv.org/abs/1912.01219)
+- [WaveNet: A Generative Model for Raw Audio](https://arxiv.org/abs/1609.03499)
+- [ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech](https://arxiv.org/abs/1807.07281)
 
 ## Examples
 
@@ -54,6 +56,8 @@ nltk.download("cmudict")
 - [Train a TransformerTTS  model with ljspeech dataset](./examples/transformer_tts)
 - [Train a FastSpeech model with ljspeech dataset](./examples/fastspeech)
 - [Train a WaveFlow model with ljspeech dataset](./examples/waveflow)
+- [Train a WaveNet model with ljspeech dataset](./examples/wavenet)
+- [Train a Clarinet model with ljspeech dataset](./examples/clarinet)
 
 ## Copyright and License
 

examples/clarinet/configs/clarinet_ljspeech.yaml

 data:
-  batch_size: 4
+  batch_size: 8
   train_clip_seconds: 0.5
   sample_rate: 22050
   hop_length: 256

examples/clarinet/synthesis.py

+# 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 sys
+import argparse
+import ruamel.yaml
+import random
+from tqdm import tqdm
+import pickle
+import numpy as np
+from tensorboardX import SummaryWriter
+
+import paddle.fluid.dygraph as dg
+from paddle import fluid
+
+from parakeet.models.wavenet import WaveNet, UpsampleNet
+from parakeet.models.clarinet import STFT, Clarinet, ParallelWaveNet
+from parakeet.data import TransformDataset, SliceDataset, RandomSampler, SequentialSampler, DataCargo
+from parakeet.utils.layer_tools import summary, freeze
+
+from utils import valid_model, eval_model, save_checkpoint, load_checkpoint, load_model
+sys.path.append("../wavenet")
+from data import LJSpeechMetaData, Transform, DataCollector
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="synthesize audio files from mel spectrogram in the validation set."
+    )
+    parser.add_argument("--config", type=str, help="path of the config file.")
+    parser.add_argument(
+        "--device", type=int, default=-1, help="device to use.")
+    parser.add_argument("--data", type=str, help="path of LJspeech dataset.")
+    parser.add_argument(
+        "checkpoint", type=str, help="checkpoint to load from.")
+    parser.add_argument(
+        "output", type=str, default="experiment", help="path to save student.")
+
+    args = parser.parse_args()
+    with open(args.config, 'rt') as f:
+        config = ruamel.yaml.safe_load(f)
+
+    ljspeech_meta = LJSpeechMetaData(args.data)
+
+    data_config = config["data"]
+    sample_rate = data_config["sample_rate"]
+    n_fft = data_config["n_fft"]
+    win_length = data_config["win_length"]
+    hop_length = data_config["hop_length"]
+    n_mels = data_config["n_mels"]
+    train_clip_seconds = data_config["train_clip_seconds"]
+    transform = Transform(sample_rate, n_fft, win_length, hop_length, n_mels)
+    ljspeech = TransformDataset(ljspeech_meta, transform)
+
+    valid_size = data_config["valid_size"]
+    ljspeech_valid = SliceDataset(ljspeech, 0, valid_size)
+    ljspeech_train = SliceDataset(ljspeech, valid_size, len(ljspeech))
+
+    teacher_config = config["teacher"]
+    n_loop = teacher_config["n_loop"]
+    n_layer = teacher_config["n_layer"]
+    filter_size = teacher_config["filter_size"]
+    context_size = 1 + n_layer * sum([filter_size**i for i in range(n_loop)])
+    print("context size is {} samples".format(context_size))
+    train_batch_fn = DataCollector(context_size, sample_rate, hop_length,
+                                   train_clip_seconds)
+    valid_batch_fn = DataCollector(
+        context_size, sample_rate, hop_length, train_clip_seconds, valid=True)
+
+    batch_size = data_config["batch_size"]
+    train_cargo = DataCargo(
+        ljspeech_train,
+        train_batch_fn,
+        batch_size,
+        sampler=RandomSampler(ljspeech_train))
+
+    # only batch=1 for validation is enabled
+    valid_cargo = DataCargo(
+        ljspeech_valid,
+        valid_batch_fn,
+        batch_size=1,
+        sampler=SequentialSampler(ljspeech_valid))
+
+    if args.device == -1:
+        place = fluid.CPUPlace()
+    else:
+        place = fluid.CUDAPlace(args.device)
+
+    with dg.guard(place):
+        # conditioner(upsampling net)
+        conditioner_config = config["conditioner"]
+        upsampling_factors = conditioner_config["upsampling_factors"]
+        upsample_net = UpsampleNet(upscale_factors=upsampling_factors)
+        freeze(upsample_net)
+
+        residual_channels = teacher_config["residual_channels"]
+        loss_type = teacher_config["loss_type"]
+        output_dim = teacher_config["output_dim"]
+        log_scale_min = teacher_config["log_scale_min"]
+        assert loss_type == "mog" and output_dim == 3, \
+            "the teacher wavenet should be a wavenet with single gaussian output"
+
+        teacher = WaveNet(n_loop, n_layer, residual_channels, output_dim,
+                          n_mels, filter_size, loss_type, log_scale_min)
+        # load & freeze upsample_net & teacher
+        freeze(teacher)
+
+        student_config = config["student"]
+        n_loops = student_config["n_loops"]
+        n_layers = student_config["n_layers"]
+        student_residual_channels = student_config["residual_channels"]
+        student_filter_size = student_config["filter_size"]
+        student_log_scale_min = student_config["log_scale_min"]
+        student = ParallelWaveNet(n_loops, n_layers, student_residual_channels,
+                                  n_mels, student_filter_size)
+
+        stft_config = config["stft"]
+        stft = STFT(
+            n_fft=stft_config["n_fft"],
+            hop_length=stft_config["hop_length"],
+            win_length=stft_config["win_length"])
+
+        lmd = config["loss"]["lmd"]
+        model = Clarinet(upsample_net, teacher, student, stft,
+                         student_log_scale_min, lmd)
+        summary(model)
+        load_model(model, args.checkpoint)
+
+        # loader
+        train_loader = fluid.io.DataLoader.from_generator(
+            capacity=10, return_list=True)
+        train_loader.set_batch_generator(train_cargo, place)
+
+        valid_loader = fluid.io.DataLoader.from_generator(
+            capacity=10, return_list=True)
+        valid_loader.set_batch_generator(valid_cargo, place)
+
+        if not os.path.exists(args.output):
+            os.makedirs(args.output)
+        eval_model(model, valid_loader, args.output, sample_rate)

examples/clarinet/train.py

+# 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 sys
+import argparse
+import ruamel.yaml
+import random
+from tqdm import tqdm
+import pickle
+import numpy as np
+from tensorboardX import SummaryWriter
+
+import paddle.fluid.dygraph as dg
+from paddle import fluid
+
+from parakeet.models.wavenet import WaveNet, UpsampleNet
+from parakeet.models.clarinet import STFT, Clarinet, ParallelWaveNet
+from parakeet.data import TransformDataset, SliceDataset, RandomSampler, SequentialSampler, DataCargo
+from parakeet.utils.layer_tools import summary, freeze
+
+from utils import make_output_tree, valid_model, save_checkpoint, load_checkpoint, load_wavenet
+sys.path.append("../wavenet")
+from data import LJSpeechMetaData, Transform, DataCollector
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="train a clarinet model with LJspeech and a trained wavenet model."
+    )
+    parser.add_argument("--config", type=str, help="path of the config file.")
+    parser.add_argument(
+        "--device", type=int, default=-1, help="device to use.")
+    parser.add_argument(
+        "--output",
+        type=str,
+        default="experiment",
+        help="path to save student.")
+    parser.add_argument("--data", type=str, help="path of LJspeech dataset.")
+    parser.add_argument("--resume", type=str, help="checkpoint to load from.")
+    parser.add_argument(
+        "--wavenet", type=str, help="wavenet checkpoint to use.")
+    args = parser.parse_args()
+    with open(args.config, 'rt') as f:
+        config = ruamel.yaml.safe_load(f)
+
+    ljspeech_meta = LJSpeechMetaData(args.data)
+
+    data_config = config["data"]
+    sample_rate = data_config["sample_rate"]
+    n_fft = data_config["n_fft"]
+    win_length = data_config["win_length"]
+    hop_length = data_config["hop_length"]
+    n_mels = data_config["n_mels"]
+    train_clip_seconds = data_config["train_clip_seconds"]
+    transform = Transform(sample_rate, n_fft, win_length, hop_length, n_mels)
+    ljspeech = TransformDataset(ljspeech_meta, transform)
+
+    valid_size = data_config["valid_size"]
+    ljspeech_valid = SliceDataset(ljspeech, 0, valid_size)
+    ljspeech_train = SliceDataset(ljspeech, valid_size, len(ljspeech))
+
+    teacher_config = config["teacher"]
+    n_loop = teacher_config["n_loop"]
+    n_layer = teacher_config["n_layer"]
+    filter_size = teacher_config["filter_size"]
+    context_size = 1 + n_layer * sum([filter_size**i for i in range(n_loop)])
+    print("context size is {} samples".format(context_size))
+    train_batch_fn = DataCollector(context_size, sample_rate, hop_length,
+                                   train_clip_seconds)
+    valid_batch_fn = DataCollector(
+        context_size, sample_rate, hop_length, train_clip_seconds, valid=True)
+
+    batch_size = data_config["batch_size"]
+    train_cargo = DataCargo(
+        ljspeech_train,
+        train_batch_fn,
+        batch_size,
+        sampler=RandomSampler(ljspeech_train))
+
+    # only batch=1 for validation is enabled
+    valid_cargo = DataCargo(
+        ljspeech_valid,
+        valid_batch_fn,
+        batch_size=1,
+        sampler=SequentialSampler(ljspeech_valid))
+
+    make_output_tree(args.output)
+
+    if args.device == -1:
+        place = fluid.CPUPlace()
+    else:
+        place = fluid.CUDAPlace(args.device)
+
+    with dg.guard(place):
+        # conditioner(upsampling net)
+        conditioner_config = config["conditioner"]
+        upsampling_factors = conditioner_config["upsampling_factors"]
+        upsample_net = UpsampleNet(upscale_factors=upsampling_factors)
+        freeze(upsample_net)
+
+        residual_channels = teacher_config["residual_channels"]
+        loss_type = teacher_config["loss_type"]
+        output_dim = teacher_config["output_dim"]
+        log_scale_min = teacher_config["log_scale_min"]
+        assert loss_type == "mog" and output_dim == 3, \
+            "the teacher wavenet should be a wavenet with single gaussian output"
+
+        teacher = WaveNet(n_loop, n_layer, residual_channels, output_dim,
+                          n_mels, filter_size, loss_type, log_scale_min)
+        freeze(teacher)
+
+        student_config = config["student"]
+        n_loops = student_config["n_loops"]
+        n_layers = student_config["n_layers"]
+        student_residual_channels = student_config["residual_channels"]
+        student_filter_size = student_config["filter_size"]
+        student_log_scale_min = student_config["log_scale_min"]
+        student = ParallelWaveNet(n_loops, n_layers, student_residual_channels,
+                                  n_mels, student_filter_size)
+
+        stft_config = config["stft"]
+        stft = STFT(
+            n_fft=stft_config["n_fft"],
+            hop_length=stft_config["hop_length"],
+            win_length=stft_config["win_length"])
+
+        lmd = config["loss"]["lmd"]
+        model = Clarinet(upsample_net, teacher, student, stft,
+                         student_log_scale_min, lmd)
+        summary(model)
+
+        # optim
+        train_config = config["train"]
+        learning_rate = train_config["learning_rate"]
+        anneal_rate = train_config["anneal_rate"]
+        anneal_interval = train_config["anneal_interval"]
+        lr_scheduler = dg.ExponentialDecay(
+            learning_rate, anneal_interval, anneal_rate, staircase=True)
+        optim = fluid.optimizer.Adam(
+            lr_scheduler, parameter_list=model.parameters())
+        gradiant_max_norm = train_config["gradient_max_norm"]
+        clipper = fluid.dygraph_grad_clip.GradClipByGlobalNorm(
+            gradiant_max_norm)
+
+        assert args.wavenet or args.resume, "you should load from a trained wavenet or resume training; training without a trained wavenet is not recommended."
+        if args.wavenet:
+            load_wavenet(model, args.wavenet)
+
+        if args.resume:
+            load_checkpoint(model, optim, args.resume)
+
+        # loader
+        train_loader = fluid.io.DataLoader.from_generator(
+            capacity=10, return_list=True)
+        train_loader.set_batch_generator(train_cargo, place)
+
+        valid_loader = fluid.io.DataLoader.from_generator(
+            capacity=10, return_list=True)
+        valid_loader.set_batch_generator(valid_cargo, place)
+
+        # train
+        max_iterations = train_config["max_iterations"]
+        checkpoint_interval = train_config["checkpoint_interval"]
+        eval_interval = train_config["eval_interval"]
+        checkpoint_dir = os.path.join(args.output, "checkpoints")
+        state_dir = os.path.join(args.output, "states")
+        log_dir = os.path.join(args.output, "log")
+        writer = SummaryWriter(log_dir)
+
+        # training loop
+        global_step = 1
+        global_epoch = 1
+        while global_step < max_iterations:
+            epoch_loss = 0.
+            for j, batch in tqdm(enumerate(train_loader), desc="[train]"):
+                audios, mels, audio_starts = batch
+                model.train()
+                loss_dict = model(
+                    audios, mels, audio_starts, clip_kl=global_step > 500)
+
+                writer.add_scalar("learning_rate",
+                                  optim._learning_rate.step().numpy()[0],
+                                  global_step)
+                for k, v in loss_dict.items():
+                    writer.add_scalar("loss/{}".format(k),
+                                      v.numpy()[0], global_step)
+
+                l = loss_dict["loss"]
+                step_loss = l.numpy()[0]
+                print("[train] loss: {:<8.6f}".format(step_loss))
+                epoch_loss += step_loss
+
+                l.backward()
+                optim.minimize(l, grad_clip=clipper)
+                optim.clear_gradients()
+
+                if global_step % eval_interval == 0:
+                    # evaluate on valid dataset
+                    valid_model(model, valid_loader, state_dir, global_step,
+                                sample_rate)
+                if global_step % checkpoint_interval == 0:
+                    save_checkpoint(model, optim, checkpoint_dir, global_step)
+
+                global_step += 1
+
+            # epoch loss
+            average_loss = epoch_loss / j
+            writer.add_scalar("average_loss", average_loss, global_epoch)
+            global_epoch += 1

examples/clarinet/utils.py

+# 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 soundfile as sf
+from tensorboardX import SummaryWriter
+from collections import OrderedDict
+
+from paddle import fluid
+import paddle.fluid.dygraph as dg
+
+
+def make_output_tree(output_dir):
+    checkpoint_dir = os.path.join(output_dir, "checkpoints")
+    if not os.path.exists(checkpoint_dir):
+        os.makedirs(checkpoint_dir)
+
+    state_dir = os.path.join(output_dir, "states")
+    if not os.path.exists(state_dir):
+        os.makedirs(state_dir)
+
+
+def valid_model(model, valid_loader, output_dir, global_step, sample_rate):
+    model.eval()
+    for i, batch in enumerate(valid_loader):
+        # print("sentence {}".format(i))
+        path = os.path.join(output_dir,
+                            "step_{}_sentence_{}.wav".format(global_step, i))
+        audio_clips, mel_specs, audio_starts = batch
+        wav_var = model.synthesis(mel_specs)
+        wav_np = wav_var.numpy()[0]
+        sf.write(path, wav_np, samplerate=sample_rate)
+        print("generated {}".format(path))
+
+
+def eval_model(model, valid_loader, output_dir, sample_rate):
+    model.eval()
+    for i, batch in enumerate(valid_loader):
+        # print("sentence {}".format(i))
+        path = os.path.join(output_dir, "sentence_{}.wav".format(i))
+        audio_clips, mel_specs, audio_starts = batch
+        wav_var = model.synthesis(mel_specs)
+        wav_np = wav_var.numpy()[0]
+        sf.write(path, wav_np, samplerate=sample_rate)
+        print("generated {}".format(path))
+
+
+def save_checkpoint(model, optim, checkpoint_dir, global_step):
+    path = os.path.join(checkpoint_dir, "step_{}".format(global_step))
+    dg.save_dygraph(model.state_dict(), path)
+    print("saving model to {}".format(path + ".pdparams"))
+    if optim:
+        dg.save_dygraph(optim.state_dict(), path)
+        print("saving optimizer to {}".format(path + ".pdopt"))
+
+
+def load_model(model, path):
+    model_dict, _ = dg.load_dygraph(path)
+    model.state_dict(model_dict)
+    print("loaded model from {}.pdparams".format(path))
+
+
+def load_checkpoint(model, optim, path):
+    model_dict, optim_dict = dg.load_dygraph(path)
+    model.state_dict(model_dict)
+    print("loaded model from {}.pdparams".format(path))
+    if optim_dict:
+        optim.set_dict(optim_dict)
+        print("loaded optimizer from {}.pdparams".format(path))
+
+
+def load_wavenet(model, path):
+    wavenet_dict, _ = dg.load_dygraph(path)
+    encoder_dict = OrderedDict()
+    teacher_dict = OrderedDict()
+    for k, v in wavenet_dict.items():
+        if k.startswith("encoder."):
+            encoder_dict[k.split('.', 1)[1]] = v
+        else:
+            # k starts with "decoder."
+            teacher_dict[k.split('.', 1)[1]] = v
+
+    model.encoder.set_dict(encoder_dict)
+    model.teacher.set_dict(teacher_dict)
+    print("loaded the encoder part and teacher part from wavenet model.")

examples/deepvoice3/README.md

@@ -23,7 +23,7 @@ The model consists of an encoder, a decoder and a converter (and a speaker embed
 
 ```text
 ├── data.py          data_processing
-├── ljspeech.yaml    (example) configuration file
+├── configs/         (example) configuration files
 ├── sentences.txt    sample sentences
 ├── synthesis.py     script to synthesize waveform from text
 ├── train.py         script to train a model
@@ -72,7 +72,7 @@ optional arguments:
 Example script:
 
 ```bash
-python train.py --config=./ljspeech.yaml --data=./LJSpeech-1.1/ --output=experiment --device=0
+python train.py --config=configs/ljspeech.yaml --data=./LJSpeech-1.1/ --output=experiment --device=0
 ```
 
 You can monitor training log via tensorboard, using the script below.
@@ -110,5 +110,5 @@ optional arguments:
 Example script:
 
 ```bash
-python synthesis.py --config=./ljspeech.yaml --device=0 experiment/checkpoints/model_step_005000000 sentences.txt generated
+python synthesis.py --config=configs/ljspeech.yaml --device=0 experiment/checkpoints/model_step_005000000 sentences.txt generated
 ```

examples/wavenet/configs/wavenet_mixture_of_gaussians.yaml

 data:
-  batch_size: 4
+  batch_size: 16
   train_clip_seconds: 0.5
   sample_rate: 22050
   hop_length: 256
@@ -30,7 +30,7 @@ train:
   snap_interval: 10000
   eval_interval: 10000
   
-  max_iterations: 200000
+  max_iterations: 2000000
 
 
 

examples/wavenet/configs/wavenet_single_gaussian.yaml

 data:
-  batch_size: 4
+  batch_size: 16
   train_clip_seconds: 0.5
   sample_rate: 22050
   hop_length: 256
@@ -30,7 +30,7 @@ train:
   snap_interval: 10000
   eval_interval: 10000
   
-  max_iterations: 200000
+  max_iterations: 2000000
 
 
 

examples/wavenet/configs/wavenet_softmax.yaml

 data:
-  batch_size: 4
+  batch_size: 16
   train_clip_seconds: 0.5
   sample_rate: 22050
   hop_length: 256
@@ -30,7 +30,7 @@ train:
   snap_interval: 10000
   eval_interval: 10000
   
-  max_iterations: 200000
+  max_iterations: 2000000