update save/load and README for deepvoice3, wavenet and clarinet
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chenfeiyu
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838d6d1021
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@ -74,7 +74,7 @@ Entries to the introduction, and the launch of training and synthsis for differe
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## Pre-trained models and audio samples
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Parakeet also releases some well-trained parameters for the example models, which can be accessed in the following tables. Each column of these tables lists resources for one model, including the url link to the pre-trained model, the dataset that the model is trained on and the total training steps, and several synthesized audio samples based on the pre-trained model.
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Parakeet also releases some well-trained parameters for the example models, which can be accessed in the following tables. Each column of these tables lists resources for one model, including the url link to the pre-trained model, the dataset that the model is trained on, and synthesized audio samples based on the pre-trained model.
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#### Vocoders
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@ -94,7 +94,7 @@ We provide the model checkpoints of WaveFlow with 64 and 128 residual channels,
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</thead>
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<tbody>
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<tr>
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<th>LJSpeech, 3020 K</th>
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<th>LJSpeech </th>
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<th>LJSpeech </th>
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</tr>
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<tr>
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@ -127,8 +127,8 @@ We provide the model checkpoints of WaveFlow with 64 and 128 residual channels,
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</thead>
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<tbody>
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<tr>
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<th>LJSpeech, 500 K</th>
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<th>LJSpeech, 2450 K</th>
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<th>LJSpeech </th>
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<th>LJSpeech </th>
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</tr>
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<tr>
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<th>
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@ -13,8 +13,8 @@ PaddlePaddle dynamic graph implementation of [WaveFlow: A Compact Flow-based Mod
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├── synthesis.py # script for speech synthesis
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├── train.py # script for model training
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├── utils.py # helper functions for e.g., model checkpointing
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├── parakeet/models/waveflow/data.py # dataset and dataloader settings for LJSpeech
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├── parakeet/models/waveflow/waveflow.py # WaveFlow model high level APIs
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├── data.py # dataset and dataloader settings for LJSpeech
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├── waveflow.py # WaveFlow model high level APIs
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└── parakeet/models/waveflow/waveflow_modules.py # WaveFlow model implementation
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```
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@ -48,12 +48,12 @@ python -u train.py \
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--config=./configs/waveflow_ljspeech.yaml \
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--root=./data/LJSpeech-1.1 \
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--name=${ModelName} --batch_size=4 \
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--parallel=false --use_gpu=true
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--use_gpu=true
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```
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#### Save and Load checkpoints
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Our model will save model parameters as checkpoints in `./runs/waveflow/${ModelName}/checkpoint/` every 10000 iterations by default.
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Our model will save model parameters as checkpoints in `./runs/waveflow/${ModelName}/checkpoint/` every 10000 iterations by default, where `${ModelName}` is the model name for one single experiment and it could be whatever you like.
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The saved checkpoint will have the format of `step-${iteration_number}.pdparams` for model parameters and `step-${iteration_number}.pdopt` for optimizer parameters.
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There are three ways to load a checkpoint and resume training (take an example that you want to load a 500000-iteration checkpoint):
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@ -68,7 +68,7 @@ export CUDA_VISIBLE_DEVICES=0,1,2,3
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python -u -m paddle.distributed.launch train.py \
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--config=./configs/waveflow_ljspeech.yaml \
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--root=./data/LJSpeech-1.1 \
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--name=${ModelName} --parallel=true --use_gpu=true
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--name=${ModelName} --use_gpu=true
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```
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Use `export CUDA_VISIBLE_DEVICES=0,1,2,3` to set the GPUs that you want to use to be visible. Then the `paddle.distributed.launch` module will use these visible GPUs to do data parallel training in multiprocessing mode.
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@ -23,7 +23,7 @@ from paddle import fluid
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import utils
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from parakeet.utils import io
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from parakeet.models.waveflow import WaveFlow
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from waveflow import WaveFlow
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def add_options_to_parser(parser):
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@ -21,9 +21,9 @@ import numpy as np
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import paddle.fluid.dygraph as dg
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from paddle import fluid
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import utils
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from parakeet.models.waveflow import WaveFlow
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from parakeet.utils import io
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import utils
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from waveflow import WaveFlow
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def add_options_to_parser(parser):
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@ -26,7 +26,7 @@ from tensorboardX import SummaryWriter
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import utils
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from parakeet.utils import io
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from parakeet.models.waveflow import WaveFlow
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from waveflow import WaveFlow
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def add_options_to_parser(parser):
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@ -40,11 +40,6 @@ def add_options_to_parser(parser):
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parser.add_argument(
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'--root', type=str, help="root path of the LJSpeech dataset")
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parser.add_argument(
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'--parallel',
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type=utils.str2bool,
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default=True,
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help="option to use data parallel training")
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parser.add_argument(
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'--use_gpu',
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type=utils.str2bool,
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@ -66,11 +61,11 @@ def add_options_to_parser(parser):
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def train(config):
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use_gpu = config.use_gpu
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parallel = config.parallel if use_gpu else False
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# Get the rank of the current training process.
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rank = dg.parallel.Env().local_rank if parallel else 0
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nranks = dg.parallel.Env().nranks if parallel else 1
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rank = dg.parallel.Env().local_rank
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nranks = dg.parallel.Env().nranks
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parallel = nranks > 1
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if rank == 0:
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# Print the whole config setting.
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@ -100,16 +95,7 @@ def train(config):
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# Build model.
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model = WaveFlow(config, checkpoint_dir, parallel, rank, nranks, tb)
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model.build()
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# Obtain the current iteration.
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if config.checkpoint is None:
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if config.iteration is None:
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iteration = io.load_latest_checkpoint(checkpoint_dir, rank)
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else:
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iteration = config.iteration
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else:
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iteration = int(config.checkpoint.split('/')[-1].split('-')[-1])
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iteration = model.build()
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while iteration < config.max_iterations:
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# Run one single training step.
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@ -21,11 +21,11 @@ import paddle.fluid.dygraph as dg
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from paddle import fluid
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from scipy.io.wavfile import write
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import utils
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from parakeet.utils import io
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from parakeet.modules import weight_norm
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from .data import LJSpeech
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from .waveflow_modules import WaveFlowLoss, WaveFlowModule
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from parakeet.models.waveflow import WaveFlowLoss, WaveFlowModule
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from data import LJSpeech
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import utils
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class WaveFlow():
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@ -93,13 +93,12 @@ class WaveFlow():
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parameter_list=waveflow.parameters())
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# Load parameters.
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io.load_parameters(
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self.checkpoint_dir,
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self.rank,
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waveflow,
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optimizer,
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iteration = io.load_parameters(
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model=waveflow,
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optimizer=optimizer,
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checkpoint_dir=self.checkpoint_dir,
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iteration=config.iteration,
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file_path=config.checkpoint)
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checkpoint_path=config.checkpoint)
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print("Rank {}: checkpoint loaded.".format(self.rank))
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# Data parallelism.
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@ -113,13 +112,11 @@ class WaveFlow():
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else:
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# Load parameters.
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io.load_parameters(
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self.checkpoint_dir,
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self.rank,
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waveflow,
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iteration = io.load_parameters(
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model=waveflow,
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checkpoint_dir=self.checkpoint_dir,
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iteration=config.iteration,
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file_path=config.checkpoint,
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dtype=self.dtype)
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checkpoint_path=config.checkpoint)
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print("Rank {}: checkpoint loaded.".format(self.rank))
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for layer in waveflow.sublayers():
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@ -128,6 +125,8 @@ class WaveFlow():
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self.waveflow = waveflow
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return iteration
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def train_step(self, iteration):
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"""Train the model for one step.
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@ -293,6 +292,5 @@ class WaveFlow():
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Returns:
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None
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"""
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io.save_latest_parameters(self.checkpoint_dir, iteration,
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self.waveflow, self.optimizer)
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io.save_latest_checkpoint(self.checkpoint_dir, iteration)
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io.save_parameters(self.checkpoint_dir, iteration, self.waveflow,
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self.optimizer)
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@ -12,4 +12,4 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from parakeet.models.waveflow.waveflow import WaveFlow
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from parakeet.models.waveflow.waveflow_modules import WaveFlowLoss, WaveFlowModule
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@ -18,6 +18,7 @@ import time
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import ruamel.yaml
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import numpy as np
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import paddle.fluid.dygraph as dg
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from paddle.fluid.framework import convert_np_dtype_to_dtype_ as convert_np_dtype
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def is_main_process():
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@ -51,8 +52,6 @@ def _load_latest_checkpoint(checkpoint_dir):
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Args:
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checkpoint_dir (str): the directory where checkpoint is saved.
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rank (int, optional): the rank of the process in multi-process setting.
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Defaults to 0.
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Returns:
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int: the latest iteration number.
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@ -90,8 +89,7 @@ def load_parameters(model,
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optimizer=None,
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checkpoint_dir=None,
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iteration=None,
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checkpoint_path=None,
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dtype="float32"):
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checkpoint_path=None):
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"""Load a specific model checkpoint from disk.
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Args:
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@ -102,37 +100,36 @@ def load_parameters(model,
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iteration (int, optional): if specified, load the specific checkpoint,
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if not specified, load the latest one. Defaults to None.
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checkpoint_path (str, optional): if specified, load the checkpoint
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stored in the checkpoint_path. Defaults to None.
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dtype (str, optional): precision of the model parameters.
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Defaults to float32.
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stored in the checkpoint_path and the argument 'checkpoint_dir' will
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be ignored. Defaults to None.
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Returns:
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iteration (int): number of iterations that the loaded checkpoint has
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been trained.
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"""
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if iteration is not None and checkpoint_dir is None:
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raise ValueError(
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"When iteration is specified, checkpoint_dir should not be None")
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if checkpoint_path is not None:
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# checkpoint is not None
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iteration = int(os.path.basename(checkpoint_path).split("-")[-1])
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else:
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elif checkpoint_dir is not None:
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if iteration is None:
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iteration = _load_latest_checkpoint(checkpoint_dir)
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if iteration == 0:
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return iteration
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checkpoint_path = os.path.join(checkpoint_dir,
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"step-{}".format(iteration))
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if iteration == 0 and not os.path.exists(checkpoint_path):
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# if step-0 exist, it is also loaded
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return iteration
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else:
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raise ValueError(
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"At least one of 'checkpoint_dir' and 'checkpoint_path' should be specified!"
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)
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local_rank = dg.parallel.Env().local_rank
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model_dict, optimizer_dict = dg.load_dygraph(checkpoint_path)
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# cast to desired data type
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state_dict = model.state_dict()
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# cast to desired data type, for mixed-precision training/inference.
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for k, v in model_dict.items():
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model_dict[k] = v.astype(dtype)
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if k in state_dict and convert_np_dtype(v.dtype) != state_dict[
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k].dtype:
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model_dict[k] = v.astype(state_dict[k].numpy().dtype)
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model.set_dict(model_dict)
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print("[checkpoint] Rank {}: loaded model from {}.pdparams".format(
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