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Merge branch 'master' into 'master' 

update save & load for deep voicde 3, wavenet and clarinet, remove the concept of epoch in training

See merge request !48
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liuyibing01 2020-03-26 18:44:46 +08:00
parent 618eb31ab4 838d6d1021
commit a3683ac297
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117
examples/clarinet/README.md
View File

@ -22,47 +22,71 @@ tar xjvf LJSpeech-1.1.tar.bz2
└── utils.py utility functions
```
## Saving & Loading
`train.py` and `synthesis.py` have 3 arguments in common, `--checkpooint`, `iteration` and `output`.
1. `output` is the directory for saving results.
During training, checkpoints are saved in `checkpoints/` in `output` and tensorboard log is save in `log/` in `output`. Other possible outputs are saved in `states/` in `outuput`.
During synthesizing, audio files and other possible outputs are save in `synthesis/` in `output`.
So after training and synthesizing with the same output directory, the file structure of the output directory looks like this.
```text
├── checkpoints/ # checkpoint directory (including *.pdparams, *.pdopt and a text file `checkpoint` that records the latest checkpoint)
├── states/ # audio files generated at validation and other possible outputs
├── log/ # tensorboard log
└── synthesis/ # synthesized audio files and other possible outputs
```
2. `--checkpoint` and `--iteration` for loading from existing checkpoint. Loading existing checkpoiont follows the following rule:
If `--checkpoint` is provided, the checkpoint specified by `--checkpoint` is loaded.
If `--checkpoint` is not provided, we try to load the model specified by `--iteration` from the checkpoint directory. If `--iteration` is not provided, we try to load the latested checkpoint from checkpoint directory.
## Train
Train the model using train.py, follow the usage displayed by `python train.py --help`.
```text
usage: train.py [-h] [--config CONFIG] [--device DEVICE] [--output OUTPUT]
[--data DATA] [--resume RESUME] [--wavenet WAVENET]
usage: train.py [-h] [--config CONFIG] [--device DEVICE] [--data DATA]
[--checkpoint CHECKPOINT | --iteration ITERATION]
[--wavenet WAVENET]
output
train a ClariNet model with LJspeech and a trained WaveNet model.
Train a ClariNet model with LJspeech and a trained WaveNet model.
positional arguments:
output path to save experiment results
optional arguments:
-h, --help show this help message and exit
--config CONFIG path of the config file.
--device DEVICE device to use.
--output OUTPUT path to save student.
--data DATA path of LJspeech dataset.
--resume RESUME checkpoint to load from.
--wavenet WAVENET wavenet checkpoint to use.
```
-h, --help show this help message and exit
--config CONFIG path of the config file
--device DEVICE device to use
--data DATA path of LJspeech dataset
--checkpoint CHECKPOINT checkpoint to resume from
--iteration ITERATION the iteration of the checkpoint to load from output directory
--wavenet WAVENET wavenet checkpoint to use
- `--config` is the configuration file to use. The provided configurations can be used directly. And you can change some values in the configuration file and train the model with a different config.
- `--data` is the path of the LJSpeech dataset, the extracted folder from the downloaded archive (the folder which contains metadata.txt).
- `--resume` is the path of the checkpoint. If it is provided, the model would load the checkpoint before trainig.
- `--output` is the directory to save results, all result are saved in this directory. The structure of the output directory is shown below.
```text
├── checkpoints # checkpoint
├── states # audio files generated at validation
└── log # tensorboard log
```
- `--device` is the device (gpu id) to use for training. `-1` means CPU.
- `--wavenet` is the path of the wavenet checkpoint to load. If you do not specify `--resume`, then this must be provided.
- `--data` is the path of the LJSpeech dataset, the extracted folder from the downloaded archive (the folder which contains `metadata.txt`).
- `--checkpoint` is the path of the checkpoint.
- `--iteration` is the iteration of the checkpoint to load from output directory.
- `output` is the directory to save results, all result are saved in this directory.
Before you start training a ClariNet model, you should have trained a WaveNet model with single Gaussian output distribution. Make sure the config of the teacher model matches that of the trained model.
See [Saving-&-Loading](#Saving-&-Loading) for details of checkpoint loading.
- `--wavenet` is the path of the wavenet checkpoint to load.
When you start training a ClariNet model without loading form a ClariNet checkpoint, you should have trained a WaveNet model with single Gaussian output distribution. Make sure the config of the teacher model matches that of the trained wavenet model.
Example script:
```bash
python train.py --config=./configs/clarinet_ljspeech.yaml --data=./LJSpeech-1.1/ --output=experiment --device=0 --conditioner=wavenet_checkpoint/conditioner --conditioner=wavenet_checkpoint/teacher
python train.py
--config=./configs/clarinet_ljspeech.yaml
--data=./LJSpeech-1.1/
--device=0
--wavenet="wavenet-step-2000000"
experiment
```
You can monitor training log via tensorboard, using the script below.
@ -75,29 +99,50 @@ tensorboard --logdir=.
## Synthesis
```text
usage: synthesis.py [-h] [--config CONFIG] [--device DEVICE] [--data DATA]
checkpoint output
[--checkpoint CHECKPOINT | --iteration ITERATION]
output
train a ClariNet model with LJspeech and a trained WaveNet model.
Synthesize audio files from mel spectrogram in the validation set.
positional arguments:
checkpoint checkpoint to load from.
output path to save student.
output path to save the synthesized audio
optional arguments:
-h, --help show this help message and exit
--config CONFIG path of the config file.
--device DEVICE device to use.
--data DATA path of LJspeech dataset.
-h, --help show this help message and exit
--config CONFIG path of the config file
--device DEVICE device to use.
--data DATA path of LJspeech dataset
--checkpoint CHECKPOINT checkpoint to resume from
--iteration ITERATION the iteration of the checkpoint to load from output directory
```
- `--config` is the configuration file to use. You should use the same configuration with which you train you model.
- `--data` is the path of the LJspeech dataset. A dataset is not needed for synthesis, but since the input is mel spectrogram, we need to get mel spectrogram from audio files.
- `checkpoint` is the checkpoint to load.
- `output_path` is the directory to save results. The output path contains the generated audio files (`*.wav`).
- `--device` is the device (gpu id) to use for training. `-1` means CPU.
- `--data` is the path of the LJspeech dataset. In principle, a dataset is not needed for synthesis, but since the input is mel spectrogram, we need to get mel spectrogram from audio files.
- `--checkpoint` is the checkpoint to load.
- `--iteration` is the iteration of the checkpoint to load from output directory.
- `output` is the directory to save synthesized audio. Audio file is saved in `synthesis/` in `output` directory.
See [Saving-&-Loading](#Saving-&-Loading) for details of checkpoint loading.
Example script:
```bash
python synthesis.py --config=./configs/wavenet_single_gaussian.yaml --data=./LJSpeech-1.1/ --device=0 experiment/checkpoints/step_500000 generated
python synthesis.py \
--config=./configs/wavenet_single_gaussian.yaml \
--data=./LJSpeech-1.1/ \
--device=0 \
--iteration=500000 \
experiment
```
or
```bash
python synthesis.py \
--config=./configs/wavenet_single_gaussian.yaml \
--data=./LJSpeech-1.1/ \
--device=0 \
--checkpoint="experiment/checkpoints/step-500000" \
experiment
```

57
examples/clarinet/synthesis.py
View File

@ -26,29 +26,41 @@ from tensorboardX import SummaryWriter
import paddle.fluid.dygraph as dg
from paddle import fluid
from parakeet.modules.weight_norm import WeightNormWrapper
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 parakeet.utils import io
from utils import valid_model, eval_model, save_checkpoint, load_checkpoint, load_model
from utils import eval_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."
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("--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("--data", type=str, help="path of LJspeech dataset")
g = parser.add_mutually_exclusive_group()
g.add_argument("--checkpoint", type=str, help="checkpoint to resume from")
g.add_argument(
"--iteration",
type=int,
help="the iteration of the checkpoint to load from output directory")
parser.add_argument(
"checkpoint", type=str, help="checkpoint to load from.")
parser.add_argument(
"output", type=str, default="experiment", help="path to save student.")
"output",
type=str,
default="experiment",
help="path to save the synthesized audio")
args = parser.parse_args()
with open(args.config, 'rt') as f:
config = ruamel.yaml.safe_load(f)
@ -136,17 +148,32 @@ if __name__ == "__main__":
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)
# load parameters
if args.checkpoint is not None:
# load from args.checkpoint
iteration = io.load_parameters(
model, checkpoint_path=args.checkpoint)
else:
# load from "args.output/checkpoints"
checkpoint_dir = os.path.join(args.output, "checkpoints")
iteration = io.load_parameters(
model, checkpoint_dir=checkpoint_dir, iteration=args.iteration)
assert iteration > 0, "A trained checkpoint is needed."
# make generation fast
for sublayer in model.sublayers():
if isinstance(sublayer, WeightNormWrapper):
sublayer.remove_weight_norm()
# data loader
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)
# the directory to save audio files
synthesis_dir = os.path.join(args.output, "synthesis")
if not os.path.exists(synthesis_dir):
os.makedirs(synthesis_dir)
eval_model(model, valid_loader, synthesis_dir, iteration, sample_rate)

135
examples/clarinet/train.py
View File

@ -30,31 +30,46 @@ 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 parakeet.utils import io
from utils import make_output_tree, valid_model, save_checkpoint, load_checkpoint, load_wavenet
from utils import make_output_tree, eval_model, load_wavenet
# import dataset from 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."
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("--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")
g = parser.add_mutually_exclusive_group()
g.add_argument("--checkpoint", type=str, help="checkpoint to resume from")
g.add_argument(
"--iteration",
type=int,
help="the iteration of the checkpoint to load from output directory")
parser.add_argument(
"--device", type=int, default=-1, help="device to use.")
"--wavenet", type=str, help="wavenet checkpoint to use")
parser.add_argument(
"--output",
"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.")
help="path to save experiment results")
args = parser.parse_args()
with open(args.config, 'rt') as f:
config = ruamel.yaml.safe_load(f)
print("Command Line args: ")
for k, v in vars(args).items():
print("{}: {}".format(k, v))
ljspeech_meta = LJSpeechMetaData(args.data)
data_config = config["data"]
@ -154,12 +169,28 @@ if __name__ == "__main__":
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)
# 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)
if args.resume:
load_checkpoint(model, optim, args.resume)
if args.checkpoint is not None:
iteration = io.load_parameters(
model, optim, checkpoint_path=args.checkpoint)
else:
iteration = io.load_parameters(
model,
optim,
checkpoint_dir=checkpoint_dir,
iteration=args.iteration)
if iteration == 0:
assert args.wavenet is not None, "When training afresh, a trained wavenet model should be provided."
load_wavenet(model, args.wavenet)
# loader
train_loader = fluid.io.DataLoader.from_generator(
@ -170,52 +201,42 @@ if __name__ == "__main__":
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)
global_step = iteration + 1
iterator = iter(tqdm(train_loader))
while global_step <= max_iterations:
try:
batch = next(iterator)
except StopIteration as e:
iterator = iter(tqdm(train_loader))
batch = next(iterator)
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)
audios, mels, audio_starts = batch
model.train()
loss_dict = model(
audios, mels, audio_starts, clip_kl=global_step > 500)
l = loss_dict["loss"]
step_loss = l.numpy()[0]
print("[train] loss: {:<8.6f}".format(step_loss))
epoch_loss += step_loss
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.backward()
optim.minimize(l, grad_clip=clipper)
optim.clear_gradients()
l = loss_dict["loss"]
step_loss = l.numpy()[0]
print("[train] global_step: {} loss: {:<8.6f}".format(global_step,
step_loss))
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)
l.backward()
optim.minimize(l, grad_clip=clipper)
optim.clear_gradients()
global_step += 1
if global_step % eval_interval == 0:
# evaluate on valid dataset
eval_model(model, valid_loader, state_dir, global_step,
sample_rate)
if global_step % checkpoint_interval == 0:
io.save_parameters(checkpoint_dir, global_step, model, optim)
# epoch loss
average_loss = epoch_loss / j
writer.add_scalar("average_loss", average_loss, global_epoch)
global_epoch += 1
global_step += 1

40
examples/clarinet/utils.py
View File

@ -32,12 +32,12 @@ def make_output_tree(output_dir):
os.makedirs(state_dir)
def valid_model(model, valid_loader, output_dir, global_step, sample_rate):
def eval_model(model, valid_loader, output_dir, iteration, 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))
"sentence_{}_step_{}.wav".format(i, iteration))
audio_clips, mel_specs, audio_starts = batch
wav_var = model.synthesis(mel_specs)
wav_np = wav_var.numpy()[0]
@ -45,42 +45,6 @@ def valid_model(model, valid_loader, output_dir, global_step, 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.set_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.set_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()

104
examples/deepvoice3/README.md
View File

@ -30,32 +30,55 @@ The model consists of an encoder, a decoder and a converter (and a speaker embed
└── utils.py utility functions
```
## Saving & Loading
`train.py` and `synthesis.py` have 3 arguments in common, `--checkpooint`, `iteration` and `output`.
1. `output` is the directory for saving results.
During training, checkpoints are saved in `checkpoints/` in `output` and tensorboard log is save in `log/` in `output`. Other possible outputs are saved in `states/` in `outuput`.
During synthesizing, audio files and other possible outputs are save in `synthesis/` in `output`.
So after training and synthesizing with the same output directory, the file structure of the output directory looks like this.
```text
├── checkpoints/ # checkpoint directory (including *.pdparams, *.pdopt and a text file `checkpoint` that records the latest checkpoint)
├── states/ # audio files generated at validation and other possible outputs
├── log/ # tensorboard log
└── synthesis/ # synthesized audio files and other possible outputs
```
2. `--checkpoint` and `--iteration` for loading from existing checkpoint. Loading existing checkpoiont follows the following rule:
If `--checkpoint` is provided, the checkpoint specified by `--checkpoint` is loaded.
If `--checkpoint` is not provided, we try to load the model specified by `--iteration` from the checkpoint directory. If `--iteration` is not provided, we try to load the latested checkpoint from checkpoint directory.
## Train
Train the model using train.py, follow the usage displayed by `python train.py --help`.
```text
usage: train.py [-h] [-c CONFIG] [-s DATA] [-r RESUME] [-o OUTPUT] [-g DEVICE]
usage: train.py [-h] [--config CONFIG] [--data DATA] [--device DEVICE]
[--checkpoint CHECKPOINT | --iteration ITERATION]
output
Train a Deep Voice 3 model with LJSpeech dataset.
positional arguments:
output path to save results
optional arguments:
-h, --help show this help message and exit
-c CONFIG, --config CONFIG
experimrnt config
-s DATA, --data DATA The path of the LJSpeech dataset.
-r RESUME, --resume RESUME
checkpoint to load
-o OUTPUT, --output OUTPUT
The directory to save result.
-g DEVICE, --device DEVICE
device to use
-h, --help show this help message and exit
--config CONFIG experimrnt config
--data DATA The path of the LJSpeech dataset.
--device DEVICE device to use
--checkpoint CHECKPOINT checkpoint to resume from.
--iteration ITERATION the iteration of the checkpoint to load from output directory
```
- `--config` is the configuration file to use. The provided `ljspeech.yaml` can be used directly. And you can change some values in the configuration file and train the model with a different config.
- `--data` is the path of the LJSpeech dataset, the extracted folder from the downloaded archive (the folder which contains metadata.txt).
- `--resume` is the path of the checkpoint. If it is provided, the model would load the checkpoint before trainig.
- `--output` is the directory to save results, all results are saved in this directory. The structure of the output directory is shown below.
- `--device` is the device (gpu id) to use for training. `-1` means CPU.
- `--checkpoint` is the path of the checkpoint.
- `--iteration` is the iteration of the checkpoint to load from output directory.
See [Saving-&-Loading](#Saving-&-Loading) for details of checkpoint loading.
- `output` is the directory to save results, all results are saved in this directory. The structure of the output directory is shown below.
```text
├── checkpoints # checkpoint
@ -67,12 +90,14 @@ optional arguments:
└── waveform # waveform (.wav files)
```
- `--device` is the device (gpu id) to use for training. `-1` means CPU.
Example script:
```bash
python train.py --config=configs/ljspeech.yaml --data=./LJSpeech-1.1/ --output=experiment --device=0
python train.py \
--config=configs/ljspeech.yaml \
--data=./LJSpeech-1.1/ \
--device=0 \
experiment
```
You can monitor training log via tensorboard, using the script below.
@ -84,31 +109,50 @@ tensorboard --logdir=.
## Synthesis
```text
usage: synthesis.py [-h] [-c CONFIG] [-g DEVICE] checkpoint text output_path
usage: synthesis.py [-h] [--config CONFIG] [--device DEVICE]
[--checkpoint CHECKPOINT | --iteration ITERATION]
text output
Synthsize waveform from a checkpoint.
Synthsize waveform with a checkpoint.
positional arguments:
checkpoint checkpoint to load.
text text file to synthesize
output_path path to save results
text text file to synthesize
output path to save synthesized audio
optional arguments:
-h, --help show this help message and exit
-c CONFIG, --config CONFIG
experiment config.
-g DEVICE, --device DEVICE
device to use
-h, --help show this help message and exit
--config CONFIG experiment config
--device DEVICE device to use
--checkpoint CHECKPOINT checkpoint to resume from
--iteration ITERATION the iteration of the checkpoint to load from output directory
```
- `--config` is the configuration file to use. You should use the same configuration with which you train you model.
- `checkpoint` is the checkpoint to load.
- `text`is the text file to synthesize.
- `output_path` is the directory to save results. The output path contains the generated audio files (`*.wav`) and attention plots (*.png) for each sentence.
- `--device` is the device (gpu id) to use for training. `-1` means CPU.
- `--checkpoint` is the path of the checkpoint.
- `--iteration` is the iteration of the checkpoint to load from output directory.
See [Saving-&-Loading](#Saving-&-Loading) for details of checkpoint loading.
- `text`is the text file to synthesize.
- `output` is the directory to save results. The generated audio files (`*.wav`) and attention plots (*.png) for are save in `synthesis/` in ouput directory.
Example script:
```bash
python synthesis.py --config=configs/ljspeech.yaml --device=0 experiment/checkpoints/model_step_005000000 sentences.txt generated
python synthesis.py \
--config=configs/ljspeech.yaml \
--device=0 \
--checkpoint="experiment/checkpoints/model_step_005000000" \
sentences.txt experiment
```
or
```bash
python synthesis.py \
--config=configs/ljspeech.yaml \
--device=0 \
--iteration=005000000 \
sentences.txt experiment
```

2
examples/deepvoice3/configs/ljspeech.yaml
View File

@ -83,7 +83,7 @@ lr_scheduler:
train:
batch_size: 16
epochs: 2000
max_iteration: 2000000
snap_interval: 1000
eval_interval: 10000

45
examples/deepvoice3/synthesis.py
View File

@ -25,25 +25,37 @@ import paddle.fluid.dygraph as dg
from tensorboardX import SummaryWriter
from parakeet.g2p import en
from parakeet.utils.layer_tools import summary
from parakeet.modules.weight_norm import WeightNormWrapper
from parakeet.utils.layer_tools import summary
from parakeet.utils import io
from utils import make_model, eval_model, plot_alignment
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Synthsize waveform with a checkpoint.")
parser.add_argument("-c", "--config", type=str, help="experiment config.")
parser.add_argument("checkpoint", type=str, help="checkpoint to load.")
parser.add_argument("--config", type=str, help="experiment config")
parser.add_argument("--device", type=int, default=-1, help="device to use")
g = parser.add_mutually_exclusive_group()
g.add_argument("--checkpoint", type=str, help="checkpoint to resume from")
g.add_argument(
"--iteration",
type=int,
help="the iteration of the checkpoint to load from output directory")
parser.add_argument("text", type=str, help="text file to synthesize")
parser.add_argument("output_path", type=str, help="path to save results")
parser.add_argument(
"-g", "--device", type=int, default=-1, help="device to use")
"output", type=str, help="path to save synthesized audio")
args = parser.parse_args()
with open(args.config, 'rt') as f:
config = ruamel.yaml.safe_load(f)
print("Command Line Args: ")
for k, v in vars(args).items():
print("{}: {}".format(k, v))
if args.device == -1:
place = fluid.CPUPlace()
else:
@ -98,8 +110,14 @@ if __name__ == "__main__":
linear_dim, use_decoder_states, converter_channels, dropout)
summary(dv3)
state, _ = dg.load_dygraph(args.checkpoint)
dv3.set_dict(state)
checkpoint_dir = os.path.join(args.output, "checkpoints")
if args.checkpoint is not None:
iteration = io.load_parameters(
dv3, checkpoint_path=args.checkpoint)
else:
iteration = io.load_parameters(
dv3, checkpoint_dir=checkpoint_dir, iteration=args.iteration)
# WARNING: don't forget to remove weight norm to re-compute each wrapped layer's weight
# removing weight norm also speeds up computation
@ -107,9 +125,6 @@ if __name__ == "__main__":
if isinstance(layer, WeightNormWrapper):
layer.remove_weight_norm()
if not os.path.exists(args.output_path):
os.makedirs(args.output_path)
transform_config = config["transform"]
c = transform_config["replace_pronunciation_prob"]
sample_rate = transform_config["sample_rate"]
@ -123,6 +138,10 @@ if __name__ == "__main__":
power = synthesis_config["power"]
n_iter = synthesis_config["n_iter"]
synthesis_dir = os.path.join(args.output, "synthesis")
if not os.path.exists(synthesis_dir):
os.makedirs(synthesis_dir)
with open(args.text, "rt", encoding="utf-8") as f:
lines = f.readlines()
for idx, line in enumerate(lines):
@ -134,7 +153,9 @@ if __name__ == "__main__":
preemphasis)
plot_alignment(
attn,
os.path.join(args.output_path, "test_{}.png".format(idx)))
os.path.join(synthesis_dir,
"test_{}_step_{}.png".format(idx, iteration)))
sf.write(
os.path.join(args.output_path, "test_{}.wav".format(idx)),
os.path.join(synthesis_dir,
"test_{}_step{}.wav".format(idx, iteration)),
wav, sample_rate)

238
examples/deepvoice3/train.py
View File

@ -17,6 +17,8 @@ import os
import argparse
import ruamel.yaml
import numpy as np
import matplotlib
matplotlib.use("agg")
from matplotlib import cm
import matplotlib.pyplot as plt
import tqdm
@ -35,33 +37,40 @@ from parakeet.data import DataCargo, PartialyRandomizedSimilarTimeLengthSampler,
from parakeet.models.deepvoice3 import Encoder, Decoder, Converter, DeepVoice3, ConvSpec
from parakeet.models.deepvoice3.loss import TTSLoss
from parakeet.utils.layer_tools import summary
from parakeet.utils import io
from data import LJSpeechMetaData, DataCollector, Transform
from utils import make_model, eval_model, save_state, make_output_tree, plot_alignment
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Train a deepvoice 3 model with LJSpeech dataset.")
parser.add_argument("-c", "--config", type=str, help="experimrnt config")
description="Train a Deep Voice 3 model with LJSpeech dataset.")
parser.add_argument("--config", type=str, help="experimrnt config")
parser.add_argument(
"-s",
"--data",
type=str,
default="/workspace/datasets/LJSpeech-1.1/",
help="The path of the LJSpeech dataset.")
parser.add_argument("-r", "--resume", type=str, help="checkpoint to load")
parser.add_argument("--device", type=int, default=-1, help="device to use")
g = parser.add_mutually_exclusive_group()
g.add_argument("--checkpoint", type=str, help="checkpoint to resume from.")
g.add_argument(
"--iteration",
type=int,
help="the iteration of the checkpoint to load from output directory")
parser.add_argument(
"-o",
"--output",
type=str,
default="result",
help="The directory to save result.")
parser.add_argument(
"-g", "--device", type=int, default=-1, help="device to use")
"output", type=str, default="experiment", help="path to save results")
args, _ = parser.parse_known_args()
with open(args.config, 'rt') as f:
config = ruamel.yaml.safe_load(f)
print("Command Line Args: ")
for k, v in vars(args).items():
print("{}: {}".format(k, v))
# =========================dataset=========================
# construct meta data
data_root = args.data
@ -151,6 +160,7 @@ if __name__ == "__main__":
query_position_rate, key_position_rate, window_backward,
window_ahead, key_projection, value_projection, downsample_factor,
linear_dim, use_decoder_states, converter_channels, dropout)
summary(dv3)
# =========================loss=========================
loss_config = config["loss"]
@ -195,7 +205,6 @@ if __name__ == "__main__":
n_iter = synthesis_config["n_iter"]
# =========================link(dataloader, paddle)=========================
# CAUTION: it does not return a DataLoader
loader = fluid.io.DataLoader.from_generator(
capacity=10, return_list=True)
loader.set_batch_generator(ljspeech_loader, places=place)
@ -208,122 +217,117 @@ if __name__ == "__main__":
make_output_tree(output_dir)
writer = SummaryWriter(logdir=log_dir)
# load model parameters
resume_path = args.resume
if resume_path is not None:
state, _ = dg.load_dygraph(args.resume)
dv3.set_dict(state)
# load parameters and optimizer, and opdate iterations done sofar
if args.checkpoint is not None:
iteration = io.load_parameters(
dv3, optim, checkpoint_path=args.checkpoint)
else:
iteration = io.load_parameters(
dv3, optim, checkpoint_dir=ckpt_dir, iteration=args.iteration)
# =========================train=========================
epoch = train_config["epochs"]
max_iter = train_config["max_iteration"]
snap_interval = train_config["snap_interval"]
save_interval = train_config["save_interval"]
eval_interval = train_config["eval_interval"]
global_step = 1
global_step = iteration + 1
iterator = iter(tqdm.tqdm(loader))
while global_step <= max_iter:
try:
batch = next(iterator)
except StopIteration as e:
iterator = iter(tqdm.tqdm(loader))
batch = next(iterator)
for j in range(1, 1 + epoch):
epoch_loss = 0.
for i, batch in tqdm.tqdm(enumerate(loader, 1)):
dv3.train() # CAUTION: don't forget to switch to train
(text_sequences, text_lengths, text_positions, mel_specs,
lin_specs, frames, decoder_positions, done_flags) = batch
downsampled_mel_specs = F.strided_slice(
mel_specs,
axes=[1],
starts=[0],
ends=[mel_specs.shape[1]],
strides=[downsample_factor])
mel_outputs, linear_outputs, alignments, done = dv3(
text_sequences, text_positions, text_lengths, None,
downsampled_mel_specs, decoder_positions)
dv3.train()
(text_sequences, text_lengths, text_positions, mel_specs,
lin_specs, frames, decoder_positions, done_flags) = batch
downsampled_mel_specs = F.strided_slice(
mel_specs,
axes=[1],
starts=[0],
ends=[mel_specs.shape[1]],
strides=[downsample_factor])
mel_outputs, linear_outputs, alignments, done = dv3(
text_sequences, text_positions, text_lengths, None,
downsampled_mel_specs, decoder_positions)
losses = criterion(mel_outputs, linear_outputs, done,
alignments, downsampled_mel_specs,
lin_specs, done_flags, text_lengths, frames)
l = losses["loss"]
l.backward()
# record learning rate before updating
writer.add_scalar("learning_rate",
optim._learning_rate.step().numpy(),
global_step)
optim.minimize(l, grad_clip=gradient_clipper)
optim.clear_gradients()
losses = criterion(mel_outputs, linear_outputs, done, alignments,
downsampled_mel_specs, lin_specs, done_flags,
text_lengths, frames)
l = losses["loss"]
l.backward()
# record learning rate before updating
writer.add_scalar("learning_rate",
optim._learning_rate.step().numpy(), global_step)
optim.minimize(l, grad_clip=gradient_clipper)
optim.clear_gradients()
# ==================all kinds of tedious things=================
# record step loss into tensorboard
epoch_loss += l.numpy()[0]
step_loss = {k: v.numpy()[0] for k, v in losses.items()}
for k, v in step_loss.items():
writer.add_scalar(k, v, global_step)
# ==================all kinds of tedious things=================
# record step loss into tensorboard
step_loss = {k: v.numpy()[0] for k, v in losses.items()}
tqdm.tqdm.write("global_step: {}\tloss: {}".format(
global_step, step_loss["loss"]))
for k, v in step_loss.items():
writer.add_scalar(k, v, global_step)
# TODO: clean code
# train state saving, the first sentence in the batch
if global_step % snap_interval == 0:
save_state(
state_dir,
writer,
# train state saving, the first sentence in the batch
if global_step % snap_interval == 0:
save_state(
state_dir,
writer,
global_step,
mel_input=downsampled_mel_specs,
mel_output=mel_outputs,
lin_input=lin_specs,
lin_output=linear_outputs,
alignments=alignments,
win_length=win_length,
hop_length=hop_length,
min_level_db=min_level_db,
ref_level_db=ref_level_db,
power=power,
n_iter=n_iter,
preemphasis=preemphasis,
sample_rate=sample_rate)
# evaluation
if global_step % eval_interval == 0:
sentences = [
"Scientists at the CERN laboratory say they have discovered a new particle.",
"There's a way to measure the acute emotional intelligence that has never gone out of style.",
"President Trump met with other leaders at the Group of 20 conference.",
"Generative adversarial network or variational auto-encoder.",
"Please call Stella.",
"Some have accepted this as a miracle without any physical explanation.",
]
for idx, sent in enumerate(sentences):
wav, attn = eval_model(
dv3, sent, replace_pronounciation_prob, min_level_db,
ref_level_db, power, n_iter, win_length, hop_length,
preemphasis)
wav_path = os.path.join(
state_dir, "waveform",
"eval_sample_{:09d}.wav".format(global_step))
sf.write(wav_path, wav, sample_rate)
writer.add_audio(
"eval_sample_{}".format(idx),
wav,
global_step,
mel_input=downsampled_mel_specs,
mel_output=mel_outputs,
lin_input=lin_specs,
lin_output=linear_outputs,
alignments=alignments,
win_length=win_length,
hop_length=hop_length,
min_level_db=min_level_db,
ref_level_db=ref_level_db,
power=power,
n_iter=n_iter,
preemphasis=preemphasis,
sample_rate=sample_rate)
attn_path = os.path.join(
state_dir, "alignments",
"eval_sample_attn_{:09d}.png".format(global_step))
plot_alignment(attn, attn_path)
writer.add_image(
"eval_sample_attn{}".format(idx),
cm.viridis(attn),
global_step,
dataformats="HWC")
# evaluation
if global_step % eval_interval == 0:
sentences = [
"Scientists at the CERN laboratory say they have discovered a new particle.",
"There's a way to measure the acute emotional intelligence that has never gone out of style.",
"President Trump met with other leaders at the Group of 20 conference.",
"Generative adversarial network or variational auto-encoder.",
"Please call Stella.",
"Some have accepted this as a miracle without any physical explanation.",
]
for idx, sent in enumerate(sentences):
wav, attn = eval_model(
dv3, sent, replace_pronounciation_prob,
min_level_db, ref_level_db, power, n_iter,
win_length, hop_length, preemphasis)
wav_path = os.path.join(
state_dir, "waveform",
"eval_sample_{:09d}.wav".format(global_step))
sf.write(wav_path, wav, sample_rate)
writer.add_audio(
"eval_sample_{}".format(idx),
wav,
global_step,
sample_rate=sample_rate)
attn_path = os.path.join(
state_dir, "alignments",
"eval_sample_attn_{:09d}.png".format(global_step))
plot_alignment(attn, attn_path)
writer.add_image(
"eval_sample_attn{}".format(idx),
cm.viridis(attn),
global_step,
dataformats="HWC")
# save checkpoint
if global_step % save_interval == 0:
io.save_parameters(ckpt_dir, global_step, dv3, optim)
# save checkpoint
if global_step % save_interval == 0:
dg.save_dygraph(
dv3.state_dict(),
os.path.join(ckpt_dir,
"model_step_{}".format(global_step)))
dg.save_dygraph(
optim.state_dict(),
os.path.join(ckpt_dir,
"model_step_{}".format(global_step)))
global_step += 1
# epoch report
writer.add_scalar("epoch_average_loss", epoch_loss / i, j)
epoch_loss = 0.
global_step += 1

107
examples/wavenet/README.md
View File

@ -22,41 +22,67 @@ tar xjvf LJSpeech-1.1.tar.bz2
└── utils.py utility functions
```
## Saving & Loading
`train.py` and `synthesis.py` have 3 arguments in common, `--checkpooint`, `iteration` and `output`.
1. `output` is the directory for saving results.
During training, checkpoints are saved in `checkpoints/` in `output` and tensorboard log is save in `log/` in `output`. Other possible outputs are saved in `states/` in `outuput`.
During synthesizing, audio files and other possible outputs are save in `synthesis/` in `output`.
So after training and synthesizing with the same output directory, the file structure of the output directory looks like this.
```text
├── checkpoints/ # checkpoint directory (including *.pdparams, *.pdopt and a text file `checkpoint` that records the latest checkpoint)
├── states/ # audio files generated at validation and other possible outputs
├── log/ # tensorboard log
└── synthesis/ # synthesized audio files and other possible outputs
```
2. `--checkpoint` and `--iteration` for loading from existing checkpoint. Loading existing checkpoiont follows the following rule:
If `--checkpoint` is provided, the checkpoint specified by `--checkpoint` is loaded.
If `--checkpoint` is not provided, we try to load the model specified by `--iteration` from the checkpoint directory. If `--iteration` is not provided, we try to load the latested checkpoint from checkpoint directory.
## Train
Train the model using train.py. For help on usage, try `python train.py --help`.
```text
usage: train.py [-h] [--data DATA] [--config CONFIG] [--output OUTPUT]
[--device DEVICE] [--resume RESUME]
usage: train.py [-h] [--data DATA] [--config CONFIG] [--device DEVICE]
[--checkpoint CHECKPOINT | --iteration ITERATION]
output
Train a WaveNet model with LJSpeech.
positional arguments:
output path to save results
optional arguments:
-h, --help show this help message and exit
--data DATA path of the LJspeech dataset.
--config CONFIG path of the config file.
--output OUTPUT path to save results.
--device DEVICE device to use.
--resume RESUME checkpoint to resume from.
-h, --help show this help message and exit
--data DATA path of the LJspeech dataset
--config CONFIG path of the config file
--device DEVICE device to use
--checkpoint CHECKPOINT checkpoint to resume from
--iteration ITERATION the iteration of the checkpoint to load from output directory
```
- `--config` is the configuration file to use. The provided configurations can be used directly. And you can change some values in the configuration file and train the model with a different config.
- `--data` is the path of the LJSpeech dataset, the extracted folder from the downloaded archive (the folder which contains metadata.txt).
- `--resume` is the path of the checkpoint. If it is provided, the model would load the checkpoint before training.
- `--output` is the directory to save results, all result are saved in this directory. The structure of the output directory is shown below.
```text
├── checkpoints # checkpoint
└── log # tensorboard log
```
- `--config` is the configuration file to use. The provided configurations can be used directly. And you can change some values in the configuration file and train the model with a different config.
- `--device` is the device (gpu id) to use for training. `-1` means CPU.
- `--checkpoint` is the path of the checkpoint.
- `--iteration` is the iteration of the checkpoint to load from output directory.
- `output` is the directory to save results, all result are saved in this directory.
See [Saving-&-Loading](#Saving-&-Loading) for details of checkpoint loading.
Example script:
```bash
python train.py --config=./configs/wavenet_single_gaussian.yaml --data=./LJSpeech-1.1/ --output=experiment --device=0
python train.py \
--config=./configs/wavenet_single_gaussian.yaml \
--data=./LJSpeech-1.1/ \
--device=0 \
experiment
```
You can monitor training log via TensorBoard, using the script below.
@ -69,29 +95,50 @@ tensorboard --logdir=.
## Synthesis
```text
usage: synthesis.py [-h] [--data DATA] [--config CONFIG] [--device DEVICE]
checkpoint output
[--checkpoint CHECKPOINT | --iteration ITERATION]
output
Synthesize valid data from LJspeech with a WaveNet model.
Synthesize valid data from LJspeech with a wavenet model.
positional arguments:
checkpoint checkpoint to load.
output path to save results.
output path to save the synthesized audio
optional arguments:
-h, --help show this help message and exit
--data DATA path of the LJspeech dataset.
--config CONFIG path of the config file.
--device DEVICE device to use.
-h, --help show this help message and exit
--data DATA path of the LJspeech dataset
--config CONFIG path of the config file
--device DEVICE device to use
--checkpoint CHECKPOINT checkpoint to resume from
--iteration ITERATION the iteration of the checkpoint to load from output directory
```
- `--data` is the path of the LJspeech dataset. In principle, a dataset is not needed for synthesis, but since the input is mel spectrogram, we need to get mel spectrogram from audio files.
- `--config` is the configuration file to use. You should use the same configuration with which you train you model.
- `--data` is the path of the LJspeech dataset. A dataset is not needed for synthesis, but since the input is mel spectrogram, we need to get mel spectrogram from audio files.
- `checkpoint` is the checkpoint to load.
- `output_path` is the directory to save results. The output path contains the generated audio files (`*.wav`).
- `--device` is the device (gpu id) to use for training. `-1` means CPU.
- `--checkpoint` is the checkpoint to load.
- `--iteration` is the iteration of the checkpoint to load from output directory.
- `output` is the directory to save synthesized audio. Audio file is saved in `synthesis/` in `output` directory.
See [Saving-&-Loading](#Saving-&-Loading) for details of checkpoint loading.
Example script:
```bash
python synthesis.py --config=./configs/wavenet_single_gaussian.yaml --data=./LJSpeech-1.1/ --device=0 experiment/checkpoints/step_500000 generated
python synthesis.py \
--config=./configs/wavenet_single_gaussian.yaml \
--data=./LJSpeech-1.1/ \
--device=0 \
--checkpoint="experiment/checkpoints/step-1000000" \
experiment
```
or
```bash
python synthesis.py \
--config=./configs/wavenet_single_gaussian.yaml \
--data=./LJSpeech-1.1/ \
--device=0 \
--iteration=1000000 \
experiment
```

44
examples/wavenet/synthesis.py
View File

@ -25,22 +25,31 @@ from parakeet.modules.weight_norm import WeightNormWrapper
from parakeet.data import SliceDataset, TransformDataset, DataCargo, SequentialSampler, RandomSampler
from parakeet.models.wavenet import UpsampleNet, WaveNet, ConditionalWavenet
from parakeet.utils.layer_tools import summary
from parakeet.utils import io
from data import LJSpeechMetaData, Transform, DataCollector
from utils import make_output_tree, valid_model, eval_model, save_checkpoint
from utils import make_output_tree, valid_model, eval_model
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Synthesize valid data from LJspeech with a wavenet model.")
parser.add_argument(
"--data", type=str, help="path of the LJspeech dataset.")
parser.add_argument("--config", type=str, help="path of the config file.")
parser.add_argument(
"--device", type=int, default=-1, help="device to use.")
"--data", type=str, help="path of the LJspeech dataset")
parser.add_argument("--config", type=str, help="path of the config file")
parser.add_argument("--device", type=int, default=-1, help="device to use")
g = parser.add_mutually_exclusive_group()
g.add_argument("--checkpoint", type=str, help="checkpoint to resume from")
g.add_argument(
"--iteration",
type=int,
help="the iteration of the checkpoint to load from output directory")
parser.add_argument("checkpoint", type=str, help="checkpoint to load.")
parser.add_argument(
"output", type=str, default="experiment", help="path to save results.")
"output",
type=str,
default="experiment",
help="path to save the synthesized audio")
args = parser.parse_args()
with open(args.config, 'rt') as f:
@ -87,7 +96,8 @@ if __name__ == "__main__":
batch_size=1,
sampler=SequentialSampler(ljspeech_valid))
make_output_tree(args.output)
if not os.path.exists(args.output):
os.makedirs(args.output)
if args.device == -1:
place = fluid.CPUPlace()
@ -111,9 +121,15 @@ if __name__ == "__main__":
model = ConditionalWavenet(encoder, decoder)
summary(model)
model_dict, _ = dg.load_dygraph(args.checkpoint)
print("Loading from {}.pdparams".format(args.checkpoint))
model.set_dict(model_dict)
# load model parameters
checkpoint_dir = os.path.join(args.output, "checkpoints")
if args.checkpoint:
iteration = io.load_parameters(
model, checkpoint_path=args.checkpoint)
else:
iteration = io.load_parameters(
model, checkpoint_dir=checkpoint_dir, iteration=args.iteration)
assert iteration > 0, "A trained model is needed."
# WARNING: don't forget to remove weight norm to re-compute each wrapped layer's weight
# removing weight norm also speeds up computation
@ -129,4 +145,8 @@ if __name__ == "__main__":
capacity=10, return_list=True)
valid_loader.set_batch_generator(valid_cargo, place)
eval_model(model, valid_loader, args.output, sample_rate)
synthesis_dir = os.path.join(args.output, "synthesis")
if not os.path.exists(synthesis_dir):
os.makedirs(synthesis_dir)
eval_model(model, valid_loader, synthesis_dir, iteration, sample_rate)

102
examples/wavenet/train.py
View File

@ -16,7 +16,7 @@ from __future__ import division
import os
import ruamel.yaml
import argparse
from tqdm import tqdm
import tqdm
from tensorboardX import SummaryWriter
from paddle import fluid
import paddle.fluid.dygraph as dg
@ -24,30 +24,37 @@ import paddle.fluid.dygraph as dg
from parakeet.data import SliceDataset, TransformDataset, DataCargo, SequentialSampler, RandomSampler
from parakeet.models.wavenet import UpsampleNet, WaveNet, ConditionalWavenet
from parakeet.utils.layer_tools import summary
from parakeet.utils import io
from data import LJSpeechMetaData, Transform, DataCollector
from utils import make_output_tree, valid_model, save_checkpoint
from utils import make_output_tree, valid_model
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Train a wavenet model with LJSpeech.")
description="Train a WaveNet model with LJSpeech.")
parser.add_argument(
"--data", type=str, help="path of the LJspeech dataset.")
parser.add_argument("--config", type=str, help="path of the config file.")
"--data", type=str, help="path of the LJspeech dataset")
parser.add_argument("--config", type=str, help="path of the config file")
parser.add_argument("--device", type=int, default=-1, help="device to use")
g = parser.add_mutually_exclusive_group()
g.add_argument("--checkpoint", type=str, help="checkpoint to resume from")
g.add_argument(
"--iteration",
type=int,
help="the iteration of the checkpoint to load from output directory")
parser.add_argument(
"--output",
type=str,
default="experiment",
help="path to save results.")
parser.add_argument(
"--device", type=int, default=-1, help="device to use.")
parser.add_argument(
"--resume", type=str, help="checkpoint to resume from.")
"output", type=str, default="experiment", help="path to save results")
args = parser.parse_args()
with open(args.config, 'rt') as f:
config = ruamel.yaml.safe_load(f)
print("Command Line Args: ")
for k, v in vars(args).items():
print("{}: {}".format(k, v))
ljspeech_meta = LJSpeechMetaData(args.data)
data_config = config["data"]
@ -126,14 +133,6 @@ if __name__ == "__main__":
clipper = fluid.dygraph_grad_clip.GradClipByGlobalNorm(
gradiant_max_norm)
if args.resume:
model_dict, optim_dict = dg.load_dygraph(args.resume)
print("Loading from {}.pdparams".format(args.resume))
model.set_dict(model_dict)
if optim_dict:
optim.set_dict(optim_dict)
print("Loading from {}.pdopt".format(args.resume))
train_loader = fluid.io.DataLoader.from_generator(
capacity=10, return_list=True)
train_loader.set_batch_generator(train_cargo, place)
@ -150,33 +149,48 @@ if __name__ == "__main__":
log_dir = os.path.join(args.output, "log")
writer = SummaryWriter(log_dir)
global_step = 1
# load parameters and optimizer, and opdate iterations done sofar
if args.checkpoint is not None:
iteration = io.load_parameters(
model, optim, checkpoint_path=args.checkpoint)
else:
iteration = io.load_parameters(
model,
optim,
checkpoint_dir=checkpoint_dir,
iteration=args.iteration)
global_step = iteration + 1
iterator = iter(tqdm.tqdm(train_loader))
while global_step <= max_iterations:
epoch_loss = 0.
for i, batch in tqdm(enumerate(train_loader)):
audio_clips, mel_specs, audio_starts = batch
try:
batch = next(iterator)
except StopIteration as e:
iterator = iter(tqdm.tqdm(train_loader))
batch = next(iterator)
model.train()
y_var = model(audio_clips, mel_specs, audio_starts)
loss_var = model.loss(y_var, audio_clips)
loss_var.backward()
loss_np = loss_var.numpy()
audio_clips, mel_specs, audio_starts = batch
epoch_loss += loss_np[0]
model.train()
y_var = model(audio_clips, mel_specs, audio_starts)
loss_var = model.loss(y_var, audio_clips)
loss_var.backward()
loss_np = loss_var.numpy()
writer.add_scalar("loss", loss_np[0], global_step)
writer.add_scalar("learning_rate",
optim._learning_rate.step().numpy()[0],
global_step)
optim.minimize(loss_var, grad_clip=clipper)
optim.clear_gradients()
print("loss: {:<8.6f}".format(loss_np[0]))
writer.add_scalar("loss", loss_np[0], global_step)
writer.add_scalar("learning_rate",
optim._learning_rate.step().numpy()[0],
global_step)
optim.minimize(loss_var, grad_clip=clipper)
optim.clear_gradients()
print("global_step: {}\tloss: {:<8.6f}".format(global_step,
loss_np[0]))
if global_step % snap_interval == 0:
valid_model(model, valid_loader, writer, global_step,
sample_rate)
if global_step % snap_interval == 0:
valid_model(model, valid_loader, writer, global_step,
sample_rate)
if global_step % checkpoint_interval == 0:
save_checkpoint(model, optim, checkpoint_dir, global_step)
if global_step % checkpoint_interval == 0:
io.save_parameters(checkpoint_dir, global_step, model, optim)
global_step += 1
global_step += 1

12
examples/wavenet/utils.py
View File

@ -49,20 +49,14 @@ def valid_model(model, valid_loader, writer, global_step, sample_rate):
sample_rate)
def eval_model(model, valid_loader, output_dir, sample_rate):
def eval_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, "sentence_{}.wav".format(i))
path = os.path.join(output_dir,
"sentence_{}_step_{}.wav".format(i, global_step))
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):
checkpoint_path = os.path.join(checkpoint_dir,
"step_{:09d}".format(global_step))
dg.save_dygraph(model.state_dict(), checkpoint_path)
dg.save_dygraph(optim.state_dict(), checkpoint_path)

3
parakeet/utils/io.py
View File

@ -52,8 +52,6 @@ def _load_latest_checkpoint(checkpoint_dir):
Args:
checkpoint_dir (str): the directory where checkpoint is saved.
rank (int, optional): the rank of the process in multi-process setting.
Defaults to 0.
Returns:
int: the latest iteration number.
@ -115,7 +113,6 @@ def load_parameters(model,
if iteration is None:
iteration = _load_latest_checkpoint(checkpoint_dir)
if iteration == 0:
# if step-0 exist, it is also loaded
return iteration
checkpoint_path = os.path.join(checkpoint_dir,
"step-{}".format(iteration))