add profiling

examples/parallelwave_gan/baker/preprocess.py

@@ -16,12 +16,9 @@ from typing import List, Dict, Any
 import soundfile as sf
 import librosa
 import numpy as np
-from config import get_cfg_default
 import argparse
 import yaml
 import json
-import dacite
-import dataclasses
 import concurrent.futures
 from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
 from pathlib import Path
@@ -30,6 +27,8 @@ from operator import itemgetter
 from praatio import tgio
 import logging
 
+from config import get_cfg_default
+
 
 def logmelfilterbank(audio,
                      sr,
@@ -229,7 +228,7 @@ def main():
 
     if args.verbose > 1:
         print(vars(args))
-        print(yaml.dump(dataclasses.asdict(C)))
+        print(C)
 
     root_dir = Path(args.rootdir)
     dumpdir = Path(args.dumpdir)

examples/parallelwave_gan/baker/pwg_updater.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import paddle
+from paddle.fluid.core import _cuda_synchronize
 from timer import timer
 
 from parakeet.datasets.data_table import DataTable
@@ -61,12 +62,15 @@ class PWGUpdater(UpdaterBase):
         self.train_iterator = iter(self.train_dataloader)
 
     def update_core(self):
+        place = paddle.fluid.framework._current_expected_place()
         with timer() as t:
+            _cuda_synchronize(place)
             try:
                 batch = next(self.train_iterator)
             except StopIteration:
                 self.train_iterator = iter(self.train_dataloader)
                 batch = next(self.train_iterator)
+            _cuda_synchronize(place)
             print(f"Loading a batch takes {t.elapse}s")
 
         wav, mel = batch
@@ -75,13 +79,17 @@ class PWGUpdater(UpdaterBase):
         noise = paddle.randn(wav.shape)
 
         with timer() as t:
+            _cuda_synchronize(place)
             wav_ = self.generator(noise, mel)
+            _cuda_synchronize(place)
             print(f"Generator takes {t.elapse}s")
 
         ## Multi-resolution stft loss
         with timer() as t:
+            _cuda_synchronize(place)
             sc_loss, mag_loss = self.criterion_stft(
                 wav_.squeeze(1), wav.squeeze(1))
+            _cuda_synchronize(place)
             print(f"Multi-resolution STFT loss takes {t.elapse}s")
 
         report("train/spectral_convergence_loss", float(sc_loss))
@@ -91,24 +99,30 @@ class PWGUpdater(UpdaterBase):
         ## Adversarial loss
         if self.state.iteration > self.discriminator_train_start_steps:
             with timer() as t:
+                _cuda_synchronize(place)
                 p_ = self.discriminator(wav_)
                 adv_loss = self.criterion_mse(p_, paddle.ones_like(p_))
+                _cuda_synchronize(place)
                 print(f"Discriminator and adversarial loss takes {t.elapse}s")
             report("train/adversarial_loss", float(adv_loss))
             gen_loss += self.lambda_adv * adv_loss
 
         report("train/generator_loss", float(gen_loss))
         with timer() as t:
+            _cuda_synchronize(place)
             self.optimizer_g.clear_grad()
             gen_loss.backward()
+            _cuda_synchronize(place)
             print(f"Backward takes {t.elapse}s.")
 
         with timer() as t:
+            _cuda_synchronize(place)
             self.optimizer_g.step()
             self.scheduler_g.step()
+            _cuda_synchronize(place)
             print(f"Update takes {t.elapse}s.")
 
-# Disctiminator
+        # Disctiminator
         if self.state.iteration > self.discriminator_train_start_steps:
             with paddle.no_grad():
                 wav_ = self.generator(noise, mel)

examples/parallelwave_gan/baker/train.py

@@ -20,7 +20,6 @@ import dataclasses
 from pathlib import Path
 
 import yaml
-import dacite
 import json
 import paddle
 import numpy as np

parakeet/modules/stft_loss.py

@@ -64,7 +64,7 @@ class STFTLoss(nn.Layer):
                  fft_size=1024,
                  shift_size=120,
                  win_length=600,
-                 window="hann_window"):
+                 window="hann"):
         """Initialize STFT loss module."""
         super().__init__()
         self.fft_size = fft_size

setup.py

@@ -64,7 +64,6 @@ setup_info = dict(
         'scipy',
         'pandas',
         'sox',
-        # 'opencc',
         'soundfile',
         'g2p_en',
         'yacs',