191 lines
6.7 KiB
Python
191 lines
6.7 KiB
Python
import itertools
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import os
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import time
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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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from scipy.io.wavfile import write
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import utils
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from data import LJSpeech
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from waveflow_modules import WaveFlowLoss, WaveFlowModule
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class WaveFlow():
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def __init__(self, config, checkpoint_dir, parallel=False, rank=0,
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nranks=1, tb_logger=None):
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self.config = config
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self.checkpoint_dir = checkpoint_dir
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self.parallel = parallel
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self.rank = rank
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self.nranks = nranks
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self.tb_logger = tb_logger
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def build(self, training=True):
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config = self.config
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dataset = LJSpeech(config, self.nranks, self.rank)
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self.trainloader = dataset.trainloader
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self.validloader = dataset.validloader
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waveflow = WaveFlowModule("waveflow", config)
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# Dry run once to create and initalize all necessary parameters.
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audio = dg.to_variable(np.random.randn(1, 16000).astype(np.float32))
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mel = dg.to_variable(
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np.random.randn(1, config.mel_bands, 63).astype(np.float32))
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waveflow(audio, mel)
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if training:
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optimizer = fluid.optimizer.AdamOptimizer(
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learning_rate=config.learning_rate)
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# Load parameters.
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utils.load_parameters(self.checkpoint_dir, self.rank,
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waveflow, optimizer,
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iteration=config.iteration,
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file_path=config.checkpoint)
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print("Rank {}: checkpoint loaded.".format(self.rank))
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# Data parallelism.
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if self.parallel:
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strategy = dg.parallel.prepare_context()
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waveflow = dg.parallel.DataParallel(waveflow, strategy)
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self.waveflow = waveflow
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self.optimizer = optimizer
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self.criterion = WaveFlowLoss(config.sigma)
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else:
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# Load parameters.
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utils.load_parameters(self.checkpoint_dir, self.rank, waveflow,
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iteration=config.iteration,
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file_path=config.checkpoint)
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print("Rank {}: checkpoint loaded.".format(self.rank))
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self.waveflow = waveflow
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def train_step(self, iteration):
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self.waveflow.train()
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start_time = time.time()
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audios, mels = next(self.trainloader)
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load_time = time.time()
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outputs = self.waveflow(audios, mels)
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loss = self.criterion(outputs)
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if self.parallel:
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# loss = loss / num_trainers
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loss = self.waveflow.scale_loss(loss)
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loss.backward()
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self.waveflow.apply_collective_grads()
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else:
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loss.backward()
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self.optimizer.minimize(loss, parameter_list=self.waveflow.parameters())
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self.waveflow.clear_gradients()
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graph_time = time.time()
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if self.rank == 0:
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loss_val = float(loss.numpy()) * self.nranks
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log = "Rank: {} Step: {:^8d} Loss: {:<8.3f} " \
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"Time: {:.3f}/{:.3f}".format(
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self.rank, iteration, loss_val,
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load_time - start_time, graph_time - load_time)
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print(log)
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tb = self.tb_logger
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tb.add_scalar("Train-Loss-Rank-0", loss_val, iteration)
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@dg.no_grad
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def valid_step(self, iteration):
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self.waveflow.eval()
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tb = self.tb_logger
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total_loss = []
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sample_audios = []
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start_time = time.time()
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for i, batch in enumerate(self.validloader()):
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audios, mels = batch
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valid_outputs = self.waveflow(audios, mels)
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valid_z, valid_log_s_list = valid_outputs
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# Visualize latent z and scale log_s.
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if self.rank == 0 and i == 0:
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tb.add_histogram("Valid-Latent_z", valid_z.numpy(), iteration)
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for j, valid_log_s in enumerate(valid_log_s_list):
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hist_name = "Valid-{}th-Flow-Log_s".format(j)
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tb.add_histogram(hist_name, valid_log_s.numpy(), iteration)
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valid_loss = self.criterion(valid_outputs)
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total_loss.append(float(valid_loss.numpy()))
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total_time = time.time() - start_time
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if self.rank == 0:
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loss_val = np.mean(total_loss)
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log = "Test | Rank: {} AvgLoss: {:<8.3f} Time {:<8.3f}".format(
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self.rank, loss_val, total_time)
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print(log)
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tb.add_scalar("Valid-Avg-Loss", loss_val, iteration)
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@dg.no_grad
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def infer(self, iteration):
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self.waveflow.eval()
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config = self.config
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sample = config.sample
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output = "{}/{}/iter-{}".format(config.output, config.name, iteration)
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os.makedirs(output, exist_ok=True)
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mels_list = [mels for _, mels in self.validloader()]
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if sample is not None:
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mels_list = [mels_list[sample]]
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for sample, mel in enumerate(mels_list):
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filename = "{}/valid_{}.wav".format(output, sample)
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print("Synthesize sample {}, save as {}".format(sample, filename))
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start_time = time.time()
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audio = self.waveflow.synthesize(mel, sigma=self.config.sigma)
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syn_time = time.time() - start_time
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audio = audio[0]
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audio_time = audio.shape[0] / self.config.sample_rate
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print("audio time {:.4f}, synthesis time {:.4f}".format(
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audio_time, syn_time))
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# Denormalize audio from [-1, 1] to [-32768, 32768] int16 range.
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audio = audio.numpy() * 32768.0
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audio = audio.astype('int16')
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write(filename, config.sample_rate, audio)
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@dg.no_grad
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def benchmark(self):
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self.waveflow.eval()
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mels_list = [mels for _, mels in self.validloader()]
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mel = fluid.layers.concat(mels_list, axis=2)
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mel = mel[:, :, :864]
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batch_size = 8
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mel = fluid.layers.expand(mel, [batch_size, 1, 1])
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for i in range(10):
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start_time = time.time()
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audio = self.waveflow.synthesize(mel, sigma=self.config.sigma)
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print("audio.shape = ", audio.shape)
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syn_time = time.time() - start_time
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audio_time = audio.shape[1] * batch_size / self.config.sample_rate
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print("audio time {:.4f}, synthesis time {:.4f}".format(
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audio_time, syn_time))
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print("{} X real-time".format(audio_time / syn_time))
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def save(self, iteration):
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utils.save_latest_parameters(self.checkpoint_dir, iteration,
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self.waveflow, self.optimizer)
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utils.save_latest_checkpoint(self.checkpoint_dir, iteration)
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