Merge branch 'master' of upstream.
This commit is contained in:
lifuchen
commit
55fa94f15d
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@ -40,7 +40,7 @@ sudo apt-get install libsndfile1
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### Install PaddlePaddle
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See [install](https://www.paddlepaddle.org.cn/install/quick) for more details. This repo requires PaddlePaddle **1.7.1** or above.
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See [install](https://www.paddlepaddle.org.cn/install/quick) for more details. This repo requires PaddlePaddle **1.8.0** or above.
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### Install Parakeet
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@ -163,11 +163,11 @@ if __name__ == "__main__":
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anneal_interval = train_config["anneal_interval"]
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lr_scheduler = dg.ExponentialDecay(
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learning_rate, anneal_interval, anneal_rate, staircase=True)
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optim = fluid.optimizer.Adam(
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lr_scheduler, parameter_list=model.parameters())
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gradiant_max_norm = train_config["gradient_max_norm"]
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clipper = fluid.dygraph_grad_clip.GradClipByGlobalNorm(
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gradiant_max_norm)
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optim = fluid.optimizer.Adam(
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lr_scheduler,
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parameter_list=model.parameters(),
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grad_clip=fluid.clip.ClipByGlobalNorm(gradiant_max_norm))
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# train
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max_iterations = train_config["max_iterations"]
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@ -229,7 +229,7 @@ if __name__ == "__main__":
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step_loss))
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l.backward()
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optim.minimize(l, grad_clip=clipper)
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optim.minimize(l)
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optim.clear_gradients()
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if global_step % eval_interval == 0:
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@ -196,8 +196,8 @@ if __name__ == "__main__":
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beta1,
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beta2,
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epsilon=epsilon,
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parameter_list=dv3.parameters())
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gradient_clipper = fluid.dygraph_grad_clip.GradClipByGlobalNorm(0.1)
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parameter_list=dv3.parameters(),
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grad_clip=fluid.clip.GradientClipByGlobalNorm(0.1))
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# generation
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synthesis_config = config["synthesis"]
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@ -258,15 +258,19 @@ if __name__ == "__main__":
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text_lengths, frames)
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l = losses["loss"]
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l.backward()
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# record learning rate before updating
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writer.add_scalar("learning_rate",
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optim._learning_rate.step().numpy(), global_step)
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optim.minimize(l, grad_clip=gradient_clipper)
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optim.minimize(l)
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optim.clear_gradients()
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# ==================all kinds of tedious things=================
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# record step loss into tensorboard
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step_loss = {k: v.numpy()[0] for k, v in losses.items()}
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step_loss = {
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k: v.numpy()[0]
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for k, v in losses.items() if v is not None
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}
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tqdm.tqdm.write("global_step: {}\tloss: {}".format(
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global_step, step_loss["loss"]))
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for k, v in step_loss.items():
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@ -93,16 +93,7 @@ def synthesize(config):
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# Build model.
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model = WaveFlow(config, checkpoint_dir)
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model.build(training=False)
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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)
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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(training=False)
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# Run model inference.
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model.infer(iteration)
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@ -81,12 +81,6 @@ class WaveFlow():
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waveflow = WaveFlowModule(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(self.dtype))
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mel = dg.to_variable(
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np.random.randn(1, config.mel_bands, 63).astype(self.dtype))
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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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@ -126,12 +126,11 @@ if __name__ == "__main__":
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anneal_interval = train_config["anneal_interval"]
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lr_scheduler = dg.ExponentialDecay(
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learning_rate, anneal_interval, anneal_rate, staircase=True)
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optim = fluid.optimizer.Adam(
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lr_scheduler, parameter_list=model.parameters())
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gradiant_max_norm = train_config["gradient_max_norm"]
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clipper = fluid.dygraph_grad_clip.GradClipByGlobalNorm(
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gradiant_max_norm)
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optim = fluid.optimizer.Adam(
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lr_scheduler,
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parameter_list=model.parameters(),
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grad_clip=fluid.clip.ClipByGlobalNorm(gradiant_max_norm))
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train_loader = fluid.io.DataLoader.from_generator(
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capacity=10, return_list=True)
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@ -149,7 +148,7 @@ if __name__ == "__main__":
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log_dir = os.path.join(args.output, "log")
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writer = SummaryWriter(log_dir)
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# load parameters and optimizer, and opdate iterations done sofar
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# load parameters and optimizer, and update iterations done so far
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if args.checkpoint is not None:
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iteration = io.load_parameters(
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model, optim, checkpoint_path=args.checkpoint)
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@ -181,7 +180,7 @@ if __name__ == "__main__":
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writer.add_scalar("learning_rate",
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optim._learning_rate.step().numpy()[0],
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global_step)
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optim.minimize(loss_var, grad_clip=clipper)
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optim.minimize(loss_var)
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optim.clear_gradients()
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print("global_step: {}\tloss: {:<8.6f}".format(global_step,
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loss_np[0]))
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@ -29,22 +29,10 @@ def conv2d(input,
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data_format="NCHW"):
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padding = tuple(pad for pad_dim in padding for pad in pad_dim)
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inputs = {
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'Input': [input],
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'Filter': [weight],
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}
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attrs = {
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'strides': stride,
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'paddings': padding,
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'dilations': dilation,
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'groups': groups,
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'use_cudnn': use_cudnn,
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'use_mkldnn': False,
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'fuse_relu_before_depthwise_conv': False,
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"padding_algorithm": "EXPLICIT",
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"data_format": data_format,
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}
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attrs = ('strides', stride, 'paddings', padding, 'dilations', dilation,
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'groups', groups, 'use_cudnn', use_cudnn, 'use_mkldnn', False,
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'fuse_relu_before_depthwise_conv', False, "padding_algorithm",
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"EXPLICIT", "data_format", data_format)
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outputs = ops.conv2d(inputs, attrs)
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out = outputs["Output"][0]
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out = ops.conv2d(input, weight, *attrs)
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return out
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@ -262,7 +262,7 @@ class TTSLoss(object):
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if compute_lin_loss:
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lin_hyp = lin_hyp[:, :-self.time_shift, :]
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lin_ref = lin_ref[:, self.time_shift:, :]
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lin_mask = lin_mask[:, self.time_shift:, :]
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lin_mask = lin_mask[:, self.time_shift:]
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lin_l1_loss = self.l1_loss(
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lin_hyp, lin_ref, lin_mask, priority_bin=self.priority_bin)
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lin_bce_loss = self.binary_divergence(lin_hyp, lin_ref, lin_mask)
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@ -273,7 +273,7 @@ class TTSLoss(object):
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if compute_mel_loss:
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mel_hyp = mel_hyp[:, :-self.time_shift, :]
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mel_ref = mel_ref[:, self.time_shift:, :]
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mel_mask = mel_mask[:, self.time_shift:, :]
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mel_mask = mel_mask[:, self.time_shift:]
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mel_l1_loss = self.l1_loss(mel_hyp, mel_ref, mel_mask)
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mel_bce_loss = self.binary_divergence(mel_hyp, mel_ref, mel_mask)
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# print("=====>", mel_l1_loss.numpy()[0], mel_bce_loss.numpy()[0])
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@ -31,8 +31,10 @@ def compute_position_embedding(radians, speaker_position_rate):
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"""
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_, embed_dim = radians.shape
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batch_size = speaker_position_rate.shape[0]
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speaker_position_rate = F.unsqueeze(speaker_position_rate, [1, 2])
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scaled_radians = speaker_position_rate * radians
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scaled_radians = F.elementwise_mul(
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F.expand(F.unsqueeze(radians, [0]), [batch_size, 1, 1]),
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speaker_position_rate,
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axis=0)
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odd_mask = (np.arange(embed_dim) % 2).astype(np.float32)
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odd_mask = dg.to_variable(odd_mask)
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@ -111,7 +111,7 @@ class ResidualBlock(dg.Layer):
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h = h[:, :, :time_steps]
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# condition
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if condition:
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if condition is not None:
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h += self.condition_proj(condition)
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# gated tanh
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@ -398,7 +398,8 @@ class WaveNet(dg.Layer):
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x_std = inv_std * (t - mu)
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exponent = F.exp(-0.5 * x_std * x_std)
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pdf_x = 1.0 / np.sqrt(2.0 * np.pi) * inv_std * exponent
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pdf_x = 1.0 / math.sqrt(2.0 * math.pi) * inv_std * exponent
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pdf_x = p_mixture * pdf_x
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# pdf_x: [bs, len]
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pdf_x = F.reduce_sum(pdf_x, dim=-1)
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@ -84,12 +84,14 @@ class WeightNormWrapper(dg.Layer):
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w_v,
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self.create_parameter(
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shape=original_weight.shape, dtype=original_weight.dtype))
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with dg.no_grad():
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F.assign(original_weight, getattr(self, w_v))
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delattr(layer, param_name)
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temp = norm_except(getattr(self, w_v), self.dim, self.power)
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self.add_parameter(
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w_g, self.create_parameter(
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shape=temp.shape, dtype=temp.dtype))
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with dg.no_grad():
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F.assign(temp, getattr(self, w_g))
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# also set this when setting up
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