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Merge branch 'reborn' of https://github.com/iclementine/Parakeet into reborn

This commit is contained in:
lfchener 2020-12-08 03:10:00 +00:00
parent a6806389f9 37d4475810
commit f255eee029
6 changed files with 227 additions and 209 deletions
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6
parakeet/models/__init__.py
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@ -12,10 +12,10 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from parakeet.models.clarinet import *
#from parakeet.models.clarinet import *
from parakeet.models.waveflow import *
from parakeet.models.wavenet import *
#from parakeet.models.wavenet import *
from parakeet.models.transformer_tts import *
from parakeet.models.deepvoice3 import *
#from parakeet.models.deepvoice3 import *
# from parakeet.models.fastspeech import *

37
parakeet/models/transformer_tts.py
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@ -273,12 +273,14 @@ class MLPPreNet(nn.Layer):
super(MLPPreNet, self).__init__()
self.lin1 = nn.Linear(d_input, d_hidden)
self.lin2 = nn.Linear(d_hidden, d_hidden)
self.lin3 = nn.Linear(d_hidden, d_hidden)
self.dropout = dropout
def forward(self, x, dropout):
l1 = F.dropout(F.relu(self.lin1(x)), self.dropout, training=self.training)
l2 = F.dropout(F.relu(self.lin2(l1)), self.dropout, training=self.training)
return l2
l3 = self.lin3(l2)
return l3
# NOTE: not used in
class CNNPreNet(nn.Layer):
@ -317,6 +319,7 @@ class CNNPostNet(nn.Layer):
Conv1dBatchNorm(c_in, c_out, kernel_size,
weight_attr=I.XavierUniform(),
padding=padding))
self.last_bn = nn.BatchNorm1D(d_output)
# for a layer that ends with a normalization layer that is targeted to
# output a non zero-central output, it may take a long time to
# train the scale and bias
@ -328,7 +331,7 @@ class CNNPostNet(nn.Layer):
x = layer(x)
if i != (len(self.convs) - 1):
x = F.tanh(x)
x = x_in + x
x = self.last_bn(x_in + x)
return x
@ -491,7 +494,7 @@ class TransformerTTS(nn.Layer):
decoder_output = paddle.concat([decoder_output, mel_output[:, -self.r:, :]], 1)
# stop condition: (if any ouput frame of the output multiframes hits the stop condition)
if paddle.any(paddle.argmax(stop_logits[0, :, :], axis=-1) == self.stop_prob_index):
if paddle.any(paddle.argmax(stop_logits[0, -self.r:, :], axis=-1) == self.stop_prob_index):
if verbose:
print("Hits stop condition.")
break
@ -526,6 +529,34 @@ class TransformerTTSLoss(nn.Layer):
stop_loss = L.masked_softmax_with_cross_entropy(
stop_logits, stop_probs.unsqueeze(-1), mask2.unsqueeze(-1))
loss = mel_loss1 + mel_loss2 + stop_loss
losses = dict(
loss=loss, # total loss
mel_loss1=mel_loss1, # ouput mel loss
mel_loss2=mel_loss2, # intermediate mel loss
stop_loss=stop_loss # stop prob loss
)
return losses
class AdaptiveTransformerTTSLoss(nn.Layer):
def __init__(self):
super(AdaptiveTransformerTTSLoss, self).__init__()
def forward(self, mel_output, mel_intermediate, mel_target, stop_logits, stop_probs):
mask = masking.feature_mask(mel_target, axis=-1, dtype=mel_target.dtype)
mask1 = paddle.unsqueeze(mask, -1)
mel_loss1 = L.masked_l1_loss(mel_output, mel_target, mask1)
mel_loss2 = L.masked_l1_loss(mel_intermediate, mel_target, mask1)
batch_size, mel_len = mask.shape
valid_lengths = mask.sum(-1).astype("int64")
last_position = F.one_hot(valid_lengths - 1, num_classes=mel_len)
stop_loss_scale = valid_lengths.sum() / batch_size - 1
mask2 = mask + last_position.scale(stop_loss_scale - 1).astype(mask.dtype)
stop_loss = L.masked_softmax_with_cross_entropy(
stop_logits, stop_probs.unsqueeze(-1), mask2.unsqueeze(-1))
loss = mel_loss1 + mel_loss2 + stop_loss
losses = dict(
loss=loss, # total loss

33
parakeet/modules/losses.py
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@ -1,3 +1,5 @@
import numba
import numpy as np
import paddle
from paddle import nn
from paddle.nn import functional as F
@ -12,7 +14,7 @@ def weighted_mean(input, weight):
Returns:
Tensor: shape(1,), weighted mean tensor with the same dtype as input.
"""
weight = paddle.cast(weight, input.dtype)
weight = paddle.cast(weight, input.dtype)
return paddle.mean(input * weight)
def masked_l1_loss(prediction, target, mask):
@ -22,3 +24,32 @@ def masked_l1_loss(prediction, target, mask):
def masked_softmax_with_cross_entropy(logits, label, mask, axis=-1):
ce = F.softmax_with_cross_entropy(logits, label, axis=axis)
return weighted_mean(ce, mask)
def diagonal_loss(attentions, input_lengths, target_lengths, g=0.2, multihead=False):
"""A metric to evaluate how diagonal a attention distribution is."""
W = guided_attentions(input_lengths, target_lengths, g)
W_tensor = paddle.to_tensor(W)
if not multihead:
return paddle.mean(attentions * W_tensor)
else:
return paddle.mean(attentions * paddle.unsqueeze(W_tensor, 1))
@numba.jit(nopython=True)
def guided_attention(N, max_N, T, max_T, g):
W = np.zeros((max_T, max_N), dtype=np.float32)
for t in range(T):
for n in range(N):
W[t, n] = 1 - np.exp(-(n / N - t / T)**2 / (2 * g * g))
# (T_dec, T_enc)
return W
def guided_attentions(input_lengths, target_lengths, g=0.2):
B = len(input_lengths)
max_input_len = input_lengths.max()
max_target_len = target_lengths.max()
W = np.zeros((B, max_target_len, max_input_len), dtype=np.float32)
for b in range(B):
W[b] = guided_attention(input_lengths[b], max_input_len,
target_lengths[b], max_target_len, g)
# (B, T_dec, T_enc)
return W

202
parakeet/modules/multihead_attention.py
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@ -1,202 +0,0 @@
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import numpy as np
import paddle.fluid as fluid
import paddle.fluid.dygraph as dg
import paddle.fluid.layers as layers
class Linear(dg.Layer):
def __init__(self,
in_features,
out_features,
is_bias=True,
dtype="float32"):
super(Linear, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.dtype = dtype
self.weight = fluid.ParamAttr(
initializer=fluid.initializer.XavierInitializer())
self.bias = is_bias
if is_bias is not False:
k = math.sqrt(1.0 / in_features)
self.bias = fluid.ParamAttr(initializer=fluid.initializer.Uniform(
low=-k, high=k))
self.linear = dg.Linear(
in_features,
out_features,
param_attr=self.weight,
bias_attr=self.bias, )
def forward(self, x):
x = self.linear(x)
return x
class ScaledDotProductAttention(dg.Layer):
def __init__(self, d_key):
"""Scaled dot product attention module.
Args:
d_key (int): the dim of key in multihead attention.
"""
super(ScaledDotProductAttention, self).__init__()
self.d_key = d_key
# please attention this mask is diff from pytorch
def forward(self,
key,
value,
query,
mask=None,
query_mask=None,
dropout=0.1):
"""
Compute scaled dot product attention.
Args:
key (Variable): shape(B, T, C), dtype float32, the input key of scaled dot product attention.
value (Variable): shape(B, T, C), dtype float32, the input value of scaled dot product attention.
query (Variable): shape(B, T, C), dtype float32, the input query of scaled dot product attention.
mask (Variable, optional): shape(B, T_q, T_k), dtype float32, the mask of key. Defaults to None.
query_mask (Variable, optional): shape(B, T_q, T_q), dtype float32, the mask of query. Defaults to None.
dropout (float32, optional): the probability of dropout. Defaults to 0.1.
Returns:
result (Variable): shape(B, T, C), the result of mutihead attention.
attention (Variable): shape(n_head * B, T, C), the attention of key.
"""
# Compute attention score
attention = layers.matmul(
query, key, transpose_y=True, alpha=self.d_key
**-0.5) #transpose the last dim in y
# Mask key to ignore padding
if mask is not None:
attention = attention + mask
attention = layers.softmax(attention, use_cudnn=True)
attention = layers.dropout(
attention, dropout, dropout_implementation='upscale_in_train')
# Mask query to ignore padding
if query_mask is not None:
attention = attention * query_mask
result = layers.matmul(attention, value)
return result, attention
class MultiheadAttention(dg.Layer):
def __init__(self,
num_hidden,
d_k,
d_q,
num_head=4,
is_bias=False,
dropout=0.1,
is_concat=True):
"""Multihead Attention.
Args:
num_hidden (int): the number of hidden layer in network.
d_k (int): the dim of key in multihead attention.
d_q (int): the dim of query in multihead attention.
num_head (int, optional): the head number of multihead attention. Defaults to 4.
is_bias (bool, optional): whether have bias in linear layers. Default to False.
dropout (float, optional): dropout probability of FFTBlock. Defaults to 0.1.
is_concat (bool, optional): whether concat query and result. Default to True.
"""
super(MultiheadAttention, self).__init__()
self.num_hidden = num_hidden
self.num_head = num_head
self.d_k = d_k
self.d_q = d_q
self.dropout = dropout
self.is_concat = is_concat
self.key = Linear(num_hidden, num_head * d_k, is_bias=is_bias)
self.value = Linear(num_hidden, num_head * d_k, is_bias=is_bias)
self.query = Linear(num_hidden, num_head * d_q, is_bias=is_bias)
self.scal_attn = ScaledDotProductAttention(d_k)
if self.is_concat:
self.fc = Linear(num_head * d_q * 2, num_hidden)
else:
self.fc = Linear(num_head * d_q, num_hidden)
self.layer_norm = dg.LayerNorm(num_hidden)
def forward(self, key, value, query_input, mask=None, query_mask=None):
"""
Compute attention.
Args:
key (Variable): shape(B, T, C), dtype float32, the input key of attention.
value (Variable): shape(B, T, C), dtype float32, the input value of attention.
query_input (Variable): shape(B, T, C), dtype float32, the input query of attention.
mask (Variable, optional): shape(B, T_query, T_key), dtype float32, the mask of key. Defaults to None.
query_mask (Variable, optional): shape(B, T_query, T_key), dtype float32, the mask of query. Defaults to None.
Returns:
result (Variable): shape(B, T, C), the result of mutihead attention.
attention (Variable): shape(num_head * B, T, C), the attention of key and query.
"""
batch_size = key.shape[0]
seq_len_key = key.shape[1]
seq_len_query = query_input.shape[1]
# Make multihead attention
key = layers.reshape(
self.key(key), [batch_size, seq_len_key, self.num_head, self.d_k])
value = layers.reshape(
self.value(value),
[batch_size, seq_len_key, self.num_head, self.d_k])
query = layers.reshape(
self.query(query_input),
[batch_size, seq_len_query, self.num_head, self.d_q])
key = layers.reshape(
layers.transpose(key, [2, 0, 1, 3]), [-1, seq_len_key, self.d_k])
value = layers.reshape(
layers.transpose(value, [2, 0, 1, 3]),
[-1, seq_len_key, self.d_k])
query = layers.reshape(
layers.transpose(query, [2, 0, 1, 3]),
[-1, seq_len_query, self.d_q])
result, attention = self.scal_attn(
key, value, query, mask=mask, query_mask=query_mask)
# concat all multihead result
result = layers.reshape(
result, [self.num_head, batch_size, seq_len_query, self.d_q])
result = layers.reshape(
layers.transpose(result, [1, 2, 0, 3]),
[batch_size, seq_len_query, -1])
if self.is_concat:
result = layers.concat([query_input, result], axis=-1)
result = layers.dropout(
self.fc(result),
self.dropout,
dropout_implementation='upscale_in_train')
result = result + query_input
result = self.layer_norm(result)
return result, attention

21
parakeet/training/cli.py Normal file
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@ -0,0 +1,21 @@
import argparse
def default_argument_parser():
parser = argparse.ArgumentParser()
# data and outpu
parser.add_argument("--config", metavar="FILE", help="path of the config file to overwrite to default config with.")
parser.add_argument("--data", metavar="DATA_DIR", help="path to the datatset.")
parser.add_argument("--output", metavar="OUTPUT_DIR", help="path to save checkpoint and log. If not provided, a directory is created in runs/ to save outputs.")
# load from saved checkpoint
parser.add_argument("--checkpoint_path", type=str, help="path of the checkpoint to load")
# running
parser.add_argument("--device", type=str, choices=["cpu", "gpu"], help="device type to use, cpu and gpu are supported.")
parser.add_argument("--nprocs", type=int, default=1, help="number of parallel processes to use.")
# overwrite extra config and default config
parser.add_argument("--opts", nargs=argparse.REMAINDER, help="options to overwrite --config file and the default config, passing in KEY VALUE pairs")
return parser

137
parakeet/utils/checkpoint.py Normal file
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@ -0,0 +1,137 @@
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import time
import numpy as np
import paddle
from paddle import distributed as dist
from parakeet.utils import mp_tools
def _load_latest_checkpoint(checkpoint_dir):
"""Get the iteration number corresponding to the latest saved checkpoint
Args:
checkpoint_dir (str): the directory where checkpoint is saved.
Returns:
int: the latest iteration number.
"""
checkpoint_record = os.path.join(checkpoint_dir, "checkpoint")
# Create checkpoint index file if not exist.
if (not os.path.isfile(checkpoint_record)):
return 0
# Fetch the latest checkpoint index.
with open(checkpoint_record, "r") as handle:
latest_checkpoint = handle.readline().split()[-1]
iteration = int(latest_checkpoint.split("-")[-1])
return iteration
def _save_checkpoint(checkpoint_dir, iteration):
"""Save the iteration number of the latest model to be checkpointed.
Args:
checkpoint_dir (str): the directory where checkpoint is saved.
iteration (int): the latest iteration number.
Returns:
None
"""
checkpoint_record = os.path.join(checkpoint_dir, "checkpoint")
# Update the latest checkpoint index.
with open(checkpoint_record, "w") as handle:
handle.write("model_checkpoint_path: step-{}".format(iteration))
def load_parameters(model,
optimizer=None,
checkpoint_dir=None,
checkpoint_path=None):
"""Load a specific model checkpoint from disk.
Args:
model (obj): model to load parameters.
optimizer (obj, optional): optimizer to load states if needed.
Defaults to None.
checkpoint_dir (str, optional): the directory where checkpoint is saved.
checkpoint_path (str, optional): if specified, load the checkpoint
stored in the checkpoint_path and the argument 'checkpoint_dir' will
be ignored. Defaults to None.
Returns:
iteration (int): number of iterations that the loaded checkpoint has
been trained.
"""
if checkpoint_path is not None:
iteration = int(os.path.basename(checkpoint_path).split("-")[-1])
elif checkpoint_dir is not None:
iteration = _load_latest_checkpoint(checkpoint_dir)
if iteration == 0:
return iteration
checkpoint_path = os.path.join(checkpoint_dir,
"step-{}".format(iteration))
else:
raise ValueError(
"At least one of 'checkpoint_dir' and 'checkpoint_path' should be specified!"
)
local_rank = dist.get_rank()
params_path = checkpoint_path + ".pdparams"
model_dict = paddle.load(params_path)
model.set_state_dict(model_dict)
print("[checkpoint] Rank {}: loaded model from {}".format(
local_rank, params_path))
optimizer_path = checkpoint_path + ".pdopt"
if optimizer and os.path.isfile(optimizer_path):
optimizer_dict = paddle.load(optimizer_path)
optimizer.set_state_dict(optimizer_dict)
print("[checkpoint] Rank {}: loaded optimizer state from {}".
format(local_rank, optimizer_path))
return iteration
@mp_tools.rank_zero_only
def save_parameters(checkpoint_dir, iteration, model, optimizer=None):
"""Checkpoint the latest trained model parameters.
Args:
checkpoint_dir (str): the directory where checkpoint is saved.
iteration (int): the latest iteration number.
model (obj): model to be checkpointed.
optimizer (obj, optional): optimizer to be checkpointed.
Defaults to None.
Returns:
None
"""
checkpoint_path = os.path.join(checkpoint_dir, "step-{}".format(iteration))
model_dict = model.state_dict()
params_path = checkpoint_path + ".pdparams"
paddle.save(model_dict, params_path)
print("[checkpoint] Saved model to {}".format(params_path))
if optimizer:
opt_dict = optimizer.state_dict()
optimizer_path = checkpoint_path + ".pdopt"
paddle.save(opt_dict, optimizer_path)
print("[checkpoint] Saved optimzier state to {}".format(
optimizer_path))
_save_checkpoint(checkpoint_dir, iteration)