220 lines
9 KiB
Python
220 lines
9 KiB
Python
import urllib.request
|
|
import torch
|
|
import os
|
|
import sys
|
|
|
|
# from https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/Tacotron2/inference.py
|
|
def checkpoint_from_distributed(state_dict):
|
|
"""
|
|
Checks whether checkpoint was generated by DistributedDataParallel. DDP
|
|
wraps model in additional "module.", it needs to be unwrapped for single
|
|
GPU inference.
|
|
:param state_dict: model's state dict
|
|
"""
|
|
ret = False
|
|
for key, _ in state_dict.items():
|
|
if key.find('module.') != -1:
|
|
ret = True
|
|
break
|
|
return ret
|
|
|
|
|
|
# from https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/Tacotron2/inference.py
|
|
def unwrap_distributed(state_dict):
|
|
"""
|
|
Unwraps model from DistributedDataParallel.
|
|
DDP wraps model in additional "module.", it needs to be removed for single
|
|
GPU inference.
|
|
:param state_dict: model's state dict
|
|
"""
|
|
new_state_dict = {}
|
|
for key, value in state_dict.items():
|
|
new_key = key.replace('module.1.', '')
|
|
new_key = new_key.replace('module.', '')
|
|
new_state_dict[new_key] = value
|
|
return new_state_dict
|
|
|
|
|
|
dependencies = ['torch']
|
|
|
|
|
|
def nvidia_ncf(pretrained=True, **kwargs):
|
|
"""Constructs an NCF model.
|
|
For detailed information on model input and output, training recipies, inference and performance
|
|
visit: github.com/NVIDIA/DeepLearningExamples and/or ngc.nvidia.com
|
|
|
|
Args:
|
|
pretrained (bool, True): If True, returns a model pretrained on ml-20m dataset.
|
|
model_math (str, 'fp32'): returns a model in given precision ('fp32' or 'fp16')
|
|
nb_users (int): number of users
|
|
nb_items (int): number of items
|
|
mf_dim (int, 64): dimension of latent space in matrix factorization
|
|
mlp_layer_sizes (list, [256,256,128,64]): sizes of layers of multi-layer-perceptron
|
|
dropout (float, 0.5): dropout
|
|
"""
|
|
|
|
from PyTorch.Recommendation.NCF import neumf as ncf
|
|
|
|
fp16 = "model_math" in kwargs and kwargs["model_math"] == "fp16"
|
|
force_reload = "force_reload" in kwargs and kwargs["force_reload"]
|
|
|
|
config = {'nb_users': None, 'nb_items': None, 'mf_dim': 64, 'mf_reg': 0.,
|
|
'mlp_layer_sizes': [256, 256, 128, 64], 'mlp_layer_regs':[0, 0, 0, 0], 'dropout': 0.5}
|
|
|
|
if pretrained:
|
|
if fp16:
|
|
checkpoint = 'https://developer.nvidia.com/joc-ncf-fp16-pyt-20190225'
|
|
else:
|
|
checkpoint = 'https://developer.nvidia.com/joc-ncf-fp32-pyt-20190225'
|
|
ckpt_file = os.path.basename(checkpoint)
|
|
if not os.path.exists(ckpt_file) or force_reload:
|
|
sys.stderr.write('Downloading checkpoint from {}\n'.format(checkpoint))
|
|
urllib.request.urlretrieve(checkpoint, ckpt_file)
|
|
ckpt = torch.load(ckpt_file)
|
|
|
|
if checkpoint_from_distributed(ckpt):
|
|
ckpt = unwrap_distributed(ckpt)
|
|
|
|
config['nb_users'] = ckpt['mf_user_embed.weight'].shape[0]
|
|
config['nb_items'] = ckpt['mf_item_embed.weight'].shape[0]
|
|
config['mf_dim'] = ckpt['mf_item_embed.weight'].shape[1]
|
|
mlp_shapes = [ckpt[k].shape for k in ckpt.keys() if 'mlp' in k and 'weight' in k and 'embed' not in k]
|
|
config['mlp_layer_sizes'] = [mlp_shapes[0][1], mlp_shapes[1][1], mlp_shapes[2][1], mlp_shapes[2][0]]
|
|
config['mlp_layer_regs'] = [0] * len(config['mlp_layer_sizes'])
|
|
|
|
else:
|
|
if 'nb_users' not in kwargs:
|
|
raise ValueError("Missing 'nb_users' argument.")
|
|
if 'nb_items' not in kwargs:
|
|
raise ValueError("Missing 'nb_items' argument.")
|
|
for k,v in kwargs.items():
|
|
if k in config.keys():
|
|
config[k] = v
|
|
config['mlp_layer_regs'] = [0] * len(config['mlp_layer_sizes'])
|
|
|
|
m = ncf.NeuMF(**config)
|
|
|
|
if fp16:
|
|
m.half()
|
|
|
|
if pretrained:
|
|
m.load_state_dict(ckpt)
|
|
|
|
return m
|
|
|
|
|
|
def nvidia_tacotron2(pretrained=True, **kwargs):
|
|
"""Constructs a Tacotron 2 model (nn.module with additional infer(input) method).
|
|
For detailed information on model input and output, training recipies, inference and performance
|
|
visit: github.com/NVIDIA/DeepLearningExamples and/or ngc.nvidia.com
|
|
|
|
Args (type[, default value]):
|
|
pretrained (bool, True): If True, returns a model pretrained on LJ Speech dataset.
|
|
model_math (str, 'fp32'): returns a model in given precision ('fp32' or 'fp16')
|
|
n_symbols (int, 148): Number of symbols used in a sequence passed to the prenet, see
|
|
https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/text/symbols.py
|
|
p_attention_dropout (float, 0.1): dropout probability on attention LSTM (1st LSTM layer in decoder)
|
|
p_decoder_dropout (float, 0.1): dropout probability on decoder LSTM (2nd LSTM layer in decoder)
|
|
max_decoder_steps (int, 1000): maximum number of generated mel spectrograms during inference
|
|
"""
|
|
|
|
from PyTorch.SpeechSynthesis.Tacotron2.tacotron2 import model as tacotron2
|
|
from PyTorch.SpeechSynthesis.Tacotron2.models import lstmcell_to_float, batchnorm_to_float
|
|
|
|
fp16 = "model_math" in kwargs and kwargs["model_math"] == "fp16"
|
|
force_reload = "force_reload" in kwargs and kwargs["force_reload"]
|
|
|
|
if pretrained:
|
|
if fp16:
|
|
checkpoint = 'https://developer.nvidia.com/joc-tacotron2-fp16-pyt-20190306'
|
|
else:
|
|
checkpoint = 'https://developer.nvidia.com/joc-tacotron2-fp32-pyt-20190306'
|
|
ckpt_file = os.path.basename(checkpoint)
|
|
if not os.path.exists(ckpt_file) or force_reload:
|
|
sys.stderr.write('Downloading checkpoint from {}\n'.format(checkpoint))
|
|
urllib.request.urlretrieve(checkpoint, ckpt_file)
|
|
ckpt = torch.load(ckpt_file)
|
|
state_dict = ckpt['state_dict']
|
|
if checkpoint_from_distributed(state_dict):
|
|
state_dict = unwrap_distributed(state_dict)
|
|
config = ckpt['config']
|
|
else:
|
|
config = {'mask_padding': False, 'n_mel_channels': 80, 'n_symbols': 148,
|
|
'symbols_embedding_dim': 512, 'encoder_kernel_size': 5,
|
|
'encoder_n_convolutions': 3, 'encoder_embedding_dim': 512,
|
|
'attention_rnn_dim': 1024, 'attention_dim': 128,
|
|
'attention_location_n_filters': 32,
|
|
'attention_location_kernel_size': 31, 'n_frames_per_step': 1,
|
|
'decoder_rnn_dim': 1024, 'prenet_dim': 256,
|
|
'max_decoder_steps': 1000, 'gate_threshold': 0.5,
|
|
'p_attention_dropout': 0.1, 'p_decoder_dropout': 0.1,
|
|
'postnet_embedding_dim': 512, 'postnet_kernel_size': 5,
|
|
'postnet_n_convolutions': 5, 'decoder_no_early_stopping': False}
|
|
for k,v in kwargs.items():
|
|
if k in config.keys():
|
|
config[k] = v
|
|
|
|
m = tacotron2.Tacotron2(**config)
|
|
|
|
if fp16:
|
|
m = batchnorm_to_float(m.half())
|
|
m = lstmcell_to_float(m)
|
|
|
|
if pretrained:
|
|
m.load_state_dict(state_dict)
|
|
|
|
return m
|
|
|
|
|
|
def nvidia_waveglow(pretrained=True, **kwargs):
|
|
"""Constructs a WaveGlow model (nn.module with additional infer(input) method).
|
|
For detailed information on model input and output, training recipies, inference and performance
|
|
visit: github.com/NVIDIA/DeepLearningExamples and/or ngc.nvidia.com
|
|
|
|
Args:
|
|
pretrained (bool): If True, returns a model pretrained on LJ Speech dataset.
|
|
model_math (str, 'fp32'): returns a model in given precision ('fp32' or 'fp16')
|
|
"""
|
|
|
|
from PyTorch.SpeechSynthesis.Tacotron2.waveglow import model as waveglow
|
|
from PyTorch.SpeechSynthesis.Tacotron2.models import batchnorm_to_float
|
|
|
|
fp16 = "model_math" in kwargs and kwargs["model_math"] == "fp16"
|
|
force_reload = "force_reload" in kwargs and kwargs["force_reload"]
|
|
|
|
if pretrained:
|
|
if fp16:
|
|
checkpoint = 'https://developer.nvidia.com/joc-waveglow-fp16-pyt-20190306'
|
|
else:
|
|
checkpoint = 'https://developer.nvidia.com/joc-waveglow-fp32-pyt-20190306'
|
|
ckpt_file = os.path.basename(checkpoint)
|
|
if not os.path.exists(ckpt_file) or force_reload:
|
|
sys.stderr.write('Downloading checkpoint from {}\n'.format(checkpoint))
|
|
urllib.request.urlretrieve(checkpoint, ckpt_file)
|
|
ckpt = torch.load(ckpt_file)
|
|
state_dict = ckpt['state_dict']
|
|
if checkpoint_from_distributed(state_dict):
|
|
state_dict = unwrap_distributed(state_dict)
|
|
config = ckpt['config']
|
|
else:
|
|
config = {'n_mel_channels': 80, 'n_flows': 12, 'n_group': 8,
|
|
'n_early_every': 4, 'n_early_size': 2,
|
|
'WN_config': {'n_layers': 8, 'kernel_size': 3,
|
|
'n_channels': 512}}
|
|
for k,v in kwargs.items():
|
|
if k in config.keys():
|
|
config[k] = v
|
|
elif k in config['WN_config'].keys():
|
|
config['WN_config'][k] = v
|
|
|
|
m = waveglow.WaveGlow(**config)
|
|
|
|
if fp16:
|
|
m = batchnorm_to_float(m.half())
|
|
for mat in m.convinv:
|
|
mat.float()
|
|
|
|
if pretrained:
|
|
m.load_state_dict(state_dict)
|
|
|
|
return m
|