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| import math
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| import os.path
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| import numpy as np
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| import torch
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| from torch import nn
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| from torch.nn import functional as F
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| from munch import Munch
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| import json
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| class AttrDict(dict):
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| def __init__(self, *args, **kwargs):
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| super(AttrDict, self).__init__(*args, **kwargs)
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| self.__dict__ = self
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| def init_weights(m, mean=0.0, std=0.01):
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| classname = m.__class__.__name__
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| if classname.find("Conv") != -1:
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| m.weight.data.normal_(mean, std)
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| def get_padding(kernel_size, dilation=1):
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| return int((kernel_size * dilation - dilation) / 2)
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| def convert_pad_shape(pad_shape):
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| l = pad_shape[::-1]
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| pad_shape = [item for sublist in l for item in sublist]
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| return pad_shape
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| def intersperse(lst, item):
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| result = [item] * (len(lst) * 2 + 1)
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| result[1::2] = lst
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| return result
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| def kl_divergence(m_p, logs_p, m_q, logs_q):
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| """KL(P||Q)"""
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| kl = (logs_q - logs_p) - 0.5
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| kl += (
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| 0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q)
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| )
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| return kl
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| def rand_gumbel(shape):
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| """Sample from the Gumbel distribution, protect from overflows."""
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| uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
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| return -torch.log(-torch.log(uniform_samples))
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| def rand_gumbel_like(x):
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| g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
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| return g
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| def slice_segments(x, ids_str, segment_size=4):
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| ret = torch.zeros_like(x[:, :, :segment_size])
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| for i in range(x.size(0)):
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| idx_str = ids_str[i]
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| idx_end = idx_str + segment_size
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| ret[i] = x[i, :, idx_str:idx_end]
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| return ret
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| def slice_segments_audio(x, ids_str, segment_size=4):
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| ret = torch.zeros_like(x[:, :segment_size])
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| for i in range(x.size(0)):
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| idx_str = ids_str[i]
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| idx_end = idx_str + segment_size
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| ret[i] = x[i, idx_str:idx_end]
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| return ret
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| def rand_slice_segments(x, x_lengths=None, segment_size=4):
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| b, d, t = x.size()
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| if x_lengths is None:
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| x_lengths = t
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| ids_str_max = x_lengths - segment_size + 1
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| ids_str = ((torch.rand([b]).to(device=x.device) * ids_str_max).clip(0)).to(
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| dtype=torch.long
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| )
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| ret = slice_segments(x, ids_str, segment_size)
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| return ret, ids_str
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| def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4):
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| position = torch.arange(length, dtype=torch.float)
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| num_timescales = channels // 2
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| log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / (
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| num_timescales - 1
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| )
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| inv_timescales = min_timescale * torch.exp(
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| torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment
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| )
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| scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
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| signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
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| signal = F.pad(signal, [0, 0, 0, channels % 2])
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| signal = signal.view(1, channels, length)
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| return signal
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| def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
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| b, channels, length = x.size()
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| signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
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| return x + signal.to(dtype=x.dtype, device=x.device)
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| def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
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| b, channels, length = x.size()
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| signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
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| return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
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| def subsequent_mask(length):
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| mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
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| return mask
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| @torch.jit.script
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| def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
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| n_channels_int = n_channels[0]
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| in_act = input_a + input_b
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| t_act = torch.tanh(in_act[:, :n_channels_int, :])
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| s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
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| acts = t_act * s_act
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| return acts
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|
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| def convert_pad_shape(pad_shape):
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| l = pad_shape[::-1]
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| pad_shape = [item for sublist in l for item in sublist]
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| return pad_shape
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| def shift_1d(x):
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| x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
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| return x
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| def sequence_mask(length, max_length=None):
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| if max_length is None:
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| max_length = length.max()
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| x = torch.arange(max_length, dtype=length.dtype, device=length.device)
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| return x.unsqueeze(0) < length.unsqueeze(1)
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|
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| def generate_path(duration, mask):
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| """
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| duration: [b, 1, t_x]
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| mask: [b, 1, t_y, t_x]
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| """
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| device = duration.device
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| b, _, t_y, t_x = mask.shape
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| cum_duration = torch.cumsum(duration, -1)
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| cum_duration_flat = cum_duration.view(b * t_x)
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| path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
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| path = path.view(b, t_x, t_y)
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| path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
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| path = path.unsqueeze(1).transpose(2, 3) * mask
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| return path
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| def clip_grad_value_(parameters, clip_value, norm_type=2):
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| if isinstance(parameters, torch.Tensor):
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| parameters = [parameters]
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| parameters = list(filter(lambda p: p.grad is not None, parameters))
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| norm_type = float(norm_type)
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| if clip_value is not None:
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| clip_value = float(clip_value)
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| total_norm = 0
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| for p in parameters:
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| param_norm = p.grad.data.norm(norm_type)
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| total_norm += param_norm.item() ** norm_type
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| if clip_value is not None:
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| p.grad.data.clamp_(min=-clip_value, max=clip_value)
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| total_norm = total_norm ** (1.0 / norm_type)
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| return total_norm
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|
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| def log_norm(x, mean=-4, std=4, dim=2):
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| """
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| normalized log mel -> mel -> norm -> log(norm)
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| """
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| x = torch.log(torch.exp(x * std + mean).norm(dim=dim))
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| return x
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| from huggingface_hub import hf_hub_download
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| def load_F0_models(path):
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| from .JDC.model import JDCNet
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| F0_model = JDCNet(num_class=1, seq_len=192)
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| if not os.path.exists(path):
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| path = hf_hub_download(repo_id="Plachta/JDCnet", filename="bst.t7")
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| params = torch.load(path, map_location="cpu")["net"]
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| F0_model.load_state_dict(params)
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| _ = F0_model.train()
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|
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| return F0_model
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| from modules.dac.model.dac import Encoder, Decoder
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| from .quantize import FAquantizer, FApredictors
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| from modules.dac.model.discriminator import Discriminator
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|
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| def build_model(args):
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| encoder = Encoder(
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| d_model=args.DAC.encoder_dim,
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| strides=args.DAC.encoder_rates,
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| d_latent=1024,
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| causal=args.causal,
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| lstm=args.lstm,
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| )
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|
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| quantizer = FAquantizer(
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| in_dim=1024,
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| n_p_codebooks=1,
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| n_c_codebooks=args.n_c_codebooks,
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| n_t_codebooks=2,
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| n_r_codebooks=3,
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| codebook_size=1024,
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| codebook_dim=8,
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| quantizer_dropout=0.5,
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| causal=args.causal,
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| separate_prosody_encoder=args.separate_prosody_encoder,
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| timbre_norm=args.timbre_norm,
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| )
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| fa_predictors = FApredictors(
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| in_dim=1024,
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| use_gr_content_f0=args.use_gr_content_f0,
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| use_gr_prosody_phone=args.use_gr_prosody_phone,
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| use_gr_residual_f0=True,
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| use_gr_residual_phone=True,
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| use_gr_timbre_content=True,
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| use_gr_timbre_prosody=args.use_gr_timbre_prosody,
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| use_gr_x_timbre=True,
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| norm_f0=args.norm_f0,
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| timbre_norm=args.timbre_norm,
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| use_gr_content_global_f0=args.use_gr_content_global_f0,
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| )
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|
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| decoder = Decoder(
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| input_channel=1024,
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| channels=args.DAC.decoder_dim,
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| rates=args.DAC.decoder_rates,
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| causal=args.causal,
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| lstm=args.lstm,
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| )
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|
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| discriminator = Discriminator(
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| rates=[],
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| periods=[2, 3, 5, 7, 11],
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| fft_sizes=[2048, 1024, 512],
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| sample_rate=args.DAC.sr,
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| bands=[(0.0, 0.1), (0.1, 0.25), (0.25, 0.5), (0.5, 0.75), (0.75, 1.0)],
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| )
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|
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| nets = Munch(
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| encoder=encoder,
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| quantizer=quantizer,
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| decoder=decoder,
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| discriminator=discriminator,
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| fa_predictors=fa_predictors,
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| )
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|
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| return nets
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|
|
|
|
| def load_checkpoint(
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| model,
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| optimizer,
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| path,
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| load_only_params=True,
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| ignore_modules=[],
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| is_distributed=False,
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| ):
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| state = torch.load(path, map_location="cpu")
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| params = state["net"]
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| for key in model:
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| if key in params and key not in ignore_modules:
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| if not is_distributed:
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|
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| for k in list(params[key].keys()):
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| if k.startswith("module."):
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| params[key][k[len("module.") :]] = params[key][k]
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| del params[key][k]
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| print("%s loaded" % key)
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| model[key].load_state_dict(params[key], strict=True)
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| _ = [model[key].eval() for key in model]
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|
|
| if not load_only_params:
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| epoch = state["epoch"] + 1
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| iters = state["iters"]
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| optimizer.load_state_dict(state["optimizer"])
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| optimizer.load_scheduler_state_dict(state["scheduler"])
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|
|
| else:
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| epoch = state["epoch"] + 1
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| iters = state["iters"]
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|
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| return model, optimizer, epoch, iters
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|
|
|
|
| def recursive_munch(d):
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| if isinstance(d, dict):
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| return Munch((k, recursive_munch(v)) for k, v in d.items())
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| elif isinstance(d, list):
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| return [recursive_munch(v) for v in d]
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| else:
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| return d
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|
|
