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https://github.com/index-tts/index-tts.git
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72c09ec0b7* indextts2 * update lfs for audio files --------- Co-authored-by: wangyining02 <wangyining02@bilibili.com>
331 lines
9.3 KiB
Python
331 lines
9.3 KiB
Python
# Copyright (c) 2023 Amphion.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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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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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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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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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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# load F0 model
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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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return F0_model
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# Generators
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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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# Discriminators
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from modules.dac.model.discriminator import Discriminator
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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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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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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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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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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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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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# strip prefix of DDP (module.), create a new OrderedDict that does not contain the prefix
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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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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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