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863d06f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torchaudio.transforms as T
def compress_latents(z: torch.Tensor, factor: int = 6) -> torch.Tensor:
B, C, T = z.shape
if T % factor != 0:
pad = factor - (T % factor)
z = torch.nn.functional.pad(z, (0, pad))
T = T + pad
return z.view(B, C, T // factor, factor).permute(0, 1, 3, 2).flatten(1, 2)
def decompress_latents(z: torch.Tensor, factor: int = 6, target_channels: int = 24) -> torch.Tensor:
B, _, T_low = z.shape
return z.view(B, target_channels, factor, T_low).permute(0, 1, 3, 2).flatten(2, 3)
def _resolve_vocab_size(char_dict_path, default=256):
import json as _json
import os as _os
if char_dict_path and _os.path.exists(char_dict_path):
try:
with open(char_dict_path, "r") as f:
cd = _json.load(f)
if isinstance(cd, dict) and "vocab_size" in cd:
return int(cd["vocab_size"])
if isinstance(cd, dict) and "char_to_id" in cd and isinstance(cd["char_to_id"], dict):
return max(cd["char_to_id"].values()) + 1
if isinstance(cd, dict):
return max(cd.values()) + 1 if cd else default
return len(cd)
except Exception:
pass
return default
def load_ttl_config(config_path="configs/tts.json"):
import json
with open(config_path, "r") as f:
full_config = json.load(f)
ttl = full_config["ttl"]
ae = full_config.get("ae", {})
dp = full_config.get("dp", {})
te = ttl["text_encoder"]
se = ttl["style_encoder"]
vf = ttl["vector_field"]
um = ttl["uncond_masker"]
char_dict_path = te.get("char_dict_path", te.get("text_embedder", {}).get("char_dict_path"))
vocab_size = _resolve_vocab_size(char_dict_path, default=256)
dp_char_dict_path = (
dp.get("sentence_encoder", {}).get("char_dict_path")
or dp.get("sentence_encoder", {}).get("text_embedder", {}).get("char_dict_path")
)
dp_vocab_size = _resolve_vocab_size(dp_char_dict_path, default=vocab_size)
ae_dec = ae.get("decoder", {})
ae_dec_cfg = {
"idim": ae_dec.get("idim", 24),
"hdim": ae_dec.get("hdim", 512),
"intermediate_dim": ae_dec.get("intermediate_dim", 2048),
"ksz": ae_dec.get("ksz", 7),
"dilation_lst": ae_dec.get("dilation_lst", [1, 2, 4, 1, 2, 4, 1, 1, 1, 1]),
"chunk_compress_factor": ae.get("chunk_compress_factor", 1),
"head": {
"idim": ae_dec.get("head", {}).get("idim", ae_dec.get("hdim", 512)),
"hdim": ae_dec.get("head", {}).get("hdim", 2048),
"odim": ae_dec.get("head", {}).get("odim", 512),
"ksz": ae_dec.get("head", {}).get("ksz", 3),
},
}
ae_enc = ae.get("encoder", {})
ae_enc_spec = ae_enc.get("spec_processor", {})
ae_enc_cfg = {
"ksz": ae_enc.get("ksz", 7),
"hdim": ae_enc.get("hdim", 512),
"intermediate_dim": ae_enc.get("intermediate_dim", 2048),
"dilation_lst": ae_enc.get("dilation_lst", [1] * 10),
"odim": ae_enc.get("odim", 24),
"idim": ae_enc.get("idim", 1253),
}
dp_se = dp.get("style_encoder", {}).get("style_token_layer", {})
return {
"full_config": full_config,
"ttl": ttl,
"ae": ae,
"dp": dp,
"vocab_size": vocab_size,
"char_dict_path": char_dict_path,
"dp_vocab_size": dp_vocab_size,
"latent_dim": ttl["latent_dim"],
"chunk_compress_factor": ttl["chunk_compress_factor"],
"compressed_channels": ttl["latent_dim"] * ttl["chunk_compress_factor"],
"normalizer_scale": ttl["normalizer"]["scale"],
"sigma_min": ttl["flow_matching"]["sig_min"],
"Ke": ttl["batch_expander"]["n_batch_expand"],
"te_d_model": te["text_embedder"]["char_emb_dim"],
"te_convnext_layers": te["convnext"]["num_layers"],
"te_expansion_factor": te["convnext"]["intermediate_dim"] // te["text_embedder"]["char_emb_dim"],
"te_attn_n_layers": te["attn_encoder"]["n_layers"],
"te_attn_p_dropout": te["attn_encoder"]["p_dropout"],
"se_d_model": se["proj_in"]["odim"],
"se_hidden_dim": se["convnext"]["intermediate_dim"],
"se_num_blocks": se["convnext"]["num_layers"],
"se_n_style": se["style_token_layer"]["n_style"],
"se_n_heads": se["style_token_layer"]["n_heads"],
"prob_both_uncond": um["prob_both_uncond"],
"prob_text_uncond": um["prob_text_uncond"],
"uncond_init_std": um["std"],
"um_text_dim": um["text_dim"],
"um_n_style": um["n_style"],
"um_style_key_dim": um["style_key_dim"],
"um_style_value_dim": um["style_value_dim"],
"vf_hidden": vf["proj_in"]["odim"],
"vf_time_dim": vf["time_encoder"]["time_dim"],
"vf_n_blocks": vf["main_blocks"]["n_blocks"],
"vf_text_dim": vf["main_blocks"]["text_cond_layer"]["text_dim"],
"vf_text_n_heads": vf["main_blocks"]["text_cond_layer"]["n_heads"],
"vf_style_dim": vf["main_blocks"]["style_cond_layer"]["style_dim"],
"vf_rotary_scale": vf["main_blocks"]["text_cond_layer"]["rotary_scale"],
"ae_dec_cfg": ae_dec_cfg,
"ae_enc_cfg": ae_enc_cfg,
"ae_sample_rate": ae.get("sample_rate", 44100),
"ae_n_fft": ae_enc_spec.get("n_fft", 2048),
"ae_hop_length": ae_enc_spec.get("hop_length", 512),
"ae_n_mels": ae_enc_spec.get("n_mels", 1253),
"dp_style_tokens": dp_se.get("n_style", 8),
"dp_style_dim": dp_se.get("style_value_dim", 16),
}
class MelSpectrogram(nn.Module):
def __init__(self, sample_rate=44100, n_fft=2048, win_length=2048,
hop_length=512, n_mels=1253, f_min=0, f_max=None):
super().__init__()
self.mel = T.MelSpectrogram(
sample_rate=sample_rate, n_fft=n_fft, win_length=win_length,
hop_length=hop_length, n_mels=n_mels, f_min=f_min, f_max=f_max,
center=True, power=1.0,
)
def forward(self, audio):
mel = torch.log(torch.clamp(self.mel(audio), min=1e-5))
return mel.squeeze(1) if mel.dim() == 4 and mel.shape[1] == 1 else mel
class MelSpectrogramNoLog(nn.Module):
def __init__(self, sample_rate=44100, n_fft=2048, win_length=2048,
hop_length=512, n_mels=1253, f_min=0, f_max=12000, power=1.0):
super().__init__()
self.mel = T.MelSpectrogram(
sample_rate=sample_rate, n_fft=n_fft, win_length=win_length,
hop_length=hop_length, n_mels=n_mels, f_min=f_min, f_max=f_max,
center=True, power=power,
)
def forward(self, audio):
mel = self.mel(audio)
return mel.squeeze(1) if mel.dim() == 4 and mel.shape[1] == 1 else mel
class LinearMelSpectrogram(nn.Module):
def __init__(self, sample_rate=44100, n_fft=2048, win_length=2048,
hop_length=512, n_mels=1253, f_min=0, f_max=None):
super().__init__()
self.spectrogram = T.Spectrogram(
n_fft=n_fft, win_length=win_length, hop_length=hop_length,
center=True, power=1.0,
)
self.mel_scale = T.MelScale(
n_mels=n_mels, sample_rate=sample_rate,
n_stft=n_fft // 2 + 1, f_min=f_min, f_max=f_max,
)
def forward(self, audio):
spec = self.spectrogram(audio)
mel = self.mel_scale(spec)
log_spec = torch.log(torch.clamp(spec, min=1e-5))
log_mel = torch.log(torch.clamp(mel, min=1e-5))
return torch.cat([log_spec, log_mel], dim=1)
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