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64ec292 1e0878a 64ec292 1e0878a 64ec292 1e0878a 64ec292 1e0878a 64ec292 1e0878a 64ec292 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 | import hydra
import torch
import torch.nn as nn
import torchaudio
from einops import rearrange
from ema_pytorch import EMA
from huggingface_hub import PyTorchModelHubMixin
from omegaconf import OmegaConf
from src.YingMusicSinger.melody.midi_extractor import MIDIExtractor
from src.YingMusicSinger.models.model import Singer
from src.YingMusicSinger.utils.cnen_tokenizer import CNENTokenizer
from src.YingMusicSinger.utils.lrc_align import (
align_lrc_put_to_front,
align_lrc_sentence_level,
)
from src.YingMusicSinger.utils.mel_spectrogram import MelodySpectrogram
from src.YingMusicSinger.utils.stable_audio_tools.vae_copysyn import StableAudioInfer
from src.YingMusicSinger.utils.smooth_ending import smooth_ending
class YingMusicSinger(nn.Module, PyTorchModelHubMixin):
def __init__(
self,
model_cfg_path,
ckpt_path=None,
vae_config_path=None,
vae_ckpt_path=None,
midi_teacher_ckpt_path=None,
is_distilled=False,
use_ema=True,
):
super().__init__()
self.cfg = OmegaConf.load(model_cfg_path)
model_cls = hydra.utils.get_class(
f"src.YingMusicSinger.models.{self.cfg.model.backbone}"
)
self.melody_input_source = self.cfg.model.melody_input_source
self.is_tts_pretrain = self.cfg.model.is_tts_pretrain
self.model = Singer(
transformer=model_cls(
**self.cfg.model.arch,
text_num_embeds=self.cfg.datasets_cfg.text_num_embeds,
mel_dim=self.cfg.model.mel_spec.n_mel_channels,
use_guidance_scale_embed=is_distilled,
),
mel_spec_kwargs=self.cfg.model.mel_spec,
is_tts_pretrain=self.is_tts_pretrain,
melody_input_source=self.melody_input_source,
cka_disabled=self.cfg.model.cka_disabled,
num_channels=None,
extra_parameters=self.cfg.extra_parameters,
distill_stage=1,
use_guidance_scale_embed=is_distilled,
)
self.vae = StableAudioInfer(
model_config_path=vae_config_path,
model_ckpt_path=vae_ckpt_path,
)
self._need_midi = self.melody_input_source in {
"some_pretrain",
"some_pretrain_fuzzdisturb",
"some_pretrain_postprocess_embedding",
}
self.midi_teacher = None
if self._need_midi:
self.midi_teacher = MIDIExtractor()
if midi_teacher_ckpt_path is not None:
self.midi_teacher._load_form_ckpt(midi_teacher_ckpt_path)
for p in self.midi_teacher.parameters():
p.requires_grad = False
self.melody_spectrogram_extract = MelodySpectrogram()
self.vae_frame_rate = 44100 / 2048
if ckpt_path is not None:
ckpt = torch.load(ckpt_path, map_location="cpu")
if use_ema:
ema_model = EMA(self.model, include_online_model=False)
ema_model.load_state_dict(ckpt["ema_model_state_dict"])
self.model = ema_model.ema_model
else:
self.model.load_state_dict(ckpt["model_state_dict"])
self.cnen_tokenizer = CNENTokenizer()
self.rear_silent_time = 1.0
@property
def device(self):
return next(self.parameters()).device
def prepare_input(
self,
ref_audio_path,
melody_audio_path,
ref_text,
target_text,
sil_len_to_end,
lrc_align_mode,
):
ref_audio, ref_audio_sr = torchaudio.load(ref_audio_path)
silence = torch.zeros(ref_audio.shape[0], int(ref_audio_sr * sil_len_to_end))
ref_wav = torch.cat([ref_audio, silence], dim=1)
ref_latent = self.vae.encode_audio(ref_wav, in_sr=ref_audio_sr).transpose(
1, 2
) # [B, T, D]
melody_audio, melody_sr = torchaudio.load(melody_audio_path)
silence = torch.zeros(melody_audio.shape[0], int(melody_sr * self.rear_silent_time))
melody_wav = torch.cat([melody_audio, silence], dim=1)
melody_latent = self.vae.encode_audio(melody_wav, in_sr=melody_sr).transpose(
1, 2
) # [B, T, D]
midi_in = torch.cat([ref_latent, melody_latent], dim=1)
if self.is_tts_pretrain:
midi_in = torch.zeros_like(midi_in)
ref_latent_len = ref_latent.shape[1]
total_len = int(ref_latent.shape[1] + melody_latent.shape[1])
if self._need_midi:
ref_mel = self.melody_spectrogram_extract(audio=ref_wav, sr=ref_audio_sr)
melody_mel = self.melody_spectrogram_extract(audio=melody_wav, sr=melody_sr)
melody_mel_spec = torch.cat([ref_mel, melody_mel], dim=2)
else:
raise NotImplementedError()
assert isinstance(ref_text, str) and isinstance(target_text, str)
text_list = [ref_text] + [target_text]
if lrc_align_mode == "put_to_front":
lrc_token, _ = align_lrc_put_to_front(
tokenizer=self.cnen_tokenizer,
lrc_start_times=None,
lrc_lines=text_list,
total_lens=total_len,
)
elif lrc_align_mode == "sentence_level":
lrc_token, _ = align_lrc_sentence_level(
tokenizer=self.cnen_tokenizer,
lrc_start_times=[0.0, ref_latent_len / self.vae_frame_rate],
lrc_lines=text_list,
total_lens=total_len,
vae_frame_rate=self.vae_frame_rate,
)
else:
raise ValueError(f"Unsupported lrc_align_mode: {lrc_align_mode}")
text_tokens = (
torch.tensor(lrc_token, dtype=torch.int64).unsqueeze(0).to(self.device)
)
midi_p, bound_p = None, None
if self._need_midi:
with torch.no_grad():
midi_p, bound_p = self.midi_teacher(melody_mel_spec.transpose(1, 2))
return (
ref_latent,
ref_latent_len,
text_tokens,
total_len,
midi_in,
midi_p,
bound_p,
)
def forward(
self,
ref_audio_path,
melody_audio_path,
ref_text,
target_text,
lrc_align_mode: str = "sentence_level",
sil_len_to_end: float = 0.5,
t_shift: float = 0.5,
nfe_step: int = 32,
cfg_strength: float = 3.0,
seed: int = 666,
is_tts_pretrain: bool = False,
):
"""
Args:
ref_audio_path: Path to the reference audio (for timbre)
melody_audio_path: Path to the melody reference audio (provides target duration and melody information)
ref_text: Text corresponding to the reference audio
target_text: Target text to be synthesized
lrc_align_mode: Lyric alignment mode "sentence_level" | "put_to_front"
sil_len_to_end: Duration of silence appended to the end of the reference audio (seconds)
t_shift: Sampling time offset
nfe_step: ODE sampling steps
cfg_strength: CFG strength
seed: Random seed
is_tts_pretrain: If True, melody is not provided (TTS mode)
"""
ref_latent, ref_latent_len, text_tokens, total_len, midi_in, midi_p, bound_p = (
self.prepare_input(
ref_audio_path=ref_audio_path,
melody_audio_path=melody_audio_path,
ref_text=ref_text,
target_text=target_text,
sil_len_to_end=sil_len_to_end,
lrc_align_mode=lrc_align_mode,
)
)
assert midi_p is not None and bound_p is not None
with torch.inference_mode():
generated_latent, _ = self.model.sample(
cond=ref_latent,
midi_in=midi_in,
text=text_tokens,
duration=total_len,
steps=nfe_step,
cfg_strength=cfg_strength,
sway_sampling_coef=None,
use_epss=False,
seed=seed,
midi_p=midi_p,
t_shift=t_shift,
bound_p=bound_p,
guidance_scale=cfg_strength,
)
generated_latent = generated_latent.to(torch.float32)
generated_latent = generated_latent[:, ref_latent_len: -int(self.vae_frame_rate*self.rear_silent_time), :]
generated_latent = generated_latent.permute(0, 2, 1) # [B, D, T]
generated_audio = self.vae.decode_audio(generated_latent)
audio = rearrange(generated_audio, "b d n -> d (b n)")
audio = audio.to(torch.float32).cpu()
audio = smooth_ending(audio, 44100)
return audio, 44100
if __name__ == "__main__":
# === Export to HuggingFace safetensors (optional) ===
# model = YingMusicSinger(
# model_cfg_path="src/YingMusicSinger/config/YingMusic_Singer.yaml",
# ckpt_path="ckpts/YingMusicSinger_model.pt",
# vae_config_path="src/YingMusicSinger/config/stable_audio_2_0_vae_20hz_official.json",
# vae_ckpt_path="ckpts/stable_audio_2_0_vae_20hz_official.ckpt",
# midi_teacher_ckpt_path="ckpts/model_ckpt_steps_100000_simplified.ckpt",
# )
# model.save_pretrained("path/to/save")
# === Inference Example ===
model = YingMusicSinger.from_pretrained("ASLP-lab/YingMusic-Singer")
model.to("cuda:0")
model.eval()
waveform, sample_rate = model(
ref_audio_path="path/to/ref_audio", # Timbre reference audio
melody_audio_path="path/to/melody_audio", # Melody-providing singing clip
ref_text="oh the reason i hold on", # Lyrics corresponding to ref_audio
target_text="oldest book broken watch|bare feet in grassy spot", # Modified target lyrics
seed=42,
)
torchaudio.save("output.wav", waveform, sample_rate=sample_rate)
print("Saved to output.wav")
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