File size: 13,860 Bytes
7344bef | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 | import os
import sys
from dotenv import load_dotenv
os.environ['HF_HUB_CACHE'] = './checkpoints/hf_cache'
import shutil
import multiprocessing
import warnings
import yaml
warnings.simplefilter('ignore')
from tqdm import tqdm
from .modules.commons import *
import librosa
import torchaudio
import torchaudio.compliance.kaldi as kaldi
from .hf_utils import load_custom_model_from_hf
import os
import sys
import torch
from .modules.commons import str2bool
# Load model and configuration
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
device = torch.device("cuda")
elif torch.backends.mps.is_available():
device = torch.device("mps")
else:
device = torch.device("cpu")
flag_vc = False
prompt_condition, mel2, style2 = None, None, None
reference_wav_name = ""
prompt_len = 3 # in seconds
ce_dit_difference = 2.0 # 2 seconds
fp16 = False
@torch.no_grad()
def custom_infer(model_set,
reference_wav,
new_reference_wav_name,
input_wav_res,
block_frame_16k,
skip_head,
skip_tail,
return_length,
diffusion_steps,
inference_cfg_rate,
max_prompt_length,
cd_difference=2.0,
):
global prompt_condition, mel2, style2
global reference_wav_name
global prompt_len
global ce_dit_difference
(
model,
semantic_fn,
f0_fn,
vocoder_fn,
campplus_model,
to_mel,
mel_fn_args,
) = model_set
sr = mel_fn_args["sampling_rate"]
hop_length = mel_fn_args["hop_size"]
if ce_dit_difference != cd_difference:
ce_dit_difference = cd_difference
print(f"Setting ce_dit_difference to {cd_difference} seconds.")
if prompt_condition is None or reference_wav_name != new_reference_wav_name or prompt_len != max_prompt_length:
prompt_len = max_prompt_length
print(f"Setting max prompt length to {max_prompt_length} seconds.")
reference_wav = reference_wav[:int(sr * prompt_len)]
reference_wav_tensor = torch.from_numpy(reference_wav).to(device)
ori_waves_16k = torchaudio.functional.resample(reference_wav_tensor, sr, 16000)
S_ori = semantic_fn(ori_waves_16k.unsqueeze(0))
feat2 = torchaudio.compliance.kaldi.fbank(
ori_waves_16k.unsqueeze(0), num_mel_bins=80, dither=0, sample_frequency=16000
)
feat2 = feat2 - feat2.mean(dim=0, keepdim=True)
style2 = campplus_model(feat2.unsqueeze(0))
mel2 = to_mel(reference_wav_tensor.unsqueeze(0))
target2_lengths = torch.LongTensor([mel2.size(2)]).to(mel2.device)
prompt_condition = model.length_regulator(
S_ori, ylens=target2_lengths, n_quantizers=3, f0=None
)[0]
reference_wav_name = new_reference_wav_name
converted_waves_16k = input_wav_res
if device.type == "mps":
start_event = torch.mps.event.Event(enable_timing=True)
end_event = torch.mps.event.Event(enable_timing=True)
torch.mps.synchronize()
else:
start_event = torch.cuda.Event(enable_timing=True)
end_event = torch.cuda.Event(enable_timing=True)
torch.cuda.synchronize()
start_event.record()
S_alt = semantic_fn(converted_waves_16k.unsqueeze(0))
end_event.record()
if device.type == "mps":
torch.mps.synchronize() # MPS - Wait for the events to be recorded!
else:
torch.cuda.synchronize() # Wait for the events to be recorded!
elapsed_time_ms = start_event.elapsed_time(end_event)
print(f"Time taken for semantic_fn: {elapsed_time_ms}ms")
ce_dit_frame_difference = int(ce_dit_difference * 50)
S_alt = S_alt[:, ce_dit_frame_difference:]
target_lengths = torch.LongTensor([(skip_head + return_length + skip_tail - ce_dit_frame_difference) / 50 * sr // hop_length]).to(S_alt.device)
print(f"target_lengths: {target_lengths}")
cond = model.length_regulator(
S_alt, ylens=target_lengths , n_quantizers=3, f0=None
)[0]
cat_condition = torch.cat([prompt_condition, cond], dim=1)
with torch.autocast(device_type=device.type, dtype=torch.float16 if fp16 else torch.float32):
vc_target = model.cfm.inference(
cat_condition,
torch.LongTensor([cat_condition.size(1)]).to(mel2.device),
mel2,
style2,
None,
n_timesteps=diffusion_steps,
inference_cfg_rate=inference_cfg_rate,
)
vc_target = vc_target[:, :, mel2.size(-1) :]
print(f"vc_target.shape: {vc_target.shape}")
vc_wave = vocoder_fn(vc_target).squeeze()
output_len = return_length * sr // 50
tail_len = skip_tail * sr // 50
output = vc_wave[-output_len - tail_len: -tail_len]
return output
def load_models(args):
global fp16
fp16 = args.fp16
print(f"Using fp16: {fp16}")
f0_fn = None
if args.checkpoint is None or args.checkpoint == "":
dit_checkpoint_path, dit_config_path = load_custom_model_from_hf("Plachta/Seed-VC",
"DiT_uvit_tat_xlsr_ema.pth",
"config_dit_mel_seed_uvit_xlsr_tiny.yml")
else:
dit_checkpoint_path = args.checkpoint
dit_config_path = args.config_path
config = yaml.safe_load(open(dit_config_path, "r"))
model_params = recursive_munch(config["model_params"])
model_params.dit_type = 'DiT'
model = build_model(model_params, stage="DiT")
hop_length = config["preprocess_params"]["spect_params"]["hop_length"]
sr = config["preprocess_params"]["sr"]
# Load checkpoints
model, _, _, _ = load_checkpoint(
model,
None,
dit_checkpoint_path,
load_only_params=True,
ignore_modules=[],
is_distributed=False,
)
for key in model:
model[key].eval()
model[key].to(device)
model.cfm.estimator.setup_caches(max_batch_size=1, max_seq_length=8192)
# Load additional modules
from .modules.campplus.DTDNN import CAMPPlus
campplus_ckpt_path = load_custom_model_from_hf(
"funasr/campplus", "campplus_cn_common.bin", config_filename=None
)
campplus_model = CAMPPlus(feat_dim=80, embedding_size=192)
campplus_model.load_state_dict(torch.load(campplus_ckpt_path, map_location="cpu"))
campplus_model.eval()
campplus_model.to(device)
vocoder_type = model_params.vocoder.type
if vocoder_type == 'bigvgan':
from .modules.bigvgan import bigvgan
bigvgan_name = model_params.vocoder.name
bigvgan_model = bigvgan.BigVGAN.from_pretrained(bigvgan_name, use_cuda_kernel=False)
# remove weight norm in the model and set to eval mode
bigvgan_model.remove_weight_norm()
bigvgan_model = bigvgan_model.eval().to(device)
vocoder_fn = bigvgan_model
elif vocoder_type == 'hifigan':
from .modules.hifigan.generator import HiFTGenerator
from .modules.hifigan.f0_predictor import ConvRNNF0Predictor
from ._paths import resolve_path
hift_config = yaml.safe_load(open(resolve_path('configs/hifigan.yml'), 'r'))
hift_gen = HiFTGenerator(**hift_config['hift'], f0_predictor=ConvRNNF0Predictor(**hift_config['f0_predictor']))
hift_path = load_custom_model_from_hf("FunAudioLLM/CosyVoice-300M", 'hift.pt', None)
hift_gen.load_state_dict(torch.load(hift_path, map_location='cpu'))
hift_gen.eval()
hift_gen.to(device)
vocoder_fn = hift_gen
elif vocoder_type == "vocos":
vocos_config = yaml.safe_load(open(model_params.vocoder.vocos.config, 'r'))
vocos_path = model_params.vocoder.vocos.path
vocos_model_params = recursive_munch(vocos_config['model_params'])
vocos = build_model(vocos_model_params, stage='mel_vocos')
vocos_checkpoint_path = vocos_path
vocos, _, _, _ = load_checkpoint(vocos, None, vocos_checkpoint_path,
load_only_params=True, ignore_modules=[], is_distributed=False)
_ = [vocos[key].eval().to(device) for key in vocos]
_ = [vocos[key].to(device) for key in vocos]
total_params = sum(sum(p.numel() for p in vocos[key].parameters() if p.requires_grad) for key in vocos.keys())
print(f"Vocoder model total parameters: {total_params / 1_000_000:.2f}M")
vocoder_fn = vocos.decoder
else:
raise ValueError(f"Unknown vocoder type: {vocoder_type}")
speech_tokenizer_type = model_params.speech_tokenizer.type
if speech_tokenizer_type == 'whisper':
# whisper
from transformers import AutoFeatureExtractor, WhisperModel
whisper_name = model_params.speech_tokenizer.name
whisper_model = WhisperModel.from_pretrained(whisper_name, torch_dtype=torch.float16).to(device)
del whisper_model.decoder
whisper_feature_extractor = AutoFeatureExtractor.from_pretrained(whisper_name)
def semantic_fn(waves_16k):
ori_inputs = whisper_feature_extractor([waves_16k.squeeze(0).cpu().numpy()],
return_tensors="pt",
return_attention_mask=True)
ori_input_features = whisper_model._mask_input_features(
ori_inputs.input_features, attention_mask=ori_inputs.attention_mask).to(device)
with torch.no_grad():
ori_outputs = whisper_model.encoder(
ori_input_features.to(whisper_model.encoder.dtype),
head_mask=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
)
S_ori = ori_outputs.last_hidden_state.to(torch.float32)
S_ori = S_ori[:, :waves_16k.size(-1) // 320 + 1]
return S_ori
elif speech_tokenizer_type == 'cnhubert':
from transformers import (
Wav2Vec2FeatureExtractor,
HubertModel,
)
hubert_model_name = config['model_params']['speech_tokenizer']['name']
hubert_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(hubert_model_name)
hubert_model = HubertModel.from_pretrained(hubert_model_name)
hubert_model = hubert_model.to(device)
hubert_model = hubert_model.eval()
hubert_model = hubert_model.half()
def semantic_fn(waves_16k):
ori_waves_16k_input_list = [
waves_16k[bib].cpu().numpy()
for bib in range(len(waves_16k))
]
ori_inputs = hubert_feature_extractor(ori_waves_16k_input_list,
return_tensors="pt",
return_attention_mask=True,
padding=True,
sampling_rate=16000).to(device)
with torch.no_grad():
ori_outputs = hubert_model(
ori_inputs.input_values.half(),
)
S_ori = ori_outputs.last_hidden_state.float()
return S_ori
elif speech_tokenizer_type == 'xlsr':
from transformers import (
Wav2Vec2FeatureExtractor,
Wav2Vec2Model,
)
model_name = config['model_params']['speech_tokenizer']['name']
output_layer = config['model_params']['speech_tokenizer']['output_layer']
wav2vec_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name)
wav2vec_model = Wav2Vec2Model.from_pretrained(model_name)
wav2vec_model.encoder.layers = wav2vec_model.encoder.layers[:output_layer]
wav2vec_model = wav2vec_model.to(device)
wav2vec_model = wav2vec_model.eval()
wav2vec_model = wav2vec_model.half()
def semantic_fn(waves_16k):
ori_waves_16k_input_list = [
waves_16k[bib].cpu().numpy()
for bib in range(len(waves_16k))
]
ori_inputs = wav2vec_feature_extractor(ori_waves_16k_input_list,
return_tensors="pt",
return_attention_mask=True,
padding=True,
sampling_rate=16000).to(device)
with torch.no_grad():
ori_outputs = wav2vec_model(
ori_inputs.input_values.half(),
)
S_ori = ori_outputs.last_hidden_state.float()
return S_ori
else:
raise ValueError(f"Unknown speech tokenizer type: {speech_tokenizer_type}")
# Generate mel spectrograms
mel_fn_args = {
"n_fft": config['preprocess_params']['spect_params']['n_fft'],
"win_size": config['preprocess_params']['spect_params']['win_length'],
"hop_size": config['preprocess_params']['spect_params']['hop_length'],
"num_mels": config['preprocess_params']['spect_params']['n_mels'],
"sampling_rate": sr,
"fmin": config['preprocess_params']['spect_params'].get('fmin', 0),
"fmax": None if config['preprocess_params']['spect_params'].get('fmax', "None") == "None" else 8000,
"center": False
}
from .modules.audio import mel_spectrogram
to_mel = lambda x: mel_spectrogram(x, **mel_fn_args)
return (
model,
semantic_fn,
f0_fn,
vocoder_fn,
campplus_model,
to_mel,
mel_fn_args,
)
|