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import numpy as np
import torch
import torch.nn.functional as F
from diffusion.unit2mel import load_model_vocoder
class DiffGtMel:
def __init__(self, project_path=None, device=None):
self.project_path = project_path
if device is not None:
self.device = device
else:
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
self.model = None
self.vocoder = None
self.args = None
def flush_model(self, project_path, ddsp_config=None):
if (self.model is None) or (project_path != self.project_path):
model, vocoder, args = load_model_vocoder(project_path, device=self.device)
if self.check_args(ddsp_config, args):
self.model = model
self.vocoder = vocoder
self.args = args
def check_args(self, args1, args2):
if args1.data.block_size != args2.data.block_size:
raise ValueError("DDSP与DIFF模型的block_size不一致")
if args1.data.sampling_rate != args2.data.sampling_rate:
raise ValueError("DDSP与DIFF模型的sampling_rate不一致")
if args1.data.encoder != args2.data.encoder:
raise ValueError("DDSP与DIFF模型的encoder不一致")
return True
def __call__(self, audio, f0, hubert, volume, acc=1, spk_id=1, k_step=0, method='pndm',
spk_mix_dict=None, start_frame=0):
input_mel = self.vocoder.extract(audio, self.args.data.sampling_rate)
out_mel = self.model(
hubert,
f0,
volume,
spk_id=spk_id,
spk_mix_dict=spk_mix_dict,
gt_spec=input_mel,
infer=True,
infer_speedup=acc,
method=method,
k_step=k_step,
use_tqdm=False)
if start_frame > 0:
out_mel = out_mel[:, start_frame:, :]
f0 = f0[:, start_frame:, :]
output = self.vocoder.infer(out_mel, f0)
if start_frame > 0:
output = F.pad(output, (start_frame * self.vocoder.vocoder_hop_size, 0))
return output
def infer(self, audio, f0, hubert, volume, acc=1, spk_id=1, k_step=0, method='pndm', silence_front=0,
use_silence=False, spk_mix_dict=None):
start_frame = int(silence_front * self.vocoder.vocoder_sample_rate / self.vocoder.vocoder_hop_size)
if use_silence:
audio = audio[:, start_frame * self.vocoder.vocoder_hop_size:]
f0 = f0[:, start_frame:, :]
hubert = hubert[:, start_frame:, :]
volume = volume[:, start_frame:, :]
_start_frame = 0
else:
_start_frame = start_frame
audio = self.__call__(audio, f0, hubert, volume, acc=acc, spk_id=spk_id, k_step=k_step,
method=method, spk_mix_dict=spk_mix_dict, start_frame=_start_frame)
if use_silence:
if start_frame > 0:
audio = F.pad(audio, (start_frame * self.vocoder.vocoder_hop_size, 0))
return audio
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