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