mirror of
https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI.git
synced 2026-06-05 09:10:25 +08:00
189 lines
6.8 KiB
Python
189 lines
6.8 KiB
Python
from io import BytesIO
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import os
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from typing import List, Optional, Tuple, Union
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import numpy as np
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import torch
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from infer.lib import jit
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try:
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# Fix "Torch not compiled with CUDA enabled"
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import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
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if torch.xpu.is_available():
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from infer.modules.ipex import ipex_init
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ipex_init()
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except Exception: # pylint: disable=broad-exception-caught
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pass
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import torch.nn as nn
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import torch.nn.functional as F
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import logging
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logger = logging.getLogger(__name__)
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from rvc.f0.mel import MelSpectrogram
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from rvc.f0.e2e import E2E
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class RMVPE:
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def __init__(self, model_path: str, is_half, device=None, use_jit=False):
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self.resample_kernel = {}
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self.resample_kernel = {}
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self.is_half = is_half
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if device is None:
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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self.device = device
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self.mel_extractor = MelSpectrogram(
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is_half=is_half,
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n_mel_channels=128,
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sampling_rate=16000,
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win_length=1024,
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hop_length=160,
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mel_fmin=30,
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mel_fmax=8000,
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device=device,
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).to(device)
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if "privateuseone" in str(device):
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import onnxruntime as ort
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ort_session = ort.InferenceSession(
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"%s/rmvpe.onnx" % os.environ["rmvpe_root"],
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providers=["DmlExecutionProvider"],
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)
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self.model = ort_session
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else:
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if str(self.device) == "cuda":
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self.device = torch.device("cuda:0")
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def get_jit_model():
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jit_model_path = model_path.rstrip(".pth")
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jit_model_path += ".half.jit" if is_half else ".jit"
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ckpt = None
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if os.path.exists(jit_model_path):
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ckpt = jit.load(jit_model_path)
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model_device = ckpt["device"]
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if model_device != str(self.device):
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del ckpt
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ckpt = None
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if ckpt is None:
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ckpt = jit.rmvpe_jit_export(
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model_path=model_path,
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mode="script",
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inputs_path=None,
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save_path=jit_model_path,
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device=device,
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is_half=is_half,
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)
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model = torch.jit.load(BytesIO(ckpt["model"]), map_location=device)
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return model
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def get_default_model():
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model = E2E(4, 1, (2, 2))
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ckpt = torch.load(model_path, map_location="cpu")
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model.load_state_dict(ckpt)
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model.eval()
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if is_half:
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model = model.half()
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else:
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model = model.float()
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return model
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if use_jit:
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if is_half and "cpu" in str(self.device):
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logger.warning(
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"Use default rmvpe model. \
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Jit is not supported on the CPU for half floating point"
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)
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self.model = get_default_model()
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else:
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self.model = get_jit_model()
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else:
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self.model = get_default_model()
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self.model = self.model.to(device)
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cents_mapping = 20 * np.arange(360) + 1997.3794084376191
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self.cents_mapping = np.pad(cents_mapping, (4, 4)) # 368
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def mel2hidden(self, mel):
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with torch.no_grad():
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n_frames = mel.shape[-1]
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n_pad = 32 * ((n_frames - 1) // 32 + 1) - n_frames
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if n_pad > 0:
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mel = F.pad(mel, (0, n_pad), mode="constant")
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if "privateuseone" in str(self.device):
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onnx_input_name = self.model.get_inputs()[0].name
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onnx_outputs_names = self.model.get_outputs()[0].name
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hidden = self.model.run(
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[onnx_outputs_names],
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input_feed={onnx_input_name: mel.cpu().numpy()},
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)[0]
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else:
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mel = mel.half() if self.is_half else mel.float()
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hidden = self.model(mel)
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return hidden[:, :n_frames]
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def decode(self, hidden, thred=0.03):
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cents_pred = self.to_local_average_cents(hidden, thred=thred)
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f0 = 10 * (2 ** (cents_pred / 1200))
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f0[f0 == 10] = 0
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# f0 = np.array([10 * (2 ** (cent_pred / 1200)) if cent_pred else 0 for cent_pred in cents_pred])
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return f0
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def infer_from_audio(self, audio, thred=0.03):
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# torch.cuda.synchronize()
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# t0 = ttime()
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if not torch.is_tensor(audio):
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audio = torch.from_numpy(audio)
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mel = self.mel_extractor(
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audio.float().to(self.device).unsqueeze(0), center=True
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)
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# print(123123123,mel.device.type)
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# torch.cuda.synchronize()
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# t1 = ttime()
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hidden = self.mel2hidden(mel)
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# torch.cuda.synchronize()
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# t2 = ttime()
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# print(234234,hidden.device.type)
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if "privateuseone" not in str(self.device):
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hidden = hidden.squeeze(0).cpu().numpy()
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else:
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hidden = hidden[0]
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if self.is_half == True:
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hidden = hidden.astype("float32")
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f0 = self.decode(hidden, thred=thred)
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# torch.cuda.synchronize()
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# t3 = ttime()
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# print("hmvpe:%s\t%s\t%s\t%s"%(t1-t0,t2-t1,t3-t2,t3-t0))
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return f0
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def to_local_average_cents(self, salience, thred=0.05):
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# t0 = ttime()
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center = np.argmax(salience, axis=1) # 帧长#index
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salience = np.pad(salience, ((0, 0), (4, 4))) # 帧长,368
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# t1 = ttime()
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center += 4
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todo_salience = []
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todo_cents_mapping = []
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starts = center - 4
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ends = center + 5
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for idx in range(salience.shape[0]):
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todo_salience.append(salience[:, starts[idx] : ends[idx]][idx])
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todo_cents_mapping.append(self.cents_mapping[starts[idx] : ends[idx]])
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# t2 = ttime()
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todo_salience = np.array(todo_salience) # 帧长，9
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todo_cents_mapping = np.array(todo_cents_mapping) # 帧长，9
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product_sum = np.sum(todo_salience * todo_cents_mapping, 1)
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weight_sum = np.sum(todo_salience, 1) # 帧长
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devided = product_sum / weight_sum # 帧长
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# t3 = ttime()
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maxx = np.max(salience, axis=1) # 帧长
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devided[maxx <= thred] = 0
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# t4 = ttime()
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# print("decode:%s\t%s\t%s\t%s" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
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return devided
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