Hev832
/

hex-rvc

@@ -1,147 +0,0 @@
-from multiprocessing import cpu_count
-from pathlib import Path
-import torch
-from fairseq import checkpoint_utils
-from scipy.io import wavfile
-from infer_pack.models import (
-    SynthesizerTrnMs256NSFsid,
-    SynthesizerTrnMs256NSFsid_nono,
-    SynthesizerTrnMs768NSFsid,
-    SynthesizerTrnMs768NSFsid_nono,
-)
-from my_utils import load_audio
-from infer import VC
-BASE_DIR = Path(__file__).resolve().parent
-class Config:
-    def __init__(self, device, is_half):
-        self.device = device
-        self.is_half = is_half
-        self.n_cpu = 0
-        self.gpu_name = None
-        self.gpu_mem = None
-        self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
-    def device_config(self) -> tuple:
-        if torch.cuda.is_available():
-            i_device = int(self.device.split(":")[-1])
-            self.gpu_name = torch.cuda.get_device_name(i_device)
-            if (
-                    ("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
-                    or "P40" in self.gpu_name.upper()
-                    or "1060" in self.gpu_name
-                    or "1070" in self.gpu_name
-                    or "1080" in self.gpu_name
-            ):
-                print("16 series/10 series P40 forced single precision")
-                self.is_half = False
-            else:
-                self.gpu_name = None
-            self.gpu_mem = int(
-                torch.cuda.get_device_properties(i_device).total_memory
-                / 1024
-                / 1024
-                / 1024
-                + 0.4
-            )
-        elif torch.backends.mps.is_available():
-            print("No supported N-card found, use MPS for inference")
-            self.device = "mps"
-        else:
-            print("No supported N-card found, use CPU for inference")
-            self.device = "cpu"
-            self.is_half = True
-        if self.n_cpu == 0:
-            self.n_cpu = cpu_count()
-        if self.is_half:
-            # 6G memory config
-            x_pad = 3
-            x_query = 10
-            x_center = 60
-            x_max = 65
-        else:
-            # 5G memory config
-            x_pad = 1
-            x_query = 6
-            x_center = 38
-            x_max = 41
-        if self.gpu_mem is not None and self.gpu_mem <= 4:
-            x_pad = 1
-            x_query = 5
-            x_center = 30
-            x_max = 32
-        return x_pad, x_query, x_center, x_max
-def load_hubert(device, is_half, model_path):
-    models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task([model_path], suffix='')
-    hubert = models[0]
-    hubert = hubert.to(device)
-    if is_half:
-        hubert = hubert.half()
-    else:
-        hubert = hubert.float()
-    hubert.eval()
-    return hubert
-def get_vc(device, is_half, config, model_path):
-    cpt = torch.load(model_path, map_location='cpu')
-    if "config" not in cpt or "weight" not in cpt:
-        raise ValueError(f'Incorrect format for {model_path}. Use a voice model trained using RVC v2 instead.')
-    tgt_sr = cpt["config"][-1]
-    cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
-    if_f0 = cpt.get("f0", 1)
-    version = cpt.get("version", "v1")
-    if version == "v1":
-        if if_f0 == 1:
-            net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
-        else:
-            net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
-    elif version == "v2":
-        if if_f0 == 1:
-            net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=is_half)
-        else:
-            net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
-    del net_g.enc_q
-    print(net_g.load_state_dict(cpt["weight"], strict=False))
-    net_g.eval().to(device)
-    if is_half:
-        net_g = net_g.half()
-    else:
-        net_g = net_g.float()
-    vc = VC(tgt_sr, config)
-    return cpt, version, net_g, tgt_sr, vc
-def rvc_infer(index_path, index_rate, input_path, output_path, pitch_change, f0_method, cpt, version, net_g, filter_radius, tgt_sr, rms_mix_rate, protect, crepe_hop_length, vc, hubert_model):
-    # Load the input audio file
-    audio = load_audio(input_path, 16000)
-    # Initialize a list to keep track of times
-    times = [0, 0, 0]
-    # Determine if F0 (fundamental frequency) is used
-    if_f0 = cpt.get('f0', 1)
-    # Run the voice conversion pipeline
-    audio_opt = vc.pipeline(
-        hubert_model, net_g, 0, audio, input_path, times,
-        pitch_change, f0_method, index_path, index_rate,
-        if_f0, filter_radius, tgt_sr, 0, rms_mix_rate,
-        version, protect, crepe_hop_length
-    )
-    # Write the output audio to a file
-    wavfile.write(output_path, tgt_sr, audio_opt)