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 vc_infer_pipeline import VC BASE_DIR = Path(__file__).resolve().parent.parent # config cpu def use_fp32_config(): for config_file in [ "32k.json", "40k.json", "48k.json", "48k_v2.json", "32k_v2.json", ]: with open(f"src/configs/{config_file}", "r") as f: strr = f.read().replace("true", "false") with open(f"src/configs/{config_file}", "w") as f: f.write(strr) class Config: def __init__(self, device, is_half): self.device = device self.is_half = is_half self.n_cpu = 2 # set cpu cores 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 for config_file in ["32k.json", "40k.json", "48k.json"]: with open(BASE_DIR / "src" / "configs" / config_file, "r") as f: strr = f.read().replace("true", "false") with open(BASE_DIR / "src" / "configs" / config_file, "w") as f: f.write(strr) with open(BASE_DIR / "src" / "trainset_preprocess_pipeline_print.py", "r") as f: strr = f.read().replace("3.7", "3.0") with open(BASE_DIR / "src" / "trainset_preprocess_pipeline_print.py", "w") as f: f.write(strr) else: self.gpu_name = None self.gpu_mem = int( torch.cuda.get_device_properties(i_device).total_memory / 1024 / 1024 / 1024 + 0.4 ) if self.gpu_mem <= 4: with open(BASE_DIR / "src" / "trainset_preprocess_pipeline_print.py", "r") as f: strr = f.read().replace("3.7", "3.0") with open(BASE_DIR / "src" / "trainset_preprocess_pipeline_print.py", "w") as f: f.write(strr) 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 = False use_fp32_config() # cpu config 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 != 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] print(f"SAMPLE RATE MODEL {model_path} IS: {tgt_sr}") 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): audio = load_audio(input_path, 16000) times = [0, 0, 0] if_f0 = cpt.get('f0', 1) 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) wavfile.write(output_path, tgt_sr, audio_opt)