import os import sys import torch import numpy as np import soundfile as sf from vc_infer_pipeline import VC from rvc.lib.utils import load_audio from rvc.lib.tools.split_audio import process_audio, merge_audio from fairseq import checkpoint_utils from rvc.lib.infer_pack.models import ( SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono, SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono, ) from rvc.configs.config import Config config = Config() torch.manual_seed(114514) hubert_model = None def load_hubert(): global hubert_model models, _, _ = checkpoint_utils.load_model_ensemble_and_task( ["hubert_base.pt"], suffix="", ) hubert_model = models[0] hubert_model = hubert_model.to(config.device) if config.is_half: hubert_model = hubert_model.half() else: hubert_model = hubert_model.float() hubert_model.eval() def vc_single( sid=0, input_audio_path=None, f0_up_key=None, f0_file=None, f0_method=None, file_index=None, index_rate=None, resample_sr=0, rms_mix_rate=1, protect=0.33, hop_length=None, output_path=None, split_audio=False, ): global tgt_sr, net_g, vc, hubert_model, version if input_audio_path is None: return "Please, load an audio!", None f0_up_key = int(f0_up_key) try: audio = load_audio(input_audio_path, 16000) audio_max = np.abs(audio).max() / 0.95 if audio_max > 1: audio /= audio_max if not hubert_model: load_hubert() if_f0 = cpt.get("f0", 1) file_index = ( file_index.strip(" ") .strip('"') .strip("\n") .strip('"') .strip(" ") .replace("trained", "added") ) if tgt_sr != resample_sr >= 16000: tgt_sr = resample_sr if split_audio == "True": result, new_dir_path = process_audio(input_audio_path) if result == "Error": return "Error with Split Audio", None dir_path = new_dir_path.strip(" ").strip('"').strip("\n").strip('"').strip(" ") if dir_path != "": paths = [ os.path.join(root, name) for root, _, files in os.walk(dir_path, topdown=False) for name in files if name.endswith(".wav") and root == dir_path ] try: for path in paths: info, opt = vc_single( sid, path, f0_up_key, None, f0_method, file_index, index_rate, resample_sr, rms_mix_rate, protect, hop_length, path, False, ) #new_dir_path except Exception as error: print(error) return "Error", None print("Finished processing segmented audio, now merging audio...") merge_timestamps_file = os.path.join(os.path.dirname(new_dir_path), f"{os.path.basename(input_audio_path).split('.')[0]}_timestamps.txt") tgt_sr, audio_opt = merge_audio(merge_timestamps_file) else: audio_opt = vc.pipeline( hubert_model, net_g, sid, audio, input_audio_path, f0_up_key, f0_method, file_index, index_rate, if_f0, filter_radius, tgt_sr, resample_sr, rms_mix_rate, version, protect, hop_length, f0_file=f0_file, ) if output_path is not None: sf.write(output_path, audio_opt, tgt_sr, format="WAV") return (tgt_sr, audio_opt) except Exception as error: print(error) def get_vc(weight_root, sid): global n_spk, tgt_sr, net_g, vc, cpt, version if sid == "" or sid == []: global hubert_model if hubert_model is not None: print("clean_empty_cache") del net_g, n_spk, vc, hubert_model, tgt_sr # ,cpt hubert_model = net_g = n_spk = vc = hubert_model = tgt_sr = None if torch.cuda.is_available(): torch.cuda.empty_cache() 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=config.is_half ) else: net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) elif version == "v2": if if_f0 == 1: net_g = SynthesizerTrnMs768NSFsid( *cpt["config"], is_half=config.is_half ) else: net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) del net_g, cpt if torch.cuda.is_available(): torch.cuda.empty_cache() cpt = None person = weight_root cpt = torch.load(person, map_location="cpu") 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=config.is_half) else: net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) elif version == "v2": if if_f0 == 1: net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.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(config.device) if config.is_half: net_g = net_g.half() else: net_g = net_g.float() vc = VC(tgt_sr, config) n_spk = cpt["config"][-3] f0up_key = sys.argv[1] filter_radius = sys.argv[2] index_rate = float(sys.argv[3]) hop_length = sys.argv[4] f0method = sys.argv[5] audio_input_path = sys.argv[6] audio_output_path = sys.argv[7] model_path = sys.argv[8] index_path = sys.argv[9] split_audio = sys.argv[10] sid = f0up_key input_audio = audio_input_path f0_pitch = f0up_key f0_file = None f0_method = f0method file_index = index_path index_rate = index_rate output_file = audio_output_path split_audio = split_audio get_vc(model_path, 0) try: result, audio_opt = vc_single( sid=0, input_audio_path=input_audio, f0_up_key=f0_pitch, f0_file=None, f0_method=f0_method, file_index=file_index, index_rate=index_rate, hop_length=hop_length, output_path=output_file, split_audio=split_audio ) if os.path.exists(output_file) and os.path.getsize(output_file) > 0: message = result else: message = result print(f"Conversion completed. Output file: '{output_file}'") except Exception as error: print(f"Voice conversion failed: {error}")