import re import os import sys import time import faiss import torch import shutil import librosa import logging import argparse import warnings import onnxruntime import logging.handlers import numpy as np import soundfile as sf import torch.nn.functional as F from tqdm import tqdm from scipy import signal from distutils.util import strtobool from fairseq import checkpoint_utils warnings.filterwarnings("ignore") sys.path.append(os.getcwd()) from main.configs.config import Config from main.library.algorithm.synthesizers import Synthesizer from main.library.utils import check_predictors, check_embedders, load_audio, process_audio, merge_audio bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000) config = Config() translations = config.translations logger = logging.getLogger(__name__) logger.propagate = False for l in ["torch", "faiss", "httpx", "fairseq", "httpcore", "faiss.loader", "numba.core", "urllib3"]: logging.getLogger(l).setLevel(logging.ERROR) if logger.hasHandlers(): logger.handlers.clear() else: console_handler = logging.StreamHandler() console_formatter = logging.Formatter(fmt="\n%(asctime)s.%(msecs)03d | %(levelname)s | %(module)s | %(message)s", datefmt="%Y-%m-%d %H:%M:%S") console_handler.setFormatter(console_formatter) console_handler.setLevel(logging.INFO) file_handler = logging.handlers.RotatingFileHandler(os.path.join("assets", "logs", "convert.log"), maxBytes=5*1024*1024, backupCount=3, encoding='utf-8') file_formatter = logging.Formatter(fmt="\n%(asctime)s.%(msecs)03d | %(levelname)s | %(module)s | %(message)s", datefmt="%Y-%m-%d %H:%M:%S") file_handler.setFormatter(file_formatter) file_handler.setLevel(logging.DEBUG) logger.addHandler(console_handler) logger.addHandler(file_handler) logger.setLevel(logging.DEBUG) def parse_arguments(): parser = argparse.ArgumentParser() parser.add_argument("--pitch", type=int, default=0) parser.add_argument("--filter_radius", type=int, default=3) parser.add_argument("--index_rate", type=float, default=0.5) parser.add_argument("--volume_envelope", type=float, default=1) parser.add_argument("--protect", type=float, default=0.33) parser.add_argument("--hop_length", type=int, default=64) parser.add_argument("--f0_method", type=str, default="rmvpe") parser.add_argument("--embedder_model", type=str, default="contentvec_base.pt") parser.add_argument("--input_path", type=str, required=True) parser.add_argument("--output_path", type=str, default="./audios/output.wav") parser.add_argument("--export_format", type=str, default="wav") parser.add_argument("--pth_path", type=str, required=True) parser.add_argument("--index_path", type=str) parser.add_argument("--f0_autotune", type=lambda x: bool(strtobool(x)), default=False) parser.add_argument("--f0_autotune_strength", type=float, default=1) parser.add_argument("--clean_audio", type=lambda x: bool(strtobool(x)), default=False) parser.add_argument("--clean_strength", type=float, default=0.7) parser.add_argument("--resample_sr", type=int, default=0) parser.add_argument("--split_audio", type=lambda x: bool(strtobool(x)), default=False) parser.add_argument("--checkpointing", type=lambda x: bool(strtobool(x)), default=False) parser.add_argument("--f0_file", type=str, default=None) parser.add_argument("--f0_onnx", type=lambda x: bool(strtobool(x)), default=False) parser.add_argument("--embedders_onnx", type=lambda x: bool(strtobool(x)), default=False) parser.add_argument("--formant_shifting", type=lambda x: bool(strtobool(x)), default=False) parser.add_argument("--formant_qfrency", type=float, default=0.8) parser.add_argument("--formant_timbre", type=float, default=0.8) return parser.parse_args() def main(): args = parse_arguments() pitch, filter_radius, index_rate, volume_envelope, protect, hop_length, f0_method, input_path, output_path, pth_path, index_path, f0_autotune, f0_autotune_strength, clean_audio, clean_strength, export_format, embedder_model, resample_sr, split_audio, checkpointing, f0_file, f0_onnx, embedders_onnx, formant_shifting, formant_qfrency, formant_timbre = args.pitch, args.filter_radius, args.index_rate, args.volume_envelope,args.protect, args.hop_length, args.f0_method, args.input_path, args.output_path, args.pth_path, args.index_path, args.f0_autotune, args.f0_autotune_strength, args.clean_audio, args.clean_strength, args.export_format, args.embedder_model, args.resample_sr, args.split_audio, args.checkpointing, args.f0_file, args.f0_onnx, args.embedders_onnx, args.formant_shifting, args.formant_qfrency, args.formant_timbre log_data = {translations['pitch']: pitch, translations['filter_radius']: filter_radius, translations['index_strength']: index_rate, translations['volume_envelope']: volume_envelope, translations['protect']: protect, "Hop length": hop_length, translations['f0_method']: f0_method, translations['audio_path']: input_path, translations['output_path']: output_path.replace('wav', export_format), translations['model_path']: pth_path, translations['indexpath']: index_path, translations['autotune']: f0_autotune, translations['clear_audio']: clean_audio, translations['export_format']: export_format, translations['hubert_model']: embedder_model, translations['split_audio']: split_audio, translations['memory_efficient_training']: checkpointing, translations["f0_onnx_mode"]: f0_onnx, translations["embed_onnx"]: embedders_onnx} if clean_audio: log_data[translations['clean_strength']] = clean_strength if resample_sr != 0: log_data[translations['sample_rate']] = resample_sr if f0_autotune: log_data[translations['autotune_rate_info']] = f0_autotune_strength if os.path.isfile(f0_file): log_data[translations['f0_file']] = f0_file if formant_shifting: log_data[translations['formant_qfrency']] = formant_qfrency log_data[translations['formant_timbre']] = formant_timbre for key, value in log_data.items(): logger.debug(f"{key}: {value}") run_convert_script(pitch=pitch, filter_radius=filter_radius, index_rate=index_rate, volume_envelope=volume_envelope, protect=protect, hop_length=hop_length, f0_method=f0_method, input_path=input_path, output_path=output_path, pth_path=pth_path, index_path=index_path, f0_autotune=f0_autotune, f0_autotune_strength=f0_autotune_strength, clean_audio=clean_audio, clean_strength=clean_strength, export_format=export_format, embedder_model=embedder_model, resample_sr=resample_sr, split_audio=split_audio, checkpointing=checkpointing, f0_file=f0_file, f0_onnx=f0_onnx, embedders_onnx=embedders_onnx, formant_shifting=formant_shifting, formant_qfrency=formant_qfrency, formant_timbre=formant_timbre) def run_batch_convert(params): path, audio_temp, export_format, cut_files, pitch, filter_radius, index_rate, volume_envelope, protect, hop_length, f0_method, pth_path, index_path, f0_autotune, f0_autotune_strength, clean_audio, clean_strength, embedder_model, resample_sr, checkpointing, f0_file, f0_onnx, formant_shifting, formant_qfrency, formant_timbre = params["path"], params["audio_temp"], params["export_format"], params["cut_files"], params["pitch"], params["filter_radius"], params["index_rate"], params["volume_envelope"], params["protect"], params["hop_length"], params["f0_method"], params["pth_path"], params["index_path"], params["f0_autotune"], params["f0_autotune_strength"], params["clean_audio"], params["clean_strength"], params["embedder_model"], params["resample_sr"], params["checkpointing"], params["f0_file"], params["f0_onnx"], params["formant_shifting"], params["formant_qfrency"], params["formant_timbre"] segment_output_path = os.path.join(audio_temp, f"output_{cut_files.index(path)}.{export_format}") if os.path.exists(segment_output_path): os.remove(segment_output_path) VoiceConverter().convert_audio(pitch=pitch, filter_radius=filter_radius, index_rate=index_rate, volume_envelope=volume_envelope, protect=protect, hop_length=hop_length, f0_method=f0_method, audio_input_path=path, audio_output_path=segment_output_path, model_path=pth_path, index_path=index_path, f0_autotune=f0_autotune, f0_autotune_strength=f0_autotune_strength, clean_audio=clean_audio, clean_strength=clean_strength, export_format=export_format, embedder_model=embedder_model, resample_sr=resample_sr, checkpointing=checkpointing, f0_file=f0_file, f0_onnx=f0_onnx, formant_shifting=formant_shifting, formant_qfrency=formant_qfrency, formant_timbre=formant_timbre) os.remove(path) if os.path.exists(segment_output_path): return segment_output_path else: logger.warning(f"{translations['not_found_convert_file']}: {segment_output_path}") sys.exit(1) def run_convert_script(pitch=0, filter_radius=3, index_rate=0.5, volume_envelope=1, protect=0.5, hop_length=64, f0_method="rmvpe", input_path=None, output_path="./output.wav", pth_path=None, index_path=None, f0_autotune=False, f0_autotune_strength=1, clean_audio=False, clean_strength=0.7, export_format="wav", embedder_model="contentvec_base.pt", resample_sr=0, split_audio=False, checkpointing=False, f0_file=None, f0_onnx=False, embedders_onnx=False, formant_shifting=False, formant_qfrency=0.8, formant_timbre=0.8): check_predictors(f0_method, f0_onnx); check_embedders(embedder_model, embedders_onnx) embedder_model += ".onnx" if embedders_onnx else ".pt" cvt = VoiceConverter() start_time = time.time() pid_path = os.path.join("assets", "convert_pid.txt") with open(pid_path, "w") as pid_file: pid_file.write(str(os.getpid())) if not pth_path or not os.path.exists(pth_path) or os.path.isdir(pth_path) or not pth_path.endswith((".pth", ".onnx")): logger.warning(translations["provide_file"].format(filename=translations["model"])) sys.exit(1) processed_segments = [] audio_temp = os.path.join("audios_temp") if not os.path.exists(audio_temp) and split_audio: os.makedirs(audio_temp, exist_ok=True) if os.path.isdir(input_path): try: logger.info(translations["convert_batch"]) audio_files = [f for f in os.listdir(input_path) if f.lower().endswith(("wav", "mp3", "flac", "ogg", "opus", "m4a", "mp4", "aac", "alac", "wma", "aiff", "webm", "ac3"))] if not audio_files: logger.warning(translations["not_found_audio"]) sys.exit(1) logger.info(translations["found_audio"].format(audio_files=len(audio_files))) for audio in audio_files: audio_path = os.path.join(input_path, audio) output_audio = os.path.join(input_path, os.path.splitext(audio)[0] + f"_output.{export_format}") if split_audio: try: cut_files, time_stamps = process_audio(logger, audio_path, audio_temp) params_list = [{"path": path, "audio_temp": audio_temp, "export_format": export_format, "cut_files": cut_files, "pitch": pitch, "filter_radius": filter_radius, "index_rate": index_rate, "volume_envelope": volume_envelope, "protect": protect, "hop_length": hop_length, "f0_method": f0_method, "pth_path": pth_path, "index_path": index_path, "f0_autotune": f0_autotune, "f0_autotune_strength": f0_autotune_strength, "clean_audio": clean_audio, "clean_strength": clean_strength, "embedder_model": embedder_model, "resample_sr": resample_sr, "checkpointing": checkpointing, "f0_file": f0_file, "f0_onnx": f0_onnx, "formant_shifting": formant_shifting, "formant_qfrency": formant_qfrency, "formant_timbre": formant_timbre} for path in cut_files] with tqdm(total=len(params_list), desc=translations["convert_audio"], ncols=100, unit="a") as pbar: for params in params_list: processed_segments.append(run_batch_convert(params)) pbar.update(1) logger.debug(pbar.format_meter(pbar.n, pbar.total, pbar.format_dict["elapsed"])) merge_audio(processed_segments, time_stamps, audio_path, output_audio, export_format) except Exception as e: logger.error(translations["error_convert_batch"].format(e=e)) finally: if os.path.exists(audio_temp): shutil.rmtree(audio_temp, ignore_errors=True) else: try: logger.info(f"{translations['convert_audio']} '{audio_path}'...") if os.path.exists(output_audio): os.remove(output_audio) with tqdm(total=1, desc=translations["convert_audio"], ncols=100, unit="a") as pbar: cvt.convert_audio(pitch=pitch, filter_radius=filter_radius, index_rate=index_rate, volume_envelope=volume_envelope, protect=protect, hop_length=hop_length, f0_method=f0_method, audio_input_path=audio_path, audio_output_path=output_audio, model_path=pth_path, index_path=index_path, f0_autotune=f0_autotune, f0_autotune_strength=f0_autotune_strength, clean_audio=clean_audio, clean_strength=clean_strength, export_format=export_format, embedder_model=embedder_model, resample_sr=resample_sr, checkpointing=checkpointing, f0_file=f0_file, f0_onnx=f0_onnx, formant_shifting=formant_shifting, formant_qfrency=formant_qfrency, formant_timbre=formant_timbre) pbar.update(1) logger.debug(pbar.format_meter(pbar.n, pbar.total, pbar.format_dict["elapsed"])) except Exception as e: logger.error(translations["error_convert"].format(e=e)) logger.info(translations["convert_batch_success"].format(elapsed_time=f"{(time.time() - start_time):.2f}", output_path=output_path.replace('wav', export_format))) except Exception as e: logger.error(translations["error_convert_batch_2"].format(e=e)) else: logger.info(f"{translations['convert_audio']} '{input_path}'...") if not os.path.exists(input_path): logger.warning(translations["not_found_audio"]) sys.exit(1) if os.path.exists(output_path): os.remove(output_path) if split_audio: try: cut_files, time_stamps = process_audio(logger, input_path, audio_temp) params_list = [{"path": path, "audio_temp": audio_temp, "export_format": export_format, "cut_files": cut_files, "pitch": pitch, "filter_radius": filter_radius, "index_rate": index_rate, "volume_envelope": volume_envelope, "protect": protect, "hop_length": hop_length, "f0_method": f0_method, "pth_path": pth_path, "index_path": index_path, "f0_autotune": f0_autotune, "f0_autotune_strength": f0_autotune_strength, "clean_audio": clean_audio, "clean_strength": clean_strength, "embedder_model": embedder_model, "resample_sr": resample_sr, "checkpointing": checkpointing, "f0_file": f0_file, "f0_onnx": f0_onnx, "formant_shifting": formant_shifting, "formant_qfrency": formant_qfrency, "formant_timbre": formant_timbre} for path in cut_files] with tqdm(total=len(params_list), desc=translations["convert_audio"], ncols=100, unit="a") as pbar: for params in params_list: processed_segments.append(run_batch_convert(params)) pbar.update(1) logger.debug(pbar.format_meter(pbar.n, pbar.total, pbar.format_dict["elapsed"])) merge_audio(processed_segments, time_stamps, input_path, output_path.replace("wav", export_format), export_format) except Exception as e: logger.error(translations["error_convert_batch"].format(e=e)) finally: if os.path.exists(audio_temp): shutil.rmtree(audio_temp, ignore_errors=True) else: try: with tqdm(total=1, desc=translations["convert_audio"], ncols=100, unit="a") as pbar: cvt.convert_audio(pitch=pitch, filter_radius=filter_radius, index_rate=index_rate, volume_envelope=volume_envelope, protect=protect, hop_length=hop_length, f0_method=f0_method, audio_input_path=input_path, audio_output_path=output_path, model_path=pth_path, index_path=index_path, f0_autotune=f0_autotune, f0_autotune_strength=f0_autotune_strength, clean_audio=clean_audio, clean_strength=clean_strength, export_format=export_format, embedder_model=embedder_model, resample_sr=resample_sr, checkpointing=checkpointing, f0_file=f0_file, f0_onnx=f0_onnx, formant_shifting=formant_shifting, formant_qfrency=formant_qfrency, formant_timbre=formant_timbre) pbar.update(1) logger.debug(pbar.format_meter(pbar.n, pbar.total, pbar.format_dict["elapsed"])) except Exception as e: logger.error(translations["error_convert"].format(e=e)) if os.path.exists(pid_path): os.remove(pid_path) logger.info(translations["convert_audio_success"].format(input_path=input_path, elapsed_time=f"{(time.time() - start_time):.2f}", output_path=output_path.replace('wav', export_format))) def change_rms(source_audio, source_rate, target_audio, target_rate, rate): rms2 = F.interpolate(torch.from_numpy(librosa.feature.rms(y=target_audio, frame_length=target_rate // 2 * 2, hop_length=target_rate // 2)).float().unsqueeze(0), size=target_audio.shape[0], mode="linear").squeeze() return (target_audio * (torch.pow(F.interpolate(torch.from_numpy(librosa.feature.rms(y=source_audio, frame_length=source_rate // 2 * 2, hop_length=source_rate // 2)).float().unsqueeze(0), size=target_audio.shape[0], mode="linear").squeeze(), 1 - rate) * torch.pow(torch.maximum(rms2, torch.zeros_like(rms2) + 1e-6), rate - 1)).numpy()) def get_providers(): ort_providers = onnxruntime.get_available_providers() if "CUDAExecutionProvider" in ort_providers: providers = ["CUDAExecutionProvider"] elif "CoreMLExecutionProvider" in ort_providers: providers = ["CoreMLExecutionProvider"] else: providers = ["CPUExecutionProvider"] return providers class Autotune: def __init__(self, ref_freqs): self.ref_freqs = ref_freqs self.note_dict = self.ref_freqs def autotune_f0(self, f0, f0_autotune_strength): autotuned_f0 = np.zeros_like(f0) for i, freq in enumerate(f0): autotuned_f0[i] = freq + (min(self.note_dict, key=lambda x: abs(x - freq)) - freq) * f0_autotune_strength return autotuned_f0 class VC: def __init__(self, tgt_sr, config): self.x_pad = config.x_pad self.x_query = config.x_query self.x_center = config.x_center self.x_max = config.x_max self.sample_rate = 16000 self.window = 160 self.t_pad = self.sample_rate * self.x_pad self.t_pad_tgt = tgt_sr * self.x_pad self.t_pad2 = self.t_pad * 2 self.t_query = self.sample_rate * self.x_query self.t_center = self.sample_rate * self.x_center self.t_max = self.sample_rate * self.x_max self.time_step = self.window / self.sample_rate * 1000 self.f0_min = 50 self.f0_max = 1100 self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700) self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700) self.device = config.device self.ref_freqs = [49.00, 51.91, 55.00, 58.27, 61.74, 65.41, 69.30, 73.42, 77.78, 82.41, 87.31, 92.50, 98.00, 103.83, 110.00, 116.54, 123.47, 130.81, 138.59, 146.83, 155.56, 164.81, 174.61, 185.00, 196.00, 207.65, 220.00, 233.08, 246.94, 261.63, 277.18, 293.66, 311.13, 329.63, 349.23, 369.99, 392.00, 415.30, 440.00, 466.16, 493.88, 523.25, 554.37, 587.33, 622.25, 659.25, 698.46, 739.99, 783.99, 830.61, 880.00, 932.33, 987.77, 1046.50] self.autotune = Autotune(self.ref_freqs) self.note_dict = self.autotune.note_dict def get_f0_pm(self, x, p_len): import parselmouth f0 = (parselmouth.Sound(x, self.sample_rate).to_pitch_ac(time_step=self.window / self.sample_rate * 1000 / 1000, voicing_threshold=0.6, pitch_floor=self.f0_min, pitch_ceiling=self.f0_max).selected_array["frequency"]) pad_size = (p_len - len(f0) + 1) // 2 if pad_size > 0 or p_len - len(f0) - pad_size > 0: f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant") return f0 def get_f0_mangio_crepe(self, x, p_len, hop_length, model="full", onnx=False): from main.library.predictors.CREPE import predict x = x.astype(np.float32) x /= np.quantile(np.abs(x), 0.999) audio = torch.unsqueeze(torch.from_numpy(x).to(self.device, copy=True), dim=0) if audio.ndim == 2 and audio.shape[0] > 1: audio = torch.mean(audio, dim=0, keepdim=True).detach() p_len = p_len or x.shape[0] // hop_length source = np.array(predict(audio.detach(), self.sample_rate, hop_length, self.f0_min, self.f0_max, model, batch_size=hop_length * 2, device=self.device, pad=True, providers=get_providers(), onnx=onnx).squeeze(0).cpu().float().numpy()) source[source < 0.001] = np.nan return np.nan_to_num(np.interp(np.arange(0, len(source) * p_len, len(source)) / p_len, np.arange(0, len(source)), source)) def get_f0_crepe(self, x, model="full", onnx=False): from main.library.predictors.CREPE import predict, mean, median f0, pd = predict(torch.tensor(np.copy(x))[None].float(), self.sample_rate, self.window, self.f0_min, self.f0_max, model, batch_size=512, device=self.device, return_periodicity=True, providers=get_providers(), onnx=onnx) f0, pd = mean(f0, 3), median(pd, 3) f0[pd < 0.1] = 0 return f0[0].cpu().numpy() def get_f0_fcpe(self, x, p_len, hop_length, onnx=False, legacy=False): from main.library.predictors.FCPE import FCPE model_fcpe = FCPE(os.path.join("assets", "models", "predictors", ("fcpe_legacy" if legacy else"fcpe") + (".onnx" if onnx else ".pt")), hop_length=int(hop_length), f0_min=int(self.f0_min), f0_max=int(self.f0_max), dtype=torch.float32, device=self.device, sample_rate=self.sample_rate, threshold=0.03, providers=get_providers(), onnx=onnx, legacy=legacy) f0 = model_fcpe.compute_f0(x, p_len=p_len) del model_fcpe return f0 def get_f0_rmvpe(self, x, legacy=False, onnx=False): from main.library.predictors.RMVPE import RMVPE rmvpe_model = RMVPE(os.path.join("assets", "models", "predictors", "rmvpe" + (".onnx" if onnx else ".pt")), device=self.device, onnx=onnx, providers=get_providers()) f0 = rmvpe_model.infer_from_audio_with_pitch(x, thred=0.03, f0_min=self.f0_min, f0_max=self.f0_max) if legacy else rmvpe_model.infer_from_audio(x, thred=0.03) del rmvpe_model return f0 def get_f0_pyworld_wrapper(self, x, filter_radius, model="harvest"): from main.library.predictors.WORLD_WRAPPER import PYWORLD pw = PYWORLD() x = x.astype(np.double) if model == "harvest": f0, t = pw.harvest(x, fs=self.sample_rate, f0_ceil=self.f0_max, f0_floor=self.f0_min, frame_period=10) elif model == "dio": f0, t = pw.dio(x, fs=self.sample_rate, f0_ceil=self.f0_max, f0_floor=self.f0_min, frame_period=10) else: raise ValueError(translations["method_not_valid"]) f0 = pw.stonemask(x, self.sample_rate, t, f0) if filter_radius > 2 or model == "dio": f0 = signal.medfilt(f0, 3) return f0 def get_f0_pyworld(self, x, filter_radius, model="harvest"): from main.library.predictors.pyworld import harvest, dio, stonemask x = x.astype(np.double) if model == "harvest": f0, t = harvest.harvest(x, fs=self.sample_rate, f0_ceil=self.f0_max, f0_floor=self.f0_min, frame_period=10) elif model == "dio": f0, t = dio.dio(x, fs=self.sample_rate, f0_ceil=self.f0_max, f0_floor=self.f0_min, frame_period=10) else: raise ValueError(translations["method_not_valid"]) f0 = stonemask.stonemask(x, self.sample_rate, t, f0) if filter_radius > 2 or model == "dio": f0 = signal.medfilt(f0, 3) return f0 def get_f0_swipe(self, x): from main.library.predictors.SWIPE import swipe f0, _ = swipe(x.astype(np.double), self.sample_rate, f0_floor=self.f0_min, f0_ceil=self.f0_max, frame_period=10, device=self.device) return f0 def get_f0_yin(self, x, hop_length, p_len): source = np.array(librosa.yin(x.astype(np.float32), sr=self.sample_rate, fmin=self.f0_min, fmax=self.f0_max, hop_length=hop_length)) source[source < 0.001] = np.nan return np.nan_to_num(np.interp(np.arange(0, len(source) * p_len, len(source)) / p_len, np.arange(0, len(source)), source)) def get_f0_pyin(self, x, hop_length, p_len): f0, _, _ = librosa.pyin(x.astype(np.float32), fmin=self.f0_min, fmax=self.f0_max, sr=self.sample_rate, hop_length=hop_length) source = np.array(f0) source[source < 0.001] = np.nan return np.nan_to_num(np.interp(np.arange(0, len(source) * p_len, len(source)) / p_len, np.arange(0, len(source)), source)) def get_f0_hybrid(self, methods_str, x, p_len, hop_length, filter_radius, onnx_mode): methods_str = re.search("hybrid\[(.+)\]", methods_str) if methods_str: methods = [method.strip() for method in methods_str.group(1).split("+")] f0_computation_stack, resampled_stack = [], [] logger.debug(translations["hybrid_methods"].format(methods=methods)) x = x.astype(np.float32) x /= np.quantile(np.abs(x), 0.999) for method in methods: f0 = None f0_methods = {"pm": lambda: self.get_f0_pm(x, p_len), "diow": lambda: self.get_f0_pyworld_wrapper(x, filter_radius, "dio"), "dio": lambda: self.get_f0_pyworld(x, filter_radius, "dio"), "mangio-crepe-tiny": lambda: self.get_f0_mangio_crepe(x, p_len, int(hop_length), "tiny", onnx=onnx_mode), "mangio-crepe-small": lambda: self.get_f0_mangio_crepe(x, p_len, int(hop_length), "small", onnx=onnx_mode), "mangio-crepe-medium": lambda: self.get_f0_mangio_crepe(x, p_len, int(hop_length), "medium", onnx=onnx_mode), "mangio-crepe-large": lambda: self.get_f0_mangio_crepe(x, p_len, int(hop_length), "large", onnx=onnx_mode), "mangio-crepe-full": lambda: self.get_f0_mangio_crepe(x, p_len, int(hop_length), "full", onnx=onnx_mode), "crepe-tiny": lambda: self.get_f0_crepe(x, "tiny", onnx=onnx_mode), "crepe-small": lambda: self.get_f0_crepe(x, "small", onnx=onnx_mode), "crepe-medium": lambda: self.get_f0_crepe(x, "medium", onnx=onnx_mode), "crepe-large": lambda: self.get_f0_crepe(x, "large", onnx=onnx_mode), "crepe-full": lambda: self.get_f0_crepe(x, "full", onnx=onnx_mode), "fcpe": lambda: self.get_f0_fcpe(x, p_len, int(hop_length), onnx=onnx_mode), "fcpe-legacy": lambda: self.get_f0_fcpe(x, p_len, int(hop_length), legacy=True, onnx=onnx_mode), "rmvpe": lambda: self.get_f0_rmvpe(x, onnx=onnx_mode), "rmvpe-legacy": lambda: self.get_f0_rmvpe(x, legacy=True, onnx=onnx_mode), "harvestw": lambda: self.get_f0_pyworld_wrapper(x, filter_radius, "harvest"), "harvest": lambda: self.get_f0_pyworld(x, filter_radius, "harvest"), "yin": lambda: self.get_f0_yin(x, int(hop_length), p_len), "pyin": lambda: self.get_f0_pyin(x, int(hop_length), p_len), "swipe": lambda: self.get_f0_swipe(x)} f0 = f0_methods.get(method, lambda: ValueError(translations["method_not_valid"]))() f0_computation_stack.append(f0) for f0 in f0_computation_stack: resampled_stack.append(np.interp(np.linspace(0, len(f0), p_len), np.arange(len(f0)), f0)) return resampled_stack[0] if len(resampled_stack) == 1 else np.nanmedian(np.vstack(resampled_stack), axis=0) def get_f0(self, x, p_len, pitch, f0_method, filter_radius, hop_length, f0_autotune, f0_autotune_strength, inp_f0=None, onnx_mode=False): f0_methods = {"pm": lambda: self.get_f0_pm(x, p_len), "diow": lambda: self.get_f0_pyworld_wrapper(x, filter_radius, "dio"), "dio": lambda: self.get_f0_pyworld(x, filter_radius, "dio"), "mangio-crepe-tiny": lambda: self.get_f0_mangio_crepe(x, p_len, int(hop_length), "tiny", onnx=onnx_mode), "mangio-crepe-small": lambda: self.get_f0_mangio_crepe(x, p_len, int(hop_length), "small", onnx=onnx_mode), "mangio-crepe-medium": lambda: self.get_f0_mangio_crepe(x, p_len, int(hop_length), "medium", onnx=onnx_mode), "mangio-crepe-large": lambda: self.get_f0_mangio_crepe(x, p_len, int(hop_length), "large", onnx=onnx_mode), "mangio-crepe-full": lambda: self.get_f0_mangio_crepe(x, p_len, int(hop_length), "full", onnx=onnx_mode), "crepe-tiny": lambda: self.get_f0_crepe(x, "tiny", onnx=onnx_mode), "crepe-small": lambda: self.get_f0_crepe(x, "small", onnx=onnx_mode), "crepe-medium": lambda: self.get_f0_crepe(x, "medium", onnx=onnx_mode), "crepe-large": lambda: self.get_f0_crepe(x, "large", onnx=onnx_mode), "crepe-full": lambda: self.get_f0_crepe(x, "full", onnx=onnx_mode), "fcpe": lambda: self.get_f0_fcpe(x, p_len, int(hop_length), onnx=onnx_mode), "fcpe-legacy": lambda: self.get_f0_fcpe(x, p_len, int(hop_length), legacy=True, onnx=onnx_mode), "rmvpe": lambda: self.get_f0_rmvpe(x, onnx=onnx_mode), "rmvpe-legacy": lambda: self.get_f0_rmvpe(x, legacy=True, onnx=onnx_mode), "harvestw": lambda: self.get_f0_pyworld_wrapper(x, filter_radius, "harvest"), "harvest": lambda: self.get_f0_pyworld(x, filter_radius, "harvest"), "yin": lambda: self.get_f0_yin(x, int(hop_length), p_len), "pyin": lambda: self.get_f0_pyin(x, int(hop_length), p_len), "swipe": lambda: self.get_f0_swipe(x)} f0 = self.get_f0_hybrid(f0_method, x, p_len, hop_length, filter_radius, onnx_mode) if "hybrid" in f0_method else f0_methods.get(f0_method, lambda: ValueError(translations["method_not_valid"]))() if f0_autotune: f0 = Autotune.autotune_f0(self, f0, f0_autotune_strength) if isinstance(f0, tuple): f0 = f0[0] f0 *= pow(2, pitch / 12) tf0 = self.sample_rate // self.window if inp_f0 is not None: replace_f0 = np.interp(list(range(np.round((inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1).astype(np.int16))), inp_f0[:, 0] * 100, inp_f0[:, 1]) f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)] = replace_f0[:f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)].shape[0]] f0_mel = 1127 * np.log(1 + f0 / 700) f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - self.f0_mel_min) * 254 / (self.f0_mel_max - self.f0_mel_min) + 1 f0_mel[f0_mel <= 1] = 1 f0_mel[f0_mel > 255] = 255 return np.rint(f0_mel).astype(np.int32), f0.copy() def extract_features(self, model, feats, version): return torch.as_tensor(model.run([model.get_outputs()[0].name, model.get_outputs()[1].name], {"feats": feats.detach().cpu().numpy()})[0 if version == "v1" else 1], dtype=torch.float32, device=feats.device) def voice_conversion(self, model, net_g, sid, audio0, pitch, pitchf, index, big_npy, index_rate, version, protect): pitch_guidance = pitch != None and pitchf != None feats = torch.from_numpy(audio0).float() if feats.dim() == 2: feats = feats.mean(-1) assert feats.dim() == 1, feats.dim() feats = feats.view(1, -1) if self.embed_suffix == ".pt": padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False) inputs = {"source": feats.to(self.device), "padding_mask": padding_mask, "output_layer": 9 if version == "v1" else 12} with torch.no_grad(): if self.embed_suffix == ".pt": logits = model.extract_features(**inputs) feats = model.final_proj(logits[0]) if version == "v1" else logits[0] else: feats = self.extract_features(model, feats, version).to(self.device) if protect < 0.5 and pitch_guidance: feats0 = feats.clone() if (not isinstance(index, type(None)) and not isinstance(big_npy, type(None)) and index_rate != 0): npy = feats[0].cpu().numpy() score, ix = index.search(npy, k=8) weight = np.square(1 / score) weight /= weight.sum(axis=1, keepdims=True) npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1) feats = (torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate + (1 - index_rate) * feats) feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1) if protect < 0.5 and pitch_guidance: feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1) p_len = audio0.shape[0] // self.window if feats.shape[1] < p_len: p_len = feats.shape[1] if pitch_guidance: pitch = pitch[:, :p_len] pitchf = pitchf[:, :p_len] if protect < 0.5 and pitch_guidance: pitchff = pitchf.clone() pitchff[pitchf > 0] = 1 pitchff[pitchf < 1] = protect pitchff = pitchff.unsqueeze(-1) feats = feats * pitchff + feats0 * (1 - pitchff) feats = feats.to(feats0.dtype) p_len = torch.tensor([p_len], device=self.device).long() audio1 = ((net_g.infer(feats.float(), p_len, pitch if pitch_guidance else None, pitchf.float() if pitch_guidance else None, sid)[0][0, 0]).data.cpu().float().numpy()) if self.suffix == ".pth" else (net_g.run([net_g.get_outputs()[0].name], ({net_g.get_inputs()[0].name: feats.cpu().numpy().astype(np.float32), net_g.get_inputs()[1].name: p_len.cpu().numpy(), net_g.get_inputs()[2].name: np.array([sid.cpu().item()], dtype=np.int64), net_g.get_inputs()[3].name: np.random.randn(1, 192, p_len).astype(np.float32), net_g.get_inputs()[4].name: pitch.cpu().numpy().astype(np.int64), net_g.get_inputs()[5].name: pitchf.cpu().numpy().astype(np.float32)} if pitch_guidance else {net_g.get_inputs()[0].name: feats.cpu().numpy().astype(np.float32), net_g.get_inputs()[1].name: p_len.cpu().numpy(), net_g.get_inputs()[2].name: np.array([sid.cpu().item()], dtype=np.int64), net_g.get_inputs()[3].name: np.random.randn(1, 192, p_len).astype(np.float32)}))[0][0, 0]) if self.embed_suffix == ".pt": del padding_mask del feats, p_len, net_g if torch.cuda.is_available(): torch.cuda.empty_cache() elif torch.backends.mps.is_available(): torch.mps.empty_cache() return audio1 def pipeline(self, model, net_g, sid, audio, pitch, f0_method, file_index, index_rate, pitch_guidance, filter_radius, tgt_sr, resample_sr, volume_envelope, version, protect, hop_length, f0_autotune, f0_autotune_strength, suffix, embed_suffix, f0_file=None, f0_onnx=False): self.suffix = suffix self.embed_suffix = embed_suffix if file_index != "" and os.path.exists(file_index) and index_rate != 0: try: index = faiss.read_index(file_index) big_npy = index.reconstruct_n(0, index.ntotal) except Exception as e: logger.error(translations["read_faiss_index_error"].format(e=e)) index = big_npy = None else: index = big_npy = None opt_ts, audio_opt = [], [] audio = signal.filtfilt(bh, ah, audio) audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect") if audio_pad.shape[0] > self.t_max: audio_sum = np.zeros_like(audio) for i in range(self.window): audio_sum += audio_pad[i : i - self.window] for t in range(self.t_center, audio.shape[0], self.t_center): opt_ts.append(t - self.t_query + np.where(np.abs(audio_sum[t - self.t_query : t + self.t_query]) == np.abs(audio_sum[t - self.t_query : t + self.t_query]).min())[0][0]) s = 0 t, inp_f0 = None, None audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect") sid = torch.tensor(sid, device=self.device).unsqueeze(0).long() p_len = audio_pad.shape[0] // self.window if hasattr(f0_file, "name"): try: with open(f0_file.name, "r") as f: raw_lines = f.read() if len(raw_lines) > 0: inp_f0 = [] for line in raw_lines.strip("\n").split("\n"): inp_f0.append([float(i) for i in line.split(",")]) inp_f0 = np.array(inp_f0, dtype=np.float32) except: logger.error(translations["error_readfile"]) inp_f0 = None if pitch_guidance: pitch, pitchf = self.get_f0(audio_pad, p_len, pitch, f0_method, filter_radius, hop_length, f0_autotune, f0_autotune_strength, inp_f0, onnx_mode=f0_onnx) pitch, pitchf = pitch[:p_len], pitchf[:p_len] if self.device == "mps": pitchf = pitchf.astype(np.float32) pitch, pitchf = torch.tensor(pitch, device=self.device).unsqueeze(0).long(), torch.tensor(pitchf, device=self.device).unsqueeze(0).float() for t in opt_ts: t = t // self.window * self.window audio_opt.append(self.voice_conversion(model, net_g, sid, audio_pad[s : t + self.t_pad2 + self.window], pitch[:, s // self.window : (t + self.t_pad2) // self.window] if pitch_guidance else None, pitchf[:, s // self.window : (t + self.t_pad2) // self.window] if pitch_guidance else None, index, big_npy, index_rate, version, protect)[self.t_pad_tgt : -self.t_pad_tgt]) s = t audio_opt.append(self.voice_conversion(model, net_g, sid, audio_pad[t:], (pitch[:, t // self.window :] if t is not None else pitch) if pitch_guidance else None, (pitchf[:, t // self.window :] if t is not None else pitchf) if pitch_guidance else None, index, big_npy, index_rate, version, protect)[self.t_pad_tgt : -self.t_pad_tgt]) audio_opt = np.concatenate(audio_opt) if volume_envelope != 1: audio_opt = change_rms(audio, self.sample_rate, audio_opt, tgt_sr, volume_envelope) if resample_sr >= self.sample_rate and tgt_sr != resample_sr: audio_opt = librosa.resample(audio_opt, orig_sr=tgt_sr, target_sr=resample_sr, res_type="soxr_vhq") audio_max = np.abs(audio_opt).max() / 0.99 if audio_max > 1: audio_opt /= audio_max if pitch_guidance: del pitch, pitchf del sid if torch.cuda.is_available(): torch.cuda.empty_cache() elif torch.backends.mps.is_available(): torch.mps.empty_cache() return audio_opt class VoiceConverter: def __init__(self): self.config = config self.hubert_model = None self.tgt_sr = None self.net_g = None self.vc = None self.cpt = None self.version = None self.n_spk = None self.use_f0 = None self.loaded_model = None self.vocoder = "Default" self.checkpointing = False def load_embedders(self, embedder_model): embedder_model_path = os.path.join("assets", "models", "embedders", embedder_model) if not os.path.exists(embedder_model_path) and not embedder_model.endswith((".pt", ".onnx")): raise FileNotFoundError(f"{translations['not_found'].format(name=translations['model'])}: {embedder_model}") try: if embedder_model.endswith(".pt"): models, _, _ = checkpoint_utils.load_model_ensemble_and_task([embedder_model_path], suffix="") self.embed_suffix = ".pt" self.hubert_model = models[0].to(self.config.device).float().eval() else: sess_options = onnxruntime.SessionOptions() sess_options.log_severity_level = 3 self.embed_suffix = ".onnx" self.hubert_model = onnxruntime.InferenceSession(embedder_model_path, sess_options=sess_options, providers=get_providers()) except Exception as e: logger.error(translations["read_model_error"].format(e=e)) def convert_audio(self, audio_input_path, audio_output_path, model_path, index_path, embedder_model, pitch, f0_method, index_rate, volume_envelope, protect, hop_length, f0_autotune, f0_autotune_strength, filter_radius, clean_audio, clean_strength, export_format, resample_sr = 0, sid = 0, checkpointing = False, f0_file = None, f0_onnx = False, formant_shifting = False, formant_qfrency=0.8, formant_timbre=0.8): try: self.get_vc(model_path, sid) audio = load_audio(logger, audio_input_path, 16000, formant_shifting=formant_shifting, formant_qfrency=formant_qfrency, formant_timbre=formant_timbre) self.checkpointing = checkpointing audio_max = np.abs(audio).max() / 0.95 if audio_max > 1: audio /= audio_max if not self.hubert_model: self.load_embedders(embedder_model) if self.tgt_sr != resample_sr >= 16000: self.tgt_sr = resample_sr target_sr = min([8000, 11025, 12000, 16000, 22050, 24000, 32000, 44100, 48000, 96000], key=lambda x: abs(x - self.tgt_sr)) audio_output = self.vc.pipeline(model=self.hubert_model, net_g=self.net_g, sid=sid, audio=audio, pitch=pitch, f0_method=f0_method, file_index=(index_path.strip().strip('"').strip("\n").strip('"').strip().replace("trained", "added")), index_rate=index_rate, pitch_guidance=self.use_f0, filter_radius=filter_radius, tgt_sr=self.tgt_sr, resample_sr=target_sr, volume_envelope=volume_envelope, version=self.version, protect=protect, hop_length=hop_length, f0_autotune=f0_autotune, f0_autotune_strength=f0_autotune_strength, suffix=self.suffix, embed_suffix=self.embed_suffix, f0_file=f0_file, f0_onnx=f0_onnx) if clean_audio: from main.tools.noisereduce import reduce_noise audio_output = reduce_noise(y=audio_output, sr=target_sr, prop_decrease=clean_strength, device=config.device) sf.write(audio_output_path, audio_output, target_sr, format=export_format) except Exception as e: logger.error(translations["error_convert"].format(e=e)) import traceback logger.debug(traceback.format_exc()) def get_vc(self, weight_root, sid): if sid == "" or sid == []: self.cleanup() if torch.cuda.is_available(): torch.cuda.empty_cache() elif torch.backends.mps.is_available(): torch.mps.empty_cache() if not self.loaded_model or self.loaded_model != weight_root: self.loaded_model = weight_root self.load_model() if self.cpt is not None: self.setup() def cleanup(self): if self.hubert_model is not None: del self.net_g, self.n_spk, self.vc, self.hubert_model, self.tgt_sr self.hubert_model = self.net_g = self.n_spk = self.vc = self.tgt_sr = None if torch.cuda.is_available(): torch.cuda.empty_cache() elif torch.backends.mps.is_available(): torch.mps.empty_cache() del self.net_g, self.cpt if torch.cuda.is_available(): torch.cuda.empty_cache() elif torch.backends.mps.is_available(): torch.mps.empty_cache() self.cpt = None def load_model(self): if os.path.isfile(self.loaded_model): if self.loaded_model.endswith(".pth"): self.cpt = torch.load(self.loaded_model, map_location="cpu") else: sess_options = onnxruntime.SessionOptions() sess_options.log_severity_level = 3 self.cpt = onnxruntime.InferenceSession(self.loaded_model, sess_options=sess_options, providers=get_providers()) else: self.cpt = None def setup(self): if self.cpt is not None: if self.loaded_model.endswith(".pth"): self.tgt_sr = self.cpt["config"][-1] self.cpt["config"][-3] = self.cpt["weight"]["emb_g.weight"].shape[0] self.use_f0 = self.cpt.get("f0", 1) self.version = self.cpt.get("version", "v1") self.vocoder = self.cpt.get("vocoder", "Default") self.net_g = Synthesizer(*self.cpt["config"], use_f0=self.use_f0, text_enc_hidden_dim=768 if self.version == "v2" else 256, vocoder=self.vocoder, checkpointing=self.checkpointing) del self.net_g.enc_q self.net_g.load_state_dict(self.cpt["weight"], strict=False) self.net_g.eval().to(self.config.device).float() self.n_spk = self.cpt["config"][-3] self.suffix = ".pth" else: import json import onnx model = onnx.load(self.loaded_model) metadata_dict = None for prop in model.metadata_props: if prop.key == "model_info": metadata_dict = json.loads(prop.value) break self.net_g = self.cpt self.tgt_sr = metadata_dict.get("sr", 32000) self.use_f0 = metadata_dict.get("f0", 1) self.suffix = ".onnx" self.vc = VC(self.tgt_sr, self.config) if __name__ == "__main__": main()