From 09fc351828112f49886343112a204b28f0ddc1b3 Mon Sep 17 00:00:00 2001 From: Ftps <63702646+Tps-F@users.noreply.github.com> Date: Sun, 17 Sep 2023 07:37:15 +0900 Subject: [PATCH] fix file location (#1239) --- infer/modules/vc/modules.py | 2 +- infer/modules/vc/pipeline.py | 10 +- modules.py | 307 ----------------------- pipeline.py | 457 ----------------------------------- 4 files changed, 6 insertions(+), 770 deletions(-) delete mode 100644 modules.py delete mode 100644 pipeline.py diff --git a/infer/modules/vc/modules.py b/infer/modules/vc/modules.py index b65ed70..306149f 100644 --- a/infer/modules/vc/modules.py +++ b/infer/modules/vc/modules.py @@ -51,7 +51,7 @@ class VC: "__type__": "update", } - if not sid: + if sid == "" or sid == []: if self.hubert_model is not None: # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的 logger.info("Clean model cache") del ( diff --git a/infer/modules/vc/pipeline.py b/infer/modules/vc/pipeline.py index 0399f29..0c22584 100644 --- a/infer/modules/vc/pipeline.py +++ b/infer/modules/vc/pipeline.py @@ -153,10 +153,10 @@ class Pipeline(object): ) f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03) - if "privateuseone" in str(self.device): # clean ortruntime memory - del self.model_rmvpe.model - del self.model_rmvpe - logger.info("Cleaning ortruntime memory") + if "privateuseone" in str(self.device): # clean ortruntime memory + del self.model_rmvpe.model + del self.model_rmvpe + logger.info("Cleaning ortruntime memory") f0 *= pow(2, f0_up_key / 12) # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()])) @@ -362,7 +362,7 @@ class Pipeline(object): ) pitch = pitch[:p_len] pitchf = pitchf[:p_len] - if self.device == "mps" or "xpu" in self.device: + if "mps" not in str(self.device) or "xpu" not in str(self.device): pitchf = pitchf.astype(np.float32) pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long() pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float() diff --git a/modules.py b/modules.py deleted file mode 100644 index 306149f..0000000 --- a/modules.py +++ /dev/null @@ -1,307 +0,0 @@ -import traceback -import logging - -logger = logging.getLogger(__name__) - -import numpy as np -import soundfile as sf -import torch -from io import BytesIO - -from infer.lib.audio import load_audio, wav2 -from infer.lib.infer_pack.models import ( - SynthesizerTrnMs256NSFsid, - SynthesizerTrnMs256NSFsid_nono, - SynthesizerTrnMs768NSFsid, - SynthesizerTrnMs768NSFsid_nono, -) -from infer.modules.vc.pipeline import Pipeline -from infer.modules.vc.utils import * - - -class VC: - def __init__(self, config): - self.n_spk = None - self.tgt_sr = None - self.net_g = None - self.pipeline = None - self.cpt = None - self.version = None - self.if_f0 = None - self.version = None - self.hubert_model = None - - self.config = config - - def get_vc(self, sid, *to_return_protect): - logger.info("Get sid: " + sid) - - to_return_protect0 = { - "visible": self.if_f0 != 0, - "value": to_return_protect[0] - if self.if_f0 != 0 and to_return_protect - else 0.5, - "__type__": "update", - } - to_return_protect1 = { - "visible": self.if_f0 != 0, - "value": to_return_protect[1] - if self.if_f0 != 0 and to_return_protect - else 0.33, - "__type__": "update", - } - - if sid == "" or sid == []: - if self.hubert_model is not None: # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的 - logger.info("Clean model cache") - del ( - self.net_g, - self.n_spk, - self.vc, - self.hubert_model, - self.tgt_sr, - ) # ,cpt - self.hubert_model = ( - self.net_g - ) = self.n_spk = self.vc = self.hubert_model = self.tgt_sr = None - if torch.cuda.is_available(): - torch.cuda.empty_cache() - ###楼下不这么折腾清理不干净 - self.if_f0 = self.cpt.get("f0", 1) - self.version = self.cpt.get("version", "v1") - if self.version == "v1": - if self.if_f0 == 1: - self.net_g = SynthesizerTrnMs256NSFsid( - *self.cpt["config"], is_half=self.config.is_half - ) - else: - self.net_g = SynthesizerTrnMs256NSFsid_nono(*self.cpt["config"]) - elif self.version == "v2": - if self.if_f0 == 1: - self.net_g = SynthesizerTrnMs768NSFsid( - *self.cpt["config"], is_half=self.config.is_half - ) - else: - self.net_g = SynthesizerTrnMs768NSFsid_nono(*self.cpt["config"]) - del self.net_g, self.cpt - if torch.cuda.is_available(): - torch.cuda.empty_cache() - return ( - {"visible": False, "__type__": "update"}, - { - "visible": True, - "value": to_return_protect0, - "__type__": "update", - }, - { - "visible": True, - "value": to_return_protect1, - "__type__": "update", - }, - "", - "", - ) - person = f'{os.getenv("weight_root")}/{sid}' - logger.info(f"Loading: {person}") - - self.cpt = torch.load(person, map_location="cpu") - self.tgt_sr = self.cpt["config"][-1] - self.cpt["config"][-3] = self.cpt["weight"]["emb_g.weight"].shape[0] # n_spk - self.if_f0 = self.cpt.get("f0", 1) - self.version = self.cpt.get("version", "v1") - - synthesizer_class = { - ("v1", 1): SynthesizerTrnMs256NSFsid, - ("v1", 0): SynthesizerTrnMs256NSFsid_nono, - ("v2", 1): SynthesizerTrnMs768NSFsid, - ("v2", 0): SynthesizerTrnMs768NSFsid_nono, - } - - self.net_g = synthesizer_class.get( - (self.version, self.if_f0), SynthesizerTrnMs256NSFsid - )(*self.cpt["config"], is_half=self.config.is_half) - - 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) - if self.config.is_half: - self.net_g = self.net_g.half() - else: - self.net_g = self.net_g.float() - - self.pipeline = Pipeline(self.tgt_sr, self.config) - n_spk = self.cpt["config"][-3] - index = {"value": get_index_path_from_model(sid), "__type__": "update"} - logger.info("Select index: " + index["value"]) - - return ( - ( - {"visible": True, "maximum": n_spk, "__type__": "update"}, - to_return_protect0, - to_return_protect1, - index, - index, - ) - if to_return_protect - else {"visible": True, "maximum": n_spk, "__type__": "update"} - ) - - def vc_single( - self, - sid, - input_audio_path, - f0_up_key, - f0_file, - f0_method, - file_index, - file_index2, - index_rate, - filter_radius, - resample_sr, - rms_mix_rate, - protect, - ): - if input_audio_path is None: - return "You need to upload 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 - times = [0, 0, 0] - - if self.hubert_model is None: - self.hubert_model = load_hubert(self.config) - - file_index = ( - ( - file_index.strip(" ") - .strip('"') - .strip("\n") - .strip('"') - .strip(" ") - .replace("trained", "added") - ) - if file_index != "" - else file_index2 - ) # 防止小白写错，自动帮他替换掉 - - audio_opt = self.pipeline.pipeline( - self.hubert_model, - self.net_g, - sid, - audio, - input_audio_path, - times, - f0_up_key, - f0_method, - file_index, - index_rate, - self.if_f0, - filter_radius, - self.tgt_sr, - resample_sr, - rms_mix_rate, - self.version, - protect, - f0_file, - ) - if self.tgt_sr != resample_sr >= 16000: - tgt_sr = resample_sr - else: - tgt_sr = self.tgt_sr - index_info = ( - "Index:\n%s." % file_index - if os.path.exists(file_index) - else "Index not used." - ) - return ( - "Success.\n%s\nTime:\nnpy: %.2fs, f0: %.2fs, infer: %.2fs." - % (index_info, *times), - (tgt_sr, audio_opt), - ) - except: - info = traceback.format_exc() - logger.warn(info) - return info, (None, None) - - def vc_multi( - self, - sid, - dir_path, - opt_root, - paths, - f0_up_key, - f0_method, - file_index, - file_index2, - index_rate, - filter_radius, - resample_sr, - rms_mix_rate, - protect, - format1, - ): - try: - dir_path = ( - dir_path.strip(" ").strip('"').strip("\n").strip('"').strip(" ") - ) # 防止小白拷路径头尾带了空格和"和回车 - opt_root = opt_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ") - os.makedirs(opt_root, exist_ok=True) - try: - if dir_path != "": - paths = [ - os.path.join(dir_path, name) for name in os.listdir(dir_path) - ] - else: - paths = [path.name for path in paths] - except: - traceback.print_exc() - paths = [path.name for path in paths] - infos = [] - for path in paths: - info, opt = self.vc_single( - sid, - path, - f0_up_key, - None, - f0_method, - file_index, - file_index2, - # file_big_npy, - index_rate, - filter_radius, - resample_sr, - rms_mix_rate, - protect, - ) - if "Success" in info: - try: - tgt_sr, audio_opt = opt - if format1 in ["wav", "flac"]: - sf.write( - "%s/%s.%s" - % (opt_root, os.path.basename(path), format1), - audio_opt, - tgt_sr, - ) - else: - path = "%s/%s.%s" % ( - opt_root, - os.path.basename(path), - format1, - ) - with BytesIO() as wavf: - sf.write(wavf, audio_opt, tgt_sr, format="wav") - wavf.seek(0, 0) - with open(path, "wb") as outf: - wav2(wavf, outf, format1) - except: - info += traceback.format_exc() - infos.append("%s->%s" % (os.path.basename(path), info)) - yield "\n".join(infos) - yield "\n".join(infos) - except: - yield traceback.format_exc() diff --git a/pipeline.py b/pipeline.py deleted file mode 100644 index 0c22584..0000000 --- a/pipeline.py +++ /dev/null @@ -1,457 +0,0 @@ -import os -import sys -import traceback -import logging - -logger = logging.getLogger(__name__) - -from functools import lru_cache -from time import time as ttime - -import faiss -import librosa -import numpy as np -import parselmouth -import pyworld -import torch -import torch.nn.functional as F -import torchcrepe -from scipy import signal - -now_dir = os.getcwd() -sys.path.append(now_dir) - -bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000) - -input_audio_path2wav = {} - - -@lru_cache -def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period): - audio = input_audio_path2wav[input_audio_path] - f0, t = pyworld.harvest( - audio, - fs=fs, - f0_ceil=f0max, - f0_floor=f0min, - frame_period=frame_period, - ) - f0 = pyworld.stonemask(audio, f0, t, fs) - return f0 - - -def change_rms(data1, sr1, data2, sr2, rate): # 1是输入音频，2是输出音频,rate是2的占比 - # print(data1.max(),data2.max()) - rms1 = librosa.feature.rms( - y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2 - ) # 每半秒一个点 - rms2 = librosa.feature.rms(y=data2, frame_length=sr2 // 2 * 2, hop_length=sr2 // 2) - rms1 = torch.from_numpy(rms1) - rms1 = F.interpolate( - rms1.unsqueeze(0), size=data2.shape[0], mode="linear" - ).squeeze() - rms2 = torch.from_numpy(rms2) - rms2 = F.interpolate( - rms2.unsqueeze(0), size=data2.shape[0], mode="linear" - ).squeeze() - rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-6) - data2 *= ( - torch.pow(rms1, torch.tensor(1 - rate)) - * torch.pow(rms2, torch.tensor(rate - 1)) - ).numpy() - return data2 - - -class Pipeline(object): - def __init__(self, tgt_sr, config): - self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = ( - config.x_pad, - config.x_query, - config.x_center, - config.x_max, - config.is_half, - ) - self.sr = 16000 # hubert输入采样率 - self.window = 160 # 每帧点数 - self.t_pad = self.sr * self.x_pad # 每条前后pad时间 - self.t_pad_tgt = tgt_sr * self.x_pad - self.t_pad2 = self.t_pad * 2 - self.t_query = self.sr * self.x_query # 查询切点前后查询时间 - self.t_center = self.sr * self.x_center # 查询切点位置 - self.t_max = self.sr * self.x_max # 免查询时长阈值 - self.device = config.device - - def get_f0( - self, - input_audio_path, - x, - p_len, - f0_up_key, - f0_method, - filter_radius, - inp_f0=None, - ): - global input_audio_path2wav - time_step = self.window / self.sr * 1000 - f0_min = 50 - f0_max = 1100 - f0_mel_min = 1127 * np.log(1 + f0_min / 700) - f0_mel_max = 1127 * np.log(1 + f0_max / 700) - if f0_method == "pm": - f0 = ( - parselmouth.Sound(x, self.sr) - .to_pitch_ac( - time_step=time_step / 1000, - voicing_threshold=0.6, - pitch_floor=f0_min, - pitch_ceiling=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" - ) - elif f0_method == "harvest": - input_audio_path2wav[input_audio_path] = x.astype(np.double) - f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10) - if filter_radius > 2: - f0 = signal.medfilt(f0, 3) - elif f0_method == "crepe": - model = "full" - # Pick a batch size that doesn't cause memory errors on your gpu - batch_size = 512 - # Compute pitch using first gpu - audio = torch.tensor(np.copy(x))[None].float() - f0, pd = torchcrepe.predict( - audio, - self.sr, - self.window, - f0_min, - f0_max, - model, - batch_size=batch_size, - device=self.device, - return_periodicity=True, - ) - pd = torchcrepe.filter.median(pd, 3) - f0 = torchcrepe.filter.mean(f0, 3) - f0[pd < 0.1] = 0 - f0 = f0[0].cpu().numpy() - elif f0_method == "rmvpe": - if not hasattr(self, "model_rmvpe"): - from infer.lib.rmvpe import RMVPE - - logger.info( - "Loading rmvpe model,%s" % "%s/rmvpe.pt" % os.environ["rmvpe_root"] - ) - self.model_rmvpe = RMVPE( - "%s/rmvpe.pt" % os.environ["rmvpe_root"], - is_half=self.is_half, - device=self.device, - ) - f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03) - - if "privateuseone" in str(self.device): # clean ortruntime memory - del self.model_rmvpe.model - del self.model_rmvpe - logger.info("Cleaning ortruntime memory") - - f0 *= pow(2, f0_up_key / 12) - # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()])) - tf0 = self.sr // self.window # 每秒f0点数 - if inp_f0 is not None: - delta_t = np.round( - (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1 - ).astype("int16") - replace_f0 = np.interp( - list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1] - ) - shape = f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)].shape[0] - f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)] = replace_f0[ - :shape - ] - # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()])) - f0bak = f0.copy() - f0_mel = 1127 * np.log(1 + f0 / 700) - f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / ( - f0_mel_max - f0_mel_min - ) + 1 - f0_mel[f0_mel <= 1] = 1 - f0_mel[f0_mel > 255] = 255 - f0_coarse = np.rint(f0_mel).astype(np.int32) - return f0_coarse, f0bak # 1-0 - - def vc( - self, - model, - net_g, - sid, - audio0, - pitch, - pitchf, - times, - index, - big_npy, - index_rate, - version, - protect, - ): # ,file_index,file_big_npy - feats = torch.from_numpy(audio0) - if self.is_half: - feats = feats.half() - else: - feats = feats.float() - if feats.dim() == 2: # double channels - feats = feats.mean(-1) - assert feats.dim() == 1, feats.dim() - feats = feats.view(1, -1) - 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, - } - t0 = ttime() - with torch.no_grad(): - logits = model.extract_features(**inputs) - feats = model.final_proj(logits[0]) if version == "v1" else logits[0] - if protect < 0.5 and pitch is not None and pitchf is not None: - 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() - if self.is_half: - npy = npy.astype("float32") - - # _, I = index.search(npy, 1) - # npy = big_npy[I.squeeze()] - - 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) - - if self.is_half: - npy = npy.astype("float16") - 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 is not None and pitchf is not None: - feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute( - 0, 2, 1 - ) - t1 = ttime() - p_len = audio0.shape[0] // self.window - if feats.shape[1] < p_len: - p_len = feats.shape[1] - if pitch is not None and pitchf is not None: - pitch = pitch[:, :p_len] - pitchf = pitchf[:, :p_len] - - if protect < 0.5 and pitch is not None and pitchf is not None: - 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() - with torch.no_grad(): - hasp = pitch is not None and pitchf is not None - arg = (feats, p_len, pitch, pitchf, sid) if hasp else (feats, p_len, sid) - audio1 = (net_g.infer(*arg)[0][0, 0]).data.cpu().float().numpy() - del hasp, arg - del feats, p_len, padding_mask - if torch.cuda.is_available(): - torch.cuda.empty_cache() - t2 = ttime() - times[0] += t1 - t0 - times[2] += t2 - t1 - return audio1 - - def pipeline( - self, - model, - net_g, - sid, - audio, - input_audio_path, - times, - f0_up_key, - f0_method, - file_index, - index_rate, - if_f0, - filter_radius, - tgt_sr, - resample_sr, - rms_mix_rate, - version, - protect, - f0_file=None, - ): - if ( - file_index != "" - # and file_big_npy != "" - # and os.path.exists(file_big_npy) == True - and os.path.exists(file_index) - and index_rate != 0 - ): - try: - index = faiss.read_index(file_index) - # big_npy = np.load(file_big_npy) - big_npy = index.reconstruct_n(0, index.ntotal) - except: - traceback.print_exc() - index = big_npy = None - else: - index = big_npy = None - audio = signal.filtfilt(bh, ah, audio) - audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect") - opt_ts = [] - 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 - audio_opt = [] - t = None - t1 = ttime() - audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect") - p_len = audio_pad.shape[0] // self.window - inp_f0 = None - if hasattr(f0_file, "name"): - try: - with open(f0_file.name, "r") as f: - lines = f.read().strip("\n").split("\n") - inp_f0 = [] - for line in lines: - inp_f0.append([float(i) for i in line.split(",")]) - inp_f0 = np.array(inp_f0, dtype="float32") - except: - traceback.print_exc() - sid = torch.tensor(sid, device=self.device).unsqueeze(0).long() - pitch, pitchf = None, None - if if_f0 == 1: - pitch, pitchf = self.get_f0( - input_audio_path, - audio_pad, - p_len, - f0_up_key, - f0_method, - filter_radius, - inp_f0, - ) - pitch = pitch[:p_len] - pitchf = pitchf[:p_len] - if "mps" not in str(self.device) or "xpu" not in str(self.device): - pitchf = pitchf.astype(np.float32) - pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long() - pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float() - t2 = ttime() - times[1] += t2 - t1 - for t in opt_ts: - t = t // self.window * self.window - if if_f0 == 1: - audio_opt.append( - self.vc( - model, - net_g, - sid, - audio_pad[s : t + self.t_pad2 + self.window], - pitch[:, s // self.window : (t + self.t_pad2) // self.window], - pitchf[:, s // self.window : (t + self.t_pad2) // self.window], - times, - index, - big_npy, - index_rate, - version, - protect, - )[self.t_pad_tgt : -self.t_pad_tgt] - ) - else: - audio_opt.append( - self.vc( - model, - net_g, - sid, - audio_pad[s : t + self.t_pad2 + self.window], - None, - None, - times, - index, - big_npy, - index_rate, - version, - protect, - )[self.t_pad_tgt : -self.t_pad_tgt] - ) - s = t - if if_f0 == 1: - audio_opt.append( - self.vc( - model, - net_g, - sid, - audio_pad[t:], - pitch[:, t // self.window :] if t is not None else pitch, - pitchf[:, t // self.window :] if t is not None else pitchf, - times, - index, - big_npy, - index_rate, - version, - protect, - )[self.t_pad_tgt : -self.t_pad_tgt] - ) - else: - audio_opt.append( - self.vc( - model, - net_g, - sid, - audio_pad[t:], - None, - None, - times, - index, - big_npy, - index_rate, - version, - protect, - )[self.t_pad_tgt : -self.t_pad_tgt] - ) - audio_opt = np.concatenate(audio_opt) - if rms_mix_rate != 1: - audio_opt = change_rms(audio, 16000, audio_opt, tgt_sr, rms_mix_rate) - if tgt_sr != resample_sr >= 16000: - audio_opt = librosa.resample( - audio_opt, orig_sr=tgt_sr, target_sr=resample_sr - ) - audio_max = np.abs(audio_opt).max() / 0.99 - max_int16 = 32768 - if audio_max > 1: - max_int16 /= audio_max - audio_opt = (audio_opt * max_int16).astype(np.int16) - del pitch, pitchf, sid - if torch.cuda.is_available(): - torch.cuda.empty_cache() - return audio_opt