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18.2 kB
| import os, sys | |
| import traceback | |
| import logging | |
| now_dir = os.getcwd() | |
| sys.path.append(now_dir) | |
| logger = logging.getLogger(__name__) | |
| import numpy as np | |
| import soundfile as sf | |
| import torch | |
| from io import BytesIO | |
| from lib.infer_libs.audio import load_audio | |
| from lib.infer_libs.audio import wav2 | |
| from lib.infer_libs.infer_pack.models import ( | |
| SynthesizerTrnMs256NSFsid, | |
| SynthesizerTrnMs256NSFsid_nono, | |
| SynthesizerTrnMs768NSFsid, | |
| SynthesizerTrnMs768NSFsid_nono, | |
| ) | |
| from lib.pipeline import Pipeline | |
| import time | |
| import glob | |
| from shutil import move | |
| from fairseq import checkpoint_utils | |
| sup_audioext = { | |
| "wav", | |
| "mp3", | |
| "flac", | |
| "ogg", | |
| "opus", | |
| "m4a", | |
| "mp4", | |
| "aac", | |
| "alac", | |
| "wma", | |
| "aiff", | |
| "webm", | |
| "ac3", | |
| } | |
| def note_to_hz(note_name): | |
| try: | |
| SEMITONES = {'C': -9, 'C#': -8, 'D': -7, 'D#': -6, 'E': -5, 'F': -4, 'F#': -3, 'G': -2, 'G#': -1, 'A': 0, 'A#': 1, 'B': 2} | |
| pitch_class, octave = note_name[:-1], int(note_name[-1]) | |
| semitone = SEMITONES[pitch_class] | |
| note_number = 12 * (octave - 4) + semitone | |
| frequency = 440.0 * (2.0 ** (1.0/12)) ** note_number | |
| return frequency | |
| except: | |
| return None | |
| def load_hubert(hubert_model_path, config): | |
| models, _, _ = checkpoint_utils.load_model_ensemble_and_task( | |
| [hubert_model_path], | |
| 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() | |
| return hubert_model.eval() | |
| 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}' | |
| person = f'{sid}' | |
| #logger.info(f"Loading: {person}") | |
| logger.info(f"Loading...") | |
| 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": False, "maximum": n_spk, "__type__": "update"}, | |
| to_return_protect0, | |
| to_return_protect1 | |
| ) | |
| if to_return_protect | |
| else {"visible": False, "maximum": n_spk, "__type__": "update"} | |
| ) | |
| def vc_single_dont_save( | |
| self, | |
| sid, | |
| input_audio_path1, | |
| f0_up_key, | |
| f0_method, | |
| file_index, | |
| file_index2, | |
| index_rate, | |
| filter_radius, | |
| resample_sr, | |
| rms_mix_rate, | |
| protect, | |
| crepe_hop_length, | |
| do_formant, | |
| quefrency, | |
| timbre, | |
| f0_min, | |
| f0_max, | |
| f0_autotune, | |
| hubert_model_path = "hubert_base.pt" | |
| ): | |
| """ | |
| Performs inference without saving | |
| Parameters: | |
| - sid (int) | |
| - input_audio_path1 (str) | |
| - f0_up_key (int) | |
| - f0_method (str) | |
| - file_index (str) | |
| - file_index2 (str) | |
| - index_rate (float) | |
| - filter_radius (int) | |
| - resample_sr (int) | |
| - rms_mix_rate (float) | |
| - protect (float) | |
| - crepe_hop_length (int) | |
| - do_formant (bool) | |
| - quefrency (float) | |
| - timbre (float) | |
| - f0_min (str) | |
| - f0_max (str) | |
| - f0_autotune (bool) | |
| - hubert_model_path (str) | |
| Returns: | |
| Tuple(Tuple(status, index_info, times), Tuple(sr, data)): | |
| - Tuple(status, index_info, times): | |
| - status (str): either "Success." or an error | |
| - index_info (str): index path if used | |
| - times (list): [npy_time, f0_time, infer_time, total_time] | |
| - Tuple(sr, data): Audio data results. | |
| """ | |
| global total_time | |
| total_time = 0 | |
| start_time = time.time() | |
| if not input_audio_path1: | |
| return "You need to upload an audio", None | |
| if not os.path.exists(input_audio_path1): | |
| return "Audio was not properly selected or doesn't exist", None | |
| f0_up_key = int(f0_up_key) | |
| if not f0_min.isdigit(): | |
| f0_min = note_to_hz(f0_min) | |
| if f0_min: | |
| print(f"Converted Min pitch: freq - {f0_min}") | |
| else: | |
| f0_min = 50 | |
| print("Invalid minimum pitch note. Defaulting to 50hz.") | |
| else: | |
| f0_min = float(f0_min) | |
| if not f0_max.isdigit(): | |
| f0_max = note_to_hz(f0_max) | |
| if f0_max: | |
| print(f"Converted Max pitch: freq - {f0_max}") | |
| else: | |
| f0_max = 1100 | |
| print("Invalid maximum pitch note. Defaulting to 1100hz.") | |
| else: | |
| f0_max = float(f0_max) | |
| try: | |
| print(f"Attempting to load {input_audio_path1}....") | |
| audio = load_audio(file=input_audio_path1, | |
| sr=16000, | |
| DoFormant=do_formant, | |
| Quefrency=quefrency, | |
| Timbre=timbre) | |
| 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(hubert_model_path, self.config) | |
| try: | |
| self.if_f0 = self.cpt.get("f0", 1) | |
| except NameError: | |
| message = "Model was not properly selected" | |
| print(message) | |
| return message, None | |
| if file_index and not file_index == "" and isinstance(file_index, str): | |
| file_index = file_index.strip(" ") \ | |
| .strip('"') \ | |
| .strip("\n") \ | |
| .strip('"') \ | |
| .strip(" ") \ | |
| .replace("trained", "added") | |
| elif file_index2: | |
| file_index = file_index2 | |
| else: | |
| file_index = "" | |
| audio_opt = self.pipeline.pipeline( | |
| self.hubert_model, | |
| self.net_g, | |
| sid, | |
| audio, | |
| input_audio_path1, | |
| 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, | |
| crepe_hop_length, | |
| f0_autotune, | |
| f0_min=f0_min, | |
| f0_max=f0_max | |
| ) | |
| if self.tgt_sr != resample_sr >= 16000: | |
| tgt_sr = resample_sr | |
| else: | |
| tgt_sr = self.tgt_sr | |
| index_info = ( | |
| "Index: %s." % file_index | |
| if isinstance(file_index, str) and os.path.exists(file_index) | |
| else "Index not used." | |
| ) | |
| end_time = time.time() | |
| total_time = end_time - start_time | |
| times.append(total_time) | |
| return ( | |
| ("Success.", index_info, times), | |
| (tgt_sr, audio_opt), | |
| ) | |
| except: | |
| info = traceback.format_exc() | |
| logger.warn(info) | |
| return ( | |
| (info, None, [None, None, None, None]), | |
| (None, None) | |
| ) | |
| def vc_single( | |
| self, | |
| sid, | |
| input_audio_path1, | |
| f0_up_key, | |
| f0_method, | |
| file_index, | |
| file_index2, | |
| index_rate, | |
| filter_radius, | |
| resample_sr, | |
| rms_mix_rate, | |
| protect, | |
| format1, | |
| crepe_hop_length, | |
| do_formant, | |
| quefrency, | |
| timbre, | |
| f0_min, | |
| f0_max, | |
| f0_autotune, | |
| hubert_model_path = "assets/hubert/hubert_base.pt" | |
| ): | |
| """ | |
| Performs inference with saving | |
| Parameters: | |
| - sid (int) | |
| - input_audio_path1 (str) | |
| - f0_up_key (int) | |
| - f0_method (str) | |
| - file_index (str) | |
| - file_index2 (str) | |
| - index_rate (float) | |
| - filter_radius (int) | |
| - resample_sr (int) | |
| - rms_mix_rate (float) | |
| - protect (float) | |
| - format1 (str) | |
| - crepe_hop_length (int) | |
| - do_formant (bool) | |
| - quefrency (float) | |
| - timbre (float) | |
| - f0_min (str) | |
| - f0_max (str) | |
| - f0_autotune (bool) | |
| - hubert_model_path (str) | |
| Returns: | |
| Tuple(Tuple(status, index_info, times), Tuple(sr, data), output_path): | |
| - Tuple(status, index_info, times): | |
| - status (str): either "Success." or an error | |
| - index_info (str): index path if used | |
| - times (list): [npy_time, f0_time, infer_time, total_time] | |
| - Tuple(sr, data): Audio data results. | |
| - output_path (str): Audio results path | |
| """ | |
| global total_time | |
| total_time = 0 | |
| start_time = time.time() | |
| if not input_audio_path1: | |
| return "You need to upload an audio", None, None | |
| if not os.path.exists(input_audio_path1): | |
| return "Audio was not properly selected or doesn't exist", None, None | |
| f0_up_key = int(f0_up_key) | |
| if not f0_min.isdigit(): | |
| f0_min = note_to_hz(f0_min) | |
| if f0_min: | |
| print(f"Converted Min pitch: freq - {f0_min}") | |
| else: | |
| f0_min = 50 | |
| print("Invalid minimum pitch note. Defaulting to 50hz.") | |
| else: | |
| f0_min = float(f0_min) | |
| if not f0_max.isdigit(): | |
| f0_max = note_to_hz(f0_max) | |
| if f0_max: | |
| print(f"Converted Max pitch: freq - {f0_max}") | |
| else: | |
| f0_max = 1100 | |
| print("Invalid maximum pitch note. Defaulting to 1100hz.") | |
| else: | |
| f0_max = float(f0_max) | |
| try: | |
| print(f"Attempting to load {input_audio_path1}...") | |
| audio = load_audio(file=input_audio_path1, | |
| sr=16000, | |
| DoFormant=do_formant, | |
| Quefrency=quefrency, | |
| Timbre=timbre) | |
| 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(hubert_model_path, self.config) | |
| try: | |
| self.if_f0 = self.cpt.get("f0", 1) | |
| except NameError: | |
| message = "Model was not properly selected" | |
| print(message) | |
| return message, None | |
| if file_index and not file_index == "" and isinstance(file_index, str): | |
| file_index = file_index.strip(" ") \ | |
| .strip('"') \ | |
| .strip("\n") \ | |
| .strip('"') \ | |
| .strip(" ") \ | |
| .replace("trained", "added") | |
| elif file_index2: | |
| file_index = file_index2 | |
| else: | |
| file_index = "" | |
| audio_opt = self.pipeline.pipeline( | |
| self.hubert_model, | |
| self.net_g, | |
| sid, | |
| audio, | |
| input_audio_path1, | |
| 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, | |
| crepe_hop_length, | |
| f0_autotune, | |
| f0_min=f0_min, | |
| f0_max=f0_max | |
| ) | |
| if self.tgt_sr != resample_sr >= 16000: | |
| tgt_sr = resample_sr | |
| else: | |
| tgt_sr = self.tgt_sr | |
| index_info = ( | |
| "Index: %s." % file_index | |
| if isinstance(file_index, str) and os.path.exists(file_index) | |
| else "Index not used." | |
| ) | |
| opt_root = os.path.join(os.getcwd(), "output") | |
| os.makedirs(opt_root, exist_ok=True) | |
| output_count = 1 | |
| while True: | |
| opt_filename = f"{os.path.splitext(os.path.basename(input_audio_path1))[0]}{os.path.basename(os.path.dirname(file_index))}{f0_method.capitalize()}_{output_count}.{format1}" | |
| current_output_path = os.path.join(opt_root, opt_filename) | |
| if not os.path.exists(current_output_path): | |
| break | |
| output_count += 1 | |
| try: | |
| if format1 in ["wav", "flac"]: | |
| sf.write( | |
| current_output_path, | |
| audio_opt, | |
| self.tgt_sr, | |
| ) | |
| else: | |
| with BytesIO() as wavf: | |
| sf.write( | |
| wavf, | |
| audio_opt, | |
| self.tgt_sr, | |
| format="wav" | |
| ) | |
| wavf.seek(0, 0) | |
| with open(current_output_path, "wb") as outf: | |
| wav2(wavf, outf, format1) | |
| except: | |
| info = traceback.format_exc() | |
| end_time = time.time() | |
| total_time = end_time - start_time | |
| times.append(total_time) | |
| return ( | |
| ("Success.", index_info, times), | |
| (tgt_sr, audio_opt), | |
| current_output_path | |
| ) | |
| except: | |
| info = traceback.format_exc() | |
| logger.warn(info) | |
| return ( | |
| (info, None, [None, None, None, None]), | |
| (None, None), | |
| None | |
| ) |