529 lines
24 KiB
Python
529 lines
24 KiB
Python
import gc
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import hashlib
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import io
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import json
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import logging
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import os
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import pickle
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import time
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from pathlib import Path
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import librosa
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import numpy as np
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# import onnxruntime
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import soundfile
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import torch
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import torchaudio
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import cluster
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import utils
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from diffusion.unit2mel import load_model_vocoder
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from inference import slicer
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from models import SynthesizerTrn
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logging.getLogger('matplotlib').setLevel(logging.WARNING)
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def read_temp(file_name):
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if not os.path.exists(file_name):
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with open(file_name, "w") as f:
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f.write(json.dumps({"info": "temp_dict"}))
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return {}
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else:
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try:
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with open(file_name, "r") as f:
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data = f.read()
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data_dict = json.loads(data)
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if os.path.getsize(file_name) > 50 * 1024 * 1024:
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f_name = file_name.replace("\\", "/").split("/")[-1]
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print(f"clean {f_name}")
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for wav_hash in list(data_dict.keys()):
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if int(time.time()) - int(data_dict[wav_hash]["time"]) > 14 * 24 * 3600:
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del data_dict[wav_hash]
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except Exception as e:
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print(e)
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print(f"{file_name} error,auto rebuild file")
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data_dict = {"info": "temp_dict"}
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return data_dict
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def write_temp(file_name, data):
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with open(file_name, "w") as f:
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f.write(json.dumps(data))
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def timeit(func):
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def run(*args, **kwargs):
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t = time.time()
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res = func(*args, **kwargs)
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print('executing \'%s\' costed %.3fs' % (func.__name__, time.time() - t))
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return res
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return run
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def format_wav(audio_path):
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if Path(audio_path).suffix == '.wav':
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return
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raw_audio, raw_sample_rate = librosa.load(audio_path, mono=True, sr=None)
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soundfile.write(Path(audio_path).with_suffix(".wav"), raw_audio, raw_sample_rate)
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def get_end_file(dir_path, end):
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file_lists = []
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for root, dirs, files in os.walk(dir_path):
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files = [f for f in files if f[0] != '.']
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dirs[:] = [d for d in dirs if d[0] != '.']
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for f_file in files:
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if f_file.endswith(end):
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file_lists.append(os.path.join(root, f_file).replace("\\", "/"))
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return file_lists
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def get_md5(content):
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return hashlib.new("md5", content).hexdigest()
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def fill_a_to_b(a, b):
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if len(a) < len(b):
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for _ in range(0, len(b) - len(a)):
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a.append(a[0])
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def mkdir(paths: list):
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for path in paths:
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if not os.path.exists(path):
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os.mkdir(path)
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def pad_array(arr, target_length):
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current_length = arr.shape[0]
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if current_length >= target_length:
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return arr
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else:
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pad_width = target_length - current_length
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pad_left = pad_width // 2
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pad_right = pad_width - pad_left
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padded_arr = np.pad(arr, (pad_left, pad_right), 'constant', constant_values=(0, 0))
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return padded_arr
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def split_list_by_n(list_collection, n, pre=0):
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for i in range(0, len(list_collection), n):
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yield list_collection[i-pre if i-pre>=0 else i: i + n]
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class F0FilterException(Exception):
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pass
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class Svc(object):
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def __init__(self, net_g_path, config_path,
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device=None,
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cluster_model_path="logs/44k/kmeans_10000.pt",
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nsf_hifigan_enhance = False,
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diffusion_model_path="logs/44k/diffusion/model_0.pt",
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diffusion_config_path="configs/diffusion.yaml",
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shallow_diffusion = False,
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only_diffusion = False,
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spk_mix_enable = False,
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feature_retrieval = False
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):
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self.net_g_path = net_g_path
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self.only_diffusion = only_diffusion
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self.shallow_diffusion = shallow_diffusion
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self.feature_retrieval = feature_retrieval
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if device is None:
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self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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else:
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self.dev = torch.device(device)
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self.net_g_ms = None
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if not self.only_diffusion:
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self.hps_ms = utils.get_hparams_from_file(config_path,True)
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self.target_sample = self.hps_ms.data.sampling_rate
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self.hop_size = self.hps_ms.data.hop_length
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self.spk2id = self.hps_ms.spk
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self.unit_interpolate_mode = self.hps_ms.data.unit_interpolate_mode if self.hps_ms.data.unit_interpolate_mode is not None else 'left'
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self.vol_embedding = self.hps_ms.model.vol_embedding if self.hps_ms.model.vol_embedding is not None else False
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self.speech_encoder = self.hps_ms.model.speech_encoder if self.hps_ms.model.speech_encoder is not None else 'vec768l12'
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self.nsf_hifigan_enhance = nsf_hifigan_enhance
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if self.shallow_diffusion or self.only_diffusion:
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if os.path.exists(diffusion_model_path) and os.path.exists(diffusion_model_path):
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self.diffusion_model,self.vocoder,self.diffusion_args = load_model_vocoder(diffusion_model_path,self.dev,config_path=diffusion_config_path)
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if self.only_diffusion:
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self.target_sample = self.diffusion_args.data.sampling_rate
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self.hop_size = self.diffusion_args.data.block_size
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self.spk2id = self.diffusion_args.spk
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self.speech_encoder = self.diffusion_args.data.encoder
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self.unit_interpolate_mode = self.diffusion_args.data.unit_interpolate_mode if self.diffusion_args.data.unit_interpolate_mode is not None else 'left'
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if spk_mix_enable:
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self.diffusion_model.init_spkmix(len(self.spk2id))
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else:
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print("No diffusion model or config found. Shallow diffusion mode will False")
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self.shallow_diffusion = self.only_diffusion = False
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# load hubert and model
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if not self.only_diffusion:
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self.load_model(spk_mix_enable)
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self.hubert_model = utils.get_speech_encoder(self.speech_encoder,device=self.dev)
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self.volume_extractor = utils.Volume_Extractor(self.hop_size)
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else:
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self.hubert_model = utils.get_speech_encoder(self.diffusion_args.data.encoder,device=self.dev)
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self.volume_extractor = utils.Volume_Extractor(self.diffusion_args.data.block_size)
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if os.path.exists(cluster_model_path):
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if self.feature_retrieval:
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with open(cluster_model_path,"rb") as f:
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self.cluster_model = pickle.load(f)
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self.big_npy = None
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self.now_spk_id = -1
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else:
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self.cluster_model = cluster.get_cluster_model(cluster_model_path)
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else:
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self.feature_retrieval=False
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if self.shallow_diffusion : self.nsf_hifigan_enhance = False
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if self.nsf_hifigan_enhance:
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from modules.enhancer import Enhancer
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self.enhancer = Enhancer('nsf-hifigan', 'pretrain/nsf_hifigan/model',device=self.dev)
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def load_model(self, spk_mix_enable=False):
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# get model configuration
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self.net_g_ms = SynthesizerTrn(
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self.hps_ms.data.filter_length // 2 + 1,
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self.hps_ms.train.segment_size // self.hps_ms.data.hop_length,
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**self.hps_ms.model)
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_ = utils.load_checkpoint(self.net_g_path, self.net_g_ms, None)
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if "half" in self.net_g_path and torch.cuda.is_available():
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_ = self.net_g_ms.half().eval().to(self.dev)
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else:
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_ = self.net_g_ms.eval().to(self.dev)
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if spk_mix_enable:
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self.net_g_ms.EnableCharacterMix(len(self.spk2id), self.dev)
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def get_unit_f0(self, wav, tran, cluster_infer_ratio, speaker, f0_filter ,f0_predictor,cr_threshold=0.05):
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f0_predictor_object = utils.get_f0_predictor(f0_predictor,hop_length=self.hop_size,sampling_rate=self.target_sample,device=self.dev,threshold=cr_threshold)
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f0, uv = f0_predictor_object.compute_f0_uv(wav)
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if f0_filter and sum(f0) == 0:
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raise F0FilterException("No voice detected")
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f0 = torch.FloatTensor(f0).to(self.dev)
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uv = torch.FloatTensor(uv).to(self.dev)
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f0 = f0 * 2 ** (tran / 12)
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f0 = f0.unsqueeze(0)
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uv = uv.unsqueeze(0)
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wav16k = librosa.resample(wav, orig_sr=self.target_sample, target_sr=16000)
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wav16k = torch.from_numpy(wav16k).to(self.dev)
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c = self.hubert_model.encoder(wav16k)
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c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[1],self.unit_interpolate_mode)
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if cluster_infer_ratio !=0:
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if self.feature_retrieval:
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speaker_id = self.spk2id.get(speaker)
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if speaker_id is None:
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raise RuntimeError("The name you entered is not in the speaker list!")
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if not speaker_id and type(speaker) is int:
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if len(self.spk2id.__dict__) >= speaker:
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speaker_id = speaker
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feature_index = self.cluster_model[speaker_id]
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feat_np = c.transpose(0,1).cpu().numpy()
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if self.big_npy is None or self.now_spk_id != speaker_id:
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self.big_npy = feature_index.reconstruct_n(0, feature_index.ntotal)
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self.now_spk_id = speaker_id
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print("starting feature retrieval...")
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score, ix = feature_index.search(feat_np, k=8)
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weight = np.square(1 / score)
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weight /= weight.sum(axis=1, keepdims=True)
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npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
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c = cluster_infer_ratio * npy + (1 - cluster_infer_ratio) * feat_np
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c = torch.FloatTensor(c).to(self.dev).transpose(0,1)
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print("end feature retrieval...")
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else:
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cluster_c = cluster.get_cluster_center_result(self.cluster_model, c.cpu().numpy().T, speaker).T
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cluster_c = torch.FloatTensor(cluster_c).to(self.dev)
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c = cluster_infer_ratio * cluster_c + (1 - cluster_infer_ratio) * c
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c = c.unsqueeze(0)
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return c, f0, uv
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def infer(self, speaker, tran, raw_path,
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cluster_infer_ratio=0,
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auto_predict_f0=False,
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noice_scale=0.4,
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f0_filter=False,
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f0_predictor='pm',
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enhancer_adaptive_key = 0,
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cr_threshold = 0.05,
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k_step = 100,
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frame = 0,
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spk_mix = False,
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second_encoding = False,
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loudness_envelope_adjustment = 1
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):
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wav, sr = librosa.load(raw_path, sr=self.target_sample)
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if spk_mix:
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c, f0, uv = self.get_unit_f0(wav, tran, 0, None, f0_filter,f0_predictor,cr_threshold=cr_threshold)
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n_frames = f0.size(1)
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sid = speaker[:, frame:frame+n_frames].transpose(0,1)
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else:
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speaker_id = self.spk2id.get(speaker)
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if not speaker_id and type(speaker) is int:
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if len(self.spk2id.__dict__) >= speaker:
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speaker_id = speaker
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if speaker_id is None:
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raise RuntimeError("The name you entered is not in the speaker list!")
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sid = torch.LongTensor([int(speaker_id)]).to(self.dev).unsqueeze(0)
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c, f0, uv = self.get_unit_f0(wav, tran, cluster_infer_ratio, speaker, f0_filter,f0_predictor,cr_threshold=cr_threshold)
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n_frames = f0.size(1)
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if "half" in self.net_g_path and torch.cuda.is_available():
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c = c.half()
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with torch.no_grad():
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start = time.time()
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vol = None
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if not self.only_diffusion:
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vol = self.volume_extractor.extract(torch.FloatTensor(wav).to(self.dev)[None,:])[None,:].to(self.dev) if self.vol_embedding else None
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audio,f0 = self.net_g_ms.infer(c, f0=f0, g=sid, uv=uv, predict_f0=auto_predict_f0, noice_scale=noice_scale,vol=vol)
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audio = audio[0,0].data.float()
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audio_mel = self.vocoder.extract(audio[None,:],self.target_sample) if self.shallow_diffusion else None
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else:
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audio = torch.FloatTensor(wav).to(self.dev)
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audio_mel = None
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if self.only_diffusion or self.shallow_diffusion:
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vol = self.volume_extractor.extract(audio[None,:])[None,:,None].to(self.dev) if vol is None else vol[:,:,None]
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if self.shallow_diffusion and second_encoding:
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audio16k = librosa.resample(audio.detach().cpu().numpy(), orig_sr=self.target_sample, target_sr=16000)
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audio16k = torch.from_numpy(audio16k).to(self.dev)
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c = self.hubert_model.encoder(audio16k)
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c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[1],self.unit_interpolate_mode)
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f0 = f0[:,:,None]
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c = c.transpose(-1,-2)
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audio_mel = self.diffusion_model(
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c,
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f0,
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vol,
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spk_id = sid,
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spk_mix_dict = None,
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gt_spec=audio_mel,
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infer=True,
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infer_speedup=self.diffusion_args.infer.speedup,
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method=self.diffusion_args.infer.method,
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k_step=k_step)
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audio = self.vocoder.infer(audio_mel, f0).squeeze()
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if self.nsf_hifigan_enhance:
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audio, _ = self.enhancer.enhance(
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audio[None,:],
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self.target_sample,
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f0[:,:,None],
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self.hps_ms.data.hop_length,
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adaptive_key = enhancer_adaptive_key)
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if loudness_envelope_adjustment != 1:
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audio = utils.change_rms(wav,self.target_sample,audio,self.target_sample,loudness_envelope_adjustment)
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use_time = time.time() - start
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print("vits use time:{}".format(use_time))
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return audio, audio.shape[-1], n_frames
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def clear_empty(self):
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# clean up vram
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torch.cuda.empty_cache()
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def unload_model(self):
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# unload model
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self.net_g_ms = self.net_g_ms.to("cpu")
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del self.net_g_ms
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if hasattr(self,"enhancer"):
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self.enhancer.enhancer = self.enhancer.enhancer.to("cpu")
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del self.enhancer.enhancer
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del self.enhancer
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gc.collect()
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def slice_inference(self,
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raw_audio_path,
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spk,
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tran,
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slice_db,
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cluster_infer_ratio,
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auto_predict_f0,
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noice_scale,
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pad_seconds=0.5,
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clip_seconds=0,
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lg_num=0,
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lgr_num =0.75,
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f0_predictor='pm',
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enhancer_adaptive_key = 0,
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cr_threshold = 0.05,
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k_step = 100,
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use_spk_mix = False,
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second_encoding = False,
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loudness_envelope_adjustment = 1
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):
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if use_spk_mix:
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if len(self.spk2id) == 1:
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spk = self.spk2id.keys()[0]
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use_spk_mix = False
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wav_path = Path(raw_audio_path).with_suffix('.wav')
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chunks = slicer.cut(wav_path, db_thresh=slice_db)
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audio_data, audio_sr = slicer.chunks2audio(wav_path, chunks)
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per_size = int(clip_seconds*audio_sr)
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lg_size = int(lg_num*audio_sr)
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lg_size_r = int(lg_size*lgr_num)
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lg_size_c_l = (lg_size-lg_size_r)//2
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lg_size_c_r = lg_size-lg_size_r-lg_size_c_l
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lg = np.linspace(0,1,lg_size_r) if lg_size!=0 else 0
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if use_spk_mix:
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assert len(self.spk2id) == len(spk)
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audio_length = 0
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for (slice_tag, data) in audio_data:
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aud_length = int(np.ceil(len(data) / audio_sr * self.target_sample))
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if slice_tag:
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audio_length += aud_length // self.hop_size
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continue
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if per_size != 0:
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datas = split_list_by_n(data, per_size,lg_size)
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else:
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datas = [data]
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for k,dat in enumerate(datas):
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pad_len = int(audio_sr * pad_seconds)
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per_length = int(np.ceil(len(dat) / audio_sr * self.target_sample))
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a_length = per_length + 2 * pad_len
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audio_length += a_length // self.hop_size
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audio_length += len(audio_data)
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spk_mix_tensor = torch.zeros(size=(len(spk), audio_length)).to(self.dev)
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for i in range(len(spk)):
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last_end = None
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for mix in spk[i]:
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if mix[3]<0. or mix[2]<0.:
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raise RuntimeError("mix value must higer Than zero!")
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begin = int(audio_length * mix[0])
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end = int(audio_length * mix[1])
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length = end - begin
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if length<=0:
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raise RuntimeError("begin Must lower Than end!")
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step = (mix[3] - mix[2])/length
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if last_end is not None:
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if last_end != begin:
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raise RuntimeError("[i]EndTime Must Equal [i+1]BeginTime!")
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last_end = end
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if step == 0.:
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spk_mix_data = torch.zeros(length).to(self.dev) + mix[2]
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else:
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spk_mix_data = torch.arange(mix[2],mix[3],step).to(self.dev)
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if(len(spk_mix_data)<length):
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num_pad = length - len(spk_mix_data)
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spk_mix_data = torch.nn.functional.pad(spk_mix_data, [0, num_pad], mode="reflect").to(self.dev)
|
|
spk_mix_tensor[i][begin:end] = spk_mix_data[:length]
|
|
|
|
spk_mix_ten = torch.sum(spk_mix_tensor,dim=0).unsqueeze(0).to(self.dev)
|
|
# spk_mix_tensor[0][spk_mix_ten<0.001] = 1.0
|
|
for i, x in enumerate(spk_mix_ten[0]):
|
|
if x == 0.0:
|
|
spk_mix_ten[0][i] = 1.0
|
|
spk_mix_tensor[:,i] = 1.0 / len(spk)
|
|
spk_mix_tensor = spk_mix_tensor / spk_mix_ten
|
|
if not ((torch.sum(spk_mix_tensor,dim=0) - 1.)<0.0001).all():
|
|
raise RuntimeError("sum(spk_mix_tensor) not equal 1")
|
|
spk = spk_mix_tensor
|
|
|
|
global_frame = 0
|
|
audio = []
|
|
for (slice_tag, data) in audio_data:
|
|
print(f'#=====segment start, {round(len(data) / audio_sr, 3)}s======')
|
|
# padd
|
|
length = int(np.ceil(len(data) / audio_sr * self.target_sample))
|
|
if slice_tag:
|
|
print('jump empty segment')
|
|
_audio = np.zeros(length)
|
|
audio.extend(list(pad_array(_audio, length)))
|
|
global_frame += length // self.hop_size
|
|
continue
|
|
if per_size != 0:
|
|
datas = split_list_by_n(data, per_size,lg_size)
|
|
else:
|
|
datas = [data]
|
|
for k,dat in enumerate(datas):
|
|
per_length = int(np.ceil(len(dat) / audio_sr * self.target_sample)) if clip_seconds!=0 else length
|
|
if clip_seconds!=0: print(f'###=====segment clip start, {round(len(dat) / audio_sr, 3)}s======')
|
|
# padd
|
|
pad_len = int(audio_sr * pad_seconds)
|
|
dat = np.concatenate([np.zeros([pad_len]), dat, np.zeros([pad_len])])
|
|
raw_path = io.BytesIO()
|
|
soundfile.write(raw_path, dat, audio_sr, format="wav")
|
|
raw_path.seek(0)
|
|
out_audio, out_sr, out_frame = self.infer(spk, tran, raw_path,
|
|
cluster_infer_ratio=cluster_infer_ratio,
|
|
auto_predict_f0=auto_predict_f0,
|
|
noice_scale=noice_scale,
|
|
f0_predictor = f0_predictor,
|
|
enhancer_adaptive_key = enhancer_adaptive_key,
|
|
cr_threshold = cr_threshold,
|
|
k_step = k_step,
|
|
frame = global_frame,
|
|
spk_mix = use_spk_mix,
|
|
second_encoding = second_encoding,
|
|
loudness_envelope_adjustment = loudness_envelope_adjustment
|
|
)
|
|
global_frame += out_frame
|
|
_audio = out_audio.cpu().numpy()
|
|
pad_len = int(self.target_sample * pad_seconds)
|
|
_audio = _audio[pad_len:-pad_len]
|
|
_audio = pad_array(_audio, per_length)
|
|
if lg_size!=0 and k!=0:
|
|
lg1 = audio[-(lg_size_r+lg_size_c_r):-lg_size_c_r] if lgr_num != 1 else audio[-lg_size:]
|
|
lg2 = _audio[lg_size_c_l:lg_size_c_l+lg_size_r] if lgr_num != 1 else _audio[0:lg_size]
|
|
lg_pre = lg1*(1-lg)+lg2*lg
|
|
audio = audio[0:-(lg_size_r+lg_size_c_r)] if lgr_num != 1 else audio[0:-lg_size]
|
|
audio.extend(lg_pre)
|
|
_audio = _audio[lg_size_c_l+lg_size_r:] if lgr_num != 1 else _audio[lg_size:]
|
|
audio.extend(list(_audio))
|
|
return np.array(audio)
|
|
|
|
class RealTimeVC:
|
|
def __init__(self):
|
|
self.last_chunk = None
|
|
self.last_o = None
|
|
self.chunk_len = 16000 # chunk length
|
|
self.pre_len = 3840 # cross fade length, multiples of 640
|
|
|
|
# Input and output are 1-dimensional numpy waveform arrays
|
|
|
|
def process(self, svc_model, speaker_id, f_pitch_change, input_wav_path,
|
|
cluster_infer_ratio=0,
|
|
auto_predict_f0=False,
|
|
noice_scale=0.4,
|
|
f0_filter=False):
|
|
|
|
import maad
|
|
audio, sr = torchaudio.load(input_wav_path)
|
|
audio = audio.cpu().numpy()[0]
|
|
temp_wav = io.BytesIO()
|
|
if self.last_chunk is None:
|
|
input_wav_path.seek(0)
|
|
|
|
audio, sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path,
|
|
cluster_infer_ratio=cluster_infer_ratio,
|
|
auto_predict_f0=auto_predict_f0,
|
|
noice_scale=noice_scale,
|
|
f0_filter=f0_filter)
|
|
|
|
audio = audio.cpu().numpy()
|
|
self.last_chunk = audio[-self.pre_len:]
|
|
self.last_o = audio
|
|
return audio[-self.chunk_len:]
|
|
else:
|
|
audio = np.concatenate([self.last_chunk, audio])
|
|
soundfile.write(temp_wav, audio, sr, format="wav")
|
|
temp_wav.seek(0)
|
|
|
|
audio, sr = svc_model.infer(speaker_id, f_pitch_change, temp_wav,
|
|
cluster_infer_ratio=cluster_infer_ratio,
|
|
auto_predict_f0=auto_predict_f0,
|
|
noice_scale=noice_scale,
|
|
f0_filter=f0_filter)
|
|
|
|
audio = audio.cpu().numpy()
|
|
ret = maad.util.crossfade(self.last_o, audio, self.pre_len)
|
|
self.last_chunk = audio[-self.pre_len:]
|
|
self.last_o = audio
|
|
return ret[self.chunk_len:2 * self.chunk_len]
|
|
|