352 lines
14 KiB
Python
352 lines
14 KiB
Python
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 time
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from pathlib import Path
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from inference import slicer
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import gc
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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 parselmouth
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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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from hubert import hubert_model
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import utils
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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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):
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self.net_g_path = net_g_path
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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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self.hps_ms = utils.get_hparams_from_file(config_path)
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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.nsf_hifigan_enhance = nsf_hifigan_enhance
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# 加载hubert
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self.hubert_model = utils.get_hubert_model().to(self.dev)
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self.load_model()
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if os.path.exists(cluster_model_path):
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self.cluster_model = cluster.get_cluster_model(cluster_model_path)
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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):
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# 获取模型配置
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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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def get_unit_f0(self, in_path, tran, cluster_infer_ratio, speaker, f0_filter ,F0_mean_pooling):
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wav, sr = librosa.load(in_path, sr=self.target_sample)
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if F0_mean_pooling == True:
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f0, uv = utils.compute_f0_uv_torchcrepe(torch.FloatTensor(wav), sampling_rate=self.target_sample, hop_length=self.hop_size,device=self.dev)
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if f0_filter and sum(f0) == 0:
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raise F0FilterException("未检测到人声")
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f0 = torch.FloatTensor(list(f0))
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uv = torch.FloatTensor(list(uv))
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if F0_mean_pooling == False:
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f0 = utils.compute_f0_parselmouth(wav, sampling_rate=self.target_sample, hop_length=self.hop_size)
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if f0_filter and sum(f0) == 0:
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raise F0FilterException("未检测到人声")
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f0, uv = utils.interpolate_f0(f0)
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f0 = torch.FloatTensor(f0)
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uv = torch.FloatTensor(uv)
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f0 = f0 * 2 ** (tran / 12)
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f0 = f0.unsqueeze(0).to(self.dev)
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uv = uv.unsqueeze(0).to(self.dev)
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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 = utils.get_hubert_content(self.hubert_model, wav_16k_tensor=wav16k)
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c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[1])
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if cluster_infer_ratio !=0:
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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_mean_pooling=False,
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enhancer_adaptive_key = 0
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):
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speaker_id = self.spk2id.__dict__.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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sid = torch.LongTensor([int(speaker_id)]).to(self.dev).unsqueeze(0)
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c, f0, uv = self.get_unit_f0(raw_path, tran, cluster_infer_ratio, speaker, f0_filter,F0_mean_pooling)
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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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audio = self.net_g_ms.infer(c, f0=f0, g=sid, uv=uv, predict_f0=auto_predict_f0, noice_scale=noice_scale)[0,0].data.float()
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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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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]
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def clear_empty(self):
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# 清理显存
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torch.cuda.empty_cache()
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def unload_model(self):
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# 卸载模型
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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_mean_pooling = False,
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enhancer_adaptive_key = 0
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):
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wav_path = raw_audio_path
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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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audio = []
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for (slice_tag, data) in audio_data:
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print(f'#=====segment start, {round(len(data) / audio_sr, 3)}s======')
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# padd
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length = int(np.ceil(len(data) / audio_sr * self.target_sample))
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if slice_tag:
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print('jump empty segment')
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_audio = np.zeros(length)
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audio.extend(list(pad_array(_audio, length)))
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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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per_length = int(np.ceil(len(dat) / audio_sr * self.target_sample)) if clip_seconds!=0 else length
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if clip_seconds!=0: print(f'###=====segment clip start, {round(len(dat) / audio_sr, 3)}s======')
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# padd
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pad_len = int(audio_sr * pad_seconds)
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dat = np.concatenate([np.zeros([pad_len]), dat, np.zeros([pad_len])])
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raw_path = io.BytesIO()
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soundfile.write(raw_path, dat, audio_sr, format="wav")
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raw_path.seek(0)
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out_audio, out_sr = self.infer(spk, tran, raw_path,
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cluster_infer_ratio=cluster_infer_ratio,
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auto_predict_f0=auto_predict_f0,
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noice_scale=noice_scale,
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F0_mean_pooling = F0_mean_pooling,
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enhancer_adaptive_key = enhancer_adaptive_key
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)
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_audio = out_audio.cpu().numpy()
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pad_len = int(self.target_sample * pad_seconds)
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_audio = _audio[pad_len:-pad_len]
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_audio = pad_array(_audio, per_length)
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if lg_size!=0 and k!=0:
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lg1 = audio[-(lg_size_r+lg_size_c_r):-lg_size_c_r] if lgr_num != 1 else audio[-lg_size:]
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lg2 = _audio[lg_size_c_l:lg_size_c_l+lg_size_r] if lgr_num != 1 else _audio[0:lg_size]
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lg_pre = lg1*(1-lg)+lg2*lg
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audio = audio[0:-(lg_size_r+lg_size_c_r)] if lgr_num != 1 else audio[0:-lg_size]
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audio.extend(lg_pre)
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_audio = _audio[lg_size_c_l+lg_size_r:] if lgr_num != 1 else _audio[lg_size:]
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audio.extend(list(_audio))
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return np.array(audio)
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class RealTimeVC:
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def __init__(self):
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self.last_chunk = None
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self.last_o = None
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self.chunk_len = 16000 # 区块长度
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self.pre_len = 3840 # 交叉淡化长度，640的倍数
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"""输入输出都是1维numpy 音频波形数组"""
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def process(self, svc_model, speaker_id, f_pitch_change, input_wav_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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import maad
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audio, sr = torchaudio.load(input_wav_path)
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audio = audio.cpu().numpy()[0]
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temp_wav = io.BytesIO()
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if self.last_chunk is None:
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input_wav_path.seek(0)
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audio, sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path,
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cluster_infer_ratio=cluster_infer_ratio,
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auto_predict_f0=auto_predict_f0,
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noice_scale=noice_scale,
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f0_filter=f0_filter)
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audio = audio.cpu().numpy()
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self.last_chunk = audio[-self.pre_len:]
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self.last_o = audio
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return audio[-self.chunk_len:]
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else:
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audio = np.concatenate([self.last_chunk, audio])
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soundfile.write(temp_wav, audio, sr, format="wav")
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temp_wav.seek(0)
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audio, sr = svc_model.infer(speaker_id, f_pitch_change, temp_wav,
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cluster_infer_ratio=cluster_infer_ratio,
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auto_predict_f0=auto_predict_f0,
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noice_scale=noice_scale,
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f0_filter=f0_filter)
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audio = audio.cpu().numpy()
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ret = maad.util.crossfade(self.last_o, audio, self.pre_len)
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self.last_chunk = audio[-self.pre_len:]
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self.last_o = audio
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return ret[self.chunk_len:2 * self.chunk_len]
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