import hashlib
import io
import json
import logging
import os
import time
from pathlib import Path
from inference import slicer
import librosa
import numpy as np
# import onnxruntime
import parselmouth
import soundfile
import torch
import torchaudio
import cluster
from hubert import hubert_model
import utils
from models import SynthesizerTrn
logging.getLogger('matplotlib').setLevel(logging.WARNING)
def read_temp(file_name):
if not os.path.exists(file_name):
with open(file_name, "w") as f:
f.write(json.dumps({"info": "temp_dict"}))
return {}
else:
try:
with open(file_name, "r") as f:
data = f.read()
data_dict = json.loads(data)
if os.path.getsize(file_name) > 50 * 1024 * 1024:
f_name = file_name.replace("\\", "/").split("/")[-1]
print(f"clean {f_name}")
for wav_hash in list(data_dict.keys()):
if int(time.time()) - int(data_dict[wav_hash]["time"]) > 14 * 24 * 3600:
del data_dict[wav_hash]
except Exception as e:
print(e)
print(f"{file_name} error,auto rebuild file")
data_dict = {"info": "temp_dict"}
return data_dict
def write_temp(file_name, data):
with open(file_name, "w") as f:
f.write(json.dumps(data))
def timeit(func):
def run(*args, **kwargs):
t = time.time()
res = func(*args, **kwargs)
print('executing \'%s\' costed %.3fs' % (func.__name__, time.time() - t))
return res
return run
def format_wav(audio_path):
if Path(audio_path).suffix == '.wav':
return
raw_audio, raw_sample_rate = librosa.load(audio_path, mono=True, sr=None)
soundfile.write(Path(audio_path).with_suffix(".wav"), raw_audio, raw_sample_rate)
def get_end_file(dir_path, end):
file_lists = []
for root, dirs, files in os.walk(dir_path):
files = [f for f in files if f[0] != '.']
dirs[:] = [d for d in dirs if d[0] != '.']
for f_file in files:
if f_file.endswith(end):
file_lists.append(os.path.join(root, f_file).replace("\\", "/"))
return file_lists
def get_md5(content):
return hashlib.new("md5", content).hexdigest()
def fill_a_to_b(a, b):
if len(a) < len(b):
for _ in range(0, len(b) - len(a)):
a.append(a[0])
def mkdir(paths: list):
for path in paths:
if not os.path.exists(path):
os.mkdir(path)
def pad_array(arr, target_length):
current_length = arr.shape[0]
if current_length >= target_length:
return arr
else:
pad_width = target_length - current_length
pad_left = pad_width // 2
pad_right = pad_width - pad_left
padded_arr = np.pad(arr, (pad_left, pad_right), 'constant', constant_values=(0, 0))
return padded_arr
class Svc(object):
def __init__(self, net_g_path, config_path,
device=None,
cluster_model_path="logs/44k/kmeans_10000.pt"):
self.net_g_path = net_g_path
if device is None:
self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
else:
self.dev = torch.device(device)
self.net_g_ms = None
self.hps_ms = utils.get_hparams_from_file(config_path)
self.target_sample = self.hps_ms.data.sampling_rate
self.hop_size = self.hps_ms.data.hop_length
self.spk2id = self.hps_ms.spk
# 加载hubert
self.hubert_model = utils.get_hubert_model().to(self.dev)
self.load_model()
if os.path.exists(cluster_model_path):
self.cluster_model = cluster.get_cluster_model(cluster_model_path)
def load_model(self):
# 获取模型配置
self.net_g_ms = SynthesizerTrn(
self.hps_ms.data.filter_length // 2 + 1,
self.hps_ms.train.segment_size // self.hps_ms.data.hop_length,
**self.hps_ms.model)
_ = utils.load_checkpoint(self.net_g_path, self.net_g_ms, None)
if "half" in self.net_g_path and torch.cuda.is_available():
_ = self.net_g_ms.half().eval().to(self.dev)
else:
_ = self.net_g_ms.eval().to(self.dev)
def get_unit_f0(self, in_path, tran, cluster_infer_ratio, speaker):
wav, sr = librosa.load(in_path, sr=self.target_sample)
f0 = utils.compute_f0_parselmouth(wav, sampling_rate=self.target_sample, hop_length=self.hop_size)
f0, uv = utils.interpolate_f0(f0)
f0 = torch.FloatTensor(f0)
uv = torch.FloatTensor(uv)
f0 = f0 * 2 ** (tran / 12)
f0 = f0.unsqueeze(0).to(self.dev)
uv = uv.unsqueeze(0).to(self.dev)
wav16k = librosa.resample(wav, orig_sr=self.target_sample, target_sr=16000)
wav16k = torch.from_numpy(wav16k).to(self.dev)
c = utils.get_hubert_content(self.hubert_model, wav_16k_tensor=wav16k)
c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[1])
if cluster_infer_ratio !=0:
cluster_c = cluster.get_cluster_center_result(self.cluster_model, c.cpu().numpy().T, speaker).T
cluster_c = torch.FloatTensor(cluster_c).to(self.dev)
c = cluster_infer_ratio * cluster_c + (1 - cluster_infer_ratio) * c
c = c.unsqueeze(0)
return c, f0, uv
def infer(self, speaker, tran, raw_path,
cluster_infer_ratio=0,
auto_predict_f0=False,
noice_scale=0.4):
speaker_id = self.spk2id[speaker]
sid = torch.LongTensor([int(speaker_id)]).to(self.dev).unsqueeze(0)
c, f0, uv = self.get_unit_f0(raw_path, tran, cluster_infer_ratio, speaker)
if "half" in self.net_g_path and torch.cuda.is_available():
c = c.half()
with torch.no_grad():
start = time.time()
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()
use_time = time.time() - start
print("vits use time:{}".format(use_time))
return audio, audio.shape[-1]
def slice_inference(self,raw_audio_path, spk, tran, slice_db,cluster_infer_ratio, auto_predict_f0,noice_scale, pad_seconds=0.5):
wav_path = raw_audio_path
chunks = slicer.cut(wav_path, db_thresh=slice_db)
audio_data, audio_sr = slicer.chunks2audio(wav_path, chunks)
audio = []
for (slice_tag, data) in audio_data:
print(f'#=====segment start, {round(len(data) / audio_sr, 3)}s======')
# padd
pad_len = int(audio_sr * pad_seconds)
data = np.concatenate([np.zeros([pad_len]), data, np.zeros([pad_len])])
length = int(np.ceil(len(data) / audio_sr * self.target_sample))
raw_path = io.BytesIO()
soundfile.write(raw_path, data, audio_sr, format="wav")
raw_path.seek(0)
if slice_tag:
print('jump empty segment')
_audio = np.zeros(length)
else:
out_audio, out_sr = self.infer(spk, tran, raw_path,
cluster_infer_ratio=cluster_infer_ratio,
auto_predict_f0=auto_predict_f0,
noice_scale=noice_scale
)
_audio = out_audio.cpu().numpy()
pad_len = int(self.target_sample * pad_seconds)
_audio = _audio[pad_len:-pad_len]
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 # 区块长度
self.pre_len = 3840 # 交叉淡化长度，640的倍数
"""输入输出都是1维numpy 音频波形数组"""
def process(self, svc_model, speaker_id, f_pitch_change, input_wav_path):
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)
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)
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]