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init
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import hashlib
import io
import json
import logging
import os
import time
from pathlib import Path
from inference import slicer
import gc
import librosa
import numpy as np
# import onnxruntime
import soundfile
import torch
import torchaudio
import cluster
import utils
from models import SynthesizerTrn
from diffusion.unit2mel import load_model_vocoder
import yaml
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
def split_list_by_n(list_collection, n, pre=0):
for i in range(0, len(list_collection), n):
yield list_collection[i-pre if i-pre>=0 else i: i + n]
class F0FilterException(Exception):
pass
class Svc(object):
def __init__(self, net_g_path, config_path,
device=None,
cluster_model_path="logs/44k/kmeans_10000.pt",
nsf_hifigan_enhance = False,
diffusion_model_path="logs/44k/diffusion/model_0.pt",
diffusion_config_path="configs/diffusion.yaml",
shallow_diffusion = False,
only_diffusion = False,
):
self.net_g_path = net_g_path
self.only_diffusion = only_diffusion
self.shallow_diffusion = shallow_diffusion
if device is None:
# self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.dev = torch.device("cpu")
else:
self.dev = torch.device(device)
self.net_g_ms = None
if not self.only_diffusion:
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
try:
self.speech_encoder = self.hps_ms.model.speech_encoder
except Exception as e:
self.speech_encoder = 'vec768l12'
self.nsf_hifigan_enhance = nsf_hifigan_enhance
if self.shallow_diffusion or self.only_diffusion:
if os.path.exists(diffusion_model_path) and os.path.exists(diffusion_model_path):
self.diffusion_model,self.vocoder,self.diffusion_args = load_model_vocoder(diffusion_model_path,self.dev,config_path=diffusion_config_path)
if self.only_diffusion:
self.target_sample = self.diffusion_args.data.sampling_rate
self.hop_size = self.diffusion_args.data.block_size
self.spk2id = self.diffusion_args.spk
self.speech_encoder = self.diffusion_args.data.encoder
else:
print("No diffusion model or config found. Shallow diffusion mode will False")
self.shallow_diffusion = self.only_diffusion = False
# load hubert and model
if not self.only_diffusion:
self.load_model()
self.hubert_model = utils.get_speech_encoder(self.speech_encoder,device=self.dev)
self.volume_extractor = utils.Volume_Extractor(self.hop_size)
else:
self.hubert_model = utils.get_speech_encoder(self.diffusion_args.data.encoder,device=self.dev)
self.volume_extractor = utils.Volume_Extractor(self.diffusion_args.data.block_size)
if os.path.exists(cluster_model_path):
self.cluster_model = cluster.get_cluster_model(cluster_model_path)
if self.shallow_diffusion : self.nsf_hifigan_enhance = False
if self.nsf_hifigan_enhance:
from modules.enhancer import Enhancer
self.enhancer = Enhancer('nsf-hifigan', 'pretrain/nsf_hifigan/model',device=self.dev)
def load_model(self):
# get model configuration
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, wav, tran, cluster_infer_ratio, speaker, f0_filter ,f0_predictor,cr_threshold=0.05):
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)
f0, uv = f0_predictor_object.compute_f0_uv(wav)
if f0_filter and sum(f0) == 0:
raise F0FilterException("No voice detected")
f0 = torch.FloatTensor(f0).to(self.dev)
uv = torch.FloatTensor(uv).to(self.dev)
f0 = f0 * 2 ** (tran / 12)
f0 = f0.unsqueeze(0)
uv = uv.unsqueeze(0)
wav16k = librosa.resample(wav, orig_sr=self.target_sample, target_sr=16000)
wav16k = torch.from_numpy(wav16k).to(self.dev)
c = self.hubert_model.encoder(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,
f0_filter=False,
f0_predictor='pm',
enhancer_adaptive_key = 0,
cr_threshold = 0.05,
k_step = 100
):
speaker_id = self.spk2id.get(speaker)
if not speaker_id and type(speaker) is int:
if len(self.spk2id.__dict__) >= speaker:
speaker_id = speaker
sid = torch.LongTensor([int(speaker_id)]).to(self.dev).unsqueeze(0)
wav, sr = librosa.load(raw_path, sr=self.target_sample)
c, f0, uv = self.get_unit_f0(wav, tran, cluster_infer_ratio, speaker, f0_filter,f0_predictor,cr_threshold=cr_threshold)
if "half" in self.net_g_path and torch.cuda.is_available():
c = c.half()
with torch.no_grad():
start = time.time()
if not self.only_diffusion:
audio,f0 = self.net_g_ms.infer(c, f0=f0, g=sid, uv=uv, predict_f0=auto_predict_f0, noice_scale=noice_scale)
audio = audio[0,0].data.float()
if self.shallow_diffusion:
audio_mel = self.vocoder.extract(audio[None,:],self.target_sample)
else:
audio = torch.FloatTensor(wav).to(self.dev)
audio_mel = None
if self.only_diffusion or self.shallow_diffusion:
vol = self.volume_extractor.extract(audio[None,:])[None,:,None].to(self.dev)
f0 = f0[:,:,None]
c = c.transpose(-1,-2)
audio_mel = self.diffusion_model(
c,
f0,
vol,
spk_id = sid,
spk_mix_dict = None,
gt_spec=audio_mel,
infer=True,
infer_speedup=self.diffusion_args.infer.speedup,
method=self.diffusion_args.infer.method,
k_step=k_step)
audio = self.vocoder.infer(audio_mel, f0).squeeze()
if self.nsf_hifigan_enhance:
audio, _ = self.enhancer.enhance(
audio[None,:],
self.target_sample,
f0[:,:,None],
self.hps_ms.data.hop_length,
adaptive_key = enhancer_adaptive_key)
use_time = time.time() - start
print("vits use time:{}".format(use_time))
return audio, audio.shape[-1]
def clear_empty(self):
# clean up vram
torch.cuda.empty_cache()
def unload_model(self):
# unload model
self.net_g_ms = self.net_g_ms.to("cpu")
del self.net_g_ms
if hasattr(self,"enhancer"):
self.enhancer.enhancer = self.enhancer.enhancer.to("cpu")
del self.enhancer.enhancer
del self.enhancer
gc.collect()
def slice_inference(self,
raw_audio_path,
spk,
tran,
slice_db,
cluster_infer_ratio,
auto_predict_f0,
noice_scale,
pad_seconds=0.5,
clip_seconds=0,
lg_num=0,
lgr_num =0.75,
f0_predictor='pm',
enhancer_adaptive_key = 0,
cr_threshold = 0.05,
k_step = 100
):
wav_path = Path(raw_audio_path).with_suffix('.wav')
chunks = slicer.cut(wav_path, db_thresh=slice_db)
audio_data, audio_sr = slicer.chunks2audio(wav_path, chunks)
per_size = int(clip_seconds*audio_sr)
lg_size = int(lg_num*audio_sr)
lg_size_r = int(lg_size*lgr_num)
lg_size_c_l = (lg_size-lg_size_r)//2
lg_size_c_r = lg_size-lg_size_r-lg_size_c_l
lg = np.linspace(0,1,lg_size_r) if lg_size!=0 else 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)))
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 = 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
)
_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]