537 lines
24 KiB
Python
537 lines
24 KiB
Python
import gc
|
|
import hashlib
|
|
import io
|
|
import json
|
|
import logging
|
|
import os
|
|
import pickle
|
|
import time
|
|
from pathlib import Path
|
|
|
|
import librosa
|
|
import numpy as np
|
|
|
|
# import onnxruntime
|
|
import soundfile
|
|
import torch
|
|
import torchaudio
|
|
|
|
import cluster
|
|
import utils
|
|
from diffusion.unit2mel import load_model_vocoder
|
|
from inference import slicer
|
|
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
|
|
|
|
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,
|
|
spk_mix_enable = False,
|
|
feature_retrieval = False
|
|
):
|
|
self.net_g_path = net_g_path
|
|
self.only_diffusion = only_diffusion
|
|
self.shallow_diffusion = shallow_diffusion
|
|
self.feature_retrieval = feature_retrieval
|
|
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
|
|
if not self.only_diffusion:
|
|
self.hps_ms = utils.get_hparams_from_file(config_path,True)
|
|
self.target_sample = self.hps_ms.data.sampling_rate
|
|
self.hop_size = self.hps_ms.data.hop_length
|
|
self.spk2id = self.hps_ms.spk
|
|
self.unit_interpolate_mode = self.hps_ms.data.unit_interpolate_mode if self.hps_ms.data.unit_interpolate_mode is not None else 'left'
|
|
self.vol_embedding = self.hps_ms.model.vol_embedding if self.hps_ms.model.vol_embedding is not None else False
|
|
self.speech_encoder = self.hps_ms.model.speech_encoder if self.hps_ms.model.speech_encoder is not None else '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
|
|
self.unit_interpolate_mode = self.diffusion_args.data.unit_interpolate_mode if self.diffusion_args.data.unit_interpolate_mode is not None else 'left'
|
|
if spk_mix_enable:
|
|
self.diffusion_model.init_spkmix(len(self.spk2id))
|
|
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(spk_mix_enable)
|
|
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):
|
|
if self.feature_retrieval:
|
|
with open(cluster_model_path,"rb") as f:
|
|
self.cluster_model = pickle.load(f)
|
|
self.big_npy = None
|
|
self.now_spk_id = -1
|
|
else:
|
|
self.cluster_model = cluster.get_cluster_model(cluster_model_path)
|
|
else:
|
|
self.feature_retrieval=False
|
|
|
|
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, spk_mix_enable=False):
|
|
# 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)
|
|
self.dtype = list(self.net_g_ms.parameters())[0].dtype
|
|
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)
|
|
if spk_mix_enable:
|
|
self.net_g_ms.EnableCharacterMix(len(self.spk2id), 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],self.unit_interpolate_mode)
|
|
|
|
if cluster_infer_ratio !=0:
|
|
if self.feature_retrieval:
|
|
speaker_id = self.spk2id.get(speaker)
|
|
if speaker_id is None:
|
|
raise RuntimeError("The name you entered is not in the speaker list!")
|
|
if not speaker_id and type(speaker) is int:
|
|
if len(self.spk2id.__dict__) >= speaker:
|
|
speaker_id = speaker
|
|
feature_index = self.cluster_model[speaker_id]
|
|
feat_np = c.transpose(0,1).cpu().numpy()
|
|
if self.big_npy is None or self.now_spk_id != speaker_id:
|
|
self.big_npy = feature_index.reconstruct_n(0, feature_index.ntotal)
|
|
self.now_spk_id = speaker_id
|
|
print("starting feature retrieval...")
|
|
score, ix = feature_index.search(feat_np, k=8)
|
|
weight = np.square(1 / score)
|
|
weight /= weight.sum(axis=1, keepdims=True)
|
|
npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
|
|
c = cluster_infer_ratio * npy + (1 - cluster_infer_ratio) * feat_np
|
|
c = torch.FloatTensor(c).to(self.dev).transpose(0,1)
|
|
print("end feature retrieval...")
|
|
else:
|
|
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,
|
|
frame = 0,
|
|
spk_mix = False,
|
|
second_encoding = False,
|
|
loudness_envelope_adjustment = 1
|
|
):
|
|
wav, sr = librosa.load(raw_path, sr=self.target_sample)
|
|
if spk_mix:
|
|
c, f0, uv = self.get_unit_f0(wav, tran, 0, None, f0_filter,f0_predictor,cr_threshold=cr_threshold)
|
|
n_frames = f0.size(1)
|
|
sid = speaker[:, frame:frame+n_frames].transpose(0,1)
|
|
else:
|
|
speaker_id = self.spk2id.get(speaker)
|
|
if not speaker_id and type(speaker) is int:
|
|
if len(self.spk2id.__dict__) >= speaker:
|
|
speaker_id = speaker
|
|
if speaker_id is None:
|
|
raise RuntimeError("The name you entered is not in the speaker list!")
|
|
sid = torch.LongTensor([int(speaker_id)]).to(self.dev).unsqueeze(0)
|
|
c, f0, uv = self.get_unit_f0(wav, tran, cluster_infer_ratio, speaker, f0_filter,f0_predictor,cr_threshold=cr_threshold)
|
|
n_frames = f0.size(1)
|
|
c = c.to(self.dtype)
|
|
f0 = f0.to(self.dtype)
|
|
uv = uv.to(self.dtype)
|
|
with torch.no_grad():
|
|
start = time.time()
|
|
vol = None
|
|
if not self.only_diffusion:
|
|
vol = self.volume_extractor.extract(torch.FloatTensor(wav).to(self.dev)[None,:])[None,:].to(self.dev) if self.vol_embedding else None
|
|
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)
|
|
audio = audio[0,0].data.float()
|
|
audio_mel = self.vocoder.extract(audio[None,:],self.target_sample) if self.shallow_diffusion else None
|
|
else:
|
|
audio = torch.FloatTensor(wav).to(self.dev)
|
|
audio_mel = None
|
|
if self.dtype != torch.float32:
|
|
c = c.to(torch.float32)
|
|
f0 = f0.to(torch.float32)
|
|
uv = uv.to(torch.float32)
|
|
if self.only_diffusion or self.shallow_diffusion:
|
|
vol = self.volume_extractor.extract(audio[None,:])[None,:,None].to(self.dev) if vol is None else vol[:,:,None]
|
|
if self.shallow_diffusion and second_encoding:
|
|
audio16k = librosa.resample(audio.detach().cpu().numpy(), orig_sr=self.target_sample, target_sr=16000)
|
|
audio16k = torch.from_numpy(audio16k).to(self.dev)
|
|
c = self.hubert_model.encoder(audio16k)
|
|
c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[1],self.unit_interpolate_mode)
|
|
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)
|
|
if loudness_envelope_adjustment != 1:
|
|
audio = utils.change_rms(wav,self.target_sample,audio,self.target_sample,loudness_envelope_adjustment)
|
|
use_time = time.time() - start
|
|
print("vits use time:{}".format(use_time))
|
|
return audio, audio.shape[-1], n_frames
|
|
|
|
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,
|
|
use_spk_mix = False,
|
|
second_encoding = False,
|
|
loudness_envelope_adjustment = 1
|
|
):
|
|
if use_spk_mix:
|
|
if len(self.spk2id) == 1:
|
|
spk = self.spk2id.keys()[0]
|
|
use_spk_mix = False
|
|
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
|
|
|
|
if use_spk_mix:
|
|
assert len(self.spk2id) == len(spk)
|
|
audio_length = 0
|
|
for (slice_tag, data) in audio_data:
|
|
aud_length = int(np.ceil(len(data) / audio_sr * self.target_sample))
|
|
if slice_tag:
|
|
audio_length += aud_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):
|
|
pad_len = int(audio_sr * pad_seconds)
|
|
per_length = int(np.ceil(len(dat) / audio_sr * self.target_sample))
|
|
a_length = per_length + 2 * pad_len
|
|
audio_length += a_length // self.hop_size
|
|
audio_length += len(audio_data)
|
|
spk_mix_tensor = torch.zeros(size=(len(spk), audio_length)).to(self.dev)
|
|
for i in range(len(spk)):
|
|
last_end = None
|
|
for mix in spk[i]:
|
|
if mix[3]<0. or mix[2]<0.:
|
|
raise RuntimeError("mix value must higer Than zero!")
|
|
begin = int(audio_length * mix[0])
|
|
end = int(audio_length * mix[1])
|
|
length = end - begin
|
|
if length<=0:
|
|
raise RuntimeError("begin Must lower Than end!")
|
|
step = (mix[3] - mix[2])/length
|
|
if last_end is not None:
|
|
if last_end != begin:
|
|
raise RuntimeError("[i]EndTime Must Equal [i+1]BeginTime!")
|
|
last_end = end
|
|
if step == 0.:
|
|
spk_mix_data = torch.zeros(length).to(self.dev) + mix[2]
|
|
else:
|
|
spk_mix_data = torch.arange(mix[2],mix[3],step).to(self.dev)
|
|
if(len(spk_mix_data)<length):
|
|
num_pad = length - len(spk_mix_data)
|
|
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]
|
|
|