so-vits-svc/inference/infer_tool.py

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]