so-vits-svc/inference/infer_tool.py

360 lines
14 KiB
Python

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
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
):
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
self.nsf_hifigan_enhance = nsf_hifigan_enhance
# load 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)
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, in_path, tran, cluster_infer_ratio, speaker, f0_filter ,f0_predictor,cr_threshold=0.05):
wav, sr = librosa.load(in_path, sr=self.target_sample)
if f0_predictor == "pm":
from modules.F0Predictor.PMF0Predictor import PMF0Predictor
f0_predictor_object = PMF0Predictor(hop_length=self.hop_size,sampling_rate=self.target_sample)
elif f0_predictor == "crepe":
from modules.F0Predictor.CrepeF0Predictor import CrepeF0Predictor
f0_predictor_object = CrepeF0Predictor(hop_length=self.hop_size,sampling_rate=self.target_sample,device=self.dev,threshold=cr_threshold)
elif f0_predictor == "harvest":
from modules.F0Predictor.HarvestF0Predictor import HarvestF0Predictor
f0_predictor_object = HarvestF0Predictor(hop_length=self.hop_size,sampling_rate=self.target_sample)
elif f0_predictor == "dio":
from modules.F0Predictor.DioF0Predictor import DioF0Predictor
f0_predictor_object = DioF0Predictor(hop_length=self.hop_size,sampling_rate=self.target_sample)
else:
raise Exception("Unknown f0 predictor")
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 = 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,
f0_filter=False,
f0_predictor='pm',
enhancer_adaptive_key = 0,
cr_threshold = 0.05
):
speaker_id = self.spk2id.__dict__.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)
c, f0, uv = self.get_unit_f0(raw_path, 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()
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()
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
):
wav_path = raw_audio_path
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
)
_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]