import hashlib
import json
import os
import time
from io import BytesIO
from pathlib import Path
import librosa
import numpy as np
import soundfile
import torch
import utils
from modules.fastspeech.pe import PitchExtractor
from network.diff.candidate_decoder import FFT
from network.diff.diffusion import GaussianDiffusion
from network.diff.net import DiffNet
from network.vocoders.base_vocoder import VOCODERS, get_vocoder_cls
from preprocessing.data_gen_utils import get_pitch_parselmouth, get_pitch_crepe, get_pitch_world
from preprocessing.hubertinfer import Hubertencoder
from utils.hparams import hparams, set_hparams
from utils.pitch_utils import denorm_f0, norm_interp_f0
if os.path.exists("chunks_temp.json"):
os.remove("chunks_temp.json")
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.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
f0_dict = read_temp("./infer_tools/f0_temp.json")
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 fill_a_to_b(a, b):
if len(a) < len(b):
for _ in range(0, len(b) - len(a)):
a.append(a[0])
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 mkdir(paths: list):
for path in paths:
if not os.path.exists(path):
os.mkdir(path)
def get_md5(content):
return hashlib.new("md5", content).hexdigest()
class Svc:
def __init__(self, project_name, config_name, hubert_gpu, model_path):
self.project_name = project_name
self.DIFF_DECODERS = {
'wavenet': lambda hp: DiffNet(hp['audio_num_mel_bins']),
'fft': lambda hp: FFT(
hp['hidden_size'], hp['dec_layers'], hp['dec_ffn_kernel_size'], hp['num_heads']),
}
self.model_path = model_path
self.dev = torch.device("cuda")
self._ = set_hparams(config=config_name, exp_name=self.project_name, infer=True,
reset=True,
hparams_str='',
print_hparams=False)
self.mel_bins = hparams['audio_num_mel_bins']
self.model = GaussianDiffusion(
phone_encoder=Hubertencoder(hparams['hubert_path']),
out_dims=self.mel_bins, denoise_fn=self.DIFF_DECODERS[hparams['diff_decoder_type']](hparams),
timesteps=hparams['timesteps'],
K_step=hparams['K_step'],
loss_type=hparams['diff_loss_type'],
spec_min=hparams['spec_min'], spec_max=hparams['spec_max'],
)
self.load_ckpt()
self.model.cuda()
hparams['hubert_gpu'] = hubert_gpu
self.hubert = Hubertencoder(hparams['hubert_path'])
self.pe = PitchExtractor().cuda()
utils.load_ckpt(self.pe, hparams['pe_ckpt'], 'model', strict=True)
self.pe.eval()
self.vocoder = get_vocoder_cls(hparams)()
def load_ckpt(self, model_name='model', force=True, strict=True):
utils.load_ckpt(self.model, self.model_path, model_name, force, strict)
def infer(self, in_path, key, acc, use_pe=True, use_crepe=True, thre=0.05, singer=False, **kwargs):
batch = self.pre(in_path, acc, use_crepe, thre)
spk_embed = batch.get('spk_embed') if not hparams['use_spk_id'] else batch.get('spk_ids')
hubert = batch['hubert']
ref_mels = batch["mels"]
energy=batch['energy']
mel2ph = batch['mel2ph']
batch['f0'] = batch['f0'] + (key / 12)
batch['f0'][batch['f0']>np.log2(hparams['f0_max'])]=0
f0 = batch['f0']
uv = batch['uv']
@timeit
def diff_infer():
outputs = self.model(
hubert.cuda(), spk_embed=spk_embed, mel2ph=mel2ph.cuda(), f0=f0.cuda(), uv=uv.cuda(),energy=energy.cuda(),
ref_mels=ref_mels.cuda(),
infer=True, **kwargs)
return outputs
outputs=diff_infer()
batch['outputs'] = self.model.out2mel(outputs['mel_out'])
batch['mel2ph_pred'] = outputs['mel2ph']
batch['f0_gt'] = denorm_f0(batch['f0'], batch['uv'], hparams)
if use_pe:
batch['f0_pred'] = self.pe(outputs['mel_out'])['f0_denorm_pred'].detach()
else:
batch['f0_pred'] = outputs.get('f0_denorm')
return self.after_infer(batch, singer, in_path)
@timeit
def after_infer(self, prediction, singer, in_path):
for k, v in prediction.items():
if type(v) is torch.Tensor:
prediction[k] = v.cpu().numpy()
# remove paddings
mel_gt = prediction["mels"]
mel_gt_mask = np.abs(mel_gt).sum(-1) > 0
mel_pred = prediction["outputs"]
mel_pred_mask = np.abs(mel_pred).sum(-1) > 0
mel_pred = mel_pred[mel_pred_mask]
mel_pred = np.clip(mel_pred, hparams['mel_vmin'], hparams['mel_vmax'])
f0_gt = prediction.get("f0_gt")
f0_pred = prediction.get("f0_pred")
if f0_pred is not None:
f0_gt = f0_gt[mel_gt_mask]
if len(f0_pred) > len(mel_pred_mask):
f0_pred = f0_pred[:len(mel_pred_mask)]
f0_pred = f0_pred[mel_pred_mask]
torch.cuda.is_available() and torch.cuda.empty_cache()
if singer:
data_path = in_path.replace("batch", "singer_data")
mel_path = data_path[:-4] + "_mel.npy"
f0_path = data_path[:-4] + "_f0.npy"
np.save(mel_path, mel_pred)
np.save(f0_path, f0_pred)
wav_pred = self.vocoder.spec2wav(mel_pred, f0=f0_pred)
return f0_gt, f0_pred, wav_pred
def temporary_dict2processed_input(self, item_name, temp_dict, use_crepe=True, thre=0.05):
'''
process data in temporary_dicts
'''
binarization_args = hparams['binarization_args']
@timeit
def get_pitch(wav, mel):
# get ground truth f0 by self.get_pitch_algorithm
global f0_dict
if use_crepe:
md5 = get_md5(wav)
if f"{md5}_gt" in f0_dict.keys():
print("load temp crepe f0")
gt_f0 = np.array(f0_dict[f"{md5}_gt"]["f0"])
coarse_f0 = np.array(f0_dict[f"{md5}_coarse"]["f0"])
else:
torch.cuda.is_available() and torch.cuda.empty_cache()
gt_f0, coarse_f0 = get_pitch_crepe(wav, mel, hparams, thre)
f0_dict[f"{md5}_gt"] = {"f0": gt_f0.tolist(), "time": int(time.time())}
f0_dict[f"{md5}_coarse"] = {"f0": coarse_f0.tolist(), "time": int(time.time())}
write_temp("./infer_tools/f0_temp.json", f0_dict)
else:
md5 = get_md5(wav)
if f"{md5}_gt_harvest" in f0_dict.keys():
print("load temp harvest f0")
gt_f0 = np.array(f0_dict[f"{md5}_gt_harvest"]["f0"])
coarse_f0 = np.array(f0_dict[f"{md5}_coarse_harvest"]["f0"])
else:
gt_f0, coarse_f0 = get_pitch_world(wav, mel, hparams)
f0_dict[f"{md5}_gt_harvest"] = {"f0": gt_f0.tolist(), "time": int(time.time())}
f0_dict[f"{md5}_coarse_harvest"] = {"f0": coarse_f0.tolist(), "time": int(time.time())}
write_temp("./infer_tools/f0_temp.json", f0_dict)
processed_input['f0'] = gt_f0
processed_input['pitch'] = coarse_f0
def get_align(mel, phone_encoded):
mel2ph = np.zeros([mel.shape[0]], int)
start_frame = 0
ph_durs = mel.shape[0] / phone_encoded.shape[0]
if hparams['debug']:
print(mel.shape, phone_encoded.shape, mel.shape[0] / phone_encoded.shape[0])
for i_ph in range(phone_encoded.shape[0]):
end_frame = int(i_ph * ph_durs + ph_durs + 0.5)
mel2ph[start_frame:end_frame + 1] = i_ph + 1
start_frame = end_frame + 1
processed_input['mel2ph'] = mel2ph
if hparams['vocoder'] in VOCODERS:
wav, mel = VOCODERS[hparams['vocoder']].wav2spec(temp_dict['wav_fn'])
else:
wav, mel = VOCODERS[hparams['vocoder'].split('.')[-1]].wav2spec(temp_dict['wav_fn'])
processed_input = {
'item_name': item_name, 'mel': mel,
'sec': len(wav) / hparams['audio_sample_rate'], 'len': mel.shape[0]
}
processed_input = {**temp_dict, **processed_input} # merge two dicts
if binarization_args['with_f0']:
get_pitch(wav, mel)
if binarization_args['with_hubert']:
st = time.time()
hubert_encoded = processed_input['hubert'] = self.hubert.encode(temp_dict['wav_fn'])
et = time.time()
dev = 'cuda' if hparams['hubert_gpu'] and torch.cuda.is_available() else 'cpu'
print(f'hubert (on {dev}) time used {et - st}')
if binarization_args['with_align']:
get_align(mel, hubert_encoded)
return processed_input
def pre(self, wav_fn, accelerate, use_crepe=True, thre=0.05):
if isinstance(wav_fn, BytesIO):
item_name = self.project_name
else:
song_info = wav_fn.split('/')
item_name = song_info[-1].split('.')[-2]
temp_dict = {'wav_fn': wav_fn, 'spk_id': self.project_name}
temp_dict = self.temporary_dict2processed_input(item_name, temp_dict, use_crepe, thre)
hparams['pndm_speedup'] = accelerate
batch = processed_input2batch([getitem(temp_dict)])
return batch
def getitem(item):
max_frames = hparams['max_frames']
spec = torch.Tensor(item['mel'])[:max_frames]
energy = (spec.exp() ** 2).sum(-1).sqrt()
mel2ph = torch.LongTensor(item['mel2ph'])[:max_frames] if 'mel2ph' in item else None
f0, uv = norm_interp_f0(item["f0"][:max_frames], hparams)
hubert = torch.Tensor(item['hubert'][:hparams['max_input_tokens']])
pitch = torch.LongTensor(item.get("pitch"))[:max_frames]
sample = {
"item_name": item['item_name'],
"hubert": hubert,
"mel": spec,
"pitch": pitch,
"energy": energy,
"f0": f0,
"uv": uv,
"mel2ph": mel2ph,
"mel_nonpadding": spec.abs().sum(-1) > 0,
}
return sample
def processed_input2batch(samples):
'''
Args:
samples: one batch of processed_input
NOTE:
the batch size is controlled by hparams['max_sentences']
'''
if len(samples) == 0:
return {}
item_names = [s['item_name'] for s in samples]
hubert = utils.collate_2d([s['hubert'] for s in samples], 0.0)
f0 = utils.collate_1d([s['f0'] for s in samples], 0.0)
pitch = utils.collate_1d([s['pitch'] for s in samples])
uv = utils.collate_1d([s['uv'] for s in samples])
energy = utils.collate_1d([s['energy'] for s in samples], 0.0)
mel2ph = utils.collate_1d([s['mel2ph'] for s in samples], 0.0) \
if samples[0]['mel2ph'] is not None else None
mels = utils.collate_2d([s['mel'] for s in samples], 0.0)
mel_lengths = torch.LongTensor([s['mel'].shape[0] for s in samples])
batch = {
'item_name': item_names,
'nsamples': len(samples),
'hubert': hubert,
'mels': mels,
'mel_lengths': mel_lengths,
'mel2ph': mel2ph,
'energy': energy,
'pitch': pitch,
'f0': f0,
'uv': uv,
}
return batch