def convert(cfg):
dataset_path = Path(utils.to_absolute_path("datasets")) / cfg.dataset.path
with open(dataset_path / "speakers.json") as file:
speakers = sorted(json.load(file))
synthesis_list_path = Path(utils.to_absolute_path(cfg.synthesis_list))
with open(synthesis_list_path) as file:
synthesis_list = json.load(file)
in_dir = Path(utils.to_absolute_path(cfg.in_dir))
out_dir = Path(utils.to_absolute_path(cfg.out_dir))
out_dir.mkdir(exist_ok=True, parents=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
encoder = Encoder(**cfg.model.encoder)
decoder = Decoder(**cfg.model.decoder)
encoder.to(device)
decoder.to(device)
print("Load checkpoint from: {}:".format(cfg.checkpoint))
checkpoint_path = utils.to_absolute_path(cfg.checkpoint)
checkpoint = torch.load(checkpoint_path, map_location=lambda storage, loc: storage)
encoder.load_state_dict(checkpoint["encoder"])
decoder.load_state_dict(checkpoint["decoder"])
encoder.eval()
decoder.eval()
for wav_path, speaker_id, out_filename in tqdm(synthesis_list):
wav_path = in_dir / wav_path
wav, _ = librosa.load(
wav_path.with_suffix(".wav"),
sr=cfg.preprocessing.sr)
wav = wav / np.abs(wav).max() * 0.999
mel = librosa.feature.melspectrogram(
preemphasis(wav, cfg.preprocessing.preemph),
sr=cfg.preprocessing.sr,
n_fft=cfg.preprocessing.n_fft,
n_mels=cfg.preprocessing.n_mels,
hop_length=cfg.preprocessing.hop_length,
win_length=cfg.preprocessing.win_length,
fmin=cfg.preprocessing.fmin,
power=1)
logmel = librosa.amplitude_to_db(mel, top_db=cfg.preprocessing.top_db)
logmel = logmel / cfg.preprocessing.top_db + 1
mel = torch.FloatTensor(logmel).unsqueeze(0).to(device)
speaker = torch.LongTensor([speakers.index(speaker_id)]).to(device)
with torch.no_grad():
z, _ = encoder.encode(mel)
output = decoder.generate(z, speaker)
path = out_dir / out_filename
librosa.output.write_wav(path.with_suffix(".wav"), output.astype(np.float32), sr=cfg.preprocessing.sr)
def encode_dataset(cfg):
out_dir = Path(utils.to_absolute_path(cfg.out_dir))
out_dir.mkdir(exist_ok=True, parents=True)
root_path = Path(utils.to_absolute_path("datasets")) / cfg.dataset.path
with open(root_path / "test.json") as file:
metadata = json.load(file)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
encoder = Encoder(**cfg.model.encoder)
encoder.to(device)
print("Load checkpoint from: {}:".format(cfg.checkpoint))
checkpoint_path = utils.to_absolute_path(cfg.checkpoint)
checkpoint = torch.load(checkpoint_path,
map_location=lambda storage, loc: storage)
encoder.load_state_dict(checkpoint["encoder"])
encoder.eval()
if cfg.save_auxiliary:
auxiliary = []
def hook(module, input, output):
auxiliary.append(output.clone())
encoder.encoder[-1].register_forward_hook(hook)
for _, _, _, path in tqdm(metadata):
path = root_path.parent / path
mel = torch.from_numpy(np.load(
path.with_suffix(".mel.npy"))).unsqueeze(0).to(device)
with torch.no_grad():
z, c, indices = encoder.encode(mel)
z = z.squeeze().cpu().numpy()
out_path = out_dir / path.stem
with open(out_path.with_suffix(".txt"), "w") as file:
np.savetxt(file, z, fmt="%.16f")
if cfg.save_auxiliary:
aux_path = out_dir.parent / "auxiliary_embedding1"
aux_path.mkdir(exist_ok=True, parents=True)
out_path = aux_path / path.stem
c = c.squeeze().cpu().numpy()
with open(out_path.with_suffix(".txt"), "w") as file:
np.savetxt(file, c, fmt="%.16f")
aux_path = out_dir.parent / "auxiliary_embedding2"
aux_path.mkdir(exist_ok=True, parents=True)
out_path = aux_path / path.stem
aux = auxiliary.pop().squeeze().cpu().numpy()
with open(out_path.with_suffix(".txt"), "w") as file:
np.savetxt(file, aux, fmt="%.16f")
def convert(cfg):
dataset_path = Path(utils.to_absolute_path(
"datasets")) / cfg.dataset.path #zerospeech/datasets/2019/english
with open(dataset_path / "speakers.json") as file: # 말하는 사람들 이름 써있는 데이터
speakers = sorted(json.load(file)) # speakers라는 객체로 저장
synthesis_list_path = Path(utils.to_absolute_path(
cfg.synthesis_list)) # ???인걸 보니 우리가 파이썬에서 돌릴때 지정해줘야함
with open(synthesis_list_path) as file:
synthesis_list = json.load(
file) # datasets/2019/english에 있는 synthesis.json보면됨
in_dir = Path(utils.to_absolute_path(
cfg.in_dir)) # ???임. zerospeech 폴더로 경로따면 될듯. (./)
out_dir = Path(utils.to_absolute_path(
cfg.out_dir)) #???임. 목소리 변환된 결과를 저장할 경로
out_dir.mkdir(exist_ok=True, parents=True)
device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu") # gpu안되면 cpu로
encoder = Encoder(
**cfg.model.encoder) #ZeroSpeech/config/model/default에 있는 encoder
decoder = Decoder(
**cfg.model.decoder) #ZeroSpeech/config/model/default에 있는 decoder
encoder.to(device) # cpu or gpu
decoder.to(device) # cpu or gpu
print("Load checkpoint from: {}:".format(cfg.checkpoint)
) ### ???로 되어있는데 pretrained, 혹은 checkpoint까지 학습된 모델 있으면 그 모델의 위치로 지정
checkpoint_path = utils.to_absolute_path(cfg.checkpoint)
checkpoint = torch.load(checkpoint_path,
map_location=lambda storage, loc: storage
) # checkpoint에 지정된 weight들을 불러옵니다
encoder.load_state_dict(checkpoint["encoder"])
decoder.load_state_dict(checkpoint["decoder"])
encoder.eval()
decoder.eval()
meter = pyloudnorm.Meter(
cfg.preprocessing.sr
) #sr:16000으로 조정?? https://www.christiansteinmetz.com/projects-blog/pyloudnorm 소음 관련같습니다..
for wav_path, speaker_id, out_filename in tqdm(
synthesis_list
): #"english/test/S002_0379088085","V002","V002_0379088085"
wav_path = in_dir / wav_path # ./english/test/S002_0379088085
wav, _ = librosa.load(wav_path.with_suffix(".wav"),
sr=cfg.preprocessing.sr)
ref_loudness = meter.integrated_loudness(wav) #인풋의 음량을 측정인듯
wav = wav / np.abs(wav).max() * 0.999
mel = librosa.feature.melspectrogram(
preemphasis(wav, cfg.preprocessing.preemph),
sr=cfg.preprocessing.sr,
n_fft=cfg.preprocessing.n_fft,
n_mels=cfg.preprocessing.n_mels,
hop_length=cfg.preprocessing.hop_length,
win_length=cfg.preprocessing.win_length,
fmin=cfg.preprocessing.fmin,
power=1)
logmel = librosa.amplitude_to_db(mel, top_db=cfg.preprocessing.top_db)
logmel = logmel / cfg.preprocessing.top_db + 1
mel = torch.FloatTensor(logmel).unsqueeze(0).to(
device) #unsqueeze()함수는 인수로 받은 위치에 새로운 차원을 삽입
#https://subinium.github.io/pytorch-Tensor-Variable/#%EB%8D%94%EB%AF%B8-%EC%B0%A8%EC%9B%90-%EC%B6%94%EA%B0%80%EC%99%80-%EC%82%AD%EC%A0%9C--squeeze--unsqueeze
#https://datascienceschool.net/view-notebook/4f3606fd839f4320a4120a56eec1e228/
speaker = torch.LongTensor([speakers.index(speaker_id)
]).to(device) # 마찬가지로 텐서로 만드는데
#텐서에는 자료형이라는 것이 있습니다. 각 데이터형별로 정의되어져 있는데,
#예를 들어 32비트의 유동 소수점은 torch.FloatTensor를, 64비트의 부호 있는 정수는 torch.LongTensor를 사용합니다.
#GPU 연산을 위한 자료형도 있습니다. 예를 들어 torch.cuda.FloatTensor가 그 예입니다.
# 즉 mel은 소수점있고 speaker는 소숫점 없으니까!
with torch.no_grad(
): # 자동미분,벡터연산한 결과의 연산기록 추적못하게 https://bob3rdnewbie.tistory.com/315
z, _ = encoder.encode(mel)
output = decoder.generate(z, speaker)
output_loudness = meter.integrated_loudness(output) #아웃풋의 음량을 측정인듯
output = pyloudnorm.normalize.loudness(output, output_loudness,
ref_loudness)
# 아웃풋의 음량을 input에 넣은 wav의 음량과 동일하게 변경
path = out_dir / out_filename
librosa.output.write_wav(path.with_suffix(".wav"),
output.astype(np.float32),
sr=cfg.preprocessing.sr)
def DDF(cfg):
filter_list_path = Path(utils.to_absolute_path(cfg.filter_list))
with open(filter_list_path) as file:
filter_list = json.load(file)
in_dir = Path(utils.to_absolute_path(cfg.in_dir))
out_dir = Path(utils.to_absolute_path(cfg.out_dir))
out_dir.mkdir(exist_ok=True, parents=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
encoder = Encoder(**cfg.model.encoder)
decoder = Decoder(**cfg.model.decoder)
encoder.to(device)
decoder.to(device)
print("Load checkpoint from: {}:".format(cfg.checkpoint))
checkpoint_path = utils.to_absolute_path(cfg.checkpoint)
checkpoint = torch.load(checkpoint_path,
map_location=lambda storage, loc: storage)
encoder.load_state_dict(checkpoint["encoder"])
decoder.load_state_dict(checkpoint["decoder"])
encoder.eval()
decoder.eval()
meter = pyloudnorm.Meter(cfg.preprocessing.sr)
#---------------------------------------
if cfg.privacy_preference == "Low":
for wav_path, speaker_id, out_filename in tqdm(filter_list):
wav_path = in_dir / wav_path
# librosa.load (it will return audio time series, and its sampling rate)
wav, _ = librosa.load(wav_path.with_suffix(".wav"),
sr=cfg.preprocessing.sr)
ref_loudness = meter.integrated_loudness(wav)
wav = wav / np.abs(wav).max() * 0.999
path = out_dir / out_filename
# to return raw recording in mel-spectrogram without any filtering
if cfg.output_type == "Embedding":
mel = librosa.feature.melspectrogram(
preemphasis(wav, cfg.preprocessing.preemph),
sr=cfg.preprocessing.sr,
n_fft=cfg.preprocessing.n_fft,
n_mels=cfg.preprocessing.n_mels,
hop_length=cfg.preprocessing.hop_length,
win_length=cfg.preprocessing.win_length,
fmin=cfg.preprocessing.fmin,
power=1)
logmel = librosa.amplitude_to_db(
mel, top_db=cfg.preprocessing.top_db)
logmel = logmel / cfg.preprocessing.top_db + 1
mel = torch.FloatTensor(logmel).squeeze().to(device).numpy()
np.savetxt(path.with_suffix(".mel.txt"), mel)
# to return raw recording in waveform without any filtering
if cfg.output_type == "Recording":
librosa.output.write_wav(path.with_suffix(".wav"),
wav.astype(np.float32),
sr=cfg.preprocessing.sr)
#---------------------------------------
if cfg.privacy_preference == "Moderate":
dataset_path = Path(
utils.to_absolute_path("Training/Datasets")) / cfg.dataset.path
with open(dataset_path / "speakers.json") as file:
speakers = sorted(json.load(file))
for wav_path, speaker_id, out_filename in tqdm(filter_list):
wav_path = in_dir / wav_path
wav, _ = librosa.load(wav_path.with_suffix(".wav"),
sr=cfg.preprocessing.sr)
ref_loudness = meter.integrated_loudness(wav)
wav = wav / np.abs(wav).max() * 0.999
mel = librosa.feature.melspectrogram(
preemphasis(wav, cfg.preprocessing.preemph),
sr=cfg.preprocessing.sr,
n_fft=cfg.preprocessing.n_fft,
n_mels=cfg.preprocessing.n_mels,
hop_length=cfg.preprocessing.hop_length,
win_length=cfg.preprocessing.win_length,
fmin=cfg.preprocessing.fmin,
power=1)
logmel = librosa.amplitude_to_db(mel,
top_db=cfg.preprocessing.top_db)
logmel = logmel / cfg.preprocessing.top_db + 1
mel = torch.FloatTensor(logmel).unsqueeze(0).to(device)
speaker = torch.LongTensor([speakers.index(speaker_id)]).to(device)
path = out_dir / out_filename
if cfg.output_type == "Recording":
with torch.no_grad():
vq, _ = encoder.encode(mel)
output = decoder.generate(vq, speaker)
output_loudness = meter.integrated_loudness(output)
output = pyloudnorm.normalize.loudness(output, output_loudness,
ref_loudness)
librosa.output.write_wav(path.with_suffix(".wav"),
output.astype(np.float32),
sr=cfg.preprocessing.sr)
if cfg.output_type == "Embedding":
with torch.no_grad():
vq, _ = encoder.encode(mel)
speaker = decoder.speaker(speaker)
vq = vq.squeeze().to(device).numpy()
speaker = speaker.squeeze().to(device).numpy()
np.savetxt(path.with_suffix(".vq.txt"), vq)
np.savetxt(path.with_suffix(".speaker.txt"), speaker)
#---------------------------------------
if cfg.privacy_preference == "High":
dataset_path = Path(
utils.to_absolute_path("Training/Datasets")) / cfg.dataset.path
with open(dataset_path / "speakers.json") as file:
speakers = sorted(json.load(file))
for wav_path, speaker_id, out_filename in tqdm(filter_list):
wav_path = in_dir / wav_path
wav, _ = librosa.load(wav_path.with_suffix(".wav"),
sr=cfg.preprocessing.sr)
ref_loudness = meter.integrated_loudness(wav)
wav = wav / np.abs(wav).max() * 0.999
mel = librosa.feature.melspectrogram(
preemphasis(wav, cfg.preprocessing.preemph),
sr=cfg.preprocessing.sr,
n_fft=cfg.preprocessing.n_fft,
n_mels=cfg.preprocessing.n_mels,
hop_length=cfg.preprocessing.hop_length,
win_length=cfg.preprocessing.win_length,
fmin=cfg.preprocessing.fmin,
power=1)
logmel = librosa.amplitude_to_db(mel,
top_db=cfg.preprocessing.top_db)
logmel = logmel / cfg.preprocessing.top_db + 1
mel = torch.FloatTensor(logmel).unsqueeze(0).to(device)
speaker = torch.LongTensor([speakers.index(speaker_id)]).to(device)
path = out_dir / out_filename
if cfg.output_type == "Recording":
with torch.no_grad():
vq, _ = encoder.encode(mel)
output = decoder.generate(vq, speaker)
output_loudness = meter.integrated_loudness(output)
output = pyloudnorm.normalize.loudness(output, output_loudness,
ref_loudness)
librosa.output.write_wav(path.with_suffix(".wav"),
output.astype(np.float32),
sr=cfg.preprocessing.sr)
if cfg.output_type == "Embedding":
with torch.no_grad():
vq, _ = encoder.encode(mel)
vq = vq.squeeze().cpu().numpy()
np.savetxt(path.with_suffix(".vq.txt"), vq)
def train_model(cfg):
tensorboard_path = Path(utils.to_absolute_path("tensorboard")) / cfg.checkpoint_dir
checkpoint_dir = Path(utils.to_absolute_path(cfg.checkpoint_dir))
writer = SummaryWriter(tensorboard_path)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
encoder = Encoder(**cfg.model.encoder)
vocoder = Vocoder(**cfg.model.vocoder)
encoder.to(device)
vocoder.to(device)
optimizer = optim.Adam(
vocoder.parameters(),
lr=cfg.training.optimizer.lr)
vocoder, optimizer = amp.initialize(vocoder, optimizer, opt_level="O1")
scheduler = optim.lr_scheduler.MultiStepLR(
optimizer, milestones=cfg.training.scheduler.milestones,
gamma=cfg.training.scheduler.gamma)
if cfg.resume:
print("Resume checkpoint from: {}:".format(cfg.resume))
resume_path = utils.to_absolute_path(cfg.resume)
checkpoint = torch.load(resume_path, map_location=lambda storage, loc: storage)
vocoder.load_state_dict(checkpoint["vocoder"])
optimizer.load_state_dict(checkpoint["optimizer"])
amp.load_state_dict(checkpoint["amp"])
scheduler.load_state_dict(checkpoint["scheduler"])
global_step = checkpoint["step"]
else:
global_step = 0
print("Resume cpc encoder from: {}:".format(cfg.cpc_checkpoint))
encoder_path = utils.to_absolute_path(cfg.cpc_checkpoint)
checkpoint = torch.load(encoder_path, map_location=lambda storage, loc: storage)
encoder.load_state_dict(checkpoint["encoder"])
encoder.eval()
root_path = Path(utils.to_absolute_path("datasets")) / cfg.dataset.path
dataset = WavDataset(
root=root_path,
hop_length=cfg.preprocessing.hop_length,
sr=cfg.preprocessing.sr,
sample_frames=cfg.training.sample_frames)
dataloader = DataLoader(
dataset,
batch_size=cfg.training.batch_size,
shuffle=True,
num_workers=cfg.training.n_workers,
pin_memory=True,
drop_last=True)
n_epochs = cfg.training.n_steps // len(dataloader) + 1
start_epoch = global_step // len(dataloader) + 1
for epoch in range(start_epoch, n_epochs + 1):
average_loss = 0
for i, (audio, mels, speakers) in enumerate(tqdm(dataloader), 1):
audio, mels, speakers = audio.to(device), mels.to(device), speakers.to(device)
optimizer.zero_grad()
with torch.no_grad():
_, _, indices = encoder.encode(mels)
output = vocoder(audio[:, :-1], indices, speakers)
loss = F.cross_entropy(output.transpose(1, 2), audio[:, 1:])
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), 1)
optimizer.step()
scheduler.step()
average_loss += (loss.item() - average_loss) / i
global_step += 1
if global_step % cfg.training.checkpoint_interval == 0:
save_checkpoint(
vocoder, optimizer, amp, scheduler, global_step, checkpoint_dir)
writer.add_scalar("loss/train", average_loss, global_step)
print("epoch:{}, loss:{:.3E}".format(epoch, average_loss))
def convert():
'''
dataset_path = Path(utils.to_absolute_path("datasets")) / cfg.dataset.path
with open(dataset_path / "speakers.json") as file:
speakers = sorted(json.load(file))
'''
dataset_path = Path('./cfg').absolute()
with open(dataset_path / "speakers.json") as file:
speakers = sorted(json.load(file))
with open(Path("./cfg/cfg.json").absolute()) as file:
para = json.load(file)
synthesis_list_path = Path('./dataset/english/synthesis.txt').absolute()
with open(synthesis_list_path) as file:
synthesis_list = json.load(file)
in_dir = Path('./dataset/english').absolute()
out_dir = Path('./output').absolute()
out_dir.mkdir(exist_ok=True, parents=True)
print(synthesis_list)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
encoder = Encoder(in_channels=para['encoder']['in_channels'],
channels=para['encoder']['channels'],
n_embeddings=para['encoder']['n_embeddings'],
embedding_dim=para['encoder']['embedding_dim'],
jitter=para['encoder']['jitter'])
decoder = Decoder(
in_channels=para['decoder']['in_channels'],
conditioning_channels=para['decoder']['conditioning_channels'],
n_speakers=para['decoder']['n_speakers'],
speaker_embedding_dim=para['decoder']['speaker_embedding_dim'],
mu_embedding_dim=para['decoder']['mu_embedding_dim'],
rnn_channels=para['decoder']['rnn_channels'],
fc_channels=para['decoder']['fc_channels'],
bits=para['decoder']['bits'],
hop_length=para['decoder']['hop_length'])
encoder.to(device)
decoder.to(device)
print("Load checkpoint from: {}:".format('./checkpoint/model.pt'))
checkpoint_path = Path('./checkpoint/model.pt').absolute()
checkpoint = torch.load(checkpoint_path,
map_location=lambda storage, loc: storage)
encoder.load_state_dict(checkpoint["encoder"])
decoder.load_state_dict(checkpoint["decoder"])
encoder.eval()
decoder.eval()
#meter = pyloudnorm.Meter(160000)
print('load finish')
for wav_path, speaker_id, out_filename in tqdm(synthesis_list):
wav_path = in_dir / wav_path
wav, _ = librosa.load(wav_path.with_suffix(".wav"),
sr=para['preprocess']['sr'])
#ref_loudness = meter.integrated_loudness(wav)
wav = wav / np.abs(wav).max() * 0.999
mel = librosa.feature.melspectrogram(
preemphasis(wav, para['preprocess']['preemph']),
sr=para['preprocess']['sr'],
n_fft=para['preprocess']['n_fft'],
n_mels=para['preprocess']['n_mels'],
hop_length=para['preprocess']['hop_length'],
win_length=para['preprocess']['win_length'],
fmin=para['preprocess']['fmin'],
power=1)
logmel = librosa.amplitude_to_db(mel,
top_db=para['preprocess']['top_db'])
logmel = logmel / para['preprocess']['top_db'] + 1
mel = torch.FloatTensor(logmel).unsqueeze(0).to(device)
speaker = torch.LongTensor([speakers.index(speaker_id)]).to(device)
with torch.no_grad():
z, _ = encoder.encode(mel)
output = decoder.generate(z, speaker)
#output_loudness = meter.integrated_loudness(output)
#output = pyloudnorm.normalize.loudness(output, output_loudness, ref_loudness)
path = out_dir / out_filename
librosa.output.write_wav(path.with_suffix(".wav"),
output.astype(np.float32),
sr=para['preprocess']['sr'])
