def log(self, epoch, flag, audio_gen, f0, audio):
# log gradients
if flag == 'train':
self.log_writer.add_scalar(
f"{self.scale_tb_prefix}_grads/G/grad/tail",
self.netG.module.last_conv_layers[-1].weight_v.grad.abs().mean(
),
epoch,
)
self.log_writer.add_scalar(
f"{self.scale_tb_prefix}_grads/G/grad/head",
self.netG.module.first_conv.weight_v.grad.abs().mean(),
epoch,
)
if self.hp_adv and epoch > self.disc_start:
self.log_writer.add_scalar(
f"{self.scale_tb_prefix}_grads/D/grad/tail",
self.netD.module.conv_layers[-1].weight_v.grad.abs().mean(
), epoch)
self.log_writer.add_scalar(
f"{self.scale_tb_prefix}_grads/D/grad/head",
self.netD.module.conv_layers[0].weight_v.grad.abs().mean(),
epoch,
)
# log losses
for key in self.loss_meter_keys:
if flag == 'train':
self.log_writer.add_scalar(
f"{self.scale_tb_prefix}_{flag}/{key}",
self.loss_meter_train[key].summarize_epoch(), epoch)
elif flag == 'val':
self.log_writer.add_scalar(
f"{self.scale_tb_prefix}_{flag}/{key}",
self.loss_meter_val[key].summarize_epoch(), epoch)
# log audios
if self.log_audio:
audios = [audio_gen, f0, audio]
names = ['audio_gen', 'f0', 'audio']
srs = [self.sr, self.sr_f0, self.sr]
for audio, name, sr in zip(audios, names, srs):
audio_numpy = audio[0].clamp(
-1, 1).squeeze().detach().cpu().numpy()
sf.write(
f"{self.scale_output_dirpath}/{name}_sample_{flag}.wav",
audio_numpy, sr)
audio = sampling.resample_torch(audio[0], sr, 16000).clamp(
-1, 1).detach().squeeze()
self.log_writer.add_audio(
f"{self.scale_tb_prefix}_{flag}/{name}",
audio,
epoch,
sample_rate=16000)
nfft = int(sr / (48000 / 4096))
sample_stft = writers.show_spec(audio_numpy, nfft=nfft, sr=sr)
self.log_writer.add_figure(
f'{self.scale_tb_prefix}_{flag}/stft_{name}', sample_stft,
epoch)
def draw_f0(Gs, samplers, in_s, max_val, loudness_list):
G_z = in_s
with torch.no_grad():
for G, sampler, loudness in zip(Gs, samplers, loudness_list):
z_in = G_z
G_z = G(z_in.detach(), loudness.detach())
G_z = resample_torch(G_z, None, None, max_val=max_val, sampler=sampler)
return G_z
def f0_transfer(real_audio, loudness_list, Gs, samplers, max_val, save_all=False):
with torch.no_grad():
prev_audios = []
prev_in = real_audio
for it, (G, sampler, loudness) in enumerate(zip(Gs, samplers, loudness_list)):
audio_curr = G(prev_in.detach(), loudness.detach())
prev_in = audio_curr[:, :, :prev_in.shape[-1]]
if save_all:
prev_audios.append(audio_curr.detach())
prev_in = resample_torch(prev_in, None, None, max_val=max_val, sampler=sampler)
audio_curr = Gs[-1](prev_in.detach(), loudness_list[-1].detach())
prev_audios.append(audio_curr.detach())
return prev_audios
def main(args):
CWD = Path(hydra.utils.get_original_cwd())
os.chdir(CWD)
# Load model args
trained_dirpath = Path(args.trained_dirpath)
run_args = torch.load(trained_dirpath / 'args.pth')
# define args from trained model
sr = run_args.sr
num_scales = run_args.num_scales
scale_factor = run_args.scale_factor
max_value = run_args.max_val
max_value_f0 = run_args.max_val_f0
cond_freq = run_args.cond_freq
# Convert filepaths
input_dirpath = Path(args.input_dirpath)
input_files = input_dirpath.glob('*.wav')
output_dirpath = trained_dirpath.joinpath(args.exp_name)
try:
output_dirpath.mkdir()
except FileExistsError:
print('Directory already exists')
# Pytorch device
device = torch.device("cuda")
#load input file
base_audio = BaseAudio(args.crepe_path, device, args.unvoiced_flag)
srs = create_srs(sr, num_scales, scale_factor)
samplers = create_samplers(srs, device=device)
if args.norm_loudness_flag:
norm_dicts = load_norm_dicts(trained_dirpath / 'loudness.json')
else:
norm_dicts = None
octave_shifts = [2**x for x in args.octaves]
# Load Trained models
Gs = load_trained_pyramid(trained_dirpath, network_params=run_args.generator_params, device=device, srs=srs)
for filepath in tqdm(input_files, desc='Generating audio file'):
for octave in tqdm(octave_shifts, desc='Octave'):
real_audio = load_audio(filepath, sr, max_value)
loudness_hop = 8 * sr // cond_freq
real_audio = real_audio[:len(real_audio) // loudness_hop * loudness_hop]
loudness_list = calc_loudness_list(audio=real_audio, srs=srs, device=device,
sr_in=sr, norm_dicts=norm_dicts)
real_audio = base_audio.forward(real_audio, sr, max_value_f0, numpy_flag=True, octave=octave)
real_audio_orig = real_audio[None, None, ...].to(device)
# resample input to the wanted scale
real_audio = resample_torch(real_audio_orig, sr, srs[0], max_val=max_value_f0)
audio_outputs = f0_transfer(real_audio,loudness_list, Gs, samplers, max_val=max_value, save_all = False)
# add f0 sine input
audio_outputs.append(real_audio_orig)
save_audios(output_dirpath, audio_outputs,f'{filepath.stem}_{octave}', [srs[-1]])
def main(args):
CWD = Path(hydra.utils.get_original_cwd())
os.chdir(CWD)
# Load model args
trained_dirpath = Path(args.trained_dirpath)
run_args = torch.load(trained_dirpath / 'args.pth')
# define args from trained model
sr = run_args.sr
num_scales = run_args.num_scales
scale_factor = run_args.scale_factor
max_value = run_args.max_val
max_value_f0 = run_args.max_val_f0
cond_freq = run_args.cond_freq
# Convert filepaths
input_dirpath = Path(args.input_dirpath)
input_files = input_dirpath.glob('*.wav')
output_dirpath = trained_dirpath.joinpath(args.exp_name)
try:
output_dirpath.mkdir()
except FileExistsError:
print('Directory already exists')
# Pytorch device
device = torch.device("cuda")
#load input file
base_audio = BaseAudio(args.crepe_path, device, args.unvoiced_flag)
srs = create_srs(sr, num_scales, scale_factor)
samplers = create_samplers(srs, device=device)
if args.norm_loudness_flag:
norm_dicts = load_norm_dicts(trained_dirpath / 'loudness.json')
else:
norm_dicts = None
octave_shifts = [2**x for x in args.octaves]
# Load Trained models
Gs = load_trained_pyramid(trained_dirpath, network_params=run_args.generator_params, device=device, srs=srs)
filepath = next(iter(input_files))
octave = next(iter(octave_shifts))
real_audio = load_audio(filepath, sr, max_value)
loudness_hop = 8 * sr // cond_freq
real_audio = real_audio[:len(real_audio) // loudness_hop * loudness_hop]
loudness_list = calc_loudness_list(audio=real_audio, srs=srs, device=device,
sr_in=sr, norm_dicts=norm_dicts)
real_audio = base_audio.forward(real_audio, sr, max_value_f0, numpy_flag=True, octave=octave)
real_audio_orig = real_audio[None, None, ...].to(device)
# resample input to the wanted scale
real_audio = resample_torch(real_audio_orig, sr, srs[0], max_val=max_value_f0)
BUFFER_SIZES = [256, 512, 1024, 2048, 4096, 8192, 16384, 32768]
num_iters = 100
times = []
for bs in BUFFER_SIZES:
this_ra = real_audio[..., :bs // 8]
this_ll = [l[..., :bs // (64000 // l.shape[-1])] for l in loudness_list]
with torch.no_grad():
for i in trange(num_iters):
start_time = time.time()
audio_outputs = f0_transfer(this_ra, this_ll, Gs, samplers, max_val=max_value, save_all = False)
time_elapsed = time.time() - start_time
times.append(
["htp", "gpu", bs, time_elapsed]
)
df = pd.DataFrame(times)
df.to_csv("htp_gpu_rtf.csv")
def main(args):
CWD = Path(hydra.utils.get_original_cwd())
os.chdir(CWD)
# Load model args
trained_dirpath = Path(args.trained_dirpath)
run_args = torch.load(trained_dirpath / 'args.pth')
# define args from trained model
sr = run_args.sr
num_scales = run_args.num_scales
scale_factor = run_args.scale_factor
max_value = run_args.max_val
max_value_f0 = run_args.max_val_f0
cond_freq = run_args.cond_freq
# Convert filepaths
input_dirpath = Path(args.input_dirpath)
input_files = input_dirpath.glob('*.wav')
output_dirpath = trained_dirpath.joinpath(args.exp_name)
try:
output_dirpath.mkdir()
except FileExistsError:
print('Directory already exists')
# Pytorch device
device = torch.device("cuda")
#load input file
base_audio = BaseAudio(args.crepe_path, device, args.unvoiced_flag)
srs = create_srs(sr, num_scales, scale_factor)
samplers = create_samplers(srs, device=device)
if args.norm_loudness_flag:
norm_dicts = load_norm_dicts(trained_dirpath / 'loudness.json')
else:
norm_dicts = None
octave_shifts = [2**x for x in args.octaves]
# Load Trained models
Gs = load_trained_pyramid(trained_dirpath, network_params=run_args.generator_params, device=device, srs=srs)
filepath = next(iter(input_files))
octave = next(iter(octave_shifts))
real_audio = load_audio(filepath, sr, max_value)
loudness_hop = 8 * sr // cond_freq
real_audio = real_audio[:len(real_audio) // loudness_hop * loudness_hop]
loudness_list = calc_loudness_list(audio=real_audio, srs=srs, device=device,
sr_in=sr, norm_dicts=norm_dicts)
real_audio = base_audio.forward(real_audio, sr, max_value_f0, numpy_flag=True, octave=octave)
real_audio_orig = real_audio[None, None, ...].to(device)
# resample input to the wanted scale
real_audio = resample_torch(real_audio_orig, sr, srs[0], max_val=max_value_f0)
num_iters = 100
times = []
with torch.no_grad():
for i in trange(num_iters):
start_time = time.time()
audio_outputs = f0_transfer(real_audio,loudness_list, Gs, samplers, max_val=max_value, save_all = False)
time_elapsed = time.time() - start_time
times.append(time_elapsed)
length_in_seconds = 4
rtfs = np.array(times) / length_in_seconds
print("Mean RTF: %.4f" % np.mean(rtfs))
print("90th percentile RTF: %.4f" % np.percentile(rtfs, 90))
