alokprasad
commented
4 years ago I removed all CUDA specific code from glow.py and inference.py , it runs now but the wave file generated is full of NULL , so no speech in it. Any idea what could be the issue?
diff --git a/denoiser.py b/denoiser.py
index 8f9ff57..2da8f3a 100644
--- a/denoiser.py
+++ b/denoiser.py
@@ -11,7 +11,7 @@ class Denoiser(torch.nn.Module):
super(Denoiser, self).__init__()
self.stft = STFT(filter_length=filter_length,
hop_length=int(filter_length/n_overlap),
- win_length=win_length).cuda()
+ win_length=win_length)
if mode == 'zeros':
mel_input = torch.zeros(
(1, 80, 88),
@@ -32,7 +32,7 @@ class Denoiser(torch.nn.Module):
self.register_buffer('bias_spec', bias_spec[:, :, 0][:, :, None])
def forward(self, audio, strength=0.1):
- audio_spec, audio_angles = self.stft.transform(audio.cuda().float())
+ audio_spec, audio_angles = self.stft.transform(audio.float())
audio_spec_denoised = audio_spec - self.bias_spec * strength
audio_spec_denoised = torch.clamp(audio_spec_denoised, 0.0)
audio_denoised = self.stft.inverse(audio_spec_denoised, audio_angles)
diff --git a/glow.py b/glow.py
index f692103..6690272 100644
--- a/glow.py
+++ b/glow.py
@@ -103,7 +103,7 @@ class Invertible1x1Conv(torch.nn.Module):
# Reverse computation
W_inverse = W.float().inverse()
W_inverse = Variable(W_inverse[..., None])
- if z.type() == 'torch.cuda.HalfTensor':
+ if z.type() == 'torch.HalfTensor':
W_inverse = W_inverse.half()
self.W_inverse = W_inverse
z = F.conv1d(z, self.W_inverse, bias=None, stride=1, padding=0)
@@ -148,8 +148,8 @@ class WN(torch.nn.Module):
# depthwise separable convolution
depthwise = torch.nn.Conv1d(n_channels, n_channels, 3,
dilation=dilation, padding=padding,
- groups=n_channels).cuda()
- pointwise = torch.nn.Conv1d(n_channels, 2*n_channels, 1).cuda()
+ groups=n_channels)
+ pointwise = torch.nn.Conv1d(n_channels, 2*n_channels, 1)
bn = torch.nn.BatchNorm1d(n_channels)
self.in_layers.append(torch.nn.Sequential(bn, depthwise, pointwise))
# res_skip_layer
@@ -245,12 +245,12 @@ class SqueezeWave(torch.nn.Module):
def infer(self, spect, sigma=1.0):
spect_size = spect.size()
l = spect.size(2)*(256 // self.n_audio_channel)
- if spect.type() == 'torch.cuda.HalfTensor':
- audio = torch.cuda.HalfTensor(spect.size(0),
+ if spect.type() == 'torch.HalfTensor':
+ audio = torch.HalfTensor(spect.size(0),
self.n_remaining_channels,
l).normal_()
else:
- audio = torch.cuda.FloatTensor(spect.size(0),
+ audio = torch.FloatTensor(spect.size(0),
self.n_remaining_channels,
l).normal_()
@@ -268,10 +268,10 @@ class SqueezeWave(torch.nn.Module):
audio = self.convinv[k](audio, reverse=True)
if k % self.n_early_every == 0 and k > 0:
- if spect.type() == 'torch.cuda.HalfTensor':
- z = torch.cuda.HalfTensor(spect.size(0), self.n_early_size, l).normal_()
+ if spect.type() == 'torch.HalfTensor':
+ z = torch.HalfTensor(spect.size(0), self.n_early_size, l).normal_()
else:
- z = torch.cuda.FloatTensor(spect.size(0), self.n_early_size, l).normal_()
+ z = torch.FloatTensor(spect.size(0), self.n_early_size, l).normal_()
audio = torch.cat((sigma*z, audio),1)
audio = audio.permute(0,2,1).contiguous().view(audio.size(0), -1).data
diff --git a/inference.py b/inference.py
index 568e6ce..f31c013 100644
--- a/inference.py
+++ b/inference.py
@@ -32,27 +32,31 @@ from scipy.io.wavfile import write
import torch
from mel2samp import files_to_list, MAX_WAV_VALUE
from denoiser import Denoiser
-
+import time
def main(mel_files, squeezewave_path, sigma, output_dir, sampling_rate, is_fp16,
denoiser_strength):
mel_files = files_to_list(mel_files)
- squeezewave = torch.load(squeezewave_path)['model']
+
+ #device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+ device = torch.device('cpu')
+ squeezewave = torch.load(squeezewave_path,map_location=device) ['model']
squeezewave = squeezewave.remove_weightnorm(squeezewave)
- squeezewave.cuda().eval()
+ squeezewave.eval()
if is_fp16:
from apex import amp
- squeezewave, _ = amp.initialize(squeezewave, [], opt_level="O3")
+ squeezewave, _ = amp.initialize(squeezewave, [])
if denoiser_strength > 0:
- denoiser = Denoiser(squeezewave).cuda()
-
+ denoiser = Denoiser(squeezewave)
+ start = time.time()
for i, file_path in enumerate(mel_files):
file_name = os.path.splitext(os.path.basename(file_path))[0]
mel = torch.load(file_path)
- mel = torch.autograd.Variable(mel.cuda())
+ mel = torch.autograd.Variable(mel)
mel = torch.unsqueeze(mel, 0)
mel = mel.half() if is_fp16 else mel
+
with torch.no_grad():
audio = squeezewave.infer(mel, sigma=sigma).float()
if denoiser_strength > 0:
@@ -65,6 +69,9 @@ def main(mel_files, squeezewave_path, sigma, output_dir, sampling_rate, is_fp16,
output_dir, "{}_synthesis.wav".format(file_name))
write(audio_path, sampling_rate, audio)
print(audio_path)
+ end = time.time()
+ print("Squeezewave vocoder time")
+ print(end-start)
if __name__ == "__main__":
BohanZhai
varungujjar
Looks like Apex library needs CUDA, when i without cuda , getting below error. any idea.
File "inference.py", line 48, in main squeezewave, _ = amp.initialize(squeezewave, [], opt_level="O3") File "/usr/local/lib/python3.6/dist-packages/apex-0.1-py3.6.egg/apex/amp/frontend.py", line 358, in initialize return _initialize(models, optimizers, _amp_state.opt_properties, num_losses, cast_model_outputs) File "/usr/local/lib/python3.6/dist-packages/apex-0.1-py3.6.egg/apex/amp/_initialize.py", line 171, in _initialize check_params_fp32(models) File "/usr/local/lib/python3.6/dist-packages/apex-0.1-py3.6.egg/apex/amp/_initialize.py", line 93, in check_params_fp32 name, param.type())) File "/usr/local/lib/python3.6/dist-packages/apex-0.1-py3.6.egg/apex/amp/_amp_state.py", line 32, in warn_or_err raise RuntimeError(msg) RuntimeError: Found param WN.0.in_layers.0.0.weight with type torch.FloatTensor, expected torch.cuda.FloatTensor. When using amp.initialize, you need to provide a model with parameters located on a CUDA device before passing it no matter what optimization level you chose. Use model.to('cuda') to use the default device.