misc nonfunctional

codes/models/gpt_voice/gpt_asr_hf2.py

@@ -9,6 +9,7 @@ from transformers.utils.model_parallel_utils import get_device_map, assert_devic
 
 from models.tacotron2.text import symbols
 from trainer.networks import register_model
+from utils.audio import plot_spectrogram
 from utils.util import opt_get
 
 
@@ -248,6 +249,7 @@ class GptAsrHf2(nn.Module):
         return text_logits
 
     def forward(self, mel_inputs, text_targets, return_attentions=False):
+        plot_spectrogram(mel_inputs[0].cpu())
         text_targets = F.pad(text_targets, (0,1))  # Pad the targets with a <0> so that all have a "stop" token.
         text_logits = self.get_logits(mel_inputs, text_targets, get_attns=return_attentions)
         if return_attentions:

codes/scripts/audio/use_vocoder.py

@@ -1,13 +1,8 @@
-import pathlib
-
-import numpy
 import torch
 from scipy.io import wavfile
-from tqdm import tqdm
-import matplotlib.pyplot as plt
-import librosa
 
 from models.waveglow.waveglow import WaveGlow
+from utils.audio import plot_spectrogram
 
 
 class Vocoder:
@@ -25,18 +20,6 @@ class Vocoder:
             return self.model.infer(mel)
 
 
-def plot_spectrogram(spec, title=None, ylabel="freq_bin", aspect="auto", xmax=None):
-    fig, axs = plt.subplots(1, 1)
-    axs.set_title(title or "Spectrogram (db)")
-    axs.set_ylabel(ylabel)
-    axs.set_xlabel("frame")
-    im = axs.imshow(librosa.power_to_db(spec), origin="lower", aspect=aspect)
-    if xmax:
-        axs.set_xlim((0, xmax))
-    fig.colorbar(im, ax=axs)
-    plt.show(block=False)
-
-
 if __name__ == '__main__':
     vocoder = Vocoder()
     m = torch.load('test_mels.pth')

codes/trainer/injectors/base_injectors.py

@@ -5,8 +5,10 @@ import torchaudio.functional
 from kornia.augmentation import RandomResizedCrop
 from torch.cuda.amp import autocast
 
+from data.audio.unsupervised_audio_dataset import load_audio
 from trainer.inject import Injector, create_injector
 from trainer.losses import extract_params_from_state
+from utils.audio import plot_spectrogram
 from utils.util import opt_get
 from utils.weight_scheduler import get_scheduler_for_opt
 
@@ -568,7 +570,7 @@ class TorchMelSpectrogramInjector(Injector):
         self.mel_fmax = opt_get(opt, ['mel_fmax'], 8000)
         self.sampling_rate = opt_get(opt, ['sampling_rate'], 22050)
         self.mel_stft = torchaudio.transforms.MelSpectrogram(n_fft=self.filter_length, hop_length=self.hop_length,
-                                                             win_length=self.win_length, power=2, normalized=True,
+                                                             win_length=self.win_length, power=2, normalized=False,
                                                              sample_rate=self.sampling_rate, f_min=self.mel_fmin,
                                                              f_max=self.mel_fmax, n_mels=self.n_mel_channels)
 
@@ -582,6 +584,14 @@ class TorchMelSpectrogramInjector(Injector):
         return {self.output: mel}
 
 
+def test_torch_mel_injector():
+    a = load_audio('D:\\data\\audio\\libritts\\train-clean-100\\19\\198\\19_198_000000_000000.wav', 22050)
+    inj = TorchMelSpectrogramInjector({'in': 'in', 'out': 'out'}, {})
+    f = inj({'in': a.unsqueeze(0)})['out']
+    plot_spectrogram(f[0])
+    print('Pause')
+
+
 class RandomAudioCropInjector(Injector):
     def __init__(self, opt, env):
         super().__init__(opt, env)
@@ -606,6 +616,10 @@ class AudioResampleInjector(Injector):
         return {self.output: torchaudio.functional.resample(inp, self.input_sr, self.output_sr)}
 
 
-if __name__ == '__main__':
+def test_audio_resample_injector():
     inj = AudioResampleInjector({'in': 'x', 'out': 'y', 'input_sample_rate': 22050, 'output_sample_rate': '1'}, None)
-    print(inj({'x':torch.rand(10,1,40800)})['y'].shape)
+    print(inj({'x':torch.rand(10,1,40800)})['y'].shape)
+
+
+if __name__ == '__main__':
+    test_torch_mel_injector()

codes/utils/audio.py

@@ -0,0 +1,14 @@
+import librosa
+import matplotlib.pyplot as plt
+
+
+def plot_spectrogram(spec, title=None, ylabel="freq_bin", aspect="auto", xmax=None):
+    fig, axs = plt.subplots(1, 1)
+    axs.set_title(title or "Spectrogram (db)")
+    axs.set_ylabel(ylabel)
+    axs.set_xlabel("frame")
+    im = axs.imshow(librosa.power_to_db(spec), origin="lower", aspect=aspect)
+    if xmax:
+        axs.set_xlim((0, xmax))
+    fig.colorbar(im, ax=axs)
+    plt.show(block=False)