misc nonfunctional
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@ -9,6 +9,7 @@ from transformers.utils.model_parallel_utils import get_device_map, assert_devic
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from models.tacotron2.text import symbols
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from trainer.networks import register_model
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from utils.audio import plot_spectrogram
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from utils.util import opt_get
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@ -248,6 +249,7 @@ class GptAsrHf2(nn.Module):
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return text_logits
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def forward(self, mel_inputs, text_targets, return_attentions=False):
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plot_spectrogram(mel_inputs[0].cpu())
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text_targets = F.pad(text_targets, (0,1)) # Pad the targets with a <0> so that all have a "stop" token.
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text_logits = self.get_logits(mel_inputs, text_targets, get_attns=return_attentions)
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if return_attentions:
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@ -1,13 +1,8 @@
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import pathlib
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import numpy
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import torch
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from scipy.io import wavfile
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from tqdm import tqdm
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import matplotlib.pyplot as plt
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import librosa
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from models.waveglow.waveglow import WaveGlow
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from utils.audio import plot_spectrogram
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class Vocoder:
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@ -25,18 +20,6 @@ class Vocoder:
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return self.model.infer(mel)
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def plot_spectrogram(spec, title=None, ylabel="freq_bin", aspect="auto", xmax=None):
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fig, axs = plt.subplots(1, 1)
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axs.set_title(title or "Spectrogram (db)")
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axs.set_ylabel(ylabel)
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axs.set_xlabel("frame")
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im = axs.imshow(librosa.power_to_db(spec), origin="lower", aspect=aspect)
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if xmax:
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axs.set_xlim((0, xmax))
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fig.colorbar(im, ax=axs)
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plt.show(block=False)
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if __name__ == '__main__':
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vocoder = Vocoder()
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m = torch.load('test_mels.pth')
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@ -5,8 +5,10 @@ import torchaudio.functional
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from kornia.augmentation import RandomResizedCrop
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from torch.cuda.amp import autocast
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from data.audio.unsupervised_audio_dataset import load_audio
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from trainer.inject import Injector, create_injector
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from trainer.losses import extract_params_from_state
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from utils.audio import plot_spectrogram
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from utils.util import opt_get
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from utils.weight_scheduler import get_scheduler_for_opt
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@ -568,7 +570,7 @@ class TorchMelSpectrogramInjector(Injector):
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self.mel_fmax = opt_get(opt, ['mel_fmax'], 8000)
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self.sampling_rate = opt_get(opt, ['sampling_rate'], 22050)
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self.mel_stft = torchaudio.transforms.MelSpectrogram(n_fft=self.filter_length, hop_length=self.hop_length,
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win_length=self.win_length, power=2, normalized=True,
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win_length=self.win_length, power=2, normalized=False,
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sample_rate=self.sampling_rate, f_min=self.mel_fmin,
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f_max=self.mel_fmax, n_mels=self.n_mel_channels)
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@ -582,6 +584,14 @@ class TorchMelSpectrogramInjector(Injector):
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return {self.output: mel}
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def test_torch_mel_injector():
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a = load_audio('D:\\data\\audio\\libritts\\train-clean-100\\19\\198\\19_198_000000_000000.wav', 22050)
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inj = TorchMelSpectrogramInjector({'in': 'in', 'out': 'out'}, {})
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f = inj({'in': a.unsqueeze(0)})['out']
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plot_spectrogram(f[0])
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print('Pause')
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class RandomAudioCropInjector(Injector):
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def __init__(self, opt, env):
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super().__init__(opt, env)
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@ -606,6 +616,10 @@ class AudioResampleInjector(Injector):
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return {self.output: torchaudio.functional.resample(inp, self.input_sr, self.output_sr)}
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if __name__ == '__main__':
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def test_audio_resample_injector():
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inj = AudioResampleInjector({'in': 'x', 'out': 'y', 'input_sample_rate': 22050, 'output_sample_rate': '1'}, None)
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print(inj({'x':torch.rand(10,1,40800)})['y'].shape)
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if __name__ == '__main__':
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test_torch_mel_injector()
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14
codes/utils/audio.py
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14
codes/utils/audio.py
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@ -0,0 +1,14 @@
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import librosa
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import matplotlib.pyplot as plt
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def plot_spectrogram(spec, title=None, ylabel="freq_bin", aspect="auto", xmax=None):
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fig, axs = plt.subplots(1, 1)
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axs.set_title(title or "Spectrogram (db)")
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axs.set_ylabel(ylabel)
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axs.set_xlabel("frame")
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im = axs.imshow(librosa.power_to_db(spec), origin="lower", aspect=aspect)
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if xmax:
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axs.set_xlim((0, xmax))
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fig.colorbar(im, ax=axs)
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plt.show(block=False)
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