Further wandb logs

codes/models/gpt_voice/gpt_asr_hf2.py

@@ -231,9 +231,9 @@ class GptAsrHf2(nn.Module):
 
     def get_logits(self, mel_inputs, text_targets, get_attns=False):
         # Pad front remove last element to set up next token prediction. Pad at front is the "START" token.
-        text_targets = F.pad(text_targets, (1,0), value=self.START_TOKEN)[:, :-1]
+        text_inputs = F.pad(text_targets, (1,0), value=self.START_TOKEN)[:, :-1]
-        text_emb = self.gpt.get_input_embeddings()(text_targets)
+        text_emb = self.gpt.get_input_embeddings()(text_inputs)
-        text_emb = text_emb + self.text_pos_embedding(torch.arange(text_emb.shape[1], device=text_targets.device))
+        text_emb = text_emb + self.text_pos_embedding(torch.arange(text_emb.shape[1], device=text_inputs.device))
         mel_emb = self.mel_encoder(mel_inputs)
         mel_emb = mel_emb.permute(0,2,1).contiguous()
         mel_emb = mel_emb + self.mel_pos_embedding(torch.arange(mel_emb.shape[1], device=mel_emb.device))

codes/scripts/audio/use_vocoder.py

@@ -4,6 +4,8 @@ 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
 
@@ -23,13 +25,22 @@ class Vocoder:
             return self.model.infer(mel)
 
 
-if __name__ == '__main__':
+def plot_spectrogram(spec, title=None, ylabel="freq_bin", aspect="auto", xmax=None):
-    path = 'data/audio'
+    fig, axs = plt.subplots(1, 1)
-    files = list(pathlib.Path(path).glob('*.npy'))
+    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)
 
-    for inp in tqdm(files):
+
-        inp = str(inp)
+if __name__ == '__main__':
-        mel = torch.tensor(numpy.load(inp)).to('cuda')
+    vocoder = Vocoder()
-        vocoder = Vocoder()
+    m = torch.load('test_mels.pth')
-        wav = vocoder.transform_mel_to_audio(mel)
+    for i, b in enumerate(m):
-        wavfile.write(f'{inp}.wav', 22050, wav[0].cpu().numpy())
+        plot_spectrogram(b.cpu())
+        wav = vocoder.transform_mel_to_audio(b)
+        wavfile.write(f'{i}.wav', 22050, wav[0].cpu().numpy())

codes/trainer/injectors/base_injectors.py

@@ -577,6 +577,7 @@ class TorchMelSpectrogramInjector(Injector):
         if len(inp.shape) == 3:  # Automatically squeeze out the channels dimension if it is present (assuming mono-audio)
             inp = inp.squeeze(1)
         assert len(inp.shape) == 2
+        self.mel_stft = self.mel_stft.to(inp.device)
         mel = self.mel_stft(inp)
         return {self.output: mel}
 

codes/trainer/injectors/spec_augment.py

@@ -81,6 +81,8 @@ class CombineMelInjector(Injector):
         self.text_key = opt['text_key']
         self.audio_lengths = opt['audio_lengths_key']
         self.text_lengths = opt['text_lengths_key']
+        self.output_audio_key = opt['output_audio_key']
+        self.output_text_key = opt['output_text_key']
         from models.tacotron2.text import symbols
         self.text_separator = len(symbols)+1  # Probably need to allow this to be set by user.
 
@@ -93,9 +95,9 @@ class CombineMelInjector(Injector):
         combined_audios = []
         combined_texts = []
         for b in range(audio.shape[0]//2):
-            a1 = audio[b*2, :audio_lengths[b*2]]
+            a1 = audio[b*2, :, :audio_lengths[b*2]]
-            a2 = audio[b*2+1, :audio_lengths[b*2+1]]
+            a2 = audio[b*2+1, :, :audio_lengths[b*2+1]]
-            a = torch.cat([a1, a2], dim=0)
+            a = torch.cat([a1, a2], dim=1)
             a = torch.nn.functional.pad(a, (0, audio.shape[-1]*2-a.shape[-1]))
             combined_audios.append(a)
 
@@ -105,8 +107,8 @@ class CombineMelInjector(Injector):
             t = torch.cat([t1, t2], dim=0)
             t = torch.nn.functional.pad(t, (0, texts.shape[-1]*2-t.shape[-1]))
             combined_texts.append(t)
-        return {self.audio_key: torch.stack(combined_audios, dim=0),
+        return {self.output_audio_key: torch.stack(combined_audios, dim=0),
-                self.text_key: torch.stack(combined_texts, dim=0)}
+                self.output_text_key: torch.stack(combined_texts, dim=0)}
 
 
 def test_mel_injector():