dos2unix

FastSpeech/synthesis.py

@@ -1,74 +1,74 @@
-import torch
-import torch.nn as nn
-import matplotlib
-import matplotlib.pyplot as plt
-import numpy as np
-import time
-import os
-
-from fastspeech import FastSpeech
-from text import text_to_sequence
-import hparams as hp
-import utils
-import audio as Audio
-import glow
-import waveglow
-
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-
-def get_FastSpeech(num):
-    checkpoint_path = "checkpoint_" + str(num) + ".pth.tar"
-    model = nn.DataParallel(FastSpeech()).to(device)
-    model.load_state_dict(torch.load(os.path.join(
-        hp.checkpoint_path, checkpoint_path))['model'])
-    model.eval()
-
-    return model
-
-
-def synthesis(model, text, alpha=1.0):
-    text = np.array(text_to_sequence(text, hp.text_cleaners))
-    text = np.stack([text])
-
-    src_pos = np.array([i+1 for i in range(text.shape[1])])
-    src_pos = np.stack([src_pos])
-    with torch.no_grad():
-        sequence = torch.autograd.Variable(
-            torch.from_numpy(text)).cuda().long()
-        src_pos = torch.autograd.Variable(
-            torch.from_numpy(src_pos)).cuda().long()
-
-        mel, mel_postnet = model.module.forward(sequence, src_pos, alpha=alpha)
-
-        return mel[0].cpu().transpose(0, 1), \
-            mel_postnet[0].cpu().transpose(0, 1), \
-            mel.transpose(1, 2), \
-            mel_postnet.transpose(1, 2)
-
-
-if __name__ == "__main__":
-    # Test
-    num = 112000
-    alpha = 1.0
-    model = get_FastSpeech(num)
-    words = "Let’s go out to the airport. The plane landed ten minutes ago."
-
-    mel, mel_postnet, mel_torch, mel_postnet_torch = synthesis(
-        model, words, alpha=alpha)
-
-    if not os.path.exists("results"):
-        os.mkdir("results")
-    Audio.tools.inv_mel_spec(mel_postnet, os.path.join(
-        "results", words + "_" + str(num) + "_griffin_lim.wav"))
-
-    wave_glow = utils.get_WaveGlow()
-    waveglow.inference.inference(mel_postnet_torch, wave_glow, os.path.join(
-        "results", words + "_" + str(num) + "_waveglow.wav"))
-
-    tacotron2 = utils.get_Tacotron2()
-    mel_tac2, _, _ = utils.load_data_from_tacotron2(words, tacotron2)
-    waveglow.inference.inference(torch.stack([torch.from_numpy(
-        mel_tac2).cuda()]), wave_glow, os.path.join("results", "tacotron2.wav"))
-
-    utils.plot_data([mel.numpy(), mel_postnet.numpy(), mel_tac2])
+import torch
+import torch.nn as nn
+import matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+import time
+import os
+
+from fastspeech import FastSpeech
+from text import text_to_sequence
+import hparams as hp
+import utils
+import audio as Audio
+import glow
+import waveglow
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+
+def get_FastSpeech(num):
+    checkpoint_path = "checkpoint_" + str(num) + ".pth.tar"
+    model = nn.DataParallel(FastSpeech()).to(device)
+    model.load_state_dict(torch.load(os.path.join(
+        hp.checkpoint_path, checkpoint_path))['model'])
+    model.eval()
+
+    return model
+
+
+def synthesis(model, text, alpha=1.0):
+    text = np.array(text_to_sequence(text, hp.text_cleaners))
+    text = np.stack([text])
+
+    src_pos = np.array([i+1 for i in range(text.shape[1])])
+    src_pos = np.stack([src_pos])
+    with torch.no_grad():
+        sequence = torch.autograd.Variable(
+            torch.from_numpy(text)).cuda().long()
+        src_pos = torch.autograd.Variable(
+            torch.from_numpy(src_pos)).cuda().long()
+
+        mel, mel_postnet = model.module.forward(sequence, src_pos, alpha=alpha)
+
+        return mel[0].cpu().transpose(0, 1), \
+            mel_postnet[0].cpu().transpose(0, 1), \
+            mel.transpose(1, 2), \
+            mel_postnet.transpose(1, 2)
+
+
+if __name__ == "__main__":
+    # Test
+    num = 112000
+    alpha = 1.0
+    model = get_FastSpeech(num)
+    words = "Let’s go out to the airport. The plane landed ten minutes ago."
+
+    mel, mel_postnet, mel_torch, mel_postnet_torch = synthesis(
+        model, words, alpha=alpha)
+
+    if not os.path.exists("results"):
+        os.mkdir("results")
+    Audio.tools.inv_mel_spec(mel_postnet, os.path.join(
+        "results", words + "_" + str(num) + "_griffin_lim.wav"))
+
+    wave_glow = utils.get_WaveGlow()
+    waveglow.inference.inference(mel_postnet_torch, wave_glow, os.path.join(
+        "results", words + "_" + str(num) + "_waveglow.wav"))
+
+    tacotron2 = utils.get_Tacotron2()
+    mel_tac2, _, _ = utils.load_data_from_tacotron2(words, tacotron2)
+    waveglow.inference.inference(torch.stack([torch.from_numpy(
+        mel_tac2).cuda()]), wave_glow, os.path.join("results", "tacotron2.wav"))
+
+    utils.plot_data([mel.numpy(), mel_postnet.numpy(), mel_tac2])