[bug] 加快编译速度

app.py

@@ -22,8 +22,6 @@ from models import SynthesizerTrn
 from text import text_to_sequence
 import torch
 from torch import no_grad, LongTensor
-import webbrowser
-import gradio.processing_utils as gr_processing_utils
 from gradio_client import utils as client_utils
 limitation = os.getenv("SYSTEM") == "spaces"  # limit text and audio length in huggingface spaces
 
@@ -45,10 +43,10 @@ def get_text(text, hps):
 def vits(text, language, speaker_id, noise_scale, noise_scale_w, length_scale):
     start = time.perf_counter()
     if not len(text):
-        return "输入文本不能为空！", None, None
+        return None
     text = text.replace('\n', ' ').replace('\r', '').replace(" ", "")
     if len(text) > 200 and limitation:
-        return f"输入文字过长！{len(text)}>200", None, None
+        return None
     if language == "中文":
         text = f"[ZH]{text}[ZH]"
     elif language == "日文":

requirements.txt

@@ -1,12 +1,12 @@
-Cython
+#Cython
 librosa
-matplotlib
+#matplotlib
 numpy
-phonemizer
-scipy
-tensorboard
+#phonemizer
+#scipy
+#tensorboard
 torch
-torchvision
+#torchvision
 Unidecode
 pyopenjtalk
 ffmpeg

utils.py

@@ -42,59 +42,59 @@ def load_checkpoint(checkpoint_path, model, optimizer=None):
   return model, optimizer, learning_rate, iteration
 
 
-def plot_spectrogram_to_numpy(spectrogram):
-  global MATPLOTLIB_FLAG
-  if not MATPLOTLIB_FLAG:
-    import matplotlib
-    matplotlib.use("Agg")
-    MATPLOTLIB_FLAG = True
-    mpl_logger = logging.getLogger('matplotlib')
-    mpl_logger.setLevel(logging.WARNING)
-  import matplotlib.pylab as plt
-  import numpy as np
-
-  fig, ax = plt.subplots(figsize=(10,2))
-  im = ax.imshow(spectrogram, aspect="auto", origin="lower",
-                  interpolation='none')
-  plt.colorbar(im, ax=ax)
-  plt.xlabel("Frames")
-  plt.ylabel("Channels")
-  plt.tight_layout()
-
-  fig.canvas.draw()
-  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-  plt.close()
-  return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
-  global MATPLOTLIB_FLAG
-  if not MATPLOTLIB_FLAG:
-    import matplotlib
-    matplotlib.use("Agg")
-    MATPLOTLIB_FLAG = True
-    mpl_logger = logging.getLogger('matplotlib')
-    mpl_logger.setLevel(logging.WARNING)
-  import matplotlib.pylab as plt
-  import numpy as np
-
-  fig, ax = plt.subplots(figsize=(6, 4))
-  im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
-                  interpolation='none')
-  fig.colorbar(im, ax=ax)
-  xlabel = 'Decoder timestep'
-  if info is not None:
-      xlabel += '\n\n' + info
-  plt.xlabel(xlabel)
-  plt.ylabel('Encoder timestep')
-  plt.tight_layout()
-
-  fig.canvas.draw()
-  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-  plt.close()
-  return data
+# def plot_spectrogram_to_numpy(spectrogram):
+#   global MATPLOTLIB_FLAG
+#   if not MATPLOTLIB_FLAG:
+#     import matplotlib
+#     matplotlib.use("Agg")
+#     MATPLOTLIB_FLAG = True
+#     mpl_logger = logging.getLogger('matplotlib')
+#     mpl_logger.setLevel(logging.WARNING)
+#   import matplotlib.pylab as plt
+#   import numpy as np
+
+#   fig, ax = plt.subplots(figsize=(10,2))
+#   im = ax.imshow(spectrogram, aspect="auto", origin="lower",
+#                   interpolation='none')
+#   plt.colorbar(im, ax=ax)
+#   plt.xlabel("Frames")
+#   plt.ylabel("Channels")
+#   plt.tight_layout()
+
+#   fig.canvas.draw()
+#   data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
+#   data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+#   plt.close()
+#   return data
+
+
+# def plot_alignment_to_numpy(alignment, info=None):
+#   global MATPLOTLIB_FLAG
+#   if not MATPLOTLIB_FLAG:
+#     import matplotlib
+#     matplotlib.use("Agg")
+#     MATPLOTLIB_FLAG = True
+#     mpl_logger = logging.getLogger('matplotlib')
+#     mpl_logger.setLevel(logging.WARNING)
+#   import matplotlib.pylab as plt
+#   import numpy as np
+
+#   fig, ax = plt.subplots(figsize=(6, 4))
+#   im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
+#                   interpolation='none')
+#   fig.colorbar(im, ax=ax)
+#   xlabel = 'Decoder timestep'
+#   if info is not None:
+#       xlabel += '\n\n' + info
+#   plt.xlabel(xlabel)
+#   plt.ylabel('Encoder timestep')
+#   plt.tight_layout()
+
+#   fig.canvas.draw()
+#   data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
+#   data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+#   plt.close()
+#   return data
 
 
 def load_audio_to_torch(full_path, target_sampling_rate):