Update utils.py

utils.py

@@ -6,9 +6,7 @@ import argparse
 import logging
 import json
 import subprocess
-import warnings
 import random
-import functools
 
 import librosa
 import numpy as np
@@ -17,8 +15,6 @@ import torch
 from torch.nn import functional as F
 from modules.commons import sequence_mask
 from hubert import hubert_model
-from modules.crepe import CrepePitchExtractor
-
 MATPLOTLIB_FLAG = False
 
 logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
@@ -50,21 +46,6 @@ f0_mel_max = 1127 * np.log(1 + f0_max / 700)
 #         factor = torch.ones(f0.shape[0], 1, 1).to(f0.device)
 #     f0_norm = (f0 - means.unsqueeze(-1)) * factor.unsqueeze(-1)
 #     return f0_norm
-
-def deprecated(func):
-    """This is a decorator which can be used to mark functions
-    as deprecated. It will result in a warning being emitted
-    when the function is used."""
-    @functools.wraps(func)
-    def new_func(*args, **kwargs):
-        warnings.simplefilter('always', DeprecationWarning)  # turn off filter
-        warnings.warn("Call to deprecated function {}.".format(func.__name__),
-                      category=DeprecationWarning,
-                      stacklevel=2)
-        warnings.simplefilter('default', DeprecationWarning)  # reset filter
-        return func(*args, **kwargs)
-    return new_func
-
 def normalize_f0(f0, x_mask, uv, random_scale=True):
     # calculate means based on x_mask
     uv_sum = torch.sum(uv, dim=1, keepdim=True)
@@ -81,18 +62,6 @@ def normalize_f0(f0, x_mask, uv, random_scale=True):
         exit(0)
     return f0_norm * x_mask
 
-def compute_f0_uv_torchcrepe(wav_numpy, p_len=None, sampling_rate=44100, hop_length=512,device=None):
-    x = wav_numpy
-    if p_len is None:
-        p_len = x.shape[0]//hop_length
-    else:
-        assert abs(p_len-x.shape[0]//hop_length) < 4, "pad length error"
-    
-    f0_min = 50
-    f0_max = 1100
-    F0Creper = CrepePitchExtractor(hop_length=hop_length,f0_min=f0_min,f0_max=f0_max,device=device)
-    f0,uv = F0Creper(x[None,:].float(),sampling_rate,pad_to=p_len)
-    return f0,uv
 
 def plot_data_to_numpy(x, y):
     global MATPLOTLIB_FLAG