Spaces:
Runtime error
Runtime error
File size: 6,657 Bytes
6a62ffb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 |
import gc
import os.path
import numpy as np
import parselmouth
import torch
import pyworld
import torchcrepe
from scipy import signal
from torch import Tensor
def get_f0_crepe_computation(
x,
f0_min,
f0_max,
p_len,
sr,
hop_length=128,
# 512 before. Hop length changes the speed that the voice jumps to a different dramatic pitch. Lower hop lengths means more pitch accuracy but longer inference time.
model="full", # Either use crepe-tiny "tiny" or crepe "full". Default is full
):
x = x.astype(np.float32) # fixes the F.conv2D exception. We needed to convert double to float.
x /= np.quantile(np.abs(x), 0.999)
torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
audio = torch.from_numpy(x).to(torch_device, copy=True)
audio = torch.unsqueeze(audio, dim=0)
if audio.ndim == 2 and audio.shape[0] > 1:
audio = torch.mean(audio, dim=0, keepdim=True).detach()
audio = audio.detach()
# print("Initiating prediction with a crepe_hop_length of: " + str(hop_length))
pitch: torch.Tensor = torchcrepe.predict(
audio,
sr,
hop_length,
f0_min,
f0_max,
model,
batch_size=hop_length * 2,
device=torch_device,
pad=True
)
p_len = p_len or x.shape[0] // hop_length
# Resize the pitch for final f0
source = np.array(pitch.squeeze(0).cpu().float().numpy())
source[source < 0.001] = np.nan
target = np.interp(
np.arange(0, len(source) * p_len, len(source)) / p_len,
np.arange(0, len(source)),
source
)
f0 = np.nan_to_num(target)
return f0 # Resized f0
def get_mangio_crepe_f0(x, f0_min, f0_max, p_len, sr, crepe_hop_length, model='full'):
# print("Performing crepe pitch extraction. (EXPERIMENTAL)")
# print("CREPE PITCH EXTRACTION HOP LENGTH: " + str(crepe_hop_length))
x = x.astype(np.float32)
x /= np.quantile(np.abs(x), 0.999)
torch_device_index = 0
torch_device = None
if torch.cuda.is_available():
torch_device = torch.device(f"cuda:{torch_device_index % torch.cuda.device_count()}")
elif torch.backends.mps.is_available():
torch_device = torch.device("mps")
else:
torch_device = torch.device("cpu")
audio = torch.from_numpy(x).to(torch_device, copy=True)
audio = torch.unsqueeze(audio, dim=0)
if audio.ndim == 2 and audio.shape[0] > 1:
audio = torch.mean(audio, dim=0, keepdim=True).detach()
audio = audio.detach()
# print(
# "Initiating f0 Crepe Feature Extraction with an extraction_crepe_hop_length of: " +
# str(crepe_hop_length)
# )
# Pitch prediction for pitch extraction
pitch: Tensor = torchcrepe.predict(
audio,
sr,
crepe_hop_length,
f0_min,
f0_max,
model,
batch_size=crepe_hop_length * 2,
device=torch_device,
pad=True
)
p_len = p_len or x.shape[0] // crepe_hop_length
# Resize the pitch
source = np.array(pitch.squeeze(0).cpu().float().numpy())
source[source < 0.001] = np.nan
target = np.interp(
np.arange(0, len(source) * p_len, len(source)) / p_len,
np.arange(0, len(source)),
source
)
return np.nan_to_num(target)
def pitch_extract(f0_method, x, f0_min, f0_max, p_len, time_step, sr, window, crepe_hop_length, filter_radius=3):
f0s = []
f0 = np.zeros(p_len)
for method in f0_method if isinstance(f0_method, list) else [f0_method]:
if method == "pm":
f0 = (
parselmouth.Sound(x, sr)
.to_pitch_ac(
time_step=time_step / 1000,
voicing_threshold=0.6,
pitch_floor=f0_min,
pitch_ceiling=f0_max,
)
.selected_array["frequency"]
)
pad_size = (p_len - len(f0) + 1) // 2
if pad_size > 0 or p_len - len(f0) - pad_size > 0:
f0 = np.pad(
f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
)
elif method in ['harvest', 'dio']:
if method == 'harvest':
f0, t = pyworld.harvest(
x.astype(np.double),
fs=sr,
f0_ceil=f0_max,
f0_floor=f0_min,
frame_period=10,
)
elif method == "dio":
f0, t = pyworld.dio(
x.astype(np.double),
fs=sr,
f0_ceil=f0_max,
f0_floor=f0_min,
frame_period=10,
)
f0 = pyworld.stonemask(x.astype(np.double), f0, t, sr)
elif method == "torchcrepe":
f0 = get_f0_crepe_computation(x, f0_min, f0_max, p_len, sr, crepe_hop_length)
elif method == "torchcrepe tiny":
f0 = get_f0_crepe_computation(x, f0_min, f0_max, p_len, sr, crepe_hop_length, "tiny")
elif method == "mangio-crepe":
f0 = get_mangio_crepe_f0(x, f0_min, f0_max, p_len, sr, crepe_hop_length)
elif method == "mangio-crepe tiny":
f0 = get_mangio_crepe_f0(x, f0_min, f0_max, p_len, sr, crepe_hop_length, 'tiny')
elif method == "rmvpe":
rmvpe_model_path = os.path.join('data', 'models', 'rmvpe')
rmvpe_model_file = os.path.join(rmvpe_model_path, 'rmvpe.pt')
if not os.path.isfile(rmvpe_model_file):
import huggingface_hub
rmvpe_model_file = huggingface_hub.hf_hub_download('lj1995/VoiceConversionWebUI', 'rmvpe.pt', local_dir=rmvpe_model_path, local_dir_use_symlinks=False)
from modules.voice_conversion.rvc.rmvpe import RMVPE
print("loading rmvpe model")
model_rmvpe = RMVPE(rmvpe_model_file, is_half=True, device=None)
f0 = model_rmvpe.infer_from_audio(x, thred=0.03)
del model_rmvpe
torch.cuda.empty_cache()
gc.collect()
f0s.append(f0)
if not f0s:
f0s = [f0]
f0s_new = []
for f0_val in f0s:
_len = f0_val.shape[0]
if _len == p_len:
f0s_new.append(f0)
continue
if _len > p_len:
f0 = f0[:p_len]
f0s_new.append(f0)
continue
if _len < p_len:
print('WARNING: len < p_len, skipping this f0')
f0 = np.nanmedian(np.stack(f0s_new, axis=0), axis=0)
if filter_radius >= 2:
f0 = signal.medfilt(f0, filter_radius)
return f0
|