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from __future__ import annotations
from logging import getLogger
from typing import Any, Literal
import numpy as np
import torch
import torchcrepe
from cm_time import timer
from numpy import dtype, float32, ndarray
from torch import FloatTensor, Tensor
from so_vits_svc_fork.utils import get_optimal_device
LOG = getLogger(__name__)
def normalize_f0(
f0: FloatTensor, x_mask: FloatTensor, uv: FloatTensor, random_scale=True
) -> FloatTensor:
# calculate means based on x_mask
uv_sum = torch.sum(uv, dim=1, keepdim=True)
uv_sum[uv_sum == 0] = 9999
means = torch.sum(f0[:, 0, :] * uv, dim=1, keepdim=True) / uv_sum
if random_scale:
factor = torch.Tensor(f0.shape[0], 1).uniform_(0.8, 1.2).to(f0.device)
else:
factor = torch.ones(f0.shape[0], 1).to(f0.device)
# normalize f0 based on means and factor
f0_norm = (f0 - means.unsqueeze(-1)) * factor.unsqueeze(-1)
if torch.isnan(f0_norm).any():
exit(0)
return f0_norm * x_mask
def interpolate_f0(
f0: ndarray[Any, dtype[float32]]
) -> tuple[ndarray[Any, dtype[float32]], ndarray[Any, dtype[float32]]]:
data = np.reshape(f0, (f0.size, 1))
vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
vuv_vector[data > 0.0] = 1.0
vuv_vector[data <= 0.0] = 0.0
ip_data = data
frame_number = data.size
last_value = 0.0
for i in range(frame_number):
if data[i] <= 0.0:
j = i + 1
for j in range(i + 1, frame_number):
if data[j] > 0.0:
break
if j < frame_number - 1:
if last_value > 0.0:
step = (data[j] - data[i - 1]) / float(j - i)
for k in range(i, j):
ip_data[k] = data[i - 1] + step * (k - i + 1)
else:
for k in range(i, j):
ip_data[k] = data[j]
else:
for k in range(i, frame_number):
ip_data[k] = last_value
else:
ip_data[i] = data[i]
last_value = data[i]
return ip_data[:, 0], vuv_vector[:, 0]
def compute_f0_parselmouth(
wav_numpy: ndarray[Any, dtype[float32]],
p_len: None | int = None,
sampling_rate: int = 44100,
hop_length: int = 512,
):
import parselmouth
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"
time_step = hop_length / sampling_rate * 1000
f0_min = 50
f0_max = 1100
f0 = (
parselmouth.Sound(x, sampling_rate)
.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")
return f0
def _resize_f0(
x: ndarray[Any, dtype[float32]], target_len: int
) -> ndarray[Any, dtype[float32]]:
source = np.array(x)
source[source < 0.001] = np.nan
target = np.interp(
np.arange(0, len(source) * target_len, len(source)) / target_len,
np.arange(0, len(source)),
source,
)
res = np.nan_to_num(target)
return res
def compute_f0_pyworld(
wav_numpy: ndarray[Any, dtype[float32]],
p_len: None | int = None,
sampling_rate: int = 44100,
hop_length: int = 512,
type_: Literal["dio", "harvest"] = "dio",
):
import pyworld
if p_len is None:
p_len = wav_numpy.shape[0] // hop_length
if type_ == "dio":
f0, t = pyworld.dio(
wav_numpy.astype(np.double),
fs=sampling_rate,
f0_ceil=f0_max,
f0_floor=f0_min,
frame_period=1000 * hop_length / sampling_rate,
)
elif type_ == "harvest":
f0, t = pyworld.harvest(
wav_numpy.astype(np.double),
fs=sampling_rate,
f0_ceil=f0_max,
f0_floor=f0_min,
frame_period=1000 * hop_length / sampling_rate,
)
f0 = pyworld.stonemask(wav_numpy.astype(np.double), f0, t, sampling_rate)
for index, pitch in enumerate(f0):
f0[index] = round(pitch, 1)
return _resize_f0(f0, p_len)
def compute_f0_crepe(
wav_numpy: ndarray[Any, dtype[float32]],
p_len: None | int = None,
sampling_rate: int = 44100,
hop_length: int = 512,
device: str | torch.device = get_optimal_device(),
model: Literal["full", "tiny"] = "full",
):
audio = torch.from_numpy(wav_numpy).to(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()
# (T) -> (1, T)
audio = audio.detach()
pitch: Tensor = torchcrepe.predict(
audio,
sampling_rate,
hop_length,
f0_min,
f0_max,
model,
batch_size=hop_length * 2,
device=device,
pad=True,
)
f0 = pitch.squeeze(0).cpu().float().numpy()
p_len = p_len or wav_numpy.shape[0] // hop_length
f0 = _resize_f0(f0, p_len)
return f0
def compute_f0(
wav_numpy: ndarray[Any, dtype[float32]],
p_len: None | int = None,
sampling_rate: int = 44100,
hop_length: int = 512,
method: Literal["crepe", "crepe-tiny", "parselmouth", "dio", "harvest"] = "dio",
**kwargs,
):
with timer() as t:
wav_numpy = wav_numpy.astype(np.float32)
wav_numpy /= np.quantile(np.abs(wav_numpy), 0.999)
if method in ["dio", "harvest"]:
f0 = compute_f0_pyworld(wav_numpy, p_len, sampling_rate, hop_length, method)
elif method == "crepe":
f0 = compute_f0_crepe(wav_numpy, p_len, sampling_rate, hop_length, **kwargs)
elif method == "crepe-tiny":
f0 = compute_f0_crepe(
wav_numpy, p_len, sampling_rate, hop_length, model="tiny", **kwargs
)
elif method == "parselmouth":
f0 = compute_f0_parselmouth(wav_numpy, p_len, sampling_rate, hop_length)
else:
raise ValueError(
"type must be dio, crepe, crepe-tiny, harvest or parselmouth"
)
rtf = t.elapsed / (len(wav_numpy) / sampling_rate)
LOG.info(f"F0 inference time: {t.elapsed:.3f}s, RTF: {rtf:.3f}")
return f0
def f0_to_coarse(f0: torch.Tensor | float):
is_torch = isinstance(f0, torch.Tensor)
f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * np.log(1 + f0 / 700)
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (f0_bin - 2) / (
f0_mel_max - f0_mel_min
) + 1
f0_mel[f0_mel <= 1] = 1
f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1
f0_coarse = (f0_mel + 0.5).long() if is_torch else np.rint(f0_mel).astype(np.int)
assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (
f0_coarse.max(),
f0_coarse.min(),
)
return f0_coarse
f0_bin = 256
f0_max = 1100.0
f0_min = 50.0
f0_mel_min = 1127 * np.log(1 + f0_min / 700)
f0_mel_max = 1127 * np.log(1 + f0_max / 700)
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