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from typing import Optional, Union
try:
from typing import Literal
except Exception:
from typing_extensions import Literal
import numpy as np
import torch
import torchcrepe
from torch import nn
from torch.nn import functional as F
#from:https://github.com/fishaudio/fish-diffusion
def repeat_expand(
content: Union[torch.Tensor, np.ndarray], target_len: int, mode: str = "nearest"
):
"""Repeat content to target length.
This is a wrapper of torch.nn.functional.interpolate.
Args:
content (torch.Tensor): tensor
target_len (int): target length
mode (str, optional): interpolation mode. Defaults to "nearest".
Returns:
torch.Tensor: tensor
"""
ndim = content.ndim
if content.ndim == 1:
content = content[None, None]
elif content.ndim == 2:
content = content[None]
assert content.ndim == 3
is_np = isinstance(content, np.ndarray)
if is_np:
content = torch.from_numpy(content)
results = torch.nn.functional.interpolate(content, size=target_len, mode=mode)
if is_np:
results = results.numpy()
if ndim == 1:
return results[0, 0]
elif ndim == 2:
return results[0]
class BasePitchExtractor:
def __init__(
self,
hop_length: int = 512,
f0_min: float = 50.0,
f0_max: float = 1100.0,
keep_zeros: bool = True,
):
"""Base pitch extractor.
Args:
hop_length (int, optional): Hop length. Defaults to 512.
f0_min (float, optional): Minimum f0. Defaults to 50.0.
f0_max (float, optional): Maximum f0. Defaults to 1100.0.
keep_zeros (bool, optional): Whether keep zeros in pitch. Defaults to True.
"""
self.hop_length = hop_length
self.f0_min = f0_min
self.f0_max = f0_max
self.keep_zeros = keep_zeros
def __call__(self, x, sampling_rate=44100, pad_to=None):
raise NotImplementedError("BasePitchExtractor is not callable.")
def post_process(self, x, sampling_rate, f0, pad_to):
if isinstance(f0, np.ndarray):
f0 = torch.from_numpy(f0).float().to(x.device)
if pad_to is None:
return f0
f0 = repeat_expand(f0, pad_to)
if self.keep_zeros:
return f0
vuv_vector = torch.zeros_like(f0)
vuv_vector[f0 > 0.0] = 1.0
vuv_vector[f0 <= 0.0] = 0.0
# 去掉0频率, 并线性插值
nzindex = torch.nonzero(f0).squeeze()
f0 = torch.index_select(f0, dim=0, index=nzindex).cpu().numpy()
time_org = self.hop_length / sampling_rate * nzindex.cpu().numpy()
time_frame = np.arange(pad_to) * self.hop_length / sampling_rate
vuv_vector = F.interpolate(vuv_vector[None,None,:],size=pad_to)[0][0]
if f0.shape[0] <= 0:
return torch.zeros(pad_to, dtype=torch.float, device=x.device),vuv_vector.cpu().numpy()
if f0.shape[0] == 1:
return torch.ones(pad_to, dtype=torch.float, device=x.device) * f0[0],vuv_vector.cpu().numpy()
# 大概可以用 torch 重写?
f0 = np.interp(time_frame, time_org, f0, left=f0[0], right=f0[-1])
#vuv_vector = np.ceil(scipy.ndimage.zoom(vuv_vector,pad_to/len(vuv_vector),order = 0))
return f0,vuv_vector.cpu().numpy()
class MaskedAvgPool1d(nn.Module):
def __init__(
self, kernel_size: int, stride: Optional[int] = None, padding: Optional[int] = 0
):
"""An implementation of mean pooling that supports masked values.
Args:
kernel_size (int): The size of the median pooling window.
stride (int, optional): The stride of the median pooling window. Defaults to None.
padding (int, optional): The padding of the median pooling window. Defaults to 0.
"""
super(MaskedAvgPool1d, self).__init__()
self.kernel_size = kernel_size
self.stride = stride or kernel_size
self.padding = padding
def forward(self, x, mask=None):
ndim = x.dim()
if ndim == 2:
x = x.unsqueeze(1)
assert (
x.dim() == 3
), "Input tensor must have 2 or 3 dimensions (batch_size, channels, width)"
# Apply the mask by setting masked elements to zero, or make NaNs zero
if mask is None:
mask = ~torch.isnan(x)
# Ensure mask has the same shape as the input tensor
assert x.shape == mask.shape, "Input tensor and mask must have the same shape"
masked_x = torch.where(mask, x, torch.zeros_like(x))
# Create a ones kernel with the same number of channels as the input tensor
ones_kernel = torch.ones(x.size(1), 1, self.kernel_size, device=x.device)
# Perform sum pooling
sum_pooled = nn.functional.conv1d(
masked_x,
ones_kernel,
stride=self.stride,
padding=self.padding,
groups=x.size(1),
)
# Count the non-masked (valid) elements in each pooling window
valid_count = nn.functional.conv1d(
mask.float(),
ones_kernel,
stride=self.stride,
padding=self.padding,
groups=x.size(1),
)
valid_count = valid_count.clamp(min=1) # Avoid division by zero
# Perform masked average pooling
avg_pooled = sum_pooled / valid_count
# Fill zero values with NaNs
avg_pooled[avg_pooled == 0] = float("nan")
if ndim == 2:
return avg_pooled.squeeze(1)
return avg_pooled
class MaskedMedianPool1d(nn.Module):
def __init__(
self, kernel_size: int, stride: Optional[int] = None, padding: Optional[int] = 0
):
"""An implementation of median pooling that supports masked values.
This implementation is inspired by the median pooling implementation in
https://gist.github.com/rwightman/f2d3849281624be7c0f11c85c87c1598
Args:
kernel_size (int): The size of the median pooling window.
stride (int, optional): The stride of the median pooling window. Defaults to None.
padding (int, optional): The padding of the median pooling window. Defaults to 0.
"""
super(MaskedMedianPool1d, self).__init__()
self.kernel_size = kernel_size
self.stride = stride or kernel_size
self.padding = padding
def forward(self, x, mask=None):
ndim = x.dim()
if ndim == 2:
x = x.unsqueeze(1)
assert (
x.dim() == 3
), "Input tensor must have 2 or 3 dimensions (batch_size, channels, width)"
if mask is None:
mask = ~torch.isnan(x)
assert x.shape == mask.shape, "Input tensor and mask must have the same shape"
masked_x = torch.where(mask, x, torch.zeros_like(x))
x = F.pad(masked_x, (self.padding, self.padding), mode="reflect")
mask = F.pad(
mask.float(), (self.padding, self.padding), mode="constant", value=0
)
x = x.unfold(2, self.kernel_size, self.stride)
mask = mask.unfold(2, self.kernel_size, self.stride)
x = x.contiguous().view(x.size()[:3] + (-1,))
mask = mask.contiguous().view(mask.size()[:3] + (-1,)).to(x.device)
# Combine the mask with the input tensor
#x_masked = torch.where(mask.bool(), x, torch.fill_(torch.zeros_like(x),float("inf")))
x_masked = torch.where(mask.bool(), x, torch.FloatTensor([float("inf")]).to(x.device))
# Sort the masked tensor along the last dimension
x_sorted, _ = torch.sort(x_masked, dim=-1)
# Compute the count of non-masked (valid) values
valid_count = mask.sum(dim=-1)
# Calculate the index of the median value for each pooling window
median_idx = (torch.div((valid_count - 1), 2, rounding_mode='trunc')).clamp(min=0)
# Gather the median values using the calculated indices
median_pooled = x_sorted.gather(-1, median_idx.unsqueeze(-1).long()).squeeze(-1)
# Fill infinite values with NaNs
median_pooled[torch.isinf(median_pooled)] = float("nan")
if ndim == 2:
return median_pooled.squeeze(1)
return median_pooled
class CrepePitchExtractor(BasePitchExtractor):
def __init__(
self,
hop_length: int = 512,
f0_min: float = 50.0,
f0_max: float = 1100.0,
threshold: float = 0.05,
keep_zeros: bool = False,
device = None,
model: Literal["full", "tiny"] = "full",
use_fast_filters: bool = True,
decoder="viterbi"
):
super().__init__(hop_length, f0_min, f0_max, keep_zeros)
if decoder == "viterbi":
self.decoder = torchcrepe.decode.viterbi
elif decoder == "argmax":
self.decoder = torchcrepe.decode.argmax
elif decoder == "weighted_argmax":
self.decoder = torchcrepe.decode.weighted_argmax
else:
raise "Unknown decoder"
self.threshold = threshold
self.model = model
self.use_fast_filters = use_fast_filters
self.hop_length = hop_length
if device is None:
self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
else:
self.dev = torch.device(device)
if self.use_fast_filters:
self.median_filter = MaskedMedianPool1d(3, 1, 1).to(device)
self.mean_filter = MaskedAvgPool1d(3, 1, 1).to(device)
def __call__(self, x, sampling_rate=44100, pad_to=None):
"""Extract pitch using crepe.
Args:
x (torch.Tensor): Audio signal, shape (1, T).
sampling_rate (int, optional): Sampling rate. Defaults to 44100.
pad_to (int, optional): Pad to length. Defaults to None.
Returns:
torch.Tensor: Pitch, shape (T // hop_length,).
"""
assert x.ndim == 2, f"Expected 2D tensor, got {x.ndim}D tensor."
assert x.shape[0] == 1, f"Expected 1 channel, got {x.shape[0]} channels."
x = x.to(self.dev)
f0, pd = torchcrepe.predict(
x,
sampling_rate,
self.hop_length,
self.f0_min,
self.f0_max,
pad=True,
model=self.model,
batch_size=1024,
device=x.device,
return_periodicity=True,
decoder=self.decoder
)
# Filter, remove silence, set uv threshold, refer to the original warehouse readme
if self.use_fast_filters:
pd = self.median_filter(pd)
else:
pd = torchcrepe.filter.median(pd, 3)
pd = torchcrepe.threshold.Silence(-60.0)(pd, x, sampling_rate, self.hop_length)
f0 = torchcrepe.threshold.At(self.threshold)(f0, pd)
if self.use_fast_filters:
f0 = self.mean_filter(f0)
else:
f0 = torchcrepe.filter.mean(f0, 3)
f0 = torch.where(torch.isnan(f0), torch.full_like(f0, 0), f0)[0]
if torch.all(f0 == 0):
rtn = f0.cpu().numpy() if pad_to is None else np.zeros(pad_to)
return rtn,rtn
return self.post_process(x, sampling_rate, f0, pad_to)