Delete modules/crepe.py

modules/crepe.py

@@ -1,327 +0,0 @@
-from typing import Optional,Union
-try:
-    from typing import Literal
-except Exception as e:
-    from typing_extensions import Literal
-import numpy as np
-import torch
-import torchcrepe
-from torch import nn
-from torch.nn import functional as F
-import scipy
-
-#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
-
-        if f0.shape[0] <= 0:
-            return torch.zeros(pad_to, dtype=torch.float, device=x.device),torch.zeros(pad_to, dtype=torch.float, device=x.device)
-
-        if f0.shape[0] == 1:
-            return torch.ones(pad_to, dtype=torch.float, device=x.device) * f0[0],torch.ones(pad_to, dtype=torch.float, device=x.device)
-    
-        # 大概可以用 torch 重写?
-        f0 = np.interp(time_frame, time_org, f0, left=f0[0], right=f0[-1])
-        vuv_vector = vuv_vector.cpu().numpy()
-        vuv_vector = np.ceil(scipy.ndimage.zoom(vuv_vector,pad_to/len(vuv_vector),order = 0))
-        
-        return f0,vuv_vector
-
-
-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,
-    ):
-        super().__init__(hop_length, f0_min, f0_max, keep_zeros)
-
-        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,
-        )
-
-        # 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, 512)
-        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]
-
-        return self.post_process(x, sampling_rate, f0, pad_to)