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 # Remove 0 frequency and apply linear interpolation 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) # Probably can be rewritten with torch? 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] if torch.all(f0 == 0): rtn = f0.cpu().numpy() if pad_to==None else np.zeros(pad_to) return rtn,rtn return self.post_process(x, sampling_rate, f0, pad_to)