Upload fcpe.py

lib/infer_libs/fcpe.py

@@ -0,0 +1,873 @@
+from typing import Union
+
+import torch.nn.functional as F
+import numpy as np
+import torch
+import torch.nn as nn
+from torch.nn.utils import weight_norm
+from torchaudio.transforms import Resample
+import os
+import librosa
+import soundfile as sf
+import torch.utils.data
+from librosa.filters import mel as librosa_mel_fn
+import math
+from functools import partial
+
+from einops import rearrange, repeat
+from local_attention import LocalAttention
+from torch import nn
+
+os.environ["LRU_CACHE_CAPACITY"] = "3"
+
+def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
+    sampling_rate = None
+    try:
+        data, sampling_rate = sf.read(full_path, always_2d=True)# than soundfile.
+    except Exception as ex:
+        print(f"'{full_path}' failed to load.\nException:")
+        print(ex)
+        if return_empty_on_exception:
+            return [], sampling_rate or target_sr or 48000
+        else:
+            raise Exception(ex)
+    
+    if len(data.shape) > 1:
+        data = data[:, 0]
+        assert len(data) > 2# check duration of audio file is > 2 samples (because otherwise the slice operation was on the wrong dimension)
+    
+    if np.issubdtype(data.dtype, np.integer): # if audio data is type int
+        max_mag = -np.iinfo(data.dtype).min # maximum magnitude = min possible value of intXX
+    else: # if audio data is type fp32
+        max_mag = max(np.amax(data), -np.amin(data))
+        max_mag = (2**31)+1 if max_mag > (2**15) else ((2**15)+1 if max_mag > 1.01 else 1.0) # data should be either 16-bit INT, 32-bit INT or [-1 to 1] float32
+    
+    data = torch.FloatTensor(data.astype(np.float32))/max_mag
+    
+    if (torch.isinf(data) | torch.isnan(data)).any() and return_empty_on_exception:# resample will crash with inf/NaN inputs. return_empty_on_exception will return empty arr instead of except
+        return [], sampling_rate or target_sr or 48000
+    if target_sr is not None and sampling_rate != target_sr:
+        data = torch.from_numpy(librosa.core.resample(data.numpy(), orig_sr=sampling_rate, target_sr=target_sr))
+        sampling_rate = target_sr
+    
+    return data, sampling_rate
+
+def dynamic_range_compression(x, C=1, clip_val=1e-5):
+    return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
+
+def dynamic_range_decompression(x, C=1):
+    return np.exp(x) / C
+
+def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
+    return torch.log(torch.clamp(x, min=clip_val) * C)
+
+def dynamic_range_decompression_torch(x, C=1):
+    return torch.exp(x) / C
+
+class STFT():
+    def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025, clip_val=1e-5):
+        self.target_sr = sr
+        
+        self.n_mels     = n_mels
+        self.n_fft      = n_fft
+        self.win_size   = win_size
+        self.hop_length = hop_length
+        self.fmin     = fmin
+        self.fmax     = fmax
+        self.clip_val = clip_val
+        self.mel_basis = {}
+        self.hann_window = {}
+    
+    def get_mel(self, y, keyshift=0, speed=1, center=False, train=False):
+        sampling_rate = self.target_sr
+        n_mels     = self.n_mels
+        n_fft      = self.n_fft
+        win_size   = self.win_size
+        hop_length = self.hop_length
+        fmin       = self.fmin
+        fmax       = self.fmax
+        clip_val   = self.clip_val
+        
+        factor = 2 ** (keyshift / 12)       
+        n_fft_new = int(np.round(n_fft * factor))
+        win_size_new = int(np.round(win_size * factor))
+        hop_length_new = int(np.round(hop_length * speed))
+        if not train:
+            mel_basis = self.mel_basis
+            hann_window = self.hann_window
+        else:
+            mel_basis = {}
+            hann_window = {}
+        
+        if torch.min(y) < -1.:
+            print('min value is ', torch.min(y))
+        if torch.max(y) > 1.:
+            print('max value is ', torch.max(y))
+        
+        mel_basis_key = str(fmax)+'_'+str(y.device)
+        if mel_basis_key not in mel_basis:
+            mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)
+            mel_basis[mel_basis_key] = torch.from_numpy(mel).float().to(y.device)
+        
+        keyshift_key = str(keyshift)+'_'+str(y.device)
+        if keyshift_key not in hann_window:
+            hann_window[keyshift_key] = torch.hann_window(win_size_new).to(y.device)
+        
+        pad_left = (win_size_new - hop_length_new) //2
+        pad_right = max((win_size_new- hop_length_new + 1) //2, win_size_new - y.size(-1) - pad_left)
+        if pad_right < y.size(-1):
+            mode = 'reflect'
+        else:
+            mode = 'constant'
+        y = torch.nn.functional.pad(y.unsqueeze(1), (pad_left, pad_right), mode = mode)
+        y = y.squeeze(1)
+        
+        spec = torch.stft(y, n_fft_new, hop_length=hop_length_new, win_length=win_size_new, window=hann_window[keyshift_key],
+                          center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=True)                          
+        spec = torch.sqrt(spec.real.pow(2) + spec.imag.pow(2) + (1e-9))
+        if keyshift != 0:
+            size = n_fft // 2 + 1
+            resize = spec.size(1)
+            if resize < size:
+                spec = F.pad(spec, (0, 0, 0, size-resize))
+            spec = spec[:, :size, :] * win_size / win_size_new   
+        spec = torch.matmul(mel_basis[mel_basis_key], spec)
+        spec = dynamic_range_compression_torch(spec, clip_val=clip_val)
+        return spec
+    
+    def __call__(self, audiopath):
+        audio, sr = load_wav_to_torch(audiopath, target_sr=self.target_sr)
+        spect = self.get_mel(audio.unsqueeze(0)).squeeze(0)
+        return spect
+
+stft = STFT()
+
+#import fast_transformers.causal_product.causal_product_cuda
+
+def softmax_kernel(data, *, projection_matrix, is_query, normalize_data=True, eps=1e-4, device = None):
+    b, h, *_ = data.shape
+    # (batch size, head, length, model_dim)
+
+    # normalize model dim
+    data_normalizer = (data.shape[-1] ** -0.25) if normalize_data else 1.
+
+    # what is ration?, projection_matrix.shape[0] --> 266
+    
+    ratio = (projection_matrix.shape[0] ** -0.5)
+
+    projection = repeat(projection_matrix, 'j d -> b h j d', b = b, h = h)
+    projection = projection.type_as(data)
+
+    #data_dash = w^T x
+    data_dash = torch.einsum('...id,...jd->...ij', (data_normalizer * data), projection)
+
+    
+    # diag_data = D**2 
+    diag_data = data ** 2
+    diag_data = torch.sum(diag_data, dim=-1)
+    diag_data = (diag_data / 2.0) * (data_normalizer ** 2)
+    diag_data = diag_data.unsqueeze(dim=-1)
+    
+    #print ()
+    if is_query:
+        data_dash = ratio * (
+            torch.exp(data_dash - diag_data -
+                    torch.max(data_dash, dim=-1, keepdim=True).values) + eps)
+    else:
+        data_dash = ratio * (
+            torch.exp(data_dash - diag_data + eps))#- torch.max(data_dash)) + eps)
+
+    return data_dash.type_as(data)
+
+def orthogonal_matrix_chunk(cols, qr_uniform_q = False, device = None):
+    unstructured_block = torch.randn((cols, cols), device = device)
+    q, r = torch.linalg.qr(unstructured_block.cpu(), mode='reduced')
+    q, r = map(lambda t: t.to(device), (q, r))
+
+    # proposed by @Parskatt
+    # to make sure Q is uniform https://arxiv.org/pdf/math-ph/0609050.pdf
+    if qr_uniform_q:
+        d = torch.diag(r, 0)
+        q *= d.sign()
+    return q.t()
+def exists(val):
+    return val is not None
+
+def empty(tensor):
+    return tensor.numel() == 0
+
+def default(val, d):
+    return val if exists(val) else d
+
+def cast_tuple(val):
+    return (val,) if not isinstance(val, tuple) else val
+
+class PCmer(nn.Module):
+    """The encoder that is used in the Transformer model."""
+    
+    def __init__(self, 
+                num_layers,
+                num_heads,
+                dim_model,
+                dim_keys,
+                dim_values,
+                residual_dropout,
+                attention_dropout):
+        super().__init__()
+        self.num_layers = num_layers
+        self.num_heads = num_heads
+        self.dim_model = dim_model
+        self.dim_values = dim_values
+        self.dim_keys = dim_keys
+        self.residual_dropout = residual_dropout
+        self.attention_dropout = attention_dropout
+
+        self._layers = nn.ModuleList([_EncoderLayer(self) for _ in range(num_layers)])
+        
+    #  METHODS  ########################################################################################################
+    
+    def forward(self, phone, mask=None):
+        
+        # apply all layers to the input
+        for (i, layer) in enumerate(self._layers):
+            phone = layer(phone, mask)
+        # provide the final sequence
+        return phone
+
+
+# ==================================================================================================================== #
+#  CLASS  _ E N C O D E R  L A Y E R                                                                                   #
+# ==================================================================================================================== #
+
+
+class _EncoderLayer(nn.Module):
+    """One layer of the encoder.
+    
+    Attributes:
+        attn: (:class:`mha.MultiHeadAttention`): The attention mechanism that is used to read the input sequence.
+        feed_forward (:class:`ffl.FeedForwardLayer`): The feed-forward layer on top of the attention mechanism.
+    """
+    
+    def __init__(self, parent: PCmer):
+        """Creates a new instance of ``_EncoderLayer``.
+        
+        Args:
+            parent (Encoder): The encoder that the layers is created for.
+        """
+        super().__init__()
+        
+        
+        self.conformer = ConformerConvModule(parent.dim_model)
+        self.norm = nn.LayerNorm(parent.dim_model)
+        self.dropout = nn.Dropout(parent.residual_dropout)
+        
+        # selfatt -> fastatt: performer!
+        self.attn = SelfAttention(dim = parent.dim_model,
+                                  heads = parent.num_heads,
+                                  causal = False)
+        
+    #  METHODS  ########################################################################################################
+
+    def forward(self, phone, mask=None):
+        
+        # compute attention sub-layer
+        phone = phone + (self.attn(self.norm(phone), mask=mask))
+        
+        phone = phone + (self.conformer(phone))
+        
+        return phone 
+
+def calc_same_padding(kernel_size):
+    pad = kernel_size // 2
+    return (pad, pad - (kernel_size + 1) % 2)
+
+# helper classes
+
+class Swish(nn.Module):
+    def forward(self, x):
+        return x * x.sigmoid()
+
+class Transpose(nn.Module):
+    def __init__(self, dims):
+        super().__init__()
+        assert len(dims) == 2, 'dims must be a tuple of two dimensions'
+        self.dims = dims
+
+    def forward(self, x):
+        return x.transpose(*self.dims)
+
+class GLU(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.dim = dim
+
+    def forward(self, x):
+        out, gate = x.chunk(2, dim=self.dim)
+        return out * gate.sigmoid()
+
+class DepthWiseConv1d(nn.Module):
+    def __init__(self, chan_in, chan_out, kernel_size, padding):
+        super().__init__()
+        self.padding = padding
+        self.conv = nn.Conv1d(chan_in, chan_out, kernel_size, groups = chan_in)
+
+    def forward(self, x):
+        x = F.pad(x, self.padding)
+        return self.conv(x)
+
+class ConformerConvModule(nn.Module):
+    def __init__(
+        self,
+        dim,
+        causal = False,
+        expansion_factor = 2,
+        kernel_size = 31,
+        dropout = 0.):
+        super().__init__()
+
+        inner_dim = dim * expansion_factor
+        padding = calc_same_padding(kernel_size) if not causal else (kernel_size - 1, 0)
+
+        self.net = nn.Sequential(
+            nn.LayerNorm(dim),
+            Transpose((1, 2)),
+            nn.Conv1d(dim, inner_dim * 2, 1),
+            GLU(dim=1),
+            DepthWiseConv1d(inner_dim, inner_dim, kernel_size = kernel_size, padding = padding),
+            #nn.BatchNorm1d(inner_dim) if not causal else nn.Identity(),
+            Swish(),
+            nn.Conv1d(inner_dim, dim, 1),
+            Transpose((1, 2)),
+            nn.Dropout(dropout)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+def linear_attention(q, k, v):
+    if v is None:
+        #print (k.size(), q.size())
+        out = torch.einsum('...ed,...nd->...ne', k, q)
+        return out
+
+    else:
+        k_cumsum = k.sum(dim = -2) 
+        #k_cumsum = k.sum(dim = -2)
+        D_inv = 1. / (torch.einsum('...nd,...d->...n', q, k_cumsum.type_as(q)) + 1e-8)
+
+        context = torch.einsum('...nd,...ne->...de', k, v)
+        #print ("TRUEEE: ", context.size(), q.size(), D_inv.size())
+        out = torch.einsum('...de,...nd,...n->...ne', context, q, D_inv)
+        return out
+
+def gaussian_orthogonal_random_matrix(nb_rows, nb_columns, scaling = 0, qr_uniform_q = False, device = None):
+    nb_full_blocks = int(nb_rows / nb_columns)
+    #print (nb_full_blocks)
+    block_list = []
+
+    for _ in range(nb_full_blocks):
+        q = orthogonal_matrix_chunk(nb_columns, qr_uniform_q = qr_uniform_q, device = device)
+        block_list.append(q)
+    # block_list[n] is a orthogonal matrix ... (model_dim * model_dim)
+    #print (block_list[0].size(), torch.einsum('...nd,...nd->...n', block_list[0], torch.roll(block_list[0],1,1)))
+    #print (nb_rows, nb_full_blocks, nb_columns)
+    remaining_rows = nb_rows - nb_full_blocks * nb_columns
+    #print (remaining_rows)
+    if remaining_rows > 0:
+        q = orthogonal_matrix_chunk(nb_columns, qr_uniform_q = qr_uniform_q, device = device)
+        #print (q[:remaining_rows].size())
+        block_list.append(q[:remaining_rows])
+
+    final_matrix = torch.cat(block_list)
+    
+    if scaling == 0:
+        multiplier = torch.randn((nb_rows, nb_columns), device = device).norm(dim = 1)
+    elif scaling == 1:
+        multiplier = math.sqrt((float(nb_columns))) * torch.ones((nb_rows,), device = device)
+    else:
+        raise ValueError(f'Invalid scaling {scaling}')
+
+    return torch.diag(multiplier) @ final_matrix
+
+class FastAttention(nn.Module):
+    def __init__(self, dim_heads, nb_features = None, ortho_scaling = 0, causal = False, generalized_attention = False, kernel_fn = nn.ReLU(), qr_uniform_q = False, no_projection = False):
+        super().__init__()
+        nb_features = default(nb_features, int(dim_heads * math.log(dim_heads)))
+
+        self.dim_heads = dim_heads
+        self.nb_features = nb_features
+        self.ortho_scaling = ortho_scaling
+
+        self.create_projection = partial(gaussian_orthogonal_random_matrix, nb_rows = self.nb_features, nb_columns = dim_heads, scaling = ortho_scaling, qr_uniform_q = qr_uniform_q)
+        projection_matrix = self.create_projection()
+        self.register_buffer('projection_matrix', projection_matrix)
+
+        self.generalized_attention = generalized_attention
+        self.kernel_fn = kernel_fn
+
+        # if this is turned on, no projection will be used
+        # queries and keys will be softmax-ed as in the original efficient attention paper
+        self.no_projection = no_projection
+
+        self.causal = causal
+
+    @torch.no_grad()
+    def redraw_projection_matrix(self):
+        projections = self.create_projection()
+        self.projection_matrix.copy_(projections)
+        del projections
+
+    def forward(self, q, k, v):
+        device = q.device
+
+        if self.no_projection:
+            q = q.softmax(dim = -1)
+            k = torch.exp(k) if self.causal else k.softmax(dim = -2)
+        else:
+            create_kernel = partial(softmax_kernel, projection_matrix = self.projection_matrix, device = device)
+            
+            q = create_kernel(q, is_query = True)
+            k = create_kernel(k, is_query = False)
+
+        attn_fn = linear_attention if not self.causal else self.causal_linear_fn
+        if v is None:
+            out = attn_fn(q, k, None)
+            return out
+        else:
+            out = attn_fn(q, k, v)
+            return out
+class SelfAttention(nn.Module):
+    def __init__(self, dim, causal = False, heads = 8, dim_head = 64, local_heads = 0, local_window_size = 256, nb_features = None, feature_redraw_interval = 1000, generalized_attention = False, kernel_fn = nn.ReLU(), qr_uniform_q = False, dropout = 0., no_projection = False):
+        super().__init__()
+        assert dim % heads == 0, 'dimension must be divisible by number of heads'
+        dim_head = default(dim_head, dim // heads)
+        inner_dim = dim_head * heads
+        self.fast_attention = FastAttention(dim_head, nb_features, causal = causal, generalized_attention = generalized_attention, kernel_fn = kernel_fn, qr_uniform_q = qr_uniform_q, no_projection = no_projection)
+
+        self.heads = heads
+        self.global_heads = heads - local_heads
+        self.local_attn = LocalAttention(window_size = local_window_size, causal = causal, autopad = True, dropout = dropout, look_forward = int(not causal), rel_pos_emb_config = (dim_head, local_heads)) if local_heads > 0 else None
+
+        #print (heads, nb_features, dim_head)
+        #name_embedding = torch.zeros(110, heads, dim_head, dim_head)
+        #self.name_embedding = nn.Parameter(name_embedding, requires_grad=True)
+        
+
+        self.to_q = nn.Linear(dim, inner_dim)
+        self.to_k = nn.Linear(dim, inner_dim)
+        self.to_v = nn.Linear(dim, inner_dim)
+        self.to_out = nn.Linear(inner_dim, dim)
+        self.dropout = nn.Dropout(dropout)
+
+    @torch.no_grad()
+    def redraw_projection_matrix(self):
+        self.fast_attention.redraw_projection_matrix()
+        #torch.nn.init.zeros_(self.name_embedding)
+        #print (torch.sum(self.name_embedding))
+    def forward(self, x, context = None, mask = None, context_mask = None, name=None, inference=False, **kwargs):
+        _, _, _, h, gh = *x.shape, self.heads, self.global_heads
+        
+        cross_attend = exists(context)
+
+        context = default(context, x)
+        context_mask = default(context_mask, mask) if not cross_attend else context_mask
+        #print (torch.sum(self.name_embedding))
+        q, k, v = self.to_q(x), self.to_k(context), self.to_v(context)
+
+        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v))
+        (q, lq), (k, lk), (v, lv) = map(lambda t: (t[:, :gh], t[:, gh:]), (q, k, v))
+
+        attn_outs = []
+        #print (name)
+        #print (self.name_embedding[name].size())
+        if not empty(q):
+            if exists(context_mask):
+                global_mask = context_mask[:, None, :, None]
+                v.masked_fill_(~global_mask, 0.)
+            if cross_attend:
+                pass
+                #print (torch.sum(self.name_embedding))
+                #out = self.fast_attention(q,self.name_embedding[name],None)
+                #print (torch.sum(self.name_embedding[...,-1:]))
+            else:
+                out = self.fast_attention(q, k, v)
+            attn_outs.append(out)
+
+        if not empty(lq):
+            assert not cross_attend, 'local attention is not compatible with cross attention'
+            out = self.local_attn(lq, lk, lv, input_mask = mask)
+            attn_outs.append(out)
+
+        out = torch.cat(attn_outs, dim = 1)
+        out = rearrange(out, 'b h n d -> b n (h d)')
+        out =  self.to_out(out)
+        return self.dropout(out)
+
+def l2_regularization(model, l2_alpha):
+    l2_loss = []
+    for module in model.modules():
+        if type(module) is nn.Conv2d:
+            l2_loss.append((module.weight ** 2).sum() / 2.0)
+    return l2_alpha * sum(l2_loss)
+
+
+class FCPEModel(nn.Module):
+    def __init__(
+            self,
+            input_channel=128,
+            out_dims=360,
+            n_layers=12,
+            n_chans=512,
+            use_siren=False,
+            use_full=False,
+            loss_mse_scale=10,
+            loss_l2_regularization=False,
+            loss_l2_regularization_scale=1,
+            loss_grad1_mse=False,
+            loss_grad1_mse_scale=1,
+            f0_max=1975.5,
+            f0_min=32.70,
+            confidence=False,
+            threshold=0.05,
+            use_input_conv=True
+    ):
+        super().__init__()
+        if use_siren is True:
+            raise ValueError("Siren is not supported yet.")
+        if use_full is True:
+            raise ValueError("Full model is not supported yet.")
+
+        self.loss_mse_scale = loss_mse_scale if (loss_mse_scale is not None) else 10
+        self.loss_l2_regularization = loss_l2_regularization if (loss_l2_regularization is not None) else False
+        self.loss_l2_regularization_scale = loss_l2_regularization_scale if (loss_l2_regularization_scale
+                                                                             is not None) else 1
+        self.loss_grad1_mse = loss_grad1_mse if (loss_grad1_mse is not None) else False
+        self.loss_grad1_mse_scale = loss_grad1_mse_scale if (loss_grad1_mse_scale is not None) else 1
+        self.f0_max = f0_max if (f0_max is not None) else 1975.5
+        self.f0_min = f0_min if (f0_min is not None) else 32.70
+        self.confidence = confidence if (confidence is not None) else False
+        self.threshold = threshold if (threshold is not None) else 0.05
+        self.use_input_conv = use_input_conv if (use_input_conv is not None) else True
+
+        self.cent_table_b = torch.Tensor(
+            np.linspace(self.f0_to_cent(torch.Tensor([f0_min]))[0], self.f0_to_cent(torch.Tensor([f0_max]))[0],
+                        out_dims))
+        self.register_buffer("cent_table", self.cent_table_b)
+
+        # conv in stack
+        _leaky = nn.LeakyReLU()
+        self.stack = nn.Sequential(
+            nn.Conv1d(input_channel, n_chans, 3, 1, 1),
+            nn.GroupNorm(4, n_chans),
+            _leaky,
+            nn.Conv1d(n_chans, n_chans, 3, 1, 1))
+
+        # transformer
+        self.decoder = PCmer(
+            num_layers=n_layers,
+            num_heads=8,
+            dim_model=n_chans,
+            dim_keys=n_chans,
+            dim_values=n_chans,
+            residual_dropout=0.1,
+            attention_dropout=0.1)
+        self.norm = nn.LayerNorm(n_chans)
+
+        # out
+        self.n_out = out_dims
+        self.dense_out = weight_norm(
+            nn.Linear(n_chans, self.n_out))
+
+    def forward(self, mel, infer=True, gt_f0=None, return_hz_f0=False, cdecoder = "local_argmax"):
+        """
+        input:
+            B x n_frames x n_unit
+        return:
+            dict of B x n_frames x feat
+        """
+        if cdecoder == "argmax":
+            self.cdecoder = self.cents_decoder
+        elif cdecoder == "local_argmax":
+            self.cdecoder = self.cents_local_decoder
+        if self.use_input_conv:
+            x = self.stack(mel.transpose(1, 2)).transpose(1, 2)
+        else:
+            x = mel
+        x = self.decoder(x)
+        x = self.norm(x)
+        x = self.dense_out(x)  # [B,N,D]
+        x = torch.sigmoid(x)
+        if not infer:
+            gt_cent_f0 = self.f0_to_cent(gt_f0)  # mel f0  #[B,N,1]
+            gt_cent_f0 = self.gaussian_blurred_cent(gt_cent_f0)  # #[B,N,out_dim]
+            loss_all = self.loss_mse_scale * F.binary_cross_entropy(x, gt_cent_f0)  # bce loss
+            # l2 regularization
+            if self.loss_l2_regularization:
+                loss_all = loss_all + l2_regularization(model=self, l2_alpha=self.loss_l2_regularization_scale)
+            x = loss_all
+        if infer:
+            x = self.cdecoder(x)
+            x = self.cent_to_f0(x)
+            if not return_hz_f0:
+                x = (1 + x / 700).log()
+        return x
+
+    def cents_decoder(self, y, mask=True):
+        B, N, _ = y.size()
+        ci = self.cent_table[None, None, :].expand(B, N, -1)
+        rtn = torch.sum(ci * y, dim=-1, keepdim=True) / torch.sum(y, dim=-1, keepdim=True)  # cents: [B,N,1]
+        if mask:
+            confident = torch.max(y, dim=-1, keepdim=True)[0]
+            confident_mask = torch.ones_like(confident)
+            confident_mask[confident <= self.threshold] = float("-INF")
+            rtn = rtn * confident_mask
+        if self.confidence:
+            return rtn, confident
+        else:
+            return rtn
+        
+    def cents_local_decoder(self, y, mask=True):
+        B, N, _ = y.size()
+        ci = self.cent_table[None, None, :].expand(B, N, -1)
+        confident, max_index = torch.max(y, dim=-1, keepdim=True)
+        local_argmax_index = torch.arange(0,9).to(max_index.device) + (max_index - 4)
+        local_argmax_index[local_argmax_index<0] = 0
+        local_argmax_index[local_argmax_index>=self.n_out] = self.n_out - 1
+        ci_l = torch.gather(ci,-1,local_argmax_index)
+        y_l = torch.gather(y,-1,local_argmax_index)
+        rtn = torch.sum(ci_l * y_l, dim=-1, keepdim=True) / torch.sum(y_l, dim=-1, keepdim=True)  # cents: [B,N,1]
+        if mask:
+            confident_mask = torch.ones_like(confident)
+            confident_mask[confident <= self.threshold] = float("-INF")
+            rtn = rtn * confident_mask
+        if self.confidence:
+            return rtn, confident
+        else:
+            return rtn
+
+    def cent_to_f0(self, cent):
+        return 10. * 2 ** (cent / 1200.)
+
+    def f0_to_cent(self, f0):
+        return 1200. * torch.log2(f0 / 10.)
+
+    def gaussian_blurred_cent(self, cents):  # cents: [B,N,1]
+        mask = (cents > 0.1) & (cents < (1200. * np.log2(self.f0_max / 10.)))
+        B, N, _ = cents.size()
+        ci = self.cent_table[None, None, :].expand(B, N, -1)
+        return torch.exp(-torch.square(ci - cents) / 1250) * mask.float()
+
+
+class FCPEInfer:
+    def __init__(self, model_path, device=None, dtype=torch.float32):
+        if device is None:
+            device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        self.device = device
+        ckpt = torch.load(model_path, map_location=torch.device(self.device))
+        self.args = DotDict(ckpt["config"])
+        self.dtype = dtype
+        model = FCPEModel(
+            input_channel=self.args.model.input_channel,
+            out_dims=self.args.model.out_dims,
+            n_layers=self.args.model.n_layers,
+            n_chans=self.args.model.n_chans,
+            use_siren=self.args.model.use_siren,
+            use_full=self.args.model.use_full,
+            loss_mse_scale=self.args.loss.loss_mse_scale,
+            loss_l2_regularization=self.args.loss.loss_l2_regularization,
+            loss_l2_regularization_scale=self.args.loss.loss_l2_regularization_scale,
+            loss_grad1_mse=self.args.loss.loss_grad1_mse,
+            loss_grad1_mse_scale=self.args.loss.loss_grad1_mse_scale,
+            f0_max=self.args.model.f0_max,
+            f0_min=self.args.model.f0_min,
+            confidence=self.args.model.confidence,
+        )
+        model.to(self.device).to(self.dtype)
+        model.load_state_dict(ckpt['model'])
+        model.eval()
+        self.model = model
+        self.wav2mel = Wav2Mel(self.args, dtype=self.dtype, device=self.device)
+
+    @torch.no_grad()
+    def __call__(self, audio, sr, threshold=0.05):
+        self.model.threshold = threshold
+        audio = audio[None,:]
+        mel = self.wav2mel(audio=audio, sample_rate=sr).to(self.dtype)
+        f0 = self.model(mel=mel, infer=True, return_hz_f0=True)
+        return f0
+
+
+class Wav2Mel:
+
+    def __init__(self, args, device=None, dtype=torch.float32):
+        # self.args = args
+        self.sampling_rate = args.mel.sampling_rate
+        self.hop_size = args.mel.hop_size
+        if device is None:
+            device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        self.device = device
+        self.dtype = dtype
+        self.stft = STFT(
+            args.mel.sampling_rate,
+            args.mel.num_mels,
+            args.mel.n_fft,
+            args.mel.win_size,
+            args.mel.hop_size,
+            args.mel.fmin,
+            args.mel.fmax
+        )
+        self.resample_kernel = {}
+
+    def extract_nvstft(self, audio, keyshift=0, train=False):
+        mel = self.stft.get_mel(audio, keyshift=keyshift, train=train).transpose(1, 2)  # B, n_frames, bins
+        return mel
+
+    def extract_mel(self, audio, sample_rate, keyshift=0, train=False):
+        audio = audio.to(self.dtype).to(self.device)
+        # resample
+        if sample_rate == self.sampling_rate:
+            audio_res = audio
+        else:
+            key_str = str(sample_rate)
+            if key_str not in self.resample_kernel:
+                self.resample_kernel[key_str] = Resample(sample_rate, self.sampling_rate, lowpass_filter_width=128)
+            self.resample_kernel[key_str] = self.resample_kernel[key_str].to(self.dtype).to(self.device)
+            audio_res = self.resample_kernel[key_str](audio)
+
+        # extract
+        mel = self.extract_nvstft(audio_res, keyshift=keyshift, train=train)  # B, n_frames, bins
+        n_frames = int(audio.shape[1] // self.hop_size) + 1
+        if n_frames > int(mel.shape[1]):
+            mel = torch.cat((mel, mel[:, -1:, :]), 1)
+        if n_frames < int(mel.shape[1]):
+            mel = mel[:, :n_frames, :]
+        return mel
+
+    def __call__(self, audio, sample_rate, keyshift=0, train=False):
+        return self.extract_mel(audio, sample_rate, keyshift=keyshift, train=train)
+
+
+class DotDict(dict):
+    def __getattr__(*args):
+        val = dict.get(*args)
+        return DotDict(val) if type(val) is dict else val
+
+    __setattr__ = dict.__setitem__
+    __delattr__ = dict.__delitem__
+
+class F0Predictor(object):
+    def compute_f0(self,wav,p_len):
+        '''
+        input: wav:[signal_length]
+               p_len:int
+        output: f0:[signal_length//hop_length]
+        '''
+        pass
+
+    def compute_f0_uv(self,wav,p_len):
+        '''
+        input: wav:[signal_length]
+               p_len:int
+        output: f0:[signal_length//hop_length],uv:[signal_length//hop_length]
+        '''
+        pass
+
+class FCPE(F0Predictor):
+    def __init__(self, model_path, hop_length=512, f0_min=50, f0_max=1100, dtype=torch.float32, device=None, sampling_rate=44100,
+                 threshold=0.05):
+        self.fcpe = FCPEInfer(model_path, device=device, dtype=dtype)
+        self.hop_length = hop_length
+        self.f0_min = f0_min
+        self.f0_max = f0_max
+        if device is None:
+            self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        else:
+            self.device = device
+        self.threshold = threshold
+        self.sampling_rate = sampling_rate
+        self.dtype = dtype
+        self.name = "fcpe"
+
+    def repeat_expand(
+            self, content: Union[torch.Tensor, np.ndarray], target_len: int, mode: str = "nearest"
+    ):
+        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]
+
+    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 = self.repeat_expand(f0, pad_to)
+
+        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).cpu().numpy(), vuv_vector.cpu().numpy()
+        if f0.shape[0] == 1:
+            return (torch.ones(pad_to, dtype=torch.float, device=x.device) * f0[
+                0]).cpu().numpy(), 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()
+
+    def compute_f0(self, wav, p_len=None):
+        x = torch.FloatTensor(wav).to(self.dtype).to(self.device)
+        if p_len is None:
+            print("fcpe p_len is None")
+            p_len = x.shape[0] // self.hop_length
+        #else:
+#            assert abs(p_len - x.shape[0] // self.hop_length) < 4, "pad length error"
+        f0 = self.fcpe(x, sr=self.sampling_rate, threshold=self.threshold)[0,:,0]
+        if torch.all(f0 == 0):
+            rtn = f0.cpu().numpy() if p_len is None else np.zeros(p_len)
+            return rtn, rtn
+        return self.post_process(x, self.sampling_rate, f0, p_len)[0]
+
+    def compute_f0_uv(self, wav, p_len=None):
+        x = torch.FloatTensor(wav).to(self.dtype).to(self.device)
+        if p_len is None:
+            p_len = x.shape[0] // self.hop_length
+        #else:
+#            assert abs(p_len - x.shape[0] // self.hop_length) < 4, "pad length error"
+        f0 = self.fcpe(x, sr=self.sampling_rate, threshold=self.threshold)[0,:,0]
+        if torch.all(f0 == 0):
+            rtn = f0.cpu().numpy() if p_len is None else np.zeros(p_len)
+            return rtn, rtn
+        return self.post_process(x, self.sampling_rate, f0, p_len)