Delete infer/lib/FCPE.py

infer/lib/FCPE.py

@@ -1,920 +0,0 @@
-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.parametrizations 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):
-    """Loads wav file to torch tensor."""
-    try:
-        data, sample_rate = sf.read(full_path, always_2d=True)
-    except Exception as error:
-        print(f"An error occurred loading {full_path}: {error}")
-        if return_empty_on_exception:
-            return [], sample_rate or target_sr or 48000
-        else:
-            raise
-
-    data = data[:, 0] if len(data.shape) > 1 else data
-    assert len(data) > 2
-
-    # Normalize data
-    max_mag = (
-        -np.iinfo(data.dtype).min
-        if np.issubdtype(data.dtype, np.integer)
-        else 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 = torch.FloatTensor(data.astype(np.float32)) / max_mag
-
-    # Handle exceptions and resample
-    if (torch.isinf(data) | torch.isnan(data)).any() and return_empty_on_exception:
-        return [], sample_rate or target_sr or 48000
-    if target_sr is not None and sample_rate != target_sr:
-        data = torch.from_numpy(
-            librosa.core.resample(
-                data.numpy(), orig_sr=sample_rate, target_sr=target_sr
-            )
-        )
-        sample_rate = target_sr
-
-    return data, sample_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):
-        sample_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))
-
-        # Optimize mel_basis and hann_window caching
-        mel_basis = self.mel_basis if not train else {}
-        hann_window = self.hann_window if not train else {}
-
-        mel_basis_key = str(fmax) + "_" + str(y.device)
-        if mel_basis_key not in mel_basis:
-            mel = librosa_mel_fn(
-                sr=sample_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)
-
-        # Padding and STFT
-        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,
-        )
-        mode = "reflect" if pad_right < y.size(-1) else "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=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))
-
-        # Handle keyshift and mel conversion
-        if keyshift != 0:
-            size = n_fft // 2 + 1
-            resize = spec.size(1)
-            spec = (
-                F.pad(spec, (0, 0, 0, size - resize))
-                if resize < size
-                else spec[:, :size, :]
-            )
-            spec = spec * 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()
-
-
-def softmax_kernel(
-    data, *, projection_matrix, is_query, normalize_data=True, eps=1e-4, device=None
-):
-    b, h, *_ = data.shape
-
-    # Normalize data
-    data_normalizer = (data.shape[-1] ** -0.25) if normalize_data else 1.0
-
-    # Project data
-    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 = torch.einsum("...id,...jd->...ij", (data_normalizer * data), projection)
-
-    # Calculate diagonal data
-    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)
-
-    # Apply softmax
-    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))
-
-    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))
-
-    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):
-    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)])
-
-    def forward(self, phone, mask=None):
-        for layer in self._layers:
-            phone = layer(phone, mask)
-        return phone
-
-
-class _EncoderLayer(nn.Module):
-    def __init__(self, parent: PCmer):
-        super().__init__()
-        self.conformer = ConformerConvModule(parent.dim_model)
-        self.norm = nn.LayerNorm(parent.dim_model)
-        self.dropout = nn.Dropout(parent.residual_dropout)
-        self.attn = SelfAttention(
-            dim=parent.dim_model, heads=parent.num_heads, causal=False
-        )
-
-    def forward(self, phone, mask=None):
-        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)
-
-
-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=0.0
-    ):
-        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
-            ),
-            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:
-        out = torch.einsum("...ed,...nd->...ne", k, q)
-        return out
-    else:
-        k_cumsum = k.sum(dim=-2)
-        D_inv = 1.0 / (torch.einsum("...nd,...d->...n", q, k_cumsum.type_as(q)) + 1e-8)
-        context = torch.einsum("...nd,...ne->...de", k, v)
-        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)
-    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)
-
-    remaining_rows = nb_rows - nb_full_blocks * nb_columns
-    if remaining_rows > 0:
-        q = orthogonal_matrix_chunk(
-            nb_columns, qr_uniform_q=qr_uniform_q, device=device
-        )
-        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
-        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=0.0,
-        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
-        )
-
-        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()
-
-    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
-        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 = []
-        if not empty(q):
-            if exists(context_mask):
-                global_mask = context_mask[:, None, :, None]
-                v.masked_fill_(~global_mask, 0.0)
-            if cross_attend:
-                pass  # TODO: Implement cross-attention
-            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 FCPE(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"
-    ):
-        if cdecoder == "argmax":
-            self.cdecoder = self.cents_decoder
-        elif cdecoder == "local_argmax":
-            self.cdecoder = self.cents_local_decoder
-
-        x = (
-            self.stack(mel.transpose(1, 2)).transpose(1, 2)
-            if self.use_input_conv
-            else mel
-        )
-        x = self.decoder(x)
-        x = self.norm(x)
-        x = self.dense_out(x)
-        x = torch.sigmoid(x)
-
-        if not infer:
-            gt_cent_f0 = self.f0_to_cent(gt_f0)
-            gt_cent_f0 = self.gaussian_blurred_cent(gt_cent_f0)
-            loss_all = self.loss_mse_scale * F.binary_cross_entropy(x, gt_cent_f0)
-            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)
-            x = (1 + x / 700).log() if not return_hz_f0 else x
-
-        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
-        )
-        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
-        return (rtn, confident) if self.confidence else 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 = torch.clamp(local_argmax_index, 0, 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
-        )
-        if mask:
-            confident_mask = torch.ones_like(confident)
-            confident_mask[confident <= self.threshold] = float("-INF")
-            rtn = rtn * confident_mask
-        return (rtn, confident) if self.confidence else rtn
-
-    def cent_to_f0(self, cent):
-        return 10.0 * 2 ** (cent / 1200.0)
-
-    def f0_to_cent(self, f0):
-        return 1200.0 * torch.log2(f0 / 10.0)
-
-    def gaussian_blurred_cent(self, cents):
-        mask = (cents > 0.1) & (cents < (1200.0 * np.log2(self.f0_max / 10.0)))
-        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), weights_only=True
-        )
-        self.args = DotDict(ckpt["config"])
-        self.dtype = dtype
-        model = FCPE(
-            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.sample_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)
-        return mel
-
-    def extract_mel(self, audio, sample_rate, keyshift=0, train=False):
-        audio = audio.to(self.dtype).to(self.device)
-        if sample_rate == self.sample_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.sample_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)
-
-        mel = self.extract_nvstft(
-            audio_res, keyshift=keyshift, train=train
-        )  # B, n_frames, bins
-        n_frames = int(audio.shape[1] // self.hop_size) + 1
-        mel = (
-            torch.cat((mel, mel[:, -1:, :]), 1) if n_frames > int(mel.shape[1]) else mel
-        )
-        mel = mel[:, :n_frames, :] if n_frames < int(mel.shape[1]) else mel
-        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):
-        pass
-
-    def compute_f0_uv(self, wav, p_len):
-        pass
-
-
-class FCPEF0Predictor(F0Predictor):
-    def __init__(
-        self,
-        model_path,
-        hop_length=512,
-        f0_min=50,
-        f0_max=1100,
-        dtype=torch.float32,
-        device=None,
-        sample_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
-        self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
-        self.threshold = threshold
-        self.sample_rate = sample_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
-        content = (
-            content[None, None]
-            if ndim == 1
-            else content[None] if ndim == 2 else content
-        )
-        assert content.ndim == 3
-        is_np = isinstance(content, np.ndarray)
-        content = torch.from_numpy(content) if is_np else content
-        results = torch.nn.functional.interpolate(content, size=target_len, mode=mode)
-        results = results.numpy() if is_np else results
-        return results[0, 0] if ndim == 1 else results[0] if ndim == 2 else results
-
-    def post_process(self, x, sample_rate, f0, pad_to):
-        f0 = (
-            torch.from_numpy(f0).float().to(x.device)
-            if isinstance(f0, np.ndarray)
-            else f0
-        )
-        f0 = self.repeat_expand(f0, pad_to) if pad_to is not None else f0
-
-        vuv_vector = torch.zeros_like(f0)
-        vuv_vector[f0 > 0.0] = 1.0
-        vuv_vector[f0 <= 0.0] = 0.0
-
-        nzindex = torch.nonzero(f0).squeeze()
-        f0 = torch.index_select(f0, dim=0, index=nzindex).cpu().numpy()
-        time_org = self.hop_length / sample_rate * nzindex.cpu().numpy()
-        time_frame = np.arange(pad_to) * self.hop_length / sample_rate
-
-        vuv_vector = F.interpolate(vuv_vector[None, None, :], size=pad_to)[0][0]
-
-        if f0.shape[0] <= 0:
-            return np.zeros(pad_to), vuv_vector.cpu().numpy()
-        if f0.shape[0] == 1:
-            return np.ones(pad_to) * f0[0], vuv_vector.cpu().numpy()
-
-        f0 = np.interp(time_frame, time_org, f0, left=f0[0], right=f0[-1])
-        return f0, vuv_vector.cpu().numpy()
-
-    def compute_f0(self, wav, p_len=None):
-        x = torch.FloatTensor(wav).to(self.dtype).to(self.device)
-        p_len = x.shape[0] // self.hop_length if p_len is None else p_len
-        f0 = self.fcpe(x, sr=self.sample_rate, threshold=self.threshold)[0, :, 0]
-        if torch.all(f0 == 0):
-            return f0.cpu().numpy() if p_len is None else np.zeros(p_len)
-        return self.post_process(x, self.sample_rate, f0, p_len)[0]
-
-    def compute_f0_uv(self, wav, p_len=None):
-        x = torch.FloatTensor(wav).to(self.dtype).to(self.device)
-        p_len = x.shape[0] // self.hop_length if p_len is None else p_len
-        f0 = self.fcpe(x, sr=self.sample_rate, threshold=self.threshold)[0, :, 0]
-        if torch.all(f0 == 0):
-            return f0.cpu().numpy() if p_len is None else np.zeros(p_len), (
-                f0.cpu().numpy() if p_len is None else np.zeros(p_len)
-            )
-        return self.post_process(x, self.sample_rate, f0, p_len)