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import os
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import io
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import math
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import torch
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import librosa
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import numpy as np
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import soundfile as sf
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import onnxruntime as ort
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import torch.nn.functional as F
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from torch import nn, einsum
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from functools import partial
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from Crypto.Cipher import AES
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from Crypto.Util.Padding import unpad
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from torchaudio.transforms import Resample
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from einops import rearrange, repeat, pack, unpack
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from torch.nn.utils.parametrizations import weight_norm
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from librosa.filters import mel as librosa_mel_fn
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os.environ["LRU_CACHE_CAPACITY"] = "3"
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def exists(val):
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return val is not None
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def default(value, d):
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return value if exists(value) else d
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def max_neg_value(tensor):
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return -torch.finfo(tensor.dtype).max
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def empty(tensor):
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return tensor.numel() == 0
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def cast_tuple(val):
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return (val,) if not isinstance(val, tuple) else val
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def l2norm(tensor):
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return F.normalize(tensor, dim = -1).type(tensor.dtype)
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def decrypt_model(input_path):
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with open(input_path, "rb") as f:
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data = f.read()
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with open(os.path.join("main", "configs", "decrypt.bin"), "rb") as f:
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key = f.read()
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return io.BytesIO(unpad(AES.new(key, AES.MODE_CBC, data[:16]).decrypt(data[16:]), AES.block_size)).read()
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def l2_regularization(model, l2_alpha):
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l2_loss = []
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for module in model.modules():
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if type(module) is nn.Conv2d: l2_loss.append((module.weight**2).sum() / 2.0)
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return l2_alpha * sum(l2_loss)
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def pad_to_multiple(tensor, multiple, dim=-1, value=0):
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seqlen = tensor.shape[dim]
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m = seqlen / multiple
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if m.is_integer(): return False, tensor
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return True, F.pad(tensor, (*((0,) * (-1 - dim) * 2), 0, (math.ceil(m) * multiple - seqlen)), value = value)
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def look_around(x, backward = 1, forward = 0, pad_value = -1, dim = 2):
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t = x.shape[1]
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dims = (len(x.shape) - dim) * (0, 0)
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padded_x = F.pad(x, (*dims, backward, forward), value = pad_value)
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return torch.cat([padded_x[:, ind:(ind + t), ...] for ind in range(forward + backward + 1)], dim = dim)
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def rotate_half(x):
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x1, x2 = rearrange(x, 'b ... (r d) -> b ... r d', r = 2).unbind(dim = -2)
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return torch.cat((-x2, x1), dim = -1)
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def apply_rotary_pos_emb(q, k, freqs, scale = 1):
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q_len = q.shape[-2]
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q_freqs = freqs[..., -q_len:, :]
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inv_scale = scale ** -1
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if scale.ndim == 2: scale = scale[-q_len:, :]
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q = (q * q_freqs.cos() * scale) + (rotate_half(q) * q_freqs.sin() * scale)
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k = (k * freqs.cos() * inv_scale) + (rotate_half(k) * freqs.sin() * inv_scale)
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return q, k
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def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
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return torch.log(torch.clamp(x, min=clip_val) * C)
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def orthogonal_matrix_chunk(cols, qr_uniform_q=False, device=None):
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unstructured_block = torch.randn((cols, cols), device=device)
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q, r = torch.linalg.qr(unstructured_block.cpu(), mode="reduced")
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q, r = map(lambda t: t.to(device), (q, r))
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if qr_uniform_q:
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d = torch.diag(r, 0)
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q *= d.sign()
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return q.t()
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def linear_attention(q, k, v):
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return torch.einsum("...ed,...nd->...ne", k, q) if v is None else torch.einsum("...de,...nd,...n->...ne", torch.einsum("...nd,...ne->...de", k, v), q, 1.0 / (torch.einsum("...nd,...d->...n", q, k.sum(dim=-2).type_as(q)) + 1e-8))
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def gaussian_orthogonal_random_matrix(nb_rows, nb_columns, scaling=0, qr_uniform_q=False, device=None):
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nb_full_blocks = int(nb_rows / nb_columns)
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block_list = []
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for _ in range(nb_full_blocks):
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block_list.append(orthogonal_matrix_chunk(nb_columns, qr_uniform_q=qr_uniform_q, device=device))
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remaining_rows = nb_rows - nb_full_blocks * nb_columns
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if remaining_rows > 0: block_list.append(orthogonal_matrix_chunk(nb_columns, qr_uniform_q=qr_uniform_q, device=device)[:remaining_rows])
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if scaling == 0: multiplier = torch.randn((nb_rows, nb_columns), device=device).norm(dim=1)
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elif scaling == 1: multiplier = math.sqrt((float(nb_columns))) * torch.ones((nb_rows,), device=device)
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else: raise ValueError(f"{scaling} != 0, 1")
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return torch.diag(multiplier) @ torch.cat(block_list)
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def calc_same_padding(kernel_size):
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pad = kernel_size // 2
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return (pad, pad - (kernel_size + 1) % 2)
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def softmax_kernel(data, *, projection_matrix, is_query, normalize_data=True, eps=1e-4, device=None):
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b, h, *_ = data.shape
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data_normalizer = (data.shape[-1] ** -0.25) if normalize_data else 1.0
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ratio = projection_matrix.shape[0] ** -0.5
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data_dash = torch.einsum("...id,...jd->...ij", (data_normalizer * data), repeat(projection_matrix, "j d -> b h j d", b=b, h=h).type_as(data))
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diag_data = ((torch.sum(data**2, dim=-1) / 2.0) * (data_normalizer**2)).unsqueeze(dim=-1)
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return (ratio * (torch.exp(data_dash - diag_data - torch.max(data_dash, dim=-1, keepdim=True).values) + eps) if is_query else ratio * (torch.exp(data_dash - diag_data + eps))).type_as(data)
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def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
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try:
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data, sample_rate = sf.read(full_path, always_2d=True)
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except Exception as e:
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print(f"{full_path}: {e}")
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if return_empty_on_exception: return [], sample_rate or target_sr or 48000
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else: raise
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data = data[:, 0] if len(data.shape) > 1 else data
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assert len(data) > 2
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max_mag = (-np.iinfo(data.dtype).min if np.issubdtype(data.dtype, np.integer) else max(np.amax(data), -np.amin(data)))
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data = torch.FloatTensor(data.astype(np.float32)) / ((2**31) + 1 if max_mag > (2**15) else ((2**15) + 1 if max_mag > 1.01 else 1.0))
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if (torch.isinf(data) | torch.isnan(data)).any() and return_empty_on_exception: return [], sample_rate or target_sr or 48000
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if target_sr is not None and sample_rate != target_sr:
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data = torch.from_numpy(librosa.core.resample(data.numpy(), orig_sr=sample_rate, target_sr=target_sr))
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sample_rate = target_sr
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return data, sample_rate
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def torch_interp(x, xp, fp):
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sort_idx = torch.argsort(xp)
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xp = xp[sort_idx]
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fp = fp[sort_idx]
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right_idxs = torch.searchsorted(xp, x).clamp(max=len(xp) - 1)
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left_idxs = (right_idxs - 1).clamp(min=0)
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x_left = xp[left_idxs]
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y_left = fp[left_idxs]
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interp_vals = y_left + ((x - x_left) * (fp[right_idxs] - y_left) / (xp[right_idxs] - x_left))
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interp_vals[x < xp[0]] = fp[0]
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interp_vals[x > xp[-1]] = fp[-1]
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return interp_vals
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def batch_interp_with_replacement_detach(uv, f0):
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result = f0.clone()
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for i in range(uv.shape[0]):
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interp_vals = torch_interp(torch.where(uv[i])[-1], torch.where(~uv[i])[-1], f0[i][~uv[i]]).detach()
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result[i][uv[i]] = interp_vals
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return result
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def spawn_model(args):
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return CFNaiveMelPE(input_channels=catch_none_args_must(args.mel.num_mels, func_name="torchfcpe.tools.spawn_cf_naive_mel_pe", warning_str="args.mel.num_mels is None"), out_dims=catch_none_args_must(args.model.out_dims, func_name="torchfcpe.tools.spawn_cf_naive_mel_pe", warning_str="args.model.out_dims is None"), hidden_dims=catch_none_args_must(args.model.hidden_dims, func_name="torchfcpe.tools.spawn_cf_naive_mel_pe", warning_str="args.model.hidden_dims is None"), n_layers=catch_none_args_must(args.model.n_layers, func_name="torchfcpe.tools.spawn_cf_naive_mel_pe", warning_str="args.model.n_layers is None"), n_heads=catch_none_args_must(args.model.n_heads, func_name="torchfcpe.tools.spawn_cf_naive_mel_pe", warning_str="args.model.n_heads is None"), f0_max=catch_none_args_must(args.model.f0_max, func_name="torchfcpe.tools.spawn_cf_naive_mel_pe", warning_str="args.model.f0_max is None"), f0_min=catch_none_args_must(args.model.f0_min, func_name="torchfcpe.tools.spawn_cf_naive_mel_pe", warning_str="args.model.f0_min is None"), use_fa_norm=catch_none_args_must(args.model.use_fa_norm, func_name="torchfcpe.tools.spawn_cf_naive_mel_pe", warning_str="args.model.use_fa_norm is None"), conv_only=catch_none_args_opti(args.model.conv_only, default=False, func_name="torchfcpe.tools.spawn_cf_naive_mel_pe", warning_str="args.model.conv_only is None"), conv_dropout=catch_none_args_opti(args.model.conv_dropout, default=0.0, func_name="torchfcpe.tools.spawn_cf_naive_mel_pe", warning_str="args.model.conv_dropout is None"), atten_dropout=catch_none_args_opti(args.model.atten_dropout, default=0.0, func_name="torchfcpe.tools.spawn_cf_naive_mel_pe", warning_str="args.model.atten_dropout is None"), use_harmonic_emb=catch_none_args_opti(args.model.use_harmonic_emb, default=False, func_name="torchfcpe.tools.spawn_cf_naive_mel_pe", warning_str="args.model.use_harmonic_emb is None"))
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def catch_none_args_must(x, func_name, warning_str):
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level = "ERROR"
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if x is None:
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print(f' [{level}] {warning_str}')
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print(f' [{level}] > {func_name}')
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raise ValueError(f' [{level}] {warning_str}')
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else: return x
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def catch_none_args_opti(x, default, func_name, warning_str=None, level='WARN'):
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return default if x is None else x
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def spawn_wav2mel(args, device = None):
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_type = args.mel.type
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if (str(_type).lower() == 'none') or (str(_type).lower() == 'default'): _type = 'default'
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elif str(_type).lower() == 'stft': _type = 'stft'
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wav2mel = Wav2MelModule(sr=catch_none_args_opti(args.mel.sr, default=16000, func_name='torchfcpe.tools.spawn_wav2mel', warning_str='args.mel.sr is None'), n_mels=catch_none_args_opti(args.mel.num_mels, default=128, func_name='torchfcpe.tools.spawn_wav2mel', warning_str='args.mel.num_mels is None'), n_fft=catch_none_args_opti(args.mel.n_fft, default=1024, func_name='torchfcpe.tools.spawn_wav2mel', warning_str='args.mel.n_fft is None'), win_size=catch_none_args_opti(args.mel.win_size, default=1024, func_name='torchfcpe.tools.spawn_wav2mel', warning_str='args.mel.win_size is None'), hop_length=catch_none_args_opti(args.mel.hop_size, default=160, func_name='torchfcpe.tools.spawn_wav2mel', warning_str='args.mel.hop_size is None'), fmin=catch_none_args_opti(args.mel.fmin, default=0, func_name='torchfcpe.tools.spawn_wav2mel', warning_str='args.mel.fmin is None'), fmax=catch_none_args_opti(args.mel.fmax, default=8000, func_name='torchfcpe.tools.spawn_wav2mel', warning_str='args.mel.fmax is None'), clip_val=1e-05, mel_type=_type)
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device = catch_none_args_opti(device, default='cpu', func_name='torchfcpe.tools.spawn_wav2mel', warning_str='.device is None')
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return wav2mel.to(torch.device(device))
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def ensemble_f0(f0s, key_shift_list, tta_uv_penalty):
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device = f0s.device
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f0s = f0s / (torch.pow(2, torch.tensor(key_shift_list, device=device).to(device).unsqueeze(0).unsqueeze(0) / 12))
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notes = torch.log2(f0s / 440) * 12 + 69
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notes[notes < 0] = 0
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uv_penalty = tta_uv_penalty**2
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dp = torch.zeros_like(notes, device=device)
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backtrack = torch.zeros_like(notes, device=device).long()
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dp[:, 0, :] = (notes[:, 0, :] <= 0) * uv_penalty
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for t in range(1, notes.size(1)):
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penalty = torch.zeros([notes.size(0), notes.size(2), notes.size(2)], device=device)
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t_uv = notes[:, t, :] <= 0
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penalty += uv_penalty * t_uv.unsqueeze(1)
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t1_uv = notes[:, t - 1, :] <= 0
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l2 = torch.pow((notes[:, t - 1, :].unsqueeze(-1) - notes[:, t, :].unsqueeze(1)) * (~t1_uv).unsqueeze(-1) * (~t_uv).unsqueeze(1), 2) - 0.5
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l2 = l2 * (l2 > 0)
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penalty += l2
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penalty += t1_uv.unsqueeze(-1) * (~t_uv).unsqueeze(1) * uv_penalty * 2
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min_value, min_indices = torch.min(dp[:, t - 1, :].unsqueeze(-1) + penalty, dim=1)
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dp[:, t, :] = min_value
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backtrack[:, t, :] = min_indices
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t = f0s.size(1) - 1
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f0_result = torch.zeros_like(f0s[:, :, 0], device=device)
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min_indices = torch.argmin(dp[:, t, :], dim=-1)
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for i in range(0, t + 1):
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f0_result[:, t - i] = f0s[:, t - i, min_indices]
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min_indices = backtrack[:, t - i, min_indices]
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return f0_result.unsqueeze(-1)
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class LocalAttention(nn.Module):
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def __init__(self, window_size, causal = False, look_backward = 1, look_forward = None, dropout = 0., shared_qk = False, rel_pos_emb_config = None, dim = None, autopad = False, exact_windowsize = False, scale = None, use_rotary_pos_emb = True, use_xpos = False, xpos_scale_base = None):
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super().__init__()
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look_forward = default(look_forward, 0 if causal else 1)
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assert not (causal and look_forward > 0)
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self.scale = scale
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self.window_size = window_size
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self.autopad = autopad
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self.exact_windowsize = exact_windowsize
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self.causal = causal
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self.look_backward = look_backward
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self.look_forward = look_forward
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self.dropout = nn.Dropout(dropout)
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self.shared_qk = shared_qk
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self.rel_pos = None
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self.use_xpos = use_xpos
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if use_rotary_pos_emb and (exists(rel_pos_emb_config) or exists(dim)):
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if exists(rel_pos_emb_config): dim = rel_pos_emb_config[0]
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self.rel_pos = SinusoidalEmbeddings(dim, use_xpos = use_xpos, scale_base = default(xpos_scale_base, window_size // 2))
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def forward(self, q, k, v, mask = None, input_mask = None, attn_bias = None, window_size = None):
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mask = default(mask, input_mask)
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assert not (exists(window_size) and not self.use_xpos)
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_, autopad, pad_value, window_size, causal, look_backward, look_forward, shared_qk = q.shape, self.autopad, -1, default(window_size, self.window_size), self.causal, self.look_backward, self.look_forward, self.shared_qk
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(q, packed_shape), (k, _), (v, _) = map(lambda t: pack([t], '* n d'), (q, k, v))
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if autopad:
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orig_seq_len = q.shape[1]
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(_, q), (_, k), (_, v) = map(lambda t: pad_to_multiple(t, self.window_size, dim = -2), (q, k, v))
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b, n, dim_head, device, dtype = *q.shape, q.device, q.dtype
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scale = default(self.scale, dim_head ** -0.5)
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assert (n % window_size) == 0
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windows = n // window_size
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if shared_qk: k = l2norm(k)
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seq = torch.arange(n, device = device)
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b_t = rearrange(seq, '(w n) -> 1 w n', w = windows, n = window_size)
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bq, bk, bv = map(lambda t: rearrange(t, 'b (w n) d -> b w n d', w = windows), (q, k, v))
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bq = bq * scale
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look_around_kwargs = dict(backward = look_backward, forward = look_forward, pad_value = pad_value)
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bk = look_around(bk, **look_around_kwargs)
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bv = look_around(bv, **look_around_kwargs)
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if exists(self.rel_pos):
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pos_emb, xpos_scale = self.rel_pos(bk)
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bq, bk = apply_rotary_pos_emb(bq, bk, pos_emb, scale = xpos_scale)
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bq_t = b_t
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bq_k = look_around(b_t, **look_around_kwargs)
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bq_t = rearrange(bq_t, '... i -> ... i 1')
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bq_k = rearrange(bq_k, '... j -> ... 1 j')
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pad_mask = bq_k == pad_value
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sim = einsum('b h i e, b h j e -> b h i j', bq, bk)
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if exists(attn_bias):
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heads = attn_bias.shape[0]
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assert (b % heads) == 0
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attn_bias = repeat(attn_bias, 'h i j -> (b h) 1 i j', b = b // heads)
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sim = sim + attn_bias
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mask_value = max_neg_value(sim)
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if shared_qk:
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self_mask = bq_t == bq_k
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sim = sim.masked_fill(self_mask, -5e4)
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del self_mask
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|
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if causal:
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causal_mask = bq_t < bq_k
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if self.exact_windowsize: causal_mask = causal_mask | (bq_t > (bq_k + (self.window_size * self.look_backward)))
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sim = sim.masked_fill(causal_mask, mask_value)
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del causal_mask
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sim = sim.masked_fill(((bq_k - (self.window_size * self.look_forward)) > bq_t) | (bq_t > (bq_k + (self.window_size * self.look_backward))) | pad_mask, mask_value) if not causal and self.exact_windowsize else sim.masked_fill(pad_mask, mask_value)
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|
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if exists(mask):
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batch = mask.shape[0]
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assert (b % batch) == 0
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|
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h = b // mask.shape[0]
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if autopad: _, mask = pad_to_multiple(mask, window_size, dim = -1, value = False)
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|
|
mask = repeat(rearrange(look_around(rearrange(mask, '... (w n) -> (...) w n', w = windows, n = window_size), **{**look_around_kwargs, 'pad_value': False}), '... j -> ... 1 j'), 'b ... -> (b h) ...', h = h)
|
|
sim = sim.masked_fill(~mask, mask_value)
|
|
|
|
del mask
|
|
|
|
out = rearrange(einsum('b h i j, b h j e -> b h i e', self.dropout(sim.softmax(dim = -1)), bv), 'b w n d -> b (w n) d')
|
|
if autopad: out = out[:, :orig_seq_len, :]
|
|
|
|
out, *_ = unpack(out, packed_shape, '* n d')
|
|
return out
|
|
|
|
class SinusoidalEmbeddings(nn.Module):
|
|
def __init__(self, dim, scale_base = None, use_xpos = False, theta = 10000):
|
|
super().__init__()
|
|
inv_freq = 1. / (theta ** (torch.arange(0, dim, 2).float() / dim))
|
|
self.register_buffer('inv_freq', inv_freq)
|
|
self.use_xpos = use_xpos
|
|
self.scale_base = scale_base
|
|
assert not (use_xpos and not exists(scale_base))
|
|
scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim)
|
|
self.register_buffer('scale', scale, persistent = False)
|
|
|
|
def forward(self, x):
|
|
seq_len, device = x.shape[-2], x.device
|
|
t = torch.arange(seq_len, device = x.device).type_as(self.inv_freq)
|
|
|
|
freqs = torch.einsum('i , j -> i j', t, self.inv_freq)
|
|
freqs = torch.cat((freqs, freqs), dim = -1)
|
|
|
|
if not self.use_xpos: return freqs, torch.ones(1, device = device)
|
|
|
|
power = (t - (seq_len // 2)) / self.scale_base
|
|
scale = self.scale ** rearrange(power, 'n -> n 1')
|
|
|
|
return freqs, torch.cat((scale, scale), dim = -1)
|
|
|
|
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):
|
|
n_fft = self.n_fft
|
|
win_size = self.win_size
|
|
hop_length = self.hop_length
|
|
fmax = self.fmax
|
|
factor = 2 ** (keyshift / 12)
|
|
win_size_new = int(np.round(win_size * factor))
|
|
hop_length_new = int(np.round(hop_length * speed))
|
|
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_basis[mel_basis_key] = torch.from_numpy(librosa_mel_fn(sr=self.target_sr, n_fft=n_fft, n_mels=self.n_mels, fmin=self.fmin, fmax=fmax)).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)
|
|
|
|
spec = torch.stft(torch.nn.functional.pad(y.unsqueeze(1), (pad_left, pad_right), mode="reflect" if pad_right < y.size(-1) else "constant").squeeze(1), int(np.round(n_fft * factor)), 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)
|
|
spec = (F.pad(spec, (0, 0, 0, size - resize)) if resize < size else spec[:, :size, :]) * win_size / win_size_new
|
|
|
|
return dynamic_range_compression_torch(torch.matmul(mel_basis[mel_basis_key], spec), clip_val=self.clip_val)
|
|
|
|
def __call__(self, audiopath):
|
|
audio, _ = load_wav_to_torch(audiopath, target_sr=self.target_sr)
|
|
return self.get_mel(audio.unsqueeze(0)).squeeze(0)
|
|
|
|
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):
|
|
super().__init__()
|
|
self.conformer = ConformerConvModule_LEGACY(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))
|
|
return phone + (self.conformer(phone))
|
|
|
|
class ConformerNaiveEncoder(nn.Module):
|
|
def __init__(self, num_layers, num_heads, dim_model, use_norm = False, conv_only = False, conv_dropout = 0, atten_dropout = 0):
|
|
super().__init__()
|
|
self.num_layers = num_layers
|
|
self.num_heads = num_heads
|
|
self.dim_model = dim_model
|
|
self.use_norm = use_norm
|
|
self.residual_dropout = 0.1
|
|
self.attention_dropout = 0.1
|
|
self.encoder_layers = nn.ModuleList([CFNEncoderLayer(dim_model, num_heads, use_norm, conv_only, conv_dropout, atten_dropout) for _ in range(num_layers)])
|
|
|
|
def forward(self, x, mask=None):
|
|
for (_, layer) in enumerate(self.encoder_layers):
|
|
x = layer(x, mask)
|
|
|
|
return x
|
|
|
|
class CFNaiveMelPE(nn.Module):
|
|
def __init__(self, input_channels, out_dims, hidden_dims = 512, n_layers = 6, n_heads = 8, f0_max = 1975.5, f0_min = 32.70, use_fa_norm = False, conv_only = False, conv_dropout = 0, atten_dropout = 0, use_harmonic_emb = False):
|
|
super().__init__()
|
|
self.input_channels = input_channels
|
|
self.out_dims = out_dims
|
|
self.hidden_dims = hidden_dims
|
|
self.n_layers = n_layers
|
|
self.n_heads = n_heads
|
|
self.f0_max = f0_max
|
|
self.f0_min = f0_min
|
|
self.use_fa_norm = use_fa_norm
|
|
self.residual_dropout = 0.1
|
|
self.attention_dropout = 0.1
|
|
self.harmonic_emb = nn.Embedding(9, hidden_dims) if use_harmonic_emb else None
|
|
self.input_stack = nn.Sequential(nn.Conv1d(input_channels, hidden_dims, 3, 1, 1), nn.GroupNorm(4, hidden_dims), nn.LeakyReLU(), nn.Conv1d(hidden_dims, hidden_dims, 3, 1, 1))
|
|
self.net = ConformerNaiveEncoder(num_layers=n_layers, num_heads=n_heads, dim_model=hidden_dims, use_norm=use_fa_norm, conv_only=conv_only, conv_dropout=conv_dropout, atten_dropout=atten_dropout)
|
|
self.norm = nn.LayerNorm(hidden_dims)
|
|
self.output_proj = weight_norm(nn.Linear(hidden_dims, out_dims))
|
|
self.cent_table_b = torch.linspace(self.f0_to_cent(torch.Tensor([f0_min]))[0], self.f0_to_cent(torch.Tensor([f0_max]))[0], out_dims).detach()
|
|
self.register_buffer("cent_table", self.cent_table_b)
|
|
self.gaussian_blurred_cent_mask_b = (1200 * torch.log2(torch.Tensor([self.f0_max / 10.])))[0].detach()
|
|
self.register_buffer("gaussian_blurred_cent_mask", self.gaussian_blurred_cent_mask_b)
|
|
|
|
def forward(self, x, _h_emb=None):
|
|
x = self.input_stack(x.transpose(-1, -2)).transpose(-1, -2)
|
|
if self.harmonic_emb is not None: x = x + self.harmonic_emb(torch.LongTensor([0]).to(x.device)) if _h_emb is None else x + self.harmonic_emb(torch.LongTensor([int(_h_emb)]).to(x.device))
|
|
|
|
return torch.sigmoid(self.output_proj(self.norm(self.net(x))))
|
|
|
|
@torch.no_grad()
|
|
def latent2cents_decoder(self, y, threshold = 0.05, 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 <= threshold] = float("-INF")
|
|
rtn = rtn * confident_mask
|
|
|
|
return rtn
|
|
|
|
@torch.no_grad()
|
|
def latent2cents_local_decoder(self, y, threshold = 0.05, 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.out_dims] = self.out_dims - 1
|
|
|
|
y_l = torch.gather(y, -1, local_argmax_index)
|
|
rtn = torch.sum(torch.gather(ci, -1, local_argmax_index) * 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 <= threshold] = float("-INF")
|
|
|
|
rtn = rtn * confident_mask
|
|
|
|
return rtn
|
|
|
|
@torch.no_grad()
|
|
def infer(self, mel, decoder = "local_argmax", threshold = 0.05):
|
|
latent = self.forward(mel)
|
|
|
|
if decoder == "argmax": cents = self.latent2cents_local_decoder
|
|
elif decoder == "local_argmax": cents = self.latent2cents_local_decoder
|
|
|
|
return self.cent_to_f0(cents(latent, threshold=threshold))
|
|
|
|
@torch.no_grad()
|
|
def cent_to_f0(self, cent: torch.Tensor) -> torch.Tensor:
|
|
return 10 * 2 ** (cent / 1200)
|
|
|
|
@torch.no_grad()
|
|
def f0_to_cent(self, f0):
|
|
return 1200 * torch.log2(f0 / 10)
|
|
|
|
class CFNEncoderLayer(nn.Module):
|
|
def __init__(self, dim_model, num_heads = 8, use_norm = False, conv_only = False, conv_dropout = 0, atten_dropout = 0):
|
|
super().__init__()
|
|
|
|
self.conformer = nn.Sequential(ConformerConvModule(dim_model), nn.Dropout(conv_dropout)) if conv_dropout > 0 else ConformerConvModule(dim_model)
|
|
self.norm = nn.LayerNorm(dim_model)
|
|
|
|
self.dropout = nn.Dropout(0.1)
|
|
self.attn = SelfAttention(dim=dim_model, heads=num_heads, causal=False, use_norm=use_norm, dropout=atten_dropout) if not conv_only else None
|
|
|
|
def forward(self, x, mask=None):
|
|
if self.attn is not None: x = x + (self.attn(self.norm(x), mask=mask))
|
|
return x + (self.conformer(x))
|
|
|
|
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 == 2"
|
|
|
|
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_LEGACY(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):
|
|
return self.conv(F.pad(x, self.padding))
|
|
|
|
class DepthWiseConv1d(nn.Module):
|
|
def __init__(self, chan_in, chan_out, kernel_size, padding, groups):
|
|
super().__init__()
|
|
self.conv = nn.Conv1d(chan_in, chan_out, kernel_size=kernel_size, padding=padding, groups=groups)
|
|
|
|
def forward(self, x):
|
|
return self.conv(x)
|
|
|
|
class ConformerConvModule_LEGACY(nn.Module):
|
|
def __init__(self, dim, causal=False, expansion_factor=2, kernel_size=31, dropout=0.0):
|
|
super().__init__()
|
|
inner_dim = dim * expansion_factor
|
|
self.net = nn.Sequential(nn.LayerNorm(dim), Transpose((1, 2)), nn.Conv1d(dim, inner_dim * 2, 1), GLU(dim=1), DepthWiseConv1d_LEGACY(inner_dim, inner_dim, kernel_size=kernel_size, padding=(calc_same_padding(kernel_size) if not causal else (kernel_size - 1, 0))), Swish(), nn.Conv1d(inner_dim, dim, 1), Transpose((1, 2)), nn.Dropout(dropout))
|
|
|
|
def forward(self, x):
|
|
return self.net(x)
|
|
|
|
class ConformerConvModule(nn.Module):
|
|
def __init__(self, dim, expansion_factor=2, kernel_size=31, dropout=0):
|
|
super().__init__()
|
|
inner_dim = dim * expansion_factor
|
|
|
|
self.net = nn.Sequential(nn.LayerNorm(dim), Transpose((1, 2)), nn.Conv1d(dim, inner_dim * 2, 1), nn.GLU(dim=1), DepthWiseConv1d(inner_dim, inner_dim, kernel_size=kernel_size, padding=calc_same_padding(kernel_size)[0], groups=inner_dim), nn.SiLU(), nn.Conv1d(inner_dim, dim, 1), Transpose((1, 2)), nn.Dropout(dropout))
|
|
|
|
def forward(self, x):
|
|
return self.net(x)
|
|
|
|
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):
|
|
if self.no_projection: q, k = q.softmax(dim=-1), (torch.exp(k) if self.causal else k.softmax(dim=-2))
|
|
else:
|
|
create_kernel = partial(softmax_kernel, projection_matrix=self.projection_matrix, device=q.device)
|
|
q, k = create_kernel(q, is_query=True), create_kernel(k, is_query=False)
|
|
|
|
attn_fn = linear_attention if not self.causal else self.causal_linear_fn
|
|
return attn_fn(q, k, None) if v is None else attn_fn(q, k, v)
|
|
|
|
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
|
|
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 = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), (self.to_q(x), self.to_k(context), self.to_v(context)))
|
|
(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): v.masked_fill_(~context_mask[:, None, :, None], 0.0)
|
|
|
|
if cross_attend: pass
|
|
else: out = self.fast_attention(q, k, v)
|
|
|
|
attn_outs.append(out)
|
|
|
|
if not empty(lq):
|
|
assert (not cross_attend), "not cross_attend"
|
|
|
|
out = self.local_attn(lq, lk, lv, input_mask=mask)
|
|
attn_outs.append(out)
|
|
|
|
return self.dropout(self.to_out(rearrange(torch.cat(attn_outs, dim=1), "b h n d -> b n (h d)")))
|
|
|
|
class HannWindow(torch.nn.Module):
|
|
def __init__(self, win_size):
|
|
super().__init__()
|
|
self.register_buffer('window', torch.hann_window(win_size), persistent=False)
|
|
|
|
def forward(self):
|
|
return self.window
|
|
|
|
class FCPE_LEGACY(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: raise ValueError("Siren not support")
|
|
if use_full: raise ValueError("Model full not support")
|
|
|
|
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)
|
|
self.stack = nn.Sequential(nn.Conv1d(input_channel, n_chans, 3, 1, 1), nn.GroupNorm(4, n_chans), nn.LeakyReLU(), nn.Conv1d(n_chans, n_chans, 3, 1, 1))
|
|
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)
|
|
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 = torch.sigmoid(self.dense_out(self.norm(self.decoder((self.stack(mel.transpose(1, 2)).transpose(1, 2) if self.use_input_conv else mel)))))
|
|
|
|
if not infer:
|
|
loss_all = self.loss_mse_scale * F.binary_cross_entropy(x, self.gaussian_blurred_cent(self.f0_to_cent(gt_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.cent_to_f0(self.cdecoder(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()
|
|
rtn = torch.sum(self.cent_table[None, None, :].expand(B, N, -1) * 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()
|
|
|
|
confident, max_index = torch.max(y, dim=-1, keepdim=True)
|
|
local_argmax_index = torch.clamp(torch.arange(0, 9).to(max_index.device) + (max_index - 4), 0, self.n_out - 1)
|
|
|
|
y_l = torch.gather(y, -1, local_argmax_index)
|
|
rtn = torch.sum(torch.gather(self.cent_table[None, None, :].expand(B, N, -1), -1, local_argmax_index) * 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):
|
|
B, N, _ = cents.size()
|
|
return torch.exp(-torch.square(self.cent_table[None, None, :].expand(B, N, -1) - cents) / 1250) * (cents > 0.1) & (cents < (1200.0 * np.log2(self.f0_max / 10.0))).float()
|
|
|
|
class InferCFNaiveMelPE(torch.nn.Module):
|
|
def __init__(self, args, state_dict):
|
|
super().__init__()
|
|
self.wav2mel = spawn_wav2mel(args, device="cpu")
|
|
self.model = spawn_model(args)
|
|
self.model.load_state_dict(state_dict)
|
|
self.model.eval()
|
|
self.args_dict = dict(args)
|
|
self.register_buffer("tensor_device_marker", torch.tensor(1.0).float(), persistent=False)
|
|
|
|
def forward(self, wav, sr, decoder_mode = "local_argmax", threshold = 0.006, key_shifts = [0]):
|
|
with torch.no_grad():
|
|
mels = rearrange(torch.stack([self.wav2mel(wav.to(self.tensor_device_marker.device), sr, keyshift=keyshift) for keyshift in key_shifts], -1), "B T C K -> (B K) T C")
|
|
f0s = rearrange(self.model.infer(mels, decoder=decoder_mode, threshold=threshold), "(B K) T 1 -> B T (K 1)", K=len(key_shifts))
|
|
|
|
return f0s
|
|
|
|
def infer(self, wav, sr, decoder_mode = "local_argmax", threshold = 0.006, f0_min = None, f0_max = None, interp_uv = False, output_interp_target_length = None, return_uv = False, test_time_augmentation = False, tta_uv_penalty = 12.0, tta_key_shifts = [0, -12, 12], tta_use_origin_uv=False):
|
|
if test_time_augmentation:
|
|
assert len(tta_key_shifts) > 0
|
|
flag = 0
|
|
|
|
if tta_use_origin_uv:
|
|
if 0 not in tta_key_shifts:
|
|
flag = 1
|
|
tta_key_shifts.append(0)
|
|
|
|
tta_key_shifts.sort(key=lambda x: (x if x >= 0 else -x / 2))
|
|
f0s = self.__call__(wav, sr, decoder_mode, threshold, tta_key_shifts)
|
|
f0 = ensemble_f0(f0s[:, :, flag:], tta_key_shifts[flag:], tta_uv_penalty)
|
|
|
|
f0_for_uv = f0s[:, :, [0]] if tta_use_origin_uv else f0
|
|
else:
|
|
f0 = self.__call__(wav, sr, decoder_mode, threshold)
|
|
f0_for_uv = f0
|
|
|
|
if f0_min is None: f0_min = self.args_dict["model"]["f0_min"]
|
|
|
|
uv = (f0_for_uv < f0_min).type(f0_for_uv.dtype)
|
|
f0 = f0 * (1 - uv)
|
|
|
|
if interp_uv: f0 = batch_interp_with_replacement_detach(uv.squeeze(-1).bool(), f0.squeeze(-1)).unsqueeze(-1)
|
|
if f0_max is not None: f0[f0 > f0_max] = f0_max
|
|
if output_interp_target_length is not None: f0 = torch.nn.functional.interpolate(f0.transpose(1, 2), size=int(output_interp_target_length), mode="nearest").transpose(1, 2)
|
|
|
|
if return_uv: return f0, torch.nn.functional.interpolate(uv.transpose(1, 2), size=int(output_interp_target_length), mode="nearest").transpose(1, 2)
|
|
else: return f0
|
|
|
|
class FCPEInfer_LEGACY:
|
|
def __init__(self, model_path, device=None, dtype=torch.float32, providers=None, onnx=False):
|
|
if device is None: device = "cuda" if torch.cuda.is_available() else "cpu"
|
|
self.wav2mel = Wav2Mel(device=device, dtype=dtype)
|
|
self.device = device
|
|
self.dtype = dtype
|
|
self.onnx = onnx
|
|
|
|
if self.onnx:
|
|
sess_options = ort.SessionOptions()
|
|
sess_options.log_severity_level = 3
|
|
|
|
self.model = ort.InferenceSession(decrypt_model(model_path), sess_options=sess_options, providers=providers)
|
|
else:
|
|
ckpt = torch.load(model_path, map_location=torch.device(self.device))
|
|
self.args = DotDict(ckpt["config"])
|
|
|
|
model = FCPE_LEGACY(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
|
|
|
|
@torch.no_grad()
|
|
def __call__(self, audio, sr, threshold=0.05):
|
|
if not self.onnx: self.model.threshold = threshold
|
|
else: self.wav2mel = Wav2Mel(device=self.device, dtype=self.dtype)
|
|
return (torch.as_tensor(self.model.run([self.model.get_outputs()[0].name], {self.model.get_inputs()[0].name: self.wav2mel(audio=audio[None, :], sample_rate=sr).to(self.dtype).detach().cpu().numpy(), self.model.get_inputs()[1].name: np.array(threshold, dtype=np.float32)})[0], dtype=self.dtype, device=self.device) if self.onnx else self.model(mel=self.wav2mel(audio=audio[None, :], sample_rate=sr).to(self.dtype), infer=True, return_hz_f0=True))
|
|
|
|
class FCPEInfer:
|
|
def __init__(self, model_path, device=None, dtype=torch.float32, providers=None, onnx=False):
|
|
if device is None: device = "cuda" if torch.cuda.is_available() else "cpu"
|
|
self.device = device
|
|
self.dtype = dtype
|
|
self.onnx = onnx
|
|
|
|
if self.onnx:
|
|
sess_options = ort.SessionOptions()
|
|
sess_options.log_severity_level = 3
|
|
|
|
self.model = ort.InferenceSession(decrypt_model(model_path), sess_options=sess_options, providers=providers)
|
|
else:
|
|
ckpt = torch.load(model_path, map_location=torch.device(device))
|
|
ckpt["config_dict"]["model"]["conv_dropout"] = ckpt["config_dict"]["model"]["atten_dropout"] = 0.0
|
|
self.args = DotDict(ckpt["config_dict"])
|
|
|
|
model = InferCFNaiveMelPE(self.args, ckpt["model"])
|
|
model = model.to(device)
|
|
|
|
model.eval()
|
|
self.model = model
|
|
|
|
@torch.no_grad()
|
|
def __call__(self, audio, sr, threshold=0.05, f0_min=50, f0_max=1100, p_len=None):
|
|
if self.onnx: self.wav2mel = Wav2Mel(device=self.device, dtype=self.dtype)
|
|
return (torch.as_tensor(self.model.run([self.model.get_outputs()[0].name], {self.model.get_inputs()[0].name: self.wav2mel(audio=audio[None, :], sample_rate=sr).to(self.dtype).detach().cpu().numpy(), self.model.get_inputs()[1].name: np.array(threshold, dtype=np.float32)})[0], dtype=self.dtype, device=self.device) if self.onnx else self.model.infer(audio[None, :], sr, threshold=threshold, f0_min=f0_min, f0_max=f0_max, output_interp_target_length=p_len))
|
|
|
|
class MelModule(torch.nn.Module):
|
|
def __init__(self, sr, n_mels, n_fft, win_size, hop_length, fmin = None, fmax = None, clip_val = 1e-5, out_stft = False):
|
|
super().__init__()
|
|
if fmin is None: fmin = 0
|
|
if fmax is None: fmax = sr / 2
|
|
|
|
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.register_buffer('mel_basis', torch.tensor(librosa_mel_fn(sr=sr, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)).float(), persistent=False)
|
|
self.hann_window = torch.nn.ModuleDict()
|
|
self.out_stft = out_stft
|
|
|
|
@torch.no_grad()
|
|
def __call__(self, y, key_shift = 0, speed = 1, center = False, no_cache_window = False):
|
|
n_fft = self.n_fft
|
|
win_size = self.win_size
|
|
hop_length = self.hop_length
|
|
clip_val = self.clip_val
|
|
|
|
factor = 2 ** (key_shift / 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))
|
|
|
|
y = y.squeeze(-1)
|
|
|
|
if torch.min(y) < -1: print('[error with torchfcpe.mel_extractor.MelModule] min ', torch.min(y))
|
|
if torch.max(y) > 1: print('[error with torchfcpe.mel_extractor.MelModule] max ', torch.max(y))
|
|
|
|
key_shift_key = str(key_shift)
|
|
if not no_cache_window:
|
|
if key_shift_key in self.hann_window: hann_window = self.hann_window[key_shift_key]
|
|
else:
|
|
hann_window = HannWindow(win_size_new).to(self.mel_basis.device)
|
|
self.hann_window[key_shift_key] = hann_window
|
|
|
|
hann_window_tensor = hann_window()
|
|
else: hann_window_tensor = torch.hann_window(win_size_new).to(self.mel_basis.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)
|
|
|
|
mode = 'reflect' if pad_right < y.size(-1) else 'constant'
|
|
|
|
spec = torch.stft(torch.nn.functional.pad(y.unsqueeze(1), (pad_left, pad_right), mode=mode).squeeze(1), n_fft_new, hop_length=hop_length_new, win_length=win_size_new, window=hann_window_tensor, 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 key_shift != 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 = spec[:, :512, :] if self.out_stft else torch.matmul(self.mel_basis, spec)
|
|
|
|
return dynamic_range_compression_torch(spec, clip_val=clip_val).transpose(-1, -2)
|
|
|
|
class Wav2MelModule(torch.nn.Module):
|
|
def __init__(self, sr, n_mels, n_fft, win_size, hop_length, fmin = None, fmax = None, clip_val = 1e-5, mel_type="default"):
|
|
super().__init__()
|
|
if fmin is None: fmin = 0
|
|
if fmax is None: fmax = sr / 2
|
|
|
|
self.sampling_rate = sr
|
|
self.n_mels = n_mels
|
|
self.n_fft = n_fft
|
|
self.win_size = win_size
|
|
self.hop_size = hop_length
|
|
self.fmin = fmin
|
|
self.fmax = fmax
|
|
self.clip_val = clip_val
|
|
|
|
self.register_buffer('tensor_device_marker', torch.tensor(1.0).float(), persistent=False)
|
|
self.resample_kernel = torch.nn.ModuleDict()
|
|
|
|
if mel_type == "default": self.mel_extractor = MelModule(sr, n_mels, n_fft, win_size, hop_length, fmin, fmax, clip_val, out_stft=False)
|
|
elif mel_type == "stft": self.mel_extractor = MelModule(sr, n_mels, n_fft, win_size, hop_length, fmin, fmax, clip_val, out_stft=True)
|
|
|
|
self.mel_type = mel_type
|
|
|
|
@torch.no_grad()
|
|
def __call__(self, audio, sample_rate, keyshift = 0, no_cache_window = False):
|
|
|
|
if sample_rate == self.sampling_rate: audio_res = audio
|
|
else:
|
|
key_str = str(sample_rate)
|
|
|
|
if key_str not in self.resample_kernel:
|
|
if len(self.resample_kernel) > 8: self.resample_kernel.clear()
|
|
self.resample_kernel[key_str] = Resample(sample_rate, self.sampling_rate, lowpass_filter_width=128).to(self.tensor_device_marker.device)
|
|
|
|
audio_res = self.resample_kernel[key_str](audio.squeeze(-1)).unsqueeze(-1)
|
|
|
|
mel = self.mel_extractor(audio_res, keyshift, no_cache_window=no_cache_window)
|
|
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
|
|
|
|
class Wav2Mel:
|
|
def __init__(self, device=None, dtype=torch.float32):
|
|
self.sample_rate = 16000
|
|
self.hop_size = 160
|
|
if device is None: device = "cuda" if torch.cuda.is_available() else "cpu"
|
|
self.device = device
|
|
self.dtype = dtype
|
|
self.stft = STFT(16000, 128, 1024, 1024, 160, 0, 8000)
|
|
self.resample_kernel = {}
|
|
|
|
def extract_nvstft(self, audio, keyshift=0, train=False):
|
|
return self.stft.get_mel(audio, keyshift=keyshift, train=train).transpose(1, 2)
|
|
|
|
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)
|
|
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)
|
|
return mel[:, :n_frames, :] if n_frames < int(mel.shape[1]) else mel
|
|
|
|
def __call__(self, audio, sample_rate, keyshift=0, train=False):
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return self.extract_mel(audio, sample_rate, keyshift=keyshift, train=train)
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class DotDict(dict):
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def __getattr__(*args):
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val = dict.get(*args)
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return DotDict(val) if type(val) is dict else val
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__setattr__ = dict.__setitem__
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__delattr__ = dict.__delitem__
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class FCPE:
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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, providers=None, onnx=False, legacy=False):
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self.fcpe = FCPEInfer_LEGACY(model_path, device=device, dtype=dtype, providers=providers, onnx=onnx) if legacy else FCPEInfer(model_path, device=device, dtype=dtype, providers=providers, onnx=onnx)
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self.hop_length = hop_length
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self.f0_min = f0_min
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self.f0_max = f0_max
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self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
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self.threshold = threshold
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self.sample_rate = sample_rate
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self.dtype = dtype
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self.legacy = legacy
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self.name = "fcpe"
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def repeat_expand(self, content, target_len, mode = "nearest"):
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ndim = content.ndim
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content = (content[None, None] if ndim == 1 else content[None] if ndim == 2 else content)
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assert content.ndim == 3
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is_np = isinstance(content, np.ndarray)
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results = torch.nn.functional.interpolate(torch.from_numpy(content) if is_np else content, size=target_len, mode=mode)
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results = results.numpy() if is_np else results
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return results[0, 0] if ndim == 1 else results[0] if ndim == 2 else results
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def post_process(self, x, sample_rate, f0, pad_to):
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f0 = (torch.from_numpy(f0).float().to(x.device) if isinstance(f0, np.ndarray) else f0)
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f0 = self.repeat_expand(f0, pad_to) if pad_to is not None else f0
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vuv_vector = torch.zeros_like(f0)
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vuv_vector[f0 > 0.0] = 1.0
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vuv_vector[f0 <= 0.0] = 0.0
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|
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nzindex = torch.nonzero(f0).squeeze()
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f0 = torch.index_select(f0, dim=0, index=nzindex).cpu().numpy()
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vuv_vector = F.interpolate(vuv_vector[None, None, :], size=pad_to)[0][0]
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|
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if f0.shape[0] <= 0: return np.zeros(pad_to), vuv_vector.cpu().numpy()
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if f0.shape[0] == 1: return np.ones(pad_to) * f0[0], vuv_vector.cpu().numpy()
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|
|
|
return np.interp(np.arange(pad_to) * self.hop_length / sample_rate, self.hop_length / sample_rate * nzindex.cpu().numpy(), f0, left=f0[0], right=f0[-1]), vuv_vector.cpu().numpy()
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|
|
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def compute_f0(self, wav, p_len=None):
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x = torch.FloatTensor(wav).to(self.dtype).to(self.device)
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|
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) if self.legacy else (self.fcpe(x, sr=self.sample_rate, threshold=self.threshold, f0_min=self.f0_min, f0_max=self.f0_max, p_len=p_len))
|
|
f0 = f0[:] if f0.dim() == 1 else f0[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)[0] |
