from copy import deepcopy import math import torch import torch.nn as nn import torch.nn.functional as F import numpy as np from ...utils.commons.hparams import hparams from ...utils.commons.gpu_mem_track import MemTracker from ..commons.layers import Embedding from ..commons.conv import ResidualBlock, ConvBlocks from ..commons.conformer.conformer import ConformerLayers from .unet import Unet def regulate_boundary(bd_logits, threshold, min_gap=18, ref_bd=None, ref_bd_min_gap=8, non_padding=None): # this doesn't preserve gradient device = bd_logits.device bd_logits = torch.sigmoid(bd_logits).data.cpu() # bd_logits[0] = bd_logits[-1] = 1e-5 # avoid itv invalid problem bd = (bd_logits > threshold).long() bd_res = torch.zeros_like(bd).long() for i in range(bd.shape[0]): bd_i = bd[i] last_bd_idx = -1 start = -1 for j in range(bd_i.shape[0]): if bd_i[j] == 1: if 0 <= start < j: continue elif start < 0: start = j else: if 0 <= start < j: if j - 1 > start: bd_idx = start + int(torch.argmax(bd_logits[i, start: j]).item()) else: bd_idx = start if bd_idx - last_bd_idx < min_gap and last_bd_idx > 0: bd_idx = round((bd_idx + last_bd_idx) / 2) bd_res[i, last_bd_idx] = 0 bd_res[i, bd_idx] = 1 last_bd_idx = bd_idx start = -1 # assert ref_bd_min_gap <= min_gap // 2 if ref_bd is not None and ref_bd_min_gap > 0: ref = ref_bd.data.cpu() for i in range(bd_res.shape[0]): ref_bd_i = ref[i] ref_bd_i_js = [] for j in range(ref_bd_i.shape[0]): if ref_bd_i[j] == 1: ref_bd_i_js.append(j) seg_sum = torch.sum(bd_res[i, max(0, j - ref_bd_min_gap): j + ref_bd_min_gap]) if seg_sum == 0: bd_res[i, j] = 1 elif seg_sum == 1 and bd_res[i, j] != 1: bd_res[i, max(0, j - ref_bd_min_gap): j + ref_bd_min_gap] = \ ref_bd_i[max(0, j - ref_bd_min_gap): j + ref_bd_min_gap] elif seg_sum > 1: for k in range(1, ref_bd_min_gap+1): if bd_res[i, max(0, j - k)] == 1 and ref_bd_i[max(0, j - k)] != 1: bd_res[i, max(0, j - k)] = 0 break if bd_res[i, min(bd_res.shape[1] - 1, j + k)] == 1 and ref_bd_i[min(bd_res.shape[1] - 1, j + k)] != 1: bd_res[i, min(bd_res.shape[1] - 1, j + k)] = 0 break bd_res[i, j] = 1 # final check assert torch.sum(bd_res[i, ref_bd_i_js]) == len(ref_bd_i_js), \ f"{torch.sum(bd_res[i, ref_bd_i_js])} {len(ref_bd_i_js)}" bd_res = bd_res.to(device) # force valid begin and end bd_res[:, 0] = 0 if non_padding is not None: for i in range(bd_res.shape[0]): bd_res[i, sum(non_padding[i]) - 1:] = 0 else: bd_res[:, -1] = 0 return bd_res class BackboneNet(nn.Module): def __init__(self, hparams): super().__init__() self.hidden_size = hidden_size = hparams['hidden_size'] self.dropout = hparams.get('dropout', 0.0) updown_rates = [2, 2, 2] channel_multiples = [1, 1, 1] if hparams.get('updown_rates', None) is not None: updown_rates = [int(i) for i in hparams.get('updown_rates', None).split('-')] if hparams.get('channel_multiples', None) is not None: channel_multiples = [float(i) for i in hparams.get('channel_multiples', None).split('-')] assert len(updown_rates) == len(channel_multiples) # convs if hparams.get('bkb_net', 'conv') == 'conv': self.net = Unet(hidden_size, down_layers=len(updown_rates), mid_layers=hparams.get('bkb_layers', 12), up_layers=len(updown_rates), kernel_size=3, updown_rates=updown_rates, channel_multiples=channel_multiples, dropout=0, is_BTC=True, constant_channels=False, mid_net=None, use_skip_layer=hparams.get('unet_skip_layer', False)) # conformer elif hparams.get('bkb_net', 'conv') == 'conformer': mid_net = ConformerLayers( hidden_size, num_layers=hparams.get('bkb_layers', 12), kernel_size=hparams.get('conformer_kernel', 9), dropout=self.dropout, num_heads=4) self.net = Unet(hidden_size, down_layers=len(updown_rates), up_layers=len(updown_rates), kernel_size=3, updown_rates=updown_rates, channel_multiples=channel_multiples, dropout=0, is_BTC=True, constant_channels=False, mid_net=mid_net, use_skip_layer=hparams.get('unet_skip_layer', False)) def forward(self, x): return self.net(x) class PitchDecoder(nn.Module): def __init__(self, hparams): super().__init__() self.hidden_size = hidden_size = hparams['hidden_size'] self.dropout = hparams.get('dropout', 0.0) self.note_bd_out = nn.Linear(hidden_size, 1) self.note_bd_temperature = max(1e-7, hparams.get('note_bd_temperature', 1.0)) # note prediction self.pitch_attn_num_head = hparams.get('pitch_attn_num_head', 1) self.multihead_dot_attn = nn.Linear(hidden_size, self.pitch_attn_num_head) self.post = ConvBlocks(hidden_size, out_dims=hidden_size, dilations=None, kernel_size=3, layers_in_block=1, c_multiple=1, dropout=self.dropout, num_layers=1, post_net_kernel=3, act_type='leakyrelu') self.pitch_out = nn.Linear(hidden_size, hparams.get('note_num', 100) + 4) self.note_num = hparams.get('note_num', 100) self.note_start = hparams.get('note_start', 30) self.pitch_temperature = max(1e-7, hparams.get('note_pitch_temperature', 1.0)) def forward(self, feat, note_bd, train=True): bsz, T, _ = feat.shape attn = torch.sigmoid(self.multihead_dot_attn(feat)) # [B, T, C] -> [B, T, num_head] attn = F.dropout(attn, self.dropout, train) attn_feat = feat.unsqueeze(3) * attn.unsqueeze(2) # [B, T, C, 1] x [B, T, 1, num_head] -> [B, T, C, num_head] attn_feat = torch.mean(attn_feat, dim=-1) # [B, T, C, num_head] -> [B, T, C] mel2note = torch.cumsum(note_bd, 1) note_length = torch.max(torch.sum(note_bd, dim=1)).item() + 1 # max length note_lengths = torch.sum(note_bd, dim=1) + 1 # [B] # print('note_length', note_length) attn = torch.mean(attn, dim=-1, keepdim=True) # [B, T, num_head] -> [B, T, 1] denom = mel2note.new_zeros(bsz, note_length, dtype=attn.dtype).scatter_add_( dim=1, index=mel2note, src=attn.squeeze(-1) ) # [B, T] -> [B, note_length] count the note frames of each note (with padding excluded) frame2note = mel2note.unsqueeze(-1).repeat(1, 1, self.hidden_size) # [B, T] -> [B, T, C], with padding included note_aggregate = frame2note.new_zeros(bsz, note_length, self.hidden_size, dtype=attn_feat.dtype).scatter_add_( dim=1, index=frame2note, src=attn_feat ) # [B, T, C] -> [B, note_length, C] note_aggregate = note_aggregate / (denom.unsqueeze(-1) + 1e-5) note_aggregate = F.dropout(note_aggregate, self.dropout, train) note_logits = self.post(note_aggregate) note_logits = self.pitch_out(note_logits) / self.pitch_temperature # note_logits = torch.clamp(note_logits, min=-16., max=16.) # don't know need it or not note_pred = torch.softmax(note_logits, dim=-1) # [B, note_length, note_num] note_pred = torch.argmax(note_pred, dim=-1) # [B, note_length] # for some reason, note idx maybe 130 (why?) note_pred[note_pred > self.note_num] = 0 note_pred[note_pred < self.note_start] = 0 return note_lengths, note_logits, note_pred class MidiExtractor(nn.Module): def __init__(self, hparams): super(MidiExtractor, self).__init__() self.hparams = deepcopy(hparams) self.hidden_size = hidden_size = hparams['hidden_size'] self.dropout = hparams.get('dropout', 0.0) self.note_bd_threshold = hparams.get('note_bd_threshold', 0.5) self.note_bd_min_gap = round(hparams.get('note_bd_min_gap', 100) * hparams['audio_sample_rate'] / 1000 / hparams['hop_size']) self.note_bd_ref_min_gap = round(hparams.get('note_bd_ref_min_gap', 50) * hparams['audio_sample_rate'] / 1000 / hparams['hop_size']) self.mel_proj = nn.Conv1d(hparams['use_mel_bins'], hidden_size, kernel_size=3, padding=1) self.mel_encoder = ConvBlocks(hidden_size, out_dims=hidden_size, dilations=None, kernel_size=3, layers_in_block=2, c_multiple=1, dropout=self.dropout, num_layers=1, post_net_kernel=3, act_type='leakyrelu') self.use_pitch = hparams.get('use_pitch_embed', True) if self.use_pitch: self.pitch_embed = Embedding(300, hidden_size, 0, 'kaiming') self.uv_embed = Embedding(3, hidden_size, 0, 'kaiming') self.use_wbd = hparams.get('use_wbd', True) if self.use_wbd: self.word_bd_embed = Embedding(3, hidden_size, 0, 'kaiming') self.cond_encoder = ConvBlocks(hidden_size, out_dims=hidden_size, dilations=None, kernel_size=3, layers_in_block=1, c_multiple=1, dropout=self.dropout, num_layers=1, post_net_kernel=3, act_type='leakyrelu') # backbone self.net = BackboneNet(hparams) # note bd prediction self.note_bd_out = nn.Linear(hidden_size, 1) self.note_bd_temperature = max(1e-7, hparams.get('note_bd_temperature', 1.0)) # note prediction self.pitch_decoder = PitchDecoder(hparams) self.reset_parameters() def run_encoder(self, mel=None, word_bd=None, pitch=None, uv=None, non_padding=None): mel_embed = self.mel_proj(mel.transpose(1, 2)).transpose(1, 2) mel_embed = self.mel_encoder(mel_embed) pitch_embed = word_bd_embed = 0 if self.use_pitch and pitch is not None and uv is not None: pitch_embed = self.pitch_embed(pitch) + self.uv_embed(uv) # [B, T, C] if self.use_wbd and word_bd is not None: word_bd_embed = self.word_bd_embed(word_bd) feat = self.cond_encoder(mel_embed + pitch_embed + word_bd_embed) return feat def forward(self, mel=None, word_bd=None, note_bd=None, pitch=None, uv=None, non_padding=None, train=True): ret = {} bsz, T, _ = mel.shape feat = self.run_encoder(mel, word_bd, pitch, uv, non_padding) feat = self.net(feat) # [B, T, C] # note bd prediction note_bd_logits = self.note_bd_out(F.dropout(feat, self.dropout, train)).squeeze(-1) / self.note_bd_temperature note_bd_logits = torch.clamp(note_bd_logits, min=-16., max=16.) ret['note_bd_logits'] = note_bd_logits # [B, T] if note_bd is None or not train: note_bd = regulate_boundary(note_bd_logits, self.note_bd_threshold, self.note_bd_min_gap, word_bd, self.note_bd_ref_min_gap, non_padding) ret['note_bd_pred'] = note_bd # [B, T] # note pitch prediction note_lengths, note_logits, note_pred = self.pitch_decoder(feat, note_bd, train) ret['note_lengths'], ret['note_logits'], ret['note_pred'] = note_lengths, note_logits, note_pred return ret def reset_parameters(self): nn.init.kaiming_normal_(self.pitch_decoder.multihead_dot_attn.weight, mode='fan_in') nn.init.kaiming_normal_(self.note_bd_out.weight, mode='fan_in') nn.init.kaiming_normal_(self.pitch_decoder.pitch_out.weight, mode='fan_in') nn.init.kaiming_normal_(self.mel_proj.weight, mode='fan_in') nn.init.constant_(self.pitch_decoder.multihead_dot_attn.bias, 0.0) nn.init.constant_(self.note_bd_out.bias, 0.0) nn.init.constant_(self.pitch_decoder.pitch_out.bias, 0.0) class WordbdExtractor(MidiExtractor): def __init__(self, hparams): super().__init__(hparams) self.use_wbd = False self.word_bd_embed = None self.note_bd_out = self.note_bd_temperature = self.pitch_decoder = None self.word_bd_threshold = hparams.get('word_bd_threshold', 0.5) self.word_bd_min_gap = round( hparams.get('word_bd_min_gap', 100) * hparams['audio_sample_rate'] / 1000 / hparams['hop_size']) self.word_bd_out = nn.Linear(self.hidden_size, 1) self.word_bd_temperature = max(1e-7, hparams.get('word_bd_temperature', 1.0)) nn.init.kaiming_normal_(self.word_bd_out.weight, mode='fan_in') nn.init.constant_(self.word_bd_out.bias, 0.0) def forward(self, mel=None, pitch=None, uv=None, non_padding=None, train=True): # gpu_tracker.track() ret = {} bsz, T, _ = mel.shape feat = self.run_encoder(mel=mel, pitch=pitch, uv=uv, non_padding=non_padding) feat = self.net(feat) # [B, T, C] word_bd_logits = self.word_bd_out(F.dropout(feat, self.dropout, train)).squeeze(-1) / self.word_bd_temperature word_bd_logits = torch.clamp(word_bd_logits, min=-16., max=16.) ret['word_bd_logits'] = word_bd_logits # [B, T] if not train: word_bd = regulate_boundary(word_bd_logits, self.word_bd_threshold, self.word_bd_min_gap, non_padding=non_padding) ret['word_bd_pred'] = word_bd # [B, T] return ret def reset_parameters(self): if self.use_pitch: nn.init.kaiming_normal_(self.pitch_embed.weight, mode='fan_in') nn.init.kaiming_normal_(self.uv_embed.weight, mode='fan_in') nn.init.kaiming_normal_(self.mel_proj.weight, mode='fan_in') if self.use_pitch: nn.init.constant_(self.pitch_embed.weight[self.pitch_embed.padding_idx], 0.0) nn.init.constant_(self.uv_embed.weight[self.uv_embed.padding_idx], 0.0)