544 lines
21 KiB
Python
544 lines
21 KiB
Python
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#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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import torch
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from funasr.register import tables
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from funasr.models.transformer.utils.nets_utils import make_pad_mask
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class mae_loss(torch.nn.Module):
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def __init__(self, normalize_length=False):
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super(mae_loss, self).__init__()
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self.normalize_length = normalize_length
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self.criterion = torch.nn.L1Loss(reduction="sum")
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def forward(self, token_length, pre_token_length):
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loss_token_normalizer = token_length.size(0)
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if self.normalize_length:
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loss_token_normalizer = token_length.sum().type(torch.float32)
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loss = self.criterion(token_length, pre_token_length)
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loss = loss / loss_token_normalizer
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return loss
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def cif(hidden, alphas, threshold):
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batch_size, len_time, hidden_size = hidden.size()
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# loop varss
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integrate = torch.zeros([batch_size], device=hidden.device)
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frame = torch.zeros([batch_size, hidden_size], device=hidden.device)
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# intermediate vars along time
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list_fires = []
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list_frames = []
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for t in range(len_time):
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alpha = alphas[:, t]
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distribution_completion = torch.ones([batch_size], device=hidden.device) - integrate
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integrate += alpha
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list_fires.append(integrate)
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fire_place = integrate >= threshold
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integrate = torch.where(
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fire_place, integrate - torch.ones([batch_size], device=hidden.device), integrate
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)
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cur = torch.where(fire_place, distribution_completion, alpha)
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remainds = alpha - cur
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frame += cur[:, None] * hidden[:, t, :]
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list_frames.append(frame)
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frame = torch.where(
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fire_place[:, None].repeat(1, hidden_size), remainds[:, None] * hidden[:, t, :], frame
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)
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fires = torch.stack(list_fires, 1)
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frames = torch.stack(list_frames, 1)
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list_ls = []
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len_labels = torch.round(alphas.sum(-1)).int()
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max_label_len = len_labels.max()
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for b in range(batch_size):
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fire = fires[b, :]
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l = torch.index_select(frames[b, :, :], 0, torch.nonzero(fire >= threshold).squeeze())
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pad_l = torch.zeros([max_label_len - l.size(0), hidden_size], device=hidden.device)
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list_ls.append(torch.cat([l, pad_l], 0))
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return torch.stack(list_ls, 0), fires
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def cif_wo_hidden(alphas, threshold):
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batch_size, len_time = alphas.size()
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# loop varss
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integrate = torch.zeros([batch_size], device=alphas.device)
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# intermediate vars along time
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list_fires = []
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for t in range(len_time):
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alpha = alphas[:, t]
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integrate += alpha
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list_fires.append(integrate)
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fire_place = integrate >= threshold
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integrate = torch.where(
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fire_place,
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integrate - torch.ones([batch_size], device=alphas.device) * threshold,
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integrate,
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)
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fires = torch.stack(list_fires, 1)
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return fires
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@tables.register("predictor_classes", "CifPredictorV3")
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class CifPredictorV3(torch.nn.Module):
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def __init__(
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self,
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idim,
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l_order,
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r_order,
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threshold=1.0,
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dropout=0.1,
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smooth_factor=1.0,
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noise_threshold=0,
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tail_threshold=0.0,
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tf2torch_tensor_name_prefix_torch="predictor",
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tf2torch_tensor_name_prefix_tf="seq2seq/cif",
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smooth_factor2=1.0,
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noise_threshold2=0,
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upsample_times=5,
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upsample_type="cnn",
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use_cif1_cnn=True,
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tail_mask=True,
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):
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super(CifPredictorV3, self).__init__()
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self.pad = torch.nn.ConstantPad1d((l_order, r_order), 0)
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self.cif_conv1d = torch.nn.Conv1d(idim, idim, l_order + r_order + 1)
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self.cif_output = torch.nn.Linear(idim, 1)
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self.dropout = torch.nn.Dropout(p=dropout)
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self.threshold = threshold
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self.smooth_factor = smooth_factor
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self.noise_threshold = noise_threshold
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self.tail_threshold = tail_threshold
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self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
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self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf
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self.upsample_times = upsample_times
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self.upsample_type = upsample_type
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self.use_cif1_cnn = use_cif1_cnn
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if self.upsample_type == "cnn":
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self.upsample_cnn = torch.nn.ConvTranspose1d(
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idim, idim, self.upsample_times, self.upsample_times
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)
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self.cif_output2 = torch.nn.Linear(idim, 1)
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elif self.upsample_type == "cnn_blstm":
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self.upsample_cnn = torch.nn.ConvTranspose1d(
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idim, idim, self.upsample_times, self.upsample_times
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)
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self.blstm = torch.nn.LSTM(
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idim, idim, 1, bias=True, batch_first=True, dropout=0.0, bidirectional=True
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)
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self.cif_output2 = torch.nn.Linear(idim * 2, 1)
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elif self.upsample_type == "cnn_attn":
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self.upsample_cnn = torch.nn.ConvTranspose1d(
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idim, idim, self.upsample_times, self.upsample_times
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)
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from funasr.models.transformer.encoder import EncoderLayer as TransformerEncoderLayer
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from funasr.models.transformer.attention import MultiHeadedAttention
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from funasr.models.transformer.positionwise_feed_forward import PositionwiseFeedForward
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positionwise_layer_args = (
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idim,
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idim * 2,
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0.1,
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)
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self.self_attn = TransformerEncoderLayer(
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idim,
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MultiHeadedAttention(4, idim, 0.1),
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PositionwiseFeedForward(*positionwise_layer_args),
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0.1,
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True, # normalize_before,
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False, # concat_after,
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)
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self.cif_output2 = torch.nn.Linear(idim, 1)
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self.smooth_factor2 = smooth_factor2
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self.noise_threshold2 = noise_threshold2
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def forward(
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self,
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hidden,
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target_label=None,
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mask=None,
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ignore_id=-1,
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mask_chunk_predictor=None,
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target_label_length=None,
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):
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h = hidden
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context = h.transpose(1, 2)
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queries = self.pad(context)
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output = torch.relu(self.cif_conv1d(queries))
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# alphas2 is an extra head for timestamp prediction
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if not self.use_cif1_cnn:
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_output = context
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else:
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_output = output
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if self.upsample_type == "cnn":
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output2 = self.upsample_cnn(_output)
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output2 = output2.transpose(1, 2)
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elif self.upsample_type == "cnn_blstm":
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output2 = self.upsample_cnn(_output)
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output2 = output2.transpose(1, 2)
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output2, (_, _) = self.blstm(output2)
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elif self.upsample_type == "cnn_attn":
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output2 = self.upsample_cnn(_output)
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output2 = output2.transpose(1, 2)
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output2, _ = self.self_attn(output2, mask)
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# import pdb; pdb.set_trace()
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alphas2 = torch.sigmoid(self.cif_output2(output2))
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alphas2 = torch.nn.functional.relu(alphas2 * self.smooth_factor2 - self.noise_threshold2)
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# repeat the mask in T demension to match the upsampled length
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if mask is not None:
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mask2 = (
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mask.repeat(1, self.upsample_times, 1)
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.transpose(-1, -2)
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.reshape(alphas2.shape[0], -1)
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)
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mask2 = mask2.unsqueeze(-1)
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alphas2 = alphas2 * mask2
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alphas2 = alphas2.squeeze(-1)
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token_num2 = alphas2.sum(-1)
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output = output.transpose(1, 2)
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output = self.cif_output(output)
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alphas = torch.sigmoid(output)
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alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold)
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if mask is not None:
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mask = mask.transpose(-1, -2).float()
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alphas = alphas * mask
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if mask_chunk_predictor is not None:
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alphas = alphas * mask_chunk_predictor
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alphas = alphas.squeeze(-1)
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mask = mask.squeeze(-1)
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if target_label_length is not None:
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target_length = target_label_length
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elif target_label is not None:
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target_length = (target_label != ignore_id).float().sum(-1)
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else:
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target_length = None
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token_num = alphas.sum(-1)
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if target_length is not None:
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alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1))
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elif self.tail_threshold > 0.0:
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hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, token_num, mask=mask)
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acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)
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if target_length is None and self.tail_threshold > 0.0:
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token_num_int = torch.max(token_num).type(torch.int32).item()
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acoustic_embeds = acoustic_embeds[:, :token_num_int, :]
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return acoustic_embeds, token_num, alphas, cif_peak, token_num2
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def get_upsample_timestamp(self, hidden, mask=None, token_num=None):
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h = hidden
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b = hidden.shape[0]
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context = h.transpose(1, 2)
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queries = self.pad(context)
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output = torch.relu(self.cif_conv1d(queries))
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# alphas2 is an extra head for timestamp prediction
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if not self.use_cif1_cnn:
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_output = context
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else:
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_output = output
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if self.upsample_type == "cnn":
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output2 = self.upsample_cnn(_output)
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output2 = output2.transpose(1, 2)
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elif self.upsample_type == "cnn_blstm":
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output2 = self.upsample_cnn(_output)
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output2 = output2.transpose(1, 2)
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output2, (_, _) = self.blstm(output2)
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elif self.upsample_type == "cnn_attn":
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output2 = self.upsample_cnn(_output)
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output2 = output2.transpose(1, 2)
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output2, _ = self.self_attn(output2, mask)
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alphas2 = torch.sigmoid(self.cif_output2(output2))
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alphas2 = torch.nn.functional.relu(alphas2 * self.smooth_factor2 - self.noise_threshold2)
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# repeat the mask in T demension to match the upsampled length
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if mask is not None:
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mask2 = (
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mask.repeat(1, self.upsample_times, 1)
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.transpose(-1, -2)
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.reshape(alphas2.shape[0], -1)
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)
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mask2 = mask2.unsqueeze(-1)
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alphas2 = alphas2 * mask2
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alphas2 = alphas2.squeeze(-1)
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_token_num = alphas2.sum(-1)
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if token_num is not None:
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alphas2 *= (token_num / _token_num)[:, None].repeat(1, alphas2.size(1))
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# re-downsample
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ds_alphas = alphas2.reshape(b, -1, self.upsample_times).sum(-1)
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ds_cif_peak = cif_wo_hidden(ds_alphas, self.threshold - 1e-4)
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# upsampled alphas and cif_peak
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us_alphas = alphas2
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us_cif_peak = cif_wo_hidden(us_alphas, self.threshold - 1e-4)
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return ds_alphas, ds_cif_peak, us_alphas, us_cif_peak
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def tail_process_fn(self, hidden, alphas, token_num=None, mask=None):
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b, t, d = hidden.size()
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tail_threshold = self.tail_threshold
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if mask is not None:
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zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device)
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ones_t = torch.ones_like(zeros_t)
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mask_1 = torch.cat([mask, zeros_t], dim=1)
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mask_2 = torch.cat([ones_t, mask], dim=1)
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mask = mask_2 - mask_1
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tail_threshold = mask * tail_threshold
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alphas = torch.cat([alphas, zeros_t], dim=1)
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alphas = torch.add(alphas, tail_threshold)
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else:
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tail_threshold = torch.tensor([tail_threshold], dtype=alphas.dtype).to(alphas.device)
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tail_threshold = torch.reshape(tail_threshold, (1, 1))
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alphas = torch.cat([alphas, tail_threshold], dim=1)
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zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device)
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hidden = torch.cat([hidden, zeros], dim=1)
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token_num = alphas.sum(dim=-1)
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token_num_floor = torch.floor(token_num)
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return hidden, alphas, token_num_floor
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def gen_frame_alignments(
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self, alphas: torch.Tensor = None, encoder_sequence_length: torch.Tensor = None
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):
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batch_size, maximum_length = alphas.size()
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int_type = torch.int32
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is_training = self.training
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if is_training:
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token_num = torch.round(torch.sum(alphas, dim=1)).type(int_type)
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else:
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token_num = torch.floor(torch.sum(alphas, dim=1)).type(int_type)
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max_token_num = torch.max(token_num).item()
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alphas_cumsum = torch.cumsum(alphas, dim=1)
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alphas_cumsum = torch.floor(alphas_cumsum).type(int_type)
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alphas_cumsum = alphas_cumsum[:, None, :].repeat(1, max_token_num, 1)
|
||
|
|
|
||
|
|
index = torch.ones([batch_size, max_token_num], dtype=int_type)
|
||
|
|
index = torch.cumsum(index, dim=1)
|
||
|
|
index = index[:, :, None].repeat(1, 1, maximum_length).to(alphas_cumsum.device)
|
||
|
|
|
||
|
|
index_div = torch.floor(torch.true_divide(alphas_cumsum, index)).type(int_type)
|
||
|
|
index_div_bool_zeros = index_div.eq(0)
|
||
|
|
index_div_bool_zeros_count = torch.sum(index_div_bool_zeros, dim=-1) + 1
|
||
|
|
index_div_bool_zeros_count = torch.clamp(
|
||
|
|
index_div_bool_zeros_count, 0, encoder_sequence_length.max()
|
||
|
|
)
|
||
|
|
token_num_mask = (~make_pad_mask(token_num, maxlen=max_token_num)).to(token_num.device)
|
||
|
|
index_div_bool_zeros_count *= token_num_mask
|
||
|
|
|
||
|
|
index_div_bool_zeros_count_tile = index_div_bool_zeros_count[:, :, None].repeat(
|
||
|
|
1, 1, maximum_length
|
||
|
|
)
|
||
|
|
ones = torch.ones_like(index_div_bool_zeros_count_tile)
|
||
|
|
zeros = torch.zeros_like(index_div_bool_zeros_count_tile)
|
||
|
|
ones = torch.cumsum(ones, dim=2)
|
||
|
|
cond = index_div_bool_zeros_count_tile == ones
|
||
|
|
index_div_bool_zeros_count_tile = torch.where(cond, zeros, ones)
|
||
|
|
|
||
|
|
index_div_bool_zeros_count_tile_bool = index_div_bool_zeros_count_tile.type(torch.bool)
|
||
|
|
index_div_bool_zeros_count_tile = 1 - index_div_bool_zeros_count_tile_bool.type(int_type)
|
||
|
|
index_div_bool_zeros_count_tile_out = torch.sum(index_div_bool_zeros_count_tile, dim=1)
|
||
|
|
index_div_bool_zeros_count_tile_out = index_div_bool_zeros_count_tile_out.type(int_type)
|
||
|
|
predictor_mask = (
|
||
|
|
(~make_pad_mask(encoder_sequence_length, maxlen=encoder_sequence_length.max()))
|
||
|
|
.type(int_type)
|
||
|
|
.to(encoder_sequence_length.device)
|
||
|
|
)
|
||
|
|
index_div_bool_zeros_count_tile_out = index_div_bool_zeros_count_tile_out * predictor_mask
|
||
|
|
|
||
|
|
predictor_alignments = index_div_bool_zeros_count_tile_out
|
||
|
|
predictor_alignments_length = predictor_alignments.sum(-1).type(
|
||
|
|
encoder_sequence_length.dtype
|
||
|
|
)
|
||
|
|
return predictor_alignments.detach(), predictor_alignments_length.detach()
|
||
|
|
|
||
|
|
|
||
|
|
@tables.register("predictor_classes", "CifPredictorV3Export")
|
||
|
|
class CifPredictorV3Export(torch.nn.Module):
|
||
|
|
def __init__(self, model, **kwargs):
|
||
|
|
super().__init__()
|
||
|
|
|
||
|
|
self.pad = model.pad
|
||
|
|
self.cif_conv1d = model.cif_conv1d
|
||
|
|
self.cif_output = model.cif_output
|
||
|
|
self.threshold = model.threshold
|
||
|
|
self.smooth_factor = model.smooth_factor
|
||
|
|
self.noise_threshold = model.noise_threshold
|
||
|
|
self.tail_threshold = model.tail_threshold
|
||
|
|
|
||
|
|
self.upsample_times = model.upsample_times
|
||
|
|
self.upsample_cnn = model.upsample_cnn
|
||
|
|
self.blstm = model.blstm
|
||
|
|
self.cif_output2 = model.cif_output2
|
||
|
|
self.smooth_factor2 = model.smooth_factor2
|
||
|
|
self.noise_threshold2 = model.noise_threshold2
|
||
|
|
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
hidden: torch.Tensor,
|
||
|
|
mask: torch.Tensor,
|
||
|
|
):
|
||
|
|
h = hidden
|
||
|
|
context = h.transpose(1, 2)
|
||
|
|
queries = self.pad(context)
|
||
|
|
output = torch.relu(self.cif_conv1d(queries))
|
||
|
|
output = output.transpose(1, 2)
|
||
|
|
|
||
|
|
output = self.cif_output(output)
|
||
|
|
alphas = torch.sigmoid(output)
|
||
|
|
alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold)
|
||
|
|
mask = mask.transpose(-1, -2).float()
|
||
|
|
alphas = alphas * mask
|
||
|
|
alphas = alphas.squeeze(-1)
|
||
|
|
token_num = alphas.sum(-1)
|
||
|
|
|
||
|
|
mask = mask.squeeze(-1)
|
||
|
|
hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, mask=mask)
|
||
|
|
acoustic_embeds, cif_peak = cif_export(hidden, alphas, self.threshold)
|
||
|
|
|
||
|
|
return acoustic_embeds, token_num, alphas, cif_peak
|
||
|
|
|
||
|
|
def get_upsample_timestmap(self, hidden, mask=None, token_num=None):
|
||
|
|
h = hidden
|
||
|
|
b = hidden.shape[0]
|
||
|
|
context = h.transpose(1, 2)
|
||
|
|
|
||
|
|
# generate alphas2
|
||
|
|
_output = context
|
||
|
|
output2 = self.upsample_cnn(_output)
|
||
|
|
output2 = output2.transpose(1, 2)
|
||
|
|
output2, (_, _) = self.blstm(output2)
|
||
|
|
alphas2 = torch.sigmoid(self.cif_output2(output2))
|
||
|
|
alphas2 = torch.nn.functional.relu(alphas2 * self.smooth_factor2 - self.noise_threshold2)
|
||
|
|
|
||
|
|
mask = (
|
||
|
|
mask.repeat(1, self.upsample_times, 1).transpose(-1, -2).reshape(alphas2.shape[0], -1)
|
||
|
|
)
|
||
|
|
mask = mask.unsqueeze(-1)
|
||
|
|
alphas2 = alphas2 * mask
|
||
|
|
alphas2 = alphas2.squeeze(-1)
|
||
|
|
_token_num = alphas2.sum(-1)
|
||
|
|
alphas2 *= (token_num / _token_num)[:, None].repeat(1, alphas2.size(1))
|
||
|
|
# upsampled alphas and cif_peak
|
||
|
|
us_alphas = alphas2
|
||
|
|
us_cif_peak = cif_wo_hidden_export(us_alphas, self.threshold - 1e-4)
|
||
|
|
return us_alphas, us_cif_peak
|
||
|
|
|
||
|
|
def tail_process_fn(self, hidden, alphas, token_num=None, mask=None):
|
||
|
|
b, t, d = hidden.size()
|
||
|
|
tail_threshold = self.tail_threshold
|
||
|
|
|
||
|
|
zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device)
|
||
|
|
ones_t = torch.ones_like(zeros_t)
|
||
|
|
|
||
|
|
mask_1 = torch.cat([mask, zeros_t], dim=1)
|
||
|
|
mask_2 = torch.cat([ones_t, mask], dim=1)
|
||
|
|
mask = mask_2 - mask_1
|
||
|
|
tail_threshold = mask * tail_threshold
|
||
|
|
alphas = torch.cat([alphas, zeros_t], dim=1)
|
||
|
|
alphas = torch.add(alphas, tail_threshold)
|
||
|
|
|
||
|
|
zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device)
|
||
|
|
hidden = torch.cat([hidden, zeros], dim=1)
|
||
|
|
token_num = alphas.sum(dim=-1)
|
||
|
|
token_num_floor = torch.floor(token_num)
|
||
|
|
|
||
|
|
return hidden, alphas, token_num_floor
|
||
|
|
|
||
|
|
|
||
|
|
@torch.jit.script
|
||
|
|
def cif_export(hidden, alphas, threshold: float):
|
||
|
|
batch_size, len_time, hidden_size = hidden.size()
|
||
|
|
threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)
|
||
|
|
|
||
|
|
# loop varss
|
||
|
|
integrate = torch.zeros([batch_size], dtype=alphas.dtype, device=hidden.device)
|
||
|
|
frame = torch.zeros([batch_size, hidden_size], dtype=hidden.dtype, device=hidden.device)
|
||
|
|
# intermediate vars along time
|
||
|
|
list_fires = []
|
||
|
|
list_frames = []
|
||
|
|
|
||
|
|
for t in range(len_time):
|
||
|
|
alpha = alphas[:, t]
|
||
|
|
distribution_completion = (
|
||
|
|
torch.ones([batch_size], dtype=alphas.dtype, device=hidden.device) - integrate
|
||
|
|
)
|
||
|
|
|
||
|
|
integrate += alpha
|
||
|
|
list_fires.append(integrate)
|
||
|
|
|
||
|
|
fire_place = integrate >= threshold
|
||
|
|
integrate = torch.where(
|
||
|
|
fire_place,
|
||
|
|
integrate - torch.ones([batch_size], dtype=alphas.dtype, device=hidden.device),
|
||
|
|
integrate,
|
||
|
|
)
|
||
|
|
cur = torch.where(fire_place, distribution_completion, alpha)
|
||
|
|
remainds = alpha - cur
|
||
|
|
|
||
|
|
frame += cur[:, None] * hidden[:, t, :]
|
||
|
|
list_frames.append(frame)
|
||
|
|
frame = torch.where(
|
||
|
|
fire_place[:, None].repeat(1, hidden_size), remainds[:, None] * hidden[:, t, :], frame
|
||
|
|
)
|
||
|
|
|
||
|
|
fires = torch.stack(list_fires, 1)
|
||
|
|
frames = torch.stack(list_frames, 1)
|
||
|
|
|
||
|
|
fire_idxs = fires >= threshold
|
||
|
|
frame_fires = torch.zeros_like(hidden)
|
||
|
|
max_label_len = frames[0, fire_idxs[0]].size(0)
|
||
|
|
for b in range(batch_size):
|
||
|
|
frame_fire = frames[b, fire_idxs[b]]
|
||
|
|
frame_len = frame_fire.size(0)
|
||
|
|
frame_fires[b, :frame_len, :] = frame_fire
|
||
|
|
|
||
|
|
if frame_len >= max_label_len:
|
||
|
|
max_label_len = frame_len
|
||
|
|
frame_fires = frame_fires[:, :max_label_len, :]
|
||
|
|
return frame_fires, fires
|
||
|
|
|
||
|
|
|
||
|
|
@torch.jit.script
|
||
|
|
def cif_wo_hidden_export(alphas, threshold: float):
|
||
|
|
batch_size, len_time = alphas.size()
|
||
|
|
|
||
|
|
# loop varss
|
||
|
|
integrate = torch.zeros([batch_size], dtype=alphas.dtype, device=alphas.device)
|
||
|
|
# intermediate vars along time
|
||
|
|
list_fires = []
|
||
|
|
|
||
|
|
for t in range(len_time):
|
||
|
|
alpha = alphas[:, t]
|
||
|
|
|
||
|
|
integrate += alpha
|
||
|
|
list_fires.append(integrate)
|
||
|
|
|
||
|
|
fire_place = integrate >= threshold
|
||
|
|
integrate = torch.where(
|
||
|
|
fire_place,
|
||
|
|
integrate - torch.ones([batch_size], device=alphas.device) * threshold,
|
||
|
|
integrate,
|
||
|
|
)
|
||
|
|
|
||
|
|
fires = torch.stack(list_fires, 1)
|
||
|
|
return fires
|