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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 torch import nn
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| | | from torch import Tensor
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| | | import logging
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| | | import numpy as np
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| | | from funasr.train_utils.device_funcs import to_device
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| | | from funasr.models.transformer.utils.nets_utils import make_pad_mask
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| | | from funasr.models.scama.utils import sequence_mask
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| | | from typing import Optional, Tuple
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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(nn.Module):
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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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| | | return fires
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| | | @tables.register("predictor_classes", "CifPredictorV3")
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| | | class CifPredictorV3(nn.Module):
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| | | class CifPredictorV3(torch.nn.Module):
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| | | def __init__(self,
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| | | idim,
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| | | l_order,
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| | | ):
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| | | super(CifPredictorV3, self).__init__()
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| | | self.pad = nn.ConstantPad1d((l_order, r_order), 0)
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| | | self.cif_conv1d = nn.Conv1d(idim, idim, l_order + r_order + 1)
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| | | self.cif_output = nn.Linear(idim, 1)
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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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| | |
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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 = nn.ConvTranspose1d(idim, idim, self.upsample_times, self.upsample_times)
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| | | self.cif_output2 = nn.Linear(idim, 1)
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| | | self.upsample_cnn = torch.nn.ConvTranspose1d(idim, idim, self.upsample_times, self.upsample_times)
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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 = nn.ConvTranspose1d(idim, idim, self.upsample_times, self.upsample_times)
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| | | self.blstm = nn.LSTM(idim, idim, 1, bias=True, batch_first=True, dropout=0.0, bidirectional=True)
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| | | self.cif_output2 = nn.Linear(idim*2, 1)
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| | | self.upsample_cnn = torch.nn.ConvTranspose1d(idim, idim, self.upsample_times, self.upsample_times)
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| | | self.blstm = torch.nn.LSTM(idim, idim, 1, bias=True, batch_first=True, dropout=0.0, bidirectional=True)
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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 = nn.ConvTranspose1d(idim, idim, self.upsample_times, self.upsample_times)
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| | | self.upsample_cnn = torch.nn.ConvTranspose1d(idim, idim, self.upsample_times, self.upsample_times)
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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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| | | True, #normalize_before,
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| | | False, #concat_after,
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| | | )
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| | | self.cif_output2 = nn.Linear(idim, 1)
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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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| | | predictor_alignments = index_div_bool_zeros_count_tile_out
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| | | predictor_alignments_length = predictor_alignments.sum(-1).type(encoder_sequence_length.dtype)
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| | | return predictor_alignments.detach(), predictor_alignments_length.detach()
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| | | @tables.register("predictor_classes", "CifPredictorV3Export")
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| | | class CifPredictorV3Export(torch.nn.Module):
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| | | def __init__(self, model, **kwargs):
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| | | super().__init__()
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| | | self.pad = model.pad
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| | | self.cif_conv1d = model.cif_conv1d
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| | | self.cif_output = model.cif_output
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| | | self.threshold = model.threshold
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| | | self.smooth_factor = model.smooth_factor
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| | | self.noise_threshold = model.noise_threshold
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| | | self.tail_threshold = model.tail_threshold
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| | | |
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| | | self.upsample_times = model.upsample_times
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| | | self.upsample_cnn = model.upsample_cnn
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| | | self.blstm = model.blstm
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| | | self.cif_output2 = model.cif_output2
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| | | self.smooth_factor2 = model.smooth_factor2
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| | | self.noise_threshold2 = model.noise_threshold2
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| | | |
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| | | def forward(self, hidden: torch.Tensor,
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| | | mask: torch.Tensor,
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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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| | | 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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| | | mask = mask.transpose(-1, -2).float()
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| | | alphas = alphas * mask
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| | | alphas = alphas.squeeze(-1)
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| | | token_num = alphas.sum(-1)
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| | | mask = mask.squeeze(-1)
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| | | hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, mask=mask)
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| | | acoustic_embeds, cif_peak = cif_export(hidden, alphas, self.threshold)
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| | | return acoustic_embeds, token_num, alphas, cif_peak
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| | | def get_upsample_timestmap(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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| | | # generate alphas2
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| | | _output = context
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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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| | | 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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| | | mask = mask.repeat(1, self.upsample_times, 1).transpose(-1, -2).reshape(alphas2.shape[0], -1)
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| | | mask = mask.unsqueeze(-1)
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| | | alphas2 = alphas2 * mask
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| | | alphas2 = alphas2.squeeze(-1)
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| | | _token_num = alphas2.sum(-1)
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| | | alphas2 *= (token_num / _token_num)[:, None].repeat(1, alphas2.size(1))
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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_export(us_alphas, self.threshold - 1e-4)
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| | | return 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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| | | 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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| | | 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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| | | @torch.jit.script
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| | | def cif_export(hidden, alphas, threshold: float):
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| | | batch_size, len_time, hidden_size = hidden.size()
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| | | threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)
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| | | # loop varss
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| | | integrate = torch.zeros([batch_size], dtype=alphas.dtype, device=hidden.device)
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| | | frame = torch.zeros([batch_size, hidden_size], dtype=hidden.dtype, 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], dtype=alphas.dtype, 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(fire_place,
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| | | integrate - torch.ones([batch_size], dtype=alphas.dtype, device=hidden.device),
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| | | integrate)
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| | | cur = torch.where(fire_place,
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| | | distribution_completion,
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| | | 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(fire_place[:, None].repeat(1, hidden_size),
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| | | remainds[:, None] * hidden[:, t, :],
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| | | frame)
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| | | fires = torch.stack(list_fires, 1)
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| | | frames = torch.stack(list_frames, 1)
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| | | fire_idxs = fires >= threshold
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| | | frame_fires = torch.zeros_like(hidden)
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| | | max_label_len = frames[0, fire_idxs[0]].size(0)
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| | | for b in range(batch_size):
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| | | frame_fire = frames[b, fire_idxs[b]]
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| | | frame_len = frame_fire.size(0)
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| | | frame_fires[b, :frame_len, :] = frame_fire
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| | | if frame_len >= max_label_len:
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| | | max_label_len = frame_len
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| | | frame_fires = frame_fires[:, :max_label_len, :]
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| | | return frame_fires, fires
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| | | @torch.jit.script
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| | | def cif_wo_hidden_export(alphas, threshold: float):
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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], dtype=alphas.dtype, 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(fire_place,
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| | | integrate - torch.ones([batch_size], device=alphas.device) * threshold,
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| | | integrate)
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| | | fires = torch.stack(list_fires, 1)
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| | | return fires |
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