From 33d3d2084403fd34b79c835d2f2fe04f6cd8f738 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 13 九月 2023 09:33:54 +0800
Subject: [PATCH] Merge branch 'main' of github.com:alibaba-damo-academy/FunASR add
---
funasr/models/predictor/cif.py | 632 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++-
1 files changed, 621 insertions(+), 11 deletions(-)
diff --git a/funasr/models/predictor/cif.py b/funasr/models/predictor/cif.py
index 8199708..5f18c4d 100644
--- a/funasr/models/predictor/cif.py
+++ b/funasr/models/predictor/cif.py
@@ -1,10 +1,15 @@
import torch
from torch import nn
-
+from torch import Tensor
+import logging
+import numpy as np
+from funasr.torch_utils.device_funcs import to_device
from funasr.modules.nets_utils import make_pad_mask
+from funasr.modules.streaming_utils.utils import sequence_mask
+from typing import Optional, Tuple
class CifPredictor(nn.Module):
- def __init__(self, idim, l_order, r_order, threshold=1.0, dropout=0.1, smooth_factor=1.0, noise_threshold=0):
+ def __init__(self, idim, l_order, r_order, threshold=1.0, dropout=0.1, smooth_factor=1.0, noise_threshold=0, tail_threshold=0.45):
super(CifPredictor, self).__init__()
self.pad = nn.ConstantPad1d((l_order, r_order), 0)
@@ -14,6 +19,7 @@
self.threshold = threshold
self.smooth_factor = smooth_factor
self.noise_threshold = noise_threshold
+ self.tail_threshold = tail_threshold
def forward(self, hidden, target_label=None, mask=None, ignore_id=-1, mask_chunk_predictor=None,
target_label_length=None):
@@ -29,10 +35,12 @@
alphas = torch.sigmoid(output)
alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold)
if mask is not None:
- alphas = alphas * mask.transpose(-1, -2).float()
+ mask = mask.transpose(-1, -2).float()
+ alphas = alphas * mask
if mask_chunk_predictor is not None:
alphas = alphas * mask_chunk_predictor
alphas = alphas.squeeze(-1)
+ mask = mask.squeeze(-1)
if target_label_length is not None:
target_length = target_label_length
elif target_label is not None:
@@ -42,8 +50,40 @@
token_num = alphas.sum(-1)
if target_length is not None:
alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1))
+ elif self.tail_threshold > 0.0:
+ hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, token_num, mask=mask)
+
acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)
+
+ if target_length is None and self.tail_threshold > 0.0:
+ token_num_int = torch.max(token_num).type(torch.int32).item()
+ acoustic_embeds = acoustic_embeds[:, :token_num_int, :]
+
return acoustic_embeds, token_num, alphas, cif_peak
+
+ def tail_process_fn(self, hidden, alphas, token_num=None, mask=None):
+ b, t, d = hidden.size()
+ tail_threshold = self.tail_threshold
+ if mask is not None:
+ 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)
+ else:
+ tail_threshold = torch.tensor([tail_threshold], dtype=alphas.dtype).to(alphas.device)
+ tail_threshold = torch.reshape(tail_threshold, (1, 1))
+ alphas = torch.cat([alphas, tail_threshold], dim=1)
+ 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
+
def gen_frame_alignments(self,
alphas: torch.Tensor = None,
@@ -95,8 +135,19 @@
class CifPredictorV2(nn.Module):
- def __init__(self, idim, l_order, r_order, threshold=1.0, dropout=0.1, smooth_factor=1.0, noise_threshold=0,
- tail_threshold=0.0):
+ def __init__(self,
+ idim,
+ l_order,
+ r_order,
+ threshold=1.0,
+ dropout=0.1,
+ smooth_factor=1.0,
+ noise_threshold=0,
+ tail_threshold=0.0,
+ tf2torch_tensor_name_prefix_torch="predictor",
+ tf2torch_tensor_name_prefix_tf="seq2seq/cif",
+ tail_mask=True,
+ ):
super(CifPredictorV2, self).__init__()
self.pad = nn.ConstantPad1d((l_order, r_order), 0)
@@ -107,6 +158,9 @@
self.smooth_factor = smooth_factor
self.noise_threshold = noise_threshold
self.tail_threshold = tail_threshold
+ self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
+ self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf
+ self.tail_mask = tail_mask
def forward(self, hidden, target_label=None, mask=None, ignore_id=-1, mask_chunk_predictor=None,
target_label_length=None):
@@ -120,10 +174,12 @@
alphas = torch.sigmoid(output)
alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold)
if mask is not None:
- alphas = alphas * mask.transpose(-1, -2).float()
+ mask = mask.transpose(-1, -2).float()
+ alphas = alphas * mask
if mask_chunk_predictor is not None:
alphas = alphas * mask_chunk_predictor
alphas = alphas.squeeze(-1)
+ mask = mask.squeeze(-1)
if target_label_length is not None:
target_length = target_label_length
elif target_label is not None:
@@ -134,7 +190,10 @@
if target_length is not None:
alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1))
elif self.tail_threshold > 0.0:
- hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, token_num)
+ if self.tail_mask:
+ hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, token_num, mask=mask)
+ else:
+ hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, token_num, mask=None)
acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)
if target_length is None and self.tail_threshold > 0.0:
@@ -143,12 +202,114 @@
return acoustic_embeds, token_num, alphas, cif_peak
- def tail_process_fn(self, hidden, alphas, token_num=None):
+ def forward_chunk(self, hidden, cache=None):
+ batch_size, len_time, hidden_size = hidden.shape
+ 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)
+
+ alphas = alphas.squeeze(-1)
+
+ token_length = []
+ list_fires = []
+ list_frames = []
+ cache_alphas = []
+ cache_hiddens = []
+
+ if cache is not None and "chunk_size" in cache:
+ alphas[:, :cache["chunk_size"][0]] = 0.0
+ if "is_final" in cache and not cache["is_final"]:
+ alphas[:, sum(cache["chunk_size"][:2]):] = 0.0
+ if cache is not None and "cif_alphas" in cache and "cif_hidden" in cache:
+ cache["cif_hidden"] = to_device(cache["cif_hidden"], device=hidden.device)
+ cache["cif_alphas"] = to_device(cache["cif_alphas"], device=alphas.device)
+ hidden = torch.cat((cache["cif_hidden"], hidden), dim=1)
+ alphas = torch.cat((cache["cif_alphas"], alphas), dim=1)
+ if cache is not None and "is_final" in cache and cache["is_final"]:
+ tail_hidden = torch.zeros((batch_size, 1, hidden_size), device=hidden.device)
+ tail_alphas = torch.tensor([[self.tail_threshold]], device=alphas.device)
+ tail_alphas = torch.tile(tail_alphas, (batch_size, 1))
+ hidden = torch.cat((hidden, tail_hidden), dim=1)
+ alphas = torch.cat((alphas, tail_alphas), dim=1)
+
+ len_time = alphas.shape[1]
+ for b in range(batch_size):
+ integrate = 0.0
+ frames = torch.zeros((hidden_size), device=hidden.device)
+ list_frame = []
+ list_fire = []
+ for t in range(len_time):
+ alpha = alphas[b][t]
+ if alpha + integrate < self.threshold:
+ integrate += alpha
+ list_fire.append(integrate)
+ frames += alpha * hidden[b][t]
+ else:
+ frames += (self.threshold - integrate) * hidden[b][t]
+ list_frame.append(frames)
+ integrate += alpha
+ list_fire.append(integrate)
+ integrate -= self.threshold
+ frames = integrate * hidden[b][t]
+
+ cache_alphas.append(integrate)
+ if integrate > 0.0:
+ cache_hiddens.append(frames / integrate)
+ else:
+ cache_hiddens.append(frames)
+
+ token_length.append(torch.tensor(len(list_frame), device=alphas.device))
+ list_fires.append(list_fire)
+ list_frames.append(list_frame)
+
+ cache["cif_alphas"] = torch.stack(cache_alphas, axis=0)
+ cache["cif_alphas"] = torch.unsqueeze(cache["cif_alphas"], axis=0)
+ cache["cif_hidden"] = torch.stack(cache_hiddens, axis=0)
+ cache["cif_hidden"] = torch.unsqueeze(cache["cif_hidden"], axis=0)
+
+ max_token_len = max(token_length)
+ if max_token_len == 0:
+ return hidden, torch.stack(token_length, 0)
+ list_ls = []
+ for b in range(batch_size):
+ pad_frames = torch.zeros((max_token_len - token_length[b], hidden_size), device=alphas.device)
+ if token_length[b] == 0:
+ list_ls.append(pad_frames)
+ else:
+ list_frames[b] = torch.stack(list_frames[b])
+ list_ls.append(torch.cat((list_frames[b], pad_frames), dim=0))
+
+ cache["cif_alphas"] = torch.stack(cache_alphas, axis=0)
+ cache["cif_alphas"] = torch.unsqueeze(cache["cif_alphas"], axis=0)
+ cache["cif_hidden"] = torch.stack(cache_hiddens, axis=0)
+ cache["cif_hidden"] = torch.unsqueeze(cache["cif_hidden"], axis=0)
+ return torch.stack(list_ls, 0), torch.stack(token_length, 0)
+
+
+ def tail_process_fn(self, hidden, alphas, token_num=None, mask=None):
b, t, d = hidden.size()
tail_threshold = self.tail_threshold
- tail_threshold = torch.tensor([tail_threshold], dtype=alphas.dtype).to(alphas.device)
- tail_threshold = torch.reshape(tail_threshold, (1, 1))
- alphas = torch.cat([alphas, tail_threshold], dim=1)
+ if mask is not None:
+ 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)
+ else:
+ tail_threshold = torch.tensor([tail_threshold], dtype=alphas.dtype).to(alphas.device)
+ tail_threshold = torch.reshape(tail_threshold, (1, 1))
+ if b > 1:
+ alphas = torch.cat([alphas, tail_threshold.repeat(b, 1)], dim=1)
+ else:
+ alphas = torch.cat([alphas, tail_threshold], dim=1)
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)
@@ -203,6 +364,62 @@
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()
+
+ def gen_tf2torch_map_dict(self):
+
+ tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch
+ tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf
+ map_dict_local = {
+ ## predictor
+ "{}.cif_conv1d.weight".format(tensor_name_prefix_torch):
+ {"name": "{}/conv1d/kernel".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": (2, 1, 0),
+ }, # (256,256,3),(3,256,256)
+ "{}.cif_conv1d.bias".format(tensor_name_prefix_torch):
+ {"name": "{}/conv1d/bias".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ }, # (256,),(256,)
+ "{}.cif_output.weight".format(tensor_name_prefix_torch):
+ {"name": "{}/conv1d_1/kernel".format(tensor_name_prefix_tf),
+ "squeeze": 0,
+ "transpose": (1, 0),
+ }, # (1,256),(1,256,1)
+ "{}.cif_output.bias".format(tensor_name_prefix_torch):
+ {"name": "{}/conv1d_1/bias".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ }, # (1,),(1,)
+ }
+ return map_dict_local
+
+ def convert_tf2torch(self,
+ var_dict_tf,
+ var_dict_torch,
+ ):
+ map_dict = self.gen_tf2torch_map_dict()
+ var_dict_torch_update = dict()
+ for name in sorted(var_dict_torch.keys(), reverse=False):
+ names = name.split('.')
+ if names[0] == self.tf2torch_tensor_name_prefix_torch:
+ name_tf = map_dict[name]["name"]
+ data_tf = var_dict_tf[name_tf]
+ if map_dict[name]["squeeze"] is not None:
+ data_tf = np.squeeze(data_tf, axis=map_dict[name]["squeeze"])
+ if map_dict[name]["transpose"] is not None:
+ data_tf = np.transpose(data_tf, map_dict[name]["transpose"])
+ data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
+ assert var_dict_torch[name].size() == data_tf.size(), "{}, {}, {} != {}".format(name, name_tf,
+ var_dict_torch[
+ name].size(),
+ data_tf.size())
+ var_dict_torch_update[name] = data_tf
+ logging.info(
+ "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_tf,
+ var_dict_tf[name_tf].shape))
+
+ return var_dict_torch_update
class mae_loss(nn.Module):
@@ -264,3 +481,396 @@
pad_l = torch.zeros([max_label_len - l.size(0), hidden_size], device=hidden.device)
list_ls.append(torch.cat([l, pad_l], 0))
return torch.stack(list_ls, 0), fires
+
+
+def cif_wo_hidden(alphas, threshold):
+ batch_size, len_time = alphas.size()
+
+ # loop varss
+ integrate = torch.zeros([batch_size], 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
+
+
+class CifPredictorV3(nn.Module):
+ def __init__(self,
+ idim,
+ l_order,
+ r_order,
+ threshold=1.0,
+ dropout=0.1,
+ smooth_factor=1.0,
+ noise_threshold=0,
+ tail_threshold=0.0,
+ tf2torch_tensor_name_prefix_torch="predictor",
+ tf2torch_tensor_name_prefix_tf="seq2seq/cif",
+ smooth_factor2=1.0,
+ noise_threshold2=0,
+ upsample_times=5,
+ upsample_type="cnn",
+ use_cif1_cnn=True,
+ tail_mask=True,
+ ):
+ super(CifPredictorV3, self).__init__()
+
+ self.pad = nn.ConstantPad1d((l_order, r_order), 0)
+ self.cif_conv1d = nn.Conv1d(idim, idim, l_order + r_order + 1)
+ self.cif_output = nn.Linear(idim, 1)
+ self.dropout = torch.nn.Dropout(p=dropout)
+ self.threshold = threshold
+ self.smooth_factor = smooth_factor
+ self.noise_threshold = noise_threshold
+ self.tail_threshold = tail_threshold
+ self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
+ self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf
+
+ self.upsample_times = upsample_times
+ self.upsample_type = upsample_type
+ self.use_cif1_cnn = use_cif1_cnn
+ if self.upsample_type == 'cnn':
+ self.upsample_cnn = nn.ConvTranspose1d(idim, idim, self.upsample_times, self.upsample_times)
+ self.cif_output2 = nn.Linear(idim, 1)
+ elif self.upsample_type == 'cnn_blstm':
+ self.upsample_cnn = nn.ConvTranspose1d(idim, idim, self.upsample_times, self.upsample_times)
+ self.blstm = nn.LSTM(idim, idim, 1, bias=True, batch_first=True, dropout=0.0, bidirectional=True)
+ self.cif_output2 = nn.Linear(idim*2, 1)
+ elif self.upsample_type == 'cnn_attn':
+ self.upsample_cnn = nn.ConvTranspose1d(idim, idim, self.upsample_times, self.upsample_times)
+ from funasr.models.encoder.transformer_encoder import EncoderLayer as TransformerEncoderLayer
+ from funasr.modules.attention import MultiHeadedAttention
+ from funasr.modules.positionwise_feed_forward import PositionwiseFeedForward
+ positionwise_layer_args = (
+ idim,
+ idim*2,
+ 0.1,
+ )
+ self.self_attn = TransformerEncoderLayer(
+ idim,
+ MultiHeadedAttention(
+ 4, idim, 0.1
+ ),
+ PositionwiseFeedForward(*positionwise_layer_args),
+ 0.1,
+ True, #normalize_before,
+ False, #concat_after,
+ )
+ self.cif_output2 = nn.Linear(idim, 1)
+ self.smooth_factor2 = smooth_factor2
+ self.noise_threshold2 = noise_threshold2
+
+ def forward(self, hidden, target_label=None, mask=None, ignore_id=-1, mask_chunk_predictor=None,
+ target_label_length=None):
+ h = hidden
+ context = h.transpose(1, 2)
+ queries = self.pad(context)
+ output = torch.relu(self.cif_conv1d(queries))
+
+ # alphas2 is an extra head for timestamp prediction
+ if not self.use_cif1_cnn:
+ _output = context
+ else:
+ _output = output
+ if self.upsample_type == 'cnn':
+ output2 = self.upsample_cnn(_output)
+ output2 = output2.transpose(1,2)
+ elif self.upsample_type == 'cnn_blstm':
+ output2 = self.upsample_cnn(_output)
+ output2 = output2.transpose(1,2)
+ output2, (_, _) = self.blstm(output2)
+ elif self.upsample_type == 'cnn_attn':
+ output2 = self.upsample_cnn(_output)
+ output2 = output2.transpose(1,2)
+ output2, _ = self.self_attn(output2, mask)
+ # import pdb; pdb.set_trace()
+ alphas2 = torch.sigmoid(self.cif_output2(output2))
+ alphas2 = torch.nn.functional.relu(alphas2 * self.smooth_factor2 - self.noise_threshold2)
+ # repeat the mask in T demension to match the upsampled length
+ if mask is not None:
+ mask2 = mask.repeat(1, self.upsample_times, 1).transpose(-1, -2).reshape(alphas2.shape[0], -1)
+ mask2 = mask2.unsqueeze(-1)
+ alphas2 = alphas2 * mask2
+ alphas2 = alphas2.squeeze(-1)
+ token_num2 = alphas2.sum(-1)
+
+ 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)
+ if mask is not None:
+ mask = mask.transpose(-1, -2).float()
+ alphas = alphas * mask
+ if mask_chunk_predictor is not None:
+ alphas = alphas * mask_chunk_predictor
+ alphas = alphas.squeeze(-1)
+ mask = mask.squeeze(-1)
+ if target_label_length is not None:
+ target_length = target_label_length
+ elif target_label is not None:
+ target_length = (target_label != ignore_id).float().sum(-1)
+ else:
+ target_length = None
+ token_num = alphas.sum(-1)
+
+ if target_length is not None:
+ alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1))
+ elif self.tail_threshold > 0.0:
+ hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, token_num, mask=mask)
+
+ acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)
+ if target_length is None and self.tail_threshold > 0.0:
+ token_num_int = torch.max(token_num).type(torch.int32).item()
+ acoustic_embeds = acoustic_embeds[:, :token_num_int, :]
+ return acoustic_embeds, token_num, alphas, cif_peak, token_num2
+
+ def get_upsample_timestamp(self, hidden, mask=None, token_num=None):
+ h = hidden
+ b = hidden.shape[0]
+ context = h.transpose(1, 2)
+ queries = self.pad(context)
+ output = torch.relu(self.cif_conv1d(queries))
+
+ # alphas2 is an extra head for timestamp prediction
+ if not self.use_cif1_cnn:
+ _output = context
+ else:
+ _output = output
+ if self.upsample_type == 'cnn':
+ output2 = self.upsample_cnn(_output)
+ output2 = output2.transpose(1,2)
+ elif self.upsample_type == 'cnn_blstm':
+ output2 = self.upsample_cnn(_output)
+ output2 = output2.transpose(1,2)
+ output2, (_, _) = self.blstm(output2)
+ elif self.upsample_type == 'cnn_attn':
+ output2 = self.upsample_cnn(_output)
+ output2 = output2.transpose(1,2)
+ output2, _ = self.self_attn(output2, mask)
+ alphas2 = torch.sigmoid(self.cif_output2(output2))
+ alphas2 = torch.nn.functional.relu(alphas2 * self.smooth_factor2 - self.noise_threshold2)
+ # repeat the mask in T demension to match the upsampled length
+ if mask is not None:
+ mask2 = mask.repeat(1, self.upsample_times, 1).transpose(-1, -2).reshape(alphas2.shape[0], -1)
+ mask2 = mask2.unsqueeze(-1)
+ alphas2 = alphas2 * mask2
+ alphas2 = alphas2.squeeze(-1)
+ _token_num = alphas2.sum(-1)
+ if token_num is not None:
+ alphas2 *= (token_num / _token_num)[:, None].repeat(1, alphas2.size(1))
+ # re-downsample
+ ds_alphas = alphas2.reshape(b, -1, self.upsample_times).sum(-1)
+ ds_cif_peak = cif_wo_hidden(ds_alphas, self.threshold - 1e-4)
+ # upsampled alphas and cif_peak
+ us_alphas = alphas2
+ us_cif_peak = cif_wo_hidden(us_alphas, self.threshold - 1e-4)
+ return ds_alphas, ds_cif_peak, 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
+ if mask is not None:
+ 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)
+ else:
+ tail_threshold = torch.tensor([tail_threshold], dtype=alphas.dtype).to(alphas.device)
+ tail_threshold = torch.reshape(tail_threshold, (1, 1))
+ alphas = torch.cat([alphas, tail_threshold], dim=1)
+ 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
+
+ def gen_frame_alignments(self,
+ alphas: torch.Tensor = None,
+ encoder_sequence_length: torch.Tensor = None):
+ batch_size, maximum_length = alphas.size()
+ int_type = torch.int32
+
+ is_training = self.training
+ if is_training:
+ token_num = torch.round(torch.sum(alphas, dim=1)).type(int_type)
+ else:
+ token_num = torch.floor(torch.sum(alphas, dim=1)).type(int_type)
+
+ max_token_num = torch.max(token_num).item()
+
+ alphas_cumsum = torch.cumsum(alphas, dim=1)
+ alphas_cumsum = torch.floor(alphas_cumsum).type(int_type)
+ 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()
+
+class BATPredictor(nn.Module):
+ def __init__(self, idim, l_order, r_order, threshold=1.0, dropout=0.1, smooth_factor=1.0, noise_threshold=0, return_accum=False):
+ super(BATPredictor, self).__init__()
+
+ self.pad = nn.ConstantPad1d((l_order, r_order), 0)
+ self.cif_conv1d = nn.Conv1d(idim, idim, l_order + r_order + 1, groups=idim)
+ self.cif_output = nn.Linear(idim, 1)
+ self.dropout = torch.nn.Dropout(p=dropout)
+ self.threshold = threshold
+ self.smooth_factor = smooth_factor
+ self.noise_threshold = noise_threshold
+ self.return_accum = return_accum
+
+ def cif(
+ self,
+ input: Tensor,
+ alpha: Tensor,
+ beta: float = 1.0,
+ return_accum: bool = False,
+ ) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
+ B, S, C = input.size()
+ assert tuple(alpha.size()) == (B, S), f"{alpha.size()} != {(B, S)}"
+
+ dtype = alpha.dtype
+ alpha = alpha.float()
+
+ alpha_sum = alpha.sum(1)
+ feat_lengths = (alpha_sum / beta).floor().long()
+ T = feat_lengths.max()
+
+ # aggregate and integrate
+ csum = alpha.cumsum(-1)
+ with torch.no_grad():
+ # indices used for scattering
+ right_idx = (csum / beta).floor().long().clip(max=T)
+ left_idx = right_idx.roll(1, dims=1)
+ left_idx[:, 0] = 0
+
+ # count # of fires from each source
+ fire_num = right_idx - left_idx
+ extra_weights = (fire_num - 1).clip(min=0)
+ # The extra entry in last dim is for
+ output = input.new_zeros((B, T + 1, C))
+ source_range = torch.arange(1, 1 + S).unsqueeze(0).type_as(input)
+ zero = alpha.new_zeros((1,))
+
+ # right scatter
+ fire_mask = fire_num > 0
+ right_weight = torch.where(
+ fire_mask,
+ csum - right_idx.type_as(alpha) * beta,
+ zero
+ ).type_as(input)
+ # assert right_weight.ge(0).all(), f"{right_weight} should be non-negative."
+ output.scatter_add_(
+ 1,
+ right_idx.unsqueeze(-1).expand(-1, -1, C),
+ right_weight.unsqueeze(-1) * input
+ )
+
+ # left scatter
+ left_weight = (
+ alpha - right_weight - extra_weights.type_as(alpha) * beta
+ ).type_as(input)
+ output.scatter_add_(
+ 1,
+ left_idx.unsqueeze(-1).expand(-1, -1, C),
+ left_weight.unsqueeze(-1) * input
+ )
+
+ # extra scatters
+ if extra_weights.ge(0).any():
+ extra_steps = extra_weights.max().item()
+ tgt_idx = left_idx
+ src_feats = input * beta
+ for _ in range(extra_steps):
+ tgt_idx = (tgt_idx + 1).clip(max=T)
+ # (B, S, 1)
+ src_mask = (extra_weights > 0)
+ output.scatter_add_(
+ 1,
+ tgt_idx.unsqueeze(-1).expand(-1, -1, C),
+ src_feats * src_mask.unsqueeze(2)
+ )
+ extra_weights -= 1
+
+ output = output[:, :T, :]
+
+ if return_accum:
+ return output, csum
+ else:
+ return output, alpha
+
+ def forward(self, hidden, target_label=None, mask=None, ignore_id=-1, mask_chunk_predictor=None, target_label_length=None):
+ h = hidden
+ context = h.transpose(1, 2)
+ queries = self.pad(context)
+ memory = self.cif_conv1d(queries)
+ output = memory + context
+ output = self.dropout(output)
+ output = output.transpose(1, 2)
+ output = torch.relu(output)
+ output = self.cif_output(output)
+ alphas = torch.sigmoid(output)
+ alphas = torch.nn.functional.relu(alphas*self.smooth_factor - self.noise_threshold)
+ if mask is not None:
+ alphas = alphas * mask.transpose(-1, -2).float()
+ if mask_chunk_predictor is not None:
+ alphas = alphas * mask_chunk_predictor
+ alphas = alphas.squeeze(-1)
+ if target_label_length is not None:
+ target_length = target_label_length
+ elif target_label is not None:
+ target_length = (target_label != ignore_id).float().sum(-1)
+ # logging.info("target_length: {}".format(target_length))
+ else:
+ target_length = None
+ token_num = alphas.sum(-1)
+ if target_length is not None:
+ # length_noise = torch.rand(alphas.size(0), device=alphas.device) - 0.5
+ # target_length = length_noise + target_length
+ alphas *= ((target_length + 1e-4) / token_num)[:, None].repeat(1, alphas.size(1))
+ acoustic_embeds, cif_peak = self.cif(hidden, alphas, self.threshold, self.return_accum)
+ return acoustic_embeds, token_num, alphas, cif_peak
--
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