From 6d3a3da8a8c7d1be9740a9b2d6fac767f8dfff17 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 30 五月 2024 19:16:52 +0800
Subject: [PATCH] docs
---
funasr/models/bicif_paraformer/cif_predictor.py | 270 +++++++++++++++++++++++++++++++----------------------
1 files changed, 156 insertions(+), 114 deletions(-)
diff --git a/funasr/models/bicif_paraformer/cif_predictor.py b/funasr/models/bicif_paraformer/cif_predictor.py
index 2cdbc16..3739c76 100644
--- a/funasr/models/bicif_paraformer/cif_predictor.py
+++ b/funasr/models/bicif_paraformer/cif_predictor.py
@@ -14,7 +14,7 @@
def __init__(self, normalize_length=False):
super(mae_loss, self).__init__()
self.normalize_length = normalize_length
- self.criterion = torch.nn.L1Loss(reduction='sum')
+ self.criterion = torch.nn.L1Loss(reduction="sum")
def forward(self, token_length, pre_token_length):
loss_token_normalizer = token_length.size(0)
@@ -43,19 +43,17 @@
list_fires.append(integrate)
fire_place = integrate >= threshold
- integrate = torch.where(fire_place,
- integrate - torch.ones([batch_size], device=hidden.device),
- integrate)
- cur = torch.where(fire_place,
- distribution_completion,
- alpha)
+ integrate = torch.where(
+ fire_place, integrate - torch.ones([batch_size], 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)
+ 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)
@@ -85,33 +83,37 @@
list_fires.append(integrate)
fire_place = integrate >= threshold
- integrate = torch.where(fire_place,
- integrate - torch.ones([batch_size], device=alphas.device)*threshold,
- integrate)
+ integrate = torch.where(
+ fire_place,
+ integrate - torch.ones([batch_size], device=alphas.device) * threshold,
+ integrate,
+ )
fires = torch.stack(list_fires, 1)
return fires
+
@tables.register("predictor_classes", "CifPredictorV3")
class CifPredictorV3(torch.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,
- ):
+ 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 = torch.nn.ConstantPad1d((l_order, r_order), 0)
@@ -128,39 +130,53 @@
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 = torch.nn.ConvTranspose1d(idim, idim, self.upsample_times, self.upsample_times)
+ if self.upsample_type == "cnn":
+ self.upsample_cnn = torch.nn.ConvTranspose1d(
+ idim, idim, self.upsample_times, self.upsample_times
+ )
self.cif_output2 = torch.nn.Linear(idim, 1)
- elif self.upsample_type == 'cnn_blstm':
- self.upsample_cnn = torch.nn.ConvTranspose1d(idim, idim, self.upsample_times, self.upsample_times)
- self.blstm = torch.nn.LSTM(idim, idim, 1, bias=True, batch_first=True, dropout=0.0, bidirectional=True)
- self.cif_output2 = torch.nn.Linear(idim*2, 1)
- elif self.upsample_type == 'cnn_attn':
- self.upsample_cnn = torch.nn.ConvTranspose1d(idim, idim, self.upsample_times, self.upsample_times)
+ elif self.upsample_type == "cnn_blstm":
+ self.upsample_cnn = torch.nn.ConvTranspose1d(
+ idim, idim, self.upsample_times, self.upsample_times
+ )
+ self.blstm = torch.nn.LSTM(
+ idim, idim, 1, bias=True, batch_first=True, dropout=0.0, bidirectional=True
+ )
+ self.cif_output2 = torch.nn.Linear(idim * 2, 1)
+ elif self.upsample_type == "cnn_attn":
+ self.upsample_cnn = torch.nn.ConvTranspose1d(
+ idim, idim, self.upsample_times, self.upsample_times
+ )
from funasr.models.transformer.encoder import EncoderLayer as TransformerEncoderLayer
from funasr.models.transformer.attention import MultiHeadedAttention
from funasr.models.transformer.positionwise_feed_forward import PositionwiseFeedForward
+
positionwise_layer_args = (
idim,
- idim*2,
+ idim * 2,
0.1,
)
self.self_attn = TransformerEncoderLayer(
idim,
- MultiHeadedAttention(
- 4, idim, 0.1
- ),
+ MultiHeadedAttention(4, idim, 0.1),
PositionwiseFeedForward(*positionwise_layer_args),
0.1,
- True, #normalize_before,
- False, #concat_after,
+ True, # normalize_before,
+ False, # concat_after,
)
self.cif_output2 = torch.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):
+ 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)
@@ -171,23 +187,27 @@
_output = context
else:
_output = output
- if self.upsample_type == 'cnn':
+ if self.upsample_type == "cnn":
output2 = self.upsample_cnn(_output)
- output2 = output2.transpose(1,2)
- elif self.upsample_type == 'cnn_blstm':
+ output2 = output2.transpose(1, 2)
+ elif self.upsample_type == "cnn_blstm":
output2 = self.upsample_cnn(_output)
- output2 = output2.transpose(1,2)
+ output2 = output2.transpose(1, 2)
output2, (_, _) = self.blstm(output2)
- elif self.upsample_type == 'cnn_attn':
+ elif self.upsample_type == "cnn_attn":
output2 = self.upsample_cnn(_output)
- output2 = output2.transpose(1,2)
+ 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 = (
+ 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)
@@ -236,22 +256,26 @@
_output = context
else:
_output = output
- if self.upsample_type == 'cnn':
+ if self.upsample_type == "cnn":
output2 = self.upsample_cnn(_output)
- output2 = output2.transpose(1,2)
- elif self.upsample_type == 'cnn_blstm':
+ output2 = output2.transpose(1, 2)
+ elif self.upsample_type == "cnn_blstm":
output2 = self.upsample_cnn(_output)
- output2 = output2.transpose(1,2)
+ output2 = output2.transpose(1, 2)
output2, (_, _) = self.blstm(output2)
- elif self.upsample_type == 'cnn_attn':
+ elif self.upsample_type == "cnn_attn":
output2 = self.upsample_cnn(_output)
- output2 = output2.transpose(1,2)
+ 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 = (
+ 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)
@@ -289,9 +313,9 @@
return hidden, alphas, token_num_floor
- def gen_frame_alignments(self,
- alphas: torch.Tensor = None,
- encoder_sequence_length: torch.Tensor = None):
+ 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
@@ -314,11 +338,15 @@
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())
+ 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)
+ 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)
@@ -329,19 +357,25 @@
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)
+ 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)
+ 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
@@ -349,23 +383,25 @@
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,
- ):
+
+ 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)
@@ -373,18 +409,18 @@
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)
@@ -392,8 +428,10 @@
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.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)
@@ -403,26 +441,26 @@
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
@@ -430,39 +468,41 @@
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
-
+ 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)
+ 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)
-
+ 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)
@@ -470,7 +510,7 @@
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, :]
@@ -480,22 +520,24 @@
@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)
-
+ integrate = torch.where(
+ fire_place,
+ integrate - torch.ones([batch_size], device=alphas.device) * threshold,
+ integrate,
+ )
+
fires = torch.stack(list_fires, 1)
- return fires
\ No newline at end of file
+ return fires
--
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