From c4882b43fce3c32cb0ce3c9fc2c164f0ce0e8213 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 30 三月 2023 16:04:13 +0800
Subject: [PATCH] export
---
funasr/export/models/encoder/sanm_encoder.py | 29 +++++++++++++++++++++++++----
1 files changed, 25 insertions(+), 4 deletions(-)
diff --git a/funasr/export/models/encoder/sanm_encoder.py b/funasr/export/models/encoder/sanm_encoder.py
index 3b7b414..118e240 100644
--- a/funasr/export/models/encoder/sanm_encoder.py
+++ b/funasr/export/models/encoder/sanm_encoder.py
@@ -9,6 +9,21 @@
from funasr.modules.positionwise_feed_forward import PositionwiseFeedForward
from funasr.export.models.modules.feedforward import PositionwiseFeedForward as PositionwiseFeedForward_export
+def subsequent_mask(size, device="cpu", dtype=torch.bool):
+ """Create mask for subsequent steps (size, size).
+
+ :param int size: size of mask
+ :param str device: "cpu" or "cuda" or torch.Tensor.device
+ :param torch.dtype dtype: result dtype
+ :rtype: torch.Tensor
+ >>> subsequent_mask(3)
+ [[1, 0, 0],
+ [1, 1, 0],
+ [1, 1, 1]]
+ """
+ ret = torch.ones(size, size, device=device, dtype=dtype)
+ return torch.tril(ret, out=ret)
+
class SANMEncoder(nn.Module):
def __init__(
self,
@@ -150,10 +165,11 @@
def prepare_mask(self, mask):
mask_3d_btd = mask[:, :, None]
+ sub_masks = subsequent_mask(mask.size(-1))
if len(mask.shape) == 2:
- mask_4d_bhlt = 1 - mask[:, None, None, :]
+ mask_4d_bhlt = 1 - sub_masks[:, None, None, :]
elif len(mask.shape) == 3:
- mask_4d_bhlt = 1 - mask[:, None, :]
+ mask_4d_bhlt = 1 - sub_masks[:, None, :]
mask_4d_bhlt = mask_4d_bhlt * -10000.0
return mask_3d_btd, mask_4d_bhlt
@@ -161,6 +177,7 @@
def forward(self,
speech: torch.Tensor,
speech_lengths: torch.Tensor,
+ vad_mask: torch.Tensor,
):
speech = speech * self._output_size ** 0.5
mask = self.make_pad_mask(speech_lengths)
@@ -173,8 +190,12 @@
encoder_outs = self.model.encoders0(xs_pad, mask)
xs_pad, masks = encoder_outs[0], encoder_outs[1]
- encoder_outs = self.model.encoders(xs_pad, mask)
- xs_pad, masks = encoder_outs[0], encoder_outs[1]
+ # encoder_outs = self.model.encoders(xs_pad, mask)
+ for layer_idx, encoder_layer in enumerate(self.model.encoders):
+ if layer_idx == len(self.model.encoders) - 1:
+ mask = (mask[0], vad_mask)
+ encoder_outs = encoder_layer(xs_pad, mask)
+ xs_pad, masks = encoder_outs[0], encoder_outs[1]
xs_pad = self.model.after_norm(xs_pad)
--
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