From 94de39dde2e616a01683c518023d0fab72b4e103 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 19 二月 2024 22:21:50 +0800
Subject: [PATCH] aishell example
---
funasr/models/ct_transformer_streaming/encoder.py | 62 +++++++++++++------------------
1 files changed, 26 insertions(+), 36 deletions(-)
diff --git a/funasr/models/ct_transformer_streaming/encoder.py b/funasr/models/ct_transformer_streaming/encoder.py
index 784baf3..95e2a4b 100644
--- a/funasr/models/ct_transformer_streaming/encoder.py
+++ b/funasr/models/ct_transformer_streaming/encoder.py
@@ -1,39 +1,29 @@
-from typing import List
-from typing import Optional
-from typing import Sequence
-from typing import Tuple
-from typing import Union
-import logging
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from funasr.models.scama.chunk_utilis import overlap_chunk
-import numpy as np
-from funasr.train_utils.device_funcs import to_device
-from funasr.models.transformer.utils.nets_utils import make_pad_mask
-from funasr.models.sanm.attention import MultiHeadedAttention
-from funasr.models.ct_transformer.attention import MultiHeadedAttentionSANMwithMask
-from funasr.models.transformer.embedding import SinusoidalPositionEncoder, StreamSinusoidalPositionEncoder
-from funasr.models.transformer.layer_norm import LayerNorm
-from funasr.models.transformer.utils.multi_layer_conv import Conv1dLinear
-from funasr.models.transformer.utils.multi_layer_conv import MultiLayeredConv1d
-from funasr.models.transformer.positionwise_feed_forward import (
- PositionwiseFeedForward, # noqa: H301
-)
-from funasr.models.transformer.utils.repeat import repeat
-from funasr.models.transformer.utils.subsampling import Conv2dSubsampling
-from funasr.models.transformer.utils.subsampling import Conv2dSubsampling2
-from funasr.models.transformer.utils.subsampling import Conv2dSubsampling6
-from funasr.models.transformer.utils.subsampling import Conv2dSubsampling8
-from funasr.models.transformer.utils.subsampling import TooShortUttError
-from funasr.models.transformer.utils.subsampling import check_short_utt
-from funasr.models.transformer.utils.mask import subsequent_mask, vad_mask
+#!/usr/bin/env python3
+# -*- encoding: utf-8 -*-
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+# MIT License (https://opensource.org/licenses/MIT)
-from funasr.models.ctc.ctc import CTC
+import torch
+from typing import List, Optional, Tuple
from funasr.register import tables
+from funasr.models.ctc.ctc import CTC
+from funasr.models.transformer.utils.repeat import repeat
+from funasr.models.transformer.layer_norm import LayerNorm
+from funasr.models.sanm.attention import MultiHeadedAttention
+from funasr.models.transformer.utils.nets_utils import make_pad_mask
+from funasr.models.transformer.utils.subsampling import check_short_utt
+from funasr.models.transformer.utils.subsampling import TooShortUttError
+from funasr.models.transformer.embedding import SinusoidalPositionEncoder
+from funasr.models.transformer.utils.multi_layer_conv import Conv1dLinear
+from funasr.models.transformer.utils.mask import subsequent_mask, vad_mask
+from funasr.models.transformer.utils.multi_layer_conv import MultiLayeredConv1d
+from funasr.models.transformer.positionwise_feed_forward import PositionwiseFeedForward
+from funasr.models.ct_transformer_streaming.attention import MultiHeadedAttentionSANMwithMask
+from funasr.models.transformer.utils.subsampling import Conv2dSubsampling, Conv2dSubsampling2, Conv2dSubsampling6, Conv2dSubsampling8
-class EncoderLayerSANM(nn.Module):
+
+class EncoderLayerSANM(torch.nn.Module):
def __init__(
self,
in_size,
@@ -51,13 +41,13 @@
self.feed_forward = feed_forward
self.norm1 = LayerNorm(in_size)
self.norm2 = LayerNorm(size)
- self.dropout = nn.Dropout(dropout_rate)
+ self.dropout = torch.nn.Dropout(dropout_rate)
self.in_size = in_size
self.size = size
self.normalize_before = normalize_before
self.concat_after = concat_after
if self.concat_after:
- self.concat_linear = nn.Linear(size + size, size)
+ self.concat_linear = torch.nn.Linear(size + size, size)
self.stochastic_depth_rate = stochastic_depth_rate
self.dropout_rate = dropout_rate
@@ -156,7 +146,7 @@
@tables.register("encoder_classes", "SANMVadEncoder")
-class SANMVadEncoder(nn.Module):
+class SANMVadEncoder(torch.nn.Module):
"""
Author: Speech Lab of DAMO Academy, Alibaba Group
@@ -306,7 +296,7 @@
assert 0 < min(interctc_layer_idx) and max(interctc_layer_idx) < num_blocks
self.interctc_use_conditioning = interctc_use_conditioning
self.conditioning_layer = None
- self.dropout = nn.Dropout(dropout_rate)
+ self.dropout = torch.nn.Dropout(dropout_rate)
def output_size(self) -> int:
return self._output_size
--
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