From 28ccfbfc51068a663a80764e14074df5edf2b5ba Mon Sep 17 00:00:00 2001
From: kongdeqiang <kongdeqiang960204@163.com>
Date: 星期五, 13 三月 2026 17:41:41 +0800
Subject: [PATCH] 提交
---
funasr/models/rwkv_bat/rwkv_encoder.py | 36 +++++++++++++++++++-----------------
1 files changed, 19 insertions(+), 17 deletions(-)
diff --git a/funasr/models/rwkv_bat/rwkv_encoder.py b/funasr/models/rwkv_bat/rwkv_encoder.py
index af702e9..a27088d 100644
--- a/funasr/models/rwkv_bat/rwkv_encoder.py
+++ b/funasr/models/rwkv_bat/rwkv_encoder.py
@@ -1,17 +1,20 @@
-"""RWKV encoder definition for Transducer models."""
-
-import math
-from typing import Dict, List, Optional, Tuple
+#!/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)
import torch
+from typing import Dict, List, Optional, Tuple
-from funasr.models.encoder.abs_encoder import AbsEncoder
+from funasr.register import tables
from funasr.models.rwkv_bat.rwkv import RWKV
from funasr.models.transformer.layer_norm import LayerNorm
-from funasr.models.rwkv_bat.rwkv_subsampling import RWKVConvInput
from funasr.models.transformer.utils.nets_utils import make_source_mask
+from funasr.models.rwkv_bat.rwkv_subsampling import RWKVConvInput
-class RWKVEncoder(AbsEncoder):
+
+@tables.register("encoder_classes", "RWKVEncoder")
+class RWKVEncoder(torch.nn.Module):
"""RWKV encoder module.
Based on https://arxiv.org/pdf/2305.13048.pdf.
@@ -41,16 +44,17 @@
att_dropout_rate: float = 0.0,
ffn_dropout_rate: float = 0.0,
dropout_rate: float = 0.0,
- subsampling_factor: int =4,
+ subsampling_factor: int = 4,
time_reduction_factor: int = 1,
kernel: int = 3,
+ **kwargs,
) -> None:
"""Construct a RWKVEncoder object."""
super().__init__()
self.embed = RWKVConvInput(
input_size,
- [output_size//4, output_size//2, output_size],
+ [output_size // 4, output_size // 2, output_size],
subsampling_factor,
conv_kernel_size=kernel,
output_size=output_size,
@@ -60,7 +64,7 @@
linear_size = output_size * 4 if linear_size is None else linear_size
attention_size = output_size if attention_size is None else attention_size
-
+
self.rwkv_blocks = torch.nn.ModuleList(
[
RWKV(
@@ -118,12 +122,12 @@
x, _ = block(x)
else:
x = self.rwkv_infer(x)
-
+
x = self.final_norm(x)
if self.time_reduction_factor > 1:
- x = x[:,::self.time_reduction_factor,:]
- olens = torch.floor_divide(olens-1, self.time_reduction_factor) + 1
+ x = x[:, :: self.time_reduction_factor, :]
+ olens = torch.floor_divide(olens - 1, self.time_reduction_factor) + 1
return x, olens, None
@@ -131,9 +135,7 @@
batch_size = xs_pad.shape[0]
- hidden_sizes = [
- self._output_size for i in range(5)
- ]
+ hidden_sizes = [self._output_size for i in range(5)]
state = [
torch.zeros(
@@ -148,7 +150,7 @@
xs_out = []
for t in range(xs_pad.shape[1]):
- x_t = xs_pad[:,t,:]
+ x_t = xs_pad[:, t, :]
for idx, block in enumerate(self.rwkv_blocks):
x_t, state = block(x_t, state=state)
xs_out.append(x_t)
--
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