From 7584bbd6f3e321cc8bc970739a7cfce29ffcc18b Mon Sep 17 00:00:00 2001
From: haoneng.lhn <haoneng.lhn@alibaba-inc.com>
Date: 星期四, 27 四月 2023 00:21:20 +0800
Subject: [PATCH] update paraformer streaming code
---
funasr/models/encoder/sanm_encoder.py | 21 ++++++++++++++++++++-
1 files changed, 20 insertions(+), 1 deletions(-)
diff --git a/funasr/models/encoder/sanm_encoder.py b/funasr/models/encoder/sanm_encoder.py
index f2502bb..7d84ad5 100644
--- a/funasr/models/encoder/sanm_encoder.py
+++ b/funasr/models/encoder/sanm_encoder.py
@@ -6,9 +6,11 @@
import logging
import torch
import torch.nn as nn
+import torch.nn.functional as F
from funasr.modules.streaming_utils.chunk_utilis import overlap_chunk
from typeguard import check_argument_types
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.attention import MultiHeadedAttention, MultiHeadedAttentionSANM, MultiHeadedAttentionSANMwithMask
from funasr.modules.embedding import SinusoidalPositionEncoder, StreamSinusoidalPositionEncoder
@@ -349,6 +351,23 @@
return (xs_pad, intermediate_outs), olens, None
return xs_pad, olens, None
+ def _add_overlap_chunk(self, feats: np.ndarray, cache: dict = {}):
+ if len(cache) == 0:
+ return feats
+ # process last chunk
+ cache["feats"] = to_device(cache["feats"], device=feats.device)
+ overlap_feats = torch.cat((cache["feats"], feats), dim=1)
+ if cache["is_final"]:
+ cache["feats"] = overlap_feats[:, -cache["chunk_size"][0]:, :]
+ if not cache["last_chunk"]:
+ padding_length = sum(cache["chunk_size"]) - overlap_feats.shape[1]
+ overlap_feats = overlap_feats.transpose(1, 2)
+ overlap_feats = F.pad(overlap_feats, (0, padding_length))
+ overlap_feats = overlap_feats.transpose(1, 2)
+ else:
+ cache["feats"] = overlap_feats[:, -(cache["chunk_size"][0] + cache["chunk_size"][2]):, :]
+ return overlap_feats
+
def forward_chunk(self,
xs_pad: torch.Tensor,
ilens: torch.Tensor,
@@ -360,7 +379,7 @@
xs_pad = xs_pad
else:
xs_pad = self.embed(xs_pad, cache)
-
+ xs_pad = self._add_overlap_chunk(xs_pad, cache)
encoder_outs = self.encoders0(xs_pad, None, None, None, None)
xs_pad, masks = encoder_outs[0], encoder_outs[1]
intermediate_outs = []
--
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