From 1254e8aee1e3200acc2b4752c6822bfc1a21b22f Mon Sep 17 00:00:00 2001
From: StevenH <hongfanmeng@gmail.com>
Date: 星期六, 26 十月 2024 12:19:07 +0800
Subject: [PATCH] optimize ComputeDecibel in fsmn-vad model by using numpy (#2174)
---
funasr/models/fsmn_vad_streaming/model.py | 24 ++++++++++++------------
1 files changed, 12 insertions(+), 12 deletions(-)
diff --git a/funasr/models/fsmn_vad_streaming/model.py b/funasr/models/fsmn_vad_streaming/model.py
index 04689be..03aaca7 100644
--- a/funasr/models/fsmn_vad_streaming/model.py
+++ b/funasr/models/fsmn_vad_streaming/model.py
@@ -8,6 +8,7 @@
import time
import math
import torch
+import numpy as np
from torch import nn
from enum import Enum
from dataclasses import dataclass
@@ -334,18 +335,17 @@
cache["stats"].data_buf_all = torch.cat(
(cache["stats"].data_buf_all, cache["stats"].waveform[0])
)
- for offset in range(
- 0, cache["stats"].waveform.shape[1] - frame_sample_length + 1, frame_shift_length
- ):
- cache["stats"].decibel.append(
- 10
- * math.log10(
- (cache["stats"].waveform[0][offset : offset + frame_sample_length])
- .square()
- .sum()
- + 0.000001
- )
- )
+
+ waveform_numpy = cache["stats"].waveform.numpy()
+
+ offsets = np.arange(0, waveform_numpy.shape[1] - frame_sample_length + 1, frame_shift_length)
+ frames = waveform_numpy[0, offsets[:, np.newaxis] + np.arange(frame_sample_length)]
+
+ decibel_numpy = 10 * np.log10(np.sum(np.square(frames), axis=1) + 0.000001)
+ decibel_numpy = decibel_numpy.tolist()
+
+ cache["stats"].decibel.extend(decibel_numpy)
+
def ComputeScores(self, feats: torch.Tensor, cache: dict = {}) -> None:
scores = self.encoder(feats, cache=cache["encoder"]).to("cpu") # return B * T * D
--
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