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
---
runtime/python/onnxruntime/funasr_onnx/utils/frontend.py | 75 ++++++++++++++++++++++---------------
1 files changed, 45 insertions(+), 30 deletions(-)
diff --git a/runtime/python/onnxruntime/funasr_onnx/utils/frontend.py b/runtime/python/onnxruntime/funasr_onnx/utils/frontend.py
index 7b38f8b..54f9deb 100644
--- a/runtime/python/onnxruntime/funasr_onnx/utils/frontend.py
+++ b/runtime/python/onnxruntime/funasr_onnx/utils/frontend.py
@@ -2,6 +2,7 @@
from pathlib import Path
from typing import Any, Dict, Iterable, List, NamedTuple, Set, Tuple, Union
import copy
+from functools import lru_cache
import numpy as np
import kaldi_native_fbank as knf
@@ -45,19 +46,19 @@
self.cmvn_file = cmvn_file
if self.cmvn_file:
- self.cmvn = self.load_cmvn()
+ self.cmvn = load_cmvn(self.cmvn_file)
self.fbank_fn = None
self.fbank_beg_idx = 0
self.reset_status()
def fbank(self, waveform: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
waveform = waveform * (1 << 15)
- self.fbank_fn = knf.OnlineFbank(self.opts)
- self.fbank_fn.accept_waveform(self.opts.frame_opts.samp_freq, waveform.tolist())
- frames = self.fbank_fn.num_frames_ready
+ fbank_fn = knf.OnlineFbank(self.opts)
+ fbank_fn.accept_waveform(self.opts.frame_opts.samp_freq, waveform.tolist())
+ frames = fbank_fn.num_frames_ready
mat = np.empty([frames, self.opts.mel_opts.num_bins])
for i in range(frames):
- mat[i, :] = self.fbank_fn.get_frame(i)
+ mat[i, :] = fbank_fn.get_frame(i)
feat = mat.astype(np.float32)
feat_len = np.array(mat.shape[0]).astype(np.int32)
return feat, feat_len
@@ -122,33 +123,47 @@
inputs = (inputs + means) * vars
return inputs
- def load_cmvn(
- self,
- ) -> np.ndarray:
- with open(self.cmvn_file, "r", encoding="utf-8") as f:
- lines = f.readlines()
+@lru_cache()
+def load_cmvn(cmvn_file: Union[str, Path]) -> np.ndarray:
+ """load cmvn file to numpy array.
- means_list = []
- vars_list = []
- for i in range(len(lines)):
- line_item = lines[i].split()
- if line_item[0] == "<AddShift>":
- line_item = lines[i + 1].split()
- if line_item[0] == "<LearnRateCoef>":
- add_shift_line = line_item[3 : (len(line_item) - 1)]
- means_list = list(add_shift_line)
- continue
- elif line_item[0] == "<Rescale>":
- line_item = lines[i + 1].split()
- if line_item[0] == "<LearnRateCoef>":
- rescale_line = line_item[3 : (len(line_item) - 1)]
- vars_list = list(rescale_line)
- continue
+ Args:
+ cmvn_file (Union[str, Path]): cmvn file path.
- means = np.array(means_list).astype(np.float64)
- vars = np.array(vars_list).astype(np.float64)
- cmvn = np.array([means, vars])
- return cmvn
+ Raises:
+ FileNotFoundError: cmvn file not exits.
+
+ Returns:
+ np.ndarray: cmvn array. shape is (2, dim).The first row is means, the second row is vars.
+ """
+
+ cmvn_file = Path(cmvn_file)
+ if not cmvn_file.exists():
+ raise FileNotFoundError("cmvn file not exits")
+
+ with open(cmvn_file, "r", encoding="utf-8") as f:
+ lines = f.readlines()
+ means_list = []
+ vars_list = []
+ for i in range(len(lines)):
+ line_item = lines[i].split()
+ if line_item[0] == "<AddShift>":
+ line_item = lines[i + 1].split()
+ if line_item[0] == "<LearnRateCoef>":
+ add_shift_line = line_item[3 : (len(line_item) - 1)]
+ means_list = list(add_shift_line)
+ continue
+ elif line_item[0] == "<Rescale>":
+ line_item = lines[i + 1].split()
+ if line_item[0] == "<LearnRateCoef>":
+ rescale_line = line_item[3 : (len(line_item) - 1)]
+ vars_list = list(rescale_line)
+ continue
+
+ means = np.array(means_list).astype(np.float64)
+ vars = np.array(vars_list).astype(np.float64)
+ cmvn = np.array([means, vars])
+ return cmvn
class WavFrontendOnline(WavFrontend):
--
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