From 544b798b32819fe2ffed1fccb44e8c2620449a53 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 16 二月 2023 17:30:51 +0800
Subject: [PATCH] Merge branch 'dev_gzf' of github.com:alibaba-damo-academy/FunASR into dev_gzf add
---
funasr/runtime/python/onnxruntime/paraformer/rapid_paraformer/utils/frontend.py | 136 +++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 136 insertions(+), 0 deletions(-)
diff --git a/funasr/runtime/python/onnxruntime/paraformer/rapid_paraformer/utils/frontend.py b/funasr/runtime/python/onnxruntime/paraformer/rapid_paraformer/utils/frontend.py
new file mode 100644
index 0000000..eb8a7c8
--- /dev/null
+++ b/funasr/runtime/python/onnxruntime/paraformer/rapid_paraformer/utils/frontend.py
@@ -0,0 +1,136 @@
+# -*- encoding: utf-8 -*-
+from pathlib import Path
+from typing import Any, Dict, Iterable, List, NamedTuple, Set, Tuple, Union
+
+import numpy as np
+from typeguard import check_argument_types
+import kaldi_native_fbank as knf
+
+root_dir = Path(__file__).resolve().parent
+
+logger_initialized = {}
+
+
+class WavFrontend():
+ """Conventional frontend structure for ASR.
+ """
+
+ def __init__(
+ self,
+ cmvn_file: str = None,
+ fs: int = 16000,
+ window: str = 'hamming',
+ n_mels: int = 80,
+ frame_length: int = 25,
+ frame_shift: int = 10,
+ filter_length_min: int = -1,
+ filter_length_max: float = -1,
+ lfr_m: int = 1,
+ lfr_n: int = 1,
+ dither: float = 1.0
+ ) -> None:
+ check_argument_types()
+
+ opts = knf.FbankOptions()
+ opts.frame_opts.samp_freq = fs
+ opts.frame_opts.dither = dither
+ opts.frame_opts.window_type = window
+ opts.frame_opts.frame_shift_ms = float(frame_shift)
+ opts.frame_opts.frame_length_ms = float(frame_length)
+ opts.mel_opts.num_bins = n_mels
+ opts.energy_floor = 0
+ opts.frame_opts.snip_edges = True
+ opts.mel_opts.debug_mel = False
+ self.opts = opts
+
+ self.filter_length_min = filter_length_min
+ self.filter_length_max = filter_length_max
+ self.lfr_m = lfr_m
+ self.lfr_n = lfr_n
+ self.cmvn_file = cmvn_file
+
+ if self.cmvn_file:
+ self.cmvn = self.load_cmvn()
+
+ def fbank(self,
+ waveform: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+ waveform = waveform * (1 << 15)
+ 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, :] = fbank_fn.get_frame(i)
+ feat = mat.astype(np.float32)
+ feat_len = np.array(mat.shape[0]).astype(np.int32)
+ return feat, feat_len
+
+ def lfr_cmvn(self, feat: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+ if self.lfr_m != 1 or self.lfr_n != 1:
+ feat = self.apply_lfr(feat, self.lfr_m, self.lfr_n)
+
+ if self.cmvn_file:
+ feat = self.apply_cmvn(feat)
+
+ feat_len = np.array(feat.shape[0]).astype(np.int32)
+ return feat, feat_len
+
+ @staticmethod
+ def apply_lfr(inputs: np.ndarray, lfr_m: int, lfr_n: int) -> np.ndarray:
+ LFR_inputs = []
+
+ T = inputs.shape[0]
+ T_lfr = int(np.ceil(T / lfr_n))
+ left_padding = np.tile(inputs[0], ((lfr_m - 1) // 2, 1))
+ inputs = np.vstack((left_padding, inputs))
+ T = T + (lfr_m - 1) // 2
+ for i in range(T_lfr):
+ if lfr_m <= T - i * lfr_n:
+ LFR_inputs.append(
+ (inputs[i * lfr_n:i * lfr_n + lfr_m]).reshape(1, -1))
+ else:
+ # process last LFR frame
+ num_padding = lfr_m - (T - i * lfr_n)
+ frame = inputs[i * lfr_n:].reshape(-1)
+ for _ in range(num_padding):
+ frame = np.hstack((frame, inputs[-1]))
+
+ LFR_inputs.append(frame)
+ LFR_outputs = np.vstack(LFR_inputs).astype(np.float32)
+ return LFR_outputs
+
+ def apply_cmvn(self, inputs: np.ndarray) -> np.ndarray:
+ """
+ Apply CMVN with mvn data
+ """
+ frame, dim = inputs.shape
+ means = np.tile(self.cmvn[0:1, :dim], (frame, 1))
+ vars = np.tile(self.cmvn[1:2, :dim], (frame, 1))
+ 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()
+
+ 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
--
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