# -*- encoding: utf-8 -*-
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from pathlib import Path
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from typing import Any, Dict, Iterable, List, NamedTuple, Set, Tuple, Union
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import numpy as np
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from typeguard import check_argument_types
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import kaldi_native_fbank as knf
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root_dir = Path(__file__).resolve().parent
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logger_initialized = {}
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class WavFrontend():
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"""Conventional frontend structure for ASR.
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"""
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def __init__(
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self,
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cmvn_file: str = None,
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fs: int = 16000,
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window: str = 'hamming',
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n_mels: int = 80,
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frame_length: int = 25,
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frame_shift: int = 10,
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lfr_m: int = 1,
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lfr_n: int = 1,
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dither: float = 1.0,
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**kwargs,
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) -> None:
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check_argument_types()
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opts = knf.FbankOptions()
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opts.frame_opts.samp_freq = fs
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opts.frame_opts.dither = dither
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opts.frame_opts.window_type = window
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opts.frame_opts.frame_shift_ms = float(frame_shift)
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opts.frame_opts.frame_length_ms = float(frame_length)
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opts.mel_opts.num_bins = n_mels
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opts.energy_floor = 0
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opts.frame_opts.snip_edges = True
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opts.mel_opts.debug_mel = False
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self.opts = opts
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self.lfr_m = lfr_m
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self.lfr_n = lfr_n
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self.cmvn_file = cmvn_file
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if self.cmvn_file:
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self.cmvn = self.load_cmvn()
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self.fbank_fn = None
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self.fbank_beg_idx = 0
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self.reset_status()
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def fbank(self,
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waveform: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
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waveform = waveform * (1 << 15)
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self.fbank_fn = knf.OnlineFbank(self.opts)
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self.fbank_fn.accept_waveform(self.opts.frame_opts.samp_freq, waveform.tolist())
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frames = self.fbank_fn.num_frames_ready
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mat = np.empty([frames, self.opts.mel_opts.num_bins])
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for i in range(frames):
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mat[i, :] = self.fbank_fn.get_frame(i)
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feat = mat.astype(np.float32)
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feat_len = np.array(mat.shape[0]).astype(np.int32)
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return feat, feat_len
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def fbank_online(self,
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waveform: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
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waveform = waveform * (1 << 15)
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# self.fbank_fn = knf.OnlineFbank(self.opts)
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self.fbank_fn.accept_waveform(self.opts.frame_opts.samp_freq, waveform.tolist())
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frames = self.fbank_fn.num_frames_ready
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mat = np.empty([frames, self.opts.mel_opts.num_bins])
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for i in range(self.fbank_beg_idx, frames):
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mat[i, :] = self.fbank_fn.get_frame(i)
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# self.fbank_beg_idx += (frames-self.fbank_beg_idx)
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feat = mat.astype(np.float32)
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feat_len = np.array(mat.shape[0]).astype(np.int32)
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return feat, feat_len
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def reset_status(self):
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self.fbank_fn = knf.OnlineFbank(self.opts)
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self.fbank_beg_idx = 0
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def lfr_cmvn(self, feat: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
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if self.lfr_m != 1 or self.lfr_n != 1:
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feat = self.apply_lfr(feat, self.lfr_m, self.lfr_n)
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if self.cmvn_file:
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feat = self.apply_cmvn(feat)
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feat_len = np.array(feat.shape[0]).astype(np.int32)
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return feat, feat_len
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@staticmethod
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def apply_lfr(inputs: np.ndarray, lfr_m: int, lfr_n: int) -> np.ndarray:
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LFR_inputs = []
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T = inputs.shape[0]
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T_lfr = int(np.ceil(T / lfr_n))
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left_padding = np.tile(inputs[0], ((lfr_m - 1) // 2, 1))
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inputs = np.vstack((left_padding, inputs))
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T = T + (lfr_m - 1) // 2
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for i in range(T_lfr):
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if lfr_m <= T - i * lfr_n:
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LFR_inputs.append(
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(inputs[i * lfr_n:i * lfr_n + lfr_m]).reshape(1, -1))
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else:
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# process last LFR frame
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num_padding = lfr_m - (T - i * lfr_n)
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frame = inputs[i * lfr_n:].reshape(-1)
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for _ in range(num_padding):
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frame = np.hstack((frame, inputs[-1]))
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LFR_inputs.append(frame)
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LFR_outputs = np.vstack(LFR_inputs).astype(np.float32)
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return LFR_outputs
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def apply_cmvn(self, inputs: np.ndarray) -> np.ndarray:
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"""
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Apply CMVN with mvn data
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"""
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frame, dim = inputs.shape
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means = np.tile(self.cmvn[0:1, :dim], (frame, 1))
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vars = np.tile(self.cmvn[1:2, :dim], (frame, 1))
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inputs = (inputs + means) * vars
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return inputs
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def load_cmvn(self,) -> np.ndarray:
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with open(self.cmvn_file, 'r', encoding='utf-8') as f:
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lines = f.readlines()
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means_list = []
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vars_list = []
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for i in range(len(lines)):
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line_item = lines[i].split()
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if line_item[0] == '<AddShift>':
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line_item = lines[i + 1].split()
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if line_item[0] == '<LearnRateCoef>':
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add_shift_line = line_item[3:(len(line_item) - 1)]
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means_list = list(add_shift_line)
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continue
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elif line_item[0] == '<Rescale>':
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line_item = lines[i + 1].split()
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if line_item[0] == '<LearnRateCoef>':
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rescale_line = line_item[3:(len(line_item) - 1)]
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vars_list = list(rescale_line)
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continue
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means = np.array(means_list).astype(np.float64)
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vars = np.array(vars_list).astype(np.float64)
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cmvn = np.array([means, vars])
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return cmvn
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def load_bytes(input):
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middle_data = np.frombuffer(input, dtype=np.int16)
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middle_data = np.asarray(middle_data)
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if middle_data.dtype.kind not in 'iu':
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raise TypeError("'middle_data' must be an array of integers")
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dtype = np.dtype('float32')
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if dtype.kind != 'f':
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raise TypeError("'dtype' must be a floating point type")
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i = np.iinfo(middle_data.dtype)
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abs_max = 2 ** (i.bits - 1)
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offset = i.min + abs_max
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array = np.frombuffer((middle_data.astype(dtype) - offset) / abs_max, dtype=np.float32)
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return array
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def test():
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path = "/nfs/zhifu.gzf/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/asr_example.wav"
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import librosa
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cmvn_file = "/nfs/zhifu.gzf/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/am.mvn"
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config_file = "/nfs/zhifu.gzf/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/config.yaml"
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from funasr.runtime.python.onnxruntime.rapid_paraformer.utils.utils import read_yaml
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config = read_yaml(config_file)
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waveform, _ = librosa.load(path, sr=None)
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frontend = WavFrontend(
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cmvn_file=cmvn_file,
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**config['frontend_conf'],
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)
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speech, _ = frontend.fbank_online(waveform) #1d, (sample,), numpy
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feat, feat_len = frontend.lfr_cmvn(speech) # 2d, (frame, 450), np.float32 -> torch, torch.from_numpy(), dtype, (1, frame, 450)
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frontend.reset_status() # clear cache
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return feat, feat_len
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if __name__ == '__main__':
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test()
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