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| import numpy as np | |
| import torch | |
| import json | |
| import math | |
| class GlobalCMVN(torch.nn.Module): | |
| def __init__(self, mean: torch.Tensor, istd: torch.Tensor, norm_var: bool = True): | |
| """ | |
| Args: | |
| mean (torch.Tensor): mean stats | |
| istd (torch.Tensor): inverse std, std which is 1.0 / std | |
| """ | |
| super().__init__() | |
| assert mean.shape == istd.shape | |
| self.norm_var = norm_var | |
| # The buffer can be accessed from this module using self.mean | |
| self.register_buffer("mean", mean) | |
| self.register_buffer("istd", istd) | |
| def forward(self, x: torch.Tensor): | |
| """ | |
| Args: | |
| x (torch.Tensor): (batch, max_len, feat_dim) | |
| Returns: | |
| (torch.Tensor): normalized feature | |
| """ | |
| x = x - self.mean | |
| if self.norm_var: | |
| x = x * self.istd | |
| return x | |
| def load_cmvn_json(json_cmvn_file): | |
| with open(json_cmvn_file) as f: | |
| cmvn_json = json.load(f) | |
| avg = cmvn_json["mean_stat"] | |
| var = cmvn_json["var_stat"] | |
| count = cmvn_json["frame_num"] | |
| for i in range(len(avg)): | |
| avg[i] /= count | |
| var[i] = var[i] / count - avg[i] * avg[i] | |
| if var[i] < 1.0e-20: | |
| var[i] = 1.0e-20 | |
| var[i] = 1.0 / math.sqrt(var[i]) | |
| cmvn = np.array([avg, var]) | |
| return cmvn | |
| def load_cmvn_kaldi(kaldi_cmvn_file): | |
| avg = [] | |
| var = [] | |
| with open(kaldi_cmvn_file, "r") as file: | |
| # kaldi binary file start with '\0B' | |
| if file.read(2) == "\0B": | |
| logging.error( | |
| "kaldi cmvn binary file is not supported, please " | |
| ) | |
| sys.exit(1) | |
| file.seek(0) | |
| arr = file.read().split() | |
| assert arr[0] == "[" | |
| assert arr[-2] == "0" | |
| assert arr[-1] == "]" | |
| feat_dim = int((len(arr) - 2 - 2) / 2) | |
| for i in range(1, feat_dim + 1): | |
| avg.append(float(arr[i])) | |
| count = float(arr[feat_dim + 1]) | |
| for i in range(feat_dim + 2, 2 * feat_dim + 2): | |
| var.append(float(arr[i])) | |
| for i in range(len(avg)): | |
| avg[i] /= count | |
| var[i] = var[i] / count - avg[i] * avg[i] | |
| if var[i] < 1.0e-20: | |
| var[i] = 1.0e-20 | |
| var[i] = 1.0 / math.sqrt(var[i]) | |
| cmvn = np.array([avg, var]) | |
| return cmvn | |
| def load_cmvn(filename, is_json): | |
| if is_json: | |
| file = load_cmvn_json(filename) | |
| else: | |
| file = load_cmvn_kaldi(filename) | |
| return file[0], file[1] | |