PaddleSpeech/utils/compute-cmvn-stats.py

#!/usr/bin/env python3
import argparse
import logging

import kaldiio
import numpy as np

from paddlespeech.audio.transform.transformation import Transformation
from paddlespeech.s2t.utils.cli_readers import file_reader_helper
from paddlespeech.s2t.utils.cli_utils import get_commandline_args
from paddlespeech.s2t.utils.cli_utils import is_scipy_wav_style
from paddlespeech.s2t.utils.cli_writers import file_writer_helper


def get_parser():
    parser = argparse.ArgumentParser(
        description="Compute cepstral mean and "
        "variance normalization statistics"
        "If wspecifier provided: per-utterance by default, "
        "or per-speaker if"
        "spk2utt option provided; if wxfilename: global",
        formatter_class=argparse.ArgumentDefaultsHelpFormatter, )
    parser.add_argument(
        "--spk2utt",
        type=str,
        help="A text file of speaker to utterance-list map. "
        "(Don't give rspecifier format, such as "
        '"ark:utt2spk")', )
    parser.add_argument(
        "--verbose", "-V", default=0, type=int, help="Verbose option")
    parser.add_argument(
        "--in-filetype",
        type=str,
        default="mat",
        choices=["mat", "hdf5", "sound.hdf5", "sound"],
        help="Specify the file format for the rspecifier. "
        '"mat" is the matrix format in kaldi', )
    parser.add_argument(
        "--out-filetype",
        type=str,
        default="mat",
        choices=["mat", "hdf5", "npy"],
        help="Specify the file format for the wspecifier. "
        '"mat" is the matrix format in kaldi', )
    parser.add_argument(
        "--preprocess-conf",
        type=str,
        default=None,
        help="The configuration file for the pre-processing", )
    parser.add_argument(
        "rspecifier",
        type=str,
        help="Read specifier for feats. e.g. ark:some.ark")
    parser.add_argument(
        "wspecifier_or_wxfilename",
        type=str,
        help="Write specifier. e.g. ark:some.ark")
    return parser


def main():
    args = get_parser().parse_args()

    logfmt = "%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s"
    if args.verbose > 0:
        logging.basicConfig(level=logging.INFO, format=logfmt)
    else:
        logging.basicConfig(level=logging.WARN, format=logfmt)
    logging.info(get_commandline_args())

    is_wspecifier = ":" in args.wspecifier_or_wxfilename

    if is_wspecifier:
        if args.spk2utt is not None:
            logging.info("Performing as speaker CMVN mode")
            utt2spk_dict = {}
            with open(args.spk2utt) as f:
                for line in f:
                    spk, utts = line.rstrip().split(None, 1)
                    for utt in utts.split():
                        utt2spk_dict[utt] = spk

            def utt2spk(x):
                return utt2spk_dict[x]

        else:
            logging.info("Performing as utterance CMVN mode")

            def utt2spk(x):
                return x

        if args.out_filetype == "npy":
            logging.warning("--out-filetype npy is allowed only for "
                            "Global CMVN mode, changing to hdf5")
            args.out_filetype = "hdf5"

    else:
        logging.info("Performing as global CMVN mode")
        if args.spk2utt is not None:
            logging.warning("spk2utt is not used for global CMVN mode")

        def utt2spk(x):
            return None

        if args.out_filetype == "hdf5":
            logging.warning("--out-filetype hdf5 is not allowed for "
                            "Global CMVN mode, changing to npy")
            args.out_filetype = "npy"

    if args.preprocess_conf is not None:
        preprocessing = Transformation(args.preprocess_conf)
        logging.info("Apply preprocessing: {}".format(preprocessing))
    else:
        preprocessing = None

    # Calculate stats for each speaker
    counts = {}
    sum_feats = {}
    square_sum_feats = {}

    idx = 0
    for idx, (utt, matrix) in enumerate(
            file_reader_helper(args.rspecifier, args.in_filetype), 1):
        if is_scipy_wav_style(matrix):
            # If data is sound file, then got as Tuple[int, ndarray]
            rate, matrix = matrix
        if preprocessing is not None:
            matrix = preprocessing(matrix, uttid_list=utt)

        spk = utt2spk(utt)

        # Init at the first seen of the spk
        if spk not in counts:
            counts[spk] = 0
            feat_shape = matrix.shape[1:]
            # Accumulate in double precision
            sum_feats[spk] = np.zeros(feat_shape, dtype=np.float64)
            square_sum_feats[spk] = np.zeros(feat_shape, dtype=np.float64)

        counts[spk] += matrix.shape[0]
        sum_feats[spk] += matrix.sum(axis=0)
        square_sum_feats[spk] += (matrix**2).sum(axis=0)
    logging.info("Processed {} utterances".format(idx))
    assert idx > 0, idx

    cmvn_stats = {}
    for spk in counts:
        feat_shape = sum_feats[spk].shape
        cmvn_shape = (2, feat_shape[0] + 1) + feat_shape[1:]
        _cmvn_stats = np.empty(cmvn_shape, dtype=np.float64)
        _cmvn_stats[0, :-1] = sum_feats[spk]
        _cmvn_stats[1, :-1] = square_sum_feats[spk]

        _cmvn_stats[0, -1] = counts[spk]
        _cmvn_stats[1, -1] = 0.0

        # You can get the mean and std as following,
        # >>> N = _cmvn_stats[0, -1]
        # >>> mean = _cmvn_stats[0, :-1] / N
        # >>> std = np.sqrt(_cmvn_stats[1, :-1] / N - mean ** 2)

        cmvn_stats[spk] = _cmvn_stats

    # Per utterance or speaker CMVN
    if is_wspecifier:
        with file_writer_helper(
                args.wspecifier_or_wxfilename,
                filetype=args.out_filetype) as writer:
            for spk, mat in cmvn_stats.items():
                writer[spk] = mat

    # Global CMVN
    else:
        matrix = cmvn_stats[None]
        if args.out_filetype == "npy":
            np.save(args.wspecifier_or_wxfilename, matrix)
        elif args.out_filetype == "mat":
            # Kaldi supports only matrix or vector
            kaldiio.save_mat(args.wspecifier_or_wxfilename, matrix)
        else:
            raise RuntimeError(
                "Not supporting: --out-filetype {}".format(args.out_filetype))


if __name__ == "__main__":
    main()