FunASR/funasr/models/eend/utils/feature.py

# Copyright 2019 Hitachi, Ltd. (author: Yusuke Fujita)
# Licensed under the MIT license.
#
# This module is for computing audio features

import numpy as np
import librosa


def get_input_dim(
    frame_size,
    context_size,
    transform_type,
):
    if transform_type.startswith("logmel23"):
        frame_size = 23
    elif transform_type.startswith("logmel"):
        frame_size = 40
    else:
        fft_size = 1 << (frame_size - 1).bit_length()
        frame_size = int(fft_size / 2) + 1
    input_dim = (2 * context_size + 1) * frame_size
    return input_dim


def transform(Y, transform_type=None, dtype=np.float32):
    """Transform STFT feature

    Args:
        Y: STFT
            (n_frames, n_bins)-shaped np.complex array
        transform_type:
            None, "log"
        dtype: output data type
            np.float32 is expected
    Returns:
        Y (numpy.array): transformed feature
    """
    Y = np.abs(Y)
    if not transform_type:
        pass
    elif transform_type == "log":
        Y = np.log(np.maximum(Y, 1e-10))
    elif transform_type == "logmel":
        n_fft = 2 * (Y.shape[1] - 1)
        sr = 16000
        n_mels = 40
        mel_basis = librosa.filters.mel(sr, n_fft, n_mels)
        Y = np.dot(Y**2, mel_basis.T)
        Y = np.log10(np.maximum(Y, 1e-10))
    elif transform_type == "logmel23":
        n_fft = 2 * (Y.shape[1] - 1)
        sr = 8000
        n_mels = 23
        mel_basis = librosa.filters.mel(sr, n_fft, n_mels)
        Y = np.dot(Y**2, mel_basis.T)
        Y = np.log10(np.maximum(Y, 1e-10))
    elif transform_type == "logmel23_mn":
        n_fft = 2 * (Y.shape[1] - 1)
        sr = 8000
        n_mels = 23
        mel_basis = librosa.filters.mel(sr, n_fft, n_mels)
        Y = np.dot(Y**2, mel_basis.T)
        Y = np.log10(np.maximum(Y, 1e-10))
        mean = np.mean(Y, axis=0)
        Y = Y - mean
    elif transform_type == "logmel23_swn":
        n_fft = 2 * (Y.shape[1] - 1)
        sr = 8000
        n_mels = 23
        mel_basis = librosa.filters.mel(sr, n_fft, n_mels)
        Y = np.dot(Y**2, mel_basis.T)
        Y = np.log10(np.maximum(Y, 1e-10))
        # b = np.ones(300)/300
        # mean = scipy.signal.convolve2d(Y, b[:, None], mode='same')

        #  simple 2-means based threshoding for mean calculation
        powers = np.sum(Y, axis=1)
        th = (np.max(powers) + np.min(powers)) / 2.0
        for i in range(10):
            th = (np.mean(powers[powers >= th]) + np.mean(powers[powers < th])) / 2
        mean = np.mean(Y[powers > th, :], axis=0)
        Y = Y - mean
    elif transform_type == "logmel23_mvn":
        n_fft = 2 * (Y.shape[1] - 1)
        sr = 8000
        n_mels = 23
        mel_basis = librosa.filters.mel(sr, n_fft, n_mels)
        Y = np.dot(Y**2, mel_basis.T)
        Y = np.log10(np.maximum(Y, 1e-10))
        mean = np.mean(Y, axis=0)
        Y = Y - mean
        std = np.maximum(np.std(Y, axis=0), 1e-10)
        Y = Y / std
    else:
        raise ValueError("Unknown transform_type: %s" % transform_type)
    return Y.astype(dtype)


def subsample(Y, T, subsampling=1):
    """Frame subsampling"""
    Y_ss = Y[::subsampling]
    T_ss = T[::subsampling]
    return Y_ss, T_ss


def splice(Y, context_size=0):
    """Frame splicing

    Args:
        Y: feature
            (n_frames, n_featdim)-shaped numpy array
        context_size:
            number of frames concatenated on left-side
            if context_size = 5, 11 frames are concatenated.

    Returns:
        Y_spliced: spliced feature
            (n_frames, n_featdim * (2 * context_size + 1))-shaped
    """
    Y_pad = np.pad(Y, [(context_size, context_size), (0, 0)], "constant")
    Y_spliced = np.lib.stride_tricks.as_strided(
        np.ascontiguousarray(Y_pad),
        (Y.shape[0], Y.shape[1] * (2 * context_size + 1)),
        (Y.itemsize * Y.shape[1], Y.itemsize),
        writeable=False,
    )
    return Y_spliced


def stft(data, frame_size=1024, frame_shift=256):
    """Compute STFT features

    Args:
        data: audio signal
            (n_samples,)-shaped np.float32 array
        frame_size: number of samples in a frame (must be a power of two)
        frame_shift: number of samples between frames

    Returns:
        stft: STFT frames
            (n_frames, n_bins)-shaped np.complex64 array
    """
    # round up to nearest power of 2
    fft_size = 1 << (frame_size - 1).bit_length()
    # HACK: The last frame is ommited
    #       as librosa.stft produces such an excessive frame
    if len(data) % frame_shift == 0:
        return librosa.stft(data, n_fft=fft_size, win_length=frame_size, hop_length=frame_shift).T[
            :-1
        ]
    else:
        return librosa.stft(data, n_fft=fft_size, win_length=frame_size, hop_length=frame_shift).T


def _count_frames(data_len, size, shift):
    # HACK: Assuming librosa.stft(..., center=True)
    n_frames = 1 + int(data_len / shift)
    if data_len % shift == 0:
        n_frames = n_frames - 1
    return n_frames


def get_frame_labels(
    kaldi_obj, rec, start=0, end=None, frame_size=1024, frame_shift=256, n_speakers=None
):
    """Get frame-aligned labels of given recording
    Args:
        kaldi_obj (KaldiData)
        rec (str): recording id
        start (int): start frame index
        end (int): end frame index
            None means the last frame of recording
        frame_size (int): number of frames in a frame
        frame_shift (int): number of shift samples
        n_speakers (int): number of speakers
            if None, the value is given from data
    Returns:
        T: label
            (n_frames, n_speakers)-shaped np.int32 array
    """
    filtered_segments = kaldi_obj.segments[kaldi_obj.segments["rec"] == rec]
    speakers = np.unique([kaldi_obj.utt2spk[seg["utt"]] for seg in filtered_segments]).tolist()
    if n_speakers is None:
        n_speakers = len(speakers)
    es = end * frame_shift if end is not None else None
    data, rate = kaldi_obj.load_wav(rec, start * frame_shift, es)
    n_frames = _count_frames(len(data), frame_size, frame_shift)
    T = np.zeros((n_frames, n_speakers), dtype=np.int32)
    if end is None:
        end = n_frames

    for seg in filtered_segments:
        speaker_index = speakers.index(kaldi_obj.utt2spk[seg["utt"]])
        start_frame = np.rint(seg["st"] * rate / frame_shift).astype(int)
        end_frame = np.rint(seg["et"] * rate / frame_shift).astype(int)
        rel_start = rel_end = None
        if start <= start_frame and start_frame < end:
            rel_start = start_frame - start
        if start < end_frame and end_frame <= end:
            rel_end = end_frame - start
        if rel_start is not None or rel_end is not None:
            T[rel_start:rel_end, speaker_index] = 1
    return T


def get_labeledSTFT(
    kaldi_obj, rec, start, end, frame_size, frame_shift, n_speakers=None, use_speaker_id=False
):
    """Extracts STFT and corresponding labels

    Extracts STFT and corresponding diarization labels for
    given recording id and start/end times

    Args:
        kaldi_obj (KaldiData)
        rec (str): recording id
        start (int): start frame index
        end (int): end frame index
        frame_size (int): number of samples in a frame
        frame_shift (int): number of shift samples
        n_speakers (int): number of speakers
            if None, the value is given from data
    Returns:
        Y: STFT
            (n_frames, n_bins)-shaped np.complex64 array,
        T: label
            (n_frmaes, n_speakers)-shaped np.int32 array.
    """
    data, rate = kaldi_obj.load_wav(rec, start * frame_shift, end * frame_shift)
    Y = stft(data, frame_size, frame_shift)
    filtered_segments = kaldi_obj.segments[rec]
    # filtered_segments = kaldi_obj.segments[kaldi_obj.segments['rec'] == rec]
    speakers = np.unique([kaldi_obj.utt2spk[seg["utt"]] for seg in filtered_segments]).tolist()
    if n_speakers is None:
        n_speakers = len(speakers)
    T = np.zeros((Y.shape[0], n_speakers), dtype=np.int32)

    if use_speaker_id:
        all_speakers = sorted(kaldi_obj.spk2utt.keys())
        S = np.zeros((Y.shape[0], len(all_speakers)), dtype=np.int32)

    for seg in filtered_segments:
        speaker_index = speakers.index(kaldi_obj.utt2spk[seg["utt"]])
        if use_speaker_id:
            all_speaker_index = all_speakers.index(kaldi_obj.utt2spk[seg["utt"]])
        start_frame = np.rint(seg["st"] * rate / frame_shift).astype(int)
        end_frame = np.rint(seg["et"] * rate / frame_shift).astype(int)
        rel_start = rel_end = None
        if start <= start_frame and start_frame < end:
            rel_start = start_frame - start
        if start < end_frame and end_frame <= end:
            rel_end = end_frame - start
        if rel_start is not None or rel_end is not None:
            T[rel_start:rel_end, speaker_index] = 1
            if use_speaker_id:
                S[rel_start:rel_end, all_speaker_index] = 1

    if use_speaker_id:
        return Y, T, S
    else:
        return Y, T