Lyra V2 – a better, faster, and more versatile speech codec

[0] https://ham.stackexchange.com/questions/72/encrypted-traffic...

    from scipy.io import wavfile
    import numpy as np
    import zstd

    sampling_rate, samples = wavfile.read(r'data/bootleg-compress/music_orig.wav')
    orig = samples.tobytes()

    naive_compressed = zstd.ZSTD_compress(orig)
    deltas = np.diff(samples, prepend=samples.dtype.type(0), axis=0) # Per-channel deltas.
    compressed_deltas = zstd.ZSTD_compress(deltas.ravel()) # Interleave channels and compress.

    decompressed_deltas = np.frombuffer(zstd.ZSTD_uncompress(compressed_deltas), dtype=samples.dtype)
    decompressed = np.cumsum(decompressed_deltas.reshape(deltas.shape), axis=0, dtype=samples.dtype)
    assert np.array_equal(samples, decompressed)

    print(len(orig))
    print(len(naive_compressed))
    print(len(compressed_deltas))

    17432876
    15518973
    12817602

Using an encoding for second order differences with storing -127 <= d <= 127 using 1 byte and the others 2 bytes (for an input of 16-bit audio) I got a ratio of ~1.50 for something that can still operate entirely at RAM speed:

    orig = samples.tobytes()
    deltas = np.diff(samples, prepend=samples.dtype.type(0), axis=0)      # Per-channel deltas.
    delta_deltas = np.diff(deltas, prepend=samples.dtype.type(0), axis=0) # Per-channel second-order differences.

    # Many small differences, encode almost all 1-byte differences using 1 byte,
    # using 3 bytes for larger differences. Interleave channels and encode.
    small = np.sum(np.abs(delta_deltas.ravel()) <= 127)
    bootleg = np.zeros(small + (len(delta_deltas.ravel()) - small) * 3, dtype=np.uint8)
    i = 0
    for dda in delta_deltas.flatten():
        if -127 <= dda <= 127:
            bootleg[i] = dda + 127
            i += 1
        else:
            bootleg[i] = 255
            bootleg[i + 1] = (dda + 2**15) % 256
            bootleg[i + 2] = (dda + 2**15) // 256
            i += 3

    compressed_bootleg = zstd.ZSTD_compress(bootleg)
    print(len(compressed_bootleg))

    decompressed_bootleg = zstd.ZSTD_uncompress(compressed_bootleg)
    result = []

    i = 0
    while i < len(bootleg):
        if bootleg[i] < 255:
            result.append(decompressed_bootleg[i] - 127)
            i += 1
        else:
            lo = decompressed_bootleg[i + 1]
            hi = decompressed_bootleg[i + 2]
            result.append(256*hi + lo - 2**15)
            i += 3

    decompressed_delta_deltas = np.array(result, dtype=samples.dtype).reshape(delta_deltas.shape)
    decompressed_deltas = np.cumsum(decompressed_delta_deltas, axis=0, dtype=samples.dtype)
    decompressed = np.cumsum(decompressed_deltas, axis=0, dtype=samples.dtype)
    assert np.array_equal(samples, decompressed)

High Fidelity the company is still around, but they pivoted multiple times radically. Initially their plan was social VR of sorts. Then they tried to make a corporate product for meetings and such, and gave up on that right before COVID19 hit!

And after that they ripped out all the 3D and VR and scaled down to a 2D, overhead spatial audio web thing. Think something like Zoom, only you have an icon that you can move around to get closer or further to other people.

The original code still lives on, we picked it up and are working on improvements. Feel free to visit out Discord (see my profile).