5 ms·

It sounds like you’re asking for uncompressed audio? That meets all of your listed requirements. 48kHz * 16bit, single channel = 768kbit/s

userbinator 4y ago
Either that or G.711.
- viraptor 4y ago
  G711 is neither high bitrate nor usable for music.
  - simfree 4y ago
    Then use G.722, it works fine for music.
    - viraptor 4y ago
      No, g722 is still a wideband speech codec. Its available frequency goes up to 7 kHz. The uncompressed audio this thread began with goes up to 22 kHz. With g722 you're losing most overtones, or even all overtones from the top of a piano. Please don't use g722 for music apart from on-hold muzak.
dale_glass 4y ago
We support that already, yup. But it never hurts to see if there's something better than that out there.
- zinekeller 4y ago
  While I also want a low-computation codec that can save space, the historical use cases unfortunately assumes a lot more CPU power to be compensated for a lot less bandwidth, so there's little research in this area, and there's no real incentive to make something like ProRes and DNxHD as if you are editing audio the SSD speeds has been so fast that you'll run into CPU problems first.
- orlp 4y ago
  You can bootleg your own fast lossless codec by doing delta-encoding on the raw PCM to get a lot of zeros and then feed it through an off-the-shelf fast compressor like snappy/lz4/zstandard/etc. It won't get remotely close to the dedicated audio algorithms, but I wouldn't be surprised if you cut your data size by a factor 2-4 and essentially no CPU cost compared to raw uncompressed audio.
  - muziq 4y ago
    You’ve not done this before have you ?
    - orlp 4y ago
      I haven't, but now I have. I took https://opus-codec.org/static/examples/samples/music_orig.wav https://opus-codec.org/static/examples/samples/music_orig.wa... from https://opus-codec.org/examples/ https://opus-codec.org/examples/. Then I wrote the following snippet of Python code: 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)) giving: 17432876 15518973 12817602 Looks like my initial estimation of 2-4 was way off (when FLAC achieves ~2 this should've been a red flag), but you do get a ~1.36x reduction in space at basically memory read speed. 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) Prints 11593846.

userbinator 4y ago

Either that or G.711.

viraptor 4y ago
G711 is neither high bitrate nor usable for music.
- simfree 4y ago
  Then use G.722, it works fine for music.
  - viraptor 4y ago
    No, g722 is still a wideband speech codec. Its available frequency goes up to 7 kHz. The uncompressed audio this thread began with goes up to 22 kHz. With g722 you're losing most overtones, or even all overtones from the top of a piano. Please don't use g722 for music apart from on-hold muzak.

dale_glass 4y ago

We support that already, yup. But it never hurts to see if there's something better than that out there.

zinekeller 4y ago
While I also want a low-computation codec that can save space, the historical use cases unfortunately assumes a lot more CPU power to be compensated for a lot less bandwidth, so there's little research in this area, and there's no real incentive to make something like ProRes and DNxHD as if you are editing audio the SSD speeds has been so fast that you'll run into CPU problems first.
orlp 4y ago
You can bootleg your own fast lossless codec by doing delta-encoding on the raw PCM to get a lot of zeros and then feed it through an off-the-shelf fast compressor like snappy/lz4/zstandard/etc. It won't get remotely close to the dedicated audio algorithms, but I wouldn't be surprised if you cut your data size by a factor 2-4 and essentially no CPU cost compared to raw uncompressed audio.
- muziq 4y ago
  You’ve not done this before have you ?
  - orlp 4y ago
    I haven't, but now I have. I took https://opus-codec.org/static/examples/samples/music_orig.wav https://opus-codec.org/static/examples/samples/music_orig.wa... from https://opus-codec.org/examples/ https://opus-codec.org/examples/. Then I wrote the following snippet of Python code: 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)) giving: 17432876 15518973 12817602 Looks like my initial estimation of 2-4 was way off (when FLAC achieves ~2 this should've been a red flag), but you do get a ~1.36x reduction in space at basically memory read speed. 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) Prints 11593846.