torch/rdovae/fec_encoder.py

"""
/* Copyright (c) 2022 Amazon
   Written by Jan Buethe and Jean-Marc Valin */
/*
   Redistribution and use in source and binary forms, with or without
   modification, are permitted provided that the following conditions
   are met:

   - Redistributions of source code must retain the above copyright
   notice, this list of conditions and the following disclaimer.

   - Redistributions in binary form must reproduce the above copyright
   notice, this list of conditions and the following disclaimer in the
   documentation and/or other materials provided with the distribution.

   THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
   ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
   LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
   A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER
   OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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   LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
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*/
"""

import os
import subprocess
import argparse

os.environ['CUDA_VISIBLE_DEVICES'] = ""

parser = argparse.ArgumentParser(description='Encode redundancy for Opus neural FEC. Designed for use with voip application and 20ms frames')

parser.add_argument('input', metavar='<input signal>', help='audio input (.wav or .raw or .pcm as int16)')
parser.add_argument('checkpoint', metavar='<weights>', help='model checkpoint')
parser.add_argument('q0', metavar='<quant level 0>', type=int, help='quantization level for most recent frame')
parser.add_argument('q1', metavar='<quant level 1>', type=int, help='quantization level for oldest frame')
parser.add_argument('output', type=str, help='output file (will be extended with .fec)')

parser.add_argument('--dump-data', type=str, default='./dump_data', help='path to dump data executable (default ./dump_data)')
parser.add_argument('--num-redundancy-frames', default=52, type=int, help='number of redundancy frames per packet (default 52)')
parser.add_argument('--extra-delay', default=0, type=int, help="last features in packet are calculated with the decoder aligned samples, use this option to add extra delay (in samples at 16kHz)")
parser.add_argument('--lossfile', type=str, help='file containing loss trace (0 for frame received, 1 for lost)')
parser.add_argument('--debug-output', action='store_true', help='if set, differently assembled features are written to disk')

args = parser.parse_args()

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

from rdovae import RDOVAE
from packets import write_fec_packets

torch.set_num_threads(4)

checkpoint = torch.load(args.checkpoint, map_location="cpu")
model = RDOVAE(*checkpoint['model_args'], **checkpoint['model_kwargs'])
model.load_state_dict(checkpoint['state_dict'], strict=False)
model.to("cpu")

lpc_order = 16

## prepare input signal
# SILK frame size is 20ms and LPCNet subframes are 10ms
subframe_size = 160
frame_size = 2 * subframe_size

# 91 samples delay to align with SILK decoded frames
silk_delay = 91

# prepend zeros to have enough history to produce the first package
zero_history = (args.num_redundancy_frames - 1) * frame_size

# dump data has a (feature) delay of 10ms
dump_data_delay = 160

total_delay = silk_delay + zero_history + args.extra_delay - dump_data_delay

# load signal
if args.input.endswith('.raw') or args.input.endswith('.pcm'):
    signal = np.fromfile(args.input, dtype='int16')

elif args.input.endswith('.wav'):
    fs, signal = wavfile.read(args.input)
else:
    raise ValueError(f'unknown input signal format: {args.input}')

# fill up last frame with zeros
padded_signal_length = len(signal) + total_delay
tail = padded_signal_length % frame_size
right_padding = (frame_size - tail) % frame_size

signal = np.concatenate((np.zeros(total_delay, dtype=np.int16), signal, np.zeros(right_padding, dtype=np.int16)))

padded_signal_file  = os.path.splitext(args.input)[0] + '_padded.raw'
signal.tofile(padded_signal_file)

# write signal and call dump_data to create features

feature_file = os.path.splitext(args.input)[0] + '_features.f32'
command = f"{args.dump_data} -test {padded_signal_file} {feature_file}"
r = subprocess.run(command, shell=True)
if r.returncode != 0:
    raise RuntimeError(f"command '{command}' failed with exit code {r.returncode}")

# load features
nb_features = model.feature_dim + lpc_order
nb_used_features = model.feature_dim

# load features
features = np.fromfile(feature_file, dtype='float32')
num_subframes = len(features) // nb_features
num_subframes = 2 * (num_subframes // 2)
num_frames = num_subframes // 2

features = np.reshape(features, (1, -1, nb_features))
features = features[:, :, :nb_used_features]
features = features[:, :num_subframes, :]

# quant_ids in reverse decoding order
quant_ids = torch.round((args.q1 + (args.q0 - args.q1) * torch.arange(args.num_redundancy_frames // 2) / (args.num_redundancy_frames // 2 - 1))).long()

print(f"using quantization levels {quant_ids}...")

# convert input to torch tensors
features = torch.from_numpy(features)


# run encoder
print("running fec encoder...")
with torch.no_grad():

    # encoding
    z, states, state_size = model.encode(features)


    # decoder on packet chunks
    input_length = args.num_redundancy_frames // 2
    offset = args.num_redundancy_frames - 1

    packets = []
    packet_sizes = []

    for i in range(offset, num_frames):
        print(f"processing frame {i - offset}...")
        # quantize / unquantize latent vectors
        zi = torch.clone(z[:, i - 2 * input_length + 2: i + 1 : 2, :])
        zi, rates = model.quantize(zi, quant_ids)
        zi = model.unquantize(zi, quant_ids)

        features = model.decode(zi, states[:, i : i + 1, :])
        packets.append(features.squeeze(0).numpy())
        packet_size = 8 * int((torch.sum(rates) + 7 + state_size) / 8)
        packet_sizes.append(packet_size)


# write packets
packet_file = args.output + '.fec' if not args.output.endswith('.fec') else args.output
write_fec_packets(packet_file, packets, packet_sizes)


print(f"average redundancy rate: {int(round(sum(packet_sizes) / len(packet_sizes) * 50 / 1000))} kbps")

# assemble features according to loss file
if args.lossfile != None:
    num_packets = len(packets)
    loss = np.loadtxt(args.lossfile, dtype='int16')
    fec_out = np.zeros((num_packets * 2, packets[0].shape[-1]), dtype='float32')
    foffset = -2
    ptr = 0
    count = 2
    for i in range(num_packets):
        if (loss[i] == 0) or (i == num_packets - 1):

            fec_out[ptr:ptr+count,:] = packets[i][foffset:, :]

            ptr    += count
            foffset = -2
            count   = 2
        else:
            count   += 2
            foffset -= 2

    fec_out_full = np.zeros((fec_out.shape[0], 36), dtype=np.float32)
    fec_out_full[:, : fec_out.shape[-1]] = fec_out

    fec_out_full.tofile(packet_file[:-4] + f'_fec.f32')


if args.debug_output:
    import itertools

    batches = [4]
    offsets = [0, 2 * args.num_redundancy_frames - 4]

    # sanity checks
    # 1. concatenate features at offset 0
    for batch, offset in itertools.product(batches, offsets):

        stop = packets[0].shape[1] - offset
        test_features = np.concatenate([packet[stop - batch: stop, :] for packet in packets[::batch//2]], axis=0)

        test_features_full = np.zeros((test_features.shape[0], nb_features), dtype=np.float32)
        test_features_full[:, :nb_used_features] = test_features[:, :]

        print(f"writing debug output {packet_file[:-4] + f'_torch_batch{batch}_offset{offset}.f32'}")
        test_features_full.tofile(packet_file[:-4] + f'_torch_batch{batch}_offset{offset}.f32')