"""Compute output/input timestamp ratio.

usage: compute_timestamp_ratio.py [options] <data_root>

options:

--hparams=<parmas> Hyper parameters [default: ].

--preset=<json> Path of preset parameters (json).

-h, --help Show this help message and exit

"""

from docopt import docopt

import sys

import numpy as np

from hparams import hparams, hparams_debug_string

import train

from train import TextDataSource, MelSpecDataSource

from nnmnkwii.datasets import FileSourceDataset

from tqdm import trange

from deepvoice3_pytorch import frontend

if __name__ == "__main__":

args = docopt(__doc__)

data_root = args["<data_root>"]

preset = args["--preset"]

# Load preset if specified

if preset is not None:

with open(preset) as f:

hparams.parse_json(f.read())

# Override hyper parameters

hparams.parse(args["--hparams"])

assert hparams.name == "deepvoice3"

train._frontend = getattr(frontend, hparams.frontend)

# Code below

X = FileSourceDataset(TextDataSource(data_root))

Mel = FileSourceDataset(MelSpecDataSource(data_root))

in_sizes = []

out_sizes = []

for i in trange(len(X)):

x, m = X[i], Mel[i]

if X.file_data_source.multi_speaker:

x = x[0]

in_sizes.append(x.shape[0])

out_sizes.append(m.shape[0])

in_sizes = np.array(in_sizes)

out_sizes = np.array(out_sizes)

input_timestamps = np.sum(in_sizes)

output_timestamps = np.sum(out_sizes) / hparams.outputs_per_step / hparams.downsample_step

print(input_timestamps, output_timestamps, output_timestamps / input_timestamps)

sys.exit(0)