#!/usr/bin/env python
from __future__ import absolute_import, division, print_function

# Make sure we can import stuff from util/
# This script needs to be run from the root of the DeepSpeech repository
import os
import sys
sys.path.insert(1, os.path.join(sys.path[0], '..'))

import argparse
import glob
import tarfile
import pandas


COLUMNNAMES = ['wav_filename', 'wav_filesize', 'transcript']


def extract(archive_path, target_dir):
    print('Extracting {} into {}...'.format(archive_path, target_dir))
    with tarfile.open(archive_path) as tar:
        tar.extractall(target_dir)


def preprocess_data(tgz_file, target_dir):
    # First extract main archive and sub-archives
    extract(tgz_file, target_dir)
    main_folder = os.path.join(target_dir, 'data_aishell')

    wav_archives_folder = os.path.join(main_folder, 'wav')
    for targz in glob.glob(os.path.join(wav_archives_folder, '*.tar.gz')):
        extract(targz, main_folder)

    # Folder structure is now:
    # - data_aishell/
    #   - train/S****/*.wav
    #   - dev/S****/*.wav
    #   - test/S****/*.wav
    #   - wav/S****.tar.gz
    #   - transcript/aishell_transcript_v0.8.txt

    # Transcripts file has one line per WAV file, where each line consists of
    # the WAV file name without extension followed by a single space followed
    # by the transcript.

    # Since the transcripts themselves can contain spaces, we split on space but
    # only once, then build a mapping from file name to transcript
    transcripts_path = os.path.join(main_folder, 'transcript', 'aishell_transcript_v0.8.txt')
    with open(transcripts_path) as fin:
        transcripts = dict((line.split(' ', maxsplit=1) for line in fin))

    def load_set(glob_path):
        set_files = []
        for wav in glob.glob(glob_path):
            try:
                wav_filename = wav
                wav_filesize = os.path.getsize(wav)
                transcript_key = os.path.splitext(os.path.basename(wav))[0]
                transcript = transcripts[transcript_key].strip('\n')
                set_files.append((wav_filename, wav_filesize, transcript))
            except KeyError:
                print('Warning: Missing transcript for WAV file {}.'.format(wav))
        return set_files

    for subset in ('train', 'dev', 'test'):
        print('Loading {} set samples...'.format(subset))
        subset_files = load_set(os.path.join(main_folder, subset, 'S*', '*.wav'))
        df = pandas.DataFrame(data=subset_files, columns=COLUMNNAMES)

        # Trim train set to under 10s by removing the last couple hundred samples
        if subset == 'train':
            durations = (df['wav_filesize'] - 44) / 16000 / 2
            df = df[durations <= 10.0]
            print('Trimming {} samples > 10 seconds'.format((durations > 10.0).sum()))

        dest_csv = os.path.join(target_dir, 'aishell_{}.csv'.format(subset))
        print('Saving {} set into {}...'.format(subset, dest_csv))
        df.to_csv(dest_csv, index=False)


def main():
    # http://www.openslr.org/33/
    parser = argparse.ArgumentParser(description='Import AISHELL corpus')
    parser.add_argument('aishell_tgz_file', help='Path to data_aishell.tgz')
    parser.add_argument('--target_dir', default='', help='Target folder to extract files into and put the resulting CSVs. Defaults to same folder as the main archive.')
    params = parser.parse_args()

    if not params.target_dir:
        params.target_dir = os.path.dirname(params.aishell_tgz_file)

    preprocess_data(params.aishell_tgz_file, params.target_dir)


if __name__ == "__main__":
    main()