#!/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 pandas
import tarfile
import wave


COLUMN_NAMES = ['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 is_file_truncated(wav_filename, wav_filesize):
    with wave.open(wav_filename, mode='rb') as fin:
        assert fin.getframerate() == 16000
        assert fin.getsampwidth() == 2
        assert fin.getnchannels() == 1

        header_duration = fin.getnframes() / fin.getframerate()
        filesize_duration = (wav_filesize - 44) / 16000 / 2

    return header_duration != filesize_duration


def preprocess_data(folder_with_archives, target_dir):
    # First extract subset archives
    for subset in ('train', 'dev', 'test'):
        extract(os.path.join(folder_with_archives, 'magicdata_{}_set.tar.gz'.format(subset)), target_dir)

    # Folder structure is now:
    # - magicdata_{train,dev,test}.tar.gz
    # - magicdata/
    #   - train/*.wav
    #   - train/TRANS.txt
    #   - dev/*.wav
    #   - dev/TRANS.txt
    #   - test/*.wav
    #   - test/TRANS.txt

    # The TRANS files are CSVs with three columns, one containing the WAV file
    # name, one containing the speaker ID, and one containing the transcription

    def load_set(set_path):
        transcripts = pandas.read_csv(os.path.join(set_path, 'TRANS.txt'), sep='\t', index_col=0)
        glob_path = os.path.join(set_path, '*', '*.wav')
        set_files = []
        for wav in glob.glob(glob_path):
            try:
                wav_filename = wav
                wav_filesize = os.path.getsize(wav)
                transcript_key = os.path.basename(wav)
                transcript = transcripts.loc[transcript_key, 'Transcription']

                # Some files in this dataset are truncated, the header duration
                # doesn't match the file size. This causes errors at training
                # time, so check here if things are fine before including a file
                if is_file_truncated(wav_filename, wav_filesize):
                    print('Warning: File {} is corrupted, header duration does '
                          'not match file size. Ignoring.'.format(wav_filename))
                    continue

                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(target_dir, subset))
        df = pandas.DataFrame(data=subset_files, columns=COLUMN_NAMES)

        # Trim train set to under 10s
        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()))
            
            with_noise = df['transcript'].str.contains(r'\[(FIL|SPK)\]')
            df = df[~with_noise]
            print('Trimming {} samples with noise ([FIL] or [SPK])'.format(sum(with_noise)))

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


def main():
    # https://openslr.org/68/
    parser = argparse.ArgumentParser(description='Import MAGICDATA corpus')
    parser.add_argument('folder_with_archives', help='Path to folder containing magicdata_{train,dev,test}.tar.gz')
    parser.add_argument('--target_dir', default='', help='Target folder to extract files into and put the resulting CSVs. Defaults to a folder called magicdata next to the archives')
    params = parser.parse_args()

    if not params.target_dir:
        params.target_dir = os.path.join(params.folder_with_archives, 'magicdata')

    preprocess_data(params.folder_with_archives, params.target_dir)


if __name__ == "__main__":
    main()