#!/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 csv
import subprocess
import progressbar

from os import path
from sox import Transformer
from threading import RLock
from multiprocessing.dummy import Pool
from multiprocessing import cpu_count
from util.downloader import SIMPLE_BAR

'''
Broadly speaking, this script takes the audio downloaded from Common Voice
for a certain language, in addition to the *.tsv files output by CorporaCreator,
and the script formats the data and transcripts to be in a state usable by
DeepSpeech.py

Usage:
        $ python3 import_cv2.py /path/to/audio/data_dir /path/to/tsv_dir

Input:
        (1) audio_dir (string) path to dir of audio downloaded from Common Voice
        (2) tsv_dir (string) path to dir containing {train,test,dev}.tsv files
            which were generated by CorporaCreator

Ouput:
        (1) csv files in format needed by DeepSpeech.py, saved into audio_dir
        (2) wav files, saved into audio_dir alongside their mp3s
'''

FIELDNAMES = ['wav_filename', 'wav_filesize', 'transcript']
SAMPLE_RATE = 16000
MAX_SECS = 10

def _preprocess_data(audio_dir, tsv_dir):
    for dataset in ['train','test','dev']:
        input_tsv= path.join(path.abspath(tsv_dir), dataset+".tsv")
        if os.path.isfile(input_tsv):
            print("Loading TSV file: ", input_tsv)
            _maybe_convert_set(audio_dir, input_tsv)
        else:
            print("ERROR: no TSV file found: ", input_tsv)

def _maybe_convert_set(audio_dir, input_tsv):
    output_csv =  path.join(audio_dir,os.path.split(input_tsv)[-1].replace('tsv', 'csv'))
    print("Saving new DeepSpeech-formatted CSV file to: ", output_csv)

    # Get audiofile path and transcript for each sentence in tsv
    samples = []
    with open(input_tsv) as input_tsv_file:
        reader = csv.DictReader(input_tsv_file, delimiter='\t')
        for row in reader:
            samples.append((row['path'], row['sentence']))

    # Keep track of how many samples are good vs. problematic
    counter = { 'all': 0, 'too_short': 0, 'too_long': 0 }
    lock = RLock()
    num_samples = len(samples)
    rows = []

    def one_sample(sample):
        """ Take a audio file, and optionally convert it to 16kHz WAV """
        mp3_filename = path.join(audio_dir, sample[0])
        if not path.splitext(mp3_filename.lower())[1] == '.mp3':
            mp3_filename += ".mp3"
        # Storing wav files next to the mp3 ones - just with a different suffix
        wav_filename = path.splitext(mp3_filename)[0] + ".wav"
        _maybe_convert_wav(mp3_filename, wav_filename)
        frames = int(subprocess.check_output(['soxi', '-s', wav_filename], stderr=subprocess.STDOUT))
        file_size = path.getsize(wav_filename)
        with lock:
            if int(frames/SAMPLE_RATE*1000/10/2) < len(str(sample[1])):
                # Excluding samples that are too short to fit the transcript
                counter['too_short'] += 1
            elif frames/SAMPLE_RATE > MAX_SECS:
                # Excluding very long samples to keep a reasonable batch-size
                counter['too_long'] += 1
            else:
                # This one is good - keep it for the target CSV
                rows.append((wav_filename, file_size, sample[1]))
            counter['all'] += 1

    print("Importing mp3 files...")
    pool = Pool(cpu_count())
    bar = progressbar.ProgressBar(max_value=num_samples, widgets=SIMPLE_BAR)
    for i, _ in enumerate(pool.imap_unordered(one_sample, samples), start=1):
        bar.update(i)
    bar.update(num_samples)
    pool.close()
    pool.join()

    with open(output_csv, 'w') as output_csv_file:
        print('Writing CSV file for DeepSpeech.py as: ', output_csv)
        writer = csv.DictWriter(output_csv_file, fieldnames=FIELDNAMES)
        writer.writeheader()
        bar = progressbar.ProgressBar(max_value=len(rows), widgets=SIMPLE_BAR)
        for filename, file_size, transcript in bar(rows):
            writer.writerow({ 'wav_filename': filename, 'wav_filesize': file_size, 'transcript': transcript })

    print('Imported %d samples.' % (counter['all'] - counter['too_short'] - counter['too_long']))
    if counter['too_short'] > 0:
        print('Skipped %d samples that were too short to match the transcript.' % counter['too_short'])
    if counter['too_long'] > 0:
        print('Skipped %d samples that were longer than %d seconds.' % (counter['too_long'], MAX_SECS))

def _maybe_convert_wav(mp3_filename, wav_filename):
    if not path.exists(wav_filename):
        transformer = Transformer()
        transformer.convert(samplerate=SAMPLE_RATE)
        transformer.build(mp3_filename, wav_filename)

if __name__ == "__main__":
    audio_dir = sys.argv[1]
    tsv_dir = sys.argv[2]
    print('Expecting your audio from Common Voice to be in: ', audio_dir)
    print('Looking for *.tsv files (generated by CorporaCreator) in: ', tsv_dir)
    _preprocess_data(audio_dir, tsv_dir)