artcraft/models/tts/NeMo/tools/ctc_segmentation/scripts/verify_segments.py

# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import re
from pathlib import Path

import pandas as pd

parser = argparse.ArgumentParser(description="Compare alignment segments generated with different window sizes")
parser.add_argument(
    "--base_dir",
    default='output',
    type=str,
    required=True,
    help='Path to directory with "logs" and "segments" folders generated during the segmentation step',
)


def _process_log_file(log_file: str):
    processes = {}
    with open(log_file, 'r') as f:
        for line in f:
            if line[0].isdigit() and 'Process-' in line:
                process_id = re.search(r'Process-\d+', line)[0]
                window_size = re.findall(r'\d+', line)[-1]
                path = re.search(r'\/.*?\.[\w:]+', line)
                if 'is processing' in line:
                    if process_id not in processes:
                        processes[process_id] = {}
                    processes[process_id]['Window Increased'] = False
                    processes[process_id]['Start Window'] = window_size
                    processes[process_id]['Final Window'] = window_size
                    base_dir, base_name = os.path.split(path[0])
                    processes[process_id]['Audio Dir'] = base_dir
                    processes[process_id]['Audio File'] = base_name
                elif 'Increasing' in line:
                    processes[process_id]['Final Window'] = window_size
                    processes[process_id]['Window Increased'] = True
                elif 'completed' in line:
                    processes[process_id]['Completed'] = True

    df = pd.DataFrame.from_dict(
        processes,
        columns=['Audio Dir', 'Audio File', 'Start Window', 'Final Window', 'Window Increased', 'Completed'],
        orient='index',
    ).reset_index()
    return df


if __name__ == '__main__':
    args = parser.parse_args()
    log_dir = os.path.join(args.base_dir, 'logs')
    if not os.path.exists(log_dir):
        raise ValueError(f'"log" directory was not found at {args.base_dir}.')

    log_files = Path(log_dir).glob('*.log')
    dfs = []
    for log in log_files:
        dfs.append(_process_log_file(log))

    dfs = pd.concat(dfs)
    summary_file = os.path.join(log_dir, 'log_summary.csv')
    dfs.to_csv(summary_file, index=False)
    print(f'Log summary saved to {summary_file}')

    segments_dir = os.path.join(args.base_dir, 'segments')
    if not os.path.exists(segments_dir):
        raise ValueError(f'"segments" directory was not found at {args.base_dir}.')

    all_files = Path(segments_dir).glob('*_segments.txt')
    all_alignment_files = {}
    for file in all_files:
        base_name = re.sub(r'^\d+_', '', file.name)
        if base_name not in all_alignment_files:
            all_alignment_files[base_name] = []
        all_alignment_files[base_name].append(file)

    verified_dir = os.path.join(args.base_dir, 'verified_segments')
    os.makedirs(verified_dir, exist_ok=True)

    def readlines(file):
        with open(file, 'r') as f:
            lines = f.readlines()
        return lines

    stats = {}
    for part, alignment_files in all_alignment_files.items():
        stats[part] = {}
        num_alignment_files = len(alignment_files)
        all_alignments = []
        for alignment in alignment_files:
            all_alignments.append(readlines(alignment))

        with open(os.path.join(verified_dir, part), 'w') as f:
            num_segments = len(all_alignments[0])
            stats[part]['Original number of segments'] = num_segments
            stats[part]['Verified segments'] = 0
            stats[part]['Original Duration, min'] = 0
            stats[part]['Verified Duration, min'] = 0

            for i in range(num_segments):
                line = all_alignments[0][i]
                valid_line = True
                if i == 0:
                    duration = 0
                else:
                    info = line.split('|')[0].split()
                    duration = (float(info[1]) - float(info[0])) / 60
                stats[part]['Original Duration, min'] += duration
                for alignment in all_alignments:
                    if line != alignment[i]:
                        valid_line = False
                if valid_line:
                    f.write(line)
                    stats[part]['Verified segments'] += 1
                    stats[part]['Verified Duration, min'] += duration

    stats = pd.DataFrame.from_dict(stats, orient='index').reset_index()
    stats['Number dropped'] = stats['Original number of segments'] - stats['Verified segments']
    stats['Duration of dropped, min'] = round(stats['Original Duration, min'] - stats['Verified Duration, min'])
    stats['% dropped, min'] = round(stats['Duration of dropped, min'] / stats['Original number of segments'] * 100)
    stats['Misalignment present'] = stats['Number dropped'] > 0
    stats['Original Duration, min'] = round(stats['Original Duration, min'])
    stats['Verified Duration, min'] = round(stats['Verified Duration, min'])
    stats.loc["Total"] = stats.sum()

    stats_file = os.path.join(args.base_dir, 'alignment_summary.csv')
    stats.to_csv(stats_file, index=False)
    print(stats)
    print(f'Alignment summary saved to {stats_file}')