tools/ctc_segmentation/scripts/cut_audio.py

# Copyright (c) 2020, 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 json
import os
from glob import glob

import numpy as np
from scipy.io import wavfile
from tqdm import tqdm

parser = argparse.ArgumentParser(description="Cut audio on the segments based on segments")
parser.add_argument("--output_dir", type=str, help="Path to output directory", required=True)
parser.add_argument(
    "--alignment",
    type=str,
    required=True,
    help="Path to a data directory with alignments or a single .txt file with timestamps - result of the ctc-segmentation",
)
parser.add_argument("--threshold", type=float, default=-5, help="Minimum score value accepted")
parser.add_argument("--offset", type=int, default=0, help="Offset, s")
parser.add_argument("--batch_size", type=int, default=64, help="Batch size for inference")
parser.add_argument(
    "--edge_duration",
    type=float,
    help="Duration of audio for mean absolute value calculation at the edges, s",
    default=0.05,
)
parser.add_argument("--sample_rate", type=int, help="Sample rate, Hz", default=16000)
parser.add_argument(
    "--max_duration",
    type=int,
    help="Maximum audio duration (seconds). Samples that are longer will be dropped",
    default=60,
)


def process_alignment(alignment_file: str, manifest: str, clips_dir: str, args):
    """ Cut original audio file into audio segments based on alignment_file

    Args:
        alignment_file: path to the file with segmented text and corresponding time stamps.
            The first line of the file contains the path to the original audio file
        manifest: path to .json manifest to save segments metadata
        clips_dir: path to a directory to save audio clips
        args: main script args
    """
    if not os.path.exists(alignment_file):
        raise ValueError(f"{alignment_file} not found")

    base_name = os.path.basename(alignment_file).replace("_segments.txt", "")

    # read the segments, note the first line contains the path to the original audio
    segments = []
    ref_text_processed = []
    ref_text_no_preprocessing = []
    ref_text_normalized = []
    with open(alignment_file, "r") as f:
        for line in f:
            line = line.split("|")
            # read audio file name from the first line
            if len(line) == 1:
                audio_file = line[0].strip()
                continue
            ref_text_processed.append(line[1].strip())
            ref_text_no_preprocessing.append(line[2].strip())
            ref_text_normalized.append(line[3].strip())
            line = line[0].split()
            segments.append((float(line[0]) + args.offset / 1000, float(line[1]) + args.offset / 1000, float(line[2])))

    # cut the audio into segments and save the final manifests at output_dir
    sampling_rate, signal = wavfile.read(audio_file)
    original_duration = len(signal) / sampling_rate

    num_samples = int(args.edge_duration * args.sample_rate)
    low_score_dur = 0
    high_score_dur = 0
    with open(manifest, "a", encoding="utf8") as f:
        for i, (st, end, score) in enumerate(segments):
            segment = signal[round(st * sampling_rate) : round(end * sampling_rate)]
            duration = len(segment) / sampling_rate
            if duration > args.max_duration:
                continue
            if duration > 0:
                text_processed = ref_text_processed[i].strip()
                text_no_preprocessing = ref_text_no_preprocessing[i].strip()
                text_normalized = ref_text_normalized[i].strip()
                if score >= args.threshold:
                    high_score_dur += duration
                    audio_filepath = os.path.join(clips_dir, f"{base_name}_{i:04}.wav")
                    wavfile.write(audio_filepath, sampling_rate, segment)

                    assert len(signal.shape) == 1 and sampling_rate == args.sample_rate, "check sampling rate"

                    info = {
                        "audio_filepath": audio_filepath,
                        "duration": duration,
                        "text": text_processed,
                        "text_no_preprocessing": text_no_preprocessing,
                        "text_normalized": text_normalized,
                        "score": round(score, 2),
                        "start_abs": float(np.mean(np.abs(segment[:num_samples]))),
                        "end_abs": float(np.mean(np.abs(segment[-num_samples:]))),
                    }
                    json.dump(info, f, ensure_ascii=False)
                    f.write("\n")
                else:
                    low_score_dur += duration

    # keep track of duration of the deleted segments
    del_duration = 0
    begin = 0

    for i, (st, end, _) in enumerate(segments):
        if st - begin > 0.01:
            segment = signal[int(begin * sampling_rate) : int(st * sampling_rate)]
            duration = len(segment) / sampling_rate
            del_duration += duration
        begin = end

    segment = signal[int(begin * sampling_rate) :]
    duration = len(segment) / sampling_rate
    del_duration += duration

    stats = (
        args.output_dir,
        base_name,
        round(original_duration),
        round(high_score_dur),
        round(low_score_dur),
        round(del_duration),
    )
    return stats


if __name__ == "__main__":
    args = parser.parse_args()
    print("Splitting audio files into segments...")

    if os.path.isdir(args.alignment):
        alignment_files = glob(f"{args.alignment}/*_segments.txt")
    else:
        alignment_files = [args.alignment]

    # create a directory to store segments with alignement confindence score avove the threshold
    args.output_dir = os.path.abspath(args.output_dir)
    clips_dir = os.path.join(args.output_dir, "clips")
    manifest_dir = os.path.join(args.output_dir, "manifests")
    os.makedirs(clips_dir, exist_ok=True)
    os.makedirs(manifest_dir, exist_ok=True)

    manifest = os.path.join(manifest_dir, "manifest.json")
    if os.path.exists(manifest):
        os.remove(manifest)

    stats_file = os.path.join(args.output_dir, "stats.tsv")
    with open(stats_file, "w") as f:
        f.write("Folder\tSegment\tOriginal dur (s)\tHigh quality dur (s)\tLow quality dur (s)\tDeleted dur (s)\n")

        high_score_dur = 0
        low_score_dur = 0
        del_duration = 0
        original_dur = 0

        for alignment_file in tqdm(alignment_files):
            stats = process_alignment(alignment_file, manifest, clips_dir, args)
            original_dur += stats[-4]
            high_score_dur += stats[-3]
            low_score_dur += stats[-2]
            del_duration += stats[-1]
            stats = "\t".join([str(t) for t in stats]) + "\n"
            f.write(stats)

        f.write(f"Total\t\t{round(high_score_dur)}\t{round(low_score_dur)}\t{del_duration}")

    print(f"Original duration  : {round(original_dur / 60)}min")
    print(f"High score segments: {round(high_score_dur / 60)}min ({round(high_score_dur/original_dur*100)}%)")
    print(f"Low score segments : {round(low_score_dur / 60)}min ({round(low_score_dur/original_dur*100)}%)")
    print(f"Deleted segments   : {round(del_duration / 60)}min ({round(del_duration/original_dur*100)}%)")
    print(f"Stats saved at {stats_file}")
    print(f"Manifest saved at {manifest}")