# Copyright (c) 2023, 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 multiprocessing as mp from itertools import repeat from pathlib import Path import librosa from tqdm import tqdm from nemo.collections.asr.parts.utils.manifest_utils import read_manifest, write_manifest from nemo.collections.asr.parts.utils.vad_utils import get_frame_labels, load_speech_segments_from_rttm """ This script generates a manifest file for synthetic data generated using the NeMo multispeaker speech data simulator. The audio created from the simulator can be used to train a VAD model using the manifest file contains the following fields: The manifest file contains the following fields: audio_filepath (str): Path to audio file. offset (float): Offset in seconds for the start of the audio file. duration (float): Duration in seconds for the audio file. text (str): Transcription of the audio file. label (list): List of frame labels for the audio file. orig_sample_rate (int): Original sample rate of the audio file. vad_frame_unit_secs (float): Duration in seconds for each frame label. Usage: python build_synthetic_vad_manifest.py \ --input_dir /path/to/synthetic/data \ --frame_length 0.04 \ --output_file /path/to/output/manifest.json """ def generate_manifest_entry(inputs): """ Generates a manifest entry for a single audio file. This function is parallelized using multiprocessing.Pool. Args: inputs (tuple): Tuple containing audio file path and frame length in seconds. inputs[0]: audio_filepath (str): Path to audio file. inputs[1]: vad_frame_unit_secs (float): Duration in seconds for each frame label. Returns: entry (dict): Dictionary containing manifest entry. """ audio_filepath, vad_frame_unit_secs = inputs audio_filepath = Path(audio_filepath) y, sr = librosa.load(str(audio_filepath)) dur = librosa.get_duration(y=y, sr=sr) manifest_path = audio_filepath.parent / Path(f"{audio_filepath.stem}.json") audio_manifest = read_manifest(manifest_path) text = " ".join([x["text"] for x in audio_manifest]) rttm_path = audio_filepath.parent / Path(f"{audio_filepath.stem}.rttm") segments = load_speech_segments_from_rttm(rttm_path) labels = get_frame_labels(segments, vad_frame_unit_secs, 0.0, dur) entry = { "audio_filepath": str(audio_filepath.absolute()), "offset": 0.0, "duration": dur, "text": text, "label": labels, "orig_sample_rate": sr, "vad_frame_unit_secs": vad_frame_unit_secs, } return entry def main(args): wav_list = list(Path(args.input_dir).glob("*.wav")) print(f"Found {len(wav_list)} in directory: {args.input_dir}") inputs = zip(wav_list, repeat(args.frame_length)) with mp.Pool(processes=mp.cpu_count()) as pool: manifest_data = list(tqdm(pool.imap(generate_manifest_entry, inputs), total=len(wav_list))) write_manifest(args.output_file, manifest_data) print(f"Manifest saved to: {args.output_file}") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("input_dir", default=None, help="Path to directory containing synthetic data") parser.add_argument( "-l", "--frame_length", default=0.04, type=float, help="Duration in seconds for each frame label" ) parser.add_argument("-o", "--output_file", default=None, help="Path to output manifest file") args = parser.parse_args() main(args)