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