import os
import sys
import soundfile as sf
from tqdm import tqdm

def is_significant_audio(file_path, silence_threshold=-40, silence_percent=90):
    """
    Check if an audio file contains significant non-silent parts.
    """
    try:
        data, samplerate = sf.read(file_path)
        if len(data) == 0:
            return False  # Empty file
        
        # Calculate audio energy
        energy = (data ** 2).mean()
        silence_ratio = (energy < silence_threshold).sum() / len(data) * 100
        return silence_ratio < silence_percent
    except Exception as e:
        print(f"Error processing {file_path}: {e}")
        return False

def filter_manifest(manifest_path, output_path, dataset_dir):
    """
    Read the manifest file, check for silence, and write filtered files.
    """
    with open(manifest_path, 'r') as f:
        lines = f.readlines()

    filtered_lines = [lines[0]]  # Keep the header
    for line in tqdm(lines[1:], desc=f"Processing {manifest_path}"):
        file_path = os.path.join(dataset_dir, line.split("\t")[0])
        if is_significant_audio(file_path):
            filtered_lines.append(line)
        else:
            print(f"Skipping file due to silence: {file_path}")

    with open(output_path, 'w') as f_out:
        f_out.writelines(filtered_lines)

if __name__ == "__main__":
    train_manifest = sys.argv[1]
    valid_manifest = sys.argv[2]
    output_dir = sys.argv[3]

    os.makedirs(output_dir, exist_ok=True)

    dataset_dir = "dataset"
    filter_manifest(train_manifest, os.path.join(output_dir, "train.tsv"), dataset_dir)
    filter_manifest(valid_manifest, os.path.join(output_dir, "valid.tsv"), dataset_dir)