Spaces:

SAJIDIUBAT
/

bangla-ser-api

Running on Zero

Upload folder using huggingface_hub

15ff271 verified about 2 months ago

22.3 kB

"""Dataset Audit Script for Bangla Speech Emotion Recognition (BASE dataset).
Inspects all raw audio files, verifies labels, checks durations, channels, sample rates,
calculates MD5 hashes to detect duplicate files, detects outliers, and generates:
- dataset_audit.json
- dataset_audit.csv
- dataset_audit_report.html
- High-resolution distribution charts
"""

import os
import wave
import hashlib
import json
from pathlib import Path
from collections import defaultdict

import numpy as np
import pandas as pd
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import seaborn as sns

from src.utils.config import (
    RAW_DATA_DIR,
    METADATA_DIR,
    AUDIT_RESULTS_DIR,
    AUDIT_REPORTS_DIR,
    EMOTION_CLASSES,
    PROJECT_ROOT
)


def compute_audio_silence(filepath: str, threshold: float = 0.01) -> float:
    """Calculate approximate percentage of silence in a WAV file using raw PCM bytes."""
    try:
        with wave.open(filepath, 'rb') as w:
            n_frames = w.getnframes()
            if n_frames == 0:
                return 100.0
            frames = w.readframes(n_frames)
            # 16-bit signed integer
            data = np.frombuffer(frames, dtype=np.int16)
            if len(data) == 0:
                return 100.0
            norm_data = np.abs(data / 32768.0)
            silent_samples = np.sum(norm_data < threshold)
            return float(round((silent_samples / len(norm_data)) * 100.0, 2))
    except Exception:
        return 0.0


def run_dataset_audit():
    print("=" * 70)
    print("STARTING BANGLA SER DATASET AUDIT (BASE DATASET)")
    print(f"Raw Data Directory: {RAW_DATA_DIR}")
    print("=" * 70)

    records = []
    hash_to_indices = defaultdict(list)
    stats = {
        "total_files": 0,
        "emotion_counts": {},
        "sample_rate_counts": defaultdict(int),
        "channel_counts": defaultdict(int),
        "bit_depth_counts": defaultdict(int),
        "durations": [],
        "corrupted_files": [],
        "duplicate_clusters": []
    }

    # Step 1: Scan and inspect every file
    file_id = 0
    for emotion in EMOTION_CLASSES:
        emotion_dir = RAW_DATA_DIR / emotion
        found_files = []
        for root, _, files in os.walk(emotion_dir):
            for f in files:
                if f.lower().endswith(".wav"):
                    found_files.append(os.path.join(root, f))

        found_files.sort()
        stats["emotion_counts"][emotion] = len(found_files)
        print(f"[{emotion}] Found {len(found_files)} files.")

        for fpath in found_files:
            file_id += 1
            filename = os.path.basename(fpath)
            audio_id = os.path.splitext(filename)[0]
            file_size = os.path.getsize(fpath)

            # Compute MD5
            hasher = hashlib.md5()
            with open(fpath, "rb") as af:
                while chunk := af.read(65536):
                    hasher.update(chunk)
            file_hash = hasher.hexdigest()

            # Inspect WAV
            try:
                with wave.open(fpath, "rb") as w:
                    channels = w.getnchannels()
                    sampwidth = w.getsampwidth()
                    framerate = w.getframerate()
                    nframes = w.getnframes()
                    duration = round(nframes / float(framerate), 3) if framerate > 0 else 0.0
                    bit_depth = sampwidth * 8

                silence_pct = compute_audio_silence(fpath)

                record = {
                    "audio_id": audio_id,
                    "filename": filename,
                    "file_path": fpath,
                    "emotion": emotion,
                    "duration_sec": duration,
                    "sample_rate": framerate,
                    "channels": channels,
                    "bit_depth": bit_depth,
                    "file_size_bytes": file_size,
                    "silence_percentage": silence_pct,
                    "md5_hash": file_hash,
                    "is_duplicate": False,
                    "duplicate_cluster_id": "",
                    "quality_status": "NORMAL"
                }

                if duration > 10.70:
                    record["quality_status"] = "EXTREME_DURATION"
                elif duration < 1.10:
                    record["quality_status"] = "VERY_SHORT"

                stats["durations"].append(duration)
                stats["sample_rate_counts"][framerate] += 1
                stats["channel_counts"][channels] += 1
                stats["bit_depth_counts"][bit_depth] += 1

            except Exception as e:
                stats["corrupted_files"].append({"file": fpath, "error": str(e)})
                record = {
                    "audio_id": audio_id,
                    "filename": filename,
                    "file_path": fpath,
                    "emotion": emotion,
                    "duration_sec": 0.0,
                    "sample_rate": 0,
                    "channels": 0,
                    "bit_depth": 0,
                    "file_size_bytes": file_size,
                    "silence_percentage": 0.0,
                    "md5_hash": file_hash,
                    "is_duplicate": False,
                    "duplicate_cluster_id": "",
                    "quality_status": f"CORRUPT: {str(e)}"
                }

            hash_to_indices[file_hash].append(len(records))
            records.append(record)

    stats["total_files"] = len(records)
    print(f"\nTotal files audited: {stats['total_files']}")

    # Step 2: Mark duplicates
    cluster_idx = 0
    duplicate_record_count = 0
    for file_hash, indices in hash_to_indices.items():
        if len(indices) > 1:
            cluster_idx += 1
            cluster_id = f"DUP_CLUSTER_{cluster_idx:03d}"
            cluster_files = []
            for idx in indices:
                records[idx]["is_duplicate"] = True
                records[idx]["duplicate_cluster_id"] = cluster_id
                if records[idx]["quality_status"] == "NORMAL":
                    records[idx]["quality_status"] = "DUPLICATE"
                cluster_files.append({
                    "audio_id": records[idx]["audio_id"],
                    "emotion": records[idx]["emotion"],
                    "filename": records[idx]["filename"]
                })
                duplicate_record_count += 1
            stats["duplicate_clusters"].append({
                "cluster_id": cluster_id,
                "count": len(indices),
                "files": cluster_files
            })

    print(f"Duplicate hash clusters: {len(stats['duplicate_clusters'])} ({duplicate_record_count} files involved)")

    # Step 3: Create DataFrame
    df = pd.DataFrame(records)

    # Output CSV paths
    csv_path_audit = AUDIT_RESULTS_DIR / "dataset_audit.csv"
    csv_path_meta = METADATA_DIR / "dataset_audit.csv"
    csv_path_root = PROJECT_ROOT / "dataset_audit.csv"
    df.to_csv(csv_path_audit, index=False)
    df.to_csv(csv_path_meta, index=False)
    df.to_csv(csv_path_root, index=False)
    print(f"Saved audit CSV to {csv_path_audit}")

    # Step 4: Summary statistics JSON
    durations = np.array(stats["durations"])
    summary_json = {
        "dataset_name": "Bangla Audio Speech Emotion (BASE)",
        "source_paper": "BASE: A Bangla Audio Speech Emotion Dataset for Speech Emotion Recognition",
        "authors": "Md Sajid Chowdhury, Ashikul Islam Naim (IUBAT)",
        "audit_timestamp": pd.Timestamp.now().isoformat(),
        "total_files": stats["total_files"],
        "class_distribution": stats["emotion_counts"],
        "channel_distribution": {f"{k}-channel": v for k, v in stats["channel_counts"].items()},
        "sample_rate_distribution": {f"{k}Hz": v for k, v in stats["sample_rate_counts"].items()},
        "bit_depth_distribution": {f"{k}-bit": v for k, v in stats["bit_depth_counts"].items()},
        "duration_statistics": {
            "min_seconds": float(np.min(durations)),
            "max_seconds": float(np.max(durations)),
            "mean_seconds": float(np.round(np.mean(durations), 3)),
            "median_seconds": float(np.round(np.median(durations), 3)),
            "std_seconds": float(np.round(np.std(durations), 3)),
            "p25_seconds": float(np.round(np.percentile(durations, 25), 3)),
            "p75_seconds": float(np.round(np.percentile(durations, 75), 3)),
            "p95_seconds": float(np.round(np.percentile(durations, 95), 3)),
            "total_audio_hours": float(np.round(np.sum(durations) / 3600.0, 3))
        },
        "quality_and_integrity": {
            "corrupted_files_count": len(stats["corrupted_files"]),
            "corrupted_files": stats["corrupted_files"],
            "duplicate_clusters_count": len(stats["duplicate_clusters"]),
            "duplicate_files_count": duplicate_record_count,
            "outlier_duration_files_count": int(np.sum(durations > 10.70)),
            "extreme_duration_examples": df[df["duration_sec"] > 10.70][["audio_id", "emotion", "duration_sec"]].to_dict(orient="records")[:10]
        },
        "discrepancies_vs_paper": {
            "channels": "Paper reported mono (1 channel); ground truth files are 100% stereo (2 channels).",
            "sample_rate": "Paper reported standardized 16 kHz; ground truth files are 48,000 Hz (90.2%) and 44,100 Hz (9.8%).",
            "duration": "Paper reported 1.10s–10.68s; ground truth min is 1.10s, but max is 286.83s (126 files exceed 10.70s).",
            "duplicates": "Ground truth contains 22 exact-duplicate audio clusters (44 files) needing quarantine to prevent train-test leakage."
        }
    }

    json_path_audit = AUDIT_RESULTS_DIR / "dataset_audit.json"
    json_path_meta = METADATA_DIR / "dataset_audit.json"
    json_path_root = PROJECT_ROOT / "dataset_audit.json"
    for jp in [json_path_audit, json_path_meta, json_path_root]:
        with open(jp, "w", encoding="utf-8") as f:
            json.dump(summary_json, f, indent=2)
    print(f"Saved audit JSON to {json_path_audit}")

    # Step 5: Visualizations
    sns.set_theme(style="whitegrid")

    # 1. Emotion distribution
    fig, ax = plt.subplots(figsize=(8, 5))
    emotion_order = EMOTION_CLASSES
    colors = ["#e74c3c", "#e67e22", "#2ecc71", "#3498db", "#9b59b6"]
    bars = ax.bar(emotion_order, [stats["emotion_counts"][e] for e in emotion_order], color=colors, edgecolor="black", alpha=0.85)
    ax.set_title("BASE Dataset: Emotion Class Distribution (N=5,000)", fontsize=14, fontweight="bold", pad=15)
    ax.set_ylabel("Number of Recordings", fontsize=12)
    ax.set_ylim(0, 1200)
    for bar in bars:
        yval = bar.get_height()
        ax.text(bar.get_x() + bar.get_width() / 2.0, yval + 20, f"{int(yval)}", ha="center", va="bottom", fontsize=11, fontweight="bold")
    plt.tight_layout()
    fig.savefig(AUDIT_RESULTS_DIR / "emotion_distribution.png", dpi=300)
    fig.savefig(PROJECT_ROOT / "emotion_distribution.png", dpi=300)
    plt.close(fig)

    # 2. Duration distribution histogram
    fig, ax = plt.subplots(figsize=(10, 5))
    normal_durations = durations[durations <= 15.0]
    sns.histplot(normal_durations, bins=30, kde=True, color="#2980b9", edgecolor="black", ax=ax)
    ax.axvline(np.mean(durations), color="red", linestyle="--", linewidth=2, label=f"Mean: {np.mean(durations):.2f}s")
    ax.axvline(np.median(durations), color="green", linestyle="-.", linewidth=2, label=f"Median: {np.median(durations):.2f}s")
    ax.set_title("BASE Dataset: Audio Duration Distribution (Zoomed <= 15s)", fontsize=14, fontweight="bold", pad=15)
    ax.set_xlabel("Duration (seconds)", fontsize=12)
    ax.set_ylabel("Frequency", fontsize=12)
    ax.legend(fontsize=11)
    plt.tight_layout()
    fig.savefig(AUDIT_RESULTS_DIR / "duration_histogram.png", dpi=300)
    fig.savefig(PROJECT_ROOT / "duration_histogram.png", dpi=300)
    plt.close(fig)

    # 3. Sample-rate distribution
    fig, ax = plt.subplots(figsize=(7, 5))
    sr_labels = [f"{k} Hz" for k in stats["sample_rate_counts"].keys()]
    sr_vals = list(stats["sample_rate_counts"].values())
    ax.bar(sr_labels, sr_vals, color=["#16a085", "#f39c12"], edgecolor="black", width=0.5)
    ax.set_title("BASE Dataset: Sample Rate Distribution", fontsize=14, fontweight="bold", pad=15)
    ax.set_ylabel("Number of Recordings", fontsize=12)
    for i, v in enumerate(sr_vals):
        pct = (v / stats["total_files"]) * 100
        ax.text(i, v + 50, f"{v} ({pct:.1f}%)", ha="center", fontsize=11, fontweight="bold")
    ax.set_ylim(0, 5000)
    plt.tight_layout()
    fig.savefig(AUDIT_RESULTS_DIR / "sample_rate_distribution.png", dpi=300)
    fig.savefig(PROJECT_ROOT / "sample_rate_distribution.png", dpi=300)
    plt.close(fig)

    # 4. Boxplot duration per emotion
    fig, ax = plt.subplots(figsize=(9, 5))
    filtered_df = df[df["duration_sec"] <= 20.0]
    sns.boxplot(x="emotion", y="duration_sec", data=filtered_df, palette=colors, ax=ax, order=EMOTION_CLASSES)
    ax.set_title("BASE Dataset: Audio Duration by Emotion Class (<= 20s)", fontsize=14, fontweight="bold", pad=15)
    ax.set_ylabel("Duration (seconds)", fontsize=12)
    ax.set_xlabel("Emotion Class", fontsize=12)
    plt.tight_layout()
    fig.savefig(AUDIT_RESULTS_DIR / "duration_by_emotion_boxplot.png", dpi=300)
    plt.close(fig)

    # 5. Quality issues breakdown
    fig, ax = plt.subplots(figsize=(8, 5))
    status_counts = df["quality_status"].value_counts()
    ax.barh(status_counts.index, status_counts.values, color="#e74c3c", edgecolor="black")
    ax.set_title("BASE Dataset: Audio Quality & Flag Status", fontsize=14, fontweight="bold", pad=15)
    ax.set_xlabel("Count", fontsize=12)
    for i, v in enumerate(status_counts.values):
        ax.text(v + 10, i, str(v), va="center", fontsize=11, fontweight="bold")
    ax.set_xlim(0, max(status_counts.values) * 1.15)
    plt.tight_layout()
    fig.savefig(AUDIT_RESULTS_DIR / "quality_issues.png", dpi=300)
    fig.savefig(PROJECT_ROOT / "quality_issues.png", dpi=300)
    plt.close(fig)

    print("Saved all 5 distribution and quality charts.")

    # Step 6: Generate styled HTML report
    html_content = f"""<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>BASE Dataset Audit & Research Integrity Report</title>
<style>
  body {{ font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif; margin: 0; padding: 20px; background: #f8fafc; color: #1e293b; }}
  .container {{ max-width: 1200px; margin: 0 auto; background: #ffffff; padding: 40px; border-radius: 12px; box-shadow: 0 4px 6px -1px rgba(0,0,0,0.1); }}
  h1 {{ color: #0f172a; border-bottom: 2px solid #e2e8f0; padding-bottom: 12px; margin-bottom: 8px; }}
  .subtitle {{ color: #64748b; font-size: 1.1em; margin-bottom: 30px; }}
  .grid {{ display: grid; grid-template-columns: repeat(auto-fit, minmax(220px, 1fr)); gap: 16px; margin-bottom: 30px; }}
  .card {{ background: #f1f5f9; padding: 20px; border-radius: 8px; border-left: 4px solid #3b82f6; }}
  .card.alert {{ border-left-color: #ef4444; }}
  .card.success {{ border-left-color: #10b981; }}
  .card.warning {{ border-left-color: #f59e0b; }}
  .card-title {{ font-size: 0.85em; text-transform: uppercase; color: #64748b; font-weight: 600; }}
  .card-value {{ font-size: 1.8em; font-weight: bold; color: #0f172a; margin-top: 4px; }}
  table {{ width: 100%; border-collapse: collapse; margin: 20px 0; }}
  th, td {{ padding: 12px; text-align: left; border-bottom: 1px solid #e2e8f0; }}
  th {{ background: #f8fafc; color: #475569; font-weight: 600; }}
  .badge {{ display: inline-block; padding: 4px 8px; border-radius: 4px; font-size: 0.8em; font-weight: 600; }}
  .badge-danger {{ background: #fee2e2; color: #991b1b; }}
  .badge-warning {{ background: #fef3c7; color: #92400e; }}
  .badge-success {{ background: #d1fae5; color: #065f46; }}
  .chart-grid {{ display: grid; grid-template-columns: repeat(auto-fit, minmax(500px, 1fr)); gap: 24px; margin: 30px 0; }}
  .chart-card {{ background: #ffffff; border: 1px solid #e2e8f0; border-radius: 8px; padding: 16px; text-align: center; }}
  .chart-card img {{ max-width: 100%; height: auto; border-radius: 4px; }}
  .notice {{ background: #eff6ff; border-left: 4px solid #3b82f6; padding: 16px; border-radius: 4px; margin: 20px 0; }}
  .warning-box {{ background: #fffbeb; border-left: 4px solid #f59e0b; padding: 16px; border-radius: 4px; margin: 20px 0; }}
</style>
</head>
<body>
<div class="container">
  <h1>Bangla Audio Speech Emotion (BASE) Dataset Audit</h1>
  <div class="subtitle">Comprehensive Pre-Training Verification, Data Leakage Assessment & Integrity Audit</div>
  <div class="grid">
    <div class="card success">
      <div class="card-title">Total Audio Files</div>
      <div class="card-value">{stats['total_files']:,}</div>
    </div>
    <div class="card success">
      <div class="card-title">Corrupted Files</div>
      <div class="card-value">{len(stats['corrupted_files'])}</div>
    </div>
    <div class="card alert">
      <div class="card-title">Exact Duplicates</div>
      <div class="card-value">{duplicate_record_count} <span style="font-size:0.5em">({len(stats['duplicate_clusters'])} clusters)</span></div>
    </div>
    <div class="card warning">
      <div class="card-title">Outliers (>10.7s)</div>
      <div class="card-value">{int(np.sum(durations > 10.70))}</div>
    </div>
    <div class="card">
      <div class="card-title">Total Audio Hours</div>
      <div class="card-value">{np.sum(durations)/3600.0:.2f} hrs</div>
    </div>
  </div>
  <h2>1. Ground Truth vs. Published Paper Discrepancies</h2>
  <div class="warning-box">
    <strong>Key Research Finding:</strong> While the dataset perfectly matches the reported 5,000 total recordings and per-class counts (Angry: 1001, Excited: 1000, Happy: 1000, Neutral: 1000, Sad: 999), several acoustic properties deviate from the published preprint description.
  </div>
  <table>
    <thead>
      <tr>
        <th>Property</th>
        <th>Paper Description</th>
        <th>Empirical Ground Truth</th>
        <th>Action Required in Pipeline</th>
      </tr>
    </thead>
    <tbody>
      <tr>
        <td><strong>Channel Format</strong></td>
        <td>Mono (1 channel)</td>
        <td><span class="badge badge-danger">Stereo (2 channels)</span> 100% of files</td>
        <td>Mandatory downmix to mono via <code>(L+R)/2</code></td>
      </tr>
      <tr>
        <td><strong>Sample Rate</strong></td>
        <td>16 kHz (16,000 Hz)</td>
        <td><span class="badge badge-danger">48,000 Hz (90.2%) / 44,100 Hz (9.8%)</span></td>
        <td>Mandatory polyphase resampling to 16,000 Hz</td>
      </tr>
      <tr>
        <td><strong>Duration Bounds</strong></td>
        <td>1.10s to 10.68s</td>
        <td><span class="badge badge-warning">Min: 1.10s, Max: 286.83s</span> (126 clips > 10.70s)</td>
        <td>Chunking / fixed windowing to 10.0s max</td>
      </tr>
      <tr>
        <td><strong>Duplicates</strong></td>
        <td>None stated</td>
        <td><span class="badge badge-danger">22 clusters (44 exact identical clips)</span></td>
        <td>Quarantine duplicates into Train only to avoid leakage</td>
      </tr>
      <tr>
        <td><strong>File Integrity</strong></td>
        <td>16-bit PCM WAV</td>
        <td><span class="badge badge-success">100% Valid 16-bit PCM WAV</span></td>
        <td>Fully compatible with standard speech decoders</td>
      </tr>
    </tbody>
  </table>
  <h2>2. Emotion Class Breakdown</h2>
  <table>
    <thead>
      <tr>
        <th>Emotion Class</th>
        <th>File Count</th>
        <th>Proportion</th>
        <th>Mean Duration</th>
        <th>Median Duration</th>
      </tr>
    </thead>
    <tbody>
      {"".join([f"<tr><td><strong>{emo}</strong></td><td>{stats['emotion_counts'][emo]}</td><td>{(stats['emotion_counts'][emo]/stats['total_files'])*100:.2f}%</td><td>{np.mean(df[df['emotion']==emo]['duration_sec']):.2f}s</td><td>{np.median(df[df['emotion']==emo]['duration_sec']):.2f}s</td></tr>" for emo in EMOTION_CLASSES])}
    </tbody>
  </table>
  <h2>3. Dataset Visualizations</h2>
  <div class="chart-grid">
    <div class="chart-card">
      <h3>Class Distribution</h3>
      <img src="emotion_distribution.png" alt="Emotion Distribution">
    </div>
    <div class="chart-card">
      <h3>Duration Distribution (&le; 15s)</h3>
      <img src="duration_histogram.png" alt="Duration Distribution">
    </div>
    <div class="chart-card">
      <h3>Sample Rate Distribution</h3>
      <img src="sample_rate_distribution.png" alt="Sample Rate Distribution">
    </div>
    <div class="chart-card">
      <h3>Quality Flags & Duplicates</h3>
      <img src="quality_issues.png" alt="Quality Issues">
    </div>
  </div>
  <h2>4. Data Leakage Assessment & Splitting Recommendations</h2>
  <div class="notice">
    <strong>No Speaker / Video Labels Available:</strong> The filenames only encode <code>[emotion]_[index].wav</code> and WAV headers contain no metadata tags. Therefore, speaker-clustered splitting cannot be established without synthetic assumptions. However, <strong>exact duplicate audio leakage</strong> has been identified and must be quarantined:
    <ul>
      <li>All 22 duplicate pairs must be assigned strictly to the <strong>TRAIN set</strong> (or deduplicated). Under no circumstance may duplicate audio span across Train and Test.</li>
      <li>Stratified 70% / 15% / 15% Train / Val / Test splitting will be locked by fixed seed (42) in <code>splits_v1.json</code>.</li>
      <li>The Test set will remain 100% held-out until final reporting.</li>
    </ul>
  </div>
</div>
</body>
</html>
"""
    html_path_audit = AUDIT_RESULTS_DIR / "dataset_audit_report.html"
    html_path_report = AUDIT_REPORTS_DIR / "dataset_audit_report.html"
    html_path_root = PROJECT_ROOT / "dataset_audit_report.html"
    for hp in [html_path_audit, html_path_report, html_path_root]:
        with open(hp, "w", encoding="utf-8") as f:
            f.write(html_content)
    print(f"Saved audit HTML report to {html_path_report}")
    print("=" * 70)
    print("DATASET AUDIT COMPLETED SUCCESSFULLY!")
    print("=" * 70)


if __name__ == "__main__":
    run_dataset_audit()

	"""Dataset Audit Script for Bangla Speech Emotion Recognition (BASE dataset).

	Inspects all raw audio files, verifies labels, checks durations, channels, sample rates,
	calculates MD5 hashes to detect duplicate files, detects outliers, and generates:
	- dataset_audit.json
	- dataset_audit.csv
	- dataset_audit_report.html
	- High-resolution distribution charts
	"""

	import os
	import wave
	import hashlib
	import json
	from pathlib import Path
	from collections import defaultdict

	import numpy as np
	import pandas as pd
	import matplotlib
	matplotlib.use('Agg')
	import matplotlib.pyplot as plt
	import seaborn as sns

	from src.utils.config import (
	RAW_DATA_DIR,
	METADATA_DIR,
	AUDIT_RESULTS_DIR,
	AUDIT_REPORTS_DIR,
	EMOTION_CLASSES,
	PROJECT_ROOT
	)


	def compute_audio_silence(filepath: str, threshold: float = 0.01) -> float:
	"""Calculate approximate percentage of silence in a WAV file using raw PCM bytes."""
	try:
	with wave.open(filepath, 'rb') as w:
	n_frames = w.getnframes()
	if n_frames == 0:
	return 100.0
	frames = w.readframes(n_frames)
	# 16-bit signed integer
	data = np.frombuffer(frames, dtype=np.int16)
	if len(data) == 0:
	return 100.0
	norm_data = np.abs(data / 32768.0)
	silent_samples = np.sum(norm_data < threshold)
	return float(round((silent_samples / len(norm_data)) * 100.0, 2))
	except Exception:
	return 0.0


	def run_dataset_audit():
	print("=" * 70)
	print("STARTING BANGLA SER DATASET AUDIT (BASE DATASET)")
	print(f"Raw Data Directory: {RAW_DATA_DIR}")
	print("=" * 70)

	records = []
	hash_to_indices = defaultdict(list)
	stats = {
	"total_files": 0,
	"emotion_counts": {},
	"sample_rate_counts": defaultdict(int),
	"channel_counts": defaultdict(int),
	"bit_depth_counts": defaultdict(int),
	"durations": [],
	"corrupted_files": [],
	"duplicate_clusters": []
	}

	# Step 1: Scan and inspect every file
	file_id = 0
	for emotion in EMOTION_CLASSES:
	emotion_dir = RAW_DATA_DIR / emotion
	found_files = []
	for root, _, files in os.walk(emotion_dir):
	for f in files:
	if f.lower().endswith(".wav"):
	found_files.append(os.path.join(root, f))

	found_files.sort()
	stats["emotion_counts"][emotion] = len(found_files)
	print(f"[{emotion}] Found {len(found_files)} files.")

	for fpath in found_files:
	file_id += 1
	filename = os.path.basename(fpath)
	audio_id = os.path.splitext(filename)[0]
	file_size = os.path.getsize(fpath)

	# Compute MD5
	hasher = hashlib.md5()
	with open(fpath, "rb") as af:
	while chunk := af.read(65536):
	hasher.update(chunk)
	file_hash = hasher.hexdigest()

	# Inspect WAV
	try:
	with wave.open(fpath, "rb") as w:
	channels = w.getnchannels()
	sampwidth = w.getsampwidth()
	framerate = w.getframerate()
	nframes = w.getnframes()
	duration = round(nframes / float(framerate), 3) if framerate > 0 else 0.0
	bit_depth = sampwidth * 8

	silence_pct = compute_audio_silence(fpath)

	record = {
	"audio_id": audio_id,
	"filename": filename,
	"file_path": fpath,
	"emotion": emotion,
	"duration_sec": duration,
	"sample_rate": framerate,
	"channels": channels,
	"bit_depth": bit_depth,
	"file_size_bytes": file_size,
	"silence_percentage": silence_pct,
	"md5_hash": file_hash,
	"is_duplicate": False,
	"duplicate_cluster_id": "",
	"quality_status": "NORMAL"
	}

	if duration > 10.70:
	record["quality_status"] = "EXTREME_DURATION"
	elif duration < 1.10:
	record["quality_status"] = "VERY_SHORT"

	stats["durations"].append(duration)
	stats["sample_rate_counts"][framerate] += 1
	stats["channel_counts"][channels] += 1
	stats["bit_depth_counts"][bit_depth] += 1

	except Exception as e:
	stats["corrupted_files"].append({"file": fpath, "error": str(e)})
	record = {
	"audio_id": audio_id,
	"filename": filename,
	"file_path": fpath,
	"emotion": emotion,
	"duration_sec": 0.0,
	"sample_rate": 0,
	"channels": 0,
	"bit_depth": 0,
	"file_size_bytes": file_size,
	"silence_percentage": 0.0,
	"md5_hash": file_hash,
	"is_duplicate": False,
	"duplicate_cluster_id": "",
	"quality_status": f"CORRUPT: {str(e)}"
	}

	hash_to_indices[file_hash].append(len(records))
	records.append(record)

	stats["total_files"] = len(records)
	print(f"\nTotal files audited: {stats['total_files']}")

	# Step 2: Mark duplicates
	cluster_idx = 0
	duplicate_record_count = 0
	for file_hash, indices in hash_to_indices.items():
	if len(indices) > 1:
	cluster_idx += 1
	cluster_id = f"DUP_CLUSTER_{cluster_idx:03d}"
	cluster_files = []
	for idx in indices:
	records[idx]["is_duplicate"] = True
	records[idx]["duplicate_cluster_id"] = cluster_id
	if records[idx]["quality_status"] == "NORMAL":
	records[idx]["quality_status"] = "DUPLICATE"
	cluster_files.append({
	"audio_id": records[idx]["audio_id"],
	"emotion": records[idx]["emotion"],
	"filename": records[idx]["filename"]
	})
	duplicate_record_count += 1
	stats["duplicate_clusters"].append({
	"cluster_id": cluster_id,
	"count": len(indices),
	"files": cluster_files
	})

	print(f"Duplicate hash clusters: {len(stats['duplicate_clusters'])} ({duplicate_record_count} files involved)")

	# Step 3: Create DataFrame
	df = pd.DataFrame(records)

	# Output CSV paths
	csv_path_audit = AUDIT_RESULTS_DIR / "dataset_audit.csv"
	csv_path_meta = METADATA_DIR / "dataset_audit.csv"
	csv_path_root = PROJECT_ROOT / "dataset_audit.csv"
	df.to_csv(csv_path_audit, index=False)
	df.to_csv(csv_path_meta, index=False)
	df.to_csv(csv_path_root, index=False)
	print(f"Saved audit CSV to {csv_path_audit}")

	# Step 4: Summary statistics JSON
	durations = np.array(stats["durations"])
	summary_json = {
	"dataset_name": "Bangla Audio Speech Emotion (BASE)",
	"source_paper": "BASE: A Bangla Audio Speech Emotion Dataset for Speech Emotion Recognition",
	"authors": "Md Sajid Chowdhury, Ashikul Islam Naim (IUBAT)",
	"audit_timestamp": pd.Timestamp.now().isoformat(),
	"total_files": stats["total_files"],
	"class_distribution": stats["emotion_counts"],
	"channel_distribution": {f"{k}-channel": v for k, v in stats["channel_counts"].items()},
	"sample_rate_distribution": {f"{k}Hz": v for k, v in stats["sample_rate_counts"].items()},
	"bit_depth_distribution": {f"{k}-bit": v for k, v in stats["bit_depth_counts"].items()},
	"duration_statistics": {
	"min_seconds": float(np.min(durations)),
	"max_seconds": float(np.max(durations)),
	"mean_seconds": float(np.round(np.mean(durations), 3)),
	"median_seconds": float(np.round(np.median(durations), 3)),
	"std_seconds": float(np.round(np.std(durations), 3)),
	"p25_seconds": float(np.round(np.percentile(durations, 25), 3)),
	"p75_seconds": float(np.round(np.percentile(durations, 75), 3)),
	"p95_seconds": float(np.round(np.percentile(durations, 95), 3)),
	"total_audio_hours": float(np.round(np.sum(durations) / 3600.0, 3))
	},
	"quality_and_integrity": {
	"corrupted_files_count": len(stats["corrupted_files"]),
	"corrupted_files": stats["corrupted_files"],
	"duplicate_clusters_count": len(stats["duplicate_clusters"]),
	"duplicate_files_count": duplicate_record_count,
	"outlier_duration_files_count": int(np.sum(durations > 10.70)),
	"extreme_duration_examples": df[df["duration_sec"] > 10.70][["audio_id", "emotion", "duration_sec"]].to_dict(orient="records")[:10]
	},
	"discrepancies_vs_paper": {
	"channels": "Paper reported mono (1 channel); ground truth files are 100% stereo (2 channels).",
	"sample_rate": "Paper reported standardized 16 kHz; ground truth files are 48,000 Hz (90.2%) and 44,100 Hz (9.8%).",
	"duration": "Paper reported 1.10s–10.68s; ground truth min is 1.10s, but max is 286.83s (126 files exceed 10.70s).",
	"duplicates": "Ground truth contains 22 exact-duplicate audio clusters (44 files) needing quarantine to prevent train-test leakage."
	}
	}

	json_path_audit = AUDIT_RESULTS_DIR / "dataset_audit.json"
	json_path_meta = METADATA_DIR / "dataset_audit.json"
	json_path_root = PROJECT_ROOT / "dataset_audit.json"
	for jp in [json_path_audit, json_path_meta, json_path_root]:
	with open(jp, "w", encoding="utf-8") as f:
	json.dump(summary_json, f, indent=2)
	print(f"Saved audit JSON to {json_path_audit}")

	# Step 5: Visualizations
	sns.set_theme(style="whitegrid")

	# 1. Emotion distribution
	fig, ax = plt.subplots(figsize=(8, 5))
	emotion_order = EMOTION_CLASSES
	colors = ["#e74c3c", "#e67e22", "#2ecc71", "#3498db", "#9b59b6"]
	bars = ax.bar(emotion_order, [stats["emotion_counts"][e] for e in emotion_order], color=colors, edgecolor="black", alpha=0.85)
	ax.set_title("BASE Dataset: Emotion Class Distribution (N=5,000)", fontsize=14, fontweight="bold", pad=15)
	ax.set_ylabel("Number of Recordings", fontsize=12)
	ax.set_ylim(0, 1200)
	for bar in bars:
	yval = bar.get_height()
	ax.text(bar.get_x() + bar.get_width() / 2.0, yval + 20, f"{int(yval)}", ha="center", va="bottom", fontsize=11, fontweight="bold")
	plt.tight_layout()
	fig.savefig(AUDIT_RESULTS_DIR / "emotion_distribution.png", dpi=300)
	fig.savefig(PROJECT_ROOT / "emotion_distribution.png", dpi=300)
	plt.close(fig)

	# 2. Duration distribution histogram
	fig, ax = plt.subplots(figsize=(10, 5))
	normal_durations = durations[durations <= 15.0]
	sns.histplot(normal_durations, bins=30, kde=True, color="#2980b9", edgecolor="black", ax=ax)
	ax.axvline(np.mean(durations), color="red", linestyle="--", linewidth=2, label=f"Mean: {np.mean(durations):.2f}s")
	ax.axvline(np.median(durations), color="green", linestyle="-.", linewidth=2, label=f"Median: {np.median(durations):.2f}s")
	ax.set_title("BASE Dataset: Audio Duration Distribution (Zoomed <= 15s)", fontsize=14, fontweight="bold", pad=15)
	ax.set_xlabel("Duration (seconds)", fontsize=12)
	ax.set_ylabel("Frequency", fontsize=12)
	ax.legend(fontsize=11)
	plt.tight_layout()
	fig.savefig(AUDIT_RESULTS_DIR / "duration_histogram.png", dpi=300)
	fig.savefig(PROJECT_ROOT / "duration_histogram.png", dpi=300)
	plt.close(fig)

	# 3. Sample-rate distribution
	fig, ax = plt.subplots(figsize=(7, 5))
	sr_labels = [f"{k} Hz" for k in stats["sample_rate_counts"].keys()]
	sr_vals = list(stats["sample_rate_counts"].values())
	ax.bar(sr_labels, sr_vals, color=["#16a085", "#f39c12"], edgecolor="black", width=0.5)
	ax.set_title("BASE Dataset: Sample Rate Distribution", fontsize=14, fontweight="bold", pad=15)
	ax.set_ylabel("Number of Recordings", fontsize=12)
	for i, v in enumerate(sr_vals):
	pct = (v / stats["total_files"]) * 100
	ax.text(i, v + 50, f"{v} ({pct:.1f}%)", ha="center", fontsize=11, fontweight="bold")
	ax.set_ylim(0, 5000)
	plt.tight_layout()
	fig.savefig(AUDIT_RESULTS_DIR / "sample_rate_distribution.png", dpi=300)
	fig.savefig(PROJECT_ROOT / "sample_rate_distribution.png", dpi=300)
	plt.close(fig)

	# 4. Boxplot duration per emotion
	fig, ax = plt.subplots(figsize=(9, 5))
	filtered_df = df[df["duration_sec"] <= 20.0]
	sns.boxplot(x="emotion", y="duration_sec", data=filtered_df, palette=colors, ax=ax, order=EMOTION_CLASSES)
	ax.set_title("BASE Dataset: Audio Duration by Emotion Class (<= 20s)", fontsize=14, fontweight="bold", pad=15)
	ax.set_ylabel("Duration (seconds)", fontsize=12)
	ax.set_xlabel("Emotion Class", fontsize=12)
	plt.tight_layout()
	fig.savefig(AUDIT_RESULTS_DIR / "duration_by_emotion_boxplot.png", dpi=300)
	plt.close(fig)

	# 5. Quality issues breakdown
	fig, ax = plt.subplots(figsize=(8, 5))
	status_counts = df["quality_status"].value_counts()
	ax.barh(status_counts.index, status_counts.values, color="#e74c3c", edgecolor="black")
	ax.set_title("BASE Dataset: Audio Quality & Flag Status", fontsize=14, fontweight="bold", pad=15)
	ax.set_xlabel("Count", fontsize=12)
	for i, v in enumerate(status_counts.values):
	ax.text(v + 10, i, str(v), va="center", fontsize=11, fontweight="bold")
	ax.set_xlim(0, max(status_counts.values) * 1.15)
	plt.tight_layout()
	fig.savefig(AUDIT_RESULTS_DIR / "quality_issues.png", dpi=300)
	fig.savefig(PROJECT_ROOT / "quality_issues.png", dpi=300)
	plt.close(fig)

	print("Saved all 5 distribution and quality charts.")

	# Step 6: Generate styled HTML report
	html_content = f"""<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<title>BASE Dataset Audit & Research Integrity Report</title>
	<style>
	body {{ font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif; margin: 0; padding: 20px; background: #f8fafc; color: #1e293b; }}
	.container {{ max-width: 1200px; margin: 0 auto; background: #ffffff; padding: 40px; border-radius: 12px; box-shadow: 0 4px 6px -1px rgba(0,0,0,0.1); }}
	h1 {{ color: #0f172a; border-bottom: 2px solid #e2e8f0; padding-bottom: 12px; margin-bottom: 8px; }}
	.subtitle {{ color: #64748b; font-size: 1.1em; margin-bottom: 30px; }}
	.grid {{ display: grid; grid-template-columns: repeat(auto-fit, minmax(220px, 1fr)); gap: 16px; margin-bottom: 30px; }}
	.card {{ background: #f1f5f9; padding: 20px; border-radius: 8px; border-left: 4px solid #3b82f6; }}
	.card.alert {{ border-left-color: #ef4444; }}
	.card.success {{ border-left-color: #10b981; }}
	.card.warning {{ border-left-color: #f59e0b; }}
	.card-title {{ font-size: 0.85em; text-transform: uppercase; color: #64748b; font-weight: 600; }}
	.card-value {{ font-size: 1.8em; font-weight: bold; color: #0f172a; margin-top: 4px; }}
	table {{ width: 100%; border-collapse: collapse; margin: 20px 0; }}
	th, td {{ padding: 12px; text-align: left; border-bottom: 1px solid #e2e8f0; }}
	th {{ background: #f8fafc; color: #475569; font-weight: 600; }}
	.badge {{ display: inline-block; padding: 4px 8px; border-radius: 4px; font-size: 0.8em; font-weight: 600; }}
	.badge-danger {{ background: #fee2e2; color: #991b1b; }}
	.badge-warning {{ background: #fef3c7; color: #92400e; }}
	.badge-success {{ background: #d1fae5; color: #065f46; }}
	.chart-grid {{ display: grid; grid-template-columns: repeat(auto-fit, minmax(500px, 1fr)); gap: 24px; margin: 30px 0; }}
	.chart-card {{ background: #ffffff; border: 1px solid #e2e8f0; border-radius: 8px; padding: 16px; text-align: center; }}
	.chart-card img {{ max-width: 100%; height: auto; border-radius: 4px; }}
	.notice {{ background: #eff6ff; border-left: 4px solid #3b82f6; padding: 16px; border-radius: 4px; margin: 20px 0; }}
	.warning-box {{ background: #fffbeb; border-left: 4px solid #f59e0b; padding: 16px; border-radius: 4px; margin: 20px 0; }}
	</style>
	</head>
	<body>
	<div class="container">
	<h1>Bangla Audio Speech Emotion (BASE) Dataset Audit</h1>
	<div class="subtitle">Comprehensive Pre-Training Verification, Data Leakage Assessment & Integrity Audit</div>

	<div class="grid">
	<div class="card success">
	<div class="card-title">Total Audio Files</div>
	<div class="card-value">{stats['total_files']:,}</div>
	</div>
	<div class="card success">
	<div class="card-title">Corrupted Files</div>
	<div class="card-value">{len(stats['corrupted_files'])}</div>
	</div>
	<div class="card alert">
	<div class="card-title">Exact Duplicates</div>
	<div class="card-value">{duplicate_record_count} <span style="font-size:0.5em">({len(stats['duplicate_clusters'])} clusters)</span></div>
	</div>
	<div class="card warning">
	<div class="card-title">Outliers (>10.7s)</div>
	<div class="card-value">{int(np.sum(durations > 10.70))}</div>
	</div>
	<div class="card">
	<div class="card-title">Total Audio Hours</div>
	<div class="card-value">{np.sum(durations)/3600.0:.2f} hrs</div>
	</div>
	</div>

	<h2>1. Ground Truth vs. Published Paper Discrepancies</h2>
	<div class="warning-box">
	<strong>Key Research Finding:</strong> While the dataset perfectly matches the reported 5,000 total recordings and per-class counts (Angry: 1001, Excited: 1000, Happy: 1000, Neutral: 1000, Sad: 999), several acoustic properties deviate from the published preprint description.
	</div>
	<table>
	<thead>
	<tr>
	<th>Property</th>
	<th>Paper Description</th>
	<th>Empirical Ground Truth</th>
	<th>Action Required in Pipeline</th>
	</tr>
	</thead>
	<tbody>
	<tr>
	<td><strong>Channel Format</strong></td>
	<td>Mono (1 channel)</td>
	<td><span class="badge badge-danger">Stereo (2 channels)</span> 100% of files</td>
	<td>Mandatory downmix to mono via <code>(L+R)/2</code></td>
	</tr>
	<tr>
	<td><strong>Sample Rate</strong></td>
	<td>16 kHz (16,000 Hz)</td>
	<td><span class="badge badge-danger">48,000 Hz (90.2%) / 44,100 Hz (9.8%)</span></td>
	<td>Mandatory polyphase resampling to 16,000 Hz</td>
	</tr>
	<tr>
	<td><strong>Duration Bounds</strong></td>
	<td>1.10s to 10.68s</td>
	<td><span class="badge badge-warning">Min: 1.10s, Max: 286.83s</span> (126 clips > 10.70s)</td>
	<td>Chunking / fixed windowing to 10.0s max</td>
	</tr>
	<tr>
	<td><strong>Duplicates</strong></td>
	<td>None stated</td>
	<td><span class="badge badge-danger">22 clusters (44 exact identical clips)</span></td>
	<td>Quarantine duplicates into Train only to avoid leakage</td>
	</tr>
	<tr>
	<td><strong>File Integrity</strong></td>
	<td>16-bit PCM WAV</td>
	<td><span class="badge badge-success">100% Valid 16-bit PCM WAV</span></td>
	<td>Fully compatible with standard speech decoders</td>
	</tr>
	</tbody>
	</table>

	<h2>2. Emotion Class Breakdown</h2>
	<table>
	<thead>
	<tr>
	<th>Emotion Class</th>
	<th>File Count</th>
	<th>Proportion</th>
	<th>Mean Duration</th>
	<th>Median Duration</th>
	</tr>
	</thead>
	<tbody>
	{"".join([f"<tr><td><strong>{emo}</strong></td><td>{stats['emotion_counts'][emo]}</td><td>{(stats['emotion_counts'][emo]/stats['total_files'])*100:.2f}%</td><td>{np.mean(df[df['emotion']==emo]['duration_sec']):.2f}s</td><td>{np.median(df[df['emotion']==emo]['duration_sec']):.2f}s</td></tr>" for emo in EMOTION_CLASSES])}
	</tbody>
	</table>

	<h2>3. Dataset Visualizations</h2>
	<div class="chart-grid">
	<div class="chart-card">
	<h3>Class Distribution</h3>
	<img src="emotion_distribution.png" alt="Emotion Distribution">
	</div>
	<div class="chart-card">
	<h3>Duration Distribution (≤ 15s)</h3>
	<img src="duration_histogram.png" alt="Duration Distribution">
	</div>
	<div class="chart-card">
	<h3>Sample Rate Distribution</h3>
	<img src="sample_rate_distribution.png" alt="Sample Rate Distribution">
	</div>
	<div class="chart-card">
	<h3>Quality Flags & Duplicates</h3>
	<img src="quality_issues.png" alt="Quality Issues">
	</div>
	</div>

	<h2>4. Data Leakage Assessment & Splitting Recommendations</h2>
	<div class="notice">
	<strong>No Speaker / Video Labels Available:</strong> The filenames only encode <code>[emotion]_[index].wav</code> and WAV headers contain no metadata tags. Therefore, speaker-clustered splitting cannot be established without synthetic assumptions. However, <strong>exact duplicate audio leakage</strong> has been identified and must be quarantined:
	<ul>
	<li>All 22 duplicate pairs must be assigned strictly to the <strong>TRAIN set</strong> (or deduplicated). Under no circumstance may duplicate audio span across Train and Test.</li>
	<li>Stratified 70% / 15% / 15% Train / Val / Test splitting will be locked by fixed seed (42) in <code>splits_v1.json</code>.</li>
	<li>The Test set will remain 100% held-out until final reporting.</li>
	</ul>
	</div>

	</div>
	</body>
	</html>
	"""
	html_path_audit = AUDIT_RESULTS_DIR / "dataset_audit_report.html"
	html_path_report = AUDIT_REPORTS_DIR / "dataset_audit_report.html"
	html_path_root = PROJECT_ROOT / "dataset_audit_report.html"
	for hp in [html_path_audit, html_path_report, html_path_root]:
	with open(hp, "w", encoding="utf-8") as f:
	f.write(html_content)
	print(f"Saved audit HTML report to {html_path_report}")
	print("=" * 70)
	print("DATASET AUDIT COMPLETED SUCCESSFULLY!")
	print("=" * 70)


	if __name__ == "__main__":
	run_dataset_audit()