Add helper scripts (create_parquet.py, upload_to_hf.py)

scripts/create_parquet.py

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+#!/usr/bin/env python3
+"""
+Create a parquet dataset from mimicstudio.db and audio_files folder.
+
+Output:
+ - data/parquetfile/dataset.parquet
+ - data/audio_files/<speaker_id>/<audio_id>.wav (copied)
+
+The parquet will contain columns: source, text, audio (relative path)
+
+Run from repo root: python3 scripts/create_parquet.py
+"""
+import sqlite3
+import os
+import shutil
+import argparse
+from pathlib import Path
+import pandas as pd
+
+
+def prepare(db_path: str, audio_root: str, out_audio_root: str, out_parquet: str, dry_run: bool = False):
+    conn = sqlite3.connect(db_path)
+    cur = conn.cursor()
+
+    # Query audiomodel table for audio_id, prompt (text), speaker_id
+    cur.execute("SELECT audio_id, prompt, speaker_id FROM audiomodel")
+    rows = cur.fetchall()
+
+    records = []
+    total = len(rows)
+    missing = 0
+
+    for idx, (audio_id, prompt, speaker_id) in enumerate(rows, start=1):
+        # Construct source path: audio_files/<speaker_id>/<audio_id>.wav
+        src_path = Path(audio_root) / speaker_id / f"{audio_id}.wav"
+        dest_dir = Path(out_audio_root) / speaker_id
+        dest_path = dest_dir / f"{audio_id}.wav"
+
+        # Use relative path that will be accessible from Colab when copying the data folder
+        rel_audio_path = os.path.join("data", "audio_files", speaker_id, f"{audio_id}.wav")
+
+        if not src_path.exists():
+            print(f"Warning: audio file not found for row {idx}/{total}: {src_path}")
+            missing += 1
+            continue
+
+        if not dry_run:
+            dest_dir.mkdir(parents=True, exist_ok=True)
+            # copy if not exists
+            if not dest_path.exists():
+                shutil.copy2(src_path, dest_path)
+
+        # Build record for parquet
+        records.append({
+            "source": speaker_id if speaker_id is not None else "0",
+            "text": prompt if prompt is not None else "",
+            "audio": rel_audio_path,
+        })
+
+        if idx % 500 == 0:
+            print(f"Processed {idx}/{total} rows...")
+
+    conn.close()
+
+    df = pd.DataFrame.from_records(records)
+
+    if not dry_run:
+        out_parquet_path = Path(out_parquet)
+        out_parquet_path.parent.mkdir(parents=True, exist_ok=True)
+        # Write parquet with pyarrow engine
+        df.to_parquet(out_parquet, index=False)
+        print(f"Wrote parquet to: {out_parquet}  (rows: {len(df)})")
+
+    print(f"Done. Total rows: {total}, written: {len(records)}, missing audio: {missing}")
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--db", default="db/mimicstudio.db", help="Path to mimicstudio.db")
+    parser.add_argument("--audio-root", default="audio_files", help="Root folder containing original audio files")
+    parser.add_argument("--out-audio-root", default="data/audio_files", help="Destination audio folder to copy into")
+    parser.add_argument("--out-parquet", default="data/dataset.parquet", help="Output parquet path (default: data/dataset.parquet)")
+    parser.add_argument("--dry-run", action="store_true", help="Don't copy or write files; just show counts")
+
+    args = parser.parse_args()
+
+    prepare(args.db, args.audio_root, args.out_audio_root, args.out_parquet, dry_run=args.dry_run)
+
+
+if __name__ == "__main__":
+    main()

scripts/upload_to_hf.py

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+#!/usr/bin/env python3
+"""
+Upload the prepared `data/` folder to a Hugging Face repo under the `data/` path.
+
+Usage:
+  # interactive login (recommended)
+  huggingface-cli login
+  python3 scripts/upload_to_hf.py --repo Aybee5/ha-tts-mixed
+
+  # or provide token via env var HUGGINGFACE_HUB_TOKEN
+  HUGGINGFACE_HUB_TOKEN=... python3 scripts/upload_to_hf.py --repo Aybee5/ha-tts-mixed
+
+This will use `huggingface_hub.upload_folder` to upload `data/` content to the repo under the `data/` folder.
+"""
+import os
+import argparse
+from huggingface_hub import upload_folder, HfApi
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--repo", required=True, help="Repo id, e.g. username/repo")
+    parser.add_argument("--path-in-repo", default="data", help="Destination path inside the repo")
+    parser.add_argument("--local-folder", default="data", help="Local folder to upload")
+    parser.add_argument("--token", default=None, help="HF token (optional; can be provided via HUGGINGFACE_HUB_TOKEN env var or huggingface-cli login)")
+    args = parser.parse_args()
+
+    token = args.token or os.environ.get("HUGGINGFACE_HUB_TOKEN")
+
+    print(f"Uploading local folder '{args.local_folder}' to repo '{args.repo}' at path '{args.path_in_repo}'")
+
+    api = HfApi()
+    # Ensure repo exists or will error
+    try:
+        upload_folder(
+            folder_path=args.local_folder,
+            repo_id=args.repo,
+            path_in_repo=args.path_in_repo,
+            token=token,
+            repo_type="dataset",
+            # allow large uploads; may still be subject to HF limits
+            max_workers=8,
+        )
+    except Exception as e:
+        print(f"Upload failed: {e}")
+
+
+if __name__ == "__main__":
+    main()