#!/usr/bin/env python3
"""
embeddings.py — Speaker Voiceprint Management

Handles extraction, storage, and matching of speaker voice embeddings
using pyannote.audio's SpeakerEmbedding pipeline.

Embeddings are stored as numpy .npy files in bridge/embeddings/.
speakers.json is updated with has_embedding / embedding_updated metadata.

Transcript segments are logged to bridge/transcript_segments.jsonl —
a line-delimited JSON log of every finalised speech segment with speaker ID,
timestamps, and text. This log is used for automatic best-segment enrolment.

Public API
----------
    registry = EmbeddingRegistry()

    # Extract from an audio file segment
    embedding = registry.extract(audio_path, start_sec=45.0, end_sec=55.0)

    # Save a voiceprint for a known speaker
    registry.save(speaker_id="SPEAKER_00", embedding=embedding)

    # Auto-enrol from a transcript log (picks the cleanest segment)
    registry.enrol_from_transcript("SPEAKER_00", audio_path, segments)

    # Find the best matching speaker for a live embedding
    match = registry.find_match(live_embedding, threshold=0.82)
    # -> ("SPEAKER_00", 0.91) or None

    # Load a stored embedding
    emb = registry.load("SPEAKER_00")  # -> np.ndarray | None

    # Delete a voiceprint
    registry.delete("SPEAKER_00")

    # List all speakers with voiceprints
    registry.list_enrolled()

    # Accumulate live audio and extract when ready
    acc = registry.make_accumulator()
    acc.push(pcm_bytes)
    if acc.ready():
        emb = acc.extract_embedding()
        match = registry.find_match(emb)
"""

from __future__ import annotations

import json
import logging
import os
from datetime import datetime, timezone
from pathlib import Path
from typing import Optional

import numpy as np

logger = logging.getLogger(__name__)

# ── Paths ──────────────────────────────────────────────────────────────────────

_HERE                   = Path(__file__).parent
EMBEDDINGS_DIR          = _HERE / "embeddings"
SPEAKERS_FILE           = _HERE / "speakers.json"
TRANSCRIPT_SEGMENTS_LOG = _HERE / "transcript_segments.jsonl"

EMBEDDING_DIM     = 512
DEFAULT_THRESHOLD = 0.82
MIN_AUDIO_SEC     = 5.0


# ── Lazy model loader ──────────────────────────────────────────────────────────

_pipeline = None

def _get_pipeline():
    """
    Load the pyannote SpeakerEmbedding pipeline on first call.
    Requires HF_TOKEN env var or huggingface-cli login.
    """
    global _pipeline
    if _pipeline is not None:
        return _pipeline

    try:
        from pyannote.audio import Model, Inference
    except ImportError as exc:
        raise RuntimeError(
            "pyannote.audio is required for voiceprint extraction. "
            "Install with: pip install pyannote.audio"
        ) from exc

    hf_token = os.environ.get("HF_TOKEN")
    if not hf_token:
        raise RuntimeError(
            "HF_TOKEN environment variable is not set. "
            "Set it in start.bat or run: huggingface-cli login"
        )

    logger.info("[Embeddings] Loading pyannote speaker embedding model...")
    try:
        model     = Model.from_pretrained("pyannote/embedding", use_auth_token=hf_token)
        _pipeline = Inference(model, window="whole")
        logger.info("[Embeddings] Speaker embedding model loaded.")
    except Exception as exc:
        raise RuntimeError(
            f"Failed to load pyannote/embedding model: {exc}\n"
            "Ensure you have accepted model conditions at "
            "https://huggingface.co/pyannote/embedding"
        ) from exc

    return _pipeline


# ── Helpers ────────────────────────────────────────────────────────────────────

def _cosine_similarity(a: np.ndarray, b: np.ndarray) -> float:
    a = a.flatten().astype(np.float32)
    b = b.flatten().astype(np.float32)
    norm_a = np.linalg.norm(a)
    norm_b = np.linalg.norm(b)
    if norm_a == 0 or norm_b == 0:
        return 0.0
    return float(np.dot(a, b) / (norm_a * norm_b))


def _load_speakers_json() -> dict:
    if SPEAKERS_FILE.exists():
        try:
            raw = json.loads(SPEAKERS_FILE.read_text(encoding="utf-8"))
            return raw if isinstance(raw, dict) else {}
        except (json.JSONDecodeError, OSError):
            pass
    return {}


def _save_speakers_json(data: dict) -> None:
    try:
        SPEAKERS_FILE.write_text(
            json.dumps(data, indent=2, ensure_ascii=False), encoding="utf-8"
        )
    except OSError as exc:
        logger.error(f"[Embeddings] Could not save speakers.json: {exc}")


def _normalise_speaker_entry(entry) -> dict:
    """Migrate flat string entries to rich dict format."""
    if isinstance(entry, str):
        return {"name": entry, "has_embedding": False,
                "embedding_updated": None, "colour": None, "notes": ""}
    if isinstance(entry, dict):
        entry.setdefault("name",              "")
        entry.setdefault("has_embedding",     False)
        entry.setdefault("embedding_updated", None)
        entry.setdefault("colour",            None)
        entry.setdefault("notes",             "")
        return entry
    return {"name": str(entry), "has_embedding": False,
            "embedding_updated": None, "colour": None, "notes": ""}


def _update_speaker_meta(speaker_id: str, has_embedding: bool) -> None:
    data  = _load_speakers_json()
    entry = _normalise_speaker_entry(data.get(speaker_id, speaker_id))
    entry["has_embedding"]     = has_embedding
    entry["embedding_updated"] = (
        datetime.now(timezone.utc).isoformat() if has_embedding else None
    )
    data[speaker_id] = entry
    _save_speakers_json(data)


# ── Transcript segment log ─────────────────────────────────────────────────────

def log_transcript_segment(
    speaker_id: str,
    text:       str,
    start_sec:  float,
    end_sec:    float,
    session_id: Optional[str] = None,
) -> None:
    """
    Append a finalised transcript segment to transcript_segments.jsonl.

    Called by bridge.py each time a is_final segment is received.
    This log is later used by enrol_from_transcript() to find the best
    clean segment for voiceprint extraction.

    Args:
        speaker_id: Diarization label, e.g. "SPEAKER_00"
        text:       Transcribed text for this segment
        start_sec:  Segment start time in seconds (from audio start)
        end_sec:    Segment end time in seconds
        session_id: Optional session identifier (e.g. date string)
    """
    record = {
        "ts":        datetime.now(timezone.utc).isoformat(),
        "session":   session_id or datetime.now(timezone.utc).strftime("%Y-%m-%d"),
        "speaker":   speaker_id,
        "start":     round(start_sec, 3),
        "end":       round(end_sec, 3),
        "duration":  round(end_sec - start_sec, 3),
        "text":      text.strip(),
    }
    try:
        with TRANSCRIPT_SEGMENTS_LOG.open("a", encoding="utf-8") as f:
            f.write(json.dumps(record, ensure_ascii=False) + "\n")
    except OSError as exc:
        logger.warning(f"[Embeddings] Could not write segment log: {exc}")


def load_transcript_segments(
    session_id: Optional[str] = None,
    speaker_id: Optional[str] = None,
) -> list[dict]:
    """
    Load transcript segments from the log file.

    Args:
        session_id: If set, only return segments from this session
        speaker_id: If set, only return segments for this speaker

    Returns:
        List of segment dicts sorted by start time
    """
    if not TRANSCRIPT_SEGMENTS_LOG.exists():
        return []

    segments = []
    try:
        with TRANSCRIPT_SEGMENTS_LOG.open(encoding="utf-8") as f:
            for line in f:
                line = line.strip()
                if not line:
                    continue
                try:
                    rec = json.loads(line)
                    if session_id and rec.get("session") != session_id:
                        continue
                    if speaker_id and rec.get("speaker") != speaker_id:
                        continue
                    segments.append(rec)
                except json.JSONDecodeError:
                    continue
    except OSError as exc:
        logger.warning(f"[Embeddings] Could not read segment log: {exc}")

    return sorted(segments, key=lambda x: x.get("start", 0))


def get_best_enrolment_segments(
    speaker_id:   str,
    session_id:   Optional[str] = None,
    min_duration: float = 8.0,
    top_n:        int   = 5,
) -> list[dict]:
    """
    Find the best candidate segments for enrolment from the transcript log.

    Ranks segments by:
    1. Duration (longer = more reliable embedding)
    2. Isolation (gap before/after to avoid speaker overlap bleed)
    3. Text length (more words = more acoustic variety = better embedding)

    Args:
        speaker_id:   Speaker to find segments for
        session_id:   Limit to a specific session (None = all sessions)
        min_duration: Minimum segment duration in seconds
        top_n:        How many candidate segments to return

    Returns:
        List of segment dicts with added "score" and "recommendation" fields,
        sorted by score descending
    """
    all_segs  = load_transcript_segments(session_id=session_id)
    spk_segs  = [s for s in all_segs
                 if s.get("speaker") == speaker_id
                 and s.get("duration", 0) >= min_duration]

    if not spk_segs:
        return []

    # Build a set of all segment boundaries for isolation scoring
    all_starts = {s["start"] for s in all_segs}
    all_ends   = {s["end"]   for s in all_segs}

    scored = []
    for seg in spk_segs:
        duration   = seg.get("duration", 0)
        text       = seg.get("text", "")
        word_count = len(text.split())

        # Gap before this segment (silence / different speaker)
        gap_before = min(
            (seg["start"] - e for e in all_ends if e <= seg["start"]),
            default=seg["start"],
        )
        # Gap after this segment
        gap_after = min(
            (s - seg["end"] for s in all_starts if s >= seg["end"]),
            default=999.0,
        )

        # Score: weight duration heavily, reward isolation, penalise short text
        isolation = min(gap_before, gap_after)
        score = (
            duration * 2.0          # main driver
            + isolation * 1.5       # isolation bonus
            + word_count * 0.1      # text richness
        )

        # Human-readable recommendation
        if duration >= 15 and isolation >= 1.0:
            rec = "Excellent"
        elif duration >= 10 and isolation >= 0.5:
            rec = "Good"
        elif duration >= 8:
            rec = "Acceptable"
        else:
            rec = "Short"

        scored.append({**seg, "score": round(score, 2), "recommendation": rec})

    return sorted(scored, key=lambda x: x["score"], reverse=True)[:top_n]


# ── Audio extraction ───────────────────────────────────────────────────────────

def extract_audio_segment(
    audio_path: Path | str,
    start_sec:  float,
    end_sec:    float,
    out_path:   Optional[Path] = None,
) -> Path:
    """
    Extract a segment of audio using miniaudio and save as a WAV file.
    Returns path to the extracted WAV (caller deletes if temp).
    """
    import tempfile
    import wave

    try:
        import miniaudio
    except ImportError as exc:
        raise RuntimeError("miniaudio is required: pip install miniaudio") from exc

    duration = end_sec - start_sec
    if duration < MIN_AUDIO_SEC:
        logger.warning(
            f"[Embeddings] Short segment ({duration:.1f}s < {MIN_AUDIO_SEC}s) "
            "— embedding may be less reliable"
        )

    sample_rate   = 16000
    frames_needed = int(duration * sample_rate)
    skip_frames   = int(start_sec * sample_rate)

    stream = miniaudio.stream_file(
        str(audio_path),
        output_format=miniaudio.SampleFormat.SIGNED16,
        nchannels=1,
        sample_rate=sample_rate,
        frames_to_read=4096,
    )

    collected: list[bytes] = []
    collected_frames       = 0
    skipped_frames         = 0

    for chunk in stream:
        chunk_bytes  = bytes(chunk)
        chunk_frames = len(chunk_bytes) // 2

        if skipped_frames < skip_frames:
            remaining_skip = skip_frames - skipped_frames
            if chunk_frames <= remaining_skip:
                skipped_frames += chunk_frames
                continue
            else:
                offset         = remaining_skip * 2
                chunk_bytes    = chunk_bytes[offset:]
                chunk_frames   = len(chunk_bytes) // 2
                skipped_frames = skip_frames

        remaining_needed = frames_needed - collected_frames
        if chunk_frames >= remaining_needed:
            collected.append(chunk_bytes[: remaining_needed * 2])
            collected_frames += remaining_needed
            break
        else:
            collected.append(chunk_bytes)
            collected_frames += chunk_frames

    if not collected:
        raise ValueError(
            f"No audio extracted from {audio_path} at {start_sec}–{end_sec}s."
        )

    pcm_data = b"".join(collected)

    if out_path is None:
        tmp      = tempfile.NamedTemporaryFile(suffix=".wav", delete=False, prefix="embedding_")
        out_path = Path(tmp.name)
        tmp.close()

    with wave.open(str(out_path), "wb") as wf:
        wf.setnchannels(1)
        wf.setsampwidth(2)
        wf.setframerate(sample_rate)
        wf.writeframes(pcm_data)

    return out_path


# ── Main registry class ────────────────────────────────────────────────────────

class EmbeddingRegistry:

    def __init__(
        self,
        embeddings_dir: Path | str = EMBEDDINGS_DIR,
        speakers_file:  Path | str = SPEAKERS_FILE,
        threshold:      float      = DEFAULT_THRESHOLD,
    ):
        self.embeddings_dir = Path(embeddings_dir)
        self.speakers_file  = Path(speakers_file)
        self.threshold      = threshold
        self.embeddings_dir.mkdir(parents=True, exist_ok=True)

    def _path(self, speaker_id: str) -> Path:
        return self.embeddings_dir / f"{speaker_id}.npy"

    # ── Storage ────────────────────────────────────────────────────────────────

    def save(self, speaker_id: str, embedding: np.ndarray) -> None:
        emb = embedding.flatten().astype(np.float32)
        np.save(str(self._path(speaker_id)), emb)
        _update_speaker_meta(speaker_id, has_embedding=True)
        logger.info(f"[Embeddings] Saved voiceprint for {speaker_id}")

    def load(self, speaker_id: str) -> Optional[np.ndarray]:
        p = self._path(speaker_id)
        if not p.exists():
            return None
        try:
            return np.load(str(p)).astype(np.float32)
        except Exception as exc:
            logger.error(f"[Embeddings] Failed to load {p}: {exc}")
            return None

    def has(self, speaker_id: str) -> bool:
        return self._path(speaker_id).exists()

    def delete(self, speaker_id: str) -> bool:
        p = self._path(speaker_id)
        if p.exists():
            p.unlink()
            _update_speaker_meta(speaker_id, has_embedding=False)
            logger.info(f"[Embeddings] Deleted voiceprint for {speaker_id}")
            return True
        return False

    def list_enrolled(self) -> list[dict]:
        enrolled = []
        data     = _load_speakers_json()
        for p in sorted(self.embeddings_dir.glob("*.npy")):
            sid   = p.stem
            entry = _normalise_speaker_entry(data.get(sid, sid))
            enrolled.append({
                "id":      sid,
                "name":    entry["name"],
                "updated": entry.get("embedding_updated"),
                "size_kb": round(p.stat().st_size / 1024, 1),
            })
        return enrolled

    # ── Extraction ─────────────────────────────────────────────────────────────

    def extract(
        self,
        audio_path: Path | str,
        start_sec:  float = 0.0,
        end_sec:    Optional[float] = None,
    ) -> np.ndarray:
        """Extract a speaker embedding from an audio file segment."""
        audio_path = Path(audio_path)
        if not audio_path.exists():
            raise FileNotFoundError(f"Audio file not found: {audio_path}")

        if end_sec is None:
            try:
                import miniaudio
                info    = miniaudio.get_file_info(str(audio_path))
                end_sec = info.duration
            except Exception:
                end_sec = start_sec + 30.0

        if end_sec - start_sec <= 0:
            raise ValueError(f"Invalid segment: start={start_sec}, end={end_sec}")

        tmp_wav = None
        try:
            tmp_wav  = extract_audio_segment(audio_path, start_sec, end_sec)
            pipeline = _get_pipeline()
            result   = pipeline(str(tmp_wav))

            if hasattr(result, "data"):
                arr = np.array(result.data).flatten()
            elif isinstance(result, np.ndarray):
                arr = result.flatten()
            else:
                arr = np.array(result).flatten()

            logger.info(
                f"[Embeddings] Extracted from {audio_path.name} "
                f"[{start_sec:.1f}s–{end_sec:.1f}s] shape={arr.shape}"
            )
            return arr.astype(np.float32)
        finally:
            if tmp_wav and tmp_wav.exists():
                try:
                    tmp_wav.unlink()
                except OSError:
                    pass

    def extract_and_save(
        self,
        speaker_id: str,
        audio_path: Path | str,
        start_sec:  float = 0.0,
        end_sec:    Optional[float] = None,
    ) -> np.ndarray:
        """Extract embedding from segment and save it immediately."""
        embedding = self.extract(audio_path, start_sec, end_sec)
        self.save(speaker_id, embedding)
        return embedding

    def enrol_from_transcript(
        self,
        speaker_id:   str,
        audio_path:   Path | str,
        session_id:   Optional[str] = None,
        min_duration: float = 8.0,
    ) -> np.ndarray:
        """
        Automatically find the best segment from the transcript log
        and use it to enrol the speaker.

        The best segment is the highest-scoring candidate from
        get_best_enrolment_segments() — longest, most isolated, most words.

        If multiple "Excellent" or "Good" segments exist, their embeddings
        are averaged for a more robust voiceprint.

        Args:
            speaker_id:   Speaker to enrol
            audio_path:   Source audio file (should be the test recording
                          that was used to generate the transcript log)
            session_id:   Limit to segments from a specific session
            min_duration: Minimum segment duration to consider

        Returns:
            The saved embedding array

        Raises:
            ValueError: if no suitable segments are found in the log
        """
        candidates = get_best_enrolment_segments(
            speaker_id   = speaker_id,
            session_id   = session_id,
            min_duration = min_duration,
            top_n        = 3,
        )

        if not candidates:
            raise ValueError(
                f"No transcript segments found for {speaker_id} "
                f"with duration >= {min_duration}s. "
                "Run a test recording first to generate the segment log."
            )

        # Use top candidate, or average top-2 if both are Excellent/Good
        top = candidates[0]
        good_candidates = [
            c for c in candidates
            if c["recommendation"] in ("Excellent", "Good")
        ]

        if len(good_candidates) >= 2:
            logger.info(
                f"[Embeddings] Averaging {len(good_candidates)} segments "
                f"for {speaker_id} (more robust voiceprint)"
            )
            embeddings = []
            for c in good_candidates:
                try:
                    emb = self.extract(audio_path, c["start"], c["end"])
                    embeddings.append(emb)
                    logger.info(
                        f"[Embeddings]   Used segment [{c['start']:.1f}s–{c['end']:.1f}s] "
                        f"'{c['text'][:50]}...'"
                    )
                except Exception as exc:
                    logger.warning(f"[Embeddings] Skipping segment: {exc}")

            if not embeddings:
                raise ValueError("All candidate segments failed extraction")

            # Average and re-normalise
            avg = np.mean(np.stack(embeddings), axis=0)
            norm = np.linalg.norm(avg)
            if norm > 0:
                avg = avg / norm
            self.save(speaker_id, avg)
            return avg

        else:
            logger.info(
                f"[Embeddings] Using single best segment for {speaker_id}: "
                f"[{top['start']:.1f}s–{top['end']:.1f}s] "
                f"({top['recommendation']}, {top['duration']:.1f}s)"
            )
            return self.extract_and_save(
                speaker_id, audio_path, top["start"], top["end"]
            )

    # ── Matching ───────────────────────────────────────────────────────────────

    def find_match(
        self,
        live_embedding: np.ndarray,
        threshold:      Optional[float] = None,
        exclude_ids:    Optional[set[str]] = None,
    ) -> Optional[tuple[str, float]]:
        """
        Find the best matching enrolled speaker for a live embedding.

        Returns (speaker_id, similarity_score) or None.
        """
        if threshold is None:
            threshold = self.threshold

        exclude_ids = exclude_ids or set()
        best_id     = None
        best_score  = -1.0

        for p in self.embeddings_dir.glob("*.npy"):
            sid = p.stem
            if sid in exclude_ids:
                continue
            stored = self.load(sid)
            if stored is None:
                continue
            score = _cosine_similarity(live_embedding, stored)
            logger.debug(f"[Embeddings] {sid}: similarity={score:.4f}")
            if score > best_score:
                best_score = score
                best_id    = sid

        if best_id and best_score >= threshold:
            logger.info(f"[Embeddings] Match: {best_id} (score={best_score:.4f})")
            return (best_id, best_score)

        logger.debug(
            f"[Embeddings] No match above threshold={threshold:.2f} "
            f"(best={best_score:.4f})"
        )
        return None

    def similarity_scores(self, live_embedding: np.ndarray) -> list[dict]:
        """Return similarity scores against all enrolled speakers, sorted descending."""
        results = []
        data    = _load_speakers_json()
        for p in self.embeddings_dir.glob("*.npy"):
            sid    = p.stem
            stored = self.load(sid)
            if stored is None:
                continue
            score = _cosine_similarity(live_embedding, stored)
            entry = _normalise_speaker_entry(data.get(sid, sid))
            results.append({
                "id":         sid,
                "name":       entry["name"],
                "similarity": round(score, 4),
                "match":      score >= self.threshold,
            })
        return sorted(results, key=lambda x: x["similarity"], reverse=True)

    # ── Live accumulator ───────────────────────────────────────────────────────

    def make_accumulator(
        self,
        min_seconds: float = 5.0,
        sample_rate: int   = 16000,
    ) -> "LiveEmbeddingAccumulator":
        return LiveEmbeddingAccumulator(self, min_seconds, sample_rate)


# ── Live audio accumulator ─────────────────────────────────────────────────────

class LiveEmbeddingAccumulator:
    """
    Buffers raw int16 PCM chunks from the live microphone stream.
    Once min_seconds of audio is accumulated, extracts an embedding
    for speaker matching or enrolment.
    """

    def __init__(
        self,
        registry:    EmbeddingRegistry,
        min_seconds: float = 5.0,
        sample_rate: int   = 16000,
    ):
        self.registry    = registry
        self.min_frames  = int(min_seconds * sample_rate)
        self.sample_rate = sample_rate
        self._frames:    list[bytes] = []
        self._n_frames   = 0

    def push(self, pcm_bytes: bytes) -> None:
        self._frames.append(pcm_bytes)
        self._n_frames += len(pcm_bytes) // 2

    def ready(self) -> bool:
        return self._n_frames >= self.min_frames

    def seconds_accumulated(self) -> float:
        return self._n_frames / self.sample_rate

    def reset(self) -> None:
        self._frames  = []
        self._n_frames = 0

    def extract_embedding(self) -> np.ndarray:
        if not self.ready():
            raise RuntimeError(
                f"Not enough audio: {self.seconds_accumulated():.1f}s accumulated, "
                f"need {self.min_frames / self.sample_rate:.1f}s"
            )
        import tempfile
        import wave

        pcm_data = b"".join(self._frames)
        tmp      = tempfile.NamedTemporaryFile(suffix=".wav", delete=False, prefix="live_emb_")
        tmp_path = Path(tmp.name)
        tmp.close()

        try:
            with wave.open(str(tmp_path), "wb") as wf:
                wf.setnchannels(1)
                wf.setsampwidth(2)
                wf.setframerate(self.sample_rate)
                wf.writeframes(pcm_data)
            return self.registry.extract(tmp_path)
        finally:
            if tmp_path.exists():
                try:
                    tmp_path.unlink()
                except OSError:
                    pass


# ── Standalone CLI ─────────────────────────────────────────────────────────────

if __name__ == "__main__":
    import argparse
    import sys

    logging.basicConfig(level=logging.INFO, format="%(message)s")

    parser = argparse.ArgumentParser(
        description="Speaker voiceprint utility",
        formatter_class=argparse.RawDescriptionHelpFormatter,
        epilog="""
Examples:
  # Enrol from a specific time range
  python embeddings.py enrol SPEAKER_00 recording.mp3 --start 45 --end 55

  # Auto-enrol using the best segment from the transcript log
  python embeddings.py auto-enrol SPEAKER_00 recording.mp3

  # Show best candidate segments for a speaker (before enrolling)
  python embeddings.py candidates SPEAKER_00

  # List all enrolled speakers
  python embeddings.py list

  # Test match against a clip
  python embeddings.py match recording.mp3 --start 120 --end 130

  # Show similarity scores for a clip
  python embeddings.py scores recording.mp3 --start 45 --end 55

  # Delete a voiceprint
  python embeddings.py delete SPEAKER_00
        """,
    )
    sub = parser.add_subparsers(dest="cmd")

    p_enrol = sub.add_parser("enrol", help="Extract and save voiceprint from time range")
    p_enrol.add_argument("speaker_id")
    p_enrol.add_argument("audio_file")
    p_enrol.add_argument("--start", type=float, default=0.0)
    p_enrol.add_argument("--end",   type=float, default=None)

    p_auto = sub.add_parser("auto-enrol", help="Enrol using best segment from transcript log")
    p_auto.add_argument("speaker_id")
    p_auto.add_argument("audio_file")
    p_auto.add_argument("--session",  default=None)
    p_auto.add_argument("--min-dur",  type=float, default=8.0)

    p_cand = sub.add_parser("candidates", help="Show best enrolment segments from transcript log")
    p_cand.add_argument("speaker_id")
    p_cand.add_argument("--session", default=None)
    p_cand.add_argument("--min-dur", type=float, default=8.0)

    sub.add_parser("list", help="List enrolled speakers")

    p_match = sub.add_parser("match", help="Find matching speaker for audio clip")
    p_match.add_argument("audio_file")
    p_match.add_argument("--start",     type=float, default=0.0)
    p_match.add_argument("--end",       type=float, default=None)
    p_match.add_argument("--threshold", type=float, default=DEFAULT_THRESHOLD)

    p_scores = sub.add_parser("scores", help="Similarity against all enrolled speakers")
    p_scores.add_argument("audio_file")
    p_scores.add_argument("--start", type=float, default=0.0)
    p_scores.add_argument("--end",   type=float, default=None)

    p_del = sub.add_parser("delete", help="Delete a stored voiceprint")
    p_del.add_argument("speaker_id")

    args     = parser.parse_args()
    registry = EmbeddingRegistry()

    if not args.cmd:
        parser.print_help()
        sys.exit(0)

    elif args.cmd == "enrol":
        emb = registry.extract_and_save(
            args.speaker_id, args.audio_file, args.start, args.end
        )
        print(f"✓ Enrolled {args.speaker_id} — shape: {emb.shape}")

    elif args.cmd == "auto-enrol":
        emb = registry.enrol_from_transcript(
            args.speaker_id, args.audio_file,
            session_id=args.session, min_duration=args.min_dur,
        )
        print(f"✓ Auto-enrolled {args.speaker_id} — shape: {emb.shape}")

    elif args.cmd == "candidates":
        cands = get_best_enrolment_segments(
            args.speaker_id, session_id=args.session, min_duration=args.min_dur
        )
        if not cands:
            print(f"No segments found for {args.speaker_id} (min {args.min_dur}s)")
        else:
            print(f"\nBest enrolment candidates for {args.speaker_id}:\n")
            print(f"  {'Start':>7}  {'End':>7}  {'Dur':>6}  {'Rating':<12}  {'Score':>6}  Text")
            print("  " + "-" * 75)
            for c in cands:
                preview = c["text"][:45] + "..." if len(c["text"]) > 45 else c["text"]
                print(
                    f"  {c['start']:>7.1f}  {c['end']:>7.1f}  "
                    f"{c['duration']:>5.1f}s  {c['recommendation']:<12}  "
                    f"{c['score']:>6.1f}  {preview}"
                )

    elif args.cmd == "list":
        enrolled = registry.list_enrolled()
        if not enrolled:
            print("No speakers enrolled yet.")
        else:
            print(f"\n{'ID':<15} {'Name':<25} {'Updated':<30} {'Size'}")
            print("-" * 75)
            for e in enrolled:
                print(
                    f"{e['id']:<15} {e['name']:<25} "
                    f"{(e['updated'] or 'unknown'):<30} {e['size_kb']} KB"
                )

    elif args.cmd == "match":
        emb   = registry.extract(args.audio_file, args.start, args.end)
        match = registry.find_match(emb, threshold=args.threshold)
        if match:
            sid, score = match
            data  = _load_speakers_json()
            entry = _normalise_speaker_entry(data.get(sid, sid))
            print(f"✓ Match: {sid} ({entry['name']}) — similarity {score:.4f}")
        else:
            print(f"✗ No match above threshold {args.threshold:.2f}")

    elif args.cmd == "scores":
        emb    = registry.extract(args.audio_file, args.start, args.end)
        scores = registry.similarity_scores(emb)
        if not scores:
            print("No enrolled speakers to compare against.")
        else:
            print(f"\n{'ID':<15} {'Name':<25} {'Similarity':<12} {'Match?'}")
            print("-" * 60)
            for s in scores:
                flag = "✓" if s["match"] else " "
                print(
                    f"{s['id']:<15} {s['name']:<25} "
                    f"{s['similarity']:<12.4f} {flag}"
                )

    elif args.cmd == "delete":
        if registry.delete(args.speaker_id):
            print(f"✓ Deleted voiceprint for {args.speaker_id}")
        else:
            print(f"No voiceprint found for {args.speaker_id}")