api/main.py

"""
Evoxtral speech-to-text server (Model layer).
Runs Voxtral-Mini-3B + evoxtral-rl locally for transcription with expressive
tags. For video files, also runs FER (MobileViT-XXS ONNX) per segment.
"""
import asyncio
import os
import re
import shutil
import subprocess
import tempfile
import time
from contextlib import asynccontextmanager
from typing import Optional

import librosa
import numpy as np
from fastapi import FastAPI, File, UploadFile, HTTPException
from fastapi.middleware.cors import CORSMiddleware

MODEL_ID   = os.environ.get("MODEL_ID",   "mistralai/Voxtral-Mini-3B-2507")
ADAPTER_ID = os.environ.get("ADAPTER_ID", "YongkangZOU/evoxtral-rl")
MAX_UPLOAD_BYTES = int(os.environ.get("MAX_UPLOAD_MB", "100")) * 1024 * 1024
TARGET_SR = 16000

# ─── STT model ────────────────────────────────────────────────────────────────

_model     = None
_processor = None
_model_dtype  = None
_model_device = None


def _init_model() -> None:
    global _model, _processor, _model_dtype, _model_device
    import torch
    from transformers import VoxtralForConditionalGeneration, AutoProcessor
    from peft import PeftModel

    # Use all available CPU cores for parallel compute
    n_threads = os.cpu_count() or 4
    torch.set_num_threads(n_threads)
    torch.set_num_interop_threads(max(1, n_threads // 2))
    print(f"[voxtral] torch threads={n_threads}, interop={max(1, n_threads // 2)}")

    # bfloat16 on both GPU and CPU — halves memory vs float32 (~6 GB vs ~12 GB)
    # PyTorch CPU supports bfloat16 natively since 1.12
    _model_dtype = torch.bfloat16
    if torch.cuda.is_available():
        device_map = "auto"
    else:
        device_map = "cpu"

    print(f"[voxtral] Loading processor {MODEL_ID} ...")
    _processor = AutoProcessor.from_pretrained(MODEL_ID)

    print(f"[voxtral] Loading base model {MODEL_ID} (dtype={_model_dtype}) ...")
    base_model = VoxtralForConditionalGeneration.from_pretrained(
        MODEL_ID,
        dtype=_model_dtype,
        device_map=device_map,
    )

    print(f"[voxtral] Applying LoRA adapter {ADAPTER_ID} ...")
    peft_model = PeftModel.from_pretrained(base_model, ADAPTER_ID)

    # Merge LoRA weights into base model and unload adapter — removes per-forward overhead
    print(f"[voxtral] Merging LoRA weights into base model ...")
    _model = peft_model.merge_and_unload()
    _model.eval()

    _model_device = next(_model.parameters()).device
    print(f"[voxtral] Model ready on {_model_device} (dtype={_model_dtype})")


def _transcribe_sync(wav_path: str) -> str:
    """Run local Voxtral inference (blocking — call via run_in_executor)."""
    import torch

    audio_array, _ = librosa.load(wav_path, sr=TARGET_SR, mono=True)

    inputs = _processor.apply_transcription_request(
        audio=[audio_array],
        format=["WAV"],
        language="en",
        model_id=MODEL_ID,
        return_tensors="pt",
    )

    # Move inputs to model device / dtype
    inputs = {
        k: (v.to(_model_device, dtype=_model_dtype)
            if v.dtype in (torch.float32, torch.float16, torch.bfloat16)
            else v.to(_model_device))
        if hasattr(v, "to") else v
        for k, v in inputs.items()
    }

    with torch.inference_mode():
        output_ids = _model.generate(**inputs, max_new_tokens=448, do_sample=False)

    input_len = inputs["input_ids"].shape[1]
    text = _processor.tokenizer.decode(
        output_ids[0][input_len:], skip_special_tokens=True
    ).strip()
    return text


# ─── FER setup ────────────────────────────────────────────────────────────────

_fer_session   = None
_fer_input_name = "input"
_face_cascade  = None
_FER_CLASSES   = ["Anger", "Contempt", "Disgust", "Fear", "Happy", "Neutral", "Sad", "Surprise"]
_VIDEO_EXTS    = {".mp4", ".mkv", ".avi", ".mov", ".m4v"}


def _is_lfs_pointer(path: str) -> bool:
    """Return True if the file looks like a Git LFS pointer (small text file)."""
    try:
        size = os.path.getsize(path)
        if size > 10_000:
            return False
        with open(path, "rb") as f:
            header = f.read(64)
        return header.startswith(b"version https://git-lfs")
    except Exception:
        return False


def _resolve_lfs_model(fer_path: str) -> str:
    """
    If fer_path is a Git LFS pointer, download the real binary.
    Returns the path to the actual model file.
    """
    import urllib.request
    real_path = fer_path + ".resolved"
    # Use HF Space's own file resolution URL to download the actual binary
    url = "https://huggingface.co/spaces/mistral-hackaton-2026/ethos/resolve/main/models/emotion_model_web.onnx"
    print(f"[voxtral] FER: file is LFS pointer — downloading from {url}")
    try:
        urllib.request.urlretrieve(url, real_path)
        size = os.path.getsize(real_path)
        print(f"[voxtral] FER: downloaded {size} bytes to {real_path}")
        return real_path
    except Exception as e:
        print(f"[voxtral] FER: download failed: {e}")
        return fer_path


def _init_fer() -> None:
    global _fer_session, _fer_input_name, _face_cascade

    candidates = [
        os.environ.get("FER_MODEL_PATH", ""),
        "/app/models/emotion_model_web.onnx",                                          # Docker
        os.path.join(os.path.dirname(__file__), "../models/emotion_model_web.onnx"),   # local: api/../models/
        os.path.join(os.path.dirname(__file__), "../../models/emotion_model_web.onnx"), # fallback
    ]
    fer_path = next((p for p in candidates if p and os.path.exists(p)), None)
    if not fer_path:
        print("[voxtral] FER model not found — facial emotion disabled")
        return

    # Debug: log file size and first bytes to diagnose LFS pointer vs real binary
    try:
        file_size = os.path.getsize(fer_path)
        with open(fer_path, "rb") as f:
            first_bytes = f.read(32).hex()
        print(f"[voxtral] FER file: {fer_path} size={file_size} first_bytes={first_bytes}")
    except Exception as e:
        print(f"[voxtral] FER file stat error: {e}")

    # If it's a Git LFS pointer, download the actual binary
    if _is_lfs_pointer(fer_path):
        print("[voxtral] FER: detected Git LFS pointer — resolving...")
        fer_path = _resolve_lfs_model(fer_path)

    try:
        import onnxruntime as rt
        print(f"[voxtral] FER: onnxruntime version = {rt.__version__}")
        _fer_session    = rt.InferenceSession(fer_path, providers=["CPUExecutionProvider"])
        _fer_input_name = _fer_session.get_inputs()[0].name
        print(f"[voxtral] FER model loaded: {fer_path} (input={_fer_input_name}, shape={_fer_session.get_inputs()[0].shape})")
    except Exception as e:
        import traceback
        print(f"[voxtral] FER model load failed: {e}")
        print(f"[voxtral] FER traceback: {traceback.format_exc()}")
        return

    try:
        import cv2
        cascade_path  = cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
        _face_cascade = cv2.CascadeClassifier(cascade_path)
        print("[voxtral] Face cascade loaded")
    except Exception as e:
        print(f"[voxtral] Face cascade load failed (FER will use center crop): {e}")


def _is_video(filename: str) -> bool:
    return os.path.splitext(filename)[1].lower() in _VIDEO_EXTS


def _fer_frame(img_bgr: np.ndarray) -> Optional[str]:
    """Detect face (or center-crop), run FER ONNX; return emotion label or None."""
    if _fer_session is None:
        return None
    try:
        import cv2
        face_crop = None

        if _face_cascade is not None:
            gray  = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY)
            faces = _face_cascade.detectMultiScale(gray, 1.1, 5, minSize=(40, 40))
            if len(faces) > 0:
                x, y, w, h = max(faces, key=lambda f: f[2] * f[3])
                pad = int(min(w, h) * 0.15)
                x1, y1 = max(0, x - pad), max(0, y - pad)
                x2, y2 = min(img_bgr.shape[1], x + w + pad), min(img_bgr.shape[0], y + h + pad)
                face_crop = img_bgr[y1:y2, x1:x2]

        if face_crop is None:
            h, w    = img_bgr.shape[:2]
            crop_h  = int(h * 0.6)
            cx      = w // 2
            half    = min(crop_h, w) // 2
            face_crop = img_bgr[:crop_h, max(0, cx - half):cx + half]

        resized = cv2.resize(face_crop, (224, 224))
        rgb     = cv2.cvtColor(resized, cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0
        # ImageNet normalization (matches original emotion-recognition.ts)
        mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)
        std  = np.array([0.229, 0.224, 0.225], dtype=np.float32)
        rgb  = (rgb - mean) / std
        tensor = np.transpose(rgb, (2, 0, 1))[np.newaxis]  # [1, 3, 224, 224]

        out = _fer_session.run(None, {_fer_input_name: tensor})[0]
        return _FER_CLASSES[int(np.argmax(out[0]))]
    except Exception as e:
        print(f"[voxtral] FER frame error: {e}")
        return None


def _fer_for_segments(
    video_path: str, segments: list[dict]
) -> tuple[dict[int, str], dict[int, str]]:
    """
    Extract ~1fps frames from video, run FER.
    Returns:
      segment_emotions : {segment_id: majority_emotion}
      timeline         : {second: emotion}  — per-second, for live panel
    """
    if _fer_session is None:
        return {}, {}

    frames_dir = tempfile.mkdtemp()
    try:
        import cv2
        from collections import Counter

        subprocess.run(
            ["ffmpeg", "-y", "-i", video_path,
             "-vf", "fps=1", "-vframes", "600",
             "-q:v", "5", os.path.join(frames_dir, "%06d.jpg")],
            capture_output=True, timeout=120,
        )
        frame_files = sorted(f for f in os.listdir(frames_dir) if f.endswith(".jpg"))
        if not frame_files:
            print("[voxtral] FER: no video frames extracted (audio-only?)")
            return {}, {}

        # Build per-second emotion map
        timeline: dict[int, str] = {}
        for fname in frame_files:
            second = int(os.path.splitext(fname)[0]) - 1
            img = cv2.imread(os.path.join(frames_dir, fname))
            if img is None:
                continue
            emo = _fer_frame(img)
            if emo:
                timeline[second] = emo

        # Majority-vote per segment
        segment_emotions: dict[int, str] = {}
        for seg in segments:
            start_s = int(seg["start"])
            end_s   = max(int(seg["end"]), start_s + 1)
            emos    = [timeline[s] for s in range(start_s, end_s) if s in timeline]
            if emos:
                segment_emotions[seg["id"]] = Counter(emos).most_common(1)[0][0]

        print(f"[voxtral] FER: {len(frame_files)} frames → {len(segment_emotions)} segs, {len(timeline)} timeline pts")
        return segment_emotions, timeline
    except Exception as e:
        print(f"[voxtral] FER extraction error: {e}")
        return {}, {}
    finally:
        shutil.rmtree(frames_dir, ignore_errors=True)


# ─── Startup ──────────────────────────────────────────────────────────────────

def _check_ffmpeg():
    if shutil.which("ffmpeg") is None:
        raise RuntimeError(
            "ffmpeg not found.\n"
            "  macOS:   brew install ffmpeg\n"
            "  Ubuntu:  sudo apt install ffmpeg"
        )


@asynccontextmanager
async def lifespan(app: FastAPI):
    _check_ffmpeg()
    print(f"[voxtral] ffmpeg: {shutil.which('ffmpeg')}")
    _init_fer()
    _init_model()
    yield


app = FastAPI(title="Evoxtral Speech-to-Text (local)", lifespan=lifespan)

app.add_middleware(
    CORSMiddleware,
    allow_origins=["http://localhost:3000", "http://127.0.0.1:3000"],
    allow_methods=["GET", "POST", "OPTIONS"],
    allow_headers=["*"],
)


@app.get("/debug-inference")
async def debug_inference():
    """Quick smoke-test: synthesize 0.5s of silence and run a minimal generate() call."""
    import traceback, torch
    if _model is None:
        return {"ok": False, "error": "model not loaded"}
    try:
        import numpy as np
        silence = np.zeros(8000, dtype=np.float32)  # 0.5 s @ 16 kHz
        import tempfile, soundfile as sf, asyncio
        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
            wav_path = f.name
        sf.write(wav_path, silence, 16000)
        loop = asyncio.get_running_loop()
        text = await loop.run_in_executor(None, _transcribe_sync, wav_path)
        import os; os.unlink(wav_path)
        return {"ok": True, "text": text, "dtype": str(_model_dtype), "device": str(_model_device)}
    except Exception as e:
        return {"ok": False, "error": str(e), "traceback": traceback.format_exc()}


@app.get("/health")
async def health():
    return {
        "status": "ok",
        "model": f"{MODEL_ID} + {ADAPTER_ID} (local)",
        "model_loaded": _model is not None,
        "ffmpeg": shutil.which("ffmpeg") is not None,
        "fer_enabled": _fer_session is not None,
        "device": str(_model_device) if _model_device else None,
        "max_upload_mb": MAX_UPLOAD_BYTES // 1024 // 1024,
    }


# ─── Audio helpers ─────────────────────────────────────────────────────────────

def _convert_to_wav_ffmpeg(path: str, target_sr: int) -> str:
    out = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
    out.close()
    rc = subprocess.run(
        ["ffmpeg", "-y", "-i", path,
         "-vn", "-acodec", "pcm_s16le", "-ar", str(target_sr), "-ac", "1",
         "-f", "wav", out.name],
        capture_output=True, timeout=120,
    )
    if rc.returncode != 0:
        os.unlink(out.name)
        raise RuntimeError(f"ffmpeg failed: {rc.stderr.decode(errors='replace')[:400]}")
    return out.name


def _load_audio(file_path: str, target_sr: int) -> np.ndarray:
    y, _ = librosa.load(file_path, sr=target_sr, mono=True)
    return y.astype(np.float32)


def _validate_upload(contents: bytes) -> None:
    if len(contents) == 0:
        raise HTTPException(status_code=400, detail="Audio file is empty")
    if len(contents) > MAX_UPLOAD_BYTES:
        raise HTTPException(
            status_code=413,
            detail=f"File too large ({len(contents)/1024/1024:.1f} MB); max {MAX_UPLOAD_BYTES//1024//1024} MB",
        )


# ─── Segmentation ──────────────────────────────────────────────────────────────

def _vad_segment(audio: np.ndarray, sr: int) -> list[tuple[int, int]]:
    intervals = librosa.effects.split(audio, top_db=28, frame_length=2048, hop_length=512)
    if len(intervals) == 0:
        return [(0, len(audio))]
    merged: list[list[int]] = [[int(intervals[0][0]), int(intervals[0][1])]]
    for s, e in intervals[1:]:
        if (int(s) - merged[-1][1]) / sr < 0.3:
            merged[-1][1] = int(e)
        else:
            merged.append([int(s), int(e)])
    result = [(s, e) for s, e in merged if (e - s) / sr >= 0.3]
    return result if result else [(0, len(audio))]


def _segments_from_vad(audio: np.ndarray, sr: int) -> tuple[list[dict], str]:
    intervals = _vad_segment(audio, sr)
    segs = [
        {"id": i + 1, "speaker": "SPEAKER_00", "start": round(s / sr, 3), "end": round(e / sr, 3)}
        for i, (s, e) in enumerate(intervals)
    ]
    print(f"[voxtral] VAD: {len(segs)} segment(s)")
    return segs, "vad"


def _split_sentences(text: str) -> list[str]:
    parts = re.split(r'(?<=[？！。?!])\s*', text)
    return [p for p in parts if p.strip()]


def _distribute_text(full_text: str, segs: list[dict]) -> list[dict]:
    if not full_text or not segs:
        return [{**s, "text": ""} for s in segs]
    if len(segs) == 1:
        return [{**segs[0], "text": full_text}]
    sentences = _split_sentences(full_text)
    if len(sentences) <= 1:
        is_cjk = len(full_text.split()) <= 1
        sentences = list(full_text) if is_cjk else full_text.split()
    total_dur = sum(s["end"] - s["start"] for s in segs)
    if total_dur <= 0:
        return [{**segs[0], "text": full_text}] + [{**s, "text": ""} for s in segs[1:]]
    is_cjk = len(full_text.split()) <= 1 and len(full_text) > 1
    sep = "" if is_cjk else " "
    n = len(sentences)
    result_texts: list[list[str]] = [[] for _ in segs]
    cumulative = 0.0
    for i, seg in enumerate(segs):
        cumulative += (seg["end"] - seg["start"]) / total_dur
        threshold = cumulative * n
        while len(result_texts[i]) + sum(len(t) for t in result_texts[:i]) < round(threshold):
            idx = sum(len(t) for t in result_texts)
            if idx >= n:
                break
            result_texts[i].append(sentences[idx])
    assigned = sum(len(t) for t in result_texts)
    result_texts[-1].extend(sentences[assigned:])
    return [{**seg, "text": sep.join(texts)} for seg, texts in zip(segs, result_texts)]


# ─── Emotion parsing from evoxtral expression tags ─────────────────────────────

_TAG_EMOTIONS: dict[str, tuple[str, float, float]] = {
    "laughs":           ("Happy",       0.70,  0.60),
    "laughing":         ("Happy",       0.70,  0.60),
    "chuckles":         ("Happy",       0.50,  0.30),
    "giggles":          ("Happy",       0.60,  0.40),
    "sighs":            ("Sad",        -0.30, -0.30),
    "sighing":          ("Sad",        -0.30, -0.30),
    "cries":            ("Sad",        -0.70,  0.40),
    "crying":           ("Sad",        -0.70,  0.40),
    "whispers":         ("Calm",        0.10, -0.50),
    "whispering":       ("Calm",        0.10, -0.50),
    "shouts":           ("Angry",      -0.50,  0.80),
    "shouting":         ("Angry",      -0.50,  0.80),
    "exclaims":         ("Excited",     0.50,  0.70),
    "gasps":            ("Surprised",   0.20,  0.70),
    "hesitates":        ("Anxious",    -0.20,  0.30),
    "stutters":         ("Anxious",    -0.20,  0.40),
    "stammers":         ("Anxious",    -0.25,  0.35),
    "mumbles":          ("Sad",        -0.20, -0.30),
    "nervous":          ("Anxious",    -0.30,  0.40),
    "frustrated":       ("Frustrated", -0.50,  0.50),
    "excited":          ("Excited",     0.50,  0.70),
    "sad":              ("Sad",        -0.60, -0.20),
    "angry":            ("Angry",      -0.60,  0.70),
    "claps":            ("Happy",       0.60,  0.50),
    "applause":         ("Happy",       0.60,  0.50),
    "clears throat":    ("Neutral",     0.00,  0.10),
    "pause":            ("Neutral",     0.00, -0.10),
    "laughs nervously": ("Anxious",    -0.10,  0.40),
}


def _parse_emotion(text: str) -> dict:
    tags = re.findall(r'\[([^\]]+)\]', text.lower())
    for tag in tags:
        tag = tag.strip()
        if tag in _TAG_EMOTIONS:
            label, valence, arousal = _TAG_EMOTIONS[tag]
            return {"emotion": label, "valence": valence, "arousal": arousal}
        for key, (label, valence, arousal) in _TAG_EMOTIONS.items():
            if key in tag:
                return {"emotion": label, "valence": valence, "arousal": arousal}
    return {"emotion": "Neutral", "valence": 0.0, "arousal": 0.0}


# ─── Endpoints ─────────────────────────────────────────────────────────────────

@app.post("/transcribe")
async def transcribe(audio: UploadFile = File(...)):
    req_start = time.perf_counter()
    filename  = audio.filename or "audio.wav"
    print(f"[voxtral] POST /transcribe filename={filename}")

    if _model is None:
        raise HTTPException(status_code=503, detail="Model not loaded")

    contents = await audio.read()
    _validate_upload(contents)

    suffix = os.path.splitext(filename)[1].lower() or ".wav"
    with tempfile.NamedTemporaryFile(suffix=suffix, delete=False) as tmp:
        tmp.write(contents)
        tmp_path = tmp.name

    wav_path = None
    try:
        wav_path = _convert_to_wav_ffmpeg(tmp_path, TARGET_SR)
        loop = asyncio.get_running_loop()
        text = await loop.run_in_executor(None, _transcribe_sync, wav_path)
    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=400, detail=f"Cannot process audio: {e}")
    finally:
        for p in (tmp_path, wav_path):
            if p and os.path.exists(p):
                try: os.unlink(p)
                except OSError: pass

    print(f"[voxtral] /transcribe done {(time.perf_counter()-req_start)*1000:.0f}ms")
    return {"text": text, "words": []}


@app.post("/transcribe-diarize")
async def transcribe_diarize(audio: UploadFile = File(...)):
    """
    Upload audio/video → transcription + VAD segmentation + emotion.
    For video files (.mp4, .mkv, .avi, .mov, .m4v), also runs FER.
    """
    req_start = time.perf_counter()
    req_id    = f"diarize-{int(req_start * 1000)}"
    filename  = audio.filename or "audio.wav"
    print(f"[voxtral] {req_id} POST /transcribe-diarize filename={filename}")

    if _model is None:
        raise HTTPException(status_code=503, detail="Model not loaded")

    contents = await audio.read()
    _validate_upload(contents)

    suffix = os.path.splitext(filename)[1].lower() or ".wav"
    if suffix not in (".wav", ".mp3", ".flac", ".ogg", ".m4a", ".webm",
                      ".mp4", ".mkv", ".avi", ".mov", ".m4v"):
        suffix = ".wav"

    with tempfile.NamedTemporaryFile(suffix=suffix, delete=False) as tmp:
        tmp.write(contents)
        tmp_path = tmp.name

    wav_path = None
    try:
        t0 = time.perf_counter()
        wav_path     = _convert_to_wav_ffmpeg(tmp_path, TARGET_SR)
        audio_array  = _load_audio(wav_path, TARGET_SR)
        print(f"[voxtral] {req_id} audio loaded shape={audio_array.shape} in {(time.perf_counter()-t0)*1000:.0f}ms")
    except Exception as e:
        for p in (tmp_path, wav_path):
            if p and os.path.exists(p):
                try: os.unlink(p)
                except OSError: pass
        raise HTTPException(status_code=400, detail=f"Cannot decode audio: {e}")

    duration = round(len(audio_array) / TARGET_SR, 3)

    # ── STT (local model, run in thread pool) ────────────────────────────────
    try:
        t0   = time.perf_counter()
        loop = asyncio.get_running_loop()
        full_text = await loop.run_in_executor(None, _transcribe_sync, wav_path)
        print(f"[voxtral] {req_id} STT done {(time.perf_counter()-t0)*1000:.0f}ms text_len={len(full_text)}")
    except Exception as e:
        import traceback as _tb
        print(f"[voxtral] {req_id} STT error: {e}\n{_tb.format_exc()}")
        raise HTTPException(status_code=500, detail=f"Transcription failed: {e}")
    finally:
        if wav_path and os.path.exists(wav_path):
            try: os.unlink(wav_path)
            except OSError: pass

    # ── VAD segmentation + text distribution ─────────────────────────────────
    raw_segs, seg_method = _segments_from_vad(audio_array, TARGET_SR)
    segs_with_text       = _distribute_text(full_text, raw_segs)

    # ── FER (video only) ─────────────────────────────────────────────────────
    has_fer              = False
    face_emotions:  dict[int, str] = {}
    fer_timeline:   dict[int, str] = {}
    if _is_video(filename) and _fer_session is not None:
        t0 = time.perf_counter()
        face_emotions, fer_timeline = await loop.run_in_executor(
            None, _fer_for_segments, tmp_path, raw_segs
        )
        has_fer = bool(face_emotions)
        print(f"[voxtral] {req_id} FER done {(time.perf_counter()-t0)*1000:.0f}ms faces={len(face_emotions)} timeline={len(fer_timeline)}")

    if tmp_path and os.path.exists(tmp_path):
        try: os.unlink(tmp_path)
        except OSError: pass

    # ── Build segments ────────────────────────────────────────────────────────
    segments = []
    for s in segs_with_text:
        emo      = _parse_emotion(s["text"])
        seg_data = {
            "id":      s["id"],
            "speaker": s["speaker"],
            "start":   s["start"],
            "end":     s["end"],
            "text":    s["text"],
            "emotion": emo["emotion"],
            "valence": emo["valence"],
            "arousal": emo["arousal"],
        }
        if s["id"] in face_emotions:
            seg_data["face_emotion"] = face_emotions[s["id"]]
        segments.append(seg_data)

    total_ms = (time.perf_counter() - req_start) * 1000
    print(f"[voxtral] {req_id} complete total={total_ms:.0f}ms segments={len(segments)} has_fer={has_fer}")

    return {
        "segments":               segments,
        "duration":               duration,
        "text":                   full_text,
        "filename":               filename,
        "diarization_method":     seg_method,
        "has_video":              has_fer,
        # Per-second face emotion timeline for live playback panel
        # Keys are strings (JSON), values are emotion labels e.g. "Happy"
        "face_emotion_timeline":  {str(k): v for k, v in fer_timeline.items()},
    }