from __future__ import annotations

import base64
import hashlib
import inspect
import os
import random
import tempfile
from pathlib import Path
from typing import Any
from time import perf_counter

import modal


app = modal.App("ai-time-machine-audio")

NEMOTRON_STT_MODEL_ID = "nvidia/nemotron-3.5-asr-streaming-0.6b"
QWEN_TTS_MODEL_ID = "Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign"

base_image = (
    modal.Image.debian_slim(python_version="3.12")
    .apt_install("ffmpeg", "git", "libsndfile1", "sox")
    .pip_install("fastapi[standard]")
)

nemotron_image = (
    base_image
    .pip_install("Cython", "packaging")
    .pip_install("git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[asr]")
)

qwen_tts_image = base_image.pip_install("qwen-tts", "soundfile")
chatterbox_tts_image = base_image.pip_install("chatterbox-tts", "soundfile", "torchaudio")

_asr_model: Any | None = None
_tts_model: Any | None = None
_chatterbox_tts_model: Any | None = None
_asr_loaded_at: float | None = None
_tts_loaded_at: float | None = None
_chatterbox_tts_loaded_at: float | None = None

MIN_CONTAINERS = int(os.getenv("TIME_MACHINE_MODAL_MIN_CONTAINERS", "1"))
MAX_CONTAINERS = int(os.getenv("TIME_MACHINE_MODAL_MAX_CONTAINERS", "1"))
ACTIVE_TTS_MODEL_FAMILY = os.getenv(
    "TIME_MACHINE_MODAL_TTS_MODEL_FAMILY",
    "chatterbox_turbo",
).strip().lower().replace("-", "_")
QWEN_TTS_ENABLED = ACTIVE_TTS_MODEL_FAMILY == "qwen"
CHATTERBOX_TTS_ENABLED = ACTIVE_TTS_MODEL_FAMILY in {"chatterbox", "chatterbox_turbo", "turbo"}
SCALEDOWN_WINDOW_SECONDS = int(os.getenv("TIME_MACHINE_MODAL_SCALEDOWN_SECONDS", "1800"))
STARTUP_TIMEOUT_SECONDS = int(os.getenv("TIME_MACHINE_MODAL_STARTUP_TIMEOUT_SECONDS", "900"))
WARMUP_TTS = os.getenv("TIME_MACHINE_MODAL_WARMUP_TTS", "1").strip().lower() in {
    "1",
    "true",
    "yes",
    "on",
}

print(
    "Modal audio service config: "
    f"active_tts={ACTIVE_TTS_MODEL_FAMILY} "
    f"stt_min_containers={MIN_CONTAINERS} "
    f"qwen_tts_registered={QWEN_TTS_ENABLED} "
    f"chatterbox_tts_registered={CHATTERBOX_TTS_ENABLED}"
)

# Persistent volume to share and cache downloaded model weights
hf_volume = modal.Volume.from_name("hf-cache-vol", create_if_missing=True)


@app.cls(
    image=nemotron_image,
    gpu="A10G",
    timeout=600,
    startup_timeout=STARTUP_TIMEOUT_SECONDS,
    scaledown_window=SCALEDOWN_WINDOW_SECONDS,
    min_containers=MIN_CONTAINERS,
    max_containers=MAX_CONTAINERS,
    volumes={"/root/.cache/huggingface": hf_volume},
)
class NemotronSTTService:
    model: Any

    @modal.enter()
    def load(self) -> None:
        self.model = _load_asr_model()

    @modal.fastapi_endpoint(method="POST", label="time-machine-nemotron-stt")
    def transcribe(self, item: dict[str, Any]) -> dict[str, Any]:
        request_started = perf_counter()
        audio_b64 = _required_string(item, "audio_b64")
        language = str(item.get("language") or "auto")
        audio_path = _write_request_audio(audio_b64)

        target_lang = _target_language(language)
        if hasattr(self.model, "set_inference_prompt"):
            self.model.set_inference_prompt(target_lang)

        preprocess_started = perf_counter()
        proc_path = _convert_to_mono_16k(audio_path)
        duration = _get_audio_duration(proc_path)
        preprocess_seconds = perf_counter() - preprocess_started

        inference_started = perf_counter()
        result = _transcribe_manifest(self.model, proc_path, target_lang, duration)
        inference_seconds = perf_counter() - inference_started
        if proc_path.exists() and proc_path != audio_path:
            proc_path.unlink(missing_ok=True)
        audio_path.unlink(missing_ok=True)

        text = _extract_transcript_text(result)
        return {
            "text": text,
            "confidence": None,
            "language": None if language == "auto" else language,
            "is_final": True,
            "timings": {
                "preprocess_seconds": round(preprocess_seconds, 3),
                "inference_seconds": round(inference_seconds, 3),
                "total_seconds": round(perf_counter() - request_started, 3),
                "model_loaded_at": _asr_loaded_at,
            },
        }


if QWEN_TTS_ENABLED:
    @app.cls(
        image=qwen_tts_image,
        gpu="A10G",
        timeout=600,
        startup_timeout=STARTUP_TIMEOUT_SECONDS,
        scaledown_window=SCALEDOWN_WINDOW_SECONDS,
        min_containers=MIN_CONTAINERS,
        max_containers=MAX_CONTAINERS,
        volumes={"/root/.cache/huggingface": hf_volume},
    )
    class QwenTTSService:
        model: Any

        @modal.enter()
        def load(self) -> None:
            self.model = _load_tts_model()
            if WARMUP_TTS:
                self._warm_up()

        @modal.fastapi_endpoint(method="POST", label="time-machine-qwen-tts")
        def synthesize(self, item: dict[str, Any]) -> dict[str, Any]:
            request_started = perf_counter()
            text = _required_string(item, "text")
            voice_profile = item.get("voice_profile")
            if not isinstance(voice_profile, dict):
                raise ValueError("voice_profile must be an object.")

            language = str(item.get("language") or "English")
            prosody_hint = item.get("prosody_hint")
            instruction = _voice_instruction(voice_profile, prosody_hint)
            voice_seed = _voice_seed(voice_profile, item.get("voice_seed"))

            inference_started = perf_counter()
            audio_bytes, duration_seconds = _synthesize_to_wav_bytes(
                self.model,
                text=text,
                language=language,
                instruction=instruction,
                seed=voice_seed,
            )
            inference_seconds = perf_counter() - inference_started

            return {
                "audio_b64": base64.b64encode(audio_bytes).decode("ascii"),
                "mime_type": "audio/wav",
                "duration_seconds": duration_seconds,
                "description": "Qwen3-TTS VoiceDesign synthesis on warm Modal GPU.",
                "timings": {
                    "inference_seconds": round(inference_seconds, 3),
                    "total_seconds": round(perf_counter() - request_started, 3),
                    "model_loaded_at": _tts_loaded_at,
                },
            }

        def _warm_up(self) -> None:
            try:
                _synthesize_to_wav_bytes(
                    self.model,
                    text="The signal is open.",
                    language="English",
                    instruction="Natural conversational voice. Pace: fast. Emotion: curious.",
                )
            except Exception as exc:
                print(f"Qwen TTS warmup failed; first request may still pay setup cost: {exc}")
else:
    print("Qwen TTS service not registered for this Modal serve run.")


if CHATTERBOX_TTS_ENABLED:
    @app.cls(
        image=chatterbox_tts_image,
        gpu=os.getenv("TIME_MACHINE_CHATTERBOX_GPU", "L4"),
        timeout=600,
        startup_timeout=STARTUP_TIMEOUT_SECONDS,
        scaledown_window=SCALEDOWN_WINDOW_SECONDS,
        min_containers=MIN_CONTAINERS,
        max_containers=MAX_CONTAINERS,
        volumes={"/root/.cache/huggingface": hf_volume},
    )
    class ChatterboxTurboTTSService:
        model: Any

        @modal.enter()
        def load(self) -> None:
            self.runtime_name, self.model = _load_chatterbox_tts_model()
            if WARMUP_TTS:
                self._warm_up()

        @modal.fastapi_endpoint(method="POST", label="time-machine-chatterbox-turbo-tts")
        def synthesize(self, item: dict[str, Any]) -> dict[str, Any]:
            request_started = perf_counter()
            text = _required_string(item, "text")
            voice_profile = item.get("voice_profile")
            if not isinstance(voice_profile, dict):
                raise ValueError("voice_profile must be an object.")

            prosody_hint = item.get("prosody_hint")
            prompt = _voice_instruction(voice_profile, prosody_hint)
            voice_seed = _voice_seed(voice_profile, item.get("voice_seed"))
            exaggeration = _float_item(item, "exaggeration", 0.65)
            cfg_weight = _float_item(item, "cfg_weight", 0.35)
            temperature = _float_item(item, "temperature", 0.8)
            latency_profile = str(item.get("latency_profile") or "turbo")
            runtime_name = str(getattr(self, "runtime_name", "turbo"))

            inference_started = perf_counter()
            audio_bytes, duration_seconds = _synthesize_chatterbox_to_wav_bytes(
                self.model,
                text=text,
                prompt=prompt,
                seed=voice_seed,
                runtime_name=runtime_name,
                exaggeration=exaggeration,
                cfg_weight=cfg_weight,
                temperature=temperature,
            )
            inference_seconds = perf_counter() - inference_started

            return {
                "audio_b64": base64.b64encode(audio_bytes).decode("ascii"),
                "mime_type": "audio/wav",
                "duration_seconds": duration_seconds,
                "description": _chatterbox_description(
                    runtime_name,
                    latency_profile,
                    exaggeration,
                    cfg_weight,
                ),
                "timings": {
                    "inference_seconds": round(inference_seconds, 3),
                    "total_seconds": round(perf_counter() - request_started, 3),
                    "model_loaded_at": _chatterbox_tts_loaded_at,
                },
            }

        def _warm_up(self) -> None:
            try:
                _synthesize_chatterbox_to_wav_bytes(
                    self.model,
                    text="The signal is open.",
                    prompt="Natural expressive character voice. Pace: fast. Emotion: curious.",
                    runtime_name=str(getattr(self, "runtime_name", "turbo")),
                    exaggeration=0.6,
                    cfg_weight=0.35,
                    temperature=0.8,
                )
            except Exception as exc:
                print(f"Chatterbox Turbo TTS warmup failed; first request may still pay setup cost: {exc}")
else:
    print("Chatterbox TTS service not registered for this Modal serve run.")


def _load_asr_model() -> Any:
    global _asr_model, _asr_loaded_at
    if _asr_model is not None:
        return _asr_model
    started = perf_counter()
    import nemo.collections.asr as nemo_asr

    print(f"Loading Modal STT model: provider=nvidia runtime=nemo model={NEMOTRON_STT_MODEL_ID}")
    _asr_model = nemo_asr.models.ASRModel.from_pretrained(model_name=NEMOTRON_STT_MODEL_ID)
    print(
        "Loaded Modal STT model: "
        f"{_asr_model.__class__.__module__}.{_asr_model.__class__.__name__} "
        f"model={NEMOTRON_STT_MODEL_ID}"
    )
    _asr_loaded_at = round(started, 3)
    return _asr_model


def _load_tts_model() -> Any:
    global _tts_model, _tts_loaded_at
    if _tts_model is not None:
        return _tts_model
    started = perf_counter()

    import torch
    from qwen_tts import Qwen3TTSModel

    kwargs: dict[str, Any] = {
        "device_map": "cuda:0" if torch.cuda.is_available() else "cpu",
        "dtype": torch.bfloat16 if torch.cuda.is_available() else torch.float32,
    }
    print(
        "Loading Modal TTS model: "
        f"provider=qwen runtime=qwen-tts model={QWEN_TTS_MODEL_ID} "
        f"device_map={kwargs['device_map']} dtype={kwargs['dtype']}"
    )
    try:
        _tts_model = Qwen3TTSModel.from_pretrained(
            QWEN_TTS_MODEL_ID,
            attn_implementation="flash_attention_2",
            **kwargs,
        )
        attention = "flash_attention_2"
    except Exception:
        _tts_model = Qwen3TTSModel.from_pretrained(
            QWEN_TTS_MODEL_ID,
            **kwargs,
        )
        attention = "default"
    print(
        "Loaded Modal TTS model: "
        f"{_tts_model.__class__.__module__}.{_tts_model.__class__.__name__} "
        f"model={QWEN_TTS_MODEL_ID} attention={attention}"
    )
    _tts_loaded_at = round(started, 3)
    return _tts_model


def _load_chatterbox_tts_model() -> tuple[str, Any]:
    global _chatterbox_tts_model, _chatterbox_tts_loaded_at
    if _chatterbox_tts_model is not None:
        return _infer_chatterbox_runtime(_chatterbox_tts_model), _chatterbox_tts_model
    started = perf_counter()

    import torch

    device = "cuda" if torch.cuda.is_available() else "cpu"
    _ensure_chatterbox_watermarker()
    runtime_name, _chatterbox_tts_model = _load_first_available_chatterbox_model(device)
    print(
        "Loaded Modal TTS model: "
        f"provider=resemble_ai runtime=chatterbox-{runtime_name} "
        f"configured_models={','.join(_chatterbox_model_candidates())} "
        f"{runtime_name} ({_chatterbox_tts_model.__class__.__module__}."
        f"{_chatterbox_tts_model.__class__.__name__}) on {device}"
    )
    _chatterbox_tts_loaded_at = round(started, 3)
    return runtime_name, _chatterbox_tts_model


def _ensure_chatterbox_watermarker() -> None:
    try:
        import perth
    except Exception as exc:
        print(f"Chatterbox Perth watermarker unavailable; using no-op watermarker: {exc}")
        return

    watermarker_cls = getattr(perth, "PerthImplicitWatermarker", None)
    if callable(watermarker_cls):
        return

    class _NoOpWatermarker:
        def apply_watermark(self, wav: Any, *args: Any, **kwargs: Any) -> Any:
            return wav

        def watermark(self, wav: Any, *args: Any, **kwargs: Any) -> Any:
            return wav

    perth.PerthImplicitWatermarker = _NoOpWatermarker
    print("Chatterbox PerthImplicitWatermarker is missing; using no-op watermarker.")


def _load_first_available_chatterbox_model(device: str) -> tuple[str, Any]:
    last_error: Exception | None = None
    for runtime_name, chatterbox_cls in _chatterbox_model_classes():
        try:
            print(f"Trying Chatterbox TTS runtime: {runtime_name}")
            return runtime_name, _load_chatterbox_from_pretrained(chatterbox_cls, device)
        except Exception as exc:
            last_error = exc
            print(f"Chatterbox {runtime_name} load failed; trying next fallback: {exc}")

    if last_error is not None:
        raise last_error
    raise RuntimeError("No Chatterbox TTS runtime is available.")


def _chatterbox_model_classes() -> list[tuple[str, Any]]:
    classes: list[tuple[str, Any]] = []
    try:
        from chatterbox.tts_turbo import ChatterboxTurboTTS

        classes.append(("turbo", ChatterboxTurboTTS))
    except Exception as exc:
        print(f"Chatterbox Turbo runtime unavailable; trying standard Chatterbox: {exc}")

    try:
        from chatterbox.tts import ChatterboxTTS

        classes.append(("standard", ChatterboxTTS))
    except Exception as exc:
        print(f"Standard Chatterbox runtime unavailable: {exc}")

    return classes


def _load_chatterbox_from_pretrained(chatterbox_cls: Any, device: str) -> Any:
    from_pretrained = chatterbox_cls.from_pretrained
    if _supports_chatterbox_model_id(from_pretrained):
        for model_id in _chatterbox_model_candidates():
            try:
                return from_pretrained(model_id, device=device)
            except Exception as exc:
                print(f"Chatterbox model id {model_id!r} failed; trying next fallback: {exc}")

    return from_pretrained(device=device)


def _supports_chatterbox_model_id(from_pretrained: Any) -> bool:
    try:
        parameters = inspect.signature(from_pretrained).parameters
    except (TypeError, ValueError):
        return False

    positional = [
        parameter
        for parameter in parameters.values()
        if parameter.kind
        in {
            inspect.Parameter.POSITIONAL_ONLY,
            inspect.Parameter.POSITIONAL_OR_KEYWORD,
        }
    ]
    model_id_parameter_names = {
        "model_id",
        "repo_id",
        "pretrained_model_name_or_path",
        "checkpoint",
    }
    return bool(positional) and positional[0].name in model_id_parameter_names


def _chatterbox_model_candidates() -> list[str]:
    raw = os.getenv("TIME_MACHINE_CHATTERBOX_TURBO_MODEL_ID", "ResembleAI/chatterbox-turbo")
    raw_list = os.getenv("TIME_MACHINE_CHATTERBOX_MODEL_IDS", raw)
    candidates = [item.strip() for item in raw_list.split(",") if item.strip()]
    if "ResembleAI/chatterbox" not in candidates:
        candidates.append("ResembleAI/chatterbox")
    return candidates


def _target_language(language: str) -> str:
    supported_langs = {
        "en-US", "en", "en-GB", "enGB",
        "es-ES", "esES", "es-US", "es",
        "zh-CN", "zh-ZH"
    }
    return language if language in supported_langs else "en"


def _transcribe_manifest(
    model: Any,
    proc_path: Path,
    target_lang: str,
    duration: float,
) -> Any:
    import json

    manifest_path = proc_path.with_suffix(".json")
    try:
        with open(manifest_path, "w", encoding="utf-8") as f:
            f.write(json.dumps({
                "audio_filepath": str(proc_path),
                "duration": duration,
                "text": "",
                "target_lang": target_lang,
                "lang": target_lang,
                "language": target_lang
            }) + "\n")
        return model.transcribe([str(manifest_path)])
    finally:
        manifest_path.unlink(missing_ok=True)


def _synthesize_to_wav_bytes(
    model: Any,
    text: str,
    language: str,
    instruction: str,
    seed: int | None = None,
) -> tuple[bytes, float]:
    if seed is not None:
        _seed_tts_generation(seed)

    wavs, sample_rate = model.generate_voice_design(
        text=text,
        language=language,
        instruct=instruction,
    )

    output_path = Path(tempfile.NamedTemporaryFile(delete=False, suffix=".wav").name)
    try:
        import soundfile as sf

        sf.write(output_path, wavs[0], sample_rate)
        audio_bytes = output_path.read_bytes()
        duration_seconds = round(len(wavs[0]) / float(sample_rate), 3)
    finally:
        output_path.unlink(missing_ok=True)

    return audio_bytes, duration_seconds


def _synthesize_chatterbox_to_wav_bytes(
    model: Any,
    text: str,
    prompt: str,
    seed: int | None = None,
    runtime_name: str | None = None,
    exaggeration: float = 0.65,
    cfg_weight: float = 0.35,
    temperature: float = 0.8,
) -> tuple[bytes, float]:
    if seed is not None:
        _seed_tts_generation(seed)

    runtime = runtime_name or _infer_chatterbox_runtime(model)
    generate_values: dict[str, Any] = {
        "text": text,
        "prompt": prompt,
        "condition_prompt": prompt,
        "temperature": temperature,
    }
    if runtime != "turbo":
        generate_values["exaggeration"] = exaggeration
        generate_values["cfg_weight"] = cfg_weight

    generate_kwargs = _supported_kwargs(model.generate, generate_values)
    if "text" in generate_kwargs:
        wav = model.generate(**generate_kwargs)
    else:
        wav = model.generate(text, **generate_kwargs)

    sample_rate = int(getattr(model, "sr", getattr(model, "sample_rate", 24000)))
    audio = _to_numpy_audio(wav)
    output_path = Path(tempfile.NamedTemporaryFile(delete=False, suffix=".wav").name)
    try:
        import soundfile as sf

        sf.write(output_path, audio, sample_rate)
        audio_bytes = output_path.read_bytes()
        duration_seconds = round(len(audio) / float(sample_rate), 3)
    finally:
        output_path.unlink(missing_ok=True)

    return audio_bytes, duration_seconds


def _chatterbox_description(
    runtime_name: str,
    latency_profile: str,
    exaggeration: float,
    cfg_weight: float,
) -> str:
    if runtime_name == "turbo":
        return f"Chatterbox Turbo TTS synthesis on warm Modal GPU ({latency_profile})."
    return (
        "Chatterbox TTS synthesis on warm Modal GPU "
        f"({latency_profile}, exaggeration={exaggeration:g}, cfg={cfg_weight:g})."
    )


def _infer_chatterbox_runtime(model: Any) -> str:
    class_path = f"{model.__class__.__module__}.{model.__class__.__name__}".lower()
    return "turbo" if "turbo" in class_path else "standard"


def _supported_kwargs(callable_obj: Any, values: dict[str, Any]) -> dict[str, Any]:
    try:
        parameters = inspect.signature(callable_obj).parameters
    except (TypeError, ValueError):
        return values
    if any(parameter.kind == inspect.Parameter.VAR_KEYWORD for parameter in parameters.values()):
        return values
    return {key: value for key, value in values.items() if key in parameters}


def _to_numpy_audio(wav: Any) -> Any:
    if isinstance(wav, tuple) and wav:
        wav = wav[0]
    if isinstance(wav, list) and wav and hasattr(wav[0], "__len__"):
        wav = wav[0]
    if hasattr(wav, "detach"):
        wav = wav.detach().cpu().numpy()
    elif hasattr(wav, "cpu"):
        wav = wav.cpu().numpy()
    elif isinstance(wav, list):
        import numpy as np

        wav = np.asarray(wav, dtype="float32")
    if hasattr(wav, "squeeze"):
        wav = wav.squeeze()
    return wav


def _seed_tts_generation(seed: int) -> None:
    normalized = seed % (2**31 - 1)
    random.seed(normalized)
    try:
        import numpy as np

        np.random.seed(normalized)
    except Exception:
        pass
    try:
        import torch

        torch.manual_seed(normalized)
        if torch.cuda.is_available():
            torch.cuda.manual_seed_all(normalized)
    except Exception:
        pass


def _convert_to_mono_16k(input_path: Path) -> Path:
    if _is_mono_16k_wav(input_path):
        return input_path

    import subprocess
    output_path = input_path.with_name(input_path.stem + "_mono_16k.wav")
    try:
        cmd = [
            "ffmpeg",
            "-y",
            "-i", str(input_path),
            "-ac", "1",
            "-ar", "16000",
            str(output_path)
        ]
        subprocess.run(cmd, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
        return output_path
    except Exception as e:
        print(f"ffmpeg conversion failed: {e}")
        return input_path


def _is_mono_16k_wav(path: Path) -> bool:
    try:
        import wave

        with wave.open(str(path), "rb") as handle:
            return (
                handle.getnchannels() == 1
                and handle.getframerate() == 16000
                and handle.getsampwidth() == 2
            )
    except Exception:
        return False


def _get_audio_duration(path: Path) -> float:
    try:
        import wave
        with wave.open(str(path), "rb") as f:
            frames = f.getnframes()
            rate = f.getframerate()
            return max(0.1, frames / float(rate))
    except Exception:
        return 10.0


def _write_request_audio(audio_b64: str) -> Path:
    audio_bytes = base64.b64decode(audio_b64)
    audio_path = Path(tempfile.NamedTemporaryFile(delete=False, suffix=".wav").name)
    audio_path.write_bytes(audio_bytes)
    return audio_path


def _extract_transcript_text(result: Any) -> str:
    if isinstance(result, str):
        return result.strip()
    if isinstance(result, list) and result:
        first = result[0]
        if isinstance(first, str):
            return first.strip()
        if hasattr(first, "text"):
            return str(first.text).strip()
        if isinstance(first, dict):
            return str(first.get("text", "")).strip()
    if hasattr(result, "text"):
        return str(result.text).strip()
    return ""


def _voice_instruction(
    voice_profile: dict[str, Any],
    prosody_hint: object,
) -> str:
    voice_id = str(voice_profile.get("voice_id") or "character")
    parts = [
        (
            f"Consistent speaker identity: {voice_id}. "
            "Keep the same timbre and apparent speaker across separate lines."
        ),
        str(voice_profile.get("description") or "Natural conversational character voice."),
        f"Pace: {voice_profile.get('pace') or 'medium'}.",
        f"Emotion: {voice_profile.get('emotion') or 'curious'}.",
    ]
    accent_hint = voice_profile.get("accent_hint")
    if accent_hint:
        parts.append(f"Accent or local color: {accent_hint}.")
    if prosody_hint:
        parts.append(f"Prosody: {prosody_hint}.")
    return " ".join(parts)


def _voice_seed(voice_profile: dict[str, Any], provided: object = None) -> int:
    try:
        parsed = int(provided)
    except (TypeError, ValueError):
        parsed = 0
    if parsed > 0:
        return parsed
    payload = "\n".join(
        [
            str(voice_profile.get("voice_id") or ""),
            str(voice_profile.get("description") or ""),
            str(voice_profile.get("pace") or ""),
            str(voice_profile.get("emotion") or ""),
            str(voice_profile.get("accent_hint") or ""),
        ]
    )
    return int(hashlib.sha256(payload.encode("utf-8")).hexdigest()[:8], 16)


def _float_item(item: dict[str, Any], key: str, default: float) -> float:
    try:
        return float(item.get(key, default))
    except (TypeError, ValueError):
        return default


def _required_string(item: dict[str, Any], key: str) -> str:
    value = item.get(key)
    if not isinstance(value, str) or not value:
        raise ValueError(f"{key} is required.")
    return value