#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""微信语音转文字模块（silk 解码 + faster-whisper ASR）。

链路：.silk → silk_v3_decoder.exe 解码为 .pcm → 加 wav 头 → faster-whisper 转文字。

用法：
    from voice2text import VoiceToText
    v2t = VoiceToText()
    text = v2t.transcribe_silk("/path/to/xxx.silk")   # 返回文字，失败返回 None
    text = v2t.transcribe_voice(username, local_id, db)  # 直接从微信库提取并转文字

说明：
    - whisper 模型懒加载（首次调用才加载，约几十秒；之后常驻内存）。
    - 中文识别，small 模型在 CPU 上平衡速度与准确率。
"""
from __future__ import annotations

import os
import subprocess
import threading
import wave

BASE_DIR = os.path.dirname(os.path.abspath(__file__))
DECODER_EXE = os.path.join(
    BASE_DIR, "third_party", "silk-v3-decoder", "silk_v3_decoder.exe"
)
WHISPER_MODEL = os.environ.get("V2T_MODEL", "small")
SAMPLE_RATE = 24000  # silk_v3_decoder 默认输出采样率

_model = None
_model_lock = threading.Lock()
_hf_ready = False


def _ensure_hf_env():
    """国内环境用 hf-mirror 下载模型，并禁用 xet（避免 401）。"""
    global _hf_ready
    if _hf_ready:
        return
    os.environ.setdefault("HF_ENDPOINT", "https://hf-mirror.com")
    os.environ.setdefault("HF_HUB_DISABLE_XET", "1")
    _hf_ready = True


def _get_model():
    global _model
    with _model_lock:
        if _model is None:
            _ensure_hf_env()
            from faster_whisper import WhisperModel
            _model = WhisperModel(WHISPER_MODEL, device="cpu", compute_type="int8")
        return _model


def _silk_to_pcm(silk_path: str, pcm_path: str) -> bool:
    if not os.path.exists(DECODER_EXE):
        return False
    try:
        r = subprocess.run(
            [DECODER_EXE, silk_path, pcm_path, "-quiet"],
            capture_output=True, timeout=60,
        )
        return r.returncode == 0 and os.path.exists(pcm_path) and os.path.getsize(pcm_path) > 0
    except Exception:
        return False


def _pcm_to_wav(pcm_path: str, wav_path: str, sample_rate: int = SAMPLE_RATE):
    with open(pcm_path, "rb") as f:
        data = f.read()
    with wave.open(wav_path, "wb") as w:
        w.setnchannels(1)
        w.setsampwidth(2)
        w.setframerate(sample_rate)
        w.writeframes(data)


def transcribe_silk(silk_path: str, sample_rate: int = SAMPLE_RATE):
    """把 .silk 语音文件转成文字；失败返回 None。"""
    if not silk_path or not os.path.exists(silk_path):
        return None
    pcm_path = silk_path + ".pcm"
    wav_path = silk_path + ".wav"
    try:
        if not _silk_to_pcm(silk_path, pcm_path):
            return None
        _pcm_to_wav(pcm_path, wav_path, sample_rate)
        segments, _info = _get_model().transcribe(
            wav_path, language="zh", beam_size=5,
        )
        text = "".join(s.text for s in segments).strip()
        return text or None
    except Exception:
        return None
    finally:
        for p in (pcm_path, wav_path):
            try:
                if os.path.exists(p):
                    os.remove(p)
            except OSError:
                pass


def transcribe_voice(username: str, local_id: int, db, save_dir: str = None):
    """从微信库提取语音并转文字：db 为 WeChatDB 实例。"""
    try:
        from wechatauto.media import MediaDownloader
        md = MediaDownloader(db, save_dir=save_dir)
        silk = md.download_voice(username, local_id, save_dir=save_dir)
        if not silk:
            return None
        return transcribe_silk(silk)
    except Exception:
        return None


class VoiceToText:
    """可复用的语音转文字器（模型懒加载、线程安全）。"""

    def __init__(self, model: str = None):
        global WHISPER_MODEL
        if model:
            WHISPER_MODEL = model

    def transcribe_silk(self, silk_path: str):
        return transcribe_silk(silk_path)

    def transcribe_voice(self, username: str, local_id: int, db, save_dir: str = None):
        return transcribe_voice(username, local_id, db, save_dir)


if __name__ == "__main__":
    import sys
    if len(sys.argv) < 2:
        print("用法: python voice2text.py <xxx.silk>")
        sys.exit(1)
    print(transcribe_silk(sys.argv[1]))