fix: 清理导出服务日志并简化whisper接口参数

- 移除exportService中的冗余console日志输出
- 简化whisper API接口，移除downloadModel和getModelStatus的payload参数
- 清理图片、表情、语音导出过程中的调试日志
- 移除数据库查询和媒体处理中的详细日志记录
- 优化代码可读性，减少控制台输出噪音

electron/transcribeWorker.ts

@@ -1,4 +1,4 @@
-import { parentPort, workerData } from 'worker_threads'
+import { parentPort, workerData } from 'worker_threads'
 import * as fs from 'fs'
 
 interface WorkerParams {
@@ -9,37 +9,23 @@ interface WorkerParams {
 }
 
 async function run() {
-    console.info('[TranscribeWorker] Worker process starting...');
-
     if (!parentPort) {
-        console.error('[TranscribeWorker] Critical Error: parentPort is null');
         return;
     }
 
     try {
-        console.info('[TranscribeWorker] Loading sherpa-onnx-node...');
         // 动态加载以捕获可能的加载错误（如 C++ 运行库缺失等）
         let sherpa: any;
         try {
             sherpa = require('sherpa-onnx-node');
-            console.info('[TranscribeWorker] sherpa-onnx-node loaded successfully.');
-        } catch (requireError) {
-            console.error('[TranscribeWorker] Failed to load sherpa-onnx-node:', requireError);
+            } catch (requireError) {
             parentPort.postMessage({ type: 'error', error: 'Failed to load speech engine: ' + String(requireError) });
             return;
         }
 
         const { modelPath, tokensPath, wavData: rawWavData, sampleRate } = workerData as WorkerParams
         const wavData = Buffer.from(rawWavData);
-        console.info('[TranscribeWorker] Params received:', {
-            modelPath,
-            tokensPath,
-            sampleRate,
-            wavDataLength: wavData?.length
-        });
-
         // 1. 初始化识别器 (SenseVoiceSmall)
-        console.info('[TranscribeWorker] Initializing OfflineRecognizer...');
         const recognizerConfig = {
             modelConfig: {
                 senseVoice: {
@@ -52,12 +38,8 @@ async function run() {
             }
         }
         const recognizer = new sherpa.OfflineRecognizer(recognizerConfig)
-        console.info('[TranscribeWorker] OfflineRecognizer initialized.');
-
         // 2. 初始化 VAD (用于流式输出效果)
         const vadPath = modelPath.replace('model.int8.onnx', 'silero_vad.onnx');
-        console.info('[TranscribeWorker] VAD Path:', vadPath);
-
         const vadConfig = {
             sileroVad: {
                 model: vadPath,
@@ -73,8 +55,6 @@ async function run() {
 
         // 检查 VAD 模型是否存在，如果不存在则退回到全量识别
         if (!fs.existsSync(vadPath)) {
-            console.warn('[TranscribeWorker] VAD model not found, falling back to full transcription.');
-
             const pcmData = wavData.slice(44)
             const samples = new Float32Array(pcmData.length / 2)
             for (let i = 0; i < samples.length; i++) {
@@ -86,15 +66,11 @@ async function run() {
             recognizer.decode(stream)
             const result = recognizer.getResult(stream)
 
-            console.info('[TranscribeWorker] Full transcription result:', result.text);
             parentPort.postMessage({ type: 'final', text: result.text })
             return
         }
 
-        console.info('[TranscribeWorker] Initializing Vad...');
         const vad = new sherpa.Vad(vadConfig, 60) // 60s max
-        console.info('[TranscribeWorker] VAD initialized.');
-
         // 3. 处理音频数据
         const pcmData = wavData.slice(44)
         const samples = new Float32Array(pcmData.length / 2)
@@ -107,7 +83,6 @@ async function run() {
         let offset = 0
         let accumulatedText = ''
 
-        console.info('[TranscribeWorker] Starting processing loop...');
         let segmentCount = 0;
 
         while (offset < samples.length) {
@@ -120,9 +95,6 @@ async function run() {
             while (!vad.isEmpty()) {
                 const segment = vad.front(false)
 
-                // Log segment detection
-                console.info(`[TranscribeWorker] VAD Segment detected. Duration: ${segment.samples.length / sampleRate}s`);
-
                 const stream = recognizer.createStream()
                 stream.acceptWaveform({ sampleRate, samples: segment.samples })
                 recognizer.decode(stream)
@@ -133,7 +105,6 @@ async function run() {
                     if (text.length > 0) {
                         accumulatedText += (accumulatedText ? ' ' : '') + text
                         segmentCount++;
-                        console.info(`[TranscribeWorker] Partial update #${segmentCount}: "${text}" -> Total: "${accumulatedText.substring(0, 50)}..."`);
                         parentPort.postMessage({ type: 'partial', text: accumulatedText })
                     }
                 }
@@ -149,26 +120,23 @@ async function run() {
         vad.flush();
         while (!vad.isEmpty()) {
             const segment = vad.front(false);
-            console.info(`[TranscribeWorker] Final VAD Segment detected. Duration: ${segment.samples.length / sampleRate}s`);
             const stream = recognizer.createStream()
             stream.acceptWaveform({ sampleRate, samples: segment.samples })
             recognizer.decode(stream)
             const result = recognizer.getResult(stream)
             if (result.text) {
                 accumulatedText += (accumulatedText ? ' ' : '') + result.text.trim()
-                console.info(`[TranscribeWorker] Final partial update: "${result.text.trim()}"`);
                 parentPort.postMessage({ type: 'partial', text: accumulatedText })
             }
             vad.pop();
         }
 
-        console.info('[TranscribeWorker] Loop finished. Final text length:', accumulatedText.length);
         parentPort.postMessage({ type: 'final', text: accumulatedText })
 
     } catch (error) {
-        console.error('[TranscribeWorker] Fatal error:', error);
         parentPort.postMessage({ type: 'error', error: String(error) })
     }
 }
 
 run();
+