feat: add comprehensive logging throughout conversion pipeline

- voice_resolver: log spec resolution and cache hits
- conversion_executor: log chapter start/end, voice resolution, timing
- conversion_planner: log plan summary
- conversion_service: log entry point params and outcome
- conversion_runner: log job params and lifecycle
- form.py: log PendingJob creation
- synthesize.py: log pipeline creation and device selection

abogen/application/conversion_executor.py

@@ -8,6 +8,7 @@ This is Stage 6 of the conversion flow unification plan.
 
 from __future__ import annotations
 
+import logging
 import time
 from contextlib import ExitStack
 from typing import Any, Callable, Dict, List, Optional, Set, Tuple
@@ -172,6 +173,14 @@ def execute_conversion(
     result = ConversionResult(metadata=plan.metadata)
     collector = MarkerCollector()
 
+    logging.info(
+        "[executor] Starting: chapters=%d intro=%s outro=%s merge=%s",
+        len(plan.chapters),
+        bool(plan.intro and plan.intro.enabled),
+        bool(plan.outro and plan.outro.enabled),
+        request.save.merge_chapters_at_end,
+    )
+
     # Determine cancellation checker
     if check_cancelled is None:
         check_cancelled = lambda: events.check_cancelled()
@@ -207,10 +216,12 @@ def execute_conversion(
 
     # Resolve voices
     base_voice_spec = request.voice or "M1"
+    logging.info("[executor] Resolving base voice: spec=%s", base_voice_spec)
     base_provider, base_voice_choice, base_speed, base_steps = _resolve_voice(
         voice_resolver, base_voice_spec, request,
         log_callback=lambda msg: events.log(msg, level="warning"),
     )
+    logging.info("[executor] Base voice resolved: provider=%s voice=%s speed=%.2f", base_provider, base_voice_choice, base_speed)
 
     # Use ExitStack for resource management
     with ExitStack() as stack:
@@ -290,12 +301,14 @@ def execute_conversion(
 
             chapter_display = f"Chapter {chapter_idx}/{len(plan.chapters)}: {chapter.title}"
             events.log(f"Processing {chapter_display}")
+            logging.info("[executor] Chapter %d/%d start: title=%s", chapter_idx, len(plan.chapters), chapter.title)
 
             # Resolve chapter voice
             chapter_provider, chapter_voice, chapter_speed, chapter_steps = _resolve_voice(
                 voice_resolver, chapter.voice_spec, request,
                 log_callback=lambda msg: events.log(msg, level="warning"),
             )
+            logging.info("[executor] Chapter %d voice: provider=%s voice=%s speed=%.2f", chapter_idx, chapter_provider, chapter_voice, chapter_speed)
             chapter_backend = pipeline_provider.get(chapter_provider, request.language, request.use_gpu)
 
             # Record chapter start for markers
@@ -517,6 +530,9 @@ def execute_conversion(
 
             # Record chapter end for markers
             collector.on_chapter_end(stats.current_time)
+            logging.info("[executor] Chapter %d/%d done: time=%.1fs", chapter_idx, len(plan.chapters), stats.current_time)
+
+        logging.info("[executor] All chapters processed, total time=%.1fs", stats.current_time)
 
         # Process outro
         if plan.outro and plan.outro.enabled and merge_chapters:

abogen/application/conversion_planner.py

@@ -8,6 +8,7 @@ This is Stage 2 of the conversion flow unification plan.
 
 from __future__ import annotations
 
+import logging
 from typing import Any, Dict, List, Optional, Tuple
 
 from abogen.application.conversion_models import (
@@ -65,6 +66,13 @@ def build_conversion_plan(request: ConversionRequest) -> ConversionPlan:
     # 7. Resolve output layout
     output_layout = resolve_output_layout(request)
 
+    logging.info(
+        "[planner] Plan built: chapters=%d intro=%s outro=%s",
+        len(chapters),
+        bool(intro and intro.enabled),
+        bool(outro and outro.enabled),
+    )
+
     return ConversionPlan(
         request=request,
         metadata=metadata,

abogen/application/conversion_service.py

@@ -15,6 +15,7 @@ The service NEVER imports from PyQt or WebUI.
 
 from __future__ import annotations
 
+import logging
 from collections import defaultdict
 from typing import Any, Dict
 
@@ -61,6 +62,10 @@ def run_conversion(
     try:
         # Stage 0: Create voice resolver
         events.log("Preparing conversion pipeline")
+        logging.info(
+            "[app] run_conversion: provider=%s language=%s voice=%s speed=%.2f",
+            request.tts_provider, request.language, request.voice, request.speed,
+        )
         resolver = _create_voice_resolver(request, pool, voice_cache)
 
         # Stage 1: Prepare TTS context
@@ -94,10 +99,12 @@ def run_conversion(
         _finalize(request, result, plan, events)
 
         events.log("Conversion complete")
+        logging.info("[app] run_conversion completed successfully")
         return result
 
     except Exception as e:
         events.log(f"Conversion failed: {e}", level="error")
+        logging.exception("[app] run_conversion failed: %s", e)
         raise
     finally:
         pool.dispose_all()

abogen/application/voice_resolver.py

@@ -7,6 +7,7 @@ PyQtVoiceResolver) with a single app-layer implementation.
 
 from __future__ import annotations
 
+import logging
 from typing import Any, Dict, Optional
 
 from abogen.application.conversion_ports import ResolvedVoice, VoiceResolver
@@ -49,6 +50,7 @@ class AppVoiceResolver:
         cache_key = f"{provider}:{resolved}" if resolved else provider
         cached = self._cache.get(cache_key)
         if cached is not None:
+            logging.info("[resolver] Cache hit: spec=%s -> provider=%s resolved=%s", voice_spec, provider, resolved)
             return ResolvedVoice(
                 provider=provider,
                 resolved_spec=resolved,
@@ -68,6 +70,8 @@ class AppVoiceResolver:
             loaded = resolved
 
         self._cache.set(cache_key, loaded)
+        logging.info("[resolver] Resolved: spec=%s -> provider=%s resolved=%s speed=%.2f steps=%s",
+                      voice_spec, provider, resolved, speed, steps)
         return ResolvedVoice(
             provider=provider,
             resolved_spec=resolved,

abogen/webui/conversion_runner.py

@@ -15,6 +15,7 @@ Engine converts Language → its own format internally.
 
 from __future__ import annotations
 
+import logging
 from pathlib import Path
 from typing import Any
 
@@ -48,6 +49,11 @@ def _build_request(job: Job) -> ConversionRequest:
     """Build a ConversionRequest from a WebUI Job."""
     source_path = Path(job.stored_path) if job.stored_path else None
 
+    logging.info(
+        "[runner] Building request: job=%s provider=%s language=%s voice=%s speed=%.2f gpu=%s",
+        job.id, job.tts_provider, job.language, job.voice, job.speed, job.use_gpu,
+    )
+
     return ConversionRequest(
         # Source
         source_path=source_path,
@@ -186,6 +192,7 @@ def run_conversion_job(job: Job) -> None:
     request = _build_request(job)
     events = WebUIEventsAdapter(job)
 
+    logging.info("[runner] Starting conversion: job=%s", job.id)
     try:
         result = run_conversion(request, events)
         _apply_result(job, result)
@@ -193,10 +200,14 @@ def run_conversion_job(job: Job) -> None:
         if job.status != JobStatus.CANCELLED:
             job.progress = 1.0
 
+        logging.info("[runner] Conversion completed: job=%s", job.id)
+
     except ConversionCancelled:
         job.status = JobStatus.CANCELLED
         job.add_log("Job cancelled", level="warning")
+        logging.info("[runner] Conversion cancelled: job=%s", job.id)
     except Exception as exc:
         job.error = str(exc)
         job.status = JobStatus.FAILED
         job.add_log(f"Job failed: {exc}", level="error")
+        logging.exception("[runner] Conversion failed: job=%s error=%s", job.id, exc)

abogen/webui/routes/utils/form.py

@@ -1,3 +1,4 @@
+import logging
 import time
 import uuid
 from typing import Any, Dict, Iterable, List, Mapping, Optional, Tuple, cast
@@ -794,6 +795,11 @@ def build_pending_job_from_extraction(
         else:
             normalization_overrides[key] = default_val
 
+    logging.info(
+        "[form] Creating PendingJob: language=%s voice=%s speed=%.2f provider=%s",
+        language, voice, speed, settings.get("tts_provider", "kokoro"),
+    )
+
     pending = PendingJob(
         id=uuid.uuid4().hex,
         original_filename=original_name,

abogen/webui/routes/utils/synthesize.py

@@ -1,4 +1,5 @@
 import io
+import logging
 import threading
 from typing import Any, Dict, Iterable, List, Mapping, Optional, Tuple
 import numpy as np
@@ -43,9 +44,11 @@ def _resolve_pipeline(language: Language, use_gpu: bool) -> Tuple[Any, bool]:
     last_error: Optional[Exception] = None
     for device in devices:
         try:
+            logging.info("[preview] Trying device=%s for language=%s", device, language)
             return get_preview_pipeline(language, device), device != "cpu"
         except Exception as exc:
             last_error = exc
+            logging.warning("[preview] Device %s failed: %s", device, exc)
 
     raise RuntimeError("Preview pipeline is unavailable") from last_error
 
@@ -55,9 +58,11 @@ def get_preview_pipeline(language: Language, device: str) -> Any:
     with _preview_pipeline_lock:
         pipeline = _preview_pipelines.get(key)
         if pipeline is not None:
+            logging.info("[preview] Using cached pipeline for %s/%s", language, device)
             return pipeline
         from abogen.tts_plugin.utils import create_pipeline
 
+        logging.info("[preview] Creating pipeline: provider=kokoro language=%s device=%s", language, device)
         pipeline = create_pipeline("kokoro", language=language, device=device)
         _preview_pipelines[key] = pipeline
         return pipeline