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refactor: consolidate voice resolution via resolve_voice_choice (#3)

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Chapter and chunk loops now call resolve_voice_choice() instead of
inlining _resolve_voice_target + cache check + resolve_voice.
Reduces 3 duplicated voice resolution blocks to 1 closure.

Tests: 1253 passed
This commit is contained in:
Artem Akymenko
2026-07-20 09:05:26 +00:00
parent c4870eece6
commit 68e5adb091
1 changed files with 4 additions and 33 deletions
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abogen/webui/conversion_runner.py
+4 -33
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@@ -539,21 +539,9 @@ def run_conversion_job(job: Job) -> None:
if not chapter_voice_spec:
chapter_voice_spec = base_voice_spec
chapter_provider, chapter_voice_resolved, chapter_speed, chapter_steps = _resolve_voice_target(
chapter_voice_spec, normalized_profiles,
job_voice=getattr(job, "voice", "M1"),
job_tts_provider=getattr(job, "tts_provider", "kokoro"),
job_supertonic_total_steps=getattr(job, "supertonic_total_steps", 5),
job_speed=getattr(job, "speed", 1.0),
chapter_provider, chapter_voice_resolved, voice_choice, chapter_speed, chapter_steps = resolve_voice_choice(
chapter_voice_spec
)
chapter_cache_key = f"{chapter_provider}:{chapter_voice_resolved}" if chapter_voice_resolved else chapter_provider
if chapter_provider == "kokoro":
voice_choice = voice_cache.get(chapter_cache_key)
if voice_choice is None:
kokoro_backend = pipeline_pool.get("kokoro", job.language, job.use_gpu, job=job)
voice_choice = resolve_voice(chapter_voice_resolved, kokoro_backend, job.use_gpu, cache=voice_cache)
else:
voice_choice = chapter_voice_resolved
chapter_audio_path: Optional[Path] = None
segments_emitted = 0
@@ -682,26 +670,9 @@ def run_conversion_job(job: Job) -> None:
chunk_steps_use = chapter_steps
chunk_voice_choice = voice_choice
else:
chunk_provider, chunk_voice_resolved, chunk_speed_use, chunk_steps_use = _resolve_voice_target(
chunk_voice_spec, normalized_profiles,
job_voice=getattr(job, "voice", "M1"),
job_tts_provider=getattr(job, "tts_provider", "kokoro"),
job_supertonic_total_steps=getattr(job, "supertonic_total_steps", 5),
job_speed=getattr(job, "speed", 1.0),
chunk_provider, chunk_voice_resolved, chunk_voice_choice, chunk_speed_use, chunk_steps_use = resolve_voice_choice(
chunk_voice_spec
)
chunk_cache_key = f"{chunk_provider}:{chunk_voice_resolved}" if chunk_voice_resolved else chunk_provider
if chunk_provider == "kokoro":
chunk_voice_choice = voice_cache.get(chunk_cache_key)
if chunk_voice_choice is None:
kokoro_backend = pipeline_pool.get("kokoro", job.language, job.use_gpu, job=job)
chunk_voice_choice = resolve_voice(
chunk_voice_resolved,
kokoro_backend,
job.use_gpu,
cache=voice_cache,
)
else:
chunk_voice_choice = chunk_voice_resolved
chunk_start = current_time
emitted = emit_text(