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unify voice caching: VoiceCache class used by both WebUI and PyQt

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- domain/voice_loader.py: VoiceCache class now used by both UIs;
  resolve_voice() and load_voice_cached() accept VoiceCache or plain dict;
  added hasattr(pipeline, 'load_single_voice') safety check from WebUI
- conversion_runner.py: replaced local _resolve_voice() with domain's
  resolve_voice(); voice_cache changed from Dict to VoiceCache instance;
  all cache access uses VoiceCache.get()/set() API
- pyqt/conversion.py: self.voice_cache changed from Dict to VoiceCache
- debug_tts_runner.py: imports resolve_voice from domain instead of
  removed _resolve_voice from conversion_runner
This commit is contained in:
Artem Akymenko
2026-07-20 08:02:34 +00:00
parent 804517f5b2
commit f38700025a
4 changed files with 35 additions and 34 deletions
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abogen/domain/voice_loader.py
+21 -13
View File
@@ -62,10 +62,10 @@ def resolve_voice(
# Check cache first
if cache and cache.contains(voice_spec):
return cache.get(voice_spec)
# Load voice
if "*" in voice_spec:
if pipeline is None:
if pipeline is None or not hasattr(pipeline, "load_single_voice"):
return voice_spec
loaded_voice = get_new_voice(pipeline, voice_spec, use_gpu)
else:
@@ -82,35 +82,43 @@ def load_voice_cached(
voice_name: str,
pipeline: Any,
use_gpu: bool,
cache: Optional[Dict[str, Any]] = None,
cache: Any = None,
) -> Any:
"""Load voice with caching (compatibility wrapper for PyQt).
This function maintains backward compatibility with the PyQt interface
while using the unified voice loading logic.
Args:
voice_name: Voice name or formula string.
pipeline: TTS pipeline instance.
use_gpu: Whether to use GPU.
cache: Optional dict to use as cache (instead of VoiceCache).
cache: Optional VoiceCache or dict to use as cache.
Returns:
Loaded voice tensor or voice name string.
"""
# Use dict cache if provided (for backward compatibility)
# Check cache (supports both VoiceCache and plain dict)
if cache is not None:
if voice_name in cache:
if isinstance(cache, VoiceCache):
if cache.contains(voice_name):
return cache.get(voice_name)
elif voice_name in cache:
return cache[voice_name]
# Load voice
if "*" in voice_name:
if pipeline is None or not hasattr(pipeline, "load_single_voice"):
return voice_name
loaded_voice = get_new_voice(pipeline, voice_name, use_gpu)
else:
loaded_voice = voice_name
# Cache it
if cache is not None:
cache[voice_name] = loaded_voice
if isinstance(cache, VoiceCache):
cache.set(voice_name, loaded_voice)
else:
cache[voice_name] = loaded_voice
return loaded_voice
abogen/pyqt/conversion.py
+2 -2
View File
@@ -44,7 +44,7 @@ from abogen.domain.audio_buffer import (
SAMPLE_RATE,
)
from abogen.domain.subtitle_generation import process_subtitle_tokens
from abogen.domain.voice_loader import load_voice_cached, resolve_voice
from abogen.domain.voice_loader import VoiceCache, load_voice_cached, resolve_voice
from abogen.domain.progress import calc_etr_str
from abogen.domain.normalization import prepare_text_for_tts
from abogen.domain.pronunciation import (
@@ -295,7 +295,7 @@ class ConversionThread(QThread):
self.use_spacy_segmentation = True # Default, will be overridden from GUI
# Set split pattern based on language and subtitle mode
self.split_pattern = get_split_pattern(lang_code, subtitle_mode)
self.voice_cache = {} # Cache for loaded voices
self.voice_cache = VoiceCache() # Cache for loaded voices
def load_voice_cached(self, voice_name, tts):
"""Load voice with caching to avoid reloading same voice.
abogen/webui/conversion_runner.py
+9 -17
View File
@@ -119,6 +119,7 @@ from abogen.domain.audio_buffer import (
from abogen.domain.audio_sink import AudioSink, open_audio_sink
from abogen.domain.pipeline_factory import PipelinePool
from abogen.domain.conversion_engine import run_tts_segment_loop, process_and_write_subtitles, SegmentStats
from abogen.domain.voice_loader import VoiceCache, resolve_voice
from abogen.domain.voice_utils import resolve_voice_target as _resolve_voice_target
@@ -216,11 +217,11 @@ def run_conversion_job(job: Job) -> None:
if provider == "kokoro":
kokoro_backend = pipeline_pool.get("kokoro", job.language, job.use_gpu, job=job)
choice = _resolve_voice(kokoro_backend, resolved, job.use_gpu)
choice = resolve_voice(resolved, kokoro_backend, job.use_gpu, cache=voice_cache)
else:
choice = resolved
voice_cache[cache_key] = choice
voice_cache.set(cache_key, choice)
return provider, resolved, choice, speed, steps
extraction = extract_from_path(job.stored_path)
@@ -360,7 +361,7 @@ def run_conversion_job(job: Job) -> None:
chapter_dir.mkdir(parents=True, exist_ok=True)
base_voice_spec = _job_voice_fallback(job)
voice_cache: Dict[str, Any] = {}
voice_cache = VoiceCache()
base_provider, base_voice_resolved, _, _ = _resolve_voice_target(
base_voice_spec, normalized_profiles,
job_voice=getattr(job, "voice", "M1"),
@@ -368,7 +369,7 @@ def run_conversion_job(job: Job) -> None:
)
if base_provider == "kokoro" and base_voice_resolved and "*" not in base_voice_resolved:
kokoro_backend = pipeline_pool.get("kokoro", job.language, job.use_gpu, job=job)
voice_cache[f"kokoro:{base_voice_resolved}"] = _resolve_voice(kokoro_backend, base_voice_resolved, job.use_gpu)
voice_cache.set(f"kokoro:{base_voice_resolved}", resolve_voice(base_voice_resolved, kokoro_backend, job.use_gpu))
processed_chars = 0
current_time = 0.0
etr_start_time = time.time()
@@ -550,8 +551,7 @@ def run_conversion_job(job: Job) -> None:
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(kokoro_backend, chapter_voice_resolved, job.use_gpu)
voice_cache[chapter_cache_key] = voice_choice
voice_choice = resolve_voice(chapter_voice_resolved, kokoro_backend, job.use_gpu, cache=voice_cache)
else:
voice_choice = chapter_voice_resolved
@@ -694,12 +694,12 @@ def run_conversion_job(job: Job) -> None:
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(
kokoro_backend,
chunk_voice_choice = resolve_voice(
chunk_voice_resolved,
kokoro_backend,
job.use_gpu,
cache=voice_cache,
)
voice_cache[chunk_cache_key] = chunk_voice_choice
else:
chunk_voice_choice = chunk_voice_resolved
@@ -1066,14 +1066,6 @@ def _prepare_project_layout(job: Job, base_dir: Path) -> tuple[Path, Path, Path,
def _resolve_voice(pipeline, voice_spec: str, use_gpu: bool):
if "*" in voice_spec:
if pipeline is None or not hasattr(pipeline, "load_single_voice"):
return voice_spec
return get_new_voice(pipeline, voice_spec, use_gpu)
return voice_spec
def _make_canceller(job: Job) -> Callable[[], None]:
def _cancel() -> None:
abogen/webui/debug_tts_runner.py
+3 -2
View File
@@ -14,7 +14,8 @@ from abogen.kokoro_text_normalization import normalize_for_pipeline
from abogen.normalization_settings import build_apostrophe_config
from abogen.text_extractor import extract_from_path
from abogen.voice_cache import ensure_voice_assets
from abogen.webui.conversion_runner import SAMPLE_RATE, _select_device, _to_float32, _resolve_voice, _spec_to_voice_ids
from abogen.webui.conversion_runner import SAMPLE_RATE, _select_device, _to_float32, _spec_to_voice_ids
from abogen.domain.voice_loader import resolve_voice
from abogen.domain.split_pattern import get_split_pattern
from abogen.tts_plugin.utils import create_pipeline
@@ -176,7 +177,7 @@ def run_debug_tts_wavs(
pass
pipeline = _load_pipeline(language, use_gpu)
voice_choice = _resolve_voice(pipeline, voice_spec, use_gpu)
voice_choice = resolve_voice(voice_spec, pipeline, use_gpu)
apostrophe_config = build_apostrophe_config(settings=settings)
normalization_settings = dict(settings)