mirror of
https://github.com/denizsafak/abogen.git
synced 2026-07-18 13:40:27 +02:00
Artem Akymenko
c094b94704
- Normalize Pipeline public API: create_pipeline(plugin_id, *, lang_code, device) - EngineConfig: add lang_code field per Architecture Amendment #1 - Kokoro plugin reads config.lang_code (fixes functional regression) - Static voice catalog in PluginManifest.voices (None = dynamic/VoiceLister) - get_voices() reads from manifest without creating Engine - Remove dead kwargs (sample_rate, auto_download, total_steps) from SuperTonic - Clean up unused imports and dead code in engine implementations - Fix test expectations for VoiceLister (mock overrides) - Add clear_preview_pipelines() for resource management
83 lines
2.8 KiB
Python
83 lines
2.8 KiB
Python
import re
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from typing import List, Tuple
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from abogen.tts_plugin.utils import get_voices
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# Calls parsing and loads the voice to gpu or cpu
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def get_new_voice(pipeline, formula, use_gpu):
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try:
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weighted_voice = parse_voice_formula(pipeline, formula)
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# device = "cuda" if use_gpu else "cpu"
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# Setting the device "cuda" gives "Error occurred: split_with_sizes(): argument 'split_sizes' (position 2)"
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# error when the device is gpu. So disabling this for now.
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device = "cpu"
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return weighted_voice.to(device)
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except Exception as e:
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raise ValueError(f"Failed to create voice: {str(e)}")
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def parse_formula_terms(formula: str) -> List[Tuple[str, float]]:
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if not formula or not formula.strip():
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raise ValueError("Empty voice formula")
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terms: List[Tuple[str, float]] = []
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kokoro_voices = get_voices("kokoro")
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for segment in formula.split("+"):
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part = segment.strip()
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if not part:
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continue
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if "*" not in part:
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raise ValueError("Each component must be in the form voice*weight")
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voice_name, raw_weight = part.split("*", 1)
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voice_name = voice_name.strip()
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if voice_name not in kokoro_voices:
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raise ValueError(f"Unknown voice: {voice_name}")
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try:
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weight = float(raw_weight.strip())
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except ValueError as exc:
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raise ValueError(f"Invalid weight for {voice_name}") from exc
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if weight <= 0:
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raise ValueError(f"Weight for {voice_name} must be positive")
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terms.append((voice_name, weight))
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if not terms:
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raise ValueError("Voice weights must sum to a positive value")
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return terms
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def parse_voice_formula(pipeline, formula):
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terms = parse_formula_terms(formula)
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total_weight = sum(weight for _, weight in terms)
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if total_weight <= 0:
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raise ValueError("Voice weights must sum to a positive value")
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weighted_sum = None
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for voice_name, weight in terms:
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normalized_weight = weight / total_weight if total_weight > 0 else weight
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voice_tensor = pipeline.load_single_voice(voice_name)
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if weighted_sum is None:
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weighted_sum = normalized_weight * voice_tensor
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else:
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weighted_sum += normalized_weight * voice_tensor
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if weighted_sum is None:
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raise ValueError("Voice formula produced no components")
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return weighted_sum
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def calculate_sum_from_formula(formula):
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weights = re.findall(r"\* *([\d.]+)", formula)
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total_sum = sum(float(weight) for weight in weights)
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return total_sum
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def extract_voice_ids(formula: str) -> List[str]:
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return [voice for voice, _ in parse_formula_terms(formula)]
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