mirror of
https://github.com/jamiepine/voicebox.git
synced 2026-09-19 14:50:38 -07:00
feat(capture): dictation, personalities, 0.5.0
Ships the Capture release end to end. Global-hotkey dictation with synthetic paste into the focused app on macOS and Windows, an on-screen pill across recording / transcribing / refining, customizable push-to- talk and toggle chords, and an accessibility-permission prompt scoped to Settings → Captures with inline re-check feedback. Voice profiles gain optional personalities that power compose / rewrite / respond actions via a local Qwen3 LLM — shared with refinement, so there is one local LLM in the app, not two. Refinement hardened with deterministic Whisper-loop collapse before the LLM sees the transcript, per-capture flag snapshots for re-runs, and a ten-transcript evaluation harness across every bundled refinement size. Version bump 0.4.5 → 0.5.0. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
This commit is contained in:
James Pine
co-authored by
Claude Opus 4.7
Claude Opus 4.7
parent
ed2eec591a
commit
87c582ad54
@@ -18,6 +18,9 @@ from typing import Protocol, Optional, Tuple, List
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from typing_extensions import runtime_checkable
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import numpy as np
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DEFAULT_LLM_MAX_TOKENS = 512
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DEFAULT_LLM_TEMPERATURE = 0.7
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from ..utils.platform_detect import get_backend_type
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LANGUAGE_CODE_TO_NAME = {
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@@ -160,11 +163,47 @@ class STTBackend(Protocol):
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...
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@runtime_checkable
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class LLMBackend(Protocol):
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"""Protocol for local LLM (chat/completion) backend implementations."""
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async def load_model(self, model_size: str) -> None:
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"""Load LLM weights and tokenizer."""
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...
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async def generate(
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self,
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prompt: str,
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system: Optional[str] = None,
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max_tokens: int = DEFAULT_LLM_MAX_TOKENS,
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temperature: float = DEFAULT_LLM_TEMPERATURE,
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model_size: Optional[str] = None,
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examples: Optional[list[tuple[str, str]]] = None,
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) -> str:
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"""Run a single-turn chat completion and return the assistant reply.
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``examples`` is an optional list of ``(user, assistant)`` pairs
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prepended to the conversation as proper chat turns — small models
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pattern-match on inline system-prompt examples (echoing them
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verbatim for unrelated inputs), but treat structured turns as
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data and generalize instead. Used by the refinement service.
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"""
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...
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def unload_model(self) -> None:
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...
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def is_loaded(self) -> bool:
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...
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# Global backend instances
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_tts_backend: Optional[TTSBackend] = None
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_tts_backends: dict[str, TTSBackend] = {}
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_tts_backends_lock = threading.Lock()
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_stt_backend: Optional[STTBackend] = None
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_llm_backends: dict[str, LLMBackend] = {}
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_llm_backends_lock = threading.Lock()
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# Supported TTS engines — keyed by engine name, value is the backend class import path.
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# The factory function uses this for the if/elif chain; the model configs live on the backend classes.
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@@ -178,6 +217,10 @@ TTS_ENGINES = {
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"kokoro": "Kokoro",
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}
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LLM_ENGINES = {
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"qwen_llm": "Qwen3 LLM",
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}
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def _get_qwen_model_configs() -> list[ModelConfig]:
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"""Return Qwen model configs with backend-aware HF repo IDs."""
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@@ -365,9 +408,66 @@ def _get_whisper_configs() -> list[ModelConfig]:
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]
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def _get_qwen_llm_configs() -> list[ModelConfig]:
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"""Return Qwen3 LLM configs with backend-aware HF repo IDs.
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MLX path uses 4-bit community quantizations for Apple Silicon; PyTorch path
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uses the upstream instruct weights.
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"""
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backend_type = get_backend_type()
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if backend_type == "mlx":
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repo_0_6 = "mlx-community/Qwen3-0.6B-4bit"
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repo_1_7 = "mlx-community/Qwen3-1.7B-4bit"
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repo_4 = "mlx-community/Qwen3-4B-4bit"
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else:
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repo_0_6 = "Qwen/Qwen3-0.6B"
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repo_1_7 = "Qwen/Qwen3-1.7B"
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repo_4 = "Qwen/Qwen3-4B"
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common_languages = [
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"en", "zh", "ja", "ko", "de", "fr", "ru", "pt", "es", "it",
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]
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return [
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ModelConfig(
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model_name="qwen3-0.6b",
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display_name="Qwen3 0.6B",
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engine="qwen_llm",
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hf_repo_id=repo_0_6,
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model_size="0.6B",
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size_mb=400 if backend_type == "mlx" else 1400,
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languages=common_languages,
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),
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ModelConfig(
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model_name="qwen3-1.7b",
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display_name="Qwen3 1.7B",
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engine="qwen_llm",
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hf_repo_id=repo_1_7,
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model_size="1.7B",
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size_mb=1100 if backend_type == "mlx" else 3500,
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languages=common_languages,
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),
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ModelConfig(
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model_name="qwen3-4b",
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display_name="Qwen3 4B",
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engine="qwen_llm",
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hf_repo_id=repo_4,
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model_size="4B",
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size_mb=2500 if backend_type == "mlx" else 8000,
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languages=common_languages,
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),
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]
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def get_all_model_configs() -> list[ModelConfig]:
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"""Return the full list of model configs (TTS + STT)."""
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return _get_qwen_model_configs() + _get_qwen_custom_voice_configs() + _get_non_qwen_tts_configs() + _get_whisper_configs()
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"""Return the full list of model configs (TTS + STT + LLM)."""
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return (
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_get_qwen_model_configs()
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+ _get_qwen_custom_voice_configs()
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+ _get_non_qwen_tts_configs()
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+ _get_whisper_configs()
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+ _get_qwen_llm_configs()
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)
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def get_tts_model_configs() -> list[ModelConfig]:
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@@ -375,6 +475,11 @@ def get_tts_model_configs() -> list[ModelConfig]:
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return _get_qwen_model_configs() + _get_qwen_custom_voice_configs() + _get_non_qwen_tts_configs()
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def get_llm_model_configs() -> list[ModelConfig]:
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"""Return only LLM model configs."""
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return _get_qwen_llm_configs()
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# Lookup helpers — these replace the if/elif chains in main.py
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@@ -440,7 +545,7 @@ async def ensure_model_cached_or_raise(engine: str, model_size: str = "default")
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def unload_model_by_config(config: ModelConfig) -> bool:
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"""Unload a model given its config. Returns True if it was loaded, False otherwise."""
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from . import get_tts_backend_for_engine
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from ..services import tts, transcribe
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from ..services import tts, transcribe, llm as llm_service
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if config.engine == "whisper":
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whisper_model = transcribe.get_whisper_model()
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@@ -449,6 +554,14 @@ def unload_model_by_config(config: ModelConfig) -> bool:
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return True
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return False
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if config.engine == "qwen_llm":
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backend = llm_service.get_llm_model()
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loaded_size = getattr(backend, "_current_model_size", None) or getattr(backend, "model_size", None)
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if backend.is_loaded() and loaded_size == config.model_size:
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backend.unload_model()
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return True
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return False
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if config.engine == "qwen":
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tts_model = tts.get_tts_model()
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loaded_size = getattr(tts_model, "_current_model_size", None) or getattr(tts_model, "model_size", None)
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@@ -476,13 +589,18 @@ def unload_model_by_config(config: ModelConfig) -> bool:
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def check_model_loaded(config: ModelConfig) -> bool:
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"""Check if a model is currently loaded."""
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from . import get_tts_backend_for_engine
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from ..services import tts, transcribe
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from ..services import tts, transcribe, llm as llm_service
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try:
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if config.engine == "whisper":
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whisper_model = transcribe.get_whisper_model()
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return whisper_model.is_loaded() and getattr(whisper_model, "model_size", None) == config.model_size
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if config.engine == "qwen_llm":
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backend = llm_service.get_llm_model()
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loaded_size = getattr(backend, "_current_model_size", None) or getattr(backend, "model_size", None)
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return backend.is_loaded() and loaded_size == config.model_size
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if config.engine == "qwen":
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tts_model = tts.get_tts_model()
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loaded_size = getattr(tts_model, "_current_model_size", None) or getattr(tts_model, "model_size", None)
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@@ -502,7 +620,7 @@ def check_model_loaded(config: ModelConfig) -> bool:
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def get_model_load_func(config: ModelConfig):
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"""Return a callable that loads/downloads the model."""
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from . import get_tts_backend_for_engine
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from ..services import tts, transcribe
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from ..services import tts, transcribe, llm as llm_service
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if config.engine == "whisper":
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return lambda: transcribe.get_whisper_model().load_model(config.model_size)
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@@ -513,6 +631,9 @@ def get_model_load_func(config: ModelConfig):
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if config.engine == "qwen_custom_voice":
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return lambda: get_tts_backend_for_engine(config.engine).load_model(config.model_size)
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if config.engine == "qwen_llm":
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return lambda: llm_service.get_llm_model().load_model(config.model_size)
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return lambda: get_tts_backend_for_engine(config.engine).load_model()
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@@ -613,9 +734,43 @@ def get_stt_backend() -> STTBackend:
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return _stt_backend
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def get_llm_backend() -> LLMBackend:
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"""Get or create the default Qwen3 LLM backend based on platform."""
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return get_llm_backend_for_engine("qwen_llm")
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def get_llm_backend_for_engine(engine: str) -> LLMBackend:
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"""Get or create an LLM backend for the given engine."""
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global _llm_backends
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if engine in _llm_backends:
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return _llm_backends[engine]
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with _llm_backends_lock:
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if engine in _llm_backends:
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return _llm_backends[engine]
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if engine == "qwen_llm":
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backend_type = get_backend_type()
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if backend_type == "mlx":
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from .qwen_llm_backend import MLXQwenLLMBackend
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backend = MLXQwenLLMBackend()
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else:
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from .qwen_llm_backend import PyTorchQwenLLMBackend
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backend = PyTorchQwenLLMBackend()
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else:
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raise ValueError(f"Unknown LLM engine: {engine}. Supported: {list(LLM_ENGINES.keys())}")
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_llm_backends[engine] = backend
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return backend
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def reset_backends():
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"""Reset backend instances (useful for testing)."""
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global _tts_backend, _tts_backends, _stt_backend
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global _tts_backend, _tts_backends, _stt_backend, _llm_backends
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_tts_backend = None
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_tts_backends.clear()
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_stt_backend = None
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_llm_backends.clear()
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@@ -0,0 +1,290 @@
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"""
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Qwen3 LLM backend implementations.
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Provides MLX (Apple Silicon, 4-bit community quants) and PyTorch
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(transformers AutoModelForCausalLM) paths that share the same
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`LLMBackend` protocol and model-load progress plumbing as the TTS
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and STT engines.
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"""
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import asyncio
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import logging
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from typing import Optional
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from . import LLMBackend, DEFAULT_LLM_MAX_TOKENS, DEFAULT_LLM_TEMPERATURE
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from .base import (
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is_model_cached,
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get_torch_device,
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empty_device_cache,
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manual_seed,
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model_load_progress,
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)
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from ..utils.hf_offline_patch import force_offline_if_cached
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logger = logging.getLogger(__name__)
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PYTORCH_HF_REPOS = {
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"0.6B": "Qwen/Qwen3-0.6B",
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"1.7B": "Qwen/Qwen3-1.7B",
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"4B": "Qwen/Qwen3-4B",
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}
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MLX_HF_REPOS = {
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"0.6B": "mlx-community/Qwen3-0.6B-4bit",
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"1.7B": "mlx-community/Qwen3-1.7B-4bit",
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"4B": "mlx-community/Qwen3-4B-4bit",
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}
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def _progress_name(model_size: str) -> str:
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return f"qwen3-{model_size.lower()}"
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def _build_messages(
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prompt: str,
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system: Optional[str],
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examples: Optional[list[tuple[str, str]]] = None,
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) -> list[dict]:
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messages: list[dict] = []
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if system:
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messages.append({"role": "system", "content": system})
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if examples:
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for user_text, assistant_text in examples:
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messages.append({"role": "user", "content": user_text})
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messages.append({"role": "assistant", "content": assistant_text})
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messages.append({"role": "user", "content": prompt})
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return messages
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class PyTorchQwenLLMBackend:
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"""Qwen3 LLM backend using HuggingFace transformers."""
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def __init__(self, model_size: str = "0.6B"):
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self.model = None
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self.tokenizer = None
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self.model_size = model_size
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self._current_model_size: Optional[str] = None
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self.device = self._get_device()
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def _get_device(self) -> str:
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return get_torch_device(allow_xpu=True, allow_directml=True, allow_mps=True)
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def is_loaded(self) -> bool:
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return self.model is not None
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def _get_model_path(self, model_size: str) -> str:
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if model_size not in PYTORCH_HF_REPOS:
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raise ValueError(f"Unknown Qwen3 size: {model_size}")
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return PYTORCH_HF_REPOS[model_size]
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def _is_model_cached(self, model_size: str) -> bool:
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return is_model_cached(self._get_model_path(model_size))
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async def load_model(self, model_size: Optional[str] = None) -> None:
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if model_size is None:
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model_size = self.model_size
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if self.model is not None and self._current_model_size == model_size:
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return
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if self.model is not None and self._current_model_size != model_size:
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self.unload_model()
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await asyncio.to_thread(self._load_model_sync, model_size)
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def _load_model_sync(self, model_size: str) -> None:
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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progress_model_name = _progress_name(model_size)
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is_cached = self._is_model_cached(model_size)
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repo = self._get_model_path(model_size)
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with model_load_progress(progress_model_name, is_cached):
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logger.info("Loading Qwen3 %s on %s...", model_size, self.device)
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with force_offline_if_cached(is_cached, progress_model_name):
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self.tokenizer = AutoTokenizer.from_pretrained(repo)
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dtype = torch.float16 if self.device in ("cuda", "mps") else torch.float32
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self.model = AutoModelForCausalLM.from_pretrained(
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repo,
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torch_dtype=dtype,
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)
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self.model.to(self.device)
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self.model.eval()
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self._current_model_size = model_size
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self.model_size = model_size
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logger.info("Qwen3 %s loaded successfully", model_size)
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def unload_model(self) -> None:
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if self.model is None:
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return
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del self.model
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del self.tokenizer
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self.model = None
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self.tokenizer = None
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self._current_model_size = None
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empty_device_cache(self.device)
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logger.info("Qwen3 unloaded")
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async def generate(
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self,
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prompt: str,
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system: Optional[str] = None,
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max_tokens: int = DEFAULT_LLM_MAX_TOKENS,
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temperature: float = DEFAULT_LLM_TEMPERATURE,
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model_size: Optional[str] = None,
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examples: Optional[list[tuple[str, str]]] = None,
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) -> str:
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await self.load_model(model_size)
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return await asyncio.to_thread(
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self._generate_sync, prompt, system, max_tokens, temperature, examples
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)
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def _generate_sync(
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self,
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prompt: str,
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system: Optional[str],
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max_tokens: int,
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temperature: float,
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examples: Optional[list[tuple[str, str]]] = None,
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) -> str:
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import torch
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messages = _build_messages(prompt, system, examples)
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text = self.tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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enable_thinking=False,
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)
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inputs = self.tokenizer(text, return_tensors="pt").to(self.device)
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do_sample = temperature > 0
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generate_kwargs = {
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"max_new_tokens": max_tokens,
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"do_sample": do_sample,
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"pad_token_id": self.tokenizer.eos_token_id,
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}
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if do_sample:
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generate_kwargs["temperature"] = temperature
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generate_kwargs["top_p"] = 0.9
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with torch.no_grad():
|
||||
output_ids = self.model.generate(**inputs, **generate_kwargs)
|
||||
|
||||
input_len = inputs["input_ids"].shape[1]
|
||||
new_tokens = output_ids[0, input_len:]
|
||||
return self.tokenizer.decode(new_tokens, skip_special_tokens=True).strip()
|
||||
|
||||
|
||||
class MLXQwenLLMBackend:
|
||||
"""Qwen3 LLM backend using mlx-lm (Apple Silicon)."""
|
||||
|
||||
def __init__(self, model_size: str = "0.6B"):
|
||||
self.model = None
|
||||
self.tokenizer = None
|
||||
self.model_size = model_size
|
||||
self._current_model_size: Optional[str] = None
|
||||
|
||||
def is_loaded(self) -> bool:
|
||||
return self.model is not None
|
||||
|
||||
def _get_model_path(self, model_size: str) -> str:
|
||||
if model_size not in MLX_HF_REPOS:
|
||||
raise ValueError(f"Unknown Qwen3 size: {model_size}")
|
||||
return MLX_HF_REPOS[model_size]
|
||||
|
||||
def _is_model_cached(self, model_size: str) -> bool:
|
||||
return is_model_cached(
|
||||
self._get_model_path(model_size),
|
||||
weight_extensions=(".safetensors", ".bin", ".npz"),
|
||||
)
|
||||
|
||||
async def load_model(self, model_size: Optional[str] = None) -> None:
|
||||
if model_size is None:
|
||||
model_size = self.model_size
|
||||
|
||||
if self.model is not None and self._current_model_size == model_size:
|
||||
return
|
||||
|
||||
if self.model is not None and self._current_model_size != model_size:
|
||||
self.unload_model()
|
||||
|
||||
await asyncio.to_thread(self._load_model_sync, model_size)
|
||||
|
||||
def _load_model_sync(self, model_size: str) -> None:
|
||||
from mlx_lm import load as mlx_load
|
||||
|
||||
progress_model_name = _progress_name(model_size)
|
||||
is_cached = self._is_model_cached(model_size)
|
||||
repo = self._get_model_path(model_size)
|
||||
|
||||
with model_load_progress(progress_model_name, is_cached):
|
||||
logger.info("Loading Qwen3 %s via MLX...", model_size)
|
||||
with force_offline_if_cached(is_cached, progress_model_name):
|
||||
loaded = mlx_load(repo)
|
||||
|
||||
# mlx_lm.load returns (model, tokenizer) by default and
|
||||
# (model, tokenizer, config) when return_config=True.
|
||||
self.model = loaded[0]
|
||||
self.tokenizer = loaded[1]
|
||||
|
||||
self._current_model_size = model_size
|
||||
self.model_size = model_size
|
||||
logger.info("Qwen3 %s (MLX) loaded successfully", model_size)
|
||||
|
||||
def unload_model(self) -> None:
|
||||
if self.model is None:
|
||||
return
|
||||
del self.model
|
||||
del self.tokenizer
|
||||
self.model = None
|
||||
self.tokenizer = None
|
||||
self._current_model_size = None
|
||||
logger.info("Qwen3 (MLX) unloaded")
|
||||
|
||||
async def generate(
|
||||
self,
|
||||
prompt: str,
|
||||
system: Optional[str] = None,
|
||||
max_tokens: int = DEFAULT_LLM_MAX_TOKENS,
|
||||
temperature: float = DEFAULT_LLM_TEMPERATURE,
|
||||
model_size: Optional[str] = None,
|
||||
examples: Optional[list[tuple[str, str]]] = None,
|
||||
) -> str:
|
||||
await self.load_model(model_size)
|
||||
return await asyncio.to_thread(
|
||||
self._generate_sync, prompt, system, max_tokens, temperature, examples
|
||||
)
|
||||
|
||||
def _generate_sync(
|
||||
self,
|
||||
prompt: str,
|
||||
system: Optional[str],
|
||||
max_tokens: int,
|
||||
temperature: float,
|
||||
examples: Optional[list[tuple[str, str]]] = None,
|
||||
) -> str:
|
||||
from mlx_lm import generate as mlx_generate
|
||||
from mlx_lm.sample_utils import make_sampler
|
||||
|
||||
messages = _build_messages(prompt, system, examples)
|
||||
chat_prompt = self.tokenizer.apply_chat_template(
|
||||
messages,
|
||||
tokenize=False,
|
||||
add_generation_prompt=True,
|
||||
enable_thinking=False,
|
||||
)
|
||||
|
||||
sampler = make_sampler(temp=temperature, top_p=0.9) if temperature > 0 else None
|
||||
text = mlx_generate(
|
||||
self.model,
|
||||
self.tokenizer,
|
||||
prompt=chat_prompt,
|
||||
max_tokens=max_tokens,
|
||||
sampler=sampler,
|
||||
verbose=False,
|
||||
)
|
||||
return text.strip()
|
||||
Reference in New Issue
Block a user