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https://github.com/jamiepine/voicebox.git
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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
@@ -20,6 +20,7 @@ class VoiceProfileCreate(BaseModel):
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preset_voice_id: Optional[str] = Field(None, max_length=100)
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design_prompt: Optional[str] = Field(None, max_length=2000)
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default_engine: Optional[str] = Field(None, max_length=50)
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personality: Optional[str] = Field(None, max_length=2000)
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class VoiceProfileResponse(BaseModel):
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@@ -36,6 +37,7 @@ class VoiceProfileResponse(BaseModel):
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preset_voice_id: Optional[str] = None
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design_prompt: Optional[str] = None
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default_engine: Optional[str] = None
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personality: Optional[str] = None
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generation_count: int = 0
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sample_count: int = 0
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created_at: datetime
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@@ -107,6 +109,7 @@ class GenerationResponse(BaseModel):
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status: str = "completed"
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error: Optional[str] = None
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is_favorited: bool = False
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source: str = "manual"
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created_at: datetime
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versions: Optional[List["GenerationVersionResponse"]] = None
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active_version_id: Optional[str] = None
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@@ -170,6 +173,196 @@ class TranscriptionResponse(BaseModel):
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duration: float
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class RefinementFlagsModel(BaseModel):
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"""Boolean toggles that drive the refinement prompt builder."""
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smart_cleanup: bool = True
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self_correction: bool = True
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preserve_technical: bool = True
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class CaptureResponse(BaseModel):
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"""Response model for a capture."""
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id: str
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audio_path: str
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source: str
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language: Optional[str] = None
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duration_ms: Optional[int] = None
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transcript_raw: str
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transcript_refined: Optional[str] = None
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stt_model: Optional[str] = None
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llm_model: Optional[str] = None
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refinement_flags: Optional[RefinementFlagsModel] = None
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created_at: datetime
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class Config:
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from_attributes = True
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class CaptureListResponse(BaseModel):
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"""Response model for paginated capture list."""
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items: List[CaptureResponse]
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total: int
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class CaptureCreateResponse(CaptureResponse):
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"""
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Response model for ``POST /captures``.
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Adds ``auto_refine`` and ``allow_auto_paste`` — the server-side settings
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captured at the moment the capture was created. The client reads these to
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decide whether to chain a refinement request and whether to fire the
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synthetic-paste pipeline, so it doesn't need a synced local copy of the
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capture_settings table across sibling Tauri webviews.
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"""
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auto_refine: bool
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allow_auto_paste: bool
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class CaptureRefineRequest(BaseModel):
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"""Request to refine a capture's transcript via the LLM."""
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flags: Optional[RefinementFlagsModel] = None
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model_size: Optional[str] = Field(default=None, pattern="^(0\\.6B|1\\.7B|4B)$")
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class CaptureRetranscribeRequest(BaseModel):
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"""Request to re-run STT on a capture's audio with a different model."""
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model: Optional[str] = Field(None, pattern="^(base|small|medium|large|turbo)$")
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language: Optional[str] = Field(None, pattern="^(en|zh|ja|ko|de|fr|ru|pt|es|it)$")
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class CaptureSettingsResponse(BaseModel):
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"""Server-persisted defaults for the capture / refine flow."""
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stt_model: str = Field(default="turbo", pattern="^(base|small|medium|large|turbo)$")
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language: str = Field(default="auto")
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auto_refine: bool = True
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llm_model: str = Field(default="0.6B", pattern="^(0\\.6B|1\\.7B|4B)$")
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smart_cleanup: bool = True
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self_correction: bool = True
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preserve_technical: bool = True
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allow_auto_paste: bool = True
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default_playback_voice_id: Optional[str] = None
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chord_push_to_talk_keys: List[str] = Field(default_factory=lambda: ["MetaRight", "AltGr"])
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chord_toggle_to_talk_keys: List[str] = Field(
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default_factory=lambda: ["MetaRight", "AltGr", "Space"]
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)
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class Config:
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from_attributes = True
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class CaptureSettingsUpdate(BaseModel):
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"""Partial update for capture settings — every field is optional."""
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stt_model: Optional[str] = Field(default=None, pattern="^(base|small|medium|large|turbo)$")
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language: Optional[str] = None
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auto_refine: Optional[bool] = None
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llm_model: Optional[str] = Field(default=None, pattern="^(0\\.6B|1\\.7B|4B)$")
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smart_cleanup: Optional[bool] = None
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self_correction: Optional[bool] = None
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preserve_technical: Optional[bool] = None
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allow_auto_paste: Optional[bool] = None
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default_playback_voice_id: Optional[str] = None
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chord_push_to_talk_keys: Optional[List[str]] = Field(default=None, min_length=1, max_length=6)
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chord_toggle_to_talk_keys: Optional[List[str]] = Field(default=None, min_length=1, max_length=6)
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class GenerationSettingsResponse(BaseModel):
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"""Server-persisted defaults for the generation flow."""
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max_chunk_chars: int = Field(default=800, ge=100, le=5000)
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crossfade_ms: int = Field(default=50, ge=0, le=500)
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normalize_audio: bool = True
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autoplay_on_generate: bool = True
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class Config:
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from_attributes = True
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class GenerationSettingsUpdate(BaseModel):
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"""Partial update for generation settings — every field is optional."""
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max_chunk_chars: Optional[int] = Field(default=None, ge=100, le=5000)
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crossfade_ms: Optional[int] = Field(default=None, ge=0, le=500)
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normalize_audio: Optional[bool] = None
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autoplay_on_generate: Optional[bool] = None
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class LLMGenerateRequest(BaseModel):
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"""Request model for LLM text generation."""
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prompt: str = Field(..., min_length=1, max_length=50000)
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system: Optional[str] = Field(None, max_length=4000)
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model_size: Optional[str] = Field(default="0.6B", pattern="^(0\\.6B|1\\.7B|4B)$")
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max_tokens: int = Field(default=512, ge=1, le=4096)
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temperature: float = Field(default=0.7, ge=0.0, le=2.0)
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# Few-shot (user, assistant) pairs prepended as real chat turns.
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# Used by the refinement service to pin tricky rules (imperatives
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# staying imperatives, technical-term punctuation) that small models
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# lose when the examples live inline in the system prompt.
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examples: Optional[List[List[str]]] = Field(default=None, max_length=8)
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class LLMGenerateResponse(BaseModel):
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"""Response model for LLM text generation."""
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text: str
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model_size: str
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# ── Profile personality endpoints ─────────────────────────────────────
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# compose / rewrite / respond return raw text; /speak chains LLM → TTS
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# and either persists as a generation (persist=true) or streams audio
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# back transiently.
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class PersonalityTextRequest(BaseModel):
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"""Body for ``/profiles/{id}/rewrite`` and ``/profiles/{id}/respond``."""
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text: str = Field(..., min_length=1, max_length=10000)
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class PersonalityTextResponse(BaseModel):
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"""Response returned by compose / rewrite / respond endpoints."""
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text: str
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model_size: str
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class PersonalitySpeakRequest(BaseModel):
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"""Body for ``/profiles/{id}/speak`` — LLM transform then TTS."""
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text: str = Field(..., min_length=1, max_length=10000)
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# When true, the generated audio is persisted as a regular row in the
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# generations table (tagged with ``source="personality_speak"``) and
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# the response returns a GenerationResponse the client polls like any
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# other generation. When false, the LLM output is fed to a synchronous
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# TTS call and the wav bytes stream back directly.
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persist: bool = True
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language: Optional[str] = Field(
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None,
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pattern="^(zh|en|ja|ko|de|fr|ru|pt|es|it|he|ar|da|el|fi|hi|ms|nl|no|pl|sv|sw|tr)$",
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)
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engine: Optional[str] = Field(
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None,
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pattern="^(qwen|qwen_custom_voice|luxtts|chatterbox|chatterbox_turbo|tada|kokoro)$",
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)
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# ``respond`` is the default because this endpoint is designed for
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# conversational / agent-style callers. Override to ``rewrite`` to
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# speak the user's text in character verbatim, or ``compose`` to
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# speak an utterance the character would come up with on its own
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# (in which case ``text`` is treated as a topical hint, not content).
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intent: str = Field(
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default="respond", pattern="^(respond|rewrite|compose)$"
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)
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class HealthResponse(BaseModel):
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"""Response model for health check."""
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