feat: criado repositório jhonny-editor no Gitea

criado repositório jhonny-editor no Gitea
adicionado script admin/deploy.command com commit automático
atualizado admin/DEV-NOTES.md com template limpo

Resumo:
- 48 arquivos alterados
- 26 novos
- 19 modificados
- 3 removidos

 22 files changed, 658 insertions(+), 568 deletions(-)

Arquivos:
  - AGENTS.md
  - admin/DEV-NOTES.md
  - admin/OpenCut.command
  - admin/commit.command
  - admin/deploy.command
  - admin/update.command
  - code/cep-plugin/index.html
  - code/cep-plugin/main.js
  - code/cep-plugin/styles.css
  - code/engine/ARQUITETURA.md
  - code/engine/arquitetura/README.md
  - code/engine/gerar_relatorio_timeline.py
  - code/engine/integracoes/apple_speech/apple_speech_transcriber.swift
  - code/engine/integracoes/apple_speech/provider_de_transcricao_apple.py
  - code/engine/integracoes/midia/__init__.py
  - code/engine/integracoes/whisper/provider_de_transcricao_local.py
  - code/engine/scanner/__init__.py
  - code/engine/scanner/coordenacao/__init__.py
  - code/engine/scanner/descoberta/__init__.py
  - code/engine/scanner/modelos.py
  - code/engine/scanner/transcricao_da_timeline.py
  - code/src/tools/discovery.ts
  - :memory:.ses
  - admin/Jhonny.command
  - admin/inativos/OpenCut.command
  - admin/inativos/update.command
  - code/docs/glossario-analise-emocional.md
  - code/docs/levantamento-apple-vision-e-apple-intelligence.md
  - code/docs/levantamento-ferramentas-analise-visual-local.md
  - code/engine/arquitetura/biblioteca-inteligente-de-videos.md
  - code/engine/executar_scanner.py
  - code/engine/integracoes/huggingface/
  - code/engine/integracoes/midia/extracao_de_metadados.py
  - code/engine/integracoes/visual/
  - code/engine/requirements-visual.txt
  - code/engine/scanner/configuracao_visual.py
  - code/engine/scanner/descoberta/descoberta_de_arquivos.py
  - code/engine/scanner/metadados.py
  - code/engine/scanner/relatorio_visual.py
  - code/engine/scanner/retakes/
  - code/engine/scanner/visual.py
  - code/engine/testes/test_analise_visual_local.py
  - code/engine/testes/test_arquivos_e_metadados.py
  - code/engine/testes/test_provider_de_transcricao_apple.py
  - code/relatorios/analise-brools/
  - code/relatorios/analise-visual/
  - code/relatorios/audio/arquivos/
  - code/relatorios/transcricao-timeline.md

code/engine/integracoes/huggingface/__init__.py

@@ -0,0 +1,4 @@
+from .provider_de_emocao_local import ProviderDeEmocaoHuggingFace
+from .provider_de_diarizacao_local import ProviderDeDiarizacaoHuggingFace
+
+__all__ = ["ProviderDeDiarizacaoHuggingFace", "ProviderDeEmocaoHuggingFace"]

code/engine/integracoes/huggingface/provider_de_diarizacao_local.py

@@ -0,0 +1,21 @@
+from pathlib import Path
+
+
+class ProviderDeDiarizacaoHuggingFace:
+    """Identifica intervalos de falas por participante em áudio local."""
+
+    def __init__(self, modelo: str) -> None:
+        from pyannote.audio import Pipeline
+        self.pipeline = Pipeline.from_pretrained(modelo)
+
+    def analisar(self, arquivo: str | Path) -> list[tuple[float, float, str]]:
+        import soundfile as sf
+        import torch
+        audio, taxa = sf.read(str(arquivo), dtype="float32")
+        if getattr(audio, "ndim", 1) > 1:
+            audio = audio.mean(axis=1)
+        saida = self.pipeline({"waveform": torch.from_numpy(audio).unsqueeze(0),
+                               "sample_rate": taxa})
+        diarizacao = getattr(saida, "exclusive_speaker_diarization", saida)
+        return [(float(turno.start), float(turno.end), str(falante))
+                for turno, _, falante in diarizacao.itertracks(yield_label=True)]

code/engine/integracoes/huggingface/provider_de_emocao_local.py

@@ -0,0 +1,35 @@
+from pathlib import Path
+from typing import Any
+
+
+class ProviderDeEmocaoHuggingFace:
+    """Classifica a emoção de uma fala localmente com Transformers."""
+
+    def __init__(self, modelo: str = "superb/wav2vec2-base-superb-er") -> None:
+        from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
+        self.modelo = modelo
+        self.processador = AutoFeatureExtractor.from_pretrained(modelo, local_files_only=True)
+        self.classificador = AutoModelForAudioClassification.from_pretrained(
+            modelo, local_files_only=True
+        )
+        self.classificador.eval()
+
+    def analisar(self, arquivo: str | Path, inicio: float, fim: float) -> dict[str, Any]:
+        import soundfile as sf
+        import torch
+        audio, taxa = sf.read(str(arquivo), start=max(0, int(inicio * 16000)),
+                              stop=max(0, int(fim * 16000)), dtype="float32")
+        if getattr(audio, "ndim", 1) > 1:
+            audio = audio.mean(axis=1)
+        entradas = self.processador(audio, sampling_rate=taxa, return_tensors="pt")
+        with torch.no_grad():
+            logits = self.classificador(**entradas).logits
+        probabilidades = torch.softmax(logits[0], dim=-1)
+        indice = int(torch.argmax(probabilidades))
+        rotulo = self.classificador.config.id2label[indice]
+        voz_aparente = {"non-neutral-female": "feminina",
+                        "non-neutral-male": "masculina"}.get(rotulo)
+        return {"emocao": self.classificador.config.id2label[indice],
+                "confianca": float(probabilidades[indice]),
+                "voz_aparente": voz_aparente,
+                "confianca_voz": float(probabilidades[indice]) if voz_aparente else None}