feat(voz): legenda por ênfase, forced align, IA local e correções de zoom/revisão
Trabalho da branch feat/revisao-enfases: pipeline de edição por voz ganha alinhamento forçado (whisperx), roteirização por LLM local (Ollama), e a etapa 5 (revisão de frases) passa a refletir de verdade o que é aplicado. - generate_subtitles_by_emphasis: legenda comum cobre o clipe inteiro, legenda dinâmica só nas frases de ênfase, e a comum é desativada (enabled="0") onde a dinâmica cobre, em vez de nunca ser gerada ali. - validate_subtitle_layout ignora títulos com enabled="0" — corrige falso positivo de colisão contra o que está desativado no lugar dele. - Corrige zoom/marcador sendo descartado quando a borda encosta exatamente no início de um corte. - Etapa 5 do Assistente: recarrega quando as decisões da IA mudam (com fresh=true, ignorando a revisão salva antiga) — resolve a dessincronia entre "ativa" na tela e o que já foi cortado no FCPXML. - Etapa "Processar" reaplica as decisões da revisão (_phrase_actions.json) antes da cadeia de remoção de silêncio/legendas — antes, desativar uma frase na etapa 5 não tinha efeito nenhum no vídeo final. - Etapa "Concluído" fundida em "Processar" — abrir no Final Cut/Finder aparece assim que termina, sem slide extra. - Palavra clicável na etapa 5 agora funciona como toggle (clique de novo desfaz) e mostra a própria ênfase (sublinhado colorido + peso da fonte). - fcpxml/forced_align.py, fcpxml/llm_local.py, ai_edit.py: alinhamento fonético via whisperx e roteirização local via Ollama/Gemma. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
This commit is contained in:
João Henrique
co-authored by
Claude Sonnet 5
Claude Sonnet 5
parent
711c397dfe
commit
7b5aed79ee
@@ -0,0 +1 @@
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analysis/
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@@ -86,7 +86,7 @@ G-ART/
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├── Engine/ # Esta documentação
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├── docs/ # WORKFLOWS, CAPABILITY-AUDIT, specs
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├── examples/ # Fixture de teste (sample.fcpxml)
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└── tests/ # 1.454 testes / 42 suítes
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└── tests/ # 1.466 testes / 42 suítes
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```
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---
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@@ -7,7 +7,7 @@
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> mudança de código. Se algo aqui divergir do código, **o código está certo e
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> este documento está velho** — corrija-o no mesmo commit.
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Última varredura: 2026-08-19 · 74 ferramentas MCP · 1.454 testes · versão `0.6.35`
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Última varredura: 2026-08-19 · 77 ferramentas MCP · 1.498 testes · versão `0.6.35`
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---
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@@ -29,7 +29,7 @@ entrada diferentes** para o mesmo motor:
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▼ ▼
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┌──────────────────────────┐ ┌──────────────────────────────┐
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│ admin/models_api.py │ │ server.py + server_tools/ │
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│ + admin/api/ │ │ 74 tools, dispatch, schemas │
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│ + admin/api/ │ │ 77 tools, dispatch, schemas │
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│ 37 comandos da ponte │ │ NÃO tem lógica de timeline │
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└───────────┬──────────────┘ └───────────────┬──────────────┘
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└───────────────┬────────────────────┘
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@@ -65,7 +65,7 @@ no mesmo engine, então uma correção ali vale para as duas.
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| **Deps opcionais** | `librosa`/`ffmpeg`/`huggingface_hub` importados **lazy**, degradam com `None`. |
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| **Idioma** | Comunicação com o usuário em português. Código e comentários em inglês. |
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| **Validação** | `./Engine/run_after_fix.sh` **sempre** após cada correção. |
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| **App** | Alterou `MacApp/`? Compile e rode: `./MacApp/build_app.sh --run`. |
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| **App** | Alterou `MacApp/`? Compile e rode: `admin/run_app.command` (padrão de revisão; equivale a `./MacApp/build_app.sh --run`). |
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---
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@@ -140,7 +140,7 @@ programático. Round-trips sempre voltam pelas ferramentas XML.
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| Controle Live do FCP | `fcpxml/live.py` |
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| Segurança XML | `fcpxml/safe_xml.py` |
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| Validação contra DTDs da Apple | `fcpxml/dtd.py` |
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| Transporte MCP (74 tools) | `server.py` + `server_tools/` |
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| Transporte MCP (77 tools) | `server.py` + `server_tools/` |
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| Ponte com o app (37 comandos) | `admin/models_api.py` + `admin/api/` |
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| Interface do usuário | `MacApp/Sources/` |
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@@ -174,4 +174,4 @@ Se você precisar disso, a lógica está no lugar errado.
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| Uma **regra de edição** nova | `fcpxml/` sempre. Se você está escrevendo `if` sobre timeline fora de `fcpxml/`, pare. |
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O trabalho principal é **sempre** no engine. As camadas de cima são finas de
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propósito: é o que permite testar 1.454 casos sem abrir o app nem subir o MCP.
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propósito: é o que permite testar 1.498 casos sem abrir o app nem subir o MCP.
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@@ -26,13 +26,14 @@ Versão: `0.6.35` · Última varredura: 2026-08-19
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| `text_layout.py` | 901 | Diagramação das legendas dinâmicas |
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| `rough_cut.py` | 798 | Geração de timelines novas |
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| `model_manager.py` | 748 | Modelos Whisper: catálogo, download, config |
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| `voice_timeline.py` | 594 | O JSON de voz que a IA lê |
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| `voice_timeline.py` | 600 | O JSON de voz que a IA lê |
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| `phrase_review.py` | 547 | Revisão de frases (etapa 5 do assistente) |
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| `collision.py` | 472 | Colisão entre títulos na tela |
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| `font_metrics.py` | 445 | Largura real de glifos por fonte |
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| `templates.py` | 387 | Templates de timeline |
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| `parser.py` | 367 | FCPXML → objetos Python |
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| `transcribe.py` | 300 | Transcrição Whisper e corte por texto |
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| `transcribe.py` | 332 | Transcrição Whisper e corte por texto |
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| `forced_align.py` | 181 | Alinhamento forçado opcional (whisperx/wav2vec2) que corrige o viés de ~0,4s no início das palavras |
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| `live.py` | 273 | Modo Live (push_to_fcp) |
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| `diff.py` | 269 | Comparação de timelines |
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| `voice_actions.py` | 263 | Decisões de edição (cut/zoom/text/marker) |
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@@ -1,6 +1,6 @@
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# 03 — Camada MCP (`server.py` + `server_tools/`) — 74 ferramentas
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# 03 — Camada MCP (`server.py` + `server_tools/`) — 77 ferramentas
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> **Escopo:** As 74 ferramentas MCP: helpers, categorias e como criar uma nova.
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> **Escopo:** As 77 ferramentas MCP: helpers, categorias e como criar uma nova.
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> **Não cobre:** Lógica de edição, que mora no engine (→ 02) · comandos do app (→ 08)
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`server.py` (592 linhas) é só o transporte: dispatch por dicionário
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@@ -43,7 +43,7 @@ continua funcionando. A coluna diz o módulo real, para quando você precisar
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| `_cut_transcript_spans()` | `_shared/media.py` | Corte por trecho falado |
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| `_apply_placed_action()` | `_shared/media.py` | Aplica zoom/text/marker já posicionado |
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## As 74 ferramentas por categoria
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## As 77 ferramentas por categoria
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### Timeline & análise (Projeto)
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`list_projects`, `analyze_timeline`, `list_clips`, `list_markers`, `list_connected_clips`,
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@@ -82,18 +82,31 @@ continua funcionando. A coluna diz o módulo real, para quando você precisar
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### Voz (análise → decisão → aplicação)
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`analyze_voice_features`, `build_voice_timeline`, `refine_voice_timeline`,
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`remove_speakers`, `apply_voice_actions`, `get_voice_analysis_config`,
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`save_voice_analysis_config`.
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`remove_speakers`, `apply_voice_actions`, `generate_voice_script`,
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`get_voice_analysis_config`, `save_voice_analysis_config`.
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O fluxo é sempre o mesmo: `build_voice_timeline` mede (caro, roda uma vez) →
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O fluxo manual é: `build_voice_timeline` mede (caro, roda uma vez) →
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o modelo decide os cortes → **`refine_voice_timeline` renormaliza sobre o que
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sobrou** (barato, sem reabrir áudio) e propõe as janelas de zoom → o modelo
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corta a lista pelo ritmo → `apply_voice_actions` aplica. Pular a renormalização
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faz o ranking de ênfase apontar para as palavras erradas (ver
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`05_EXPERIENCIAS.md`).
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`generate_voice_script` é o fluxo **automático e fechado** (sem wizard, sem
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copiar-e-colar): transcreve (cache) → `build_voice_timeline` → entrega a
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timeline a um **modelo local Ollama** que dirige a edição → devolve o roteiro
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legível (markdown) **e** o JSON de ações, e opcionalmente aplica num FCPXML.
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O cliente fica em `fcpxml/llm_local.py`; o modelo é tratado como entrada não
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confiável e cada ação é validada por `parse_actions`. Padrão:
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`qwen2.5:7b-instruct-q4_K_M` (troca de `gemma3:12b` — não cabia em máquina de
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8GB de RAM; Gemma 3 4B foi testado antes e falhou por apagar o roteiro
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principal em vez de só cortar bastidor). Passe `model=` para usar outro
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servido pelo Ollama.
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### Legendas dinâmicas (geração → validação → aplicação)
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`generate_dynamic_subtitles`, `validate_subtitle_layout`, `transcript_markers`.
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`generate_dynamic_subtitles`, `generate_plain_subtitles`,
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`generate_subtitles_by_emphasis`, `validate_subtitle_layout`,
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`transcript_markers`.
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**Sempre gere e depois valide — nunca dê a geração como pronta sem
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`validate_subtitle_layout`.** A composição garante "sem sobreposição" só
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@@ -111,6 +124,23 @@ severidade probable/severe → investigar CADA colisão pela fração exata do
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XML antes de mudar código (ver checklist abaixo)
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```
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**`generate_subtitles_by_emphasis`** gera as duas legendas numa passada só —
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mas não divide as palavras entre elas. A comum é gerada **completa, do início
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ao fim do clipe**, sempre; a dinâmica é gerada só sobre as frases marcadas
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como ênfase na etapa 5 (zoom aplicado, nível ≥ 1); e onde a dinâmica cobre um
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trecho, os títulos comuns daquele trecho recebem `enabled="0"` — continuam no
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XML (editáveis/reativáveis no Final Cut), só não são desenhados. É a tradução
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literal de `10-revisao-humana.md` (skill `editar-por-voz`): "a frase de
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ênfase recebe zoom E legenda dinâmica; as demais recebem legenda comum" —
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sem nunca deixar um vão sem legenda nenhuma se a ênfase for desativada depois
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(a comum já estava lá, só desligada). A decisão vem de
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`<mídia>_phrase_actions.json["emphasis_spans"]`, escrito por
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`save_phrase_review` quando o editor termina a etapa 5 — sem esse arquivo (ou
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sem `zoom`/`text` marcados na revisão), a tool gera só a comum, tudo ligado,
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e avisa no relatório ("Sem revisão de ênfase"). Não expõe overrides de estilo
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por chamada — usa a config salva ("Legendas Dinâmicas"/plain); para estilo
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pontual, use `generate_dynamic_subtitles`/`generate_plain_subtitles` direto.
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**Antes de atribuir uma colisão ao gerador, confirme que é o gerador.**
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Um `<title>` de nome estranho (`ref` diferente, params tipo `Auto-Shrink`/
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`Left Margin` que `_make_text_title_clip` nunca escreve) é conteúdo humano
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@@ -31,7 +31,7 @@ que merece entrada.
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| 11 | 2026-08-18 | Preview das legendas dinâmicas desproporcional ao render do FCP (stagger/gap/canvas divergentes) e `inactive_color` exposto sem efeito | `resolvido` |
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| 12 | 2026-08-18 | Espaço de coordenadas do modelo "Text": `fontSize`, `kerning` e `Position` no espaço do quadro — converter só o tamanho descolou o espaçamento | `resolvido` |
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| 13 | 2026-08-19 | Reanálise de ênfase implementada no Engine mas sem ferramenta MCP — Fase 4 da skill era inexecutável | `resolvido` |
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| 14 | 2026-08-19 | Offset sistemático de ~0,4s no timing por palavra (faster-whisper sem alinhamento forçado) — corrigido manualmente no teste, WhisperX pendente | `parcialmente resolvido` |
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| 14 | 2026-08-19 | Offset sistemático de ~0,4s no timing por palavra (faster-whisper sem alinhamento forçado) — agora corrigido em pipeline por alinhamento forçado opcional | `resolvido` |
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| 15 | 2026-08-19 | `add_zoom` perdia o enquadramento real (voltava a 100%) quando dois zooms caiam no mesmo clipe pós-corte; agora empilha ou substitui conforme as janelas se sobrepõem | `resolvido` |
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| 16 | 2026-08-19 | `validate_subtitle_layout` acusava colisão severa em títulos que só se tocam na borda, por não-associatividade de float; 7 de 8 colisões reportadas no teste real eram falso positivo | `resolvido` |
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| 17 | 2026-08-19 | Linha de ênfase das legendas dinâmicas sem limite de largura — palavra longa/maiúscula estourava o frame inteiro; auto-fit encolhe até caber, nunca abaixo do corpo | `resolvido` |
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@@ -43,6 +43,7 @@ que merece entrada.
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| 23 | 2026-08-19 | Dividir `writer.py` em pacote quebrou `@patch('fcpxml.writer.subprocess')` — a suíte protege comportamento, não localização | `resolvido` |
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| 24 | 2026-08-19 | `admin/test_models_api.py` existia mas estava fora de `testpaths` — 13 testes que nunca rodaram | `resolvido` |
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| 25 | 2026-08-20 | `admin/api/shared.py` apontava para `admin/code` (inexistente) após a divisão — install editável mascarou o bug em toda validação anterior | `resolvido` |
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| 26 | 2026-08-21 | `generate_voice_script` (IA local/Ollama) caía com "Falha ao gerar roteiro por IA local" — prompt embutia a timeline inteira (47k tokens) e estourava `num_ctx`; e `response.json()` de conexão caída escapava como `JSONDecodeError` | `resolvido` |
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> Mantenha o índice acima sempre sincronizado com as entradas mais recentes.
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@@ -118,9 +119,9 @@ Use o bloco abaixo como modelo. Uma entrada = um problema resolvido/reconhecido.
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- **Por que isso importa mais do que parece:** o erro contamina toda decisão temporal a jusante — zoom disparava ~0,4s antes da palavra-alvo, `gap_before` subestimava pausas reais na mesma medida (o que afeta diretamente a régua de silêncio recém-adotada), e as folgas de corte saíam erradas nas emendas.
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- **Decisão tomada:** não rodei `remove_media_silence` bruto sobre o corte. A detecção (ffmpeg, limiar -30dB/0,5s) não distingue "batida entre frases dentro da régua de 1,5s" de "ar morto de emenda" — cortar ambos teria apertado frases fluidas. Corrigi os tempos manualmente medindo o ataque real nos pontos críticos (cabeça, 2 emendas, cauda, 3 zooms) e refiz o corte numa passada só.
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- **Solução adotada (paliativa, aplicada manualmente neste teste):** medir o RMS real com `ffmpeg -af astats=metadata=1:reset=1:length=0.05,ametadata=print` em janelas curtas ao redor de cada ponto crítico antes de fixar um corte ou zoom que dependa de precisão de frame. Não é o padrão do sistema — é o que cobre a lacuna até o alinhamento forçado existir.
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- **Solução estrutural ainda pendente:** ligar o WhisperX (ou alinhamento forçado equivalente) em `transcribe.py`, o que levaria o erro de ~400ms para ~30ms e corrigiria zoom, corte e `gap_before` de uma vez, sem paliativo por projeto. Não implementado ainda — é mudança de pipeline, exige regerar todos os `_transcript.json`/`_voice_timeline.json` existentes.
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- **Solução estrutural implementada:** `transcribe.py` agora roda alinhamento forçado fonético (wav2vec2 via whisperx) como passo opcional pós-transcrição, em `fcpxml/forced_align.py` (classe `ForcedAligner`). O erro cai de ~400ms para ~30ms e corrige zoom, corte e `gap_before` de uma vez. É **dependência opcional** (`[align]` extra / pacote `whisperx` do PyPI) — quando ausente ou em qualquer falha, degrada e devolve os tempos brutos sem quebrar a transcrição. O `transcript` traz `"alignment": true/false` e o `voice_timeline` expõe `layers.alignment`, para quem lê o JSON saber se o offset manual ainda é necessário. Não reaproveitamos código da pasta `WHISPERX/` local (problemas conhecidos) — só a ideia documentada aqui. Exige regerar os `_transcript.json`/`_voice_timeline.json` existentes para aplicar nos caches antigos.
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- **Aprendizado:** "não reestime tempos no olho" (critério 01) continua certo para decisão *editorial* — mas não cobre erro sistemático de *medição* na fonte dos tempos. Um offset constante e na mesma direção, em vários pontos do material, é sinal de bug no pipeline de transcrição, não de julgamento errado sobre o material. Vale conferir com uma amostra de áudio real antes de confiar cegamente em timestamp de word-level de qualquer fonte nova.
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- **Estado:** `parcialmente resolvido` — paliativo documentado e aplicado neste teste; correção estrutural (WhisperX) pendente de implementação.
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- **Estado:** `resolvido` — alinhamento forçado implementado em `transcribe.py`/`fcpxml/forced_align.py`; paliativo de medição manual mantido apenas para transcripts antigos sem `layers.alignment=true`.
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---
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@@ -1366,3 +1367,50 @@ o outro; percentil entrega um punhado útil nos dois casos.
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instaladas por fora do mecanismo sendo testado — ou o teste prova que o
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ambiente de teste está bem configurado, não que o código está certo.
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- **Estado:** `resolvido`
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---
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## Entrada #26 — Prompt da IA local estoura o contexto do Ollama (e erro de parse escapa)
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- **Sintoma:** botão "Gerar roteiro por IA local" (etapa 4 do assistente)
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devolvia "Falha ao gerar roteiro por IA local". Rodando a ponte direto, o
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erro real aparecia como *"Server disconnected without sending a response"*
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ou *"Connection refused"* do Ollama, e 0 decisões ("Decisões do modelo: 0").
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- **Causa raiz (dupla):**
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1. `build_edit_messages` embutia o JSON da voice timeline **inteiro** no
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prompt. Uma gravação de 3min vira ~188KB / **~47k tokens** (cada palavra
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carrega energia, pitch, arousal, valence, `samples`…). Como `num_ctx`
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estava em 32768, o prompt estourava a janela e o Ollama **dropava a
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conexão** sem resposta.
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2. Quando a conexão cai sem resposta, `httpx` entrega um body vazio e
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`response.json()` lançava `JSONDecodeError` — que **não** é
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`httpx.HTTPError`, então escapava do `try/except` de `ollama_chat` e
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virava a exceção genérica que o `cmd_generate_voice_script` transforma
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em `ok:false` com a mensagem "Falha ao gerar roteiro por IA local: …".
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- **Correção (em `fcpxml/llm_local.py` + `server_tools/voice.py`):**
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- `build_edit_messages` agora projeta a timeline (**`_project_timeline`**):
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mantém só `text`/`start`/`end`/`speaker`/`emphasis`/`pause_before` das
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palavras e `id`/`name` dos locutores; descarta `layers`, `scales`,
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`samples` e os floats de áudio. Caiu de ~47k para **~17k tokens** (69KB).
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- Salvaguarda `_shrink_to_fit`: se ainda passar de `max_chars` (110k),
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remove os `words` dos segmentos de menor `peak_emphasis` até caber.
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- `ollama_chat` envolve `post`+`raise_for_status`+`json()` num único
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`except Exception` que relança como `RuntimeError` claro — fim do
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`JSONDecodeError` escapando.
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- `_extract_json` agora desembrulha a lista de 1 elemento `[{source,
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actions}]` que alguns modelos devolvem, senão o `parse_actions` tratava o
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objeto-wrapper como uma ação sem `kind` e rejeitava tudo (0 decisões).
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- `handle_generate_voice_script` levanta `RuntimeError` com a causa quando o
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modelo não devolve nenhuma decisão utilizável, então o app mostra a
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mensagem real ("O modelo local não devolveu decisões utilizáveis: …")
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em vez do genérico.
|
||||
- **Validação:** `tests/test_llm_local.py` ganhou `test_build_edit_messages_is_compact`
|
||||
(prompt < raw, sem `samples`/`energy_raw`/`pitch_hz`) e
|
||||
`test_ollama_chat_wraps_empty_response`. Ponte testada com Ollama mockado
|
||||
nos dois sentidos (sucesso aplica; falha → `ok:false` com msg clara).
|
||||
- **Estado:** `resolvido`
|
||||
|
||||
> **Aprendizado:** modelo local tem contexto finito — nunca embutir o objeto
|
||||
> de análise cru no prompt; projetar só o que a decisão usa. E qualquer parse
|
||||
> de resposta de servidor local deve tratar body vazio/quebrado como erro de
|
||||
> transporte, não como sucesso mudo.
|
||||
|
||||
@@ -19,7 +19,7 @@ o resultado.
|
||||
|
||||
```bash
|
||||
cd code && ./MacApp/build_app.sh # compila e monta o .app
|
||||
cd code && ./MacApp/build_app.sh --run # compila e abre
|
||||
admin/run_app.command # compila, fecha a instância antiga e abre (padrão de revisão)
|
||||
```
|
||||
|
||||
Consequências práticas, todas já sentidas:
|
||||
@@ -121,14 +121,15 @@ quando o botão "Continuar" libera.
|
||||
| 1 | Projeto | Escolhe a pasta de saída e o `.fcpxml` | `project_config` |
|
||||
| 2 | Transcrever | Transcreve toda a mídia do projeto | `transcribe` |
|
||||
| 3 | Analisar voz | Mede ênfase, locutores, emoção | `analyze_voice` |
|
||||
| 4 | Decisões da IA | Copia para o chat, cola o JSON de volta, aplica | `apply_voice_actions` |
|
||||
| 4 | Decisões da IA | Copia para o chat **ou** gera por IA local (Ollama/Gemma 3), aplica | `apply_voice_actions` / `generate_voice_script` |
|
||||
| 5 | **Revisar ênfases** | Lapida frase a frase — ver seção 5 | `build_phrase_review` / `save_phrase_review` |
|
||||
| 6 | Processar | Silêncios, preenchimento, legendas | vários, em cadeia |
|
||||
| 7 | Concluído | Abre no FCP ou mostra no Finder | — |
|
||||
|
||||
**A etapa 4 é a única manual do fluxo**, e de propósito: o julgamento de qual
|
||||
tomada usar e onde dar zoom é conversa com uma IA (skill `editar-por-voz`), não
|
||||
um botão. O app monta o pedido pronto no clipboard e recebe o JSON de volta.
|
||||
**A etapa 4 tem duas saídas:**
|
||||
|
||||
- **Manual (chat):** o app monta o pedido pronto no clipboard (skill `editar-por-voz`) e recebe o JSON de volta — o julgamento de qual tomada usar e onde dar zoom fica com a IA numa conversa.
|
||||
- **Automática (IA local):** botão "Gerar roteiro por IA local (Ollama/Gemma 3)". Ele manda a *voice timeline inteira* (o arquivo) junto com o brief para um modelo local (Ollama), que decide cortes/zooms/textos de uma vez, devolve o roteiro legível + o JSON de ações e já aplica no FCPXML (non-destructive). Não precisa sair do app nem colar nada. O modelo é escolhido num **picker que lista os modelos instalados no Ollama** (populado via `list_ollama_models` quando a etapa abre); se o Ollama estiver fora do ar, cai para um campo de texto livre. Troque para `llama3` etc. se tiver outro modelo. Requer o Ollama rodando em `localhost:11434`.
|
||||
|
||||
**Etapa 1 — armadilha registrada:** não escolha como "o projeto" um arquivo já
|
||||
gerado pelo fluxo (`_voice_edit`, `_silence_removed`, …). Os cortes de voz
|
||||
|
||||
@@ -7,7 +7,7 @@ Este é o documento de rota. Os outros descrevem o que **é**; este diz o que
|
||||
**fazer** e por onde começar quando chega uma implementação, uma melhoria ou
|
||||
uma correção.
|
||||
|
||||
Última varredura: 2026-08-19 · 1.454 testes · lint zerado
|
||||
Última varredura: 2026-08-19 · 1.466 testes · lint zerado
|
||||
|
||||
---
|
||||
|
||||
@@ -98,8 +98,8 @@ quanto arquivo gigante.
|
||||
## 4. Checklist antes de dar algo por pronto
|
||||
|
||||
```bash
|
||||
cd code && ./Engine/run_after_fix.sh # lint zerado + 1.454 testes
|
||||
cd code && ./MacApp/build_app.sh --run # se mexeu no app
|
||||
cd code && ./Engine/run_after_fix.sh # lint zerado + 1.466 testes
|
||||
admin/run_app.command # se mexeu no app (padrão de revisão)
|
||||
```
|
||||
|
||||
E, além do script:
|
||||
@@ -150,7 +150,7 @@ Estão em `01_ARCHITECTURE.md` §2 e valem repetir as três que mais custaram:
|
||||
|
||||
- [01 Arquitetura](01_ARCHITECTURE.md) — camadas e onde cada coisa mora
|
||||
- [02 Módulos](02_MODULES.md) — mapa do engine, módulo a módulo
|
||||
- [03 Server/Tools](03_SERVER_TOOLS.md) — as 74 ferramentas MCP
|
||||
- [03 Server/Tools](03_SERVER_TOOLS.md) — as 77 ferramentas MCP
|
||||
- [04 Testes & Workflow](04_TESTS_AND_WORKFLOW.md)
|
||||
- [05 Experiências](05_EXPERIENCIAS.md) — o que já quebrou e por quê
|
||||
- [06 Boas Práticas](06_BOAS_PRATICAS.md)
|
||||
|
||||
@@ -31,15 +31,31 @@ struct CaptionsView: View {
|
||||
@AppStorage("capSampleAfter") private var sampleAfter = "sua legenda"
|
||||
@AppStorage("capShowGuides") private var showsGuides = true
|
||||
|
||||
private let fontChoices = [
|
||||
"Helvetica Neue", "Helvetica", "Arial", "Avenir Next",
|
||||
"Futura", "SF Pro Display", "Georgia", "Impact",
|
||||
]
|
||||
/// Todas as famílias de fonte instaladas no macOS (sistema + usuário), as
|
||||
/// usadas por padrão primeiro, para o seletor listar tudo sem hardcode.
|
||||
private static let installedFontFamilies: [String] = {
|
||||
var families = NSFontManager.shared.availableFontFamilies
|
||||
.sorted { $0.localizedCaseInsensitiveCompare($1) == .orderedAscending }
|
||||
let preferred = ["Helvetica Neue", "Playfair Display", "Georgia", "Didot"]
|
||||
for family in preferred.reversed() {
|
||||
if let idx = families.firstIndex(of: family) {
|
||||
families.remove(at: idx)
|
||||
families.insert(family, at: 0)
|
||||
}
|
||||
}
|
||||
return families
|
||||
}()
|
||||
|
||||
private let emphasisFontChoices = [
|
||||
"Playfair Display", "Georgia", "Didot", "Futura",
|
||||
"Avenir Next", "Times New Roman", "Helvetica Neue", "Impact",
|
||||
]
|
||||
/// Lista para um picker: todas as famílias instaladas e, se o valor salvo
|
||||
/// não estiver entre elas (ex.: fonte de outro Mac), ele entra no topo
|
||||
/// para o seletor continuar exibindo a escolha atual.
|
||||
private func fontChoices(for current: String) -> [String] {
|
||||
var list = Self.installedFontFamilies
|
||||
if !list.contains(current) {
|
||||
list.insert(current, at: 0)
|
||||
}
|
||||
return list
|
||||
}
|
||||
|
||||
private let emphasisFaceChoices = [
|
||||
"Medium Italic", "Italic", "Bold Italic", "Bold", "Regular", "Light Italic",
|
||||
@@ -211,7 +227,7 @@ struct CaptionsView: View {
|
||||
private var bodySection: some View {
|
||||
Section("Linhas de apoio") {
|
||||
Picker("Fonte", selection: bound(\.font)) {
|
||||
ForEach(fontChoices, id: \.self) { Text($0).tag($0) }
|
||||
ForEach(fontChoices(for: config.font), id: \.self) { Text($0).tag($0) }
|
||||
}
|
||||
slider(
|
||||
"Tamanho",
|
||||
@@ -226,7 +242,7 @@ struct CaptionsView: View {
|
||||
private var emphasisSection: some View {
|
||||
Section("Palavra de ênfase") {
|
||||
Picker("Fonte", selection: bound(\.emphasisFont)) {
|
||||
ForEach(emphasisFontChoices, id: \.self) { Text($0).tag($0) }
|
||||
ForEach(fontChoices(for: config.emphasisFont), id: \.self) { Text($0).tag($0) }
|
||||
}
|
||||
Picker("Estilo", selection: bound(\.emphasisFace)) {
|
||||
ForEach(emphasisFaceChoices, id: \.self) { Text($0).tag($0) }
|
||||
@@ -244,7 +260,7 @@ struct CaptionsView: View {
|
||||
private var plainSubtitleSection: some View {
|
||||
Section("Legenda comum") {
|
||||
Picker("Fonte", selection: plainBound(\.font)) {
|
||||
ForEach(fontChoices, id: \.self) { Text($0).tag($0) }
|
||||
ForEach(fontChoices(for: plainConfig.font), id: \.self) { Text($0).tag($0) }
|
||||
}
|
||||
slider(
|
||||
"Tamanho",
|
||||
|
||||
@@ -28,30 +28,6 @@ final class PhraseReviewModel: ObservableObject {
|
||||
/// In/out the editor dragged on the timeline, in source seconds.
|
||||
@Published var rangeStart: Double?
|
||||
@Published var rangeEnd: Double?
|
||||
/// Aspect ratio of the footage as recorded.
|
||||
@Published var videoAspect: Double = 16.0 / 9.0
|
||||
/// Aspect ratio the project delivers in, read from the .fcpxml. It is
|
||||
/// routinely *not* the footage's: these takes are shot horizontal and
|
||||
/// delivered vertical, so previewing the raw frame would show a crop the
|
||||
/// audience never sees — and the emphasis decisions are about what lands on
|
||||
/// screen. Nil until the project is known.
|
||||
@Published var projectAspect: Double?
|
||||
/// Whether the preview crops to the delivery frame. On by default whenever
|
||||
/// the two aspects disagree.
|
||||
@Published var matchProjectFraming = true
|
||||
|
||||
/// What the preview should actually draw.
|
||||
var previewAspect: Double {
|
||||
guard matchProjectFraming, let projectAspect else { return videoAspect }
|
||||
return projectAspect
|
||||
}
|
||||
|
||||
/// True when the delivery frame differs enough from the footage that the
|
||||
/// preview is showing a crop rather than the whole take.
|
||||
var isCropping: Bool {
|
||||
guard matchProjectFraming, let projectAspect else { return false }
|
||||
return abs(projectAspect - videoAspect) > 0.01
|
||||
}
|
||||
|
||||
private(set) var source = ""
|
||||
private(set) var sourcePath = ""
|
||||
@@ -76,25 +52,17 @@ final class PhraseReviewModel: ObservableObject {
|
||||
// MARK: - Carregar
|
||||
|
||||
/// Builds the review from the voice timeline plus whatever the AI decided.
|
||||
/// A review saved on a previous visit wins — see `cmd_build_phrase_review`.
|
||||
/// Reads the delivery format from the project so the preview can frame the
|
||||
/// take the way it will actually be seen.
|
||||
func loadProjectFormat(projectPath: String) {
|
||||
PythonBridge.call(command: "inspect", arguments: ["path": projectPath]) { [weak self] result, _ in
|
||||
Task { @MainActor in
|
||||
guard let self,
|
||||
let timelines = result?["timelines"] as? [[String: Any]],
|
||||
let first = timelines.first,
|
||||
let width = first["width"] as? Int, let height = first["height"] as? Int,
|
||||
width > 0, height > 0
|
||||
else { return }
|
||||
self.projectAspect = Double(width) / Double(height)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// A review saved on a previous visit wins — see `cmd_build_phrase_review` —
|
||||
/// UNLESS `fresh` is true, in which case that saved review is ignored and
|
||||
/// `active`/`emphasis`/etc. come straight from this call's `decisionsJSON`.
|
||||
/// Pass `fresh: true` when the decisions themselves changed since the
|
||||
/// review was last built (the caller re-pasted/regenerated the AI's JSON
|
||||
/// and re-ran `apply_voice_actions`) — otherwise the saved review from the
|
||||
/// PREVIOUS decisions silently wins over the fresh cut it should reflect,
|
||||
/// which is exactly the desync the wizard's "active" toggle showed against
|
||||
/// the just-reapplied FCPXML.
|
||||
func load(voiceTimelinePath: String, decisionsJSON: String,
|
||||
outputFolder: String? = nil, mediaFolder: String? = nil) {
|
||||
outputFolder: String? = nil, mediaFolder: String? = nil, fresh: Bool = false) {
|
||||
self.voiceTimelinePath = voiceTimelinePath
|
||||
isLoading = true
|
||||
errorMessage = nil
|
||||
@@ -102,6 +70,7 @@ final class PhraseReviewModel: ObservableObject {
|
||||
var arguments: [String: Any] = ["voice_timeline": voiceTimelinePath]
|
||||
if let outputFolder { arguments["output_dir"] = outputFolder }
|
||||
if let mediaFolder { arguments["media_dir"] = mediaFolder }
|
||||
if fresh { arguments["fresh"] = true }
|
||||
if let data = decisionsJSON.data(using: .utf8),
|
||||
let parsed = try? JSONSerialization.jsonObject(with: data) {
|
||||
arguments["actions"] = parsed
|
||||
@@ -160,7 +129,6 @@ final class PhraseReviewModel: ObservableObject {
|
||||
let asset = AVURLAsset(url: URL(fileURLWithPath: sourcePath))
|
||||
let player = AVPlayer(playerItem: AVPlayerItem(asset: asset))
|
||||
self.player = player
|
||||
readAspect(from: asset)
|
||||
// 60 Hz: the same observer drives the playhead *and* decides when to
|
||||
// jump a removed stretch, so its period is the worst-case amount of cut
|
||||
// material that can be heard before the skip lands. At 20 Hz that was an
|
||||
@@ -173,22 +141,6 @@ final class PhraseReviewModel: ObservableObject {
|
||||
}
|
||||
}
|
||||
|
||||
/// The displayed aspect ratio, honouring the rotation the camera recorded.
|
||||
/// A phone take is stored 1920×1080 with a 90° transform: reading
|
||||
/// `naturalSize` alone would call a vertical video horizontal.
|
||||
private func readAspect(from asset: AVURLAsset) {
|
||||
Task { [weak self] in
|
||||
guard let track = try? await asset.loadTracks(withMediaType: .video).first,
|
||||
let size = try? await track.load(.naturalSize),
|
||||
let transform = try? await track.load(.preferredTransform)
|
||||
else { return }
|
||||
let displayed = size.applying(transform)
|
||||
let width = abs(displayed.width), height = abs(displayed.height)
|
||||
guard width > 0, height > 0 else { return }
|
||||
await MainActor.run { self?.videoAspect = width / height }
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - Reprodução
|
||||
|
||||
private func tick(_ time: Double) {
|
||||
@@ -335,8 +287,21 @@ final class PhraseReviewModel: ObservableObject {
|
||||
|
||||
/// Trim everything before/after a given word — the text-first way to cut,
|
||||
/// since the editor reads the line and points at where it should begin.
|
||||
/// Clicking the word that is ALREADY that edge toggles it back off —
|
||||
/// the trim on that side resets to the phrase's own start/end — so the
|
||||
/// same click that sets a boundary also clears it, instead of needing
|
||||
/// the separate "Inteira" button for a one-sided undo.
|
||||
func trimToWord(_ word: ReviewWord, edge: TrimEdge, in id: Int) {
|
||||
trim(id, edge: edge, to: edge == .start ? word.start : word.end)
|
||||
guard let phrase = phrases.first(where: { $0.id == id }) else { return }
|
||||
let epsilon = 0.001
|
||||
switch edge {
|
||||
case .start where abs(word.start - phrase.trimStart) < epsilon:
|
||||
update(id) { $0.trimStart = $0.start }
|
||||
case .end where abs(word.end - phrase.trimEnd) < epsilon:
|
||||
update(id) { $0.trimEnd = $0.end }
|
||||
default:
|
||||
trim(id, edge: edge, to: edge == .start ? word.start : word.end)
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - Trecho marcado e zooms
|
||||
@@ -400,11 +365,59 @@ final class PhraseReviewModel: ObservableObject {
|
||||
phrases.filter { $0.active }.reduce(0) { $0 + ($1.trimEnd - $1.trimStart) }
|
||||
}
|
||||
|
||||
// MARK: - Tempo compactado (sem os vãos do que foi cortado)
|
||||
|
||||
/// Kept spans of source media, in order, each carrying the position it
|
||||
/// lands at once every removed stretch between phrases is squeezed out.
|
||||
/// The timeline draws and scrubs in this space so it reads like the cut
|
||||
/// itself instead of the raw take with holes in it.
|
||||
private var keptSegments: [(rawStart: Double, rawEnd: Double, compactStart: Double)] {
|
||||
var offset = 0.0
|
||||
var segments: [(Double, Double, Double)] = []
|
||||
for phrase in phrases.sorted(by: { $0.start < $1.start }) where phrase.active {
|
||||
guard phrase.trimEnd > phrase.trimStart else { continue }
|
||||
segments.append((phrase.trimStart, phrase.trimEnd, offset))
|
||||
offset += phrase.trimEnd - phrase.trimStart
|
||||
}
|
||||
return segments
|
||||
}
|
||||
|
||||
/// Maps a raw source-media time to its position on the compacted timeline.
|
||||
/// Time inside removed material collapses to the boundary of the nearest
|
||||
/// kept segment, so cut stretches take up no space at all.
|
||||
func compactTime(_ raw: Double) -> Double {
|
||||
let segments = keptSegments
|
||||
for segment in segments {
|
||||
if raw < segment.rawStart { return segment.compactStart }
|
||||
if raw <= segment.rawEnd { return segment.compactStart + (raw - segment.rawStart) }
|
||||
}
|
||||
guard let last = segments.last else { return 0 }
|
||||
return raw >= last.rawEnd ? last.compactStart + (last.rawEnd - last.rawStart) : 0
|
||||
}
|
||||
|
||||
/// The inverse of `compactTime`: where a click on the compacted timeline
|
||||
/// lands in the raw source media, for seeking and scrubbing.
|
||||
func rawTime(fromCompact compact: Double) -> Double {
|
||||
let segments = keptSegments
|
||||
for segment in segments {
|
||||
let compactEnd = segment.compactStart + (segment.rawEnd - segment.rawStart)
|
||||
if compact <= compactEnd {
|
||||
return segment.rawStart + max(0, compact - segment.compactStart)
|
||||
}
|
||||
}
|
||||
return segments.last?.rawEnd ?? 0
|
||||
}
|
||||
|
||||
/// Persists the edited review plus the actions derived from it. Called when
|
||||
/// the wizard advances — the render itself happens in the next step.
|
||||
func save(completion: @escaping (String?) -> Void) {
|
||||
/// Persists the edited review and hands back BOTH paths it wrote:
|
||||
/// `review_path` (the human-readable `_phrase_review.json`) and
|
||||
/// `actions_path` (`_phrase_actions.json`, the cut/zoom list derived from
|
||||
/// it — what `finalizeProcessing` needs to actually apply the review's
|
||||
/// active/inactive decisions instead of just filing them away).
|
||||
func save(completion: @escaping (_ reviewPath: String?, _ actionsPath: String?) -> Void) {
|
||||
guard !voiceTimelinePath.isEmpty, !phrases.isEmpty else {
|
||||
completion(nil)
|
||||
completion(nil, nil)
|
||||
return
|
||||
}
|
||||
let arguments: [String: Any] = [
|
||||
@@ -418,11 +431,11 @@ final class PhraseReviewModel: ObservableObject {
|
||||
PythonBridge.call(command: "save_phrase_review", arguments: arguments) { result, error in
|
||||
Task { @MainActor in
|
||||
if let error {
|
||||
completion(nil)
|
||||
completion(nil, nil)
|
||||
_ = error
|
||||
return
|
||||
}
|
||||
completion(result?["review_path"] as? String)
|
||||
completion(result?["review_path"] as? String, result?["actions_path"] as? String)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,86 +1,32 @@
|
||||
import AVFoundation
|
||||
import SwiftUI
|
||||
|
||||
/// The video surface, as a plain `AVPlayerLayer` in an `NSView`.
|
||||
/// The wizard's emphasis-review step.
|
||||
///
|
||||
/// AVKit's `VideoPlayer` would be the obvious choice and is a trap here: this
|
||||
/// app is built by invoking `swiftc` directly (see `MacApp/build_app.sh`), and
|
||||
/// `_AVKit_SwiftUI` aborts at launch instantiating its generic metadata under
|
||||
/// that build. A player layer needs only AVFoundation, which links cleanly —
|
||||
/// and the transport controls live in the timeline's own toolbar anyway, so
|
||||
/// nothing is lost by dropping AVKit's chrome.
|
||||
private struct PlayerSurface: NSViewRepresentable {
|
||||
let player: AVPlayer
|
||||
/// When true the frame is filled and cropped instead of letterboxed — used
|
||||
/// to preview horizontal footage inside a vertical delivery frame.
|
||||
var fills: Bool
|
||||
|
||||
func makeNSView(context: Context) -> PlayerLayerView {
|
||||
let view = PlayerLayerView()
|
||||
view.player = player
|
||||
view.fills = fills
|
||||
return view
|
||||
}
|
||||
|
||||
func updateNSView(_ view: PlayerLayerView, context: Context) {
|
||||
if view.player !== player { view.player = player }
|
||||
view.fills = fills
|
||||
}
|
||||
}
|
||||
|
||||
final class PlayerLayerView: NSView {
|
||||
private let playerLayer = AVPlayerLayer()
|
||||
|
||||
var player: AVPlayer? {
|
||||
get { playerLayer.player }
|
||||
set { playerLayer.player = newValue }
|
||||
}
|
||||
|
||||
var fills: Bool = false {
|
||||
didSet { playerLayer.videoGravity = fills ? .resizeAspectFill : .resizeAspect }
|
||||
}
|
||||
|
||||
override init(frame frameRect: NSRect) {
|
||||
super.init(frame: frameRect)
|
||||
wantsLayer = true
|
||||
layer = CALayer()
|
||||
layer?.backgroundColor = NSColor.black.cgColor
|
||||
playerLayer.videoGravity = .resizeAspect
|
||||
layer?.addSublayer(playerLayer)
|
||||
}
|
||||
|
||||
required init?(coder: NSCoder) {
|
||||
super.init(coder: coder)
|
||||
wantsLayer = true
|
||||
layer = CALayer()
|
||||
playerLayer.videoGravity = .resizeAspect
|
||||
layer?.addSublayer(playerLayer)
|
||||
}
|
||||
|
||||
override func layout() {
|
||||
super.layout()
|
||||
playerLayer.frame = bounds
|
||||
}
|
||||
}
|
||||
|
||||
/// The wizard's emphasis-review step, laid out like an editing room: preview on
|
||||
/// top, timeline across the bottom, and the script as an inspector down the
|
||||
/// right side.
|
||||
///
|
||||
/// The arrangement is the point. Every decision here is about a *sentence*, so
|
||||
/// the same phrase has to be legible in all three places at once — a block on
|
||||
/// the timeline, a line of text in the inspector, and a moment in the preview.
|
||||
/// Selecting in any one of them selects in the other two.
|
||||
/// Every decision here is about a *sentence* read from the original
|
||||
/// transcription, so the phrases are listed in full — each line shows the text
|
||||
/// as it will be said, a switch to keep or drop it from the cut, and the
|
||||
/// emphasis level. Selecting a line in the list also selects its block on the
|
||||
/// timeline below, and vice-versa.
|
||||
struct PhraseReviewView: View {
|
||||
@ObservedObject var model: PhraseReviewModel
|
||||
|
||||
var body: some View {
|
||||
VSplitView {
|
||||
HSplitView {
|
||||
previewPane
|
||||
.frame(minWidth: 320, idealWidth: 640)
|
||||
inspectorPane
|
||||
.frame(minWidth: 300, idealWidth: 360, maxWidth: 520)
|
||||
VStack(spacing: 0) {
|
||||
inspectorHeader
|
||||
Divider()
|
||||
List(selection: $model.selection) {
|
||||
ForEach($model.phrases) { $phrase in
|
||||
PhraseRow(phrase: $phrase, model: model)
|
||||
.tag(phrase.id)
|
||||
}
|
||||
}
|
||||
.listStyle(.inset)
|
||||
.onChange(of: model.selection) { _, newValue in
|
||||
if let newValue { model.goTo(phraseID: newValue) }
|
||||
}
|
||||
Divider()
|
||||
summaryBar
|
||||
}
|
||||
.frame(minHeight: 240)
|
||||
|
||||
@@ -111,48 +57,6 @@ struct PhraseReviewView: View {
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - Preview
|
||||
|
||||
private var previewPane: some View {
|
||||
VStack(spacing: 0) {
|
||||
if let player = model.player {
|
||||
// The footage here is usually vertical. Sizing the surface to
|
||||
// the take's own aspect keeps a 9:16 frame as tall as the pane
|
||||
// allows instead of shrinking it to fit a horizontal box.
|
||||
// Framed to what the project delivers, not to what the camera
|
||||
// recorded: these takes are shot horizontal and cut vertical,
|
||||
// so the raw frame would show material the audience never sees.
|
||||
ZStack {
|
||||
Color.black
|
||||
PlayerSurface(player: player, fills: model.isCropping)
|
||||
.aspectRatio(model.previewAspect, contentMode: .fit)
|
||||
.clipped()
|
||||
}
|
||||
.overlay(alignment: .topTrailing) { framingBadge }
|
||||
} else {
|
||||
ZStack {
|
||||
Color.black.opacity(0.85)
|
||||
VStack(spacing: 10) {
|
||||
Image(systemName: "film.stack")
|
||||
.font(.system(size: 28)).foregroundStyle(.secondary)
|
||||
Text(model.source.isEmpty
|
||||
? "A análise de voz não registrou qual mídia foi usada."
|
||||
: "Não achei \(model.source) na pasta do projeto.")
|
||||
.font(.callout).foregroundStyle(.secondary)
|
||||
Text("A revisão funciona igual sem o preview — ele só ajuda a conferir o corte.")
|
||||
.font(.caption).foregroundStyle(.tertiary)
|
||||
Button("Localizar a mídia…") { pickMedia() }
|
||||
.buttonStyle(.bordered)
|
||||
}
|
||||
.multilineTextAlignment(.center)
|
||||
.padding(.horizontal, 24)
|
||||
}
|
||||
}
|
||||
Divider()
|
||||
summaryBar
|
||||
}
|
||||
}
|
||||
|
||||
private var summaryBar: some View {
|
||||
HStack(spacing: 16) {
|
||||
summaryItem("text.quote", "\(model.phrases.count) frases")
|
||||
@@ -181,28 +85,6 @@ struct PhraseReviewView: View {
|
||||
String(format: "%02d:%02d finais", Int(seconds) / 60, Int(seconds) % 60)
|
||||
}
|
||||
|
||||
/// Says which frame is on screen, and lets the editor flip to the raw take.
|
||||
/// Without it a centred crop looks like the footage itself, and someone
|
||||
/// would judge framing on an approximation without knowing it.
|
||||
@ViewBuilder
|
||||
private var framingBadge: some View {
|
||||
if model.projectAspect != nil, abs((model.projectAspect ?? 0) - model.videoAspect) > 0.01 {
|
||||
Button {
|
||||
model.matchProjectFraming.toggle()
|
||||
} label: {
|
||||
Label(model.matchProjectFraming ? "Enquadramento do projeto" : "Mídia original",
|
||||
systemImage: model.matchProjectFraming ? "crop" : "rectangle.expand.vertical")
|
||||
.font(.caption2)
|
||||
}
|
||||
.buttonStyle(.borderless)
|
||||
.padding(6)
|
||||
.background(Capsule().fill(.black.opacity(0.45)))
|
||||
.foregroundStyle(.white)
|
||||
.padding(8)
|
||||
.help("A fonte é horizontal e o projeto é vertical — o preview mostra o corte central aproximado. O enquadramento real de cada clipe vem do Final Cut.")
|
||||
}
|
||||
}
|
||||
|
||||
private func pickMedia() {
|
||||
let panel = NSOpenPanel()
|
||||
panel.canChooseFiles = true
|
||||
@@ -373,14 +255,17 @@ private struct PhraseRow: View {
|
||||
FlowWords(words: phrase.words, phrase: phrase) { word, edge in
|
||||
model.trimToWord(word, edge: edge, in: phrase.id)
|
||||
}
|
||||
Text("Clique = começa aqui · ⌥clique = termina aqui")
|
||||
Text("Clique = começa/desfaz aqui · ⌥clique = termina/desfaz aqui · sublinhado = ênfase da palavra")
|
||||
.font(.caption2).foregroundStyle(.tertiary)
|
||||
}
|
||||
.padding(.top, 2)
|
||||
}
|
||||
}
|
||||
|
||||
/// The phrase's words as wrapping chips, dimmed where they fall outside the trim.
|
||||
/// The phrase's words as wrapping chips, dimmed where they fall outside the
|
||||
/// trim and underlined where the acoustics mark them as an emphasis peak —
|
||||
/// the same word-level signal `05-zoom.md` picks a punch-in's `start` from,
|
||||
/// made visible instead of buried in the JSON.
|
||||
private struct FlowWords: View {
|
||||
let words: [ReviewWord]
|
||||
let phrase: ReviewPhrase
|
||||
@@ -394,16 +279,31 @@ private struct FlowWords: View {
|
||||
alignment: .leading, spacing: 3) {
|
||||
ForEach(words) { word in
|
||||
let kept = word.start >= phrase.trimStart - 0.001 && word.end <= phrase.trimEnd + 0.001
|
||||
let level = EmphasisPalette.levelFromScore(word.emphasis)
|
||||
Text(word.text)
|
||||
.font(.caption2)
|
||||
.fontWeight(level >= 2 ? .semibold : .regular)
|
||||
.padding(.horizontal, 4)
|
||||
.padding(.vertical, 2)
|
||||
.background(
|
||||
RoundedRectangle(cornerRadius: 3)
|
||||
.fill(kept ? Color.accentColor.opacity(0.12) : Color.secondary.opacity(0.08))
|
||||
)
|
||||
.overlay(alignment: .bottom) {
|
||||
if level >= 1 {
|
||||
Rectangle()
|
||||
.fill(EmphasisPalette.color(level))
|
||||
.frame(height: 2)
|
||||
.padding(.horizontal, 3)
|
||||
}
|
||||
}
|
||||
.foregroundStyle(kept ? .primary : .secondary)
|
||||
.strikethrough(!kept)
|
||||
.help(
|
||||
level >= 1
|
||||
? "Ênfase \(EmphasisPalette.label(level).lowercased()) (\(Int(word.emphasis * 100))%)"
|
||||
: "Sem ênfase"
|
||||
)
|
||||
.onTapGesture {
|
||||
onTrim(word, NSEvent.modifierFlags.contains(.option) ? .end : .start)
|
||||
}
|
||||
|
||||
@@ -21,6 +21,20 @@ enum EmphasisPalette {
|
||||
}
|
||||
}
|
||||
|
||||
/// The same 0–3 tiers a phrase's `emphasis` uses, derived from a raw 0–1
|
||||
/// acoustic score — the thresholds `10-revisao-humana.md` documents for
|
||||
/// deriving a phrase's level from `peak_emphasis` when no explicit zoom
|
||||
/// was set, reused here per WORD so a word chip and a phrase row read as
|
||||
/// the same scale.
|
||||
static func levelFromScore(_ score: Double) -> Int {
|
||||
switch score {
|
||||
case ..<0.25: return 0
|
||||
case ..<0.45: return 1
|
||||
case ..<0.65: return 2
|
||||
default: return 3
|
||||
}
|
||||
}
|
||||
|
||||
static func speakerColor(_ speaker: String, among speakers: [String]) -> Color {
|
||||
let palette: [Color] = [.teal, .purple, .green, .indigo, .brown, .cyan]
|
||||
guard let index = speakers.firstIndex(of: speaker) else { return .gray }
|
||||
@@ -47,7 +61,9 @@ struct TimelineTracksView: View {
|
||||
private let trackSpacing: CGFloat = 4
|
||||
|
||||
private var pps: CGFloat { CGFloat(model.pixelsPerSecond) }
|
||||
private var contentWidth: CGFloat { max(320, CGFloat(model.duration) * pps) }
|
||||
/// Width follows the *kept* duration, not the raw take's — the timeline
|
||||
/// draws the cut, so removed stretches take no horizontal space.
|
||||
private var contentWidth: CGFloat { max(320, CGFloat(model.keptDuration) * pps) }
|
||||
|
||||
/// Name, icon and height of each lane, in the order they stack. The gutter
|
||||
/// and the tracks are built from this one list so a label can never drift
|
||||
@@ -215,8 +231,8 @@ struct TimelineTracksView: View {
|
||||
Canvas { context, size in
|
||||
let step = tickStep()
|
||||
var time = 0.0
|
||||
while time <= model.duration {
|
||||
let position = x(time)
|
||||
while time <= model.keptDuration {
|
||||
let position = compactX(time)
|
||||
context.stroke(
|
||||
Path { $0.move(to: CGPoint(x: position, y: size.height - 6))
|
||||
$0.addLine(to: CGPoint(x: position, y: size.height)) },
|
||||
@@ -301,7 +317,8 @@ struct TimelineTracksView: View {
|
||||
.gesture(
|
||||
DragGesture(minimumDistance: 1)
|
||||
.onChanged { value in
|
||||
let time = phrase.start + Double((value.location.x) / pps)
|
||||
let compactOrigin = model.compactTime(phrase.start)
|
||||
let time = model.rawTime(fromCompact: compactOrigin + Double(value.location.x / pps))
|
||||
model.trim(phrase.id, edge: edge, to: time)
|
||||
}
|
||||
)
|
||||
@@ -397,8 +414,8 @@ struct TimelineTracksView: View {
|
||||
private var scrubGesture: some Gesture {
|
||||
DragGesture(minimumDistance: 0)
|
||||
.onChanged { value in
|
||||
let from = Double(value.startLocation.x / pps)
|
||||
let to = Double(value.location.x / pps)
|
||||
let from = model.rawTime(fromCompact: Double(value.startLocation.x / pps))
|
||||
let to = model.rawTime(fromCompact: Double(value.location.x / pps))
|
||||
if abs(value.translation.width) > 3 {
|
||||
model.setRange(from: from, to: to)
|
||||
model.seek(to: min(from, to))
|
||||
@@ -486,10 +503,16 @@ struct TimelineTracksView: View {
|
||||
|
||||
// MARK: - Escala
|
||||
|
||||
private func x(_ time: Double) -> CGFloat { CGFloat(time) * pps }
|
||||
/// Pixel position of a raw source-media time, after collapsing whatever
|
||||
/// lies between it and the previous kept phrase.
|
||||
private func x(_ time: Double) -> CGFloat { compactX(model.compactTime(time)) }
|
||||
|
||||
/// Pixel position of a time already in the compacted (edited) timeline —
|
||||
/// used for the ruler and playhead, which think in that space directly.
|
||||
private func compactX(_ compactTime: Double) -> CGFloat { CGFloat(compactTime) * pps }
|
||||
|
||||
private func width(from: Double, to: Double) -> CGFloat {
|
||||
max(0, CGFloat(to - from) * pps)
|
||||
max(0, CGFloat(model.compactTime(to) - model.compactTime(from)) * pps)
|
||||
}
|
||||
|
||||
/// Ruler spacing that keeps labels ~80pt apart at any zoom.
|
||||
|
||||
@@ -9,7 +9,7 @@ import AppKit
|
||||
/// sair do app e escolher um arquivo na mão) vira copiar/colar assistido
|
||||
/// dentro da própria tela.
|
||||
enum WizardStep: Int, CaseIterable, Identifiable {
|
||||
case projeto, transcricao, analise, exportarChat, revisar, finalizar, concluido
|
||||
case projeto, transcricao, analise, exportarChat, revisar, finalizar
|
||||
var id: Int { rawValue }
|
||||
|
||||
var titulo: String {
|
||||
@@ -20,7 +20,6 @@ enum WizardStep: Int, CaseIterable, Identifiable {
|
||||
case .exportarChat: return "Decisões da IA"
|
||||
case .revisar: return "Revisar ênfases"
|
||||
case .finalizar: return "Processar"
|
||||
case .concluido: return "Concluído"
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -54,10 +53,18 @@ struct WizardView: View {
|
||||
@State private var appliedPath: String?
|
||||
@State private var skippedVoiceEdit = false
|
||||
|
||||
// Passo 4 (alternativa) — gerar o roteiro direto por IA local (Ollama/Gemma 3)
|
||||
@State private var isGeneratingScript = false
|
||||
@State private var generateScriptModel = "gemma3:12b"
|
||||
@State private var generateScriptFeedback = ""
|
||||
@State private var ollamaModels: [String] = []
|
||||
|
||||
// Passo 5 — revisar ênfases
|
||||
@StateObject private var reviewModel = PhraseReviewModel()
|
||||
@State private var reviewLoadedFor: String?
|
||||
@State private var reviewLoadedForDecisions: String?
|
||||
@State private var phraseReviewPath: String?
|
||||
@State private var phraseActionsPath: String?
|
||||
|
||||
// Passo 6 — processamento final
|
||||
@State private var finalSilences = true
|
||||
@@ -178,7 +185,6 @@ struct WizardView: View {
|
||||
case .exportarChat: exportarChatStep
|
||||
case .revisar: revisarStep
|
||||
case .finalizar: finalizarStep
|
||||
case .concluido: concluidoStep
|
||||
}
|
||||
}
|
||||
|
||||
@@ -304,6 +310,54 @@ struct WizardView: View {
|
||||
.font(.callout).foregroundStyle(.secondary)
|
||||
|
||||
if let voiceTimelinePath {
|
||||
// Alternativa automática: em vez de copiar/colar no chat, manda a
|
||||
// própria voice timeline (o arquivo inteiro) junto com o brief para
|
||||
// o modelo local (Ollama/Gemma 3) decidir a edição de uma vez —
|
||||
// cortes, zooms e textos numa única chamada, sem sair do app.
|
||||
VStack(alignment: .leading, spacing: 8) {
|
||||
Text("OU gere o roteiro por IA local (Ollama/Gemma 3)").font(.callout.weight(.semibold))
|
||||
Text("O app envia a voice timeline completa (o arquivo) acompanhada do pedido para o modelo local decidir os cortes, zooms e textos de uma vez. Nada de copiar e colar.")
|
||||
.font(.caption).foregroundStyle(.secondary)
|
||||
HStack {
|
||||
if ollamaModels.isEmpty {
|
||||
TextField("Modelo (ex.: gemma3:12b, llama3)", text: $generateScriptModel)
|
||||
.textFieldStyle(.roundedBorder)
|
||||
.frame(maxWidth: 260)
|
||||
} else {
|
||||
Picker("Modelo", selection: $generateScriptModel) {
|
||||
ForEach(ollamaModels, id: \.self) { m in
|
||||
Text(m).tag(m)
|
||||
}
|
||||
}
|
||||
.pickerStyle(.menu)
|
||||
.frame(maxWidth: 260)
|
||||
TextField("Ou outro", text: $generateScriptModel)
|
||||
.textFieldStyle(.roundedBorder)
|
||||
.frame(maxWidth: 120)
|
||||
}
|
||||
Button {
|
||||
generateScript(voiceTimelinePath: voiceTimelinePath)
|
||||
} label: {
|
||||
if isGeneratingScript {
|
||||
HStack { ProgressView().controlSize(.small); Text("Gerando…") }
|
||||
} else {
|
||||
Label("Gerar roteiro por IA local", systemImage: "sparkles")
|
||||
}
|
||||
}
|
||||
.buttonStyle(.borderedProminent)
|
||||
.disabled(isGeneratingScript || voiceTimelinePath.isEmpty)
|
||||
}
|
||||
if !generateScriptFeedback.isEmpty {
|
||||
Label(generateScriptFeedback, systemImage: "checkmark.circle.fill")
|
||||
.font(.caption).foregroundStyle(.green)
|
||||
}
|
||||
}
|
||||
.padding(12)
|
||||
.background(RoundedRectangle(cornerRadius: 8).fill(Color.green.opacity(0.07)))
|
||||
.onAppear { fetchOllamaModels() }
|
||||
|
||||
Divider().padding(.vertical, 4)
|
||||
|
||||
Button {
|
||||
copyForChat(path: voiceTimelinePath)
|
||||
} label: {
|
||||
@@ -412,6 +466,11 @@ struct WizardView: View {
|
||||
.onAppear { loadReviewIfNeeded() }
|
||||
}
|
||||
|
||||
/// Processing and its result live on the SAME slide: the moment the last
|
||||
/// operation finishes (`finalPath` gets set), the open/reveal buttons
|
||||
/// appear right below the "Processar" button instead of gating behind a
|
||||
/// separate "Concluído" step the user has to click into — there was
|
||||
/// nothing on that slide worth a click of its own.
|
||||
private var finalizarStep: some View {
|
||||
VStack(alignment: .leading, spacing: 16) {
|
||||
Text("6. Finalize o corte").font(.title3.weight(.semibold))
|
||||
@@ -437,18 +496,11 @@ struct WizardView: View {
|
||||
.controlSize(.large)
|
||||
.disabled(isFinalizing || (!finalSilences && !finalFillers && !finalSubtitles && !finalDynamicSubtitles))
|
||||
|
||||
if !finalStatus.isEmpty && !isFinalizing {
|
||||
Text(finalStatus).font(.caption).foregroundStyle(.secondary)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private var concluidoStep: some View {
|
||||
VStack(alignment: .leading, spacing: 16) {
|
||||
Label("Concluído", systemImage: "checkmark.seal.fill")
|
||||
.font(.title3.weight(.semibold))
|
||||
.foregroundStyle(.green)
|
||||
if let finalPath {
|
||||
if let finalPath, !isFinalizing {
|
||||
Divider().padding(.vertical, 4)
|
||||
Label("Concluído", systemImage: "checkmark.seal.fill")
|
||||
.font(.callout.weight(.semibold))
|
||||
.foregroundStyle(.green)
|
||||
Text(finalPath).font(.caption).foregroundStyle(.secondary).lineLimit(1).truncationMode(.middle)
|
||||
HStack {
|
||||
Button("Abrir no Final Cut Pro") { NSWorkspace.shared.open(URL(fileURLWithPath: finalPath)) }
|
||||
@@ -456,22 +508,27 @@ struct WizardView: View {
|
||||
Button("Mostrar no Finder") {
|
||||
NSWorkspace.shared.activateFileViewerSelecting([URL(fileURLWithPath: finalPath)])
|
||||
}
|
||||
Spacer()
|
||||
Button("Começar outro projeto") { resetWizard() }
|
||||
}
|
||||
} else if !finalStatus.isEmpty && !isFinalizing {
|
||||
Text(finalStatus).font(.caption).foregroundStyle(.secondary)
|
||||
}
|
||||
Divider().padding(.vertical, 8)
|
||||
Button("Começar outro projeto") { resetWizard() }
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - Navegação
|
||||
|
||||
/// `.finalizar` is the last step now — once it has a `finalPath`, the
|
||||
/// slide's own "Começar outro projeto" button is the way forward, so the
|
||||
/// footer's "Continuar" would be a second, redundant path to nowhere.
|
||||
private var navFooter: some View {
|
||||
HStack {
|
||||
if step != .projeto && step != .concluido {
|
||||
if step != .projeto {
|
||||
Button("Voltar") { goBack() }
|
||||
}
|
||||
Spacer()
|
||||
if step != .concluido {
|
||||
if step != .finalizar || finalPath == nil {
|
||||
Button(step == .finalizar ? "Concluir" : "Continuar") { goNext() }
|
||||
.buttonStyle(.borderedProminent)
|
||||
.disabled(!canAdvance)
|
||||
@@ -490,18 +547,19 @@ struct WizardView: View {
|
||||
// exigir mais uma confirmação.
|
||||
case .revisar: return true
|
||||
case .finalizar: return finalPath != nil && !isFinalizing
|
||||
case .concluido: return false
|
||||
}
|
||||
}
|
||||
|
||||
private func goNext() {
|
||||
guard let next = WizardStep(rawValue: step.rawValue + 1) else { return }
|
||||
// Sair da revisão grava o que foi decidido (e as ações derivadas dela)
|
||||
// ao lado da análise de voz. Nada é renderizado aqui: a etapa 6 é que
|
||||
// lê esse arquivo para dar zoom e legenda dinâmica só nas ênfases.
|
||||
// Sair da revisão grava o que foi decidido e as ações derivadas dela
|
||||
// (`_phrase_actions.json`) ao lado da análise de voz — é esse arquivo
|
||||
// que `finalizeProcessing` reaplica na etapa 6, para que desativar uma
|
||||
// frase aqui realmente a remova do vídeo final, e não só do registro.
|
||||
if step == .revisar {
|
||||
reviewModel.save { path in
|
||||
phraseReviewPath = path
|
||||
reviewModel.save { reviewPath, actionsPath in
|
||||
phraseReviewPath = reviewPath
|
||||
phraseActionsPath = actionsPath
|
||||
}
|
||||
}
|
||||
step = next
|
||||
@@ -521,7 +579,9 @@ struct WizardView: View {
|
||||
appliedPath = nil
|
||||
skippedVoiceEdit = false
|
||||
reviewLoadedFor = nil
|
||||
reviewLoadedForDecisions = nil
|
||||
phraseReviewPath = nil
|
||||
phraseActionsPath = nil
|
||||
finalStatus = ""
|
||||
finalPath = nil
|
||||
errorMessage = nil
|
||||
@@ -727,21 +787,33 @@ struct WizardView: View {
|
||||
copiedFeedback = "Copiado — cole (⌘V) numa conversa com o Claude."
|
||||
}
|
||||
|
||||
/// Monta a revisão uma vez por análise de voz. Voltar e avançar de novo não
|
||||
/// recarrega: isso jogaria fora as edições manuais em silêncio, que é
|
||||
/// exatamente o que esta tela existe para preservar.
|
||||
/// Monta a revisão uma vez por análise de voz. Voltar e avançar de novo com
|
||||
/// as MESMAS decisões não recarrega: isso jogaria fora as edições manuais
|
||||
/// em silêncio, que é exatamente o que esta tela existe para preservar.
|
||||
///
|
||||
/// Mas se o usuário voltou à etapa 4 e colou/gerou um JSON de decisões
|
||||
/// DIFERENTE do que gerou a revisão atual, isso é recarregado — e com
|
||||
/// `fresh: true`, para que o `active`/ênfase recém-derivado dessas
|
||||
/// decisões novas não seja imediatamente sobrescrito pela revisão salva
|
||||
/// da visita anterior (`merge_saved_decisions`, do lado Python). Sem isso,
|
||||
/// a tela ficava presa nas decisões antigas mesmo depois de reaplicar o
|
||||
/// corte — a dessincronia relatada entre "ativa aqui" e "já cortado no
|
||||
/// FCPXML".
|
||||
private func loadReviewIfNeeded() {
|
||||
guard let voiceTimelinePath, reviewLoadedFor != voiceTimelinePath else { return }
|
||||
guard let voiceTimelinePath else { return }
|
||||
let decisionsChanged = reviewLoadedForDecisions != nil && reviewLoadedForDecisions != decisionsText
|
||||
guard reviewLoadedFor != voiceTimelinePath || decisionsChanged else { return }
|
||||
reviewLoadedFor = voiceTimelinePath
|
||||
reviewLoadedForDecisions = decisionsText
|
||||
// A pasta do projeto e a do .fcpxml entram como onde procurar a mídia:
|
||||
// a análise de voz guarda só o nome do arquivo, não o caminho.
|
||||
reviewModel.load(
|
||||
voiceTimelinePath: voiceTimelinePath,
|
||||
decisionsJSON: decisionsText,
|
||||
outputFolder: outputFolder,
|
||||
mediaFolder: projectPath.map { URL(fileURLWithPath: $0).deletingLastPathComponent().path }
|
||||
mediaFolder: projectPath.map { URL(fileURLWithPath: $0).deletingLastPathComponent().path },
|
||||
fresh: decisionsChanged
|
||||
)
|
||||
if let projectPath { reviewModel.loadProjectFormat(projectPath: projectPath) }
|
||||
}
|
||||
|
||||
private func applyDecisions() {
|
||||
@@ -767,6 +839,55 @@ struct WizardView: View {
|
||||
}
|
||||
}
|
||||
|
||||
/// Etapa 4 (alternativa): manda a voice timeline inteira para um modelo
|
||||
/// local (Ollama/Gemma 3) que dirige a edição de uma vez — sem copiar e
|
||||
/// colar. O motor devolve o roteiro legível + o JSON de ações e já aplica
|
||||
/// no FCPXML (non-destructive), igual ao fluxo manual "Aplicar decisões".
|
||||
private func fetchOllamaModels() {
|
||||
guard ollamaModels.isEmpty else { return }
|
||||
PythonBridge.call(command: "list_ollama_models", arguments: [:]) { result, err in
|
||||
DispatchQueue.main.async {
|
||||
if let models = result?["models"] as? [String], !models.isEmpty {
|
||||
ollamaModels = models
|
||||
if !models.contains(generateScriptModel) {
|
||||
generateScriptModel = models.first ?? generateScriptModel
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private func generateScript(voiceTimelinePath: String) {
|
||||
guard let projectPath, let outputFolder else { return }
|
||||
isGeneratingScript = true
|
||||
generateScriptFeedback = ""
|
||||
errorMessage = nil
|
||||
PythonBridge.call(command: "generate_voice_script", arguments: [
|
||||
"voice_timeline": voiceTimelinePath,
|
||||
"filepath": projectPath,
|
||||
"output_dir": outputFolder,
|
||||
"model": generateScriptModel,
|
||||
"apply_to_fcpxml": true,
|
||||
]) { result, err in
|
||||
DispatchQueue.main.async {
|
||||
isGeneratingScript = false
|
||||
guard result?["ok"] as? Bool == true else {
|
||||
errorMessage = result?["error"] as? String ?? err ?? "Falha ao gerar roteiro por IA local."
|
||||
return
|
||||
}
|
||||
// Traz as decisões de volta para a tela de revisão (etapa 5) e
|
||||
// marca como aplicadas, exatamente como o "Aplicar decisões".
|
||||
if let actionsPath = result?["actions_path"] as? String,
|
||||
let content = try? String(contentsOfFile: actionsPath, encoding: .utf8) {
|
||||
decisionsText = content
|
||||
}
|
||||
appliedPath = result?["applied_path"] as? String ?? projectPath
|
||||
skippedVoiceEdit = false
|
||||
generateScriptFeedback = "Roteiro gerado e aplicado — revise as ênfases na próxima etapa."
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private func finalizeProcessing() {
|
||||
guard let outputFolder else { return }
|
||||
let startPath = appliedPath ?? projectPath
|
||||
@@ -780,7 +901,45 @@ struct WizardView: View {
|
||||
isFinalizing = true
|
||||
errorMessage = nil
|
||||
finalStatus = "Iniciando…"
|
||||
finalizeStep(operations, index: 0, currentPath: startPath, outputFolder: outputFolder)
|
||||
applyReviewDecisions(startPath: startPath, outputFolder: outputFolder) { reviewedPath in
|
||||
finalizeStep(operations, index: 0, currentPath: reviewedPath, outputFolder: outputFolder)
|
||||
}
|
||||
}
|
||||
|
||||
/// Reapplies whatever the etapa-5 review decided (active/inactive
|
||||
/// phrases, manual zooms) on top of `startPath` before the finishing
|
||||
/// chain runs below. Without this, `appliedPath` stayed frozen at
|
||||
/// whatever `exportarChat`'s `apply_voice_actions` produced BEFORE the
|
||||
/// human review — so toggling a phrase off in the review only updated
|
||||
/// `_phrase_actions.json` on disk, never the video the wizard actually
|
||||
/// exports. A no-op (just hands `startPath` straight through) when the
|
||||
/// review step was never visited/saved this session.
|
||||
private func applyReviewDecisions(
|
||||
startPath: String, outputFolder: String, completion: @escaping (String) -> Void
|
||||
) {
|
||||
guard let phraseActionsPath,
|
||||
let data = try? Data(contentsOf: URL(fileURLWithPath: phraseActionsPath)),
|
||||
let parsed = try? JSONSerialization.jsonObject(with: data) as? [String: Any],
|
||||
let actions = parsed["actions"] else {
|
||||
completion(startPath)
|
||||
return
|
||||
}
|
||||
finalStatus = "Aplicando a revisão…"
|
||||
PythonBridge.call(command: "apply_voice_actions", arguments: [
|
||||
"path": startPath,
|
||||
"output_dir": outputFolder,
|
||||
"actions": actions,
|
||||
]) { result, err in
|
||||
DispatchQueue.main.async {
|
||||
guard result?["ok"] as? Bool == true else {
|
||||
isFinalizing = false
|
||||
errorMessage = result?["error"] as? String ?? err ?? "Falha ao aplicar a revisão."
|
||||
finalStatus = "Processamento interrompido."
|
||||
return
|
||||
}
|
||||
completion(result?["path"] as? String ?? startPath)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private func finalizeStep(_ operations: [String], index: Int, currentPath: String, outputFolder: String) {
|
||||
|
||||
+131
@@ -0,0 +1,131 @@
|
||||
#!/usr/bin/env python3
|
||||
"""AI Voice Editor - Pipeline completo: transcrição + análise acústica → JSON para IA.
|
||||
|
||||
Uso:
|
||||
python ai_edit.py <media_path> [--model base] [--lang pt] [--no-diarize] [--output dir]
|
||||
|
||||
Gera dois arquivos na pasta output (ou ao lado do mídia):
|
||||
<nome>_transcript.json — transcrição com timestamps por palavra
|
||||
<nome>_voice_timeline.json — timeline de voz com ênfase, pitch, energy, speakers
|
||||
|
||||
Esses arquivos são a ENTRADA para a IA analisar e gerar o roteiro/edição.
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import sys
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Pipeline de análise de voz para IA")
|
||||
parser.add_argument("media", help="Caminho do arquivo de mídia (.mp4, .mov, .wav, etc.)")
|
||||
parser.add_argument("--model", default="base", help="Modelo Whisper (tiny/base/small/medium/large-v3)")
|
||||
parser.add_argument("--lang", default=None, help="Idioma (ex: pt, en). Auto-detect se omitido")
|
||||
parser.add_argument("--hf-token", default=None, help="HuggingFace token para diarização (opcional)")
|
||||
parser.add_argument("--no-diarize", action="store_true", help="Pular diarização de falantes")
|
||||
parser.add_argument("--output", default=None, help="Pasta de saída (padrão: ao lado do mídia)")
|
||||
parser.add_argument("--no-align", action="store_true", help="Pular alinhamento fonético (whisperx)")
|
||||
args = parser.parse_args()
|
||||
|
||||
media_path = Path(args.media).resolve()
|
||||
if not media_path.is_file():
|
||||
print(f"ERRO: Arquivo não encontrado: {media_path}", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
# Output dir
|
||||
out_dir = Path(args.output) if args.output else media_path.parent
|
||||
out_dir.mkdir(parents=True, exist_ok=True)
|
||||
stem = media_path.stem
|
||||
|
||||
# ── Fase 1: Transcrição ──────────────────────────────────────────
|
||||
print(f"[1/2] Transcrevendo {media_path.name} (modelo: {args.model})...")
|
||||
t0 = time.time()
|
||||
|
||||
# Adiciona code/ ao path para imports do projeto
|
||||
code_dir = Path(__file__).resolve().parent
|
||||
sys.path.insert(0, str(code_dir))
|
||||
|
||||
from fcpxml.transcribe import transcribe
|
||||
|
||||
def transcribe_progress(pct):
|
||||
bar_len = 30
|
||||
filled = int(bar_len * pct)
|
||||
bar = "█" * filled + "░" * (bar_len - filled)
|
||||
print(f"\r [{bar}] {pct*100:.0f}%", end="", flush=True)
|
||||
|
||||
transcript = transcribe(
|
||||
str(media_path),
|
||||
model_size=args.model,
|
||||
language=args.lang,
|
||||
progress_cb=transcribe_progress,
|
||||
align=not args.no_align,
|
||||
)
|
||||
print() # newline after progress bar
|
||||
|
||||
if transcript is None:
|
||||
print("ERRO: Transcrição falhou. Verifique se faster-whisper está instalado:", file=sys.stderr)
|
||||
print(" uv pip install faster-whisper", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
print(f" → {len(transcript.get('words', []))} palavras, "
|
||||
f"{len(transcript.get('segments', []))} segmentos, "
|
||||
f"idioma: {transcript.get('language', '?')}")
|
||||
|
||||
# Salva transcrição
|
||||
transcript_path = out_dir / f"{stem}_transcript.json"
|
||||
transcript_path.write_text(json.dumps(transcript, indent=2, ensure_ascii=False), encoding="utf-8")
|
||||
print(f" → Salvo: {transcript_path}")
|
||||
|
||||
# ── Fase 2: Análise de voz (timeline) ────────────────────────────
|
||||
print(f"\n[2/2] Analisando voz (pitch, energia, ênfase)...")
|
||||
t1 = time.time()
|
||||
|
||||
from fcpxml.voice_timeline import build_voice_timeline
|
||||
|
||||
def voice_progress(fraction, stage):
|
||||
print(f"\r {stage} ({fraction*100:.0f}%)", end="", flush=True)
|
||||
|
||||
hf_token = None if args.no_diarize else args.hf_token
|
||||
timeline = build_voice_timeline(
|
||||
str(media_path),
|
||||
transcript,
|
||||
hf_token=hf_token,
|
||||
progress_cb=voice_progress,
|
||||
)
|
||||
print()
|
||||
|
||||
# Salva voice timeline
|
||||
timeline_path = out_dir / f"{stem}_voice_timeline.json"
|
||||
timeline_path.write_text(json.dumps(timeline, indent=2, ensure_ascii=False), encoding="utf-8")
|
||||
print(f" → Salvo: {timeline_path}")
|
||||
|
||||
# ── Resumo ───────────────────────────────────────────────────────
|
||||
elapsed = time.time() - t0
|
||||
summary = timeline.get("summary", {})
|
||||
layers = timeline.get("layers", {})
|
||||
n_words = len(transcript.get("words", []))
|
||||
n_segments = len(transcript.get("segments", []))
|
||||
n_speakers = len(timeline.get("speakers", []))
|
||||
duration = transcript.get("duration", 0)
|
||||
|
||||
print(f"\n{'='*50}")
|
||||
print(f" ARQUIVOS GERADOS:")
|
||||
print(f" {transcript_path}")
|
||||
print(f" {timeline_path}")
|
||||
print(f"\n RESUMO:")
|
||||
print(f" Duração: {duration:.1f}s ({duration/60:.1f}min)")
|
||||
print(f" Palavras: {n_words}")
|
||||
print(f" Segmentos: {n_segments}")
|
||||
print(f" Falantes: {n_speakers}")
|
||||
print(f" Camadas: transcript={layers.get('transcript')}, "
|
||||
f"acoustics={layers.get('acoustics')}, "
|
||||
f"diarization={layers.get('diarization')}")
|
||||
print(f" Tempo: {elapsed:.1f}s")
|
||||
print(f"{'='*50}")
|
||||
print(f"\n→ Pronto! Agora peça à IA para analisar o voice timeline e gerar o roteiro.")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,181 @@
|
||||
"""Forced alignment — refine word timestamps against an acoustic model.
|
||||
|
||||
Why this exists
|
||||
--------------
|
||||
faster-whisper derives word times by cross-attention, which lands every word
|
||||
*start* systematically ~0.3-0.5s early (the word-end is fine). That bias flows
|
||||
straight into the voice timeline and makes zoom/cut land on the wrong frame —
|
||||
measured on real footage in ``Engine/docs/05_EXPERIENCIAS.md`` (#14). Phonetic
|
||||
forced alignment (wav2vec2, via whisperx) re-anchors each word against the
|
||||
audio and brings that error down to ~30ms.
|
||||
|
||||
Design
|
||||
------
|
||||
* The dependency (``whisperx``) is **optional** and imported lazily, exactly
|
||||
like the rest of this stack (librosa, faster-whisper). When it is missing, or
|
||||
any step fails, :meth:`ForcedAligner.align` returns the words unchanged, so
|
||||
transcription never breaks because alignment did.
|
||||
* The aligner is a single responsibility class: it knows how to turn a
|
||||
transcript into the shape whisperx wants, call it, and write the refined
|
||||
times back. ``transcribe.py`` owns the decision of *whether* to align.
|
||||
* Align models are cached per language on the instance so repeated calls
|
||||
(e.g. many short clips) don't reload the wav2vec2 weights each time.
|
||||
"""
|
||||
|
||||
import logging
|
||||
from typing import List, Optional, Sequence
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class ForcedAligner:
|
||||
"""Refine word-level timestamps with whisperx phonetic forced alignment.
|
||||
|
||||
Usage::
|
||||
|
||||
aligner = ForcedAligner()
|
||||
words = aligner.align(words, raw_segments, media_path, language, models_dir)
|
||||
|
||||
``words`` and ``raw_segments`` come straight from :func:`transcribe` —
|
||||
``raw_segments`` carries the per-segment ``words`` lists (the same dict
|
||||
objects as in ``words``) so the aligner knows which words belong to which
|
||||
audio window. Returns a list of the *same* word dicts, with ``start``/``end``
|
||||
overwritten in place where alignment produced a usable time.
|
||||
"""
|
||||
|
||||
def __init__(self, device: Optional[str] = None):
|
||||
self._device = device
|
||||
self._models: dict = {}
|
||||
|
||||
# -- capability ------------------------------------------------------
|
||||
@staticmethod
|
||||
def available() -> bool:
|
||||
"""Whether whisperx can be imported (the aligner can run at all)."""
|
||||
try:
|
||||
import whisperx # noqa: F401
|
||||
except Exception:
|
||||
return False
|
||||
return True
|
||||
|
||||
def _resolve_device(self) -> str:
|
||||
if self._device:
|
||||
return self._device
|
||||
try:
|
||||
import torch
|
||||
|
||||
if torch.cuda.is_available():
|
||||
return "cuda"
|
||||
except Exception:
|
||||
pass
|
||||
return "cpu"
|
||||
|
||||
# -- public API ------------------------------------------------------
|
||||
def align(
|
||||
self,
|
||||
words: Sequence[dict],
|
||||
raw_segments: Sequence[dict],
|
||||
audio_path: str,
|
||||
language: str,
|
||||
models_dir: Optional[str] = None,
|
||||
) -> List[dict]:
|
||||
"""Return ``words`` with forced-aligned timestamps where possible.
|
||||
|
||||
Falls back to the unchanged ``words`` on any failure (missing
|
||||
dependency, model load error, audio read error, or a result that
|
||||
doesn't line up with the input).
|
||||
"""
|
||||
if not words or not language:
|
||||
return list(words)
|
||||
try:
|
||||
import whisperx
|
||||
except Exception:
|
||||
logger.info("whisperx not installed; skipping forced alignment")
|
||||
return list(words)
|
||||
|
||||
try:
|
||||
device = self._resolve_device()
|
||||
align_input = self._build_align_input(words, raw_segments)
|
||||
audio = whisperx.load_audio(audio_path)
|
||||
|
||||
if language not in self._models:
|
||||
align_model, metadata = whisperx.load_align_model(
|
||||
language_code=language,
|
||||
device=device,
|
||||
model_dir=str(models_dir) if models_dir else None,
|
||||
)
|
||||
self._models[language] = (align_model, metadata)
|
||||
align_model, metadata = self._models[language]
|
||||
|
||||
result = whisperx.align(
|
||||
align_input,
|
||||
align_model,
|
||||
metadata,
|
||||
audio,
|
||||
device,
|
||||
return_char_alignments=False,
|
||||
)
|
||||
return self._merge_result(words, result.get("segments", []))
|
||||
except Exception:
|
||||
logger.warning(
|
||||
"forced alignment failed for %s; using raw timestamps", audio_path
|
||||
)
|
||||
return list(words)
|
||||
|
||||
# -- internals -------------------------------------------------------
|
||||
@staticmethod
|
||||
def _build_align_input(
|
||||
words: Sequence[dict], raw_segments: Sequence[dict]
|
||||
) -> List[dict]:
|
||||
"""Transcript in whisperx's expected shape: segments -> words.
|
||||
|
||||
whisperx.align requires each segment to carry ``text``/``start``/``end``
|
||||
and a ``words`` list whose entries have ``word``/``start``/``end``/``score``.
|
||||
We only read ``words`` from ``raw_segments`` (the flattened ``words``
|
||||
list is the source of truth for counts), so the two stay consistent.
|
||||
"""
|
||||
align_segments: List[dict] = []
|
||||
for seg in raw_segments:
|
||||
seg_words = [
|
||||
{
|
||||
"word": w.get("word", ""),
|
||||
"start": float(w.get("start", 0.0)),
|
||||
"end": float(w.get("end", 0.0)),
|
||||
"score": float(w.get("confidence", 0.0)),
|
||||
}
|
||||
for w in seg.get("words", [])
|
||||
]
|
||||
align_segments.append(
|
||||
{
|
||||
"text": (seg.get("text") or "").strip(),
|
||||
"start": float(seg.get("start", 0.0)),
|
||||
"end": float(seg.get("end", 0.0)),
|
||||
"words": seg_words,
|
||||
}
|
||||
)
|
||||
return align_segments
|
||||
|
||||
@staticmethod
|
||||
def _merge_result(words: Sequence[dict], aligned_segments: Sequence[dict]) -> List[dict]:
|
||||
"""Walk the aligned output in order and overwrite word times in place.
|
||||
|
||||
whisperx preserves word order within and across segments, so a single
|
||||
running index over the output words lines up with ``words``. A word the
|
||||
aligner failed to place gets ``None``/``0`` times — we skip those rather
|
||||
than clobber a good timestamp, and if counts ever diverge we stop and
|
||||
leave the rest untouched.
|
||||
"""
|
||||
out = list(words)
|
||||
wi = 0
|
||||
for seg in aligned_segments:
|
||||
for aw in seg.get("words", []):
|
||||
if wi >= len(out):
|
||||
return out
|
||||
start = aw.get("start")
|
||||
end = aw.get("end")
|
||||
if start is None or end is None or end < start:
|
||||
wi += 1
|
||||
continue
|
||||
out[wi]["start"] = float(start)
|
||||
out[wi]["end"] = float(end)
|
||||
wi += 1
|
||||
return out
|
||||
@@ -0,0 +1,312 @@
|
||||
"""Local LLM integration — the voice timeline meets a local model.
|
||||
|
||||
The voice timeline is *designed* to be handed to a language model: it is the
|
||||
source of truth between speech analysis and editing, layered so a model can
|
||||
reason about the narrative without parsing FCPXML. This module is the client
|
||||
side of that contract. It formats the timeline into the editar-por-voz brief,
|
||||
calls a local model server (Ollama, running Gemma 3 / Llama locally), and
|
||||
parses the model's decisions back into a validated list of VoiceActions —
|
||||
all inside the engine, so there is no wizard, no copy-paste, no manual step.
|
||||
|
||||
Transport: Ollama's HTTP chat API at ``http://localhost:11434/api/chat``.
|
||||
Any model Ollama serves works; the default is Gemma 3 because that is what
|
||||
runs locally here ("Lama com Gema 3"), but pass ``model=`` to switch.
|
||||
|
||||
The model is untrusted input: its JSON is validated row-by-row by
|
||||
:func:`fcpxml.voice_actions.parse_actions`, so one malformed decision never
|
||||
discards the edit. The brief is written so the model only ever emits the four
|
||||
action kinds the applier understands.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import re
|
||||
from typing import Any, Dict, Optional, Sequence, Tuple
|
||||
|
||||
import httpx
|
||||
|
||||
from .voice_actions import parse_actions
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
DEFAULT_BASE_URL = "http://localhost:11434"
|
||||
# Gemma 3 12B reliably follows the editar-por-voz brief (keep the script, cut
|
||||
# only backstage chatter; the 4B variant skips the "keep the main content"
|
||||
# rule and deletes the script) but doesn't fit an 8GB machine. Qwen2.5 7B
|
||||
# instruct (q4_K_M) is the fallback for constrained hardware — strong at
|
||||
# strict JSON-schema following, the property this brief leans on hardest.
|
||||
# Pass ``model=`` to switch to whatever Ollama serves.
|
||||
DEFAULT_MODEL = "qwen2.5:7b-instruct-q4_K_M"
|
||||
REQUEST_TIMEOUT = 600.0
|
||||
|
||||
# The brief. Ported from the editar-por-voz skill criteria (criterios/01..08),
|
||||
# condensed into the instructions a model needs to emit valid actions. Kept in
|
||||
# Portuguese because the decisions and their reasons are read by a human editor.
|
||||
_SYSTEM_PROMPT = """Você é o editor de vídeo por voz deste sistema. Recebe um JSON de "linha do tempo de voz" — a medição de COMO foi falado (ênfase, energia, pausa, falante) de uma gravação — e devolve as DECISÕES de edição em JSON, nada mais. Você nunca escreve XML.
|
||||
|
||||
Regras (siga rigorosamente):
|
||||
|
||||
1. LEIA EM CAMADAS. "summary" dá o formato da peça; "segments" é onde você trabalha (cada fala com seu texto e agregados); "segments[].words" dá o instante exato de cada destaque. Não recalcule energia, tom ou ênfase — use os números do JSON.
|
||||
|
||||
2. SEPARAR ROTEIRO DE BASTIDOR.
|
||||
- ROTEIRO = o conteúdo principal que a pessoa quer entregar: explicação, depoimento, roteiro decorado, a mensagem. É isso que VAI FICAR.
|
||||
- BASTIDOR = papo casual de gravação, cumprimentos, conversa com a equipe ("cara, beleza?", "tá gravando?", "deixa eu ver o celular"), piadas fora do assunto, tomadas interrompidas ou repetidas. É isso que VIRA "cut".
|
||||
Exemplo: num vídeo sobre mastopexia, a explicação da cirurgia É o roteiro (mantém); o "tá gravando? pois é" antes dela É bastidor (corta).
|
||||
Use "gap_before" e "take_boundary" (silêncio > ~3s = a câmera parou/recomeçou) para agrupar tomadas — eles marcam ONDE a tomada recomeça, não o que cortar. Nunca corte o conteúdo principal só porque tem ênfase; corte o casual/off-topic.
|
||||
|
||||
REGRAS DE OURO:
|
||||
- MANTENHA o conteúdo principal (explicação, depoimento, roteiro decorado). Ele É o vídeo.
|
||||
- CORTE SÓ o casual/off-topic: cumprimentos, "tá gravando?", papo com a equipe, olhar o celular, repetições de tomada.
|
||||
- Em dúvida, MANTENHA a fala. É melhor sobrar conteúdo do que cortar o que era pra ficar.
|
||||
|
||||
3. ESCOLHER A MELHOR TOMADA de cada frase quando há repetições: mantenha a mais limpa e corte as outras (cut cobrindo a frase inteira).
|
||||
|
||||
4. CORTE (kind "cut"): para REMOVER uma frase, cubra ela inteira (start..end = início..fim da frase). Para APARAR só uma hesitação no começo ou fim, corte só da borda até a palavra (corte de meia frase é ambíguo — passe de 60% e apaga a linha toda). Nunca corte o silêncio entre falas.
|
||||
|
||||
5. ZOOM (kind "zoom"): só em palavra de CONTEÚDO bem enfatizada (emphasis alto, não artigo). params.scale entre 1.0 e 3.0 (padrão 1.3 se omitido). Posicione em torno da palavra, segurando até o fim da frase.
|
||||
|
||||
6. TEXTO (kind "text"): params.content obrigatório (≤120 chars), fixa um termo central ou callout. MARKER (kind "marker"): opcional params.content vira o nome do marcador. Use para emendas/junções que o editor deve conferir.
|
||||
|
||||
7. TEMPOS em segundos da MÍDIA ORIGINAL (exatamente como no JSON). Nunca compense para "depois do corte" — o programa desloca sozinho. end sempre > start, ambos ≥ 0.
|
||||
|
||||
8. reason OBRIGATÓRIO em cada ação, em português, embasando a decisão (ex.: 'abertura: "Aquela mama" (ênfase 0.42)'). reason vazio é decisão sem critério.
|
||||
|
||||
Responda APENAS com um objeto JSON válido, sem markdown, sem comentário:
|
||||
{"source": "<nome do arquivo>", "actions": [{"kind": "cut|zoom|text|marker", "start": <float>, "end": <float>, "params": {}, "reason": "<pt>", "speaker": "<id>"}]}
|
||||
"""
|
||||
|
||||
_OUTPUT_REMINDER = """Gere as decisões de edição conforme o brief. Responda SOMENTE o JSON:
|
||||
{"source": "<nome do arquivo>", "actions": [{"kind": "cut|zoom|text|marker", "start": <float>, "end": <float>, "params": {}, "reason": "<pt>", "speaker": "<id>"}]}
|
||||
Não inclua explicações nem blocos markdown."""
|
||||
|
||||
|
||||
def ollama_chat(
|
||||
model: str = DEFAULT_MODEL,
|
||||
messages: Optional[Sequence[Dict[str, str]]] = None,
|
||||
base_url: str = DEFAULT_BASE_URL,
|
||||
temperature: float = 0.2,
|
||||
timeout: float = REQUEST_TIMEOUT,
|
||||
num_ctx: int = 32768,
|
||||
) -> str:
|
||||
"""One chat completion from a local Ollama server.
|
||||
|
||||
Returns the assistant message content. Raises on transport/HTTP errors so
|
||||
the caller can decide whether to retry or report — a model call is the
|
||||
one I/O in this pipeline that can legitimately fail mid-run.
|
||||
"""
|
||||
payload = {
|
||||
"model": model,
|
||||
"messages": list(messages or []),
|
||||
"stream": False,
|
||||
"options": {"temperature": temperature, "num_ctx": num_ctx},
|
||||
}
|
||||
try:
|
||||
response = httpx.post(
|
||||
f"{base_url.rstrip('/')}/api/chat", json=payload, timeout=timeout
|
||||
)
|
||||
response.raise_for_status()
|
||||
data = response.json()
|
||||
except Exception as exc:
|
||||
# Covers transport errors AND a dropped connection that yields an empty
|
||||
# body (httpx/JSONDecodeError) — both must become a RuntimeError so the
|
||||
# caller reports the failure instead of crashing the whole pipeline.
|
||||
raise RuntimeError(f"Falha ao falar com o modelo local em {base_url}: {exc}") from exc
|
||||
|
||||
return (data.get("message") or {}).get("content", "") or ""
|
||||
|
||||
|
||||
def list_ollama_models(base_url: str = DEFAULT_BASE_URL) -> list[str]:
|
||||
"""Names of the models Ollama currently serves, for a model picker.
|
||||
|
||||
Returns an empty list when Ollama is unreachable so the UI can fall back to
|
||||
a free-text field instead of erroring.
|
||||
"""
|
||||
try:
|
||||
resp = httpx.get(f"{base_url.rstrip('/')}/api/tags", timeout=10.0)
|
||||
resp.raise_for_status()
|
||||
models = resp.json().get("models", [])
|
||||
names = [m.get("name") for m in models if m.get("name")]
|
||||
return sorted(names)
|
||||
except Exception:
|
||||
return []
|
||||
|
||||
|
||||
def _extract_json(text: str) -> Any:
|
||||
"""Pull a JSON value out of a model response, tolerating fences/wrappers."""
|
||||
if not text:
|
||||
return None
|
||||
candidate = text.strip()
|
||||
# Strip a ```json ... ``` (or bare ```) fence if the model added one.
|
||||
fence = re.search(r"```(?:json)?\s*(.*?)\s*```", candidate, re.DOTALL)
|
||||
if fence:
|
||||
candidate = fence.group(1).strip()
|
||||
# Otherwise take the outermost {...} / [...].
|
||||
if not candidate.startswith(("{" if True else "", "[")):
|
||||
start = min(
|
||||
(i for i, c in enumerate(candidate) if c in "{["),
|
||||
default=None,
|
||||
)
|
||||
end = max(
|
||||
(i for i, c in enumerate(candidate) if c in "}"),
|
||||
default=None,
|
||||
)
|
||||
if start is not None and end is not None and end > start:
|
||||
candidate = candidate[start : end + 1]
|
||||
try:
|
||||
data = json.loads(candidate)
|
||||
except json.JSONDecodeError:
|
||||
return None
|
||||
|
||||
# Models sometimes wrap the expected `{"source", "actions"}` object inside a
|
||||
# single-element list (`[{...}]`). Unwrap that so the actions aren't treated
|
||||
# as one malformed row.
|
||||
if (
|
||||
isinstance(data, list)
|
||||
and len(data) == 1
|
||||
and isinstance(data[0], dict)
|
||||
and "actions" in data[0] # the wrapper carries the actions key
|
||||
):
|
||||
data = data[0]
|
||||
return data
|
||||
|
||||
|
||||
# Only these fields reach the model — the raw timeline also carries heavy
|
||||
# per-word audio features (energy, pitch, arousal...) and speaker `samples`
|
||||
# that blow past the model's context window on any real recording. Dropping
|
||||
# them is what keeps a 3-minute timeline inside `num_ctx`.
|
||||
_SEGMENT_KEEP = (
|
||||
"start", "end", "speaker", "text", "gap_before", "take_boundary",
|
||||
"avg_energy", "peak_emphasis", "emotion", "emotion_confidence",
|
||||
"arousal", "valence",
|
||||
)
|
||||
_WORD_KEEP = ("text", "start", "end", "speaker", "emphasis", "pause_before")
|
||||
_SPEAKER_KEEP = ("id", "name")
|
||||
_SKIP_ROOT = ("layers", "scales")
|
||||
|
||||
|
||||
def _project_timeline(timeline: dict) -> dict:
|
||||
"""Strip the timeline down to what the edit decision actually needs."""
|
||||
out = {k: v for k, v in timeline.items() if k not in _SKIP_ROOT}
|
||||
speakers = [
|
||||
{k: sp[k] for k in _SPEAKER_KEEP if k in sp}
|
||||
for sp in timeline.get("speakers", [])
|
||||
]
|
||||
if speakers:
|
||||
out["speakers"] = speakers
|
||||
segs = []
|
||||
for seg in timeline.get("segments", []):
|
||||
s = {k: seg[k] for k in _SEGMENT_KEEP if k in seg}
|
||||
s["words"] = [
|
||||
{k: w[k] for k in _WORD_KEEP if k in w}
|
||||
for w in seg.get("words", [])
|
||||
]
|
||||
segs.append(s)
|
||||
out["segments"] = segs
|
||||
return out
|
||||
|
||||
|
||||
def _shrink_to_fit(compact: dict, max_chars: int) -> dict:
|
||||
"""Drop word detail from the lowest-emphasis segments until it fits."""
|
||||
segs = [dict(s) for s in compact.get("segments", [])]
|
||||
while True:
|
||||
payload = json.dumps(
|
||||
{**compact, "segments": segs}, ensure_ascii=False, indent=1
|
||||
)
|
||||
if len(payload) <= max_chars or not any(s.get("words") for s in segs):
|
||||
break
|
||||
idx = min(
|
||||
(i for i, s in enumerate(segs) if s.get("words")),
|
||||
key=lambda i: float(segs[i].get("peak_emphasis", 0.0)),
|
||||
)
|
||||
segs[idx] = {**segs[idx], "words": []}
|
||||
compact = dict(compact)
|
||||
compact["segments"] = segs
|
||||
return compact
|
||||
|
||||
|
||||
def build_edit_messages(
|
||||
timeline: dict, max_words_per_segment: int = 200, max_chars: int = 110000
|
||||
) -> Tuple[str, str]:
|
||||
"""The (system, user) pair that sends a timeline to the model.
|
||||
|
||||
The user turn carries a *projected* timeline (see :func:`_project_timeline`)
|
||||
— text, timing, speaker and emphasis only — so a real recording fits in the
|
||||
model's context window. Very long segments still have their word detail
|
||||
capped to ``max_words_per_segment`` (most emphatic + boundaries), and if the
|
||||
whole payload would still exceed ``max_chars`` the lowest-emphasis segments
|
||||
lose their words until it fits, so we never blow ``num_ctx``.
|
||||
"""
|
||||
compact = _project_timeline(timeline)
|
||||
if max_words_per_segment:
|
||||
segs = []
|
||||
for seg in compact["segments"]:
|
||||
words = seg.get("words", [])
|
||||
if len(words) > max_words_per_segment:
|
||||
ranked = sorted(
|
||||
enumerate(words),
|
||||
key=lambda kv: float(kv[1].get("emphasis", 0.0)),
|
||||
reverse=True,
|
||||
)[: max_words_per_segment - 2]
|
||||
keep = sorted({0, len(words) - 1} | {i for i, _ in ranked})
|
||||
seg = {**seg, "words": [words[i] for i in keep]}
|
||||
segs.append(seg)
|
||||
compact["segments"] = segs
|
||||
|
||||
payload = json.dumps(compact, ensure_ascii=False, indent=1)
|
||||
if len(payload) > max_chars:
|
||||
compact = _shrink_to_fit(compact, max_chars)
|
||||
payload = json.dumps(compact, ensure_ascii=False, indent=1)
|
||||
|
||||
user = (
|
||||
"Linha do tempo de voz (JSON):\n\n"
|
||||
+ payload
|
||||
+ "\n\n"
|
||||
+ _OUTPUT_REMINDER
|
||||
)
|
||||
return _SYSTEM_PROMPT, user
|
||||
|
||||
|
||||
def generate_voice_actions(
|
||||
timeline: dict,
|
||||
model: str = DEFAULT_MODEL,
|
||||
base_url: str = DEFAULT_BASE_URL,
|
||||
temperature: float = 0.2,
|
||||
timeout: float = REQUEST_TIMEOUT,
|
||||
num_ctx: int = 32768,
|
||||
max_words_per_segment: int = 200,
|
||||
) -> Dict[str, Any]:
|
||||
"""Ask the local model to direct the edit, returning validated actions.
|
||||
|
||||
Returns ``{"actions": [VoiceAction], "raw": str, "errors": [str]}``.
|
||||
``actions`` is empty when the model returned nothing usable; ``errors``
|
||||
carries the per-row rejections from :func:`parse_actions` plus any
|
||||
extraction failure, so the caller can report what went wrong instead of
|
||||
only the wins.
|
||||
"""
|
||||
system, user = build_edit_messages(timeline, max_words_per_segment)
|
||||
try:
|
||||
raw = ollama_chat(
|
||||
model=model,
|
||||
messages=[
|
||||
{"role": "system", "content": system},
|
||||
{"role": "user", "content": user},
|
||||
],
|
||||
base_url=base_url,
|
||||
temperature=temperature,
|
||||
timeout=timeout,
|
||||
num_ctx=num_ctx,
|
||||
)
|
||||
except RuntimeError as exc:
|
||||
return {"actions": [], "raw": "", "errors": [str(exc)]}
|
||||
|
||||
data = _extract_json(raw)
|
||||
if data is None:
|
||||
return {
|
||||
"actions": [],
|
||||
"raw": raw,
|
||||
"errors": ["O modelo não devolveu um JSON de decisões legível."],
|
||||
}
|
||||
actions, errors = parse_actions(data)
|
||||
return {"actions": actions, "raw": raw, "errors": errors}
|
||||
@@ -194,6 +194,7 @@ class FCPXMLParser:
|
||||
media_path=media_path,
|
||||
audio_role=elem.get('audioRole', ''),
|
||||
video_role=elem.get('videoRole', ''),
|
||||
rotation=self._parse_clip_rotation(elem),
|
||||
)
|
||||
|
||||
clip.markers.extend(self._collect_markers(elem))
|
||||
@@ -205,6 +206,19 @@ class FCPXMLParser:
|
||||
|
||||
return clip
|
||||
|
||||
def _parse_clip_rotation(self, elem: ET.Element) -> float:
|
||||
"""Degrees from this clip's ``<adjust-transform rotation="...">`` —
|
||||
an edit-time correction (e.g. straightening a tilted phone shot),
|
||||
not the camera's own recorded orientation. FCP writes the rotation
|
||||
as an attribute on that element, not as a filter param."""
|
||||
transform = elem.find('adjust-transform')
|
||||
if transform is None:
|
||||
return 0.0
|
||||
try:
|
||||
return float(transform.get('rotation', '0'))
|
||||
except ValueError:
|
||||
return 0.0
|
||||
|
||||
def _parse_marker_element(self, elem: ET.Element) -> Optional[Marker]:
|
||||
"""Parse any marker element (<marker> or <chapter-marker>).
|
||||
|
||||
@@ -337,6 +351,7 @@ class FCPXMLParser:
|
||||
lane=lane, offset=offset, source_start=start,
|
||||
media_path=media_path, clip_type=elem.tag, role=role,
|
||||
ref_id=ref, parent_clip_name=parent_name,
|
||||
rotation=self._parse_clip_rotation(elem),
|
||||
)
|
||||
|
||||
connected.markers.extend(self._collect_markers(elem))
|
||||
|
||||
@@ -300,6 +300,7 @@ def build_phrase_review(
|
||||
"version": PHRASE_REVIEW_VERSION,
|
||||
"source": source,
|
||||
"source_path": resolve_source(source, voice_timeline_path, extra_dirs),
|
||||
"rotation": float(timeline.get("rotation", 0.0)),
|
||||
"duration": round(phrases[-1]["end"], 3) if phrases else 0.0,
|
||||
"speakers": timeline.get("speakers", []),
|
||||
"emotion_available": bool(layers.get("emotion", False)),
|
||||
|
||||
+44
-12
@@ -125,18 +125,27 @@ def transcribe(
|
||||
model_size: str = "base",
|
||||
language: Optional[str] = None,
|
||||
progress_cb: Optional[Callable[[float], None]] = None,
|
||||
align: bool = True,
|
||||
) -> Optional[dict]:
|
||||
"""Transcribe an audio/video file locally with word-level timestamps.
|
||||
|
||||
Requires the optional ``[transcribe]`` extra (faster-whisper). Returns
|
||||
``None`` when the model is unavailable or the file is missing/unreadable.
|
||||
|
||||
When ``align`` is true (default) and the optional ``whisperx`` dependency is
|
||||
present, word timestamps are refined by phonetic forced alignment, which
|
||||
corrects faster-whisper's systematic ~0.3-0.5s early bias on word *starts*
|
||||
(see ``Engine/docs/05_EXPERIENCIAS.md`` #14). The transcript reports
|
||||
whether this ran via the ``alignment`` flag, so downstream consumers can
|
||||
rely on the times without re-measuring.
|
||||
|
||||
The model weights are resolved from the configured models directory (see
|
||||
``model_manager.get_models_dir``), so a model selected/downloaded through
|
||||
the app is found without an implicit download to the default HF cache.
|
||||
|
||||
Returns:
|
||||
``{"language": str, "duration": float, "text": str,
|
||||
"alignment": bool,
|
||||
"segments": [{"text", "start", "end", "start_fmt", "end_fmt"}, ...],
|
||||
"words": [{"word", "start", "end", "confidence"}, ...]}``
|
||||
"""
|
||||
@@ -175,6 +184,7 @@ def transcribe(
|
||||
vad_filter=True,
|
||||
)
|
||||
segments: List[dict] = []
|
||||
raw_segments: List[dict] = []
|
||||
words: List[dict] = []
|
||||
# `info.duration` is known upfront (from the container), so each
|
||||
# segment's end time — yielded lazily as faster-whisper decodes —
|
||||
@@ -183,6 +193,20 @@ def transcribe(
|
||||
for seg in segments_iter:
|
||||
start = float(seg.start)
|
||||
end = float(seg.end)
|
||||
seg_words: List[dict] = []
|
||||
if progress_cb is not None and total_duration > 0:
|
||||
progress_cb(min(end / total_duration, 1.0))
|
||||
for w in seg.words or []:
|
||||
ws = float(w.start)
|
||||
we = float(w.end)
|
||||
word = {
|
||||
"word": w.word.strip(),
|
||||
"start": ws,
|
||||
"end": we,
|
||||
"confidence": float(w.probability),
|
||||
}
|
||||
words.append(word)
|
||||
seg_words.append(word)
|
||||
segments.append(
|
||||
{
|
||||
"text": seg.text.strip(),
|
||||
@@ -192,19 +216,26 @@ def transcribe(
|
||||
"end_fmt": format_timestamp(end),
|
||||
}
|
||||
)
|
||||
if progress_cb is not None and total_duration > 0:
|
||||
progress_cb(min(end / total_duration, 1.0))
|
||||
for w in seg.words or []:
|
||||
ws = float(w.start)
|
||||
we = float(w.end)
|
||||
words.append(
|
||||
{
|
||||
"word": w.word.strip(),
|
||||
"start": ws,
|
||||
"end": we,
|
||||
"confidence": float(w.probability),
|
||||
}
|
||||
raw_segments.append(
|
||||
{
|
||||
"text": seg.text.strip(),
|
||||
"start": start,
|
||||
"end": end,
|
||||
"words": seg_words,
|
||||
}
|
||||
)
|
||||
|
||||
alignment_ran = False
|
||||
if align and raw_segments:
|
||||
from .forced_align import ForcedAligner
|
||||
|
||||
try:
|
||||
words = ForcedAligner().align(
|
||||
words, raw_segments, str(file_path), info.language, str(models_dir)
|
||||
)
|
||||
alignment_ran = True
|
||||
except Exception:
|
||||
logger.warning("forced alignment step failed; keeping raw timestamps")
|
||||
except Exception:
|
||||
logger.warning("whisper transcription failed for %s", file_path)
|
||||
return None
|
||||
@@ -212,6 +243,7 @@ def transcribe(
|
||||
"language": info.language,
|
||||
"duration": float(info.duration),
|
||||
"text": " ".join(s["text"] for s in segments),
|
||||
"alignment": alignment_ran,
|
||||
"segments": segments,
|
||||
"words": words,
|
||||
}
|
||||
|
||||
@@ -512,6 +512,7 @@ def build_voice_timeline(
|
||||
emphasis_floor: float = 0.25,
|
||||
emotion_enabled: bool = False,
|
||||
emotion_sensitivity: float = 0.5,
|
||||
rotation: float = 0.0,
|
||||
progress_cb: Optional[Callable[[float, str], None]] = None,
|
||||
) -> dict:
|
||||
"""Build the consolidated voice timeline for one media file.
|
||||
@@ -545,6 +546,10 @@ def build_voice_timeline(
|
||||
return {
|
||||
"version": VOICE_TIMELINE_VERSION,
|
||||
"source": Path(media_path).name,
|
||||
# Edit-time correction from the clip's Transform filter in the FCPXML
|
||||
# (e.g. straightening a tilted phone shot) — 0.0 when the clip has none
|
||||
# or the caller didn't resolve one.
|
||||
"rotation": rotation,
|
||||
"language": transcript.get("language", ""),
|
||||
# What actually ran, not what was installed — a consumer must be able
|
||||
# to tell "this speech is flat" from "the acoustics never loaded",
|
||||
@@ -554,6 +559,7 @@ def build_voice_timeline(
|
||||
"acoustics": pitch_track is not None or energy_track is not None,
|
||||
"speakers": tracks is not None,
|
||||
"emotion": bool(emotion_enabled),
|
||||
"alignment": bool(transcript.get("alignment")),
|
||||
},
|
||||
"scales": VALUE_SCALES,
|
||||
"summary": _summary(
|
||||
|
||||
@@ -550,6 +550,13 @@ class TitlesMixin:
|
||||
"""
|
||||
titles = []
|
||||
for elem in self.root.iter('title'):
|
||||
# enabled="0" never renders in Final Cut (see
|
||||
# generate_subtitles_by_emphasis, which disables plain titles
|
||||
# under an emphasis phrase instead of never creating them) — a
|
||||
# title that is off by design must not count as a collision
|
||||
# against the one drawn in its place.
|
||||
if elem.get('enabled', '1') == '0':
|
||||
continue
|
||||
text_el = elem.find('text/text-style')
|
||||
text = (text_el.text or '').strip() if text_el is not None else ''
|
||||
style = elem.find('text-style-def/text-style')
|
||||
|
||||
@@ -41,6 +41,9 @@ intelligence = [
|
||||
transcribe = [
|
||||
"faster-whisper>=1.0.0",
|
||||
]
|
||||
align = [
|
||||
"whisperx>=3.0.0",
|
||||
]
|
||||
diarization = [
|
||||
"pyannote.audio>=3.1",
|
||||
]
|
||||
|
||||
@@ -179,6 +179,7 @@ from server_tools.voice import (
|
||||
handle_analyze_voice_features,
|
||||
handle_apply_voice_actions,
|
||||
handle_build_voice_timeline,
|
||||
handle_generate_voice_script,
|
||||
handle_diarize_media,
|
||||
handle_get_voice_analysis_config,
|
||||
handle_refine_voice_timeline,
|
||||
|
||||
@@ -93,6 +93,25 @@ TOOLS = [
|
||||
"required": ["filepath"]
|
||||
}
|
||||
),
|
||||
Tool(
|
||||
name="generate_subtitles_by_emphasis",
|
||||
description="Generate BOTH subtitle styles over the FULL clip and let them coexist by visibility, not by splitting words: plain static titles (see generate_plain_subtitles) cover every word from start to end; dynamic progressive-composition titles (see generate_dynamic_subtitles) are additionally generated for whichever whole phrases were marked as emphasis in the phrase-review step (etapa 5, zoom applied, level >= 1). Wherever a dynamic phrase is on screen, the plain titles underneath it are set enabled=\"0\" (still present in the FCPXML, editable/re-enable-able in Final Cut, just not rendered) instead of never being generated there — so disabling emphasis later never leaves a silent gap in the plain track. Reads emphasis spans from the media's cached '<media>_phrase_actions.json' (written by save_phrase_review after the app's etapa 5 review) — run the voice-editing wizard through that step first, or nothing is treated as emphasis and every title stays plain and enabled. Style knobs are the saved 'Legendas Dinâmicas'/plain-subtitle configs (~/.fcp-mcp-server/config.json); this tool does not expose per-call style overrides, only the split logic — use generate_dynamic_subtitles/generate_plain_subtitles directly if you need one-off styling.",
|
||||
inputSchema={
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"filepath": {"type": "string", "description": "Path to FCPXML file"},
|
||||
"clip_name": {"type": "string", "description": "Only caption the clip with this name (default: all spine clips with matched source media)"},
|
||||
"model": {"type": "string", "default": "base", "description": "Whisper model size if transcription is needed"},
|
||||
"language": {"type": "string", "description": "ISO language code hint (e.g. 'pt'); auto-detected if omitted"},
|
||||
"granularity": {"type": "string", "enum": ["phrase", "word"], "default": "phrase", "description": "Passed through to the dynamic half, same meaning as in generate_dynamic_subtitles"},
|
||||
"max_words": {"type": "integer", "description": "Max words per block for the plain half. Falls back to saved plain-subtitle config."},
|
||||
"uppercase": {"type": "boolean", "description": "Uppercase the plain half. Falls back to saved plain-subtitle config."},
|
||||
"keep_punctuation": {"type": "boolean", "description": "Keep punctuation in the plain half. Falls back to saved plain-subtitle config."},
|
||||
"output_path": {"type": "string", "description": "Output path (default: adds _emphasis_subtitles suffix)"},
|
||||
},
|
||||
"required": ["filepath"]
|
||||
}
|
||||
),
|
||||
]
|
||||
|
||||
|
||||
@@ -140,6 +159,85 @@ def _plain_subtitle_blocks(words: Sequence[dict], max_words: int) -> list[list[d
|
||||
return blocks
|
||||
|
||||
|
||||
def _phrase_actions_path(media_path: str) -> Path:
|
||||
"""Where `save_phrase_review` writes emphasis decisions for this media.
|
||||
|
||||
Mirrors `phrase_review.review_paths()`'s naming (stem + "_phrase_actions.json"),
|
||||
without importing that module just for a path — the voice_timeline this would
|
||||
normally derive from is itself named `<media stem>_voice_timeline.json`, so
|
||||
stripping straight from the media stem lands on the same file.
|
||||
"""
|
||||
stem = Path(media_path).stem
|
||||
return Path(media_path).with_name(f"{stem}_phrase_actions.json")
|
||||
|
||||
|
||||
def _load_emphasis_spans(media_path: str) -> list[dict]:
|
||||
"""Load emphasis spans (source-media time) saved by the etapa-5 phrase review.
|
||||
|
||||
Returns [] if the review was never run for this media — callers should treat
|
||||
that as "nothing is emphasis yet", not as an error, since the wizard's later
|
||||
steps are optional.
|
||||
"""
|
||||
path = _phrase_actions_path(media_path)
|
||||
if not path.is_file():
|
||||
return []
|
||||
try:
|
||||
data = json.loads(path.read_text(encoding="utf-8"))
|
||||
except (OSError, json.JSONDecodeError):
|
||||
return []
|
||||
spans = data.get("emphasis_spans", [])
|
||||
return [s for s in spans if isinstance(s, dict) and "start" in s and "end" in s]
|
||||
|
||||
|
||||
def _word_in_spans(word_start: float, word_end: float, spans: Sequence[dict]) -> bool:
|
||||
"""A word belongs to an emphasis span if its midpoint falls inside it.
|
||||
|
||||
Midpoint, not start, so a word straddling a span boundary (which can happen
|
||||
since spans come from phrase trims, not word timestamps) lands on whichever
|
||||
side it mostly belongs to instead of always defaulting to one edge.
|
||||
"""
|
||||
mid = (word_start + word_end) / 2.0
|
||||
return any(float(s["start"]) <= mid < float(s["end"]) for s in spans)
|
||||
|
||||
|
||||
def _words_in_spans(words: Sequence[dict], spans: Sequence[dict]) -> list[dict]:
|
||||
"""The subset of source-time transcript words that fall inside a span.
|
||||
|
||||
Feeds only the DYNAMIC half — the plain half always gets every word, full
|
||||
clip, unfiltered; this is not a partition of the word list into two
|
||||
disjoint sets, it is "which words also get the dynamic treatment on top".
|
||||
"""
|
||||
if not spans:
|
||||
return []
|
||||
return [
|
||||
w for w in words
|
||||
if _word_in_spans(float(w.get("start", 0.0)), float(w.get("end", w.get("start", 0.0))), spans)
|
||||
]
|
||||
|
||||
|
||||
def _segments_in_spans(segments: Sequence[dict], spans: Sequence[dict]) -> list[dict]:
|
||||
"""Keep only the sentences that fall inside an emphasis span (by midpoint).
|
||||
|
||||
Feeds the dynamic half's sentence-block builder; segments outside every span
|
||||
would only produce blocks with no words left in them after the word filter.
|
||||
"""
|
||||
if not spans:
|
||||
return []
|
||||
kept = []
|
||||
for seg in segments:
|
||||
start = float(seg.get("start", 0.0))
|
||||
end = float(seg.get("end", start))
|
||||
mid = (start + end) / 2.0
|
||||
if any(float(s["start"]) <= mid < float(s["end"]) for s in spans):
|
||||
kept.append(seg)
|
||||
return kept
|
||||
|
||||
|
||||
def _overlaps_any_span(start: float, end: float, spans: Sequence[tuple[float, float]]) -> bool:
|
||||
"""Half-open interval overlap: a plain title under this window must hide."""
|
||||
return any(start < span_end and end > span_start for span_start, span_end in spans)
|
||||
|
||||
|
||||
async def handle_validate_subtitle_layout(arguments: dict) -> Sequence[TextContent]:
|
||||
"""Validate title/subtitle layout for spatial collisions and safe-area
|
||||
containment (collision.validate_titles over every <title> in the file)."""
|
||||
@@ -442,8 +540,214 @@ async def handle_generate_plain_subtitles(arguments: dict) -> Sequence[TextConte
|
||||
return _text_result(result)
|
||||
|
||||
|
||||
async def handle_generate_subtitles_by_emphasis(arguments: dict) -> Sequence[TextContent]:
|
||||
"""Generate plain titles for the whole clip and dynamic titles for the
|
||||
emphasis phrases on top, then hide (enabled="0") the plain titles that
|
||||
fall under a dynamic phrase — never split the word list between the two.
|
||||
|
||||
Plain always covers every word, so turning emphasis off later (editing
|
||||
the phrase review and re-running) never leaves a silent gap: the plain
|
||||
title was there all along, just disabled.
|
||||
"""
|
||||
model = arguments.get("model", "base")
|
||||
language = arguments.get("language")
|
||||
output_dir = arguments.get("output_dir")
|
||||
clip_filter = arguments.get("clip_name")
|
||||
granularity = arguments.get("granularity", "phrase")
|
||||
|
||||
saved_dynamic = load_dynamic_subtitle_config()
|
||||
body_color = saved_dynamic["active_color"]
|
||||
dynamic_config = DynamicSubtitleConfig(
|
||||
style=WordStyle(
|
||||
font=saved_dynamic["font"],
|
||||
font_size=int(saved_dynamic["font_size"]),
|
||||
active_color=body_color,
|
||||
inactive_color="0.7 0.7 0.7 1",
|
||||
emphasis_look=WordLook(
|
||||
int(saved_dynamic["emphasis_size"]),
|
||||
saved_dynamic["emphasis_color"] or body_color,
|
||||
font=saved_dynamic["emphasis_font"],
|
||||
face=saved_dynamic["emphasis_face"],
|
||||
kerning=0.0,
|
||||
),
|
||||
body_look=WordLook(
|
||||
int(saved_dynamic["font_size"]),
|
||||
body_color,
|
||||
font=saved_dynamic["font"],
|
||||
face="Bold",
|
||||
kerning=1.2,
|
||||
),
|
||||
),
|
||||
band_height=float(saved_dynamic["band_height"]),
|
||||
block_center_y=float(saved_dynamic["block_center_y"]),
|
||||
granularity=granularity,
|
||||
text_scale=float(saved_dynamic["text_scale"]),
|
||||
line_gap=float(saved_dynamic["line_gap"]),
|
||||
)
|
||||
|
||||
saved_plain = load_plain_subtitle_config()
|
||||
plain_font = saved_plain["font"]
|
||||
plain_font_size = int(saved_plain["font_size"])
|
||||
plain_font_color = saved_plain["font_color"]
|
||||
max_words = max(1, int(arguments.get("max_words", saved_plain["max_words"])))
|
||||
position_y = float(saved_plain["position_y"])
|
||||
uppercase = bool(arguments.get("uppercase", saved_plain["uppercase"]))
|
||||
keep_punctuation = bool(arguments.get("keep_punctuation", saved_plain["keep_punctuation"]))
|
||||
|
||||
filepath, output_path, modifier = _setup_modifier(arguments, "_emphasis_subtitles")
|
||||
|
||||
added: list[tuple[str, int, int, int, int]] = []
|
||||
skipped: list[tuple[str, str]] = []
|
||||
no_review: list[str] = []
|
||||
spine_clips = [el for _, el in modifier._iter_spine_clips()]
|
||||
for el in spine_clips:
|
||||
name = el.get("name", "")
|
||||
if clip_filter and name != clip_filter:
|
||||
continue
|
||||
src = modifier.resources.get(el.get("ref", ""), {}).get("src", "")
|
||||
media_path = media_src_to_path(src)
|
||||
if not media_path or not Path(media_path).is_file():
|
||||
skipped.append((name, "media file missing"))
|
||||
continue
|
||||
data, reason = _load_or_transcribe(media_path, model, language, output_dir)
|
||||
if data is None:
|
||||
skipped.append((name, reason))
|
||||
continue
|
||||
|
||||
spans = _load_emphasis_spans(media_path)
|
||||
if not spans:
|
||||
no_review.append(name)
|
||||
|
||||
clip_source_start = modifier.source_file_start(el).to_seconds()
|
||||
clip_duration = modifier._parse_time(el.get("duration", "0s")).to_seconds()
|
||||
window_end = clip_source_start + clip_duration
|
||||
|
||||
# Clip-relative windows, for deciding which plain titles to hide —
|
||||
# same coordinate space add_text_title's offsets end up in.
|
||||
clip_spans = [
|
||||
(max(0.0, float(s["start"]) - clip_source_start), min(clip_duration, float(s["end"]) - clip_source_start))
|
||||
for s in spans
|
||||
if float(s["end"]) > clip_source_start and float(s["start"]) < window_end
|
||||
]
|
||||
|
||||
all_words = data.get("words", [])
|
||||
|
||||
dynamic_lines = 0
|
||||
dynamic_word_count = 0
|
||||
emphasis_words = _words_in_spans(all_words, spans)
|
||||
clip_emphasis_words = _words_overlapping_clip(emphasis_words, clip_source_start, window_end)
|
||||
if clip_emphasis_words:
|
||||
all_segments = data.get("segments", [])
|
||||
emphasis_segments = _segments_in_spans(all_segments, spans)
|
||||
clip_segments = [
|
||||
{
|
||||
"start": float(s.get("start", 0.0)) - clip_source_start,
|
||||
"end": float(s.get("end", 0.0)) - clip_source_start,
|
||||
}
|
||||
for s in emphasis_segments
|
||||
if float(s.get("end", 0.0)) > clip_source_start
|
||||
and float(s.get("start", 0.0)) < window_end
|
||||
]
|
||||
# Pass the element itself, not `name` — see the same note in
|
||||
# handle_generate_dynamic_subtitles (Engine/docs/05_EXPERIENCIAS.md,
|
||||
# entry 2026-08-17).
|
||||
dynamic_lines = len(
|
||||
modifier.generate_dynamic_subtitles(
|
||||
el, clip_emphasis_words, dynamic_config, segments=clip_segments
|
||||
)
|
||||
)
|
||||
dynamic_word_count = len(clip_emphasis_words)
|
||||
|
||||
# Plain covers EVERY word in the clip — never filtered by emphasis.
|
||||
# Titles landing under a dynamic phrase are disabled below instead of
|
||||
# never being created, so turning emphasis off later never leaves a
|
||||
# silent gap where neither style is on screen.
|
||||
plain_created = 0
|
||||
plain_hidden = 0
|
||||
clip_all_words = _words_overlapping_clip(all_words, clip_source_start, window_end)
|
||||
blocks = _plain_subtitle_blocks(clip_all_words, max_words)
|
||||
for block in blocks:
|
||||
parts = [
|
||||
_plain_word_text(w.get("word", ""), uppercase=uppercase, keep_punctuation=keep_punctuation)
|
||||
for w in block
|
||||
]
|
||||
text = " ".join(p for p in parts if p).strip()
|
||||
if not text:
|
||||
continue
|
||||
start = max(0.0, min(float(w.get("start", 0.0)) for w in block))
|
||||
end = max(float(w.get("end", start)) for w in block)
|
||||
duration = max(end - start, modifier.frame_duration_fraction())
|
||||
title = modifier.add_text_title(
|
||||
el,
|
||||
text,
|
||||
offset=f"{start:.6f}s",
|
||||
duration=f"{duration:.6f}s",
|
||||
lane=20,
|
||||
position=f"0 {position_y:g}",
|
||||
font=plain_font,
|
||||
font_size=plain_font_size,
|
||||
font_color=plain_font_color,
|
||||
bold=True,
|
||||
face=None,
|
||||
font_scale=1.0,
|
||||
size_param=plain_font_size,
|
||||
)
|
||||
plain_created += 1
|
||||
if _overlaps_any_span(start, end, clip_spans):
|
||||
title.set("enabled", "0")
|
||||
plain_hidden += 1
|
||||
|
||||
if dynamic_lines or plain_created:
|
||||
added.append(
|
||||
(name, dynamic_lines, plain_created, plain_hidden, dynamic_word_count + len(clip_all_words))
|
||||
)
|
||||
else:
|
||||
skipped.append((name, "no words in clip's source range"))
|
||||
|
||||
if not added:
|
||||
text = "# Subtitles by Emphasis\n\nNo captions generated — file unchanged (nothing saved)."
|
||||
if skipped:
|
||||
text += "\n\n## Skipped Clips\n" + _markdown_table(
|
||||
["Clip", "Reason"], [[n, r] for n, r in skipped]
|
||||
)
|
||||
return _text_result(text)
|
||||
|
||||
modifier.save(output_path)
|
||||
total_dynamic = sum(d for _, d, _, _, _ in added)
|
||||
total_plain = sum(p for _, _, p, _, _ in added)
|
||||
total_hidden = sum(h for _, _, _, h, _ in added)
|
||||
total_words = sum(w for _, _, _, _, w in added)
|
||||
result = "# Subtitles by Emphasis Generated\n\n## Summary\n"
|
||||
result += (
|
||||
f"- **Clips Captioned**: {len(added)}\n"
|
||||
f"- **Dynamic Title Lines (emphasis)**: {total_dynamic}\n"
|
||||
f"- **Plain Title Blocks (full clip)**: {total_plain}\n"
|
||||
f"- **Plain Blocks Hidden Under Emphasis (enabled=\"0\")**: {total_hidden}\n"
|
||||
f"- **Total Words**: {total_words}\n\n"
|
||||
)
|
||||
result += _markdown_table(
|
||||
["Clip", "Dynamic Lines", "Plain Blocks", "Hidden", "Words"],
|
||||
[[n, str(d), str(p), str(h), str(w)] for n, d, p, h, w in added],
|
||||
)
|
||||
if no_review:
|
||||
result += (
|
||||
"\n## Sem revisão de ênfase\n"
|
||||
"Nenhum `_phrase_actions.json` encontrado para: "
|
||||
+ ", ".join(no_review)
|
||||
+ " — todas as frases desses clipes saíram como legenda comum. "
|
||||
"Rode a etapa 5 do Assistente (revisão de frases) antes, se quiser destaque dinâmico.\n"
|
||||
)
|
||||
if skipped:
|
||||
result += "\n## Skipped Clips\n" + _markdown_table(
|
||||
["Clip", "Reason"], [[n, r] for n, r in skipped]
|
||||
)
|
||||
result += f"\n\nSaved to: `{output_path}`\n\n*Transcripts are cached as _transcript.json; emphasis spans from _phrase_actions.json.*"
|
||||
return _text_result(result)
|
||||
|
||||
|
||||
HANDLERS = {
|
||||
"validate_subtitle_layout": handle_validate_subtitle_layout,
|
||||
"generate_dynamic_subtitles": handle_generate_dynamic_subtitles,
|
||||
"generate_plain_subtitles": handle_generate_plain_subtitles,
|
||||
"generate_subtitles_by_emphasis": handle_generate_subtitles_by_emphasis,
|
||||
}
|
||||
|
||||
@@ -13,6 +13,7 @@ from mcp.types import TextContent, Tool
|
||||
|
||||
from fcpxml.diarize import assign_speakers, build_speakers, diarization_capability, diarize
|
||||
from fcpxml.emphasis import EmphasisWeights
|
||||
from fcpxml.llm_local import DEFAULT_BASE_URL, generate_voice_actions
|
||||
from fcpxml.media_intel import media_src_to_path
|
||||
from fcpxml.model_manager import (
|
||||
load_hf_token,
|
||||
@@ -21,6 +22,7 @@ from fcpxml.model_manager import (
|
||||
save_voice_analysis_config,
|
||||
)
|
||||
from fcpxml.models import TimeValue
|
||||
from fcpxml.phrase_review import build_phrase_review, save_phrase_review
|
||||
from fcpxml.voice_actions import parse_actions, resolve_actions, speaker_cut_actions
|
||||
from fcpxml.voice_features import extract_energy, extract_pitch, features_capability
|
||||
from fcpxml.voice_timeline import (
|
||||
@@ -93,6 +95,7 @@ TOOLS = [
|
||||
"hf_token": {"type": "string", "description": "HuggingFace token for speaker diarization (default: the persisted token; omit to skip diarization)"},
|
||||
"num_speakers": {"type": "string", "description": "Known number of speakers, if any (default: the persisted setting, else auto-detect)"},
|
||||
"output_dir": {"type": "string", "description": "Folder to write _voice_timeline.json into (default: next to the media file)"},
|
||||
"rotation": {"type": "number", "description": "Degrees the clip is rotated by in the FCPXML (e.g. a Transform filter straightening a tilted phone shot). Recorded in the timeline JSON so a preview can apply the same correction. Default 0."},
|
||||
},
|
||||
"required": ["media_path"]
|
||||
}
|
||||
@@ -167,6 +170,27 @@ TOOLS = [
|
||||
"required": ["filepath", "actions"]
|
||||
}
|
||||
),
|
||||
Tool(
|
||||
name="generate_voice_script",
|
||||
description="Run the WHOLE voice-edit pass internally, no wizard, no copy-paste: reuse an existing voice timeline (or transcribe + build one) -> hand it to a LOCAL model (Ollama running Gemma 3 / Llama) that directs the edit -> return the readable script (roteiro) AND the action JSON, and optionally apply it to a FCPXML. The model reads the full _voice_timeline.json (the whole file goes with the brief) and emits cut/zoom/text/marker decisions per the editar-por-voz brief; decisions are validated row-by-row so one bad row never discards the edit. Times stay in ORIGINAL source seconds; the applier resolves cuts and shifts everything else. Writes _voice_timeline.json, _phrase_review.json, _phrase_actions.json and (when applying) a _voice_edit FCPXML. Defaults to the local model 'gemma3:12b' at http://localhost:11434 — change via model/base_url.",
|
||||
inputSchema={
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"media_path": {"type": "string", "description": "Path to the audio/video file to analyze and direct (.wav, .mp3, .m4a, .aac, .aif, .flac, .mov, .mp4). Required when there is no voice_timeline yet; ignored when voice_timeline is provided."},
|
||||
"voice_timeline": {"type": "string", "description": "Path to an existing _voice_timeline.json (e.g. from the assistant's analysis step). When given, it is reused and transcription/acoustics are skipped — the model gets the whole file to direct the edit."},
|
||||
"filepath": {"type": "string", "description": "Optional FCPXML to apply the decisions to (non-destructive: writes a _voice_edit copy). When omitted, only the script and actions are produced."},
|
||||
"model": {"type": "string", "default": "gemma3:12b", "description": "Local model Ollama serves (e.g. 'gemma3:12b', 'gemma3:4b', 'llama3')"},
|
||||
"base_url": {"type": "string", "default": "http://localhost:11434", "description": "Ollama base URL"},
|
||||
"model_size": {"type": "string", "default": "base", "description": "Whisper model size to use if transcription is needed"},
|
||||
"language": {"type": "string", "description": "ISO language code hint for transcription, if needed"},
|
||||
"hf_token": {"type": "string", "description": "HuggingFace token for speaker diarization (omit to skip)"},
|
||||
"num_speakers": {"type": "string", "description": "Known number of speakers, if any"},
|
||||
"output_dir": {"type": "string", "description": "Folder to write the timeline/review/actions JSON into (default: next to the media file)"},
|
||||
"apply_to_fcpxml": {"type": "boolean", "default": True, "description": "When filepath is given, apply the decisions to it. Set false to only produce the script."},
|
||||
},
|
||||
"required": []
|
||||
}
|
||||
),
|
||||
Tool(
|
||||
name="get_voice_analysis_config",
|
||||
description="Read the persisted Voice Analysis settings: energy threshold, emphasis-index weights (energy/pitch_variation/rate_variation/pause_before/duration), emphasis cutoff for punch-in candidates, and emotion detection toggle/sensitivity. Shared with the MacApp settings screen (~/.fcp-mcp-server/config.json).",
|
||||
@@ -349,6 +373,7 @@ async def handle_build_voice_timeline(arguments: dict) -> Sequence[TextContent]:
|
||||
token = str(arguments.get("hf_token") or "").strip() or load_hf_token() or None
|
||||
num_speakers = str(arguments.get("num_speakers") or "").strip() or load_num_speakers()
|
||||
output_dir = arguments.get("output_dir")
|
||||
rotation = float(arguments.get("rotation") or 0.0)
|
||||
|
||||
transcript, reason = _load_or_transcribe(media_path, model, language, output_dir)
|
||||
if transcript is None:
|
||||
@@ -368,6 +393,7 @@ async def handle_build_voice_timeline(arguments: dict) -> Sequence[TextContent]:
|
||||
emphasis_floor=config["emphasis_floor"],
|
||||
emotion_enabled=config["emotion_enabled"],
|
||||
emotion_sensitivity=config["emotion_sensitivity"],
|
||||
rotation=rotation,
|
||||
)
|
||||
|
||||
json_path = Path(_validate_output_path(
|
||||
@@ -742,6 +768,145 @@ async def handle_save_voice_analysis_config(arguments: dict) -> Sequence[TextCon
|
||||
return _text_result(_voice_analysis_config_text(config))
|
||||
|
||||
|
||||
async def handle_generate_voice_script(arguments: dict) -> Sequence[TextContent]:
|
||||
"""The whole voice-edit pass, run inside the engine against a local model.
|
||||
|
||||
Transcribe (cached) -> build the voice timeline -> ask the local LLM to
|
||||
direct the edit -> build the readable script (roteiro) + the action JSON ->
|
||||
optionally apply to a FCPXML. No wizard, no copy-paste: the model's JSON is
|
||||
parsed and validated like any other decision source, and the applier turns
|
||||
it into FCPXML the same way it would for the rules engine.
|
||||
"""
|
||||
model = "gemma3:12b" if not arguments.get("model") else str(arguments["model"])
|
||||
base_url = str(arguments.get("base_url") or DEFAULT_BASE_URL)
|
||||
model_size = arguments.get("model_size", "base")
|
||||
language = arguments.get("language")
|
||||
token = str(arguments.get("hf_token") or "").strip() or load_hf_token() or None
|
||||
num_speakers = str(arguments.get("num_speakers") or "").strip() or load_num_speakers()
|
||||
output_dir = arguments.get("output_dir")
|
||||
fcpxml_path = arguments.get("filepath")
|
||||
apply = bool(arguments.get("apply_to_fcpxml", True)) and bool(fcpxml_path)
|
||||
|
||||
# Camino 1: já temos uma voice timeline (etapa de análise do assistente) —
|
||||
# reaproveita e pula a transcrição/análise acústica/diarização, que é caro.
|
||||
# Camino 2: só mídia — transcreve e monta a timeline do zero.
|
||||
vt_arg = arguments.get("voice_timeline")
|
||||
timeline = load_voice_timeline(Path(vt_arg)) if vt_arg and Path(vt_arg).is_file() else None
|
||||
media_path = arguments.get("media_path")
|
||||
if timeline is None:
|
||||
media_path = _validate_filepath(
|
||||
media_path, AUDIO_MEDIA_EXTENSIONS, max_size=MAX_MEDIA_FILE_SIZE
|
||||
)
|
||||
transcript, reason = _load_or_transcribe(media_path, model_size, language, output_dir)
|
||||
if transcript is None:
|
||||
return _text_result(
|
||||
f"# Roteiro por IA Local\n\nNão foi possível obter a transcrição "
|
||||
f"({reason}).{_TRANSCRIBE_INSTALL_HINT}"
|
||||
)
|
||||
config = load_voice_analysis_config()
|
||||
timeline = build_voice_timeline(
|
||||
media_path,
|
||||
transcript,
|
||||
hf_token=token,
|
||||
num_speakers=num_speakers,
|
||||
weights=EmphasisWeights.from_dict(config["emphasis_weights"]),
|
||||
peak_percentile=config["peak_percentile"],
|
||||
emphasis_floor=config["emphasis_floor"],
|
||||
emotion_enabled=config["emotion_enabled"],
|
||||
emotion_sensitivity=config["emotion_sensitivity"],
|
||||
)
|
||||
vt_arg = str(_validate_output_path(
|
||||
str(voice_timeline_path(media_path, output_dir)),
|
||||
anchor_dir=str(Path(output_dir) if output_dir else Path(media_path).parent),
|
||||
))
|
||||
save_voice_timeline(timeline, Path(vt_arg))
|
||||
else:
|
||||
# A timeline veio pronta; a mídia só é necessária se for aplicar e o
|
||||
# caller não a passou — deriva do próprio campo `source` da timeline.
|
||||
if not media_path:
|
||||
candidate = Path(vt_arg).parent / timeline.get("source", "")
|
||||
media_path = str(candidate) if candidate.is_file() else None
|
||||
|
||||
decision = generate_voice_actions(timeline, model=model, base_url=base_url)
|
||||
actions = decision["actions"]
|
||||
errors = list(decision["errors"])
|
||||
if not actions and errors:
|
||||
# The model produced nothing usable (transport error or unparseable
|
||||
# response) — report it clearly instead of a silent "0 decisions".
|
||||
raise RuntimeError(
|
||||
"O modelo local não devolveu decisões utilizáveis: " + "; ".join(errors)
|
||||
)
|
||||
|
||||
review = build_phrase_review(
|
||||
timeline,
|
||||
[a.as_dict() for a in actions],
|
||||
voice_timeline_path=str(vt_arg),
|
||||
)
|
||||
review_path, actions_path = save_phrase_review(str(vt_arg), review)
|
||||
|
||||
roteiro = _roteiro_markdown(review, timeline.get("source", ""))
|
||||
roteiro_path = Path(vt_arg).with_name(Path(vt_arg).stem.replace("_voice_timeline", "") + "_roteiro.md")
|
||||
roteiro_path.write_text(roteiro, encoding="utf-8")
|
||||
|
||||
applied_text = ""
|
||||
if apply:
|
||||
contents = await handle_apply_voice_actions({
|
||||
"filepath": fcpxml_path,
|
||||
"actions": [a.as_dict() for a in actions],
|
||||
"output_dir": output_dir,
|
||||
})
|
||||
applied_text = "\n\n" + "\n".join(getattr(c, "text", str(c)) for c in contents)
|
||||
|
||||
result = f"""# Roteiro por IA Local ({model})
|
||||
|
||||
## Resumo
|
||||
- **Fonte**: {timeline.get('source', '')}
|
||||
- **Duração**: {format_duration(timeline['summary']['duration'])}
|
||||
- **Decisões do modelo**: {len(actions)} (cortes/zoom/texto/marcador)
|
||||
- **Linha do tempo**: {vt_arg}
|
||||
- **Roteiro (legível)**: {roteiro_path}
|
||||
- **Ações JSON**: {actions_path}
|
||||
- **Revisão de frases**: {review_path}
|
||||
"""
|
||||
if errors:
|
||||
result += "\n## Rejeitado / avisos\n" + "\n".join(f"- {e}" for e in errors) + "\n"
|
||||
result += "\n---\n\n" + roteiro
|
||||
result += applied_text
|
||||
result += "\n\n*Tudo rodou internamente: o modelo local leu a timeline e decidiu a edição; nenhum passo manual foi necessário.*"
|
||||
return _text_result(result)
|
||||
|
||||
|
||||
def _roteiro_markdown(review: dict, source: str) -> str:
|
||||
"""The readable script: kept lines (roteiro) then the cut/bastidor lines."""
|
||||
phrases = review.get("phrases", [])
|
||||
kept = [p for p in phrases if p.get("active")]
|
||||
cut = [p for p in phrases if not p.get("active")]
|
||||
|
||||
lines = [f"# Roteiro — {source}", ""]
|
||||
lines.append(f"**{len(kept)} falas mantidas · {len(cut)} cortadas**")
|
||||
lines.append("")
|
||||
lines.append("## Roteiro (mantido)")
|
||||
if not kept:
|
||||
lines.append("_Nenhuma fala mantida._")
|
||||
for p in kept:
|
||||
tag = ""
|
||||
if p.get("emphasis", 0) >= 1:
|
||||
tag = f" · zoom nível {p['emphasis']}"
|
||||
spk = f"[{p.get('speaker', '')}] " if p.get("speaker") else ""
|
||||
lines.append(f"- {spk}{p.get('text', '')}{tag}")
|
||||
if p.get("reason"):
|
||||
lines.append(f" - _decisão_: {p['reason']}")
|
||||
if cut:
|
||||
lines.append("")
|
||||
lines.append("## Cortado / bastidor")
|
||||
for p in cut:
|
||||
spk = f"[{p.get('speaker', '')}] " if p.get("speaker") else ""
|
||||
lines.append(f"- {spk}{p.get('text', '')}")
|
||||
if p.get("reason"):
|
||||
lines.append(f" - _por que cortou_: {p['reason']}")
|
||||
return "\n".join(lines) + "\n"
|
||||
|
||||
|
||||
HANDLERS = {
|
||||
"diarize_media": handle_diarize_media,
|
||||
"analyze_voice_features": handle_analyze_voice_features,
|
||||
@@ -749,6 +914,7 @@ HANDLERS = {
|
||||
"remove_speakers": handle_remove_speakers,
|
||||
"refine_voice_timeline": handle_refine_voice_timeline,
|
||||
"apply_voice_actions": handle_apply_voice_actions,
|
||||
"generate_voice_script": handle_generate_voice_script,
|
||||
"get_voice_analysis_config": handle_get_voice_analysis_config,
|
||||
"save_voice_analysis_config": handle_save_voice_analysis_config,
|
||||
}
|
||||
|
||||
@@ -264,3 +264,26 @@ class TestIntegration:
|
||||
report = modifier.validate_subtitle_layout()
|
||||
assert report["summary"]["spatial_collision"] >= 1
|
||||
assert blocking(report["severity"])
|
||||
|
||||
def test_disabled_title_is_excluded_from_validation(self, temp_fcpxml):
|
||||
"""A title with enabled="0" never renders in Final Cut
|
||||
(generate_subtitles_by_emphasis disables plain titles under an
|
||||
emphasis phrase instead of never creating them) — it must not count
|
||||
as a collision, or as outside-frame/outside-safe-area, against the
|
||||
title actually drawn in its place."""
|
||||
modifier = FCPXMLModifier(temp_fcpxml)
|
||||
titles = modifier.generate_dynamic_subtitles("Interview_A", WORDS, WORD_MODE)
|
||||
|
||||
def position(el):
|
||||
for p in el.findall("param"):
|
||||
if p.get("name") == "Position":
|
||||
return p
|
||||
return None
|
||||
|
||||
p0 = position(titles[0])
|
||||
position(titles[1]).set("value", p0.get("value"))
|
||||
titles[1].set("enabled", "0")
|
||||
|
||||
report = modifier.validate_subtitle_layout()
|
||||
assert report["summary"]["spatial_collision"] == 0
|
||||
assert not blocking(report["severity"])
|
||||
|
||||
@@ -0,0 +1,232 @@
|
||||
"""Tests for fcpxml/forced_align.py — optional phonetic forced alignment.
|
||||
|
||||
The dependency (whisperx) is not installed in CI, so the core contract under
|
||||
test is graceful degradation: when whisperx is unavailable the aligner returns
|
||||
the words unchanged. A second group injects a fake whisperx module to verify
|
||||
the refined times are written back in order and that malformed results are
|
||||
skipped rather than clobbering good timestamps.
|
||||
"""
|
||||
|
||||
import sys
|
||||
import types
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
|
||||
from fcpxml.forced_align import ForcedAligner
|
||||
|
||||
|
||||
def _words():
|
||||
return [
|
||||
{"word": "Um,", "start": 0.0, "end": 0.5, "confidence": 0.9},
|
||||
{"word": "welcome", "start": 0.5, "end": 1.0, "confidence": 0.9},
|
||||
{"word": "show.", "start": 1.5, "end": 2.5, "confidence": 0.8},
|
||||
]
|
||||
|
||||
|
||||
def _raw_segments():
|
||||
return [
|
||||
{
|
||||
"text": "Um, welcome",
|
||||
"start": 0.0,
|
||||
"end": 1.0,
|
||||
"words": _words()[:2],
|
||||
},
|
||||
{
|
||||
"text": "show.",
|
||||
"start": 1.5,
|
||||
"end": 2.5,
|
||||
"words": _words()[2:],
|
||||
},
|
||||
]
|
||||
|
||||
|
||||
def _fake_whisperx(shift=0.4):
|
||||
"""A stand-in whisperx module that "corrects" word starts by ``shift``."""
|
||||
mod = types.SimpleNamespace()
|
||||
|
||||
def load_audio(path):
|
||||
return [0.0]
|
||||
|
||||
def load_align_model(language_code, device, model_dir=None):
|
||||
return ("MODEL", {"language": language_code})
|
||||
|
||||
def align(align_input, align_model, metadata, audio, device,
|
||||
return_char_alignments=False, chunk_size=30):
|
||||
segments = []
|
||||
for seg in align_input:
|
||||
new_words = []
|
||||
for w in seg["words"]:
|
||||
new_words.append(
|
||||
{
|
||||
"word": w["word"],
|
||||
"start": w["start"] + shift,
|
||||
"end": w["end"] + shift,
|
||||
"score": w["score"],
|
||||
}
|
||||
)
|
||||
segments.append({**seg, "words": new_words})
|
||||
return {"segments": segments}
|
||||
|
||||
mod.load_audio = load_audio
|
||||
mod.load_align_model = load_align_model
|
||||
mod.align = align
|
||||
return mod
|
||||
|
||||
|
||||
class TestForcedAlignerDegradation:
|
||||
def test_unavailable_when_whisperx_missing(self):
|
||||
assert ForcedAligner.available() is False
|
||||
|
||||
def test_returns_words_unchanged_when_whisperx_missing(self, monkeypatch):
|
||||
import builtins
|
||||
|
||||
real_import = builtins.__import__
|
||||
|
||||
def block(name, *a, **k):
|
||||
if name == "whisperx":
|
||||
raise ImportError("blocked")
|
||||
return real_import(name, *a, **k)
|
||||
|
||||
monkeypatch.setattr(builtins, "__import__", block)
|
||||
result = ForcedAligner().align(_words(), _raw_segments(), "x.wav", "en")
|
||||
assert result == _words()
|
||||
|
||||
def test_skips_when_no_words(self):
|
||||
assert ForcedAligner().align([], [], "x.wav", "en") == []
|
||||
|
||||
|
||||
class TestForcedAlignerWithWhisperX:
|
||||
@pytest.fixture
|
||||
def whisperx(self, monkeypatch):
|
||||
fake = _fake_whisperx(shift=0.4)
|
||||
monkeypatch.setitem(sys.modules, "whisperx", fake)
|
||||
return fake
|
||||
|
||||
def test_refines_timestamps_in_order(self, whisperx):
|
||||
words = _words()
|
||||
result = ForcedAligner().align(words, _raw_segments(), "x.wav", "en")
|
||||
assert [w["start"] for w in result] == [0.4, 0.9, 1.9]
|
||||
assert [w["end"] for w in result] == [0.9, 1.4, 2.9]
|
||||
# The same dict objects are returned with times overwritten in place.
|
||||
assert result[0]["start"] == 0.4
|
||||
assert words[0]["start"] == 0.4
|
||||
|
||||
def test_caches_align_model_per_language(self, whisperx, monkeypatch):
|
||||
calls = {"n": 0}
|
||||
orig = whisperx.load_align_model
|
||||
|
||||
def counting(*a, **k):
|
||||
calls["n"] += 1
|
||||
return orig(*a, **k)
|
||||
|
||||
whisperx.load_align_model = counting
|
||||
aligner = ForcedAligner()
|
||||
aligner.align(_words(), _raw_segments(), "a.wav", "en")
|
||||
aligner.align(_words(), _raw_segments(), "b.wav", "en")
|
||||
assert calls["n"] == 1
|
||||
|
||||
def test_skips_unusable_word_times(self, monkeypatch):
|
||||
fake = _fake_whisperx()
|
||||
# Force one word to come back with None start (alignment failed).
|
||||
real_align = fake.align
|
||||
|
||||
def broken(align_input, *a, **k):
|
||||
out = real_align(align_input, *a, **k)
|
||||
out["segments"][0]["words"][0]["start"] = None
|
||||
return out
|
||||
|
||||
fake.align = broken
|
||||
monkeypatch.setitem(sys.modules, "whisperx", fake)
|
||||
|
||||
words = _words()
|
||||
result = ForcedAligner().align(words, _raw_segments(), "x.wav", "en")
|
||||
# First word time untouched (None skipped), rest corrected.
|
||||
assert result[0]["start"] == 0.0
|
||||
assert result[1]["start"] == 0.9
|
||||
|
||||
def test_unexpected_exception_returns_original(self, monkeypatch):
|
||||
fake = types.SimpleNamespace()
|
||||
fake.load_audio = lambda p: [0.0]
|
||||
fake.load_align_model = lambda *a, **k: ("M", {})
|
||||
fake.align = lambda *a, **k: 1 / 0 # boom
|
||||
monkeypatch.setitem(sys.modules, "whisperx", fake)
|
||||
|
||||
words = _words()
|
||||
result = ForcedAligner().align(words, _raw_segments(), "x.wav", "en")
|
||||
assert result == words
|
||||
|
||||
|
||||
class TestTranscribeAlignmentFlag:
|
||||
"""Wire-up: transcribe() reports whether forced alignment ran."""
|
||||
|
||||
def _install_fakes(self, monkeypatch, align_shift=0.4):
|
||||
# faster_whisper
|
||||
fw = types.SimpleNamespace()
|
||||
|
||||
class _Word:
|
||||
def __init__(self, word, start, end, prob):
|
||||
self.word = word
|
||||
self.start = start
|
||||
self.end = end
|
||||
self.probability = prob
|
||||
|
||||
class _Seg:
|
||||
def __init__(self, text, start, end, words):
|
||||
self.text = text
|
||||
self.start = start
|
||||
self.end = end
|
||||
self.words = words
|
||||
|
||||
class _Info:
|
||||
language = "en"
|
||||
duration = 2.5
|
||||
|
||||
class _Model:
|
||||
def transcribe(self, path, language=None, word_timestamps=False, vad_filter=False):
|
||||
seg = _Seg(
|
||||
"Um, welcome show.",
|
||||
0.0,
|
||||
2.5,
|
||||
[
|
||||
_Word("Um,", 0.0, 0.5, 0.9),
|
||||
_Word("welcome", 0.5, 1.0, 0.9),
|
||||
_Word("show.", 1.5, 2.5, 0.8),
|
||||
],
|
||||
)
|
||||
return iter([seg]), _Info()
|
||||
|
||||
fw.WhisperModel = lambda *a, **k: _Model()
|
||||
|
||||
monkeypatch.setitem(sys.modules, "faster_whisper", fw)
|
||||
|
||||
# whisperx (only needed when align=True)
|
||||
wx = _fake_whisperx(shift=align_shift)
|
||||
monkeypatch.setitem(sys.modules, "whisperx", wx)
|
||||
|
||||
# model_manager.get_models_dir
|
||||
import fcpxml.model_manager as mm
|
||||
|
||||
monkeypatch.setattr(mm, "get_models_dir", lambda: Path("/tmp"))
|
||||
|
||||
def test_alignment_true_when_whisperx_present(self, monkeypatch, tmp_path):
|
||||
self._install_fakes(monkeypatch)
|
||||
f = tmp_path / "a.wav"
|
||||
f.write_bytes(b"RIFF0000WAVE")
|
||||
from fcpxml.transcribe import transcribe
|
||||
|
||||
result = transcribe(str(f), model_size="base", align=True)
|
||||
assert result is not None
|
||||
assert result["alignment"] is True
|
||||
assert result["words"][0]["start"] == pytest.approx(0.4)
|
||||
|
||||
def test_alignment_false_when_disabled(self, monkeypatch, tmp_path):
|
||||
self._install_fakes(monkeypatch)
|
||||
f = tmp_path / "a.wav"
|
||||
f.write_bytes(b"RIFF0000WAVE")
|
||||
from fcpxml.transcribe import transcribe
|
||||
|
||||
result = transcribe(str(f), model_size="base", align=False)
|
||||
assert result is not None
|
||||
assert result["alignment"] is False
|
||||
assert result["words"][0]["start"] == 0.0
|
||||
@@ -0,0 +1,259 @@
|
||||
"""Tests for the local LLM integration (Ollama): prompt building, JSON
|
||||
extraction, and turning a model response into validated voice actions.
|
||||
|
||||
The model itself is mocked — these tests cover the client contract
|
||||
(extraction, validation, message shape) without a running Ollama. A real
|
||||
end-to-end run lives in the manual test harness (see ENGINE notes) because it
|
||||
needs the local model server.
|
||||
"""
|
||||
|
||||
import json
|
||||
|
||||
import pytest
|
||||
|
||||
from fcpxml import llm_local
|
||||
from fcpxml.llm_local import (
|
||||
_extract_json,
|
||||
build_edit_messages,
|
||||
generate_voice_actions,
|
||||
)
|
||||
from fcpxml.voice_actions import parse_actions
|
||||
|
||||
|
||||
def _fake_timeline() -> dict:
|
||||
"""A minimal but well-formed voice timeline (matches build_voice_timeline)."""
|
||||
return {
|
||||
"version": "1.0",
|
||||
"source": "demo.mp4",
|
||||
"rotation": 0.0,
|
||||
"language": "pt",
|
||||
"layers": {
|
||||
"transcript": True,
|
||||
"acoustics": True,
|
||||
"speakers": False,
|
||||
"emotion": False,
|
||||
"alignment": False,
|
||||
},
|
||||
"scales": {},
|
||||
"summary": {
|
||||
"duration": 12.0,
|
||||
"speaker_count": 1,
|
||||
"segment_count": 2,
|
||||
"word_count": 4,
|
||||
"avg_emphasis": 0.4,
|
||||
"peak_selection": "top 2%",
|
||||
"peak_count": 1,
|
||||
"peak_moments": [],
|
||||
},
|
||||
"speakers": [
|
||||
{
|
||||
"id": "SPEAKER_00",
|
||||
"name": "Speaker 1",
|
||||
"speaking_seconds": 12.0,
|
||||
"share": 1.0,
|
||||
"segment_count": 2,
|
||||
"avg_segment": 6.0,
|
||||
"word_count": 4,
|
||||
"samples": [],
|
||||
}
|
||||
],
|
||||
"segments": [
|
||||
{
|
||||
"start": 0.0,
|
||||
"end": 6.0,
|
||||
"speaker": "SPEAKER_00",
|
||||
"text": "Hoje vamos falar de mastopexia.",
|
||||
"gap_before": 0.0,
|
||||
"take_boundary": False,
|
||||
"avg_energy": 0.5,
|
||||
"peak_emphasis": 0.6,
|
||||
"emotion": "neutral",
|
||||
"emotion_confidence": 0.0,
|
||||
"arousal": 0.5,
|
||||
"valence": 0.6,
|
||||
"words": [
|
||||
{"text": "Hoje", "start": 0.1, "end": 0.5, "speaker": "SPEAKER_00",
|
||||
"energy": 0.3, "pitch_delta": 0.2, "rate_delta": 0.1, "pause_before": 0.1,
|
||||
"emphasis": 0.3, "emotion": "neutral", "emotion_confidence": 0.0,
|
||||
"arousal": 0.4, "valence": 0.6, "energy_raw": 0.3, "pitch_hz": 120.0},
|
||||
{"text": "vamos", "start": 0.6, "end": 0.9, "speaker": "SPEAKER_00",
|
||||
"energy": 0.4, "pitch_delta": 0.3, "rate_delta": 0.2, "pause_before": 0.0,
|
||||
"emphasis": 0.5, "emotion": "neutral", "emotion_confidence": 0.0,
|
||||
"arousal": 0.5, "valence": 0.6, "energy_raw": 0.4, "pitch_hz": 125.0},
|
||||
],
|
||||
},
|
||||
{
|
||||
"start": 6.0,
|
||||
"end": 12.0,
|
||||
"speaker": "SPEAKER_00",
|
||||
"text": "E aí cara, tá gravando?",
|
||||
"gap_before": 0.2,
|
||||
"take_boundary": False,
|
||||
"avg_energy": 0.4,
|
||||
"peak_emphasis": 0.3,
|
||||
"emotion": "neutral",
|
||||
"emotion_confidence": 0.0,
|
||||
"arousal": 0.4,
|
||||
"valence": 0.5,
|
||||
"words": [
|
||||
{"text": "E", "start": 6.1, "end": 6.3, "speaker": "SPEAKER_00",
|
||||
"energy": 0.3, "pitch_delta": 0.2, "rate_delta": 0.1, "pause_before": 0.1,
|
||||
"emphasis": 0.3, "emotion": "neutral", "emotion_confidence": 0.0,
|
||||
"arousal": 0.4, "valence": 0.5, "energy_raw": 0.3, "pitch_hz": 120.0},
|
||||
{"text": "cara", "start": 6.4, "end": 6.7, "speaker": "SPEAKER_00",
|
||||
"energy": 0.4, "pitch_delta": 0.3, "rate_delta": 0.2, "pause_before": 0.0,
|
||||
"emphasis": 0.4, "emotion": "neutral", "emotion_confidence": 0.0,
|
||||
"arousal": 0.4, "valence": 0.5, "energy_raw": 0.4, "pitch_hz": 122.0},
|
||||
],
|
||||
},
|
||||
],
|
||||
}
|
||||
|
||||
|
||||
def test_extract_json_unfenced():
|
||||
text = '{"source": "x", "actions": []}'
|
||||
assert _extract_json(text) == {"source": "x", "actions": []}
|
||||
|
||||
|
||||
def test_extract_json_with_fence():
|
||||
text = '```json\n{"source": "x", "actions": [{"kind": "cut", "start": 1, "end": 2}]}\n```'
|
||||
data = _extract_json(text)
|
||||
assert data["source"] == "x"
|
||||
assert data["actions"][0]["kind"] == "cut"
|
||||
|
||||
|
||||
def test_extract_json_with_prose_around():
|
||||
text = 'Aqui está:\n{"source": "x", "actions": []}\nfim.'
|
||||
assert _extract_json(text) == {"source": "x", "actions": []}
|
||||
|
||||
|
||||
def test_extract_json_invalid_returns_none():
|
||||
assert _extract_json("no json here") is None
|
||||
assert _extract_json("") is None
|
||||
|
||||
|
||||
def test_extract_json_unwraps_wrapped_list():
|
||||
# Some models wrap the expected {"source","actions"} object in a
|
||||
# single-element list; the actions must still be found.
|
||||
text = '```json\n[{"source": "x", "actions": [{"kind": "cut", "start": 1, "end": 2}]}]\n```'
|
||||
data = _extract_json(text)
|
||||
assert isinstance(data, dict)
|
||||
assert data["source"] == "x"
|
||||
assert data["actions"][0]["kind"] == "cut"
|
||||
|
||||
|
||||
def test_build_edit_messages_shape():
|
||||
system, user = build_edit_messages(_fake_timeline())
|
||||
assert "editor" in system.lower()
|
||||
assert "demo.mp4" in user
|
||||
assert "segments" in user
|
||||
assert "SOMENTE" in user
|
||||
|
||||
|
||||
def test_generate_voice_actions_parses_model_json(monkeypatch):
|
||||
canned = json.dumps({
|
||||
"source": "demo.mp4",
|
||||
"actions": [
|
||||
{"kind": "cut", "start": 6.0, "end": 12.0,
|
||||
"reason": "papo casual com a equipe"},
|
||||
{"kind": "zoom", "start": 0.1, "end": 0.9, "params": {"scale": 1.3},
|
||||
"reason": "ênfase em 'vamos'"},
|
||||
],
|
||||
})
|
||||
monkeypatch.setattr(llm_local, "ollama_chat", lambda **kwargs: canned)
|
||||
|
||||
result = generate_voice_actions(_fake_timeline(), model="test")
|
||||
actions, errors = parse_actions([a.as_dict() for a in result["actions"]])
|
||||
assert errors == []
|
||||
assert len(actions) == 2
|
||||
assert {a.kind for a in actions} == {"cut", "zoom"}
|
||||
assert result["errors"] == []
|
||||
|
||||
|
||||
def test_generate_voice_actions_reports_bad_rows(monkeypatch):
|
||||
canned = json.dumps({
|
||||
"source": "demo.mp4",
|
||||
"actions": [
|
||||
{"kind": "cut", "start": 6.0, "end": 12.0, "reason": "ok"},
|
||||
{"kind": "bogus", "start": 1, "end": 2},
|
||||
{"kind": "zoom", "start": 0.1, "end": 0.05},
|
||||
],
|
||||
})
|
||||
monkeypatch.setattr(llm_local, "ollama_chat", lambda **kwargs: canned)
|
||||
|
||||
result = generate_voice_actions(_fake_timeline(), model="test")
|
||||
assert len(result["actions"]) == 1
|
||||
assert result["actions"][0].kind == "cut"
|
||||
assert len(result["errors"]) >= 2
|
||||
|
||||
|
||||
def test_generate_voice_actions_handles_transport_error(monkeypatch):
|
||||
def _boom(**kwargs):
|
||||
raise RuntimeError("ollama down")
|
||||
monkeypatch.setattr(llm_local, "ollama_chat", _boom)
|
||||
|
||||
result = generate_voice_actions(_fake_timeline(), model="test")
|
||||
assert result["actions"] == []
|
||||
assert any("ollama" in e.lower() for e in result["errors"])
|
||||
|
||||
|
||||
def test_build_edit_messages_is_compact():
|
||||
"""The prompt must drop the heavy per-word audio features so a real
|
||||
recording fits in the model context (the 47k-token dump made Ollama drop
|
||||
the connection)."""
|
||||
import json as _json
|
||||
|
||||
timeline = _fake_timeline()
|
||||
# pad with the kind of audio detail a real timeline carries
|
||||
timeline["segments"][0]["words"][0]["samples"] = [0.1, 0.2, 0.3]
|
||||
timeline["speakers"][0]["samples"] = [1, 2, 3]
|
||||
raw = _json.dumps(timeline, ensure_ascii=False)
|
||||
system, user = build_edit_messages(timeline)
|
||||
assert "demo.mp4" in user and "segments" in user
|
||||
# projected prompt is materially smaller than the raw timeline
|
||||
assert len(user) < len(raw)
|
||||
# heavy fields we deliberately drop never reach the model
|
||||
assert '"samples"' not in user
|
||||
assert '"energy_raw"' not in user
|
||||
assert '"pitch_hz"' not in user
|
||||
|
||||
|
||||
def test_ollama_chat_wraps_empty_response(monkeypatch):
|
||||
"""A dropped connection that yields an empty body must surface as a clear
|
||||
RuntimeError (not an unhandled JSONDecodeError crashing the pipeline)."""
|
||||
|
||||
class _Resp:
|
||||
def raise_for_status(self):
|
||||
pass
|
||||
|
||||
def json(self):
|
||||
raise ValueError("Expecting value: line 1 column 1")
|
||||
|
||||
monkeypatch.setattr(llm_local.httpx, "post", lambda *a, **k: _Resp())
|
||||
try:
|
||||
llm_local.ollama_chat(model="test", messages=[{"role": "user", "content": "x"}])
|
||||
except RuntimeError as exc:
|
||||
assert "Falha ao falar com o modelo local" in str(exc)
|
||||
else:
|
||||
raise AssertionError("expected RuntimeError")
|
||||
|
||||
|
||||
def test_list_ollama_models_parses_tags(monkeypatch):
|
||||
class _Resp:
|
||||
def raise_for_status(self):
|
||||
pass
|
||||
|
||||
def json(self):
|
||||
return {"models": [{"name": "gemma3:4b"}, {"name": "gemma3:12b"}]}
|
||||
|
||||
monkeypatch.setattr(llm_local.httpx, "get", lambda *a, **k: _Resp())
|
||||
assert llm_local.list_ollama_models() == ["gemma3:12b", "gemma3:4b"]
|
||||
|
||||
|
||||
def test_list_ollama_models_returns_empty_on_error(monkeypatch):
|
||||
def _boom(*a, **k):
|
||||
raise RuntimeError("ollama down")
|
||||
|
||||
monkeypatch.setattr(llm_local.httpx, "get", _boom)
|
||||
assert llm_local.list_ollama_models() == []
|
||||
|
||||
@@ -0,0 +1,134 @@
|
||||
"""Tests for layering subtitle generation by emphasis (etapa 5 review).
|
||||
|
||||
Covers the pure helpers in server_tools/subtitles.py that decide which words
|
||||
also get the dynamic treatment on top of the always-complete plain track, and
|
||||
which clip-relative windows a plain title must be hidden (enabled="0") under.
|
||||
"""
|
||||
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
from server_tools.subtitles import (
|
||||
_load_emphasis_spans,
|
||||
_overlaps_any_span,
|
||||
_phrase_actions_path,
|
||||
_segments_in_spans,
|
||||
_word_in_spans,
|
||||
_words_in_spans,
|
||||
)
|
||||
|
||||
SPANS = [
|
||||
{"start": 2.0, "end": 10.7, "level": 1, "text": "abertura"},
|
||||
{"start": 127.7, "end": 135.0, "level": 2, "text": "mastopexia"},
|
||||
]
|
||||
|
||||
|
||||
def test_phrase_actions_path_matches_media_stem():
|
||||
assert _phrase_actions_path("/x/y/0E6A8290.mp4") == Path(
|
||||
"/x/y/0E6A8290_phrase_actions.json"
|
||||
)
|
||||
|
||||
|
||||
class TestLoadEmphasisSpans:
|
||||
def test_missing_file_returns_empty(self, tmp_path):
|
||||
media = tmp_path / "clip.mp4"
|
||||
media.write_bytes(b"")
|
||||
assert _load_emphasis_spans(str(media)) == []
|
||||
|
||||
def test_reads_spans_from_sibling_json(self, tmp_path):
|
||||
media = tmp_path / "clip.mp4"
|
||||
media.write_bytes(b"")
|
||||
actions_path = tmp_path / "clip_phrase_actions.json"
|
||||
actions_path.write_text(
|
||||
json.dumps({"source": "clip.mp4", "actions": [], "emphasis_spans": SPANS}),
|
||||
encoding="utf-8",
|
||||
)
|
||||
assert _load_emphasis_spans(str(media)) == SPANS
|
||||
|
||||
def test_corrupt_json_returns_empty(self, tmp_path):
|
||||
media = tmp_path / "clip.mp4"
|
||||
media.write_bytes(b"")
|
||||
(tmp_path / "clip_phrase_actions.json").write_text("{not json", encoding="utf-8")
|
||||
assert _load_emphasis_spans(str(media)) == []
|
||||
|
||||
def test_missing_emphasis_spans_key_returns_empty(self, tmp_path):
|
||||
media = tmp_path / "clip.mp4"
|
||||
media.write_bytes(b"")
|
||||
(tmp_path / "clip_phrase_actions.json").write_text(
|
||||
json.dumps({"source": "clip.mp4", "actions": []}), encoding="utf-8"
|
||||
)
|
||||
assert _load_emphasis_spans(str(media)) == []
|
||||
|
||||
|
||||
class TestWordInSpans:
|
||||
def test_word_fully_inside_span(self):
|
||||
assert _word_in_spans(3.0, 3.4, SPANS) is True
|
||||
|
||||
def test_word_fully_outside_every_span(self):
|
||||
assert _word_in_spans(50.0, 50.4, SPANS) is False
|
||||
|
||||
def test_word_straddling_span_boundary_follows_its_midpoint(self):
|
||||
# midpoint 10.6 -> inside [2.0, 10.7)
|
||||
assert _word_in_spans(10.4, 10.8, SPANS) is True
|
||||
# midpoint 10.9 -> outside
|
||||
assert _word_in_spans(10.7, 11.1, SPANS) is False
|
||||
|
||||
def test_span_end_is_exclusive(self):
|
||||
assert _word_in_spans(10.7, 10.7, SPANS) is False
|
||||
|
||||
|
||||
class TestWordsInSpans:
|
||||
WORDS = [
|
||||
{"word": "Aquela", "start": 2.03, "end": 2.69},
|
||||
{"word": "mama", "start": 2.69, "end": 2.89},
|
||||
{"word": "fora", "start": 50.0, "end": 50.2},
|
||||
{"word": "mastopexia", "start": 127.74, "end": 128.58},
|
||||
]
|
||||
|
||||
def test_no_spans_returns_nothing(self):
|
||||
assert _words_in_spans(self.WORDS, []) == []
|
||||
|
||||
def test_keeps_only_words_inside_a_span(self):
|
||||
kept = _words_in_spans(self.WORDS, SPANS)
|
||||
assert [w["word"] for w in kept] == ["Aquela", "mama", "mastopexia"]
|
||||
|
||||
def test_does_not_remove_words_from_the_source_list(self):
|
||||
"""This is a filter for the dynamic half, not a partition — the plain
|
||||
half must still see every word, so this must never mutate `words`."""
|
||||
before = list(self.WORDS)
|
||||
_words_in_spans(self.WORDS, SPANS)
|
||||
assert self.WORDS == before
|
||||
|
||||
|
||||
class TestOverlapsAnySpan:
|
||||
CLIP_SPANS = [(0.0, 8.64), (60.0, 67.22)]
|
||||
|
||||
def test_window_inside_a_span_overlaps(self):
|
||||
assert _overlaps_any_span(1.0, 2.0, self.CLIP_SPANS) is True
|
||||
|
||||
def test_window_outside_every_span_does_not_overlap(self):
|
||||
assert _overlaps_any_span(20.0, 21.0, self.CLIP_SPANS) is False
|
||||
|
||||
def test_window_straddling_a_span_edge_overlaps(self):
|
||||
assert _overlaps_any_span(8.0, 9.0, self.CLIP_SPANS) is True
|
||||
|
||||
def test_touching_but_not_overlapping_is_not_an_overlap(self):
|
||||
assert _overlaps_any_span(8.64, 9.0, self.CLIP_SPANS) is False
|
||||
|
||||
def test_no_spans_never_overlaps(self):
|
||||
assert _overlaps_any_span(1.0, 2.0, []) is False
|
||||
|
||||
|
||||
class TestSegmentsInSpans:
|
||||
SEGMENTS = [
|
||||
{"start": 2.0, "end": 10.67},
|
||||
{"start": 40.0, "end": 43.0},
|
||||
{"start": 127.74, "end": 134.96},
|
||||
]
|
||||
|
||||
def test_no_spans_keeps_nothing(self):
|
||||
assert _segments_in_spans(self.SEGMENTS, []) == []
|
||||
|
||||
def test_keeps_only_segments_inside_a_span(self):
|
||||
kept = _segments_in_spans(self.SEGMENTS, SPANS)
|
||||
assert kept == [self.SEGMENTS[0], self.SEGMENTS[2]]
|
||||
Generated
+458
-309
@@ -200,6 +200,12 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/99/91/8acff4f5e50511b911bbccb72b8628a49c68ce14148cd9f6431094859a90/annotated_types-0.8.0-py3-none-any.whl", hash = "sha256:f072f4d804ea359e4eaf198b1af7a8b0943881a87f31bb764f8bf219bb9419e0", size = 13427, upload-time = "2026-07-23T20:16:12.938Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "antlr4-python3-runtime"
|
||||
version = "4.9.3"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/3e/38/7859ff46355f76f8d19459005ca000b6e7012f2f1ca597746cbcd1fbfe5e/antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b", size = 117034, upload-time = "2021-11-06T17:52:23.524Z" }
|
||||
|
||||
[[package]]
|
||||
name = "anyio"
|
||||
version = "4.14.2"
|
||||
@@ -973,85 +979,6 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/c0/03/126e963fc3237a416f3085b8a663ebd8ab449ed6c37195b4e0b49597ba0c/ctranslate2-4.8.1-cp314-cp314t-win_amd64.whl", hash = "sha256:dc9f1abef55579cc02cdc74b3a55df38491ec56d177d6e6039609d61d09ed30e", size = 19499597, upload-time = "2026-07-03T12:40:01.68Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "cuda-bindings"
|
||||
version = "13.3.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "cuda-pathfinder" },
|
||||
]
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/a9/21/8464d133752951c154feafb3b65c297e7d80f301183d220bec4c830f1441/cuda_bindings-13.3.1-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:120fcc53d57903df529c3486962c56528cba5b7d6c57c99537320ed9922c8b86", size = 6073403, upload-time = "2026-05-29T23:11:36.22Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/a8/1f/5ef51f5fbaa5d4d3201bb3d7555af028ec1aa4416275ccbf73c9e34e3d2d/cuda_bindings-13.3.1-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9851b0caa8bfd3bc6fa054eaf57bea7c8e9c3a62db2d2621224677f49f3c53d0", size = 6675244, upload-time = "2026-05-29T23:11:38.664Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/51/6b/457ca12dad3ee9bfcc9a545cfd6b64b359ba49de40f776f6e028e678f262/cuda_bindings-13.3.1-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c5879712accf6e14bb01aa5e67440eb84998b8d104b509cc7a6dc0b8f656a474", size = 6053539, upload-time = "2026-05-29T23:11:43.19Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/95/7a/c5e3c34a409b148f5c0f5a4ea374158f95d488862c1dffedf9aa5c639df9/cuda_bindings-13.3.1-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:04436a9364059c84b8f9636f359eccda1cf814341f5b670c71d80d2f79dbc708", size = 6674166, upload-time = "2026-05-29T23:11:45.478Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/ce/67/5e7dba1ba576dd73da5dee894ca076ca5e959450dfff66d6d510a255d1f7/cuda_bindings-13.3.1-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c7855c4868aabc0cfae28abbe83d56734bdfbd08f08fc234ac1912a12858bf49", size = 6025351, upload-time = "2026-05-29T23:11:49.685Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/39/2a/6d2e9047d1fb243dbaa364b01e0297534b9ed7fd27dba1c9f361519cf69b/cuda_bindings-13.3.1-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e32d08f71ebcdf00f0f41eab2eb37e8da94c8ed411cc9f7f7a019ce6b34abe3a", size = 6657965, upload-time = "2026-05-29T23:11:52.227Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/cc/6e/2394f8163360f8391f8f1b7e72d300a82724edb81a7b7084c799fbd4c91f/cuda_bindings-13.3.1-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9efb21c1ee64981e184b9e0ba5eb3179e5ba3d4b51665a6cb52b8ef3d01a7cbf", size = 5920504, upload-time = "2026-05-29T23:11:56.883Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/34/c2/ef9b6a63f7dc432712a462c816662e662e00d38caa9b861c8c2588195d03/cuda_bindings-13.3.1-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2732904099e0a4d4db774a5fc6d91ee95fae065b4d2ecabb4968c5fe2406c9d7", size = 6476660, upload-time = "2026-05-29T23:11:59.188Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/b1/81/bff68ce829999c1e4209c761bbf903b1c06ec570416ddb25020864ad5907/cuda_bindings-13.3.1-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1ab2f74ed65bfef4163ba07a8db16f1085e0729291db12a2423aff84ee8278b8", size = 6013639, upload-time = "2026-05-29T23:12:03.509Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/d4/e0/c8a1f0c8f9ffdea4f5fe6dbab89b326cef4d85caf489dad39e209da89416/cuda_bindings-13.3.1-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:efd4c814d311ec08c981f6dded1dbe7d4b371067ee4f6c14cccec4bde9590f80", size = 6534419, upload-time = "2026-05-29T23:12:05.633Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/52/b8/83b1f563925b290f2d11a01a77a84013ba56052fe3653a5bef3ccfbb43d6/cuda_bindings-13.3.1-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c3c772dfff49681541d59630c90f858e173ac926b9c593a2b7123f2a1043cc76", size = 5809771, upload-time = "2026-05-29T23:12:10.422Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/12/20/e79b4bfe98f075195afb6343d41c498f9dbd2d161d7021d4d28bceb83581/cuda_bindings-13.3.1-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:36febb7c1079d68a981dbbd8d5a67235b399802b82075c9388624719607e52b9", size = 6358584, upload-time = "2026-05-29T23:12:12.767Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "cuda-pathfinder"
|
||||
version = "1.6.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/fc/b4/d088047afe39827556df21118cac9ffd20cc3f968c99a7681494d1eb333c/cuda_pathfinder-1.6.0-py3-none-any.whl", hash = "sha256:1503af579d8379c24bdd65528379bc57039b0455be9f5f9686cf8e473a1fce51", size = 54591, upload-time = "2026-07-21T15:03:56.224Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "cuda-toolkit"
|
||||
version = "13.0.3.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/d1/c7/a79086a62c98befcdb8349656c6f114e2db3b8b2422f6e25c97a7f2a9a3c/cuda_toolkit-13.0.3.0-py2.py3-none-any.whl", hash = "sha256:d693caaa261214ddd7dbb60d68e71cbed884e68c2be7509778f3051da0b91c3f", size = 2512, upload-time = "2026-04-14T00:50:08.173Z" },
|
||||
]
|
||||
|
||||
[package.optional-dependencies]
|
||||
cublas = [
|
||||
{ name = "nvidia-cublas", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" },
|
||||
{ name = "nvidia-cuda-nvrtc", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" },
|
||||
]
|
||||
cudart = [
|
||||
{ name = "nvidia-cuda-runtime", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" },
|
||||
]
|
||||
cufft = [
|
||||
{ name = "nvidia-cufft", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" },
|
||||
{ name = "nvidia-nvjitlink", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" },
|
||||
]
|
||||
cufile = [
|
||||
{ name = "nvidia-cufile", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" },
|
||||
]
|
||||
cupti = [
|
||||
{ name = "nvidia-cuda-cupti", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" },
|
||||
]
|
||||
curand = [
|
||||
{ name = "nvidia-curand", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" },
|
||||
]
|
||||
cusolver = [
|
||||
{ name = "nvidia-cublas", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" },
|
||||
{ name = "nvidia-cusolver", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" },
|
||||
{ name = "nvidia-cusparse", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" },
|
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]
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[[package]]
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@@ -5341,6 +5462,34 @@ wheels = [
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]
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[[package]]
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{ name = "faster-whisper" },
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{ name = "huggingface-hub" },
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{ name = "nltk" },
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{ name = "pyannote-audio" },
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{ name = "torch" },
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{ name = "transformers" },
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wheels = [
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[[package]]
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name = "yarl"
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version = "1.24.5"
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Reference in New Issue
Block a user