Registro público
Informe de salud del softwareesquema 0.12.0 · métricas 1.13.0 · 2026-07-17 23:19 UTC

usefulsensors / moonshine

Very low latency speech to text, intent recognition, and text to speech, for building voice agents and interfaces

C++ · CLicencia propia★ 8729 estrellas⑂ 476 forksdesde oct 2024Ver en GitHub ↗

usefulsensors/moonshine tiene un índice de salud de 65 sobre 100, lo que lo sitúa en la banda Moderado. Su puntuación más alta es Vitality (88/100) y la más baja, Security (1/100). Se actualizó por última vez hace 1 día. 2 personas concentran la mayor parte del trabajo reciente.

65
global / 100
Moderado

Índice de salud del software

Las métricas se agrupan en categorías ponderadas sobre una escala de 1 a 100. El resultado global parte de su media; cuando la evidencia pública activa la Política de Jurisdicciones de Alto Riesgo, la calificación se ajusta y recibe el límite 49 (En riesgo). Preparación para IA queda fuera.

65
Excelente85-100Ejemplar; cumple prácticamente todos los criterios evaluados
Bueno70-84Saludable; carencias menores
Moderado50-69Aceptable con carencias notables; se recomienda revisión
En riesgo30-49Debilidades significativas; su adopción exige cautela
Crítico1-29Problemas graves (proyecto abandonado, un solo mantenedor, sin higiene)
VitalidadComunidad yAdopciónSostenibilidady GobernanzaCalidad deIngenieríaSeguridadPreparaciónpara IA

Perfil de puntuación

Cada eje es una categoría. La forma importa más que la media: un proyecto sano llena toda la figura, mientras que un perfil de picos y cráteres indica que la fortaleza en una dimensión enmascara el riesgo en otra.

Titularidad

Moonshine AIOrganización
446 seguidores74 repositorios públicosdesde ene 2022

Este repositorio está respaldado por una organización: una custodia compartida y responsable que puede sobrevivir a cualquier mantenedor individual.

Ecosistemas de paquetes

RegistroPaqueteVersiónDescargas / mesVersionesÚltima publicaciónEtiquetas
PyPImoonshine-voiceapunta a otro repositorio; no se puntúa0.0.6925.59136hace 1 díaspeechtranscriptionvoiceaion-devicestt

Métricas por categoría

Vitalidad

¿Está vivo el proyecto: se escribe código y se publican versiones?

88Excelente · 22% del índice global
Cómo se puntúa
36/36Recencia de push — último push hace 1 días
18/36Cadencia de commits — 26/52 semanas con commits
18/18Volumen de commits — 484 commits en el último año
0/10OpenSSF Scorecard: Maintained — sin datos
Datos de entrada utilizados
commits_last_year484
human_commit_share
days_since_last_push1
active_weeks_last_year26
Cómo se puntúa
27/27Publica versiones — 16 versiones publicadas
36/36Recencia de las versiones — última versión hace 1 días
27/27Cadencia de publicación — una versión cada ~10,3 días
0/10OpenSSF Scorecard: Signed-Releases — sin datos
Datos de entrada utilizados
releases_count16
latest_release_tagv0.0.69
releases_from_tagsno
days_since_latest_release1
mean_days_between_releases10,3

Comunidad y Adopción

¿Tiene el proyecto usuarios, descargas, atención y unas condiciones acogedoras para quienes contribuyen?

70Bueno · 18% del índice global
Cómo se puntúa
60/60Estrellas — 8729 estrellas
22.3/25Forks — 476 forks
9.9/15Observadores — 62 observadores
Datos de entrada utilizados
forks476
stars8729
watchers62
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
Cómo se puntúa
22.5/22.5README
16.9/22.5Licencia — archivo de licencia presente, no es una licencia reconocida
0/18Guía CONTRIBUTING
0/13.5Código de conducta
0/7.2Plantilla de issues
0/6.3Plantilla de PR
Datos de entrada utilizados
has_readme
has_license
has_contributingno
has_issue_templateno
has_code_of_conductno
has_pull_request_templateno

Sostenibilidad y Gobernanza

¿Sobrevivirá el proyecto a sus personas: factor bus, capacidad de respuesta, quién lo respalda y mantenimiento del paquete?

72Bueno · 24% del índice global
Cómo se puntúa
25.2/54Factor bus — la mitad de los commits recae en 2 contribuyente(s)
15.3/22.5Distribución de commits — el principal contribuyente firma el 32% de los commits
13.5/13.5Amplitud de contribuyentes — 15 contribuyentes
0/10OpenSSF Scorecard: Contributors — sin datos
Datos de entrada utilizados
bus_factor2
contributors_sampled15
top_contributor_share0,321
Cómo se puntúa
45.3/46.8Resolución de issues — 97% de issues cerradas
27.9/38.3Aceptación de PR — 62/85 PR decididos fusionados
0/15OpenSSF Scorecard: Code-Review — sin datos
Datos de entrada utilizados
merged_prs62
open_issues3
closed_issues91
issue_closed_ratio0,968
closed_unmerged_prs23
Cómo se puntúa
30/30Respaldo de la propiedad — propiedad de una organización
0/20Dominio verificado
19.1/25Alcance del propietario — 446 seguidores de moonshine-ai
21.9/25Trayectoria — 74 repos públicos, cuenta de ~4 años
Datos de entrada utilizados
followers446
owner_typeOrganization
is_verified
owner_loginmoonshine-ai
public_repos74
account_age_days1630

Calidad de Ingeniería

¿Existen unas prácticas mínimas de ingeniería y documentación?

76Bueno · 20% del índice global
Cómo se puntúa
24/24Flujos de trabajo de CI — 1 flujo(s) de trabajo
24/24Pruebas presentes
0/16Configuración de linter
0/9.6Hooks de pre-commit
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — sin datos
Datos de entrada utilizados
has_ci
has_tests
has_editorconfigno
has_linter_configno
has_precommit_configno

Documentación

100Excelente
Cómo se puntúa
30/30README
25/25Directorio de documentación
15/15Sitio de documentación / página del proyecto — https://moonshine.ai
10/10Descripción del repositorio
10/10Topics — 5 topics
10/10Wiki
Datos de entrada utilizados
topicsintent-recognition, stt, tts, voice, voice-recognition
has_wiki
homepagehttps://moonshine.ai
has_readme
has_docs_dir
has_description

Seguridad

¿Son sólidas las prácticas visibles de seguridad y de cadena de suministro, sin exposición jurisdiccional de alto riesgo sin resolver?

1Crítico · 16% del índice global
Cómo se puntúa
0/30Política de seguridad (SECURITY.md)
0/25Configuración de Dependabot
0/25Lockfiles de dependencias
0/20Flujo de trabajo de CodeQL
Datos de entrada utilizados
sourcefile_signals
lockfiles
manifestsbuild.gradle.kts, python/pyproject.toml, python/setup.py
has_codeql_workflowno
has_security_policyno
has_dependabot_configno

Preparación para IA

¿Hasta qué punto está el repositorio preparado para desarrollarse y mantenerse con agentes de codificación de IA? Es una insignia independiente y experimental — peso 0,0, de modo que se presenta por separado y no afecta a la puntuación de salud global.

45En riesgo · 0% del índice global
Cómo se puntúa
0/45Instrucciones para agentes — sin CLAUDE.md / AGENTS.md / reglas de editor
0/15Documentación legible por máquinas (llms.txt)
0/40Historial de commits legible — sin datos
Datos de entrada utilizados
has_llms_txtno
legible_history_share
agent_instruction_files
agent_instruction_max_bytes
Excluidos de la puntuación (sin datos o no aplicable): Historial de commits legible. Los pesos restantes se han renormalizado.
Cómo se puntúa
0/18Arranque con un solo comando
22/22Pruebas automatizadas
0/11Configuración de lint / formato
11/11Verificación estática de tipos — C++ (tipado estático)
10/10Entorno reproducible — Dockerfile
0/10Práctica demostrada con agentes — sin datos
0/8Mantenimiento automatizado — sin datos
0/10OpenSSF Scorecard: Pinned-Dependencies — sin datos
Datos de entrada utilizados
has_nixno
has_tests
lockfiles
has_dockerfile
typed_language
bootstrap_files
has_devcontainerno
has_linter_configno
typecheck_configs
agent_commit_share
toolchain_manifests
dependency_bot_commit_share
Excluidos de la puntuación (sin datos o no aplicable): Práctica demostrada con agentes, Mantenimiento automatizado. Los pesos restantes se han renormalizado.
Cómo se puntúa
45/45Código verificable por tipos — C++ (tipado estático)
53.7/55Tamaños de archivo manejables — 30/1254 archivos fuente de más de 60 KB
Datos de entrada utilizados
primary_languageC++
largest_source_bytes15.335.389
source_files_sampled1254
oversized_source_files30
Cómo se puntúa
0/40Esquema de API (OpenAPI/GraphQL/proto)
0/20Servidor MCP
40/40Ejemplos ejecutables — examples, notebooks
Datos de entrada utilizados
example_dirsexamples, notebooks
has_mcp_signalno
api_schema_files

Datos clave

8729estrellas de GitHub
15contribuidores
484commits en los últimos 12 meses
1días desde el último push
16versiones publicadas
2factor bus
3issues abiertas
Maven, PyPIecosistemas de paquetes

Advertencias de recopilación de datos

  • pypi package 'moonshine-voice' points at a different repository (https://github.com/moonshine-ai/moonshine); excluded from ecosystem scoring
  • OpenSSF Scorecard timed out after 240s; skipping Scorecard checks

Más detalle

Dependencias directas 6
RegistroPaqueteRestricción de versiónManifiesto
PyPInumpypython/pyproject.toml
PyPIsounddevicepython/pyproject.toml
PyPIrequestspython/pyproject.toml
PyPItqdmpython/pyproject.toml
PyPIfilelockpython/pyproject.toml
PyPIplatformdirspython/pyproject.toml
Todas las dependencias 30

Conjunto completo de dependencias resueltas según el grafo de dependencias de GitHub: 6 paquetes directos y 24 indirectos (transitivos). El cierre transitivo es completo cuando el repositorio incluye un lockfile.

RegistroPaqueteVersiónRelación
PyPIfilelockdirecta
PyPInumpydirecta
PyPIplatformdirsdirecta
PyPIrequestsdirecta
PyPIsounddevicedirecta
PyPItqdmdirecta
PyPIai-edge-litertindirecta
PyPIai-edge-quantizerindirecta
PyPIauditwheelindirecta
PyPIbuildindirecta
PyPIdatasetsindirecta
PyPIfaster-whisperindirecta
PyPIflatbuffersindirecta
PyPIfugashiindirecta
PyPIhuggingface-hubindirecta
PyPIjiwerindirecta
PyPIkokoroindirecta
PyPIlibrosaindirecta
PyPIlitert-torchindirecta
PyPImoonshine-voiceindirecta
PyPInbmakeindirecta
PyPIpiper-ttsindirecta
PyPIpyarrowindirecta
PyPIpytestindirecta
PyPIsetuptoolsindirecta
PyPIsoundfileindirecta
PyPItorchindirecta
PyPItorchaudioindirecta
PyPItwineindirecta
PyPIwheelindirecta
Informe JSON sin procesar legible por máquina
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                "status": "missed",
                "details": [
                  {
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                    "params": {}
                  }
                ],
                "max_points": 45
              },
              {
                "key": "machine_readable_docs_llms_txt",
                "name": "Machine-readable docs (llms.txt)",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 15
              },
              {
                "key": "legible_commit_history",
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                "detail": "no data",
                "points": 0,
                "status": "excluded",
                "details": [
                  {
                    "code": "no_data",
                    "params": {}
                  }
                ],
                "max_points": 40
              }
            ]
          },
          {
            "key": "ai_verify_loop",
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            "name": "Verify loop (build / test / typecheck)",
            "note": "Excluded from scoring (no data or not applicable): Demonstrated agent practice, Automated maintenance. Remaining weights renormalized.",
            "notes": [
              {
                "code": "excluded_no_data",
                "params": {
                  "components": [
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              },
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              "toolchain_manifests": [],
              "dependency_bot_commit_share": null
            },
            "components": [
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                "key": "one_command_bootstrap",
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              },
              {
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                "status": "met",
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              },
              {
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              },
              {
                "key": "static_type_checking",
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                "detail": "C++ (statically typed)",
                "points": 11,
                "status": "met",
                "details": [
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                ],
                "max_points": 11
              },
              {
                "key": "reproducible_environment",
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                "status": "met",
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                ],
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              },
              {
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                ],
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              {
                "key": "automated_maintenance",
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                "details": [
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                  }
                ],
                "max_points": 8
              },
              {
                "key": "openssf_scorecard_pinned_dependencies",
                "name": "OpenSSF Scorecard: Pinned-Dependencies",
                "detail": "OpenSSF Scorecard unavailable",
                "points": 0,
                "status": "excluded",
                "details": [
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                  }
                ],
                "max_points": 10
              }
            ]
          },
          {
            "key": "ai_code_legibility",
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            "notes": [],
            "value": 99,
            "inputs": {
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            },
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                "detail": "C++ (statically typed)",
                "points": 45,
                "status": "met",
                "details": [
                  {
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                    "params": {
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                    }
                  }
                ],
                "max_points": 45
              },
              {
                "key": "manageable_file_sizes",
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                "detail": "30/1254 source files over 60KB",
                "points": 53.7,
                "status": "partial",
                "details": [
                  {
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                    }
                  }
                ],
                "max_points": 55
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            ]
          },
          {
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            "notes": [],
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            "inputs": {
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              ],
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            },
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              },
              {
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              },
              {
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                "points": 40,
                "status": "met",
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            ]
          }
        ],
        "description": "How well is the repo equipped to be developed and maintained with AI coding agents? An independent, experimental badge — weight 0.0, so it is surfaced on its own and does not affect the overall health score."
      }
    ],
    "metrics_version": "1.13.0"
  },
  "warnings": [
    "pypi package 'moonshine-voice' points at a different repository (https://github.com/moonshine-ai/moonshine); excluded from ecosystem scoring",
    "OpenSSF Scorecard timed out after 240s; skipping Scorecard checks"
  ],
  "report_type": "repository",
  "generated_at": "2026-07-17T23:19:14.895972Z",
  "schema_version": "0.12.0",
  "badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/u/usefulsensors/moonshine.svg",
  "full_name": "usefulsensors/moonshine",
  "license_state": "custom",
  "license_spdx": null
}

Las puntuaciones son señales, no garantías. Reflejan prácticas públicamente visibles en GitHub; no son una auditoría de código ni una garantía de seguridad.

Los datos ausentes se excluyen y los pesos se renormalizan; nunca se puntúan como cero. La metodología es versionada y abierta: métricas v1.13.0, esquema v0.12.0 — metodología completa · wiki de métricas.

Cómo se sitúa un resultado dentro del registro general: estadísticas agregadasPyPI.