El endpoint Convert devuelve una respuesta JSON que contiene un mensaje indicando el estado de creación del audio, la URL del archivo MP3 generado y metadatos como el total de caracteres procesados y los caracteres restantes disponibles para conversión.

Los campos clave en los datos de respuesta incluyen "mensaje" (estado de la creación de audio), "audio_src" (URL del archivo de audio generado), "error" (indica éxito o fallo), "total_chars" (número de caracteres procesados) y "remaining_chars" (caracteres restantes para la conversión).

El endpoint Convert acepta parámetros como "text" (el texto a convertir), "voice" (para especificar el género: masculino, femenino o neutral) y "language" (por ejemplo, "hi_IN" para hindi). Los usuarios también pueden usar los prefijos "text_premium_" o "ssml_premium_" para voces premium.

Los datos de respuesta están organizados en un formato JSON. Incluye un objeto de nivel superior con campos para el mensaje, la URL de la fuente de audio, el estado de error y los recuentos de caracteres, lo que facilita su análisis y uso en aplicaciones.

Los casos de uso típicos de la API de texto a voz en hindi incluyen la creación de asistentes de voz, la mejora de la accesibilidad para usuarios con discapacidad visual, la generación de contenido de audio para el aprendizaje en línea y la provisión de experiencias de servicio al cliente interactivas a través de la voz.

Los usuarios pueden personalizar sus solicitudes seleccionando el género de voz deseado (masculino, femenino, neutral) y especificando el código de idioma. Además, pueden elegir entre opciones de voz estándar y premium utilizando los prefijos apropiados en el código de idioma.

La API principalmente admite la salida de audio en formato MP3. El texto de entrada puede ser texto sin formato o SSML (Lenguaje de Marcado de Síntesis de Voz) para características de habla más avanzadas, lo que permite un mayor control sobre la pronunciación y la entonación.

La precisión de los datos se mantiene a través de algoritmos avanzados y redes neuronales que analizan e interpretan datos textuales. Las mejoras continuas en el aprendizaje automático y la IA mejoran la calidad de la síntesis de voz, asegurando que la salida se asemeje de cerca al habla natural.

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