What languages are supported for transcription?

Parakeel models support 50+ languages including English, Portuguese, Spanish, French, German, Mandarin, Japanese, and many more. Specify the language for best results.

Can I clone a specific voice?

Yes! Use the `clone_voice` tool with a reference audio sample (a few seconds is enough) and the text you want the cloned voice to speak.

What is speaker diarization?

Speaker diarization identifies 'who spoke when' in an audio recording. It segments the audio by speaker and returns timestamps for each speaker's turns.

What audio formats are supported?

The API supports WAV, MP3, FLAC, OGG, and most common audio formats. For best transcription accuracy, use high-quality WAV or FLAC files at 16kHz or higher sample rate.

NVIDIA Audio MCP Server for CrewAI 10 tools — connect in under 2 minutes

Name: NVIDIA Audio
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to NVIDIA Audio through the Vinkius — pass the Edge URL in the `mcps` parameter and every NVIDIA Audio tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="NVIDIA Audio Specialist",
    goal="Help users interact with NVIDIA Audio effectively",
    backstory=(
        "You are an expert at leveraging NVIDIA Audio tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token — get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in NVIDIA Audio "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About NVIDIA Audio MCP Server

Connect NVIDIA Audio to any AI agent and unlock professional-grade audio processing — transcribe speech to text, generate natural voices, translate audio across languages, perform speaker diarization, and clone voices through natural conversation.

When paired with CrewAI, NVIDIA Audio becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call NVIDIA Audio tools autonomously — one agent queries data, another analyzes results, a third compiles reports — all orchestrated through the Vinkius with zero configuration overhead.

What you can do

Speech-to-Text — Transcribe audio files with high accuracy using Parakeel models
Text-to-Speech — Convert text to natural-sounding speech
Audio Translation — Translate spoken audio directly to another language
Speaker Diarization — Identify and separate different speakers in audio
Voice Cloning — Clone a voice from a sample and generate new speech
Noise Cancellation — Remove background noise from recordings
Audio Classification — Classify audio as speech, music, noise, etc.
Punctuation Restoration — Add punctuation to raw speech-to-text output

The NVIDIA Audio MCP Server exposes 10 tools through the Vinkius. Connect it to CrewAI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect NVIDIA Audio to CrewAI via MCP

Follow these steps to integrate the NVIDIA Audio MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py — CrewAI auto-discovers 10 tools from NVIDIA Audio

Why Use CrewAI with the NVIDIA Audio MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with NVIDIA Audio through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles — one agent researches, another analyzes, a third generates reports — each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass the Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

NVIDIA Audio + CrewAI Use Cases

Practical scenarios where CrewAI combined with the NVIDIA Audio MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries NVIDIA Audio for raw data, then a second analyst agent cross-references findings and flags anomalies — all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries NVIDIA Audio, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain NVIDIA Audio tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries NVIDIA Audio against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

NVIDIA Audio MCP Tools for CrewAI (10)

These 10 tools become available when you connect NVIDIA Audio to CrewAI via MCP:

audio_translation

Provide target language. Translate spoken audio to another language

cancel_noise

Remove background noise from audio

classify_audio

) with confidence scores. Classify the type of sound in an audio file

clone_voice

Clone a voice from a reference audio and generate speech

list_audio_models

List available audio models on NVIDIA API Catalog

punctuate_text

Add punctuation and capitalization to raw text

speaker_diarization

Identify different speakers in an audio file

speech_to_text

Supports multiple languages. Provide a public audio URL (MP3, WAV, etc). Transcribe speech from audio to text (Whisper-style)

summarize_audio

Summarize an audio transcript

text_to_speech

Optional voice parameter for different voices. Convert text to natural-sounding speech

Example Prompts for NVIDIA Audio in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with NVIDIA Audio immediately.

"Transcribe this meeting recording: https://example.com/meeting.mp3"

"Convert this text to speech: 'Welcome to our presentation today.'"

"Identify different speakers in this call: https://example.com/call.wav"

Troubleshooting NVIDIA Audio MCP Server with CrewAI

Common issues when connecting NVIDIA Audio to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts — check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

The Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

NVIDIA Audio + CrewAI FAQ

Common questions about integrating NVIDIA Audio MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily — when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.