audio-transcriber

Audio Transcriber

CLI or API | MCP | Agent

!PyPI - Version !MCP Server !PyPI - Downloads !GitHub Repo stars !GitHub forks !GitHub contributors !PyPI - License !GitHub !GitHub last commit (by committer) !GitHub pull requests !GitHub closed pull requests !GitHub issues !GitHub top language !GitHub language count !GitHub repo size !GitHub repo file count (file type) !PyPI - Wheel !PyPI - Implementation

Version: 2.0.0

Documentation — Installation, deployment, and usage across the CLI, Python API, MCP server, and A2A agent are maintained in the official documentation.

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Overview

Audio Transcriber is a production-grade Agent and Model Context Protocol (MCP) server designed to interface directly with Transcribe your .wav .mp4 .mp3 .flac files to text or record your own audio!.

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Key Features

• Consolidated Action-Routed MCP Tools: Minimizes token overhead and eliminates tool bloat in LLM contexts by grouping methods into optimized, togglable tool modules.
• Enterprise-Grade Security: Comprehensive support for Eunomia policies, OIDC token delegation, and granular execution context tracking.
• Integrated Graph Agent: Built-in Pydantic AI agent supporting the Agent Control Protocol (ACP) and standard Web interfaces (AG-UI).
• Native Telemetry & Tracing: Out-of-the-box OpenTelemetry exports and native Langfuse tracing.

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CLI or API

This agent wraps the Transcribe your .wav .mp4 .mp3 .flac files to text or record your own audio! API. You can interact with it programmatically or via its integrated execution entrypoints.

Detailed instructions on how to use the underlying API wrappers, extended schema bindings, and developer SDK references are maintained in docs/index.md.

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MCP

This server utilizes dynamic Action-Routed tools to optimize token overhead and maximize IDE compatibility.

Available MCP Tools

The table below is auto-generated from the live server — do not edit by hand.

<!-- MCP-TOOLS-TABLE:START -->

Condensed action-routed tools (default — MCP_TOOL_MODE=condensed)

| MCP Tool | Toggle Env Var | Description | |----------|----------------|-------------| | health_check | MISCTOOL | | | transcribe_audio | AUDIO_PROCESSINGTOOL | Transcribes audio from a provided file or by recording from the microphone. |

Verbose 1:1 API-mapped tools (MCP_TOOL_MODE=verbose or both)

<details> <summary>7 per-operation tools — one per public API method (click to expand)</summary>

| MCP Tool | Toggle Env Var | Description | |----------|----------------|-------------| | audio_transcriber_export | AUDIO_TRANSCRIBERTOOL | Export transcription to specified formats. | | audio_transcriber_initiate_stream | AUDIO_TRANSCRIBERTOOL | Initiate the audio input stream. | | audio_transcriber_interact | AUDIO_TRANSCRIBERTOOL | Interact with PersonaPlex server via WebSocket. | | audio_transcriber_record | AUDIO_TRANSCRIBERTOOL | Record audio for a specified duration or until stopped. | | audio_transcriber_save_stream | AUDIO_TRANSCRIBERTOOL | Save the recorded frames to a WAV file. | | audio_transcriber_stop_stream | AUDIO_TRANSCRIBERTOOL | Stop and close the audio stream. | | audio_transcriber_transcribe | AUDIO_TRANSCRIBERTOOL | Transcribe the audio file using the initialized backend. |

</details>

_2 action-routed tool(s) (default) · 7 verbose 1:1 tool(s). Each is enabled unless its <DOMAIN>TOOL toggle is set false; MCP_TOOL_MODE selects the surface (condensed default · verbose 1:1 · both). Auto-generated — do not edit._ <!-- MCP-TOOLS-TABLE:END -->

Detailed tool schemas, parameter shapes, and validation constraints are preserved in docs/usage.md.

Dynamic Tool Selection & Visibility

This MCP server supports dynamic toolset selection and visibility filtering at runtime. This allows you to restrict the set of exposed tools in order to prevent blowing up the LLM's context window.

You can configure tool filtering via multiple input channels:

• CLI Arguments: Pass --tools or --toolsets (or their disabled counterparts --disabled-tools and --disabled-toolsets) during startup.
• Environment Variables: Define standard environment variables:
• MCP_ENABLED_TOOLS / MCP_DISABLED_TOOLS
• MCP_ENABLED_TAGS / MCP_DISABLED_TAGS
• HTTP SSE Request Headers: Pass custom headers during transport initialization:
• x-mcp-enabled-tools / x-mcp-disabled-tools
• x-mcp-enabled-tags / x-mcp-disabled-tags
• HTTP SSE Request Query Parameters: Append query parameters directly to your transport connection URL:
• ?tools=tool1,tool2
• ?tags=tag1

When query strings or parameters are supplied, an LLM-free Knowledge Graph resolution layer (using DynamicToolOrchestrator) matches query intents against known tool tags, names, or descriptions, with safe fallback and automated 24-hour background cache refreshing.

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MCP Configuration Examples

<!-- MCP-CONFIG-EXAMPLES:START -->

Install the connector-focused [mcp] extra. Examples use audio-transcriber[mcp] to add FastMCP / FastAPI through agent-utilities[mcp]; the required Agent Utilities core still carries epistemic-graph[full]. The [agent-runtime] extra additionally enables model orchestration.

stdio Transport (local IDEs — Cursor, Claude Desktop, VS Code)

{
  "mcpServers": {
    "audio-transcriber-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "audio-transcriber[mcp]",
        "audio-transcriber-mcp"
      ],
      "env": {
        "MCP_TOOL_MODE": "intent",
        "AUDIO_PROCESSINGTOOL": "True",
        "MISCTOOL": "True",
        "TRANSCRIBE_DIRECTORY": "/path/to/transcribe_directory",
        "WHISPER_MODEL": "base"
      }
    }
  }
}

Runtime references require an alias-aware launcher such as GraphOS. Other launchers must omit those entries and inject the resolved values through their own runtime secret boundary.

Streamable-HTTP Transport (networked / production)

{
  "mcpServers": {
    "audio-transcriber-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "audio-transcriber[mcp]",
        "audio-transcriber-mcp",
        "--transport",
        "streamable-http",
        "--port",
        "8000"
      ],
      "env": {
        "TRANSPORT": "streamable-http",
        "HOST": "127.0.0.1",
        "PORT": "8000",
        "MCP_TOOL_MODE": "intent",
        "AUDIO_PROCESSINGTOOL": "True",
        "MISCTOOL": "True",
        "TRANSCRIBE_DIRECTORY": "/path/to/transcribe_directory",
        "WHISPER_MODEL": "base"
      }
    }
  }
}

Alternatively, connect to a pre-deployed Streamable-HTTP instance by url:

{
  "mcpServers": {
    "audio-transcriber-mcp": {
      "url": "http://localhost:8000/audio-transcriber-mcp/mcp"
    }
  }
}

Run a reviewed container image as a least-privilege stdio child (no listener or published port):

docker run -i --rm \
  --read-only \
  --cap-drop=ALL \
  --security-opt=no-new-privileges \
  --pids-limit=256 \
  --tmpfs /tmp:rw,noexec,nosuid,nodev,size=64m \
  -e TRANSPORT=stdio \
  -e MCP_TOOL_MODE=intent \
  -e AUDIO_PROCESSINGTOOL=True \
  -e MISCTOOL=True \
  -e TRANSCRIBE_DIRECTORY=/path/to/transcribe_directory \
  -e WHISPER_MODEL=base \
  registry.example.invalid/audio-transcriber@sha256:<digest> audio-transcriber-mcp

For containerized network HTTP, supply an authenticated TLS ingress (or direct server TLS), exact MCP_ALLOWED_HOSTS, and an exact trusted-proxy CIDR policy through the operator-owned deployment profile. The generator does not emit an unauthenticated non-loopback listener.

_Auto-generated from the code-read env surface (MCP_TOOL_MODE + package vars) — do not edit._ <!-- MCP-CONFIG-EXAMPLES:END -->

<!-- BEGIN GENERATED: additional-deployment-options -->

Additional Deployment Options

audio-transcriber can run as a local stdio process or container, or behind a remote network boundary. The Deployment guide carries the detailed transport contract.

• Local container — launch a reviewed immutable image as a least-privilege

stdio child with no listener or published port.

• Remote URL — connect through an operator-supplied authenticated HTTPS

ingress. Keep its URL, outbound identity references, trust profile, and exact MCP_ALLOWED_HOSTS in AgentConfig. <!-- END GENERATED: additional-deployment-options -->

Agent

This repository features a fully integrated Pydantic AI Graph Agent. It communicates over the Agent Control Protocol (ACP) and interacts seamlessly with the Agent Web UI (AG-UI) and Terminal interface.

Running the Agent CLI

To start the interactive command-line agent:

# Configure transcription (optional)
export WHISPER_MODEL="base"
export TRANSCRIBE_DIRECTORY="/path/to/transcribe_directory"

# Run the agent server
audio-transcriber-agent --provider openai --model-id gpt-4o

Docker Compose Orchestration

The following docker/agent.compose.yml configures the Agent, Web UI, and Terminal Interface together:

version: '3.8'

services:
  audio-transcriber-mcp:
    image: example/audio-transcriber:mcp
    container_name: audio-transcriber-mcp
    hostname: audio-transcriber-mcp
    restart: always
    env_file:
      - ../.env
    environment:
      - PYTHONUNBUFFERED=1
      - HOST=0.0.0.0
      - PORT=8000
      - TRANSPORT=streamable-http
    ports:
      - "8000:8000"
    healthcheck:
      test: ["CMD", "python3", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 10s
    logging:
      driver: json-file
      options:
        max-size: "10m"
        max-file: "3"

  audio-transcriber-agent:
    image: example/audio-transcriber@sha256:<digest>
    container_name: audio-transcriber-agent
    hostname: audio-transcriber-agent
    restart: always
    depends_on:
      - audio-transcriber-mcp
    env_file:
      - ../.env
    command: [ "audio-transcriber-agent" ]
    environment:
      - PYTHONUNBUFFERED=1
      - HOST=0.0.0.0
      - PORT=9014
      - MCP_URL=http://audio-transcriber-mcp:8000/mcp
      - PROVIDER=${PROVIDER:-openai}
      - MODEL_ID=${MODEL_ID:-gpt-4o}
      - ENABLE_WEB_UI=True
      - ENABLE_OTEL=True
    ports:
      - "9014:9014"
    healthcheck:
      test: ["CMD", "python3", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:9014/health')"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 10s
    logging:
      driver: json-file
      options:
        max-size: "10m"
        max-file: "3"

Detailed graph node architecture explanations, custom skill configurations, and agentic trace guides are available in docs/deployment.md.

---

Security & Governance

Built directly upon the enterprise-ready agent-utilities core, standard security parameters are fully supported:

Access Control & Policy Enforcement

• Eunomia Policies: Fine-grained, policy-driven tool authorization. Supports none, local embedded (mcp_policies.json), or centralized remote modes.
• OIDC Token Delegation: Compliant with RFC 8693 token exchange for flowing authenticating user credentials from Web UI / ACP → Agent → MCP.
• Scoped Credentials: Execution context runs restricted to the specific caller identity.

Runtime Security Grid

| Feature | Functionality | Enablement | |---------|---------------|------------| | Tool Guard | Sensitivity inspection with human-in-the-loop validation | Enabled by default | | Prompt Injection Defense | Input scanning, repetition monitoring, and recursive loop blocks | Enabled by default | | Context Safety Guard | Stuck-loop detectors and contextual overflow preemptive alerts | Enabled by default |

---

Environment Variables

<!-- ENV-VARS-TABLE:START -->

Package environment variables

| Variable | Example | Description | |----------|---------|-------------| | HOST | 0.0.0.0 | | | PORT | 8000 | | | TRANSPORT | stdio | options: stdio, streamable-http, sse | | ENABLE_OTEL | True | | | OTEL_EXPORTER_OTLP_ENDPOINT | http://localhost:8080/api/public/otel | | | OTEL_EXPORTER_OTLP_PUBLIC_KEY | pk-... | | | OTEL_EXPORTER_OTLP_SECRET_KEY | sk-... | | | OTEL_EXPORTER_OTLP_PROTOCOL | http/protobuf | | | EUNOMIA_TYPE | none | options: none, embedded, remote | | EUNOMIA_POLICY_FILE | mcp_policies.json | | | EUNOMIA_REMOTE_URL | http://eunomia-server:8000 | | | TRANSCRIBE_DIRECTORY | /path/to/transcribe_directory | Directory where transcripts are written (defaults to the data dir under audio-transcriber) | | MISCTOOL | True | | | AUDIO_PROCESSINGTOOL | True | | | WHISPER_MODEL | base | Standard OpenAI Whisper model to use for local transcription (e.g., base, tiny, small) |

Inherited agent-utilities variables (apply to every connector)

| Variable | Example | Description | |----------|---------|-------------| | MCP_TOOL_MODE | condensed | Tool surface: condensed | verbose | both | | MCP_ENABLED_TOOLS | — | Comma-separated tool allow-list | | MCP_DISABLED_TOOLS | — | Comma-separated tool deny-list | | MCP_ENABLED_TAGS | — | Comma-separated tag allow-list | | MCP_DISABLED_TAGS | — | Comma-separated tag deny-list | | MCP_CLIENT_AUTH | — | Outbound MCP auth (oidc-client-credentials for fleet calls) | | OIDC_CLIENT_ID | — | OIDC client id (service-account auth) | | OIDC_CLIENT_SECRET | — | OIDC client secret (service-account auth) | | DEBUG | False | Verbose logging | | PYTHONUNBUFFERED | 1 | Unbuffered stdout (recommended in containers) | | MCP_URL | http://localhost:8000/mcp | URL of the MCP server the agent connects to | | PROVIDER | openai | LLM provider for the agent | | MODEL_ID | gpt-4o | Model id for the agent | | ENABLE_WEB_UI | True | Serve the AG-UI web interface |

_15 package + 14 inherited variable(s). Auto-generated from .env.example + the shared agent-utilities set — do not edit._ <!-- ENV-VARS-TABLE:END -->

Every variable the server reads, grouped by purpose.

Transcription

| Variable | Description | Default | |----------|-------------|---------| | WHISPER_MODEL | Local OpenAI Whisper model (e.g. base, tiny, small) | base | | TRANSCRIBE_DIRECTORY | Directory where transcripts are written | data dir |

MCP server / transport

| Variable | Description | Default | |----------|-------------|---------| | TRANSPORT | stdio, streamable-http, or sse | stdio | | HOST | Bind host (HTTP transports) | 0.0.0.0 | | PORT | Bind port (HTTP transports) | 8000 | | MCP_TOOL_MODE | Tool surface: condensed, verbose, or both | condensed | | MCP_ENABLED_TOOLS / MCP_DISABLED_TOOLS | Comma-separated tool allow/deny list | — | | MCP_ENABLED_TAGS / MCP_DISABLED_TAGS | Comma-separated tag allow/deny list | — | | DEBUG | Verbose logging | False | | PYTHONUNBUFFERED | Unbuffered stdout (recommended in containers) | 1 |

Tool toggles

Each action-routed tool can be disabled individually via its toggle env var (set to false). See the Available MCP Tools table above for the authoritative names.

| Variable | Description | Default | |----------|-------------|---------| | MISCTOOL | Toggle the miscellaneous / health-check tool | True | | AUDIO_PROCESSINGTOOL | Toggle the audio-processing (transcription) tool | True |

Telemetry & governance

| Variable | Description | Default | |----------|-------------|---------| | ENABLE_OTEL | Enable OpenTelemetry export | True | | OTEL_EXPORTER_OTLP_ENDPOINT | OTLP collector endpoint | — | | OTEL_EXPORTER_OTLP_PUBLIC_KEY / OTEL_EXPORTER_OTLP_SECRET_KEY | OTLP auth keys | — | | OTEL_EXPORTER_OTLP_PROTOCOL | OTLP protocol (e.g. http/protobuf) | — | | EUNOMIA_TYPE | Authorization mode: none, embedded, remote | none | | EUNOMIA_POLICY_FILE | Embedded policy file | mcp_policies.json | | EUNOMIA_REMOTE_URL | Remote Eunomia server URL | — |

Agent CLI (full [agent] runtime only)

| Variable | Description | Default | |----------|-------------|---------| | MCP_URL | URL of the MCP server the agent connects to | http://localhost:8000/mcp | | PROVIDER | LLM provider (e.g. openai) | openai | | MODEL_ID | Model id (e.g. gpt-4o) | gpt-4o | | ENABLE_WEB_UI | Serve the AG-UI web interface | True |

See .env.example for a copy-paste starting point.

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Installation

Pick the extra that matches what you want to run:

| Extra | Installs | Use when | |-------|----------|----------| | audio-transcriber[mcp] | Connector-focused MCP server (agent-utilities[mcp] — FastMCP/FastAPI + epistemic-graph[full]) | You only run the MCP server (smallest install / image) | | audio-transcriber[agent] | Agent runtime (agent-utilities[agent-runtime,logfire] — model orchestration + epistemic-graph[full]) | You run the integrated agent | | audio-transcriber[all] | Everything (mcp + agent) | Development / both surfaces |

# Connector-focused MCP server (includes the shared graph engine)
uv pip install "audio-transcriber[mcp]"

# Agent runtime (adds model orchestration to the shared graph engine)
uv pip install "audio-transcriber[agent]"

# Everything (development)
uv pip install "audio-transcriber[all]"      # or: python -m pip install "audio-transcriber[all]"

Container images (:mcp vs :agent)

One multi-stage docker/Dockerfile builds two right-sized images, selected by --target:

| Image tag | Build target | Contents | Entrypoint | |-----------|--------------|----------|------------| | example/audio-transcriber:mcp | --target mcp | audio-transcriber[mcp] — connector-focused, includes epistemic-graph[full]; no model-orchestration stack | audio-transcriber-mcp | | example/audio-transcriber@sha256:<digest> | --target agent (default) | audio-transcriber[agent] — agent runtime, model orchestration + epistemic-graph[full] | audio-transcriber-agent |

docker build --target mcp   -t example/audio-transcriber:mcp    docker/   # connector-focused MCP server
docker build --target agent -t example/audio-transcriber:agent-local docker/   # agent runtime

docker/mcp.compose.yml runs the connector-focused :mcp server; docker/agent.compose.yml runs the agent (immutable agent digest) with a co-located :mcp sidecar.

Knowledge-graph database (epistemic-graph)

Both [mcp] and [agent] carry the epistemic-graph engine through the required Agent Utilities core dependency (epistemic-graph[full]). The [mcp] extra keeps the server connector-focused; [agent] additionally enables model orchestration. Local deployments can use the bundled engine. For production or shared state, run epistemic-graph as a dedicated database service and configure the runtime to use it. Deployment recipes (single-node + Raft HA), connection configuration, and architecture diagrams are documented in the epistemic-graph deployment guide.

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Repository Owners

<img width="100%" height="180em" src="https://github-readme-stats.vercel.app/api?username=example&show_icons=true&hide_border=true&&count_private=true&include_all_commits=true" />

!GitHub followers !GitHub User's stars

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Documentation

The complete documentation is published as the official documentation site and is the recommended reference for installation, deployment, and day-to-day operation.

| Page | Contents | |---|---| | Installation | pip, source, extras, prebuilt Docker image | | Deployment | run the MCP server and agent, Compose, Caddy + Technitium, env config | | Usage | the MCP tool, the AudioTranscriber API, the CLI | | Overview | capability summary and ecosystem role | | Concepts | concept registry (CONCEPT:AUDIO-*) |

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Contribute

Contributions are welcome! Please ensure code quality by executing local checks before submitting pull requests:

• Format code using ruff format .
• Lint code using ruff check .
• Validate type-safety with mypy .
• Execute test suites using pytest

<!-- BEGIN agent-utilities-deployment (generated; do not edit between markers) -->

Deploy with agent-utilities-deployment

Provision this package with the consolidated agent-utilities-deployment workflow. It selects an installed-package, editable-source, or immutable-container path; records only runtime secret and TLS-profile references in AgentConfig; and runs doctor, registration, policy, observability, and rollback gates. Ask your agent to "deploy audio-transcriber with agent-utilities-deployment".

| Install mode | Command | |------|---------| | Installed package | uv tool install "audio-transcriber[mcp]", then run audio-transcriber-mcp | | Editable source | uv pip install -e ".[agent]", then run audio-transcriber-mcp | | Immutable container | deploy registry.example.invalid/audio-transcriber@sha256:<digest> through the operator-selected orchestrator |

The repository embeds no deployment profile, credential value, certificate path, or environment-specific endpoint. Supply those at runtime through AgentConfig and the configured secret provider.

<!-- END agent-utilities-deployment -->

<!-- GOVERNED-CAPABILITY:START -->

Governed capability contract

This package ships a compact canonical skill surface with specialist procedures kept as referenced workflows. The current MCP tools, skill metadata, connector_manifest.yml, ontology, mappings, shapes, fixtures, migrations, tool-schema fingerprints, and certification metadata form one versioned capability contract. Validate them together; do not rely on stale tool names or historical per-task skill wrappers.

Runtime endpoints, credentials, certificate trust, tenant identity, retention, and observability policy are deployment inputs and are never packaged values. See Configuration, trust, and privacy before enabling a network transport, connector ingestion, GraphOS delegation, or trace export. <!-- GOVERNED-CAPABILITY:END -->