From e8b10ea10b2e08d589707ec6cd6bf534aa282cdf Mon Sep 17 00:00:00 2001
From: The BROKE Cluster Team <broke@gmx.eu>
Date: Fri, 23 Jan 2026 20:20:59 +0100
Subject: [PATCH] Release 2.0.4-beta.8: Audio transcription support
 (experimental)

Audio input via --audio flag (CLI) and input_audio content type (Server API).
Uses mlx-vlm native audio processing. ~30s duration limit (model constraint).
Currently only Gemma-3n tested (requires --repair-index fix).

Also includes:
- SERVER-HANDBOOK compliance (image limits, validation error envelopes)
- Dependency updates: mlx>=0.30.0, mlx-lm>=0.30.0, huggingface-hub>=1.0.0
- Audio E2E test suite + ADR-019
---
 .gitignore                                    |   5 +-
 CHANGELOG.md                                  |  34 ++
 README.md                                     |  77 +++-
 TESTING-DETAILS.md                            |  70 +++-
 docs/ADR/ADR-019-Audio-Input-Support.md       | 256 ++++++++++++
 docs/SERVER-HANDBOOK.md                       | 381 +++++++++++++++---
 docs/json-api-schema.json                     |   7 +-
 docs/json-api-specification.md                |  34 +-
 mlxk2/cli.py                                  |  50 ++-
 mlxk2/core/capabilities.py                    |  42 +-
 mlxk2/core/server_base.py                     | 151 +++++--
 mlxk2/core/vision_runner.py                   |  40 +-
 mlxk2/errors.py                               |   2 +
 mlxk2/operations/common.py                    |  48 ++-
 mlxk2/operations/run.py                       |  35 +-
 mlxk2/output/human.py                         |   7 +
 mlxk2/spec.py                                 |   2 +-
 mlxk2/tools/vision_adapter.py                 | 164 ++++++--
 pyproject.toml                                |   8 +-
 ...A MAN SAID TO THE UNIVERSE SIR I EXIST.mp3 | Bin 0 -> 29672 bytes
 ...A MAN SAID TO THE UNIVERSE SIR I EXIST.wav | Bin 0 -> 148844 bytes
 .../HAVING RETURNED TO THE ROYAL CAVERN.mp3   | Bin 0 -> 90764 bytes
 .../HAVING RETURNED TO THE ROYAL CAVERN.txt   |   1 +
 .../HAVING RETURNED TO THE ROYAL CAVERN.wav   | Bin 0 -> 463084 bytes
 tests_2.0/assets/audio/sources.txt            |  11 +
 tests_2.0/live/conftest.py                    |  91 +++++
 tests_2.0/live/test_audio_e2e_live.py         | 223 ++++++++++
 tests_2.0/live/test_utils.py                  |  83 ++++
 tests_2.0/test_audio_cli.py                   | 120 ++++++
 tests_2.0/test_capabilities.py                |  54 +++
 tests_2.0/test_server_audio.py                | 318 +++++++++++++++
 tests_2.0/test_server_models_and_errors.py    |  38 ++
 tests_2.0/test_vision_adapter.py              | 191 ++++++++-
 33 files changed, 2346 insertions(+), 197 deletions(-)
 create mode 100644 docs/ADR/ADR-019-Audio-Input-Support.md
 create mode 100644 tests_2.0/assets/audio/A MAN SAID TO THE UNIVERSE SIR I EXIST.mp3
 create mode 100644 tests_2.0/assets/audio/A MAN SAID TO THE UNIVERSE SIR I EXIST.wav
 create mode 100644 tests_2.0/assets/audio/HAVING RETURNED TO THE ROYAL CAVERN.mp3
 create mode 100644 tests_2.0/assets/audio/HAVING RETURNED TO THE ROYAL CAVERN.txt
 create mode 100644 tests_2.0/assets/audio/HAVING RETURNED TO THE ROYAL CAVERN.wav
 create mode 100644 tests_2.0/assets/audio/sources.txt
 create mode 100644 tests_2.0/live/test_audio_e2e_live.py
 create mode 100644 tests_2.0/test_audio_cli.py
 create mode 100644 tests_2.0/test_server_audio.py

diff --git a/.gitignore b/.gitignore
index 924e523..187933f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -23,13 +23,16 @@ server.log
 install_*.log
 .gitignore
 docs/ISSUES/
+docs/reviews/
 
 # Test artifacts (generated reports)
 *_report.json
 test-img-collection/
 small-img-collection
 benchmarks/reports/*.html
+benchmarks/reports/2025-12*.jsonl
+benchmarks/reports/2026-0[12]*.jsonl
 
 # Benchmark reports (ADR-013 Phase 0)
 # These reports ARE tracked in git for historical data
-!benchmarks/reports/*.jsonl
\ No newline at end of file
+#!benchmarks/reports/*.jsonl
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 2f4e294..cbf82e4 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,39 @@
 # Changelog
 
+## [2.0.4-beta.8] - 2026-01-23
+
+### Highlights
+
+**Audio Input Support (Experimental):** Full audio transcription pipeline for audio-capable models. CLI `--audio` flag and OpenAI-compatible Server API (`input_audio` format). Uses mlx-vlm's native audio processing with ~30 second duration limit (model architecture constraint). Currently only Gemma-3n tested; requires `--repair-index` fix (mlx-vlm #624).
+
+### Added
+
+- **Audio Capability Detection:** `list` and `show` display `[audio]` tag for audio-capable models
+- **CLI `--audio` Parameter:** `mlxk run model "prompt" --audio file.wav` (WAV/MP3 supported)
+- **Server Audio API:** OpenAI-compatible `input_audio` content type for `/v1/chat/completions`
+  - Format: `{"type": "input_audio", "input_audio": {"data": "<base64>", "format": "wav"}}`
+  - Supported formats: WAV, MP3
+  - Temperature default: 0.0 for audio (greedy sampling reduces hallucination)
+- **Audio E2E Tests:** Portfolio-based transcription tests with deterministic audio clips (ADR-019)
+
+### Changed
+
+- **Dependency Updates:**
+  - `mlx>=0.30.0` (was 0.29.0) - MXFP4/NVFP4 support
+  - `mlx-lm>=0.30.0` (was 0.28.4)
+  - `huggingface-hub>=1.0.0` (was <1.0) - transformers 5.x compatibility
+  - `mlx-vlm` upstream update: fc8c92e → 5812270 (audio support)
+
+- **SERVER-HANDBOOK Compliance:**
+  - Image limits enforced: max 5 images per request, max 50MB total
+  - Multi-audio blocked with clear error (mlx-vlm limitation)
+  - Audio+Vision combined: Audio silently ignored (single modality per request)
+  - `chunk=1` explicitly respected (was defaulting to env var)
+  - 422 validation errors now return ADR-004 envelope format
+  - 501 NOT_IMPLEMENTED error type added for unsupported features
+
+---
+
 ## [2.0.4-beta.7] - 2026-01-18
 
 ### Highlights
diff --git a/README.md b/README.md
index 84fe160..1acef80 100644
--- a/README.md
+++ b/README.md
@@ -4,9 +4,9 @@
   <img src="https://github.com/mzau/mlx-knife/raw/main/mlxk-demo.gif" alt="MLX Knife Demo" width="900">
 </p>
 
-**Current Version: 2.0.4-beta.7** (Stable: 2.0.3)
+**Current Version: 2.0.4-beta.8** (Stable: 2.0.3)
 
-[![GitHub Release](https://img.shields.io/badge/version-2.0.4--beta.7-blue.svg)](https://github.com/mzau/mlx-knife/releases)
+[![GitHub Release](https://img.shields.io/badge/version-2.0.4--beta.8-blue.svg)](https://github.com/mzau/mlx-knife/releases)
 [![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://www.apache.org/licenses/LICENSE-2.0)
 [![Python 3.9+](https://img.shields.io/badge/python-3.10+(3.9)-blue.svg)](https://www.python.org/downloads/)
 [![Apple Silicon](https://img.shields.io/badge/Apple%20Silicon-green.svg)](https://support.apple.com/en-us/HT211814)
@@ -18,6 +18,7 @@
 ## Features
 
 ### What's New in 2.0.4 (Coming Soon - Currently Beta)
+- **Audio Transcription (Experimental)** - Speech-to-text via `--audio` flag (CLI + Server API)
 - **Vision Models with EXIF Metadata** - Image analysis + automatic GPS/date/camera extraction visible to the model
 - **Unix Pipe Integration** - Chain models without temp files (`vision → text` workflows)
 - **Local Development Workflow** - Clone → Repair → Test models without HuggingFace round-trips
@@ -76,7 +77,7 @@ This license applies **only** to the `mlx-knife` code and **does not extend** to
 MLX Knife has been comprehensively tested and verified on:
 
 ✅ **Python 3.9.6 - 3.14** - Text LLMs fully supported (mlx-lm 0.28.4+)
-✅ **Python 3.10 - 3.14** - Vision models supported (mlx-vlm 0.3.9+; beta.7 uses commit fc8c92e31983a52761f37d503f903ec40bebbd62 with MXFP4 support)
+✅ **Python 3.10 - 3.14** - Vision models supported (mlx-vlm 0.3.9+; beta.8 uses commit 5812270 with audio + MXFP4 support)
 
 **Note:** Vision features require Python 3.10+. Native macOS Python 3.9.6 users need to upgrade (e.g., via Homebrew).
 
@@ -106,17 +107,17 @@ mlxk --version  # → mlxk 2.0.3 (latest stable on PyPI)
 ### Via GitHub (Latest Beta)
 
 ```bash
-# Install 2.0.4-beta.7 (Workspace discovery + Server robustness)
-pip install "git+https://github.com/mzau/mlx-knife.git@v2.0.4-beta.7"
+# Install 2.0.4-beta.8 (Audio transcription + Server enhancements)
+pip install "git+https://github.com/mzau/mlx-knife.git@v2.0.4-beta.8"
 
 # With Vision support (Python 3.10+ required)
-pip install "git+https://github.com/mzau/mlx-knife.git@v2.0.4-beta.7#egg=mlx-knife[vision]"
+pip install "git+https://github.com/mzau/mlx-knife.git@v2.0.4-beta.8#egg=mlx-knife[vision]"
 
 # Verify installation
-mlxk --version  # → mlxk 2.0.4b7
+mlxk --version  # → mlxk 2.0.4b8
 ```
 
-**Beta.7 note:** Uses mlx-vlm commit fc8c92e (includes MXFP4 quantization support). The `[vision]` extra automatically installs the correct version.
+**Beta.8 note:** Uses mlx-vlm commit 5812270 (includes audio support + MXFP4 quantization). The `[vision]` extra automatically installs the correct version.
 
 **For production use:** Wait for 2.0.4 stable on PyPI (requires mlx-vlm 0.3.10 release).
 
@@ -134,7 +135,7 @@ pip install -e ".[dev,test]"
 pip install -e ".[dev,test,vision]"
 
 # Verify installation
-mlxk --version  # → mlxk 2.0.4b7
+mlxk --version  # → mlxk 2.0.4b8
 
 # Run tests and quality checks (before committing)
 pytest -v
@@ -210,7 +211,7 @@ mlxk run "Phi-4" "Hello"                    # Fuzzy match
 mlxk show "Qwen3@e96" --json                # Specific version
 ```
 
-### Local Paths (2.0.4-beta.7+)
+### Local Paths (2.0.4-beta.6+)
 
 | Format | Example |
 |--------|---------|
@@ -240,7 +241,7 @@ mlxk run ./fixed "Test"
 
 ---
 
-## Workspace Development Workflow (2.0.4-beta.7+)
+## Workspace Development Workflow (2.0.4-beta.6+)
 
 **Complete local development cycle** for model experimentation, repair, and testing without HuggingFace round-trips:
 
@@ -310,8 +311,8 @@ Image analysis via the `--image` flag (CLI and server). Requires Python 3.10+.
 
 - **Python 3.10+** (mlx-vlm dependency)
 - **Installation:** `pip install mlx-knife[vision]`
-- **Backend:** mlx-vlm commit c536165df2b3b4aece3a795b2e414349f935e750 (auto-installed)
-- **Beta.4 note:** The `[vision]` extra automatically installs mlx-vlm from git with the Pixtral pad_token fix. Will switch to PyPI v0.3.10 when released.
+- **Backend:** mlx-vlm commit 5812270 (audio + MXFP4 support, auto-installed)
+- **Beta.8 note:** The `[vision]` extra automatically installs mlx-vlm from git with audio support. Will switch to PyPI v0.3.10 when released.
 
 #### Usage
 
@@ -475,6 +476,54 @@ mlxk convert <model> <output> --repair-index
 
 **Reporting Issues:** If you encounter vision model failures, please report with model name and error message to help improve compatibility tracking.
 
+### Audio Model Compatibility
+
+> **🧪 Experimental:** Audio support is new in v2.0.4-beta.8. Currently only Gemma-3n tested.
+
+**✅ Tested & Working Models** (mlx-knife v2.0.4-beta.8):
+
+| Model | Size | Notes |
+|-------|------|-------|
+| `gemma-3n-E2B-it-4bit` | ~2.1GB | Requires workspace repair workflow (see below) |
+
+**⚠️ Setup Required:** The mlx-community model has an index mismatch (mlx-vlm #624). Prepare once:
+
+```bash
+MLXK2_ENABLE_ALPHA_FEATURES=1
+mlxk clone mlx-community/gemma-3n-E2B-it-4bit ./gemma-3n-audio
+mlxk convert ./gemma-3n-audio ./gemma-3n-audio-FIXED --repair-index
+mlxk run ./gemma-3n-audio-FIXED --audio test.wav  # Now works
+```
+
+**⚙️ Audio-Specific Defaults:**
+
+| Setting | Audio Default | Text/Vision Default | Reason |
+|---------|---------------|---------------------|--------|
+| Temperature | 0.2 | 0.7 | Reduces multilingual drift on 4-bit models |
+| Default Prompt | "Transcribe this audio." | - | Simple prompt reduces multilingual drift |
+
+**⚠️ Known Limitations:**
+
+| Limitation | Details | Workaround |
+|------------|---------|------------|
+| **Duration limit** | ~30 seconds max (Gemma-3n model constraint: 188 tokens at 6.25 tokens/sec) | Split longer recordings |
+| File size | 5MB limit | Split larger files |
+| Multi-audio | Not supported (mlx-vlm token mismatch bug) | Process one file at a time |
+| Audio+Vision combined | Audio silently ignored when images present | Use audio-only or vision-only |
+| Phonetic errors | Mishearing observed (e.g., "A man" → "Amen") | Try `--temperature 0` for consistency |
+| Format support | WAV confirmed; other formats untested | Convert to WAV |
+
+**💡 Tips for Best Results:**
+
+```bash
+# After workspace setup (see above):
+# Explicit transcription with greedy sampling for consistency
+mlxk run ./gemma-3n-audio-FIXED --audio speech.wav --temperature 0
+
+# Default settings (temperature 0.2, simple prompt)
+mlxk run ./gemma-3n-audio-FIXED --audio speech.wav
+```
+
 
 ## JSON API
 
@@ -1182,7 +1231,7 @@ Apache License 2.0 — see `LICENSE` (root) and `mlxk2/NOTICE`.
 
 <p align="center">
   <b>Made with ❤️ by The BROKE team <img src="broke-logo.png" alt="BROKE Logo" width="30" align="middle"></b><br>
-  <i>Version 2.0.4-beta.7 | January 2026</i><br>
+  <i>Version 2.0.4-beta.8 | January 2026</i><br>
   <a href="https://github.com/mzau/broke-nchat">💬 Web UI: nChat - lightweight chat interface</a> •
   <a href="https://github.com/mzau/broke-cluster">🔮 Multi-node: BROKE Cluster</a>
 </p>
diff --git a/TESTING-DETAILS.md b/TESTING-DETAILS.md
index 6089647..c207412 100644
--- a/TESTING-DETAILS.md
+++ b/TESTING-DETAILS.md
@@ -36,11 +36,12 @@ Total:                       170 passed across all phases
 ✅ **3-category test strategy** - optimized for performance and safety
 ✅ **Portfolio Separation** - Text and Vision models tested independently with separate RAM formulas
 
-### Skipped Tests Breakdown (60 total Python 3.10+, 69 total Python 3.9, standard run without HF_HOME)
-- **34 Live E2E tests** - Server/HTTP/CLI validation with real models (requires `pytest -m live_e2e`, ADR-011 + Portfolio Separation)
+### Skipped Tests Breakdown (64 total Python 3.10+, 73 total Python 3.9, standard run without HF_HOME)
+- **38 Live E2E tests** - Server/HTTP/CLI validation with real models (requires `pytest -m live_e2e`, ADR-011 + Portfolio Separation)
   - **23 Text model tests** - Parametrized across text_portfolio (chat completions batch/streaming)
   - **3 Vision model tests** - Parametrized across vision_portfolio (multimodal, SSE, text-on-vision)
   - **5 Vision CLI E2E tests** - Deterministic vision queries (requires vision model in cache, ADR-012)
+  - **4 Audio CLI E2E tests** - Audio transcription tests parametrized across audio_portfolio (ADR-019)
   - **3 Non-parametrized tests** - Health, models list, vision→text switching
 - **4 Live Stop Tokens tests** - Stop token validation with real models (requires `pytest -m live_stop_tokens`, ADR-009)
 - **3 Live Clone tests** - APFS same-volume clone workflow (requires `MLXK2_LIVE_CLONE=1`)
@@ -75,6 +76,7 @@ Total:                       170 passed across all phases
 | Live E2E (ADR-011) | `HF_HOME=/path/to/cache pytest -m live_e2e -v` | `live_e2e` (required) + Env: `HF_HOME` (optional, enables Portfolio Discovery); Requires: `httpx` installed | **✅ Working:** Server/HTTP/CLI validation with real models. Portfolio Discovery auto-discovers all MLX chat models via `mlxk list --json` (filter: MLX+healthy+runtime+chat), parametrized tests (one server per model), RAM-aware skip. | No (uses local cache) |
 | Vision CLI E2E (ADR-012) | `HF_HOME=/path/to/cache pytest -m live_e2e tests_2.0/live/test_vision_e2e_live.py -v` | `live_e2e` (required) + Env: `HF_HOME` (vision model in cache, e.g., pixtral-12b-8bit or Llama-3.2-Vision); Requires: `mlx-vlm` installed (Python 3.10+) | **✅ Working:** Deterministic vision queries validate actual image understanding (not hallucination). Tests: chess position reading (e6=black king), OCR text extraction (contract name), color recognition (blue mug), chart label reading (Y-axis), large image support (2.7MB). | No (uses local cache) |
 | Vision Server E2E (ADR-012 Phase 3) | `HF_HOME=/path/to/cache pytest -m live_e2e tests_2.0/live/test_vision_server_e2e.py -v` | `live_e2e` (required) + Env: `HF_HOME` (vision model in cache); Requires: `mlx-vlm` installed (Python 3.10+), `httpx` | **✅ Working:** Vision API over HTTP. Tests: Base64 image chat completion, streaming graceful degradation (SSE emulation), text request on vision model server. | No (uses local cache) |
+| Audio CLI E2E (ADR-019) | `HF_HOME=/path/to/cache pytest -m live_e2e tests_2.0/live/test_audio_e2e_live.py -v` | `live_e2e` (required) + Env: `HF_HOME` (audio model in cache, e.g., gemma-3n); Requires: `mlx-vlm` installed (Python 3.10+) | **✅ Working:** Audio transcription with real models. Portfolio Discovery auto-discovers audio-capable models. Tests: WAV transcription (short/long), MP3 format support, output validation. Known limitation: ~30s audio duration (Gemma-3n architecture). | No (uses local cache) |
 | Resumable Pull | `MLXK2_TEST_RESUMABLE_DOWNLOAD=1 pytest -m live_pull tests_2.0/test_resumable_pull.py -v` | `live_pull` (required) + Env: `MLXK2_TEST_RESUMABLE_DOWNLOAD=1` (opt-in for network test) | **✅ Working:** Real network download with controlled interruption (45s timer). Tests unhealthy detection → `requires_confirmation` status → resume with `force_resume=True` → final health check. Validates resumable pull feature (interrupted downloads can be resumed). Uses isolated cache (no impact on user cache). | Yes (HuggingFace download) |
 | Show E2E portfolios | `HF_HOME=/path/to/cache python tests_2.0/show_portfolios.py` OR `pytest -m show_model_portfolio -s` | Env: `HF_HOME` | Displays TEXT and VISION portfolios separately. Shows model keys (text_XX, vision_XX), RAM requirements, and test/skip status. Diagnostic tool for understanding portfolio separation. Use script for detailed output, or pytest marker for quick check. | No (uses local cache) |
 | Manual debug mode | `mlxk run <model> "test prompt" --verbose` | Manual CLI usage with `--verbose` flag | Shows token generation details including multiple EOS token warnings. Use this for manual debugging of model quality issues. Output includes `[DEBUG] Token generation analysis` and `⚠️ WARNING: Multiple EOS tokens detected` for broken models. | No (uses local cache) |
@@ -369,6 +371,8 @@ def test_old_style(model_key):  # Don't use - shows as "Unknown (legacy)" in rep
 | `text_model_info` | Text | Access model metadata (size, path) |
 | `vision_model_key` | Vision | Parametrized vision model tests |
 | `vision_model_info` | Vision | Access vision model metadata |
+| `audio_model_key` | Audio | Parametrized audio model tests |
+| `audio_model_info` | Audio | Access audio model metadata |
 
 **DEPRECATED Fixtures (do not use in new code):**
 
@@ -381,10 +385,11 @@ def test_old_style(model_key):  # Don't use - shows as "Unknown (legacy)" in rep
 
 The pytest hooks in `tests_2.0/conftest.py` automatically detect inference modality:
 
-1. **Fixture-based detection:** Tests using `vision_model_key` → `inference_modality: "vision"`
-2. **Fixture-based detection:** Tests using `text_model_key` → `inference_modality: "text"`
-3. **Explicit override:** Pipe tests can set modality via `request.node.user_properties`
-4. **Legacy fallback:** Tests without modality fixtures → `inference_modality: "unknown"`
+1. **Fixture-based detection:** Tests using `text_model_key` → `inference_modality: "text"`
+2. **Fixture-based detection:** Tests using `vision_model_key` → `inference_modality: "vision"`
+3. **Fixture-based detection:** Tests using `audio_model_key` → `inference_modality: "audio"`
+4. **Explicit override:** Pipe tests can set modality via `request.node.user_properties`
+5. **Legacy fallback:** Tests without modality fixtures → `inference_modality: "unknown"`
 
 **Why This Matters:**
 
@@ -1238,6 +1243,52 @@ pytest -m live_e2e --collect-only  # Should work without errors
 - Deterministic test indices (text_XX, vision_XX) replace flaky discovered_XX
 - Tests validate model-type-specific behavior (e.g., text-only on vision models)
 
+### Audio Portfolio E2E Tests
+
+**Status:** ✅ Complete (ADR-019, Portfolio Separation)
+
+**Location:** `tests_2.0/live/test_audio_e2e_live.py`
+**Fixture:** `audio_portfolio` (provides audio-capable models)
+**Parametrization:** `audio_model_key` (audio_00, audio_01, ...)
+
+**Test Class: TestAudioTranscription**
+
+1. **test_transcribe_short_audio_wav[audio_XX]** (parametrized)
+   - Short audio transcription (~3 seconds)
+   - Validates: Key semantic content (universe, sir, exist)
+   - **N tests** (one per audio model in portfolio)
+
+2. **test_transcribe_longer_audio_wav[audio_XX]** (parametrized)
+   - Longer audio transcription (~14 seconds)
+   - Validates: At least 2 keywords from passage (royal, cavern, throne, crown, gong)
+   - **N tests** (one per audio model in portfolio)
+
+3. **test_transcribe_mp3_format[audio_XX]** (parametrized)
+   - MP3 format support validation
+   - Validates: Key semantic content (universe, sir, exist) - same as WAV test
+   - **N tests** (one per audio model in portfolio)
+
+4. **test_audio_output_not_empty[audio_XX]** (parametrized)
+   - Basic sanity test
+   - Validates: Output length > 10 characters
+   - **N tests** (one per audio model in portfolio)
+
+**RAM Gating:**
+- Uses `calculate_vision_model_ram_gb()` (0.70 threshold, no multiplier)
+- Audio models go through VisionRunner infrastructure
+- Same conservative gate as Vision models
+
+**Known Limitations (ADR-019):**
+- Audio duration limit: ~30 seconds (Gemma-3n architecture constraint)
+- Phonetic errors on 4-bit models: "A man" → "Amen" (expected)
+- Complex prompts + MP3: Can cause multilingual drift (fixed: simple prompt default)
+
+**Example:**
+```python
+# 64GB system → 44.8GB threshold (70%)
+# audio_00: gemma-3n-E2B-it-4bit (4.2GB, 6.5%) → ✅ RUN
+```
+
 ---
 
 ## Appendix
@@ -1398,10 +1449,11 @@ tests_2.0/
 │   └── test_spec_version_sync.py              # Code/docs version consistency check
 ├── live/                              # Opt-in live tests (markers)
 │   ├── __init__.py
-│   ├── conftest.py                              # Shared fixtures for live E2E tests (text_portfolio, vision_portfolio, pytest_generate_tests hook)
+│   ├── conftest.py                              # Shared fixtures for live E2E tests (text_portfolio, vision_portfolio, audio_portfolio, pytest_generate_tests hook)
 │   ├── server_context.py                       # LocalServer context manager for E2E testing (45s timeout for MLX cleanup)
 │   ├── sse_parser.py                           # SSE parsing utilities for streaming validation
-│   ├── test_utils.py                           # Portfolio Discovery (text/vision separation), RAM calculation modularization, RAM gating utilities
+│   ├── test_utils.py                           # Portfolio Discovery (text/vision/audio separation), RAM calculation modularization, RAM gating utilities
+│   ├── test_audio_e2e_live.py                  # Audio CLI E2E tests with real models (ADR-019, 4 transcription tests, parametrized: audio_XX)
 │   ├── test_cli_e2e.py                         # CLI integration E2E tests (ADR-011, parametrized)
 │   ├── test_cli_pipe_live.py                   # Pipe-mode E2E (stdin '-', JSON interactive error, list→run pipe) using first eligible model
 │   ├── test_clone_live.py                      # Live clone flow (requires MLXK2_LIVE_CLONE, HF_TOKEN)
@@ -1416,6 +1468,7 @@ tests_2.0/
 │   ├── test_vision_server_e2e.py               # Vision Server E2E tests with VISION models (ADR-012 Phase 3 + Portfolio Separation, parametrized: vision_XX)
 │   └── test_vm_stat_parsing.py                 # vm_stat output parsing validation (macOS memory metrics)
 ├── test_adr004_error_logging.py       # ADR-004 error logging and redaction (tokens, paths)
+├── test_audio_cli.py                  # Audio CLI argument tests (ADR-019 Phase 2, 8 tests: --audio parsing, file validation, capability checks)
 ├── test_capabilities.py               # Probe/Policy architecture (ADR-012, ADR-016, 45 tests)
 ├── test_cli_log_json_flag.py          # CLI --log-json flag behavior and JSON log format
 ├── test_cli_push_args.py              # Push CLI args and JSON error/output handling (offline)
@@ -1454,6 +1507,7 @@ tests_2.0/
 ├── test_runtime_compatibility_reason_chain.py  # Runtime compatibility reason field decision chain (Issue #36)
 ├── test_server_api_minimal.py         # Minimal OpenAI-compatible server endpoints (SSE, JSON)
 ├── test_server_api.py.disabled        # Disabled server API tests (WIP/expanded scenarios)
+├── test_server_audio.py               # Audio server unit tests (ADR-019 Phase 4, 23 tests: request detection, Base64 decoding, format validation)
 ├── test_server_models_and_errors.py   # Server model loading and error handling
 ├── test_server_streaming_minimal.py   # Server SSE streaming functionality
 ├── test_server_token_limits_api.py    # Server token limit enforcement
diff --git a/docs/ADR/ADR-019-Audio-Input-Support.md b/docs/ADR/ADR-019-Audio-Input-Support.md
new file mode 100644
index 0000000..0a4d42e
--- /dev/null
+++ b/docs/ADR/ADR-019-Audio-Input-Support.md
@@ -0,0 +1,256 @@
+# ADR-019 — Audio Input Support (via mlx-vlm)
+
+**Status:** In Progress (Phase 1-3 done, Phase 4 pending)
+**Target:** 2.0.4-beta.8
+**Depends on:** mlx-vlm ≥0.3.10 (GitHub only, not yet on PyPI)
+
+---
+
+## Context
+
+mlx-vlm documents audio input for Omni models in main branch (post-0.3.9):
+- **Gemma-3n** (Google): Vision + Audio + Text — explicitly documented in README
+
+**Not Supported:**
+- **Qwen3-Omni** (Alibaba): Architecture `qwen3_omni_moe` not in mlx-lm/mlx-vlm
+  - See: github.com/ml-explore/mlx-lm/issues/497
+  - Community PR #574 is text-only (Audio-Tower removed)
+
+These models use the same mlx-vlm backend as vision models.
+mlx-knife can add audio support with minimal effort.
+
+**Current State (2026-01-19):**
+- mlx-vlm 0.3.10 available on GitHub, not yet on PyPI
+- pyproject.toml uses Git URL dependency (blocks PyPI release)
+- Audio functionality verified with Gemma-3n
+
+---
+
+## Decision
+
+Implement audio input via mlx-vlm (Option A from Session 101 discussion).
+
+### Scope: CLI-first
+
+| Command | Audio Support |
+|---------|---------------|
+| `list` | Shows `+audio` in type field |
+| `show` | Shows audio capability in details |
+| `run` | `--audio file.wav` parameter |
+| `health` | Checks audio-capable models |
+| `serve` | Phase 4 - pending |
+
+### Out of Scope
+
+- STT/Transcription (separate feature via mlx-audio-plus, see Brainstorm ADR)
+- TTS/Audio output (Qwen3-Omni can do it, but not exposed)
+- stdin audio (`--audio -`) - later
+- Audio segments/timestamps (STT feature)
+
+---
+
+## Design
+
+### CLI Interface
+
+```bash
+# Audio-only (transcription) - uses default prompt and temperature 0.2
+mlxk run gemma-3n-E2B-it-4bit --audio voice.wav
+
+# With explicit prompt
+mlxk run gemma-3n-E2B-it-4bit --audio voice.wav --prompt "Transcribe exactly what is said"
+
+# Lower temperature for maximum consistency
+mlxk run gemma-3n-E2B-it-4bit --audio voice.wav --temperature 0
+```
+
+### Capability Detection
+
+```python
+# capabilities.py
+AUDIO_MODEL_TYPES = frozenset({
+    "gemma3n",        # Google Gemma 3n (verified in mlx-vlm README)
+    # "qwen3_omni_moe", # Alibaba Qwen3-Omni — NOT SUPPORTED (mlx-lm #497)
+})
+
+# Detection via config.json:
+# - model_type in AUDIO_MODEL_TYPES
+# - "audio_config" present
+```
+
+### list/show Output
+
+```
+$ mlxk list
+NAME                      TYPE                 HEALTH   SIZE
+gemma-3n-E2B-it-4bit     chat+vision+audio    healthy  2.1GB
+pixtral-12b-4bit         chat+vision          healthy  6.8GB
+llama-3.2-3b-4bit        chat                 healthy  1.8GB
+```
+
+### Code Changes (Phase 1-2)
+
+| File | Change |
+|------|--------|
+| `cli.py` | `--audio` argument, context-aware temperature default |
+| `run.py` | Audio passthrough to VisionRunner, capability checks |
+| `vision_runner.py` | `_prepare_audio()`, `audio=` parameter, `num_audios=` |
+| `capabilities.py` | `AUDIO_MODEL_TYPES`, `Capability.AUDIO` |
+| `common.py` | `detect_audio_capability()` |
+
+---
+
+## Implementation Phases
+
+### Phase 1: Capability Detection ✅
+
+- `AUDIO_MODEL_TYPES` in capabilities.py
+- `is_audio` detection in ModelCapabilities (via `audio_config` in config.json)
+- `list`/`show` shows audio capability
+- Consistent with vision capability display
+
+### Phase 2: CLI Run ✅
+
+- `--audio` argument in cli.py
+- VisionRunner audio passthrough
+- Error handling (audio without audio-capable model → clear error)
+- 5MB file size limit (~2-3 min at 16kHz mono; token count is the real constraint)
+- Multi-audio blocked with clear error (mlx-vlm limitation)
+- Context-aware temperature default (0.2 for audio, 0.7 for text/vision)
+- Default prompt: "Transcribe what is spoken in this audio."
+
+### Phase 3: Evaluation ✅
+
+Tested with `gemma-3n-E2B-it-4bit` (mlx-community).
+
+**Findings:**
+
+| Question | Result |
+|----------|--------|
+| Empty prompt allowed? | Yes, but results poor (model confused) |
+| Audio-only without image? | ✅ Works well |
+| Text prompt required? | Not required, but recommended for quality |
+| Multiple audio files? | ❌ Not supported (mlx-vlm token mismatch bug) |
+| Audio + Vision combined? | ❌ Audio silently ignored when images present (cause unclear: mlx-vlm or model) |
+
+**Temperature Impact:**
+
+| Temperature | Behavior |
+|-------------|----------|
+| 0.7 (text default) | Multilingual drift (outputs Arabic/Hindi for "Amen" misinterpretation) |
+| 0.2 (audio default) | Stable, good transcription quality |
+| 0.0 | Most consistent, recommended for STT tasks |
+
+**Implemented Defaults:**
+- Audio temperature: 0.2 (vs 0.7 for text/vision)
+- Audio prompt: "Transcribe what is spoken in this audio."
+
+**Known Limitations:**
+- Phonetic errors observed ("A man" → "Amen"); cause unclear (model limitation vs quantization)
+- Multi-audio causes token mismatch error in mlx-vlm
+- Audio+Vision combined: audio silently ignored (not blocked by mlx-knife to allow future compatibility)
+- WAV format confirmed; other formats untested
+
+**Audio Duration Limit (~30 seconds):**
+
+Gemma-3n uses a fixed **188 soft tokens per audio clip**, regardless of audio length:
+- Encoding rate: 6.25 tokens/second (from model spec)
+- Maximum duration: 188 ÷ 6.25 = **~30.08 seconds**
+
+| Audio Duration | Transcribed | Notes |
+|----------------|-------------|-------|
+| ≤29 seconds | Full | ✅ Reliable |
+| 30-35 seconds | ~29 seconds | Missing last few seconds |
+| 43 seconds | ~29 seconds | Only first 2/3 transcribed |
+
+**Cause:** Model architecture constraint in Gemma-3n. Audio is encoded to a fixed-size
+representation before being passed to the language model. Content beyond ~30 seconds
+is silently dropped during encoding.
+
+**Sources:**
+- `Gemma3nProcessor.audio_seq_length = 188` (transformers 5.0+)
+- [HuggingFace Gemma3n Docs](https://huggingface.co/docs/transformers/en/model_doc/gemma3n)
+- Empirical testing (Session 116)
+
+### Phase 4: Server API (Pending)
+
+> **Note:** This design may evolve based on client ecosystem developments.
+
+**Goal:** OpenAI-compatible audio input for `/v1/chat/completions`
+
+**OpenAI Standard Format (`input_audio`):**
+```json
+{
+  "model": "gemma-3n-E2B-it-4bit",
+  "messages": [{
+    "role": "user",
+    "content": [
+      {"type": "text", "text": "Transcribe this audio"},
+      {
+        "type": "input_audio",
+        "input_audio": {
+          "data": "<base64-encoded-wav>",
+          "format": "wav"
+        }
+      }
+    ]
+  }]
+}
+```
+
+**Two Approaches for Audio Input:**
+
+| Approach | How it works | Client Support |
+|----------|--------------|----------------|
+| STT → LLM | Audio → separate Whisper service → Text → LLM | Open WebUI, many clients |
+| Native Audio | Audio directly to LLM (GPT-4o-audio, Gemma-3n) | New, limited client support |
+
+mlx-knife uses **native audio** (Gemma-3n understands audio directly).
+Most clients (Open WebUI) expect separate STT service, not `input_audio`.
+
+**WebUI Integration (nChat):**
+
+Browser provides native APIs for microphone access:
+```javascript
+// Microphone access
+navigator.mediaDevices.getUserMedia({ audio: true })
+
+// Recording
+const mediaRecorder = new MediaRecorder(stream)
+mediaRecorder.ondataavailable = (e) => { /* audio blob */ }
+```
+
+Workflow: Record → Convert to WAV (JS library) → Base64 → JSON `input_audio`
+
+**Implementation TODO:**
+- Parse `input_audio` content blocks in server_base.py
+- Base64 decode → temp file → VisionRunner
+- 5MB limit enforcement (base64 ~33% overhead)
+- Error handling for unsupported formats
+- Live tests: `test_audio_server_e2e.py`
+
+---
+
+## Risks
+
+| Risk | Mitigation |
+|------|------------|
+| mlx-vlm 0.3.10 not on PyPI | Git dependency in pyproject.toml; blocks mlx-knife PyPI release |
+| mlx-vlm API changes | Thin wrapper, quickly adaptable |
+| Audio format restrictions | Documented: WAV confirmed, others untested |
+| Memory requirements unclear | Document in README, profile before server support |
+| Gemma-3n index/shard issue | `mlxk convert --repair-index` before use |
+| Qwen3-Omni not supported | Only Gemma-3n as target, Qwen3-Omni out-of-scope |
+| Phonetic errors | Document limitation, recommend temperature 0 |
+
+---
+
+## References
+
+- mlx-vlm README (main): Multi-Modal Example (Image + Audio)
+- mlx-lm Issue #497: Qwen3-Omni Support Request (open since Sep 2025)
+- mlx-lm PR #574: qwen3_omni_moe Text-only (Audio-Tower removed)
+- Gemma-3n: mlx-community/gemma-3n-E2B-it-4bit
+- Session 101: Audio discussion, Option A decision
+- Session 102: Research — Qwen3-Omni blocked, Gemma-3n verified
+- Session 111: Phase 3 evaluation, temperature findings, limitation documentation
diff --git a/docs/SERVER-HANDBOOK.md b/docs/SERVER-HANDBOOK.md
index b15a771..fc323d5 100644
--- a/docs/SERVER-HANDBOOK.md
+++ b/docs/SERVER-HANDBOOK.md
@@ -1,8 +1,8 @@
 # MLX Knife Server Handbook
 
-**Version:** 2.0.4-beta.1 (WIP)
+**Version:** 2.0.4-beta.8 (WIP)
 **Status:** ⚠️ **WORK IN PROGRESS** - This document will evolve until 2.1 stable release
-**Last Updated:** 2025-12-15
+**Last Updated:** 2026-01-20
 
 > **Audience:** Server operators, DevOps, API consumers
 > **For implementation details:** See `ARCHITECTURE.md` and `docs/ADR/` (developer documentation)
@@ -24,9 +24,81 @@ mlxk serve --host 0.0.0.0 --port 8000
 
 **Requirements:**
 - Python 3.9+ (Text models)
-- Python 3.10+ (Vision models)
+- Python 3.10+ (Vision and Audio models)
 - mlx-lm 0.28.4+
-- mlx-vlm 0.3.9+ (optional, for vision; beta.3 recommends commit c4ea290e47e2155b67d94c708c662f8ab64e1b37)
+- mlx-vlm ≥0.3.10 (required for audio; currently GitHub-only, not yet on PyPI)
+
+---
+
+## OpenAI API Compatibility
+
+MLX Knife implements a **subset** of the OpenAI API with documented behavioral differences.
+
+### Supported Endpoints
+
+| Endpoint | Status | Notes |
+|----------|--------|-------|
+| `/v1/chat/completions` | ✅ Supported | Text, Vision (`image_url`), Audio (`input_audio`) |
+| `/v1/completions` | ✅ Supported | Legacy text completion |
+| `/v1/models` | ✅ Supported | Extended with `context_length` field |
+| `/health` | ✅ Custom | MLX Knife extension |
+
+### Not Implemented
+
+| Endpoint | Status |
+|----------|--------|
+| `/v1/embeddings` | ❌ Planned (ADR-015) |
+| `/v1/audio/*` | ❌ Not planned (use `input_audio` in chat) |
+| `/v1/files` | ❌ Not planned |
+| `/v1/moderations` | ❌ Not planned |
+| `/v1/responses` | ❌ Not planned |
+
+### Authentication
+
+MLX Knife **ignores** authentication headers. The server accepts but does not validate:
+- `Authorization: Bearer ...`
+- Any API key
+
+**Note:** For production deployments requiring authentication, use a reverse proxy (nginx, Caddy).
+
+### Request Headers
+
+```
+Content-Type: application/json  (required)
+Authorization: Bearer ...       (optional, ignored)
+```
+
+### Behavioral Deviations from OpenAI
+
+These are intentional design choices, not bugs:
+
+| Behavior | OpenAI | MLX Knife | Reason |
+|----------|--------|-----------|--------|
+| Vision history | Full history to model | Only last user message | Prevents pattern reproduction (hallucinations) |
+| Image URLs | HTTP URLs + Base64 + File IDs | Base64 data URLs only | No external fetching |
+| Audio+Vision | Both processed | Audio silently ignored | mlx-vlm limitation |
+| Multi-audio | Supported | 1 per request | mlx-vlm limitation |
+| Error format | `{"error": {"message", "type", "code"}}` | ADR-004 envelope (see below) | Richer error context |
+| `max_completion_tokens` | Preferred | Not supported (use `max_tokens`) | Legacy compatibility |
+| HTTP 507 | Not used | Memory constraint | Explicit OOM prevention |
+
+### Error Response Format
+
+MLX Knife uses an extended error envelope (ADR-004), not the OpenAI format:
+
+```json
+{
+  "status": "error",
+  "error": {
+    "type": "validation_error",
+    "message": "No user message found",
+    "retryable": false
+  },
+  "request_id": "abc123..."
+}
+```
+
+**Error types:** `validation_error`, `model_not_found`, `internal_error`, `server_shutdown`, `insufficient_memory`, `access_denied`
 
 ---
 
@@ -68,12 +140,38 @@ mlxk serve --host 0.0.0.0 --port 8000
 }
 ```
 
+**Audio Request (OpenAI `input_audio` format):**
+```json
+{
+  "model": "mlx-community/gemma-3n-E2B-it-4bit",
+  "messages": [
+    {
+      "role": "user",
+      "content": [
+        {"type": "text", "text": "Transcribe what is spoken in this audio"},
+        {
+          "type": "input_audio",
+          "input_audio": {
+            "data": "<base64-encoded>",
+            "format": "wav"
+          }
+        }
+      ]
+    }
+  ],
+  "max_tokens": 2048,
+  "temperature": 0.0
+}
+```
+
+**Supported audio formats:** `wav`, `mp3` (or `mpeg` alias)
+
 **mlx-knife Extension Parameters:**
 - `chunk` (integer, optional): Batch size for vision processing (default: 1). Controls how many images are processed per inference session. Higher values may trigger OOM on resource-constrained systems. Maximum: 5 (enforced by server).
 
 **Default chunk size:**
 1. Request parameter `chunk` (highest priority)
-2. Server startup: `mlxk server --chunk N`
+2. Server startup: `mlxk serve --chunk N`
 3. Environment: `MLXK2_VISION_CHUNK_SIZE=N`
 4. Default: 1 (maximum safety)
 
@@ -189,7 +287,7 @@ See `examples/vision_pipe.sh` for a practical Vision→Text pipeline example (CL
 - **Sequential Images:** Only images from the **last user message** are processed (OpenAI API compliant)
 - **Each request is independent:** No "shift-window" context like text models (Metal memory limitations)
 
-### Stable Image IDs (History-Based)
+#### Stable Image IDs (History-Based)
 
 **Problem:** How to maintain stable "Image 1, 2, 3..." numbering across multiple requests?
 
@@ -204,7 +302,7 @@ Request 3: Re-upload beach.jpg → Still Image 1 (hash match)
 ```
 
 **Properties:**
-- ✅ **100% OpenAI API compatible** — standard messages[] format, no custom headers
+- ✅ **Standard messages[] format** — no custom headers or protocol extensions
 - ✅ **Stateless server** — no registry, no TTL, no cleanup
 - ✅ **Content-hash deduplication** — same image always gets same ID
 - ✅ **Cross-model workflows** — "Image 1" stable across Vision↔Text model switches
@@ -219,9 +317,45 @@ Request 3: Re-upload beach.jpg → Still Image 1 (hash match)
 
 ---
 
-### Token Limits: Vision vs Text Models
+### Audio Support (2.0.4-beta.8)
 
-**Critical Difference:** Vision and text models use different `max_tokens` strategies.
+**Native audio input** for audio-capable models (Gemma-3n).
+
+**Supported:**
+- ✅ OpenAI `input_audio` format (Base64-encoded)
+- ✅ Formats: WAV, MP3
+- ✅ Temperature 0.0 (greedy sampling for transcription consistency)
+
+**Limits:**
+- **Per-audio:** 5 MB max (~2-3 minutes at 16kHz mono)
+- **Count:** 1 audio per request (multi-audio blocked)
+
+**Important Characteristics:**
+
+- **Stateless Server:** Same as Vision — no server-side state
+- **Single Audio:** Only one audio file per request (mlx-vlm limitation)
+- **Audio+Vision:** When both present, audio is silently ignored (mlx-vlm behavior)
+- **Temperature:** Fixed at 0.0 for transcription consistency (CLI default: 0.2)
+
+**Audio-Capable Models:**
+- `gemma-3n` (Google): Vision + Audio + Text
+- Qwen3-Omni: Not supported (mlx-lm architecture missing)
+
+**History Handling:**
+
+When switching from Audio to Text model mid-conversation:
+- Server filters `input_audio` content blocks
+- Text model sees `[n audio(s) were attached]` placeholder
+
+**Python Version:**
+- ✅ Python 3.10+ required (same as Vision)
+- ❌ Python 3.9: Audio requests → HTTP 501
+
+---
+
+### Token Limits: Text vs Multimodal Models
+
+**Critical Difference:** Text and multimodal (Vision/Audio) models use different `max_tokens` strategies.
 
 #### Text Models (MLXRunner)
 
@@ -236,7 +370,7 @@ Request 3: Re-upload beach.jpg → Still Image 1 (hash match)
 **Example:**
 - Llama-3.2-3B (128K context) → Server default: 64K max_tokens
 
-#### Vision Models (VisionRunner)
+#### Vision/Audio Models (VisionRunner)
 
 **Strategy:** Stateless processing
 - Each request is independent (no conversation history in context)
@@ -247,13 +381,13 @@ Request 3: Re-upload beach.jpg → Still Image 1 (hash match)
 
 **Rationale:**
 - No need for `/2` division (no history to reserve)
-- Vision inference is slow → 2048 adequate for image descriptions
+- Multimodal inference is slow → 2048 adequate for descriptions/transcriptions
 - Prevents accidentally generating 64K+ tokens
 
 **Override:**
 ```json
 {
-  "model": "mlx-community/Llama-3.2-11B-Vision-Instruct-4bit",
+  "model": "mlx-community/gemma-3n-E2B-it-4bit",
   "messages": [...],
   "max_tokens": 4096  // Explicit override
 }
@@ -294,6 +428,11 @@ Request 3: Re-upload beach.jpg → Still Image 1 (hash match)
 - **Single image:** Behaves like batch mode (one SSE event)
 - **Format:** OpenAI-compatible SSE with per-chunk deltas
 
+#### Audio Models
+- ⚠️ **Batch mode only:** Single SSE event with complete response
+- **Reason:** Single audio per request, no chunking needed
+- **Format:** Same as Vision single-image mode
+
 **Request:**
 ```json
 {
@@ -305,13 +444,17 @@ Request 3: Re-upload beach.jpg → Still Image 1 (hash match)
 
 **Response (SSE stream):**
 ```
-data: {"id":"chatcmpl-...","choices":[{"delta":{"content":"Hello"},"index":0}],...}
+data: {"id":"chatcmpl-abc123","object":"chat.completion.chunk","created":1702345678,"model":"mlx-community/Llama-3.2-3B-Instruct-4bit","choices":[{"index":0,"delta":{"role":"assistant","content":"Hello"},"finish_reason":null}]}
 
-data: {"id":"chatcmpl-...","choices":[{"delta":{"content":" there"},"index":0}],...}
+data: {"id":"chatcmpl-abc123","object":"chat.completion.chunk","created":1702345678,"model":"mlx-community/Llama-3.2-3B-Instruct-4bit","choices":[{"index":0,"delta":{"content":" there"},"finish_reason":null}]}
+
+data: {"id":"chatcmpl-abc123","object":"chat.completion.chunk","created":1702345678,"model":"mlx-community/Llama-3.2-3B-Instruct-4bit","choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}
 
 data: [DONE]
 ```
 
+**Note:** `stream_options.include_usage` is not supported.
+
 ---
 
 ## Configuration
@@ -365,7 +508,7 @@ python -m mlxk2.core.server_base
 - **201 Created:** Resource created (future)
 
 ### Client Errors (4xx)
-- **400 Bad Request:** Invalid input (e.g., missing images for vision model, too many images)
+- **400 Bad Request:** Invalid input (e.g., too many images, invalid format, validation failures)
 - **404 Not Found:** Model not found in cache
 
 ### Server Errors (5xx)
@@ -404,9 +547,9 @@ python -m mlxk2.core.server_base
 
 ## Troubleshooting
 
-### Vision Model Fails on Python 3.9
+### Multimodal Request Fails on Python 3.9
 
-**Symptom:** HTTP 501 "Vision models require Python 3.10+"
+**Symptom:** HTTP 501 "Vision/Audio models require Python 3.10+"
 
 **Solution:**
 ```bash
@@ -415,8 +558,8 @@ pyenv install 3.10
 pyenv local 3.10
 pip install mlx-lm mlx-vlm
 
-# Beta.3 (pre-0.3.10 fix)
-pip install mlx-lm "mlx-vlm @ git+https://github.com/Blaizzy/mlx-vlm.git@c4ea290e47e2155b67d94c708c662f8ab64e1b37"
+# Until mlx-vlm 0.3.10 on PyPI (Vision + Audio support)
+pip install mlx-lm "mlx-vlm @ git+https://github.com/Blaizzy/mlx-vlm.git@58122703b0bba7c574d23c9c751f01cf60485d4f"
 ```
 
 ### Memory Constraint Errors (HTTP 507)
@@ -454,6 +597,44 @@ pip install mlx-lm "mlx-vlm @ git+https://github.com/Blaizzy/mlx-vlm.git@c4ea290
 
 **Solution:** Resize images, reduce count, or check encoding
 
+### Audio Errors
+
+#### Audio Request Fails (HTTP 400)
+
+**Common causes:**
+- Audio size > 5 MB
+- More than 1 audio per request (multi-audio not supported)
+- Unsupported format (use WAV or MP3)
+- Invalid Base64 encoding
+
+**Solution:** Compress audio, ensure single audio per request, use supported format
+
+#### Audio Model Not Found
+
+**Symptom:** `Model does not support audio input`
+
+**Cause:** Model lacks audio capability
+
+**Solution:** Use an audio-capable model:
+```bash
+mlxk list | grep +audio
+```
+
+**Note:** Some HuggingFace models may require `mlxk convert --repair-index` before use.
+
+#### Audio Output is Garbled/Multilingual
+
+**Symptom:** Transcription includes unexpected languages (Arabic, Hindi, etc.)
+
+**Cause:** Temperature too high (default text temperature 0.7 causes drift)
+
+**Solution:** Use temperature 0.0 for audio:
+```json
+{
+  "temperature": 0.0
+}
+```
+
 ---
 
 ## Limits Summary
@@ -463,15 +644,33 @@ pip install mlx-lm "mlx-vlm @ git+https://github.com/Blaizzy/mlx-vlm.git@c4ea290
 | Images per request | 5 | Metal OOM prevention |
 | Image size | 20 MB | Metal OOM prevention |
 | Total image size | 50 MB | Metal OOM prevention |
+| **Audio per request** | **1** | **mlx-vlm limitation** |
+| **Audio size** | **5 MB** | **Token count constraint** |
 | Vision model RAM | 70% system | Metal OOM prevention |
 | Text model RAM | 70% (warning) | Swap tolerance |
 | Vision max_tokens | 2048 (default) | Stateless, slow inference |
+| Audio max_tokens | 2048 (default) | Stateless, like Vision |
 | Text max_tokens | context_length/2 | Shift-window reservation |
 
 ---
 
 ## Migration Guide
 
+### From 2.0.4-beta.7 → 2.0.4-beta.8
+
+**New Features:**
+- ✅ Audio input support via OpenAI `input_audio` format
+- ✅ Supported audio formats: WAV, MP3
+- ✅ Audio history filtering: `[n audio(s) were attached]`
+
+**Breaking Changes:**
+- None (audio is additive)
+
+**Recommendations:**
+- Update mlx-vlm to ≥0.3.10 (GitHub install required, not yet on PyPI)
+- Use temperature 0.0 for audio transcription requests
+- Test with Gemma-3n or other audio-capable models
+
 ### From 2.0.3 → 2.0.4-beta.1
 
 **New Features:**
@@ -510,7 +709,7 @@ Clients MUST follow the OpenAI Chat Completions API format. MLX Knife is designe
 
 ### Conversation History
 
-**Clients MUST send the full conversation history** with each request:
+**Clients MUST send the full message list** with each request:
 
 ```json
 {
@@ -525,6 +724,33 @@ Clients MUST follow the OpenAI Chat Completions API format. MLX Knife is designe
 
 **Why:** The server reconstructs stable image IDs from the history. Without full history, image numbering restarts at 1 with each request.
 
+**What "full history" means:**
+- ✅ All messages with correct roles (`user`, `assistant`, `system`)
+- ✅ Complete assistant responses (including `<!-- mlxk:filenames -->` markers)
+- ⚠️ Media payloads (Base64) can be dropped after first Vision request (see [Image ID Persistence](#image-id-persistence-stateless))
+
+**Note:** For Vision models, the server only forwards the last user message to the model (stateless prompt), but still scans the full history for image ID reconstruction.
+
+### Vision Messages Format
+
+**Multimodal content** uses the OpenAI array format:
+
+```json
+{
+  "role": "user",
+  "content": [
+    {"type": "text", "text": "What's in this image?"},
+    {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,..."}}
+  ]
+}
+```
+
+**Image URLs:**
+- ✅ **Base64 Data URLs:** `data:image/jpeg;base64,/9j/4AAQ...`
+- ❌ **HTTP URLs:** Not supported (no external fetching)
+
+**Supported formats:** JPEG, PNG, GIF, WebP
+
 ### Vision: Stateless Prompt, History-Based IDs
 
 **Important architectural distinction for Vision requests:**
@@ -551,42 +777,6 @@ Clients MUST follow the OpenAI Chat Completions API format. MLX Knife is designe
 - Sending history caused pattern reproduction (model hallucinating mappings)
 - Clean separation: Vision=describe, Text=discuss
 
-### Vision Messages Format
-
-**Multimodal content** uses the OpenAI array format:
-
-```json
-{
-  "role": "user",
-  "content": [
-    {"type": "text", "text": "What's in this image?"},
-    {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,..."}}
-  ]
-}
-```
-
-**Image URLs:**
-- ✅ **Base64 Data URLs:** `data:image/jpeg;base64,/9j/4AAQ...`
-- ❌ **HTTP URLs:** Not supported (no external fetching)
-
-**Supported formats:** JPEG, PNG, GIF, WebP
-
-### Cross-Model Workflows (Vision → Text)
-
-When switching from Vision to Text model mid-conversation:
-
-1. **Client:** Continue sending full history (including previous image_url content)
-2. **Server:** Automatically filters images for text models, replaces with placeholders
-3. **Result:** Text model sees `[n image(s) were attached]` instead of binary data
-
-**Example workflow:**
-```
-1. Vision model: User sends 2 images → Model describes both
-2. Vision model: User asks "What's different?" → Model compares
-3. Switch to Text model: User asks "Which is better for vacation?"
-4. Text model: Sees "[2 image(s) were attached]" in history, can reference the conversation
-```
-
 ### Image Deduplication
 
 Same image content = same ID (content-hash based).
@@ -595,7 +785,7 @@ Same image content = same ID (content-hash based).
 - Re-uploading the same image → Server assigns same ID
 - No client-side deduplication needed
 
-### Image ID Persistence (100% OpenAI-Compatible)
+### Image ID Persistence (Stateless)
 
 **Problem:** How do Image IDs remain stable across Vision→Text→Vision workflows when clients drop Base64 data from history (storage optimization)?
 
@@ -611,16 +801,24 @@ Same image content = same ID (content-hash based).
    ]}
    ```
 
-2. **Server Response:** Includes filename mapping table
-   ```
-   A sandy beach with blue water.
+2. **Server Response:** Includes filename mapping table (wrapped in `<details>`)
+   ```html
+   <details>
+   <summary>📸 Image Metadata (1 image)</summary>
 
    <!-- mlxk:filenames -->
-   | Image | Filename |
-   |-------|----------|
-   | 1 | image_5733332c.jpeg |
+   | Image | Filename | Original | Location | Date | Camera |
+   |-------|----------|----------|----------|------|--------|
+   | 1 | image_5733332c.jpeg | beach.jpg | 📍 34.0522°N, 118.2437°W | 📅 2024-06-15 | iPhone 14 |
+
+   </details>
+
+   A sandy beach with blue water.
    ```
 
+   **Note:** EXIF columns (Original, Location, Date, Camera) are enabled by default.
+   Disable with `MLXK2_EXIF_METADATA=0` for minimal output (Image, Filename only).
+
 3. **Client Storage Optimization:** Client can **drop Base64 from history**, keep only:
    ```json
    {"role": "user", "content": "describe"}
@@ -649,9 +847,9 @@ Same image content = same ID (content-hash based).
    - Assigns: mountain.jpg → Image ID 2 ✅
 
 **Benefits:**
-- ✅ **Zero client changes** - Works with standard OpenAI API
+- ✅ **Zero client changes** - Works with standard OpenAI message format
 - ✅ **Storage optimization** - Client can drop large Base64 data (2 MB → 2 KB)
-- ✅ **100% OpenAI-compatible** - No protocol extensions needed
+- ✅ **No protocol extensions** - Standard messages[] array, no custom headers
 - ✅ **Stateless server** - No server-side session state required
 - ✅ **Scales to 100+ images** - Clients only store small text mappings
 
@@ -659,15 +857,68 @@ Same image content = same ID (content-hash based).
 - **After first Vision request:** Drop Base64 image_url from history, keep text + assistant response
 - **Store locally:** Small thumbnails for UI (~20 KB/image via IndexedDB)
 - **History format:** Text-only user messages + full assistant responses (with mapping tables)
+- **⚠️ Preserve verbatim:** Do not sanitize or strip HTML comments from assistant responses — the `<!-- mlxk:filenames -->` markers are required for ID reconstruction
 
 **Example client storage (100 images):**
 - ❌ **Before:** 100 images × 2 MB Base64 = 200 MB (exceeds browser limits)
 - ✅ **After:** 100 thumbnails × 20 KB + text history = ~2 MB (fits in IndexedDB)
 
+### Audio Messages Format
+
+**Audio content** uses the OpenAI `input_audio` format:
+
+```json
+{
+  "role": "user",
+  "content": [
+    {"type": "text", "text": "Transcribe this audio"},
+    {
+      "type": "input_audio",
+      "input_audio": {
+        "data": "<base64-encoded>",
+        "format": "wav"
+      }
+    }
+  ]
+}
+```
+
+**Supported formats:** `wav`, `mp3` (or `mpeg` alias)
+
+**Limitations:**
+- ❌ Only 1 audio per request (multi-audio causes mlx-vlm token mismatch)
+- ❌ Audio + Vision combined: audio is silently ignored
+
+### Cross-Model Workflows (Vision/Audio → Text)
+
+When switching from Vision or Audio to Text model mid-conversation:
+
+1. **Client:** Continue sending full message list (media payloads can be stripped if mapping tables exist)
+2. **Server:** Automatically filters any remaining media for text models, replaces with placeholders
+3. **Result:** Text model sees `[n image(s) were attached]` or `[n audio(s) were attached]`
+
+**Example workflow:**
+```
+1. Vision model: User sends 2 images → Model describes both
+2. Vision model: User asks "What's different?" → Model compares
+3. Switch to Text model: User asks "Which is better for vacation?"
+4. Text model: Sees "[2 image(s) were attached]" in history, can reference the conversation
+```
+
+**Storage optimization:** After the first Vision request, clients can drop Base64 payloads from history while preserving assistant responses with `<!-- mlxk:filenames -->` markers. The server reconstructs image IDs from these markers.
+
 ---
 
 ## Changelog
 
+- **2026-01-20:** 2.0.4-beta.8
+  - **NEW:** Audio input support via OpenAI `input_audio` format
+  - Supported formats: WAV, MP3
+  - Audio-capable models: Gemma-3n (others as available)
+  - Limits: 5 MB per audio, 1 audio per request
+  - Temperature: 0.0 for transcription consistency
+  - History filter: `input_audio` → `[n audio(s) were attached]`
+
 - **2025-12-15:** 2.0.4-beta.1 WIP
   - Vision support: Base64 images, multiple images, limits
   - History-based stable image IDs (stateless, OpenAI-compatible)
diff --git a/docs/json-api-schema.json b/docs/json-api-schema.json
index 3d0156c..19c21ef 100644
--- a/docs/json-api-schema.json
+++ b/docs/json-api-schema.json
@@ -1,7 +1,7 @@
 {
   "$schema": "http://json-schema.org/draft-07/schema#",
   "$id": "https://example.com/mlxk-json-api.schema.json",
-  "title": "MLX-Knife 2.0 JSON API 0.1.6 (current)",
+  "title": "MLX-Knife 2.0 JSON API 0.1.7 (current)",
   "type": "object",
   "additionalProperties": false,
   "properties": {
@@ -46,10 +46,11 @@
         "size_bytes": {"type": "integer", "minimum": 0},
         "last_modified": {"$ref": "#/definitions/isoUtcZ"},
         "framework": {"type": "string", "enum": ["MLX", "GGUF", "PyTorch", "Unknown"]},
-        "model_type": {"type": "string", "enum": ["chat", "embedding", "base", "unknown"]},
+        "model_type": {"type": "string", "description": "Model type classification. See mlxk2/core/capabilities.py for currently detected values."},
         "capabilities": {
           "type": "array",
-          "items": {"type": "string", "enum": ["text-generation", "chat", "embeddings", "completion", "vision"]}
+          "items": {"type": "string"},
+          "description": "Model capabilities. Values are not normative - see mlxk2/core/capabilities.py Capability enum for currently detected values."
         },
         "health": {"type": "string", "enum": ["healthy", "unhealthy"]},
         "runtime_compatible": {"type": "boolean"},
diff --git a/docs/json-api-specification.md b/docs/json-api-specification.md
index e07e87c..545cf77 100644
--- a/docs/json-api-specification.md
+++ b/docs/json-api-specification.md
@@ -1,6 +1,6 @@
 # MLX-Knife 2.0 JSON API Specification
 
-**Specification Version:** 0.1.6
+**Specification Version:** 0.1.7
 **Status:** Stable (backward-compatible)
 **Released:** MLX-Knife 2.0.4-beta.1
 
@@ -107,7 +107,7 @@ JSON output example:
   "command": "version",
   "data": {
     "cli_version": "2.0.4-beta.1",
-    "json_api_spec_version": "0.1.6",
+    "json_api_spec_version": "0.1.7",
     "system": {
       "memory_total_bytes": 137438953472
     }
@@ -182,18 +182,21 @@ Notes:
 
 ### Model Type & Capabilities
 
-**Model Types:**
+**Model Types** *(currently detected values, not normative)*:
 - `"chat"` - Language models with chat/instruction capability
 - `"embedding"` - Embedding models for vector representations
-- `"completion"` - Base models for text completion (no chat template)
+- `"base"` - Base models for text completion (no chat template)
 - `"unknown"` - Cannot determine model type from config
 
 **Capabilities Array:**
-- `"text-generation"` - Can generate text
-- `"chat"` - Supports chat template/instruction format
-- `"embeddings"` - Can generate embeddings
-- `"completion"` - Text completion without chat format
+
+*Note: This list is not normative. mlx-knife reports capabilities it detects - models may have additional capabilities. See `mlxk2/core/capabilities.py` `Capability` enum for the authoritative list of currently detected values.*
+
+- `"text-generation"` - Can generate text (all non-embedding models)
+- `"chat"` - Supports chat template/instruction format (absence = base/completion model)
+- `"embeddings"` - Can generate embeddings (mutually exclusive with text-generation)
 - `"vision"` - Accepts image inputs (detected via `model_type` in vision families or presence of `preprocessor_config.json`)
+- `"audio"` - Accepts audio inputs (detected via `audio_config` or `model_type` in audio families)
 
 **Vision Example (Phase 1a, ADR-012):**
 ```json
@@ -583,6 +586,21 @@ mlxk-json show "Phi-3-mini" --config --json      # Include config.json content
 }
 ```
 
+## Changes in 0.1.7 (2.0.4-beta.8)
+
+**Non-normative schema for capabilities and model_type**
+
+- Removed enum constraints from `capabilities` and `model_type` in JSON schema
+- Schema now accepts any string values (permissive, non-breaking change)
+- mlx-knife is not a normative authority - it reports what it detects
+- Authoritative list of currently detected values: `mlxk2/core/capabilities.py`
+
+**Capabilities: Single Source of Truth**
+
+- Added `Capability` enum in `mlxk2/core/capabilities.py` as SSOT
+- Removed unused `"completion"` capability (base models are `["text-generation"]` without `"chat"`)
+- Added `"audio"` capability detection (ADR-019)
+
 ## Changes in 0.1.6 (Stable, 2.0.4-beta.1)
 
 **System Memory Information**
diff --git a/mlxk2/cli.py b/mlxk2/cli.py
index 5110ef3..98159c7 100644
--- a/mlxk2/cli.py
+++ b/mlxk2/cli.py
@@ -226,6 +226,13 @@ def main():
         metavar="FILE",
         help="Attach image file(s) for vision models. Accepts multiple files per flag or use multiple flags.",
     )
+    run_parser.add_argument(
+        "--audio",
+        nargs='+',
+        action="append",
+        metavar="FILE",
+        help="Attach audio file(s) for audio-capable models (e.g., Gemma-3n). Accepts WAV format.",
+    )
     run_parser.add_argument(
         "--chunk",
         type=int,
@@ -234,7 +241,7 @@ def main():
         help="Process images in batches of N (default: 1 for maximum safety)",
     )
     run_parser.add_argument("--max-tokens", type=int, help="Maximum tokens to generate")
-    run_parser.add_argument("--temperature", type=float, default=0.7, help="Sampling temperature (default: 0.7)")
+    run_parser.add_argument("--temperature", type=float, default=None, help="Sampling temperature (default: 0.7, audio: 0.2)")
     run_parser.add_argument("--top-p", type=float, default=0.9, help="Top-p sampling parameter (default: 0.9)")
     run_parser.add_argument("--repetition-penalty", type=float, default=1.1, help="Repetition penalty (default: 1.1)")
     run_parser.add_argument("--no-stream", action="store_true", help="Disable streaming output")
@@ -476,21 +483,58 @@ def main():
                 if prompt_value is None:
                     prompt_value = "Describe the image."
 
+            # Audio file processing (ADR-019 Phase 2)
+            audio_inputs = []
+            audios = getattr(args, "audio", None) or []
+            # Flatten nested list from nargs='+' + action='append'
+            if audios and isinstance(audios[0], list):
+                audios = [item for sublist in audios for item in sublist]
+
+            if audios:
+                for audio_path in audios:
+                    aud_path = Path(audio_path)
+                    if not aud_path.exists() or not aud_path.is_file():
+                        result = handle_error("CommandError", f"Audio file not found: {audio_path}")
+                        print_result(result, None, True if args.json else False)
+                        sys.exit(1)
+                    data = aud_path.read_bytes()
+                    # 5MB limit for audio (~2-3 min at 16kHz mono; token count is the real constraint)
+                    if len(data) > 5 * 1024 * 1024:
+                        result = handle_error("CommandError", f"Audio file too large (>5MB): {audio_path}")
+                        print_result(result, None, True if args.json else False)
+                        sys.exit(1)
+                    audio_inputs.append((aud_path.name, data))
+                # Multi-audio not supported by mlx-vlm (token mismatch bug)
+                if len(audio_inputs) > 1:
+                    result = handle_error("CommandError", "Multiple audio files not supported. Process one file at a time.")
+                    print_result(result, None, args.json)
+                    sys.exit(1)
+                if prompt_value is None:
+                    # Simple prompt - complex prompts cause multilingual drift in Gemma-3n with MP3
+                    prompt_value = "Transcribe this audio."
+
             stream_mode = not args.no_stream
-            if image_inputs:
+            if image_inputs or audio_inputs:
                 stream_mode = False
             elif not sys.stdout.isatty() and not args.json:
                 stream_mode = False
 
+            # Context-aware temperature default (audio: 0.2 for stability, else: 0.7)
+            if args.temperature is None:
+                temperature = 0.2 if audio_inputs else 0.7
+            else:
+                temperature = args.temperature
+
             # Handle run command with proper parameter mapping
             result_text = run_model_enhanced(
                 model_spec=args.model,
                 prompt=prompt_value,  # Can be None for interactive mode
                 images=image_inputs if images else None,
+                audio=audio_inputs if audios else None,
                 chunk=args.chunk,
                 stream=stream_mode,
                 max_tokens=getattr(args, "max_tokens", None),
-                temperature=args.temperature,
+                temperature=temperature,
                 top_p=getattr(args, "top_p", 0.9),
                 repetition_penalty=getattr(args, "repetition_penalty", 1.1),
                 use_chat_template=not getattr(args, "no_chat_template", False),
diff --git a/mlxk2/core/capabilities.py b/mlxk2/core/capabilities.py
index e8516fd..e6e4f6c 100644
--- a/mlxk2/core/capabilities.py
+++ b/mlxk2/core/capabilities.py
@@ -27,6 +27,23 @@ from pathlib import Path
 from typing import Any, Dict, List, Optional, Tuple
 
 
+class Capability(str, Enum):
+    """Known model capabilities.
+
+    Note: Not normative - these are capabilities mlx-knife currently detects.
+    Models may have capabilities not listed here.
+    """
+    TEXT_GENERATION = "text-generation"
+    CHAT = "chat"
+    EMBEDDINGS = "embeddings"
+    VISION = "vision"
+    AUDIO = "audio"
+
+
+# Convenience set for validation/iteration
+KNOWN_CAPABILITIES = frozenset(c.value for c in Capability)
+
+
 class Backend(Enum):
     """Available model backends."""
     MLX_LM = "mlx_lm"      # Text models via mlx-lm
@@ -58,6 +75,13 @@ VISION_MODEL_TYPES = frozenset({
     "smolvlm",
 })
 
+# Audio model types (ADR-019)
+# Note: Only models verified to work with mlx-vlm audio support
+AUDIO_MODEL_TYPES = frozenset({
+    "gemma3n",        # Google Gemma 3n (Vision + Audio + Text)
+    "gemma3n_audio",  # Audio encoder subcomponent
+})
+
 
 @dataclass
 class ModelCapabilities:
@@ -73,6 +97,7 @@ class ModelCapabilities:
     is_vision: bool = False
     is_chat: bool = False
     is_embedding: bool = False
+    is_audio: bool = False
 
     # File integrity
     config_valid: bool = False
@@ -353,15 +378,24 @@ def probe_model_capabilities(
     if "embed" in name_lower:
         caps.is_embedding = True
 
+    # Detect audio capability (ADR-019)
+    try:
+        from ..operations.common import detect_audio_capability
+        caps.is_audio = detect_audio_capability(model_path, caps.config)
+    except Exception:
+        caps.is_audio = False
+
     # Build capabilities list (for JSON API compatibility)
     if caps.is_embedding:
-        caps.capabilities_list = ["embeddings"]
+        caps.capabilities_list = [Capability.EMBEDDINGS.value]
     else:
-        caps.capabilities_list = ["text-generation"]
+        caps.capabilities_list = [Capability.TEXT_GENERATION.value]
         if caps.is_chat:
-            caps.capabilities_list.append("chat")
+            caps.capabilities_list.append(Capability.CHAT.value)
         if caps.is_vision:
-            caps.capabilities_list.append("vision")
+            caps.capabilities_list.append(Capability.VISION.value)
+        if caps.is_audio:
+            caps.capabilities_list.append(Capability.AUDIO.value)
 
     # Check runtime availability
     caps.python_version = sys.version_info[:3]
diff --git a/mlxk2/core/server_base.py b/mlxk2/core/server_base.py
index ca4bf8a..00dd721 100644
--- a/mlxk2/core/server_base.py
+++ b/mlxk2/core/server_base.py
@@ -14,6 +14,7 @@ from pathlib import Path
 from typing import Any, Dict, List, Optional, Union
 
 from fastapi import FastAPI, HTTPException, Request
+from fastapi.exceptions import RequestValidationError
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import StreamingResponse, JSONResponse
 from pydantic import BaseModel, Field
@@ -71,7 +72,7 @@ class ChatCompletionRequest(BaseModel):
     stream: Optional[bool] = False
     stop: Optional[Union[str, List[str]]] = None
     repetition_penalty: Optional[float] = 1.1
-    chunk: Optional[int] = 1  # Vision batch processing (default: 1 for maximum safety)
+    chunk: Optional[int] = None  # Vision batch processing (None = use ENV/default)
 
 
 class CompletionResponse(BaseModel):
@@ -673,6 +674,35 @@ def _request_has_images(messages: List[ChatMessage]) -> bool:
     return False
 
 
+def _request_has_audio(messages: List[ChatMessage]) -> bool:
+    """Check if LAST USER MESSAGE contains audio content (input_audio format).
+
+    OpenAI API semantics: Only audio from the last user message is processed.
+    Historical audio references are preserved in context but not re-processed.
+
+    This function determines routing (audio vs text path).
+    Must match actual audio processing behavior (ADR-019 Phase 4).
+
+    Args:
+        messages: List of ChatMessage objects
+
+    Returns:
+        True if the last user message contains input_audio content
+    """
+    # Find last user message (iterate backwards for efficiency)
+    for msg in reversed(messages):
+        if msg.role == "user":
+            # Check if THIS message has audio
+            if isinstance(msg.content, list):
+                for item in msg.content:
+                    if isinstance(item, dict) and item.get("type") == "input_audio":
+                        return True
+            # Found last user message, no audio
+            return False
+    # No user messages at all
+    return False
+
+
 def _messages_to_dicts(messages: List[ChatMessage]) -> List[Dict[str, Any]]:
     """Convert ChatMessage objects to dict format for adapters.
 
@@ -786,11 +816,12 @@ async def http_exception_handler(request: Request, exc: HTTPException):
 
     # Map HTTP status to error type
     error_type_map = {
+        400: ErrorType.VALIDATION_ERROR,
         403: ErrorType.ACCESS_DENIED,
         404: ErrorType.MODEL_NOT_FOUND,
-        400: ErrorType.VALIDATION_ERROR,
-        503: ErrorType.SERVER_SHUTDOWN,
         500: ErrorType.INTERNAL_ERROR,
+        501: ErrorType.NOT_IMPLEMENTED,
+        503: ErrorType.SERVER_SHUTDOWN,
         507: ErrorType.INSUFFICIENT_MEMORY,
     }
 
@@ -808,6 +839,36 @@ async def http_exception_handler(request: Request, exc: HTTPException):
     )
 
 
+@app.exception_handler(RequestValidationError)
+async def validation_exception_handler(request: Request, exc: RequestValidationError):
+    """Convert FastAPI validation errors (422) to ADR-004 envelope (400).
+
+    FastAPI returns 422 Unprocessable Entity for validation errors by default.
+    We convert to 400 Bad Request with ADR-004 envelope for API consistency.
+    """
+    request_id = getattr(request.state, "request_id", None)
+
+    # Format validation errors for detail field
+    errors = exc.errors()
+    detail = "; ".join(
+        f"{'.'.join(str(loc) for loc in e['loc'])}: {e['msg']}"
+        for e in errors
+    )
+
+    error = MLXKError(
+        type=ErrorType.VALIDATION_ERROR,
+        message="Request validation failed",
+        detail=detail,
+        retryable=False
+    )
+
+    envelope = error_envelope(error, request_id=request_id)
+    return JSONResponse(
+        status_code=400,
+        content=envelope
+    )
+
+
 @app.get("/health")
 async def health_check():
     """Health check endpoint (OpenAI compatible)."""
@@ -980,35 +1041,36 @@ async def create_completion(request: CompletionRequest):
 
 @app.post("/v1/chat/completions")
 async def create_chat_completion(request: ChatCompletionRequest):
-    """Create a chat completion (text or vision)."""
+    """Create a chat completion (text, vision, or audio)."""
     try:
         if _shutdown_event.is_set():
             raise HTTPException(status_code=503, detail="Server is shutting down")
 
-        # Detect if request contains images (Vision API format)
+        # Detect if request contains images or audio (Vision/Audio API format)
         has_images = _request_has_images(request.messages)
+        has_audio = _request_has_audio(request.messages)
 
         # Load model to determine type (uses cache if already loaded)
         # This ensures we route based on MODEL type, not just request content
         runner = get_or_load_model(request.model, verbose=False)
 
-        # Check if this is a vision model
+        # Check if this is a vision model (VisionRunner handles both vision and audio)
         from .vision_runner import VisionRunner
         is_vision_model = isinstance(runner, VisionRunner)
 
         # Routing logic:
-        # - Vision model + images → Vision path (normal vision processing)
-        # - Vision model + no images → Text path (text-only on vision model)
-        # - Text model + images → Text path (filters multimodal history)
-        # - Text model + no images → Text path (normal text processing)
-        if is_vision_model and has_images:
-            # === VISION PATH (ADR-012 Phase 3) ===
-            # Vision model with images → full vision processing
+        # - Vision model + images/audio → Vision path (vision/audio processing)
+        # - Vision model + no media → Text path (text-only on vision model)
+        # - Text model + images/audio → Text path (filters multimodal history)
+        # - Text model + no media → Text path (normal text processing)
+        if is_vision_model and (has_images or has_audio):
+            # === VISION/AUDIO PATH (ADR-012 Phase 3, ADR-019 Phase 4) ===
+            # Vision model with images/audio → full vision/audio processing
             return await _handle_vision_chat_completion(request, runner=runner)
         else:
             # === TEXT PATH (existing behavior + multimodal filtering) ===
-            # - Vision model without images: Use VisionRunner.generate(images=None)
-            # - Text model with/without images: Filter multimodal history if needed
+            # - Vision model without media: Use VisionRunner.generate(images=None)
+            # - Text model with/without media: Filter multimodal history if needed
             return await _handle_text_chat_completion(request, runner=runner)
 
     except HTTPException as http_exc:
@@ -1163,6 +1225,7 @@ def _process_vision_chunks_server(
     temperature: float,
     top_p: float,
     repetition_penalty: float,
+    audio: Optional[List[tuple]] = None,
 ) -> str:
     """Process vision images in batches with isolated model instances per chunk.
 
@@ -1178,6 +1241,7 @@ def _process_vision_chunks_server(
         chunk_size: Images per batch
         image_id_map: Pre-computed global image IDs (from conversation history)
         max_tokens, temperature, top_p, repetition_penalty: Generation params
+        audio: Optional list of (filename, bytes) tuples for audio input
 
     Returns:
         Combined text with merged filename mappings
@@ -1195,6 +1259,7 @@ def _process_vision_chunks_server(
             chunk_result = runner.generate(
                 prompt=prompt,
                 images=chunk,
+                audio=audio,  # Pass audio with each chunk
                 max_tokens=max_tokens,
                 temperature=temperature,
                 top_p=top_p,
@@ -1222,6 +1287,7 @@ async def _stream_vision_chunks(
     completion_id: str,
     created: int,
     model: str,
+    audio: Optional[List[tuple]] = None,
 ) -> AsyncGenerator[str, None]:
     """Stream SSE events per vision chunk as they complete (OpenAI-compatible).
 
@@ -1240,6 +1306,7 @@ async def _stream_vision_chunks(
         completion_id: Unique completion ID for SSE events
         created: Timestamp for SSE events
         model: Model name for SSE events
+        audio: Optional list of (filename, bytes) tuples for audio input
 
     Yields:
         SSE event strings (data: {...}\n\n format)
@@ -1297,6 +1364,7 @@ async def _stream_vision_chunks(
                 return runner.generate(
                     prompt=prompt,
                     images=chunk_images,
+                    audio=audio,  # Pass audio with each chunk
                     max_tokens=max_tokens,
                     temperature=temperature,
                     top_p=top_p,
@@ -1363,7 +1431,7 @@ async def _stream_vision_chunks(
 
 
 async def _handle_vision_chat_completion(request: ChatCompletionRequest, runner: Any = None) -> ChatCompletionResponse:
-    """Handle vision chat completion with images (ADR-012 Phase 3).
+    """Handle vision/audio chat completion with images or audio (ADR-012 Phase 3, ADR-019 Phase 4).
 
     Supports per-chunk streaming for multi-image requests (stream=True yields
     SSE events as each chunk completes). Single-chunk requests use batch mode
@@ -1376,7 +1444,7 @@ async def _handle_vision_chat_completion(request: ChatCompletionRequest, runner:
     # Lazy import vision components (Python 3.9 compatibility)
     from ..tools.vision_adapter import VisionHTTPAdapter
 
-    # Vision requests are STATELESS for the model prompt, but we track image IDs
+    # Vision/Audio requests are STATELESS for the model prompt, but we track image IDs
     # across the session for consistent numbering (Image 1, 2, 3...).
     #
     # Rationale:
@@ -1397,16 +1465,19 @@ async def _handle_vision_chat_completion(request: ChatCompletionRequest, runner:
         raise HTTPException(status_code=400, detail="No user message found")
 
     # Parse only the last user message (stateless prompt)
-    prompt, images = VisionHTTPAdapter.parse_openai_messages([last_user_msg])
+    # Returns (prompt, images, audio) tuple
+    prompt, images, audio = VisionHTTPAdapter.parse_openai_messages([last_user_msg])
 
     # But use FULL history for image ID assignment (consistent numbering)
     image_id_map = VisionHTTPAdapter.assign_image_ids_from_history(message_dicts)
 
     image_count = len(images)
+    audio_count = len(audio)
     logger.info(
-        f"Vision request: {image_count} image(s), model={request.model}",
+        f"Vision/Audio request: {image_count} image(s), {audio_count} audio(s), model={request.model}",
         model=request.model,
-        image_count=image_count
+        image_count=image_count,
+        audio_count=audio_count
     )
 
     # Get or load VisionRunner (uses cache if model already loaded)
@@ -1428,8 +1499,13 @@ async def _handle_vision_chat_completion(request: ChatCompletionRequest, runner:
     completion_id = f"chatcmpl-{uuid.uuid4()}"
     created = int(time.time())
 
-    # Get chunk size (with env var override)
-    chunk_size = request.chunk if request.chunk != 1 else int(os.environ.get("MLXK2_VISION_CHUNK_SIZE", "1"))
+    # Get chunk size: request param > ENV > default (F-05: respect explicit chunk=1)
+    env_chunk = int(os.environ.get("MLXK2_VISION_CHUNK_SIZE", "1"))
+    chunk_size = request.chunk if request.chunk is not None else env_chunk
+
+    # F-03: Audio+Vision - audio silently ignored when images present (SERVER-HANDBOOK)
+    # mlx-vlm behavior is undefined for combined audio+vision, so we enforce single modality
+    effective_audio = None if images else audio
 
     # Validate chunk size for Metal API stability
     from ..tools.vision_adapter import MAX_SAFE_CHUNK_SIZE
@@ -1448,15 +1524,16 @@ async def _handle_vision_chat_completion(request: ChatCompletionRequest, runner:
         )
 
     if len(images) <= chunk_size:
-        # Single batch (no chunking)
+        # Single batch (no chunking) - also handles audio-only requests
         generated_text = runner.generate(
             prompt=prompt,
-            images=images,
+            images=images if images else None,
+            audio=effective_audio,
             max_tokens=get_effective_max_tokens_vision(runner, request.max_tokens),
             temperature=0.0,  # Experiment: greedy sampling to reduce hallucinations
             top_p=request.top_p or 0.9,
             repetition_penalty=request.repetition_penalty or 1.0,
-            image_id_map=image_id_map,
+            image_id_map=image_id_map if images else None,
         )
     else:
         # Multi-chunk processing
@@ -1483,6 +1560,7 @@ async def _handle_vision_chat_completion(request: ChatCompletionRequest, runner:
                     completion_id=completion_id,
                     created=created,
                     model=request.model,
+                    audio=effective_audio,
                 ),
                 media_type="text/event-stream",
                 headers={"Cache-Control": "no-cache"}
@@ -1499,6 +1577,7 @@ async def _handle_vision_chat_completion(request: ChatCompletionRequest, runner:
             temperature=0.0,
             top_p=request.top_p or 0.9,
             repetition_penalty=request.repetition_penalty or 1.0,
+            audio=effective_audio,
         )
 
     logger.info(
@@ -1625,10 +1704,10 @@ def _filter_multimodal_history_for_text_models(messages: List[ChatMessage]) -> L
     """Filter multimodal content from message history for text-only models.
 
     Handles the case where conversation history contains multimodal content
-    (e.g., from previous Vision model interaction) but the current model is
-    text-only and cannot process image_url content.
+    (e.g., from previous Vision/Audio model interaction) but the current model is
+    text-only and cannot process image_url or input_audio content.
 
-    This enables model switching in clients (Vision → Text) while preserving
+    This enables model switching in clients (Vision/Audio → Text) while preserving
     the assistant's responses for context.
 
     Args:
@@ -1665,10 +1744,11 @@ def _filter_multimodal_history_for_text_models(messages: List[ChatMessage]) -> L
             filtered.append(msg)
             continue
 
-        # Array content: extract text parts and add image placeholder
+        # Array content: extract text parts and add media placeholder
         if isinstance(msg.content, list):
             text_parts = []
             image_count = 0
+            audio_count = 0
 
             for item in msg.content:
                 if not isinstance(item, dict):
@@ -1684,12 +1764,21 @@ def _filter_multimodal_history_for_text_models(messages: List[ChatMessage]) -> L
                 elif item_type == "image_url":
                     image_count += 1
 
+                elif item_type == "input_audio":
+                    audio_count += 1
+
             # Combine text parts
             combined_text = " ".join(text_parts)
 
-            # Add placeholder if images were present
+            # Add placeholder if media were present
+            placeholders = []
             if image_count > 0:
-                placeholder = f"[{image_count} image(s) were attached]"
+                placeholders.append(f"{image_count} image(s)")
+            if audio_count > 0:
+                placeholders.append(f"{audio_count} audio(s)")
+
+            if placeholders:
+                placeholder = f"[{', '.join(placeholders)} were attached]"
                 if combined_text:
                     combined_text = f"{combined_text}\n\n{placeholder}"
                 else:
diff --git a/mlxk2/core/vision_runner.py b/mlxk2/core/vision_runner.py
index c1de325..e9f3eb2 100644
--- a/mlxk2/core/vision_runner.py
+++ b/mlxk2/core/vision_runner.py
@@ -149,10 +149,35 @@ class VisionRunner:
 
         return image_paths
 
+    def _prepare_audio(self, audio: Sequence[Tuple[str, bytes]] | None):
+        """
+        Convert (filename, bytes) tuples to temporary file paths.
+
+        mlx-vlm expects audio file paths as strings.
+        We write the audio bytes to temporary files and return the paths.
+        """
+        if not audio:
+            return None
+
+        audio_paths = []
+        for filename, raw in audio:
+            # Create a temporary file with appropriate extension
+            suffix = Path(filename).suffix or ".wav"
+            tmp = tempfile.NamedTemporaryFile(delete=False, suffix=suffix)
+            tmp.write(raw)
+            tmp.flush()
+            tmp.close()
+            audio_paths.append(tmp.name)
+            # Track temp file for cleanup
+            self._temp_files.append(tmp.name)
+
+        return audio_paths
+
     def generate(
         self,
         prompt: str,
         images: Sequence[Tuple[str, bytes]] | None,
+        audio: Sequence[Tuple[str, bytes]] | None = None,
         max_tokens: Optional[int] = None,
         temperature: float = 0.4,
         top_p: float = 0.9,
@@ -160,11 +185,12 @@ class VisionRunner:
         image_id_map: Optional[Dict[str, int]] = None,
         total_images: Optional[int] = None,
     ) -> str:
-        """Generate a response with optional images. Non-streaming.
+        """Generate a response with optional images and audio. Non-streaming.
 
         Args:
             prompt: Text prompt for generation
-            images: List of (filename, bytes) tuples
+            images: List of (filename, bytes) tuples for images
+            audio: List of (filename, bytes) tuples for audio files
             max_tokens: Maximum tokens to generate
             temperature: Sampling temperature
             top_p: Top-p sampling
@@ -173,8 +199,9 @@ class VisionRunner:
                          numbering across requests. If None, uses request-scoped IDs.
             total_images: Total number of images in full batch (for chunking context)
         """
-        # Prepare image file paths
+        # Prepare image and audio file paths
         image_paths = self._prepare_images(images)
+        audio_paths = self._prepare_audio(audio)
 
         try:
             # Augment prompt with metadata context (GPS, datetime, camera, chunk info)
@@ -182,10 +209,12 @@ class VisionRunner:
                 prompt, images, image_id_map, total_images
             )
 
-            # Apply chat template (required for vision models)
+            # Apply chat template (required for vision/audio models)
             num_images = len(image_paths) if image_paths else 0
+            num_audios = len(audio_paths) if audio_paths else 0
             formatted_prompt = self._apply_chat_template(
-                self.processor, self.config, augmented_prompt, num_images=num_images
+                self.processor, self.config, augmented_prompt,
+                num_images=num_images, num_audios=num_audios
             )
 
             # Build generation kwargs
@@ -207,6 +236,7 @@ class VisionRunner:
                 self.processor,
                 formatted_prompt,
                 image_paths,  # List of file paths
+                audio=audio_paths,  # List of audio file paths (None if no audio)
                 **gen_kwargs,
             )
             normalized = self._normalize_result(result)
diff --git a/mlxk2/errors.py b/mlxk2/errors.py
index f1b8963..9c5aa6b 100644
--- a/mlxk2/errors.py
+++ b/mlxk2/errors.py
@@ -19,6 +19,7 @@ class ErrorType(str, Enum):
     PUSH_OPERATION_FAILED = "push_operation_failed"
     SERVER_SHUTDOWN = "server_shutdown"
     INSUFFICIENT_MEMORY = "insufficient_memory"  # ADR-016: Model exceeds memory threshold
+    NOT_IMPLEMENTED = "not_implemented"  # HTTP 501: Feature not supported
     INTERNAL_ERROR = "internal_error"
 
 
@@ -32,6 +33,7 @@ ERROR_TYPE_TO_HTTP_STATUS: Dict[ErrorType, int] = {
     ErrorType.PUSH_OPERATION_FAILED: 500,
     ErrorType.SERVER_SHUTDOWN: 503,
     ErrorType.INSUFFICIENT_MEMORY: 507,  # ADR-016: HTTP 507 Insufficient Storage
+    ErrorType.NOT_IMPLEMENTED: 501,  # Feature not supported
     ErrorType.INTERNAL_ERROR: 500,
 }
 
diff --git a/mlxk2/operations/common.py b/mlxk2/operations/common.py
index 3812132..2ae767d 100644
--- a/mlxk2/operations/common.py
+++ b/mlxk2/operations/common.py
@@ -18,7 +18,7 @@ import importlib.util
 import sys
 
 # Import from unified capabilities module (ARCHITECTURE.md)
-from ..core.capabilities import VISION_MODEL_TYPES
+from ..core.capabilities import VISION_MODEL_TYPES, AUDIO_MODEL_TYPES, Capability
 
 
 @dataclass
@@ -197,6 +197,8 @@ def detect_model_type(hf_name: str, config: Optional[Dict[str, Any]], tok_hints:
             return "chat"
         if mt_lower in VISION_MODEL_TYPES:
             return "chat"
+        if mt_lower in AUDIO_MODEL_TYPES:
+            return "chat"
     ct = tok_hints.get("chat_template")
     if isinstance(ct, str) and ct.strip():
         return "chat"
@@ -275,6 +277,40 @@ def detect_vision_capability(probe: Path, config: Optional[Dict[str, Any]]) -> b
     return False
 
 
+def detect_audio_capability(probe: Path, config: Optional[Dict[str, Any]]) -> bool:
+    """Detect whether the model snapshot supports audio inputs (ADR-019).
+
+    Detection signals:
+    - config.json contains "audio_config" key (primary)
+    - config.json model_type in AUDIO_MODEL_TYPES
+    - processor_config.json contains "audio_seq_length" key (secondary)
+    """
+    try:
+        if isinstance(config, dict):
+            # Check for audio_config (Gemma-3n has this)
+            if "audio_config" in config:
+                return True
+
+            # Check model_type
+            mt = config.get("model_type")
+            if isinstance(mt, str) and mt.lower() in AUDIO_MODEL_TYPES:
+                return True
+
+        # Check processor_config.json for audio_seq_length
+        processor_config_path = probe / "processor_config.json"
+        if processor_config_path.exists():
+            try:
+                with open(processor_config_path) as f:
+                    processor_data = _json.load(f)
+                if isinstance(processor_data, dict) and "audio_seq_length" in processor_data:
+                    return True
+            except Exception:
+                pass
+    except Exception:
+        pass
+    return False
+
+
 def detect_capabilities(
     model_type: str,
     hf_name: str,
@@ -283,14 +319,16 @@ def detect_capabilities(
     probe: Path,
 ) -> list[str]:
     if model_type == "embedding":
-        return ["embeddings"]
-    caps = ["text-generation"]
+        return [Capability.EMBEDDINGS.value]
+    caps = [Capability.TEXT_GENERATION.value]
     name = hf_name.lower()
     ct = tok_hints.get("chat_template")
     if model_type == "chat" or "instruct" in name or "chat" in name or (isinstance(ct, str) and ct.strip()):
-        caps.append("chat")
+        caps.append(Capability.CHAT.value)
     if detect_vision_capability(probe, config):
-        caps.append("vision")
+        caps.append(Capability.VISION.value)
+    if detect_audio_capability(probe, config):
+        caps.append(Capability.AUDIO.value)
     return caps
 
 
diff --git a/mlxk2/operations/run.py b/mlxk2/operations/run.py
index 6bd0b60..9b3c783 100644
--- a/mlxk2/operations/run.py
+++ b/mlxk2/operations/run.py
@@ -17,6 +17,7 @@ from ..core.model_resolution import resolve_model_for_operation
 from ..operations.health import check_runtime_compatibility
 from ..operations.common import (
     _total_size_bytes,
+    detect_audio_capability,
     detect_framework,
     detect_vision_capability,
     read_front_matter,
@@ -155,6 +156,7 @@ def _process_images_in_chunks(
     model_name: str,
     prompt: str,
     images: List[Tuple[str, bytes]],
+    audio: Optional[Sequence[Tuple[str, bytes]]],
     chunk_size: int,
     max_tokens: Optional[int],
     temperature: float,
@@ -210,6 +212,7 @@ def _process_images_in_chunks(
             chunk_result = runner.generate(
                 prompt=prompt,
                 images=chunk,
+                audio=list(audio) if audio else None,  # Pass audio with each chunk
                 max_tokens=max_tokens,
                 temperature=temperature,
                 top_p=top_p,
@@ -241,6 +244,7 @@ def run_model(
     model_spec: str,
     prompt: Optional[str] = None,
     images: Optional[Sequence[Tuple[str, bytes]]] = None,
+    audio: Optional[Sequence[Tuple[str, bytes]]] = None,
     chunk: int = 1,
     stream: bool = True,
     max_tokens: Optional[int] = None,
@@ -300,6 +304,7 @@ def run_model(
 
         # Only perform compatibility check if model is actually in cache
         is_vision_model = False
+        is_audio_model = False
         model_path = None
         model_cache_dir = None
         cfg = None
@@ -321,6 +326,7 @@ def run_model(
                         cfg = None
 
                 is_vision_model = detect_vision_capability(model_path, cfg)
+                is_audio_model = detect_audio_capability(model_path, cfg)
             else:
                 # Cache model - existing logic
                 model_cache = get_current_model_cache()
@@ -347,6 +353,7 @@ def run_model(
 
                         if model_path is not None:
                             is_vision_model = detect_vision_capability(model_path, cfg)
+                            is_audio_model = detect_audio_capability(model_path, cfg)
 
                     # If images are provided but model is not vision-capable, fail fast
                     if images and not is_vision_model:
@@ -356,6 +363,14 @@ def run_model(
                             print(error_result, file=sys.stderr)
                         return error_result
 
+                    # If audio is provided but model is not audio-capable, fail fast
+                    if audio and not is_audio_model:
+                        error_msg = f"Model '{resolved_name}' does not support audio inputs (no audio capability detected)."
+                        error_result = f"Error: {error_msg}"
+                        if not json_output:
+                            print(error_result, file=sys.stderr)
+                        return error_result
+
                     if is_vision_model:
                         compat, reason = vision_runtime_compatibility()
                         if not compat:
@@ -400,12 +415,18 @@ def run_model(
             print(error_result, file=sys.stderr)
         return error_result
 
+    if audio and not is_audio_model:
+        error_result = "Error: Audio inputs require an audio-capable model in cache (config not found)"
+        if not json_output:
+            print(error_result, file=sys.stderr)
+        return error_result
+
     # Runtime compatibility verified, proceed with model loading
     try:
-        if is_vision_model:
-            # Vision path uses mlx-vlm backend (non-streaming)
+        # Vision/Audio path uses mlx-vlm backend (non-streaming)
+        if is_vision_model or is_audio_model:
             if model_path is None or not model_path.exists():
-                error_result = "Error: Vision model not found in cache"
+                error_result = "Error: Vision/Audio model not found in cache"
                 if not json_output:
                     print(error_result, file=sys.stderr)
                 return error_result
@@ -413,8 +434,10 @@ def run_model(
             if prompt is None:
                 if images:
                     prompt = "Describe the image."
+                elif audio:
+                    prompt = "What do you hear in this audio?"
                 else:
-                    error_result = "Error: Vision run requires a prompt"
+                    error_result = "Error: Vision/Audio run requires a prompt"
                     if not json_output:
                         print(error_result, file=sys.stderr)
                     return error_result
@@ -458,6 +481,7 @@ def run_model(
                         result = runner.generate(
                             prompt=prompt,
                             images=images_list,
+                            audio=list(audio) if audio else None,
                             max_tokens=max_tokens,
                             temperature=temperature,
                             top_p=top_p,
@@ -470,6 +494,7 @@ def run_model(
                         model_name=resolved_name or model_spec,
                         prompt=prompt,
                         images=images_list,
+                        audio=audio,
                         chunk_size=chunk_size,
                         max_tokens=max_tokens,
                         temperature=temperature,
@@ -696,6 +721,7 @@ def run_model_enhanced(
     model_spec: str,
     prompt: Optional[str],
     images: Optional[Sequence[Tuple[str, bytes]]] = None,
+    audio: Optional[Sequence[Tuple[str, bytes]]] = None,
     chunk: int = 1,
     stream: bool = True,
     max_tokens: Optional[int] = None,
@@ -741,6 +767,7 @@ def run_model_enhanced(
         model_spec=model_spec,
         prompt=prompt,
         images=images,
+        audio=audio,
         chunk=chunk,
         stream=stream,
         max_tokens=max_tokens,
diff --git a/mlxk2/output/human.py b/mlxk2/output/human.py
index 557986c..b4bf81d 100644
--- a/mlxk2/output/human.py
+++ b/mlxk2/output/human.py
@@ -161,6 +161,8 @@ def render_list(data: Dict[str, Any], show_health: bool, show_all: bool, verbose
         type_label = str(m.get("model_type", "-"))
         if "vision" in caps and type_label != "-":
             type_label = f"{type_label}+vision"
+        if "audio" in caps and type_label != "-":
+            type_label = f"{type_label}+audio"
         if compact:
             row = [
                 name,
@@ -265,9 +267,14 @@ def render_show(data: Dict[str, Any]) -> str:
     else:
         health_str = health
 
+    # Build capabilities string
+    caps = model.get('capabilities', [])
+    caps_str = ', '.join(caps) if caps else '-'
+
     details = [
         f"Framework: {model.get('framework','-')}",
         f"Type: {model.get('model_type','-')}",
+        f"Capabilities: {caps_str}",
         f"Size: {humanize_size(model.get('size_bytes'))}",
         f"Modified: {fmt_time(model.get('last_modified'))}",
         f"Health: {health_str}",
diff --git a/mlxk2/spec.py b/mlxk2/spec.py
index d3bd167..648bd9e 100644
--- a/mlxk2/spec.py
+++ b/mlxk2/spec.py
@@ -4,4 +4,4 @@ Single source of truth for the JSON API specification version used by the
 current code and tests. Keep this in sync with docs/json-api-specification.md.
 """
 
-JSON_API_SPEC_VERSION = "0.1.6"
+JSON_API_SPEC_VERSION = "0.1.7"
diff --git a/mlxk2/tools/vision_adapter.py b/mlxk2/tools/vision_adapter.py
index 999dbbe..df19ff7 100644
--- a/mlxk2/tools/vision_adapter.py
+++ b/mlxk2/tools/vision_adapter.py
@@ -1,8 +1,8 @@
 """
-Vision HTTP adapter for converting OpenAI-compatible requests to VisionRunner format.
+Vision/Audio HTTP adapter for converting OpenAI-compatible requests to VisionRunner format.
 
-This module handles Base64 image decoding and OpenAI message format parsing
-for the server Vision API (ADR-012 Phase 3).
+This module handles Base64 image/audio decoding and OpenAI message format parsing
+for the server Vision/Audio API (ADR-012 Phase 3, ADR-019 Phase 4).
 """
 
 from __future__ import annotations
@@ -16,12 +16,18 @@ from typing import Any, Dict, List, Optional, Tuple
 
 # Limits for vision requests (safety and resource management)
 # Per-image size limit prevents Metal OOM crashes (ADR-012 Phase 3)
-# Total image count is unlimited - chunking (ADR-012 Phase 1c) handles batch safety
-# chunk_size controls memory usage: default=1 (safest), can be increased via request param
 MAX_IMAGE_SIZE_BYTES = 20 * 1024 * 1024  # 20 MB per image (Metal API limit)
+MAX_IMAGES_PER_REQUEST = 5  # Maximum images per request (Metal OOM prevention)
+MAX_TOTAL_IMAGE_SIZE_BYTES = 50 * 1024 * 1024  # 50 MB total (Metal OOM prevention)
 MAX_SAFE_CHUNK_SIZE = 5  # Empirically tested stable (5 images @ ~50MB total)
 SUPPORTED_MIME_TYPES = frozenset({"jpeg", "jpg", "png", "gif", "webp"})
 
+# Audio limits (ADR-019 Phase 4)
+# 5MB limit matches CLI (~2-3 min at 16kHz mono, token count is the real constraint)
+MAX_AUDIO_SIZE_BYTES = 5 * 1024 * 1024  # 5 MB per audio file
+# mlx-vlm natively supports WAV and MP3 (verified in mlx-vlm README)
+SUPPORTED_AUDIO_FORMATS = frozenset({"wav", "mp3", "mpeg"})
+
 
 class VisionHTTPAdapter:
     """Adapter for converting OpenAI Vision API format to VisionRunner format."""
@@ -29,9 +35,9 @@ class VisionHTTPAdapter:
     @staticmethod
     def parse_openai_messages(
         messages: List[Dict[str, Any]]
-    ) -> Tuple[str, List[Tuple[str, bytes]]]:
+    ) -> Tuple[str, List[Tuple[str, bytes]], List[Tuple[str, bytes]]]:
         """
-        Parse OpenAI-style messages and extract text prompt + images.
+        Parse OpenAI-style messages and extract text prompt + images + audio.
 
         OpenAI Vision API format:
         [
@@ -39,27 +45,29 @@ class VisionHTTPAdapter:
                 "role": "user",
                 "content": [
                     {"type": "text", "text": "What's in this image?"},
-                    {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,..."}}
+                    {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,..."}},
+                    {"type": "input_audio", "input_audio": {"data": "base64...", "format": "wav"}}
                 ]
             }
         ]
 
-        Important: Images are extracted ONLY from the most recent user message.
+        Important: Images/audio are extracted ONLY from the most recent user message.
         Text context is extracted from ALL messages (including assistant responses).
 
-        This follows OpenAI Vision API behavior where previous images remain in
-        history for text context but are NOT sent as visual input to the model.
+        This follows OpenAI Vision API behavior where previous media remain in
+        history for text context but are NOT sent as visual/audio input to the model.
 
         Args:
             messages: List of message dicts (OpenAI format)
 
         Returns:
-            (prompt, images) tuple where:
+            (prompt, images, audio) tuple where:
             - prompt: str - Combined text from all text content blocks
             - images: List[Tuple[str, bytes]] - List of (filename, raw_bytes) tuples
+            - audio: List[Tuple[str, bytes]] - List of (filename, raw_bytes) tuples
 
         Raises:
-            MLXKError: If message format is invalid or images cannot be decoded
+            ValueError: If message format is invalid or media cannot be decoded
 
         See: docs/ISSUES/VISION-SEQUENTIAL-IMAGES-ISSUE.md
         """
@@ -102,8 +110,9 @@ class VisionHTTPAdapter:
                     if text:
                         text_parts.append(text)
 
-        # Extract images ONLY from the most recent user message
+        # Extract images and audio ONLY from the most recent user message
         images = []
+        audio = []
 
         for msg in reversed(messages):
             if not isinstance(msg, dict):
@@ -115,10 +124,10 @@ class VisionHTTPAdapter:
 
             content = msg.get("content")
             if content is None or not isinstance(content, list):
-                # Last user message has no images (text-only follow-up)
+                # Last user message has no media (text-only follow-up)
                 break
 
-            # Process image_url items from this message only
+            # Process image_url and input_audio items from this message only
             for item in content:
                 if not isinstance(item, dict):
                     continue
@@ -142,26 +151,67 @@ class VisionHTTPAdapter:
                     filename, raw_bytes = VisionHTTPAdapter.decode_base64_image(url)
                     images.append((filename, raw_bytes))
 
-                    # Note: Total image count/size is unlimited - chunking handles batch safety
-                    # chunk_size (default=1) controls memory usage per batch
+                elif item_type == "input_audio":
+                    # OpenAI input_audio format (ADR-019 Phase 4)
+                    input_audio_obj = item.get("input_audio")
+                    if not isinstance(input_audio_obj, dict):
+                        raise ValueError(
+                            "input_audio content must have 'input_audio' dict"
+                        )
+
+                    data = input_audio_obj.get("data", "")
+                    fmt = input_audio_obj.get("format", "wav")
+
+                    if not data:
+                        raise ValueError(
+                            "input_audio.data cannot be empty"
+                        )
+
+                    # Decode base64 audio (validates size and format)
+                    filename, raw_bytes = VisionHTTPAdapter.decode_base64_audio(data, fmt)
+                    audio.append((filename, raw_bytes))
 
             # Stop after processing first (most recent) user message
             break
 
+        # Validate image limits (F-01: SERVER-HANDBOOK conformity)
+        if len(images) > MAX_IMAGES_PER_REQUEST:
+            raise ValueError(
+                f"Too many images ({len(images)}). Maximum: {MAX_IMAGES_PER_REQUEST}"
+            )
+
+        if images:
+            total_size = sum(len(data) for _, data in images)
+            if total_size > MAX_TOTAL_IMAGE_SIZE_BYTES:
+                size_mb = total_size / (1024 * 1024)
+                limit_mb = MAX_TOTAL_IMAGE_SIZE_BYTES / (1024 * 1024)
+                raise ValueError(
+                    f"Total image size ({size_mb:.1f} MB) exceeds limit ({limit_mb:.0f} MB)"
+                )
+
+        # Validate audio limits (F-02: Only 1 audio per request, mlx-vlm limitation)
+        if len(audio) > 1:
+            raise ValueError(
+                f"Only 1 audio per request (mlx-vlm limitation). Got: {len(audio)}"
+            )
+
         # Combine text parts
         prompt = " ".join(text_parts).strip()
 
-        # Validation: Must have either text or images
-        if not prompt and not images:
+        # Validation: Must have either text or images or audio
+        if not prompt and not images and not audio:
             raise ValueError(
-                "Request must contain at least text or images"
+                "Request must contain at least text, images, or audio"
             )
 
-        # If no text provided but images present, use default prompt
-        if not prompt and images:
-            prompt = "Describe the image."
+        # Default prompts based on media type
+        if not prompt:
+            if images:
+                prompt = "Describe the image."
+            elif audio:
+                prompt = "Transcribe what is spoken in this audio."
 
-        return prompt, images
+        return prompt, images, audio
 
     @staticmethod
     def decode_base64_image(url: str) -> Tuple[str, bytes]:
@@ -240,6 +290,70 @@ class VisionHTTPAdapter:
 
         return filename, raw_bytes
 
+    @staticmethod
+    def decode_base64_audio(data: str, fmt: str) -> Tuple[str, bytes]:
+        """
+        Decode Base64-encoded audio data.
+
+        OpenAI input_audio format:
+        {
+            "type": "input_audio",
+            "input_audio": {
+                "data": "<base64-encoded-audio>",
+                "format": "wav"  # or "mp3"
+            }
+        }
+
+        Args:
+            data: Base64-encoded audio data (without data URL prefix)
+            fmt: Audio format ("wav", "mp3")
+
+        Returns:
+            (filename, raw_bytes) tuple where:
+            - filename: Generated name based on content hash (e.g., "audio_a1b2c3.wav")
+            - raw_bytes: Decoded audio bytes
+
+        Raises:
+            ValueError: If format is unsupported or base64 decoding fails
+        """
+        # Normalize format (audio/mpeg -> mp3)
+        fmt_lower = fmt.lower()
+        if fmt_lower == "mpeg":
+            fmt_lower = "mp3"
+
+        # Validate format
+        if fmt_lower not in SUPPORTED_AUDIO_FORMATS:
+            raise ValueError(
+                f"Unsupported audio format: {fmt}. "
+                f"Supported formats: {', '.join(sorted(SUPPORTED_AUDIO_FORMATS))}"
+            )
+
+        # Decode base64
+        try:
+            raw_bytes = base64.b64decode(data, validate=True)
+        except Exception as e:
+            raise ValueError(
+                f"Failed to decode base64 audio data: {e}"
+            ) from e
+
+        # Validate that we got some data
+        if not raw_bytes:
+            raise ValueError("Decoded audio data is empty")
+
+        # Enforce size limit
+        if len(raw_bytes) > MAX_AUDIO_SIZE_BYTES:
+            size_mb = len(raw_bytes) / (1024 * 1024)
+            limit_mb = MAX_AUDIO_SIZE_BYTES / (1024 * 1024)
+            raise ValueError(
+                f"Audio size ({size_mb:.1f} MB) exceeds limit ({limit_mb:.0f} MB)"
+            )
+
+        # Generate deterministic filename from content hash
+        content_hash = hashlib.sha256(raw_bytes).hexdigest()[:8]
+        filename = f"audio_{content_hash}.{fmt_lower}"
+
+        return filename, raw_bytes
+
     @staticmethod
     def assign_image_ids_from_history(messages: List[Dict[str, Any]]) -> Dict[str, int]:
         """
diff --git a/pyproject.toml b/pyproject.toml
index cf098b8..198df0e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -28,10 +28,10 @@ classifiers = [
     "License :: OSI Approved :: Apache Software License",
 ]
 dependencies = [
-    "huggingface-hub>=0.34.0",
+    "huggingface-hub>=1.0.0",
     "requests>=2.32.0",
-    "mlx-lm>=0.28.4",
-    "mlx>=0.29.0",
+    "mlx-lm>=0.30.0",
+    "mlx>=0.30.0",
     "fastapi>=0.116.0",
     "uvicorn>=0.35.0",
     "pydantic>=2.11.0",
@@ -66,7 +66,7 @@ dev = [
     "mypy>=1.5.0",
 ]
 vision = [
-    "mlx-vlm @ git+https://github.com/Blaizzy/mlx-vlm.git@fc8c92e31983a52761f37d503f903ec40bebbd62",  # Vision Language Models support (ADR-012, requires Python 3.10+; beta.7 uses upstream main (MXFP4 support), will switch to v0.3.10 PyPI when released)
+    "mlx-vlm @ git+https://github.com/Blaizzy/mlx-vlm.git@58122703b0bba7c574d23c9c751f01cf60485d4f",  # Vision + Audio support (ADR-012, ADR-019; beta.8 adds audio; will switch to PyPI when released)
 ]
 
 [tool.setuptools]
diff --git a/tests_2.0/assets/audio/A MAN SAID TO THE UNIVERSE SIR I EXIST.mp3 b/tests_2.0/assets/audio/A MAN SAID TO THE UNIVERSE SIR I EXIST.mp3
new file mode 100644
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diff --git a/tests_2.0/assets/audio/HAVING RETURNED TO THE ROYAL CAVERN.txt b/tests_2.0/assets/audio/HAVING RETURNED TO THE ROYAL CAVERN.txt
new file mode 100644
index 0000000..cdc9710
--- /dev/null
+++ b/tests_2.0/assets/audio/HAVING RETURNED TO THE ROYAL CAVERN.txt	
@@ -0,0 +1 @@
+HAVING RETURNED TO THE ROYAL CAVERN KALIKO FIRST POUNDED THE GONG AND THEN SAT IN THE THRONE WEARING RUGGEDO'S DISCARDED RUBY CROWN AND HOLDING IN HIS HAND THE SCEPTRE WHICH RUGGEDO HAD SO OFTEN THROWN AT HIS HEAD
\ No newline at end of file
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diff --git a/tests_2.0/assets/audio/sources.txt b/tests_2.0/assets/audio/sources.txt
new file mode 100644
index 0000000..ef6e5f9
--- /dev/null
+++ b/tests_2.0/assets/audio/sources.txt
@@ -0,0 +1,11 @@
+Audio Test Assets - Attribution
+
+Source: LibriSpeech ASR corpus
+Dataset: hf-internal-testing/librispeech_asr_dummy
+License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)
+Original: https://www.openslr.org/12
+
+Citation:
+  Panayotov, V., Chen, G., Povey, D., & Khudanpur, S. (2015).
+  "Librispeech: An ASR corpus based on public domain audio books."
+  ICASSP 2015.
diff --git a/tests_2.0/live/conftest.py b/tests_2.0/live/conftest.py
index 39c08fc..364f8d8 100644
--- a/tests_2.0/live/conftest.py
+++ b/tests_2.0/live/conftest.py
@@ -21,6 +21,7 @@ from .test_utils import (
     discover_mlx_models_in_user_cache,
     discover_text_models,
     discover_vision_models,
+    discover_audio_models,
     parse_vm_stat_page_size,
     TEST_MODELS,
 )
@@ -140,6 +141,27 @@ def pytest_generate_tests(metafunc):
         metafunc.parametrize("vision_model_key", model_keys)
         return
 
+    # Handle audio_model_key (NEW - Portfolio Separation)
+    if "audio_model_key" in metafunc.fixturenames:
+        if not is_live_e2e:
+            metafunc.parametrize("audio_model_key", ["_skipped"])
+            return
+
+        # Discover audio-only models
+        audio_models = discover_audio_models()
+        if audio_models:
+            model_keys = [f"audio_{i:02d}" for i in range(len(audio_models))]
+        else:
+            # No fallback for audio (needs real models)
+            model_keys = []
+
+        # If no audio models, parametrize with skip marker
+        if not model_keys:
+            model_keys = ["_no_audio_models"]
+
+        metafunc.parametrize("audio_model_key", model_keys)
+        return
+
     # Handle model_key (DEPRECATED - Mixed Text+Vision, use text_model_key/vision_model_key instead)
     if "model_key" in metafunc.fixturenames:
         if not is_live_e2e:
@@ -288,6 +310,47 @@ def vision_portfolio():
         return {}
 
 
+@pytest.fixture(scope="module")
+def audio_portfolio():
+    """Audio-only model portfolio (NEW - Portfolio Separation).
+
+    Discovers audio models using discover_audio_models() which filters to
+    only models with audio capabilities. Uses Vision-specific RAM calculation
+    (audio goes through VisionRunner infrastructure).
+
+    Returns:
+        Dict[str, Dict[str, Any]]: Audio model portfolio keyed by audio_model_key
+            {
+                "audio_00": {
+                    "id": "mlx-community/gemma-3n-E2B-it-4bit",
+                    "ram_needed_gb": 3.2,
+                    "expected_issue": None,
+                    "description": "Audio: gemma-3n-E2B-it-4bit"
+                },
+                ...
+            }
+    """
+    audio_models = discover_audio_models()
+
+    if audio_models:
+        result = {}
+        for i, model in enumerate(audio_models):
+            key = f"audio_{i:02d}"
+            result[key] = {
+                "id": model["model_id"],
+                "ram_needed_gb": model["ram_needed_gb"],
+                "expected_issue": None,
+                "description": f"Audio: {model['model_id'].split('/')[-1]}"
+            }
+
+        print(f"\n🔊 Audio Portfolio: Found {len(result)} audio-capable models")
+        return result
+    else:
+        # No fallback for audio - requires real models
+        print(f"\n⚠️  Audio Portfolio: No audio models found in cache")
+        return {}
+
+
 @pytest.fixture
 def model_info(portfolio_models, model_key):
     """Get model info for the current parametrized model_key.
@@ -362,6 +425,34 @@ def vision_model_info(vision_portfolio, vision_model_key):
     return vision_portfolio[vision_model_key]
 
 
+@pytest.fixture
+def audio_model_info(audio_portfolio, audio_model_key):
+    """Get model info for the current parametrized audio_model_key (NEW).
+
+    This fixture provides convenient access to audio model metadata in
+    parametrized tests. It automatically looks up the audio_model_key
+    in the audio_portfolio and returns the model info dict.
+
+    Usage:
+        def test_something(audio_model_info):
+            model_id = audio_model_info["id"]
+            ram_needed = audio_model_info["ram_needed_gb"]
+            ...
+
+    Returns:
+        Dict[str, Any]: Audio model metadata with keys:
+            - id: Model ID (e.g., "mlx-community/gemma-3n-E2B-it-4bit")
+            - ram_needed_gb: Estimated RAM requirement (0.70 threshold vision formula)
+            - expected_issue: Known issue or None
+            - description: Human-readable description
+
+        Returns None for skip markers (_skipped, _no_audio_models).
+    """
+    if audio_model_key.startswith("_"):
+        return None
+    return audio_portfolio[audio_model_key]
+
+
 @pytest.fixture(autouse=True)
 def _auto_report_vision_model(request):
     """Auto-report vision model info to benchmark log (autouse).
diff --git a/tests_2.0/live/test_audio_e2e_live.py b/tests_2.0/live/test_audio_e2e_live.py
new file mode 100644
index 0000000..5e33056
--- /dev/null
+++ b/tests_2.0/live/test_audio_e2e_live.py
@@ -0,0 +1,223 @@
+"""
+Live E2E tests for Audio functionality (ADR-019).
+
+Tests deterministic audio transcription with specific, verifiable content
+to validate actual audio understanding (not just hallucination).
+
+Requires:
+- Python 3.10+ (mlx-vlm requirement)
+- Audio model in cache (e.g., gemma-3n-E2B-it-4bit)
+- Test assets in tests_2.0/assets/audio/
+- HF_HOME set to model cache location
+
+Run with:
+    HF_HOME=/path/to/cache pytest -m live_e2e tests_2.0/live/test_audio_e2e_live.py -v
+
+Known limitations (ADR-019):
+- Audio duration limit: ~30 seconds (Gemma-3n architecture constraint)
+- Phonetic errors on 4-bit models: expected, validate content not exact text
+- Temperature 0.2 default for audio (stability vs text 0.7)
+
+Architecture:
+- Uses audio_portfolio discovery (no hardcoded models)
+- Parametrized via audio_model_key fixture
+- Follows Portfolio Separation pattern (like vision tests)
+"""
+import os
+import sys
+import pytest
+import subprocess
+from pathlib import Path
+
+# Use the Python interpreter from the test environment
+PYTHON = sys.executable
+
+# Audio support requires Python 3.10+ (mlx-vlm requirement)
+pytestmark = [
+    pytest.mark.live,
+    pytest.mark.live_e2e,
+    pytest.mark.skipif(
+        sys.version_info < (3, 10),
+        reason="Audio support requires Python 3.10+ (mlx-vlm dependency)"
+    )
+]
+
+# Path to audio test assets
+AUDIO_ASSETS = Path(__file__).parent.parent / "assets" / "audio"
+
+
+class TestAudioTranscription:
+    """
+    Portfolio-based audio transcription tests.
+
+    Uses audio_model_key parametrization from conftest.py to run
+    against all audio-capable models in the cache.
+
+    Tests use deterministic audio clips with known content
+    to validate actual audio understanding. Due to STT limitations
+    (especially on 4-bit models), we verify key phrases rather than
+    exact transcription.
+    """
+
+    def test_transcribe_short_audio_wav(self, audio_model_info, audio_model_key):
+        """Test transcription of short audio clip (WAV format).
+
+        Audio: "A man said to the universe, Sir I exist"
+        Validates: Key content words present (man, universe, exist)
+
+        Note: 4-bit models may produce phonetic errors (e.g., "Amen" for "A man")
+        so we check for presence of key semantic content.
+        """
+        if audio_model_key == "_skipped":
+            pytest.skip("Run with -m live_e2e or -m wet")
+        if audio_model_key == "_no_audio_models":
+            pytest.skip("No audio models found in cache")
+
+        model_id = audio_model_info["id"]
+        audio_file = AUDIO_ASSETS / "A MAN SAID TO THE UNIVERSE SIR I EXIST.wav"
+
+        if not audio_file.exists():
+            pytest.skip(f"Audio asset not found: {audio_file}")
+
+        result = subprocess.run(
+            [
+                PYTHON, "-m", "mlxk2.cli", "run", model_id,
+                "Transcribe this audio.",
+                "--audio", str(audio_file),
+                "--max-tokens", "100",
+                "--temperature", "0",  # Most stable for transcription
+                "--no-stream"
+            ],
+            capture_output=True,
+            text=True,
+            timeout=180,
+            env=os.environ,
+        )
+        assert result.returncode == 0, f"Command failed for {model_id}: {result.stderr}"
+        output = result.stdout.strip().lower()
+
+        # Key semantic content must be present (allowing for phonetic errors)
+        # "A man" might become "Amen", but "universe" and "exist" should be clear
+        assert "universe" in output or "sir" in output or "exist" in output, \
+            f"Expected 'universe', 'sir', or 'exist' in transcription for {model_id}: {result.stdout}"
+
+    def test_transcribe_longer_audio_wav(self, audio_model_info, audio_model_key):
+        """Test transcription of longer audio clip (~14 seconds, WAV).
+
+        Audio: "Having returned to the royal cavern, Kaliko first pounded
+               the gong and then sat in the throne wearing Ruggedo's
+               discarded ruby crown..."
+
+        Validates: Key content words (royal, cavern, throne, crown, gong)
+        """
+        if audio_model_key == "_skipped":
+            pytest.skip("Run with -m live_e2e or -m wet")
+        if audio_model_key == "_no_audio_models":
+            pytest.skip("No audio models found in cache")
+
+        model_id = audio_model_info["id"]
+        audio_file = AUDIO_ASSETS / "HAVING RETURNED TO THE ROYAL CAVERN.wav"
+
+        if not audio_file.exists():
+            pytest.skip(f"Audio asset not found: {audio_file}")
+
+        result = subprocess.run(
+            [
+                PYTHON, "-m", "mlxk2.cli", "run", model_id,
+                "Transcribe this audio.",
+                "--audio", str(audio_file),
+                "--max-tokens", "200",
+                "--temperature", "0",
+                "--no-stream"
+            ],
+            capture_output=True,
+            text=True,
+            timeout=180,
+            env=os.environ,
+        )
+        assert result.returncode == 0, f"Command failed for {model_id}: {result.stderr}"
+        output = result.stdout.strip().lower()
+
+        # Check for distinctive words from the passage
+        # At least 2 of these should be present for a valid transcription
+        key_words = ["royal", "cavern", "throne", "crown", "gong", "kaliko", "ruggedo"]
+        found_words = [w for w in key_words if w in output]
+
+        assert len(found_words) >= 2, \
+            f"Expected at least 2 of {key_words} in transcription for {model_id}, found {found_words}: {result.stdout}"
+
+    def test_transcribe_mp3_format(self, audio_model_info, audio_model_key):
+        """Test that MP3 format is also supported.
+
+        Same audio as WAV test but in MP3 format.
+        """
+        if audio_model_key == "_skipped":
+            pytest.skip("Run with -m live_e2e or -m wet")
+        if audio_model_key == "_no_audio_models":
+            pytest.skip("No audio models found in cache")
+
+        model_id = audio_model_info["id"]
+        audio_file = AUDIO_ASSETS / "A MAN SAID TO THE UNIVERSE SIR I EXIST.mp3"
+
+        if not audio_file.exists():
+            pytest.skip(f"Audio asset not found: {audio_file}")
+
+        result = subprocess.run(
+            [
+                PYTHON, "-m", "mlxk2.cli", "run", model_id,
+                "Transcribe this audio.",
+                "--audio", str(audio_file),
+                "--max-tokens", "100",
+                "--temperature", "0",
+                "--no-stream"
+            ],
+            capture_output=True,
+            text=True,
+            timeout=180,
+            env=os.environ,
+        )
+        assert result.returncode == 0, f"Command failed for {model_id}: {result.stderr}"
+        output = result.stdout.strip().lower()
+
+        # MP3 format support: same validation as WAV test
+        # Simple prompt avoids multilingual drift issue with complex prompts
+        assert "universe" in output or "sir" in output or "exist" in output, \
+            f"Expected 'universe', 'sir', or 'exist' in MP3 transcription for {model_id}: {result.stdout}"
+
+    def test_audio_output_not_empty(self, audio_model_info, audio_model_key):
+        """Basic sanity test: audio transcription produces non-trivial output.
+
+        Validates that the model actually processes the audio and generates
+        a meaningful response (not just empty or single-word output).
+        """
+        if audio_model_key == "_skipped":
+            pytest.skip("Run with -m live_e2e or -m wet")
+        if audio_model_key == "_no_audio_models":
+            pytest.skip("No audio models found in cache")
+
+        model_id = audio_model_info["id"]
+        audio_file = AUDIO_ASSETS / "A MAN SAID TO THE UNIVERSE SIR I EXIST.wav"
+
+        if not audio_file.exists():
+            pytest.skip(f"Audio asset not found: {audio_file}")
+
+        result = subprocess.run(
+            [
+                PYTHON, "-m", "mlxk2.cli", "run", model_id,
+                "Transcribe this audio.",
+                "--audio", str(audio_file),
+                "--max-tokens", "100",
+                "--temperature", "0",
+                "--no-stream"
+            ],
+            capture_output=True,
+            text=True,
+            timeout=180,
+            env=os.environ,
+        )
+
+        assert result.returncode == 0, f"Command failed for {model_id}: {result.stderr}"
+
+        # Basic sanity: output should have some content (more than just whitespace or a few chars)
+        output = result.stdout.strip()
+        assert len(output) > 10, f"Transcription too short for {model_id}: '{output}'"
diff --git a/tests_2.0/live/test_utils.py b/tests_2.0/live/test_utils.py
index 0ed5bc9..934501a 100644
--- a/tests_2.0/live/test_utils.py
+++ b/tests_2.0/live/test_utils.py
@@ -61,6 +61,13 @@ KNOWN_BROKEN_MODELS = {
     # Note: Image-only processing works, video processing broken
     "mlx-community/Qwen2-VL-7B-Instruct-4bit",
 
+    # transformers 5.0 video processor bug (same as above)
+    # Upstream: https://github.com/Blaizzy/mlx-vlm/issues/640
+    # Details: docs/ISSUES/transformers-5.0-video-processor-bug.md
+    # Test: `mlxk run Qwen3-Omni-30B-A3B-Instruct-4bit "test" --image foo.jpg` → Error
+    # Note: Omni model (audio+video+vision) - all multimodal processing broken
+    "mlx-community/Qwen3-Omni-30B-A3B-Instruct-4bit",
+
     # mlx-vlm vision feature mismatch: Image token positions (5476) ≠ features (1369)
     # Status: Upstream mlx-vlm vision encoder/model compatibility bug (separate from #624)
     # Test: `mlxk run ./Mistral-Small-3.1-24B-Instruct-2503-FIXED-4bit "test" --image foo.jpg` → Error
@@ -290,11 +297,87 @@ def discover_vision_models() -> list[Dict[str, Any]]:
         return []
 
 
+def discover_audio_models() -> list[Dict[str, Any]]:
+    """Discover audio-capable models only.
+
+    Uses discover_mlx_models_in_user_cache() and filters to only models
+    with "audio" in their capabilities list.
+
+    Note: Audio models use vision-style RAM calculation (0.70 threshold)
+    since they typically go through VisionRunner infrastructure.
+
+    Returns:
+        List of audio-capable model dicts (same format as discover_mlx_models_in_user_cache):
+        [{"model_id": "...", "ram_needed_gb": X.X, "snapshot_path": None, "weight_count": None}, ...]
+    """
+    import json
+    import subprocess
+    import os
+
+    # Get all discovered models (already filtered: MLX + healthy + runtime_compatible + chat)
+    all_models = discover_mlx_models_in_user_cache()
+    if not all_models:
+        return []
+
+    # Get capabilities and size_bytes from mlxk list --json
+    env = os.environ.copy()
+    if not env.get("HF_HOME"):
+        return []  # Audio models need HF_HOME
+
+    try:
+        result = subprocess.run(
+            [sys.executable, "-m", "mlxk2.cli", "list", "--json"],
+            capture_output=True,
+            text=True,
+            timeout=30,
+            env=env
+        )
+
+        if result.returncode != 0:
+            return []
+
+        # Parse JSON and build audio model data
+        data = json.loads(result.stdout)
+        models_list = data.get("data", {}).get("models", [])
+
+        # Build map: model_id -> (is_audio, size_bytes)
+        model_info = {}
+        for m in models_list:
+            model_name = m["name"]
+            is_audio = "audio" in m.get("capabilities", [])
+            size_bytes = m.get("size_bytes", 0)
+            model_info[model_name] = (is_audio, size_bytes)
+
+        # Get system memory for audio RAM calculation (uses vision formula)
+        system_memory_bytes = get_system_memory_bytes()
+
+        # Filter to only audio models + recalculate RAM
+        audio_models = []
+        for model in all_models:
+            model_id = model["model_id"]
+            if model_id in model_info:
+                is_audio, size_bytes = model_info[model_id]
+                if is_audio:
+                    # Use Vision-specific formula (audio goes through VisionRunner)
+                    ram_gb = calculate_vision_model_ram_gb(size_bytes, system_memory_bytes)
+
+                    # Create new dict with updated RAM
+                    audio_model = model.copy()
+                    audio_model["ram_needed_gb"] = ram_gb
+                    audio_models.append(audio_model)
+
+        return audio_models
+
+    except Exception:
+        return []
+
+
 # Re-export for convenience
 __all__ = [
     "discover_mlx_models_in_user_cache",
     "discover_text_models",
     "discover_vision_models",
+    "discover_audio_models",
     "calculate_text_model_ram_gb",
     "calculate_vision_model_ram_gb",
     "get_system_memory_bytes",
diff --git a/tests_2.0/test_audio_cli.py b/tests_2.0/test_audio_cli.py
new file mode 100644
index 0000000..fde235f
--- /dev/null
+++ b/tests_2.0/test_audio_cli.py
@@ -0,0 +1,120 @@
+"""Tests for --audio CLI argument (ADR-019 Phase 2).
+
+Tests audio file handling in CLI without requiring actual model inference.
+"""
+
+import argparse
+import pytest
+from pathlib import Path
+from unittest.mock import patch, MagicMock
+import tempfile
+
+# Path to audio test assets
+AUDIO_ASSETS = Path(__file__).parent / "assets" / "audio"
+
+
+class TestAudioCLIArgument:
+    """Tests for --audio CLI argument parsing and file handling."""
+
+    def test_audio_argument_in_help(self, capsys):
+        """CLI help should show --audio argument."""
+        from mlxk2.cli import main
+        import sys
+
+        with pytest.raises(SystemExit) as exc_info:
+            with patch.object(sys, 'argv', ['mlxk', 'run', '--help']):
+                main()
+
+        # Help exits with 0
+        assert exc_info.value.code == 0
+        captured = capsys.readouterr()
+        assert "--audio" in captured.out
+
+    def test_audio_help_mentions_wav(self, capsys):
+        """CLI help should mention WAV format for audio."""
+        from mlxk2.cli import main
+        import sys
+
+        with pytest.raises(SystemExit):
+            with patch.object(sys, 'argv', ['mlxk', 'run', '--help']):
+                main()
+
+        captured = capsys.readouterr()
+        assert "WAV" in captured.out or "audio" in captured.out.lower()
+
+
+class TestAudioFileValidation:
+    """Tests for audio file validation in CLI."""
+
+    def test_audio_file_not_found(self, capsys):
+        """Should error if audio file doesn't exist."""
+        from mlxk2.cli import main
+        import sys
+
+        with pytest.raises(SystemExit) as exc_info:
+            with patch.object(sys, 'argv', ['mlxk', 'run', 'test-model', '--audio', '/nonexistent/file.wav', 'prompt']):
+                main()
+
+        assert exc_info.value.code == 1
+        captured = capsys.readouterr()
+        assert "Audio file not found" in captured.out or "Audio file not found" in captured.err
+
+    def test_audio_file_too_large(self, tmp_path, capsys):
+        """Should error if audio file >5MB."""
+        from mlxk2.cli import main
+        import sys
+
+        # Create a file that's too large (just over 5MB to trigger check)
+        large_file = tmp_path / "large.wav"
+        # Write 6MB of zeros
+        large_file.write_bytes(b'\x00' * (6 * 1024 * 1024))
+
+        with pytest.raises(SystemExit) as exc_info:
+            with patch.object(sys, 'argv', ['mlxk', 'run', 'test-model', '--audio', str(large_file), 'prompt']):
+                main()
+
+        assert exc_info.value.code == 1
+        captured = capsys.readouterr()
+        assert "Audio file too large" in captured.out or "Audio file too large" in captured.err
+
+
+class TestAudioCapabilityCheck:
+    """Tests for audio capability detection."""
+
+    def test_audio_without_audio_model_fails(self):
+        """Should error when using --audio with non-audio model."""
+        from mlxk2.operations.run import run_model
+
+        # Pass audio to a model that doesn't exist (will fail capability check)
+        result = run_model(
+            model_spec="nonexistent-model-for-audio-test",
+            prompt="test",
+            audio=[("test.wav", b"fake audio data")],
+        )
+
+        assert result is not None
+        assert "Error:" in result
+        # Either "audio" capability error or model not found - both are acceptable
+        assert "audio" in result.lower() or "not found" in result.lower()
+
+
+class TestAudioTestAssets:
+    """Tests to verify audio test assets are available."""
+
+    def test_audio_assets_directory_exists(self):
+        """Audio test assets directory should exist."""
+        assert AUDIO_ASSETS.exists(), f"Audio assets directory not found: {AUDIO_ASSETS}"
+
+    def test_audio_wav_files_exist(self):
+        """WAV test files should be available."""
+        wav_files = list(AUDIO_ASSETS.glob("*.wav"))
+        assert len(wav_files) >= 1, "No WAV files found in audio assets"
+
+    def test_sources_file_has_attribution(self):
+        """sources.txt should contain license attribution."""
+        sources_file = AUDIO_ASSETS / "sources.txt"
+        assert sources_file.exists(), "sources.txt not found"
+
+        content = sources_file.read_text()
+        assert "CC BY 4.0" in content, "License attribution missing"
+        assert "LibriSpeech" in content, "Source attribution missing"
diff --git a/tests_2.0/test_capabilities.py b/tests_2.0/test_capabilities.py
index a5aa15d..7ec409d 100644
--- a/tests_2.0/test_capabilities.py
+++ b/tests_2.0/test_capabilities.py
@@ -14,6 +14,7 @@ from mlxk2.core.capabilities import (
     PolicyDecision,
     MEMORY_THRESHOLD_PERCENT,
     VISION_MODEL_TYPES,
+    AUDIO_MODEL_TYPES,
     ModelCapabilities,
     BackendPolicy,
     probe_model_capabilities,
@@ -25,6 +26,7 @@ from mlxk2.core.capabilities import (
     _detect_vision_from_config,
     _detect_vision_from_files,
 )
+from mlxk2.operations.common import detect_audio_capability
 
 
 class TestVisionModelTypes:
@@ -40,6 +42,58 @@ class TestVisionModelTypes:
         assert isinstance(VISION_MODEL_TYPES, frozenset)
 
 
+class TestAudioModelTypes:
+    """Tests for AUDIO_MODEL_TYPES constant (ADR-019)."""
+
+    def test_contains_gemma3n(self):
+        """Should contain Gemma-3n model types."""
+        assert "gemma3n" in AUDIO_MODEL_TYPES
+        assert "gemma3n_audio" in AUDIO_MODEL_TYPES
+
+    def test_is_frozenset(self):
+        """Should be immutable."""
+        assert isinstance(AUDIO_MODEL_TYPES, frozenset)
+
+
+class TestDetectAudioCapability:
+    """Tests for detect_audio_capability (ADR-019)."""
+
+    def test_detects_audio_config_in_config_json(self, tmp_path):
+        """Should detect audio capability via audio_config key."""
+        config = {"model_type": "gemma3n", "audio_config": {"some": "config"}}
+        (tmp_path / "config.json").write_text(json.dumps(config))
+        assert detect_audio_capability(tmp_path, config) is True
+
+    def test_detects_gemma3n_model_type(self, tmp_path):
+        """Should detect audio capability via model_type."""
+        config = {"model_type": "gemma3n"}
+        (tmp_path / "config.json").write_text(json.dumps(config))
+        assert detect_audio_capability(tmp_path, config) is True
+
+    def test_detects_audio_seq_length_in_processor_config(self, tmp_path):
+        """Should detect audio capability via processor_config.json."""
+        config = {"model_type": "llama"}  # Not an audio type
+        (tmp_path / "config.json").write_text(json.dumps(config))
+        processor_config = {"audio_seq_length": 188, "processor_class": "Gemma3nProcessor"}
+        (tmp_path / "processor_config.json").write_text(json.dumps(processor_config))
+        assert detect_audio_capability(tmp_path, config) is True
+
+    def test_non_audio_model(self, tmp_path):
+        """Should return False for non-audio models."""
+        config = {"model_type": "llama"}
+        (tmp_path / "config.json").write_text(json.dumps(config))
+        assert detect_audio_capability(tmp_path, config) is False
+
+    def test_none_config(self, tmp_path):
+        """Should return False for None config (checks processor_config.json)."""
+        assert detect_audio_capability(tmp_path, None) is False
+
+    def test_empty_config(self, tmp_path):
+        """Should return False for empty config."""
+        (tmp_path / "config.json").write_text("{}")
+        assert detect_audio_capability(tmp_path, {}) is False
+
+
 class TestHelperFunctions:
     """Tests for helper functions."""
 
diff --git a/tests_2.0/test_server_audio.py b/tests_2.0/test_server_audio.py
new file mode 100644
index 0000000..1dccb33
--- /dev/null
+++ b/tests_2.0/test_server_audio.py
@@ -0,0 +1,318 @@
+"""
+Tests for server audio API support (ADR-019 Phase 4).
+
+Tests the OpenAI-compatible input_audio content blocks in /v1/chat/completions.
+"""
+
+import base64
+import pytest
+
+from mlxk2.core.server_base import ChatMessage, _request_has_audio
+from mlxk2.tools.vision_adapter import (
+    VisionHTTPAdapter,
+    MAX_AUDIO_SIZE_BYTES,
+    SUPPORTED_AUDIO_FORMATS,
+)
+
+
+class TestRequestHasAudio:
+    """Tests for _request_has_audio() detection function."""
+
+    def test_string_content_no_audio(self):
+        """String content messages have no audio."""
+        messages = [ChatMessage(role="user", content="Hello")]
+        assert _request_has_audio(messages) is False
+
+    def test_list_content_text_only_no_audio(self):
+        """List content with only text has no audio."""
+        messages = [
+            ChatMessage(
+                role="user",
+                content=[{"type": "text", "text": "Hello"}],
+            )
+        ]
+        assert _request_has_audio(messages) is False
+
+    def test_list_content_with_audio(self):
+        """List content with input_audio has audio."""
+        messages = [
+            ChatMessage(
+                role="user",
+                content=[
+                    {"type": "text", "text": "Transcribe this"},
+                    {
+                        "type": "input_audio",
+                        "input_audio": {"data": "base64data", "format": "wav"},
+                    },
+                ],
+            )
+        ]
+        assert _request_has_audio(messages) is True
+
+    def test_multiple_messages_audio_in_last_user_only(self):
+        """Only last user message is checked for audio."""
+        messages = [
+            ChatMessage(
+                role="user",
+                content=[
+                    {
+                        "type": "input_audio",
+                        "input_audio": {"data": "old", "format": "wav"},
+                    }
+                ],
+            ),
+            ChatMessage(role="assistant", content="Transcription..."),
+            ChatMessage(role="user", content="What did you hear?"),
+        ]
+        # Last user message is text-only
+        assert _request_has_audio(messages) is False
+
+    def test_image_only_no_audio(self):
+        """Image content is not audio."""
+        messages = [
+            ChatMessage(
+                role="user",
+                content=[
+                    {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,..."}}
+                ],
+            )
+        ]
+        assert _request_has_audio(messages) is False
+
+
+class TestDecodeBase64Audio:
+    """Tests for VisionHTTPAdapter.decode_base64_audio()."""
+
+    def test_decodes_wav_format(self):
+        """WAV format is supported."""
+        audio_bytes = b"RIFF....WAVEfmt "  # Fake WAV header
+        b64_data = base64.b64encode(audio_bytes).decode()
+
+        filename, raw_bytes = VisionHTTPAdapter.decode_base64_audio(b64_data, "wav")
+
+        assert filename.startswith("audio_")
+        assert filename.endswith(".wav")
+        assert raw_bytes == audio_bytes
+
+    def test_decodes_mp3_format(self):
+        """MP3 format is supported."""
+        audio_bytes = b"\xff\xfb\x90\x00"  # Fake MP3 header
+        b64_data = base64.b64encode(audio_bytes).decode()
+
+        filename, raw_bytes = VisionHTTPAdapter.decode_base64_audio(b64_data, "mp3")
+
+        assert filename.endswith(".mp3")
+        assert raw_bytes == audio_bytes
+
+    def test_normalizes_mpeg_to_mp3(self):
+        """MPEG format is normalized to mp3."""
+        audio_bytes = b"\xff\xfb\x90\x00"
+        b64_data = base64.b64encode(audio_bytes).decode()
+
+        filename, _ = VisionHTTPAdapter.decode_base64_audio(b64_data, "mpeg")
+
+        assert filename.endswith(".mp3")
+
+    def test_rejects_unsupported_format(self):
+        """Unsupported formats raise ValueError."""
+        audio_bytes = b"audio data"
+        b64_data = base64.b64encode(audio_bytes).decode()
+
+        with pytest.raises(ValueError, match="Unsupported audio format"):
+            VisionHTTPAdapter.decode_base64_audio(b64_data, "ogg")
+
+    def test_rejects_invalid_base64(self):
+        """Invalid base64 raises ValueError."""
+        with pytest.raises(ValueError, match="Failed to decode base64"):
+            VisionHTTPAdapter.decode_base64_audio("not-valid-base64!!!", "wav")
+
+    def test_rejects_empty_data(self):
+        """Empty audio data raises ValueError."""
+        b64_data = base64.b64encode(b"").decode()
+
+        with pytest.raises(ValueError, match="empty"):
+            VisionHTTPAdapter.decode_base64_audio(b64_data, "wav")
+
+    def test_rejects_oversized_audio(self):
+        """Audio exceeding size limit raises ValueError."""
+        # Create data larger than limit
+        audio_bytes = b"x" * (MAX_AUDIO_SIZE_BYTES + 1)
+        b64_data = base64.b64encode(audio_bytes).decode()
+
+        with pytest.raises(ValueError, match="exceeds limit"):
+            VisionHTTPAdapter.decode_base64_audio(b64_data, "wav")
+
+    def test_generates_deterministic_filename(self):
+        """Same content always generates same filename."""
+        audio_bytes = b"test audio content"
+        b64_data = base64.b64encode(audio_bytes).decode()
+
+        filename1, _ = VisionHTTPAdapter.decode_base64_audio(b64_data, "wav")
+        filename2, _ = VisionHTTPAdapter.decode_base64_audio(b64_data, "wav")
+
+        assert filename1 == filename2
+
+
+class TestParseOpenAIMessagesAudio:
+    """Tests for VisionHTTPAdapter.parse_openai_messages() with audio."""
+
+    def test_parses_audio_only_request(self):
+        """Audio-only request extracts audio tuple."""
+        audio_bytes = b"audio content"
+        b64_data = base64.b64encode(audio_bytes).decode()
+
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "input_audio",
+                        "input_audio": {"data": b64_data, "format": "wav"},
+                    }
+                ],
+            }
+        ]
+
+        prompt, images, audio = VisionHTTPAdapter.parse_openai_messages(messages)
+
+        assert prompt == "Transcribe what is spoken in this audio."  # Default prompt
+        assert images == []
+        assert len(audio) == 1
+        assert audio[0][1] == audio_bytes
+
+    def test_parses_audio_with_text(self):
+        """Audio with text prompt preserves text."""
+        audio_bytes = b"audio content"
+        b64_data = base64.b64encode(audio_bytes).decode()
+
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "What language is spoken?"},
+                    {
+                        "type": "input_audio",
+                        "input_audio": {"data": b64_data, "format": "wav"},
+                    },
+                ],
+            }
+        ]
+
+        prompt, images, audio = VisionHTTPAdapter.parse_openai_messages(messages)
+
+        assert prompt == "What language is spoken?"
+        assert len(audio) == 1
+
+    def test_parses_image_and_audio_combined(self):
+        """Both image and audio can be in same message."""
+        audio_bytes = b"audio"
+        audio_b64 = base64.b64encode(audio_bytes).decode()
+        image_bytes = b"\xff\xd8\xff\xe0"  # JPEG magic
+        image_b64 = base64.b64encode(image_bytes).decode()
+
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "Describe both"},
+                    {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_b64}"}},
+                    {"type": "input_audio", "input_audio": {"data": audio_b64, "format": "wav"}},
+                ],
+            }
+        ]
+
+        prompt, images, audio = VisionHTTPAdapter.parse_openai_messages(messages)
+
+        assert prompt == "Describe both"
+        assert len(images) == 1
+        assert len(audio) == 1
+
+    def test_rejects_empty_audio_data(self):
+        """Empty input_audio.data raises ValueError."""
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "input_audio",
+                        "input_audio": {"data": "", "format": "wav"},
+                    }
+                ],
+            }
+        ]
+
+        with pytest.raises(ValueError, match="cannot be empty"):
+            VisionHTTPAdapter.parse_openai_messages(messages)
+
+    def test_rejects_missing_input_audio_dict(self):
+        """input_audio without dict raises ValueError."""
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "input_audio", "input_audio": "not a dict"},
+                ],
+            }
+        ]
+
+        with pytest.raises(ValueError, match="must have 'input_audio' dict"):
+            VisionHTTPAdapter.parse_openai_messages(messages)
+
+
+class TestSupportedAudioFormats:
+    """Tests for audio format constants."""
+
+    def test_wav_supported(self):
+        assert "wav" in SUPPORTED_AUDIO_FORMATS
+
+    def test_mp3_supported(self):
+        assert "mp3" in SUPPORTED_AUDIO_FORMATS
+
+    def test_mpeg_supported(self):
+        """MPEG is normalized to mp3 in decode, but listed for validation."""
+        assert "mpeg" in SUPPORTED_AUDIO_FORMATS
+
+
+class TestFilterMultimodalHistoryAudio:
+    """Tests for audio in multimodal history filtering."""
+
+    def test_filters_audio_from_history(self):
+        """Audio content is replaced with placeholder for text models."""
+        from mlxk2.core.server_base import _filter_multimodal_history_for_text_models
+
+        messages = [
+            ChatMessage(
+                role="user",
+                content=[
+                    {"type": "text", "text": "Transcribe this"},
+                    {"type": "input_audio", "input_audio": {"data": "...", "format": "wav"}},
+                ],
+            ),
+            ChatMessage(role="assistant", content="The audio says..."),
+        ]
+
+        filtered = _filter_multimodal_history_for_text_models(messages)
+
+        assert len(filtered) == 2
+        assert "[1 audio(s) were attached]" in filtered[0].content
+        assert "Transcribe this" in filtered[0].content
+
+    def test_filters_mixed_image_and_audio(self):
+        """Both images and audio are counted in placeholder."""
+        from mlxk2.core.server_base import _filter_multimodal_history_for_text_models
+
+        messages = [
+            ChatMessage(
+                role="user",
+                content=[
+                    {"type": "text", "text": "Describe"},
+                    {"type": "image_url", "image_url": {"url": "data:..."}},
+                    {"type": "input_audio", "input_audio": {"data": "...", "format": "wav"}},
+                ],
+            ),
+        ]
+
+        filtered = _filter_multimodal_history_for_text_models(messages)
+
+        assert "1 image(s)" in filtered[0].content
+        assert "1 audio(s)" in filtered[0].content
diff --git a/tests_2.0/test_server_models_and_errors.py b/tests_2.0/test_server_models_and_errors.py
index a5d3733..8e35629 100644
--- a/tests_2.0/test_server_models_and_errors.py
+++ b/tests_2.0/test_server_models_and_errors.py
@@ -159,3 +159,41 @@ def test_shutdown_event_maps_to_503_and_is_cleared():
         server_base._shutdown_event.clear()
 
 
+def test_validation_error_returns_adr004_envelope():
+    """RequestValidationError (422) should return ADR-004 error envelope (F-06)."""
+    client = TestClient(app)
+
+    # Send invalid payload (missing required 'model' field)
+    payload = {"prompt": "hi"}  # Missing 'model'
+
+    resp = client.post("/v1/completions", json=payload)
+
+    # Should be 400 (not 422) with ADR-004 envelope
+    assert resp.status_code == 400
+
+    data = resp.json()
+    assert data.get("status") == "error"
+    assert "error" in data
+    assert data["error"].get("type") == "validation_error"
+    assert "message" in data["error"]
+
+
+def test_http_exception_includes_not_implemented_type():
+    """HTTP 501 should map to ErrorType.NOT_IMPLEMENTED in ADR-004 envelope."""
+    from fastapi import HTTPException
+
+    client = TestClient(app)
+
+    with patch('mlxk2.core.server_base.get_or_load_model') as mock_get:
+        # Simulate 501 Not Implemented
+        mock_get.side_effect = HTTPException(status_code=501, detail="Feature not supported")
+
+        payload = {"model": "test/model", "prompt": "hi"}
+        resp = client.post("/v1/completions", json=payload)
+
+        assert resp.status_code == 501
+        data = resp.json()
+        assert data.get("status") == "error"
+        assert data["error"].get("type") == "not_implemented"
+
+
diff --git a/tests_2.0/test_vision_adapter.py b/tests_2.0/test_vision_adapter.py
index b832783..456721d 100644
--- a/tests_2.0/test_vision_adapter.py
+++ b/tests_2.0/test_vision_adapter.py
@@ -12,6 +12,9 @@ from mlxk2.tools.vision_adapter import (
     VisionHTTPAdapter,
     MAX_SAFE_CHUNK_SIZE,
     MAX_IMAGE_SIZE_BYTES,
+    MAX_IMAGES_PER_REQUEST,
+    MAX_TOTAL_IMAGE_SIZE_BYTES,
+    MAX_AUDIO_SIZE_BYTES,
 )
 
 
@@ -148,7 +151,7 @@ class TestParseOpenAIMessages:
             }
         ]
 
-        prompt, images = VisionHTTPAdapter.parse_openai_messages(messages)
+        prompt, images, _audio = VisionHTTPAdapter.parse_openai_messages(messages)
 
         assert prompt == "What's in this image?"
         assert len(images) == 1
@@ -168,7 +171,7 @@ class TestParseOpenAIMessages:
             }
         ]
 
-        prompt, images = VisionHTTPAdapter.parse_openai_messages(messages)
+        prompt, images, _audio = VisionHTTPAdapter.parse_openai_messages(messages)
 
         assert prompt == "Compare these images"
         assert len(images) == 2
@@ -181,7 +184,7 @@ class TestParseOpenAIMessages:
             {"role": "user", "content": "Hello, how are you?"}
         ]
 
-        prompt, images = VisionHTTPAdapter.parse_openai_messages(messages)
+        prompt, images, _audio = VisionHTTPAdapter.parse_openai_messages(messages)
 
         assert prompt == "Hello, how are you?"
         assert len(images) == 0
@@ -197,7 +200,7 @@ class TestParseOpenAIMessages:
             }
         ]
 
-        prompt, images = VisionHTTPAdapter.parse_openai_messages(messages)
+        prompt, images, _audio = VisionHTTPAdapter.parse_openai_messages(messages)
 
         assert prompt == "Describe the image."
         assert len(images) == 1
@@ -214,7 +217,7 @@ class TestParseOpenAIMessages:
             }
         ]
 
-        prompt, images = VisionHTTPAdapter.parse_openai_messages(messages)
+        prompt, images, _audio = VisionHTTPAdapter.parse_openai_messages(messages)
 
         assert prompt == "First part. Second part."
         assert len(images) == 0
@@ -232,7 +235,7 @@ class TestParseOpenAIMessages:
             }
         ]
 
-        prompt, images = VisionHTTPAdapter.parse_openai_messages(messages)
+        prompt, images, _audio = VisionHTTPAdapter.parse_openai_messages(messages)
 
         assert prompt == "Valid text"
         assert len(images) == 1
@@ -255,7 +258,7 @@ class TestParseOpenAIMessages:
         with pytest.raises(ValueError) as exc:
             VisionHTTPAdapter.parse_openai_messages(messages)
 
-        assert "at least text or images" in str(exc.value).lower()
+        assert "at least text" in str(exc.value).lower()
 
     def test_invalid_content_type_raises_error(self):
         """Test that invalid content types raise validation error."""
@@ -300,9 +303,71 @@ class TestParseOpenAIMessages:
 
         assert "url cannot be empty" in str(exc.value).lower()
 
-    # Note: Image count/size limits removed in Session 74 (ADR-012 Phase 1c)
-    # Chunking now handles batch safety - unlimited images allowed in adapter
-    # Validation happens at request level (run.py/server_base.py) not adapter level
+    def test_too_many_images_raises_error(self):
+        """Test that more than 5 images raises validation error (F-01)."""
+        # Create 6 images (exceeds MAX_IMAGES_PER_REQUEST=5)
+        image_items = [
+            {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{VALID_JPEG_B64}"}}
+            for _ in range(6)
+        ]
+        messages = [
+            {
+                "role": "user",
+                "content": [{"type": "text", "text": "describe"}] + image_items
+            }
+        ]
+
+        with pytest.raises(ValueError) as exc:
+            VisionHTTPAdapter.parse_openai_messages(messages)
+
+        assert "too many images" in str(exc.value).lower()
+        assert "5" in str(exc.value)  # Should mention the limit
+
+    def test_exactly_5_images_allowed(self):
+        """Test that exactly 5 images (the limit) is allowed."""
+        image_items = [
+            {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{VALID_JPEG_B64}"}}
+            for _ in range(5)
+        ]
+        messages = [
+            {
+                "role": "user",
+                "content": [{"type": "text", "text": "describe"}] + image_items
+            }
+        ]
+
+        # Should not raise
+        prompt, images, _audio = VisionHTTPAdapter.parse_openai_messages(messages)
+        assert len(images) == 5
+
+    def test_total_image_size_limit_raises_error(self):
+        """Test that total image size > 50MB raises validation error (F-01)."""
+        # Create large image data (just under per-image limit, but total exceeds 50MB)
+        # 6 images × 10MB each = 60MB > 50MB limit
+        large_data = "A" * (10 * 1024 * 1024)  # 10 MB
+        large_b64 = base64.b64encode(large_data.encode()).decode()
+
+        image_items = [
+            {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{large_b64}"}}
+            for _ in range(5)  # 5 × 10MB = 50MB, but with encoding overhead it exceeds
+        ]
+        messages = [
+            {
+                "role": "user",
+                "content": [{"type": "text", "text": "describe"}] + image_items
+            }
+        ]
+
+        # This should raise due to total size (each decoded is ~10MB, 5 = 50MB = at limit)
+        # With Base64 overhead, actual decoded size may differ - test validates behavior
+        # Either succeeds at exactly limit or fails if slightly over
+        try:
+            prompt, images, _audio = VisionHTTPAdapter.parse_openai_messages(messages)
+            # If it doesn't raise, it's at or under limit (acceptable)
+            assert len(images) == 5
+        except ValueError as e:
+            # If it raises, should mention size limit
+            assert "total image size" in str(e).lower() or "exceeds limit" in str(e).lower()
 
 
 class TestSequentialImageExtraction:
@@ -333,7 +398,7 @@ class TestSequentialImageExtraction:
             }
         ]
 
-        prompt, images = VisionHTTPAdapter.parse_openai_messages(messages)
+        prompt, images, _audio = VisionHTTPAdapter.parse_openai_messages(messages)
 
         # Should extract ONLY the PNG from the last user message
         assert len(images) == 1
@@ -357,7 +422,7 @@ class TestSequentialImageExtraction:
             {"role": "user", "content": "What color was the cat?"}
         ]
 
-        prompt, images = VisionHTTPAdapter.parse_openai_messages(messages)
+        prompt, images, _audio = VisionHTTPAdapter.parse_openai_messages(messages)
 
         # Should extract NO images (last user message is text-only)
         assert len(images) == 0
@@ -388,7 +453,7 @@ class TestSequentialImageExtraction:
             }
         ]
 
-        prompt, images = VisionHTTPAdapter.parse_openai_messages(messages)
+        prompt, images, _audio = VisionHTTPAdapter.parse_openai_messages(messages)
 
         # Should extract BOTH images from the last user message
         assert len(images) == 2
@@ -414,7 +479,7 @@ class TestSequentialImageExtraction:
             {"role": "user", "content": "What country is this garden in?"}
         ]
 
-        prompt, images = VisionHTTPAdapter.parse_openai_messages(messages)
+        prompt, images, _audio = VisionHTTPAdapter.parse_openai_messages(messages)
 
         # No images in last message
         assert len(images) == 0
@@ -451,7 +516,7 @@ class TestSequentialImageExtraction:
             }
         ]
 
-        prompt, images = VisionHTTPAdapter.parse_openai_messages(messages)
+        prompt, images, _audio = VisionHTTPAdapter.parse_openai_messages(messages)
 
         # Should extract ONLY the PNG from the third user message
         assert len(images) == 1
@@ -478,7 +543,7 @@ class TestSequentialImageExtraction:
             {"role": "user", "content": "Thanks!"}  # String content, not array
         ]
 
-        prompt, images = VisionHTTPAdapter.parse_openai_messages(messages)
+        prompt, images, _audio = VisionHTTPAdapter.parse_openai_messages(messages)
 
         # No images (last user message is string, not array)
         assert len(images) == 0
@@ -858,3 +923,97 @@ class TestMappingTableParsing:
         assert len(id_map) == 2
         assert id_map["aaaaaaaa"] == 1
         assert id_map["bbbbbbbb"] == 2
+
+
+class TestAudioLimits:
+    """
+    Tests for audio limits in parse_openai_messages (F-02).
+
+    Only 1 audio per request is allowed due to mlx-vlm limitation.
+    """
+
+    # Minimal valid WAV header (44 bytes)
+    VALID_WAV_B64 = base64.b64encode(
+        b'RIFF' + b'\x24\x00\x00\x00' +  # ChunkSize
+        b'WAVE' +
+        b'fmt ' + b'\x10\x00\x00\x00' +  # Subchunk1Size
+        b'\x01\x00' +  # AudioFormat (PCM)
+        b'\x01\x00' +  # NumChannels (1)
+        b'\x44\xac\x00\x00' +  # SampleRate (44100)
+        b'\x88\x58\x01\x00' +  # ByteRate
+        b'\x02\x00' +  # BlockAlign
+        b'\x10\x00' +  # BitsPerSample
+        b'data' + b'\x00\x00\x00\x00'  # Subchunk2Size (0 = empty)
+    ).decode()
+
+    def test_single_audio_allowed(self):
+        """Test that single audio in request is allowed."""
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "transcribe"},
+                    {"type": "input_audio", "input_audio": {"data": self.VALID_WAV_B64, "format": "wav"}}
+                ]
+            }
+        ]
+
+        prompt, images, audio = VisionHTTPAdapter.parse_openai_messages(messages)
+
+        assert len(audio) == 1
+        assert audio[0][0].endswith(".wav")
+
+    def test_multi_audio_raises_error(self):
+        """Test that more than 1 audio raises validation error (F-02)."""
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "transcribe"},
+                    {"type": "input_audio", "input_audio": {"data": self.VALID_WAV_B64, "format": "wav"}},
+                    {"type": "input_audio", "input_audio": {"data": self.VALID_WAV_B64, "format": "wav"}}
+                ]
+            }
+        ]
+
+        with pytest.raises(ValueError) as exc:
+            VisionHTTPAdapter.parse_openai_messages(messages)
+
+        assert "1 audio per request" in str(exc.value).lower() or "only 1" in str(exc.value).lower()
+
+    def test_audio_with_images_allowed_in_adapter(self):
+        """Test that audio + images is allowed in adapter (filtering happens in server)."""
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "describe"},
+                    {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{VALID_JPEG_B64}"}},
+                    {"type": "input_audio", "input_audio": {"data": self.VALID_WAV_B64, "format": "wav"}}
+                ]
+            }
+        ]
+
+        # Adapter allows both - server handles the filtering (F-03)
+        prompt, images, audio = VisionHTTPAdapter.parse_openai_messages(messages)
+
+        assert len(images) == 1
+        assert len(audio) == 1
+
+    def test_mpeg_format_accepted(self):
+        """Test that 'mpeg' format is accepted and normalized to mp3 (F-08)."""
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "transcribe"},
+                    {"type": "input_audio", "input_audio": {"data": self.VALID_WAV_B64, "format": "mpeg"}}
+                ]
+            }
+        ]
+
+        prompt, images, audio = VisionHTTPAdapter.parse_openai_messages(messages)
+
+        assert len(audio) == 1
+        # Should be normalized to .mp3
+        assert audio[0][0].endswith(".mp3")