[docs]: add tutorial for transcription v1 api

Author: davidgao7

tutorials/17-whisper-api-transcription.md

@@ -0,0 +1,99 @@
+# Tutorial: Whisper Transcription API in vLLM Production Stack
+
+## Overview
+
+This tutorial introduces the newly added `/v1/audio/transcriptions` endpoint in the `vllm-router`, enabling users to transcribe `.wav` audio files using OpenAI’s `whisper-small` model.
+
+## Prerequisites
+
+* Access to a machine with a GPU (e.g. via [RunPod](https://runpod.io/))
+* Python 3.12 environment (recommended with `uv`)
+* `vllm` and `production-stack` cloned and installed
+* `vllm` installed with audio support:
+
+  ```bash
+  pip install vllm[audio]
+  ```
+
+## 1. Serving the Whisper Model
+
+Start a vLLM backend with the `whisper-small` model:
+
+```bash
+vllm serve \
+  --task transcription openai/whisper-small \
+  --host 0.0.0.0 --port 8002
+```
+
+## 2. Running the Router
+
+Create and run a router connected to the Whisper backend:
+
+```bash
+#!/bin/bash
+if [[ $# -ne 2 ]]; then
+    echo "Usage: $0 <router_port> <backend_url>"
+    exit 1
+fi
+
+uv run python3 -m vllm_router.app \
+  --host 0.0.0.0 --port "$1" \
+  --service-discovery static \
+  --static-backends "$2" \
+  --static-models "openai/whisper-small" \
+  --static-model-types "transcription" \
+  --routing-logic roundrobin \
+  --log-stats \
+  --engine-stats-interval 10 \
+  --request-stats-window 10
+```
+
+Example usage:
+
+```bash
+./run-router.sh 8000 http://localhost:8002
+```
+
+## 3. Sending a Transcription Request
+
+Use `curl` to send a `.wav` file to the transcription endpoint:
+
+* You can test with any `.wav` audio file of your choice.
+
+```bash
+curl -v http://localhost:8000/v1/audio/transcriptions \
+  -F 'file=@/path/to/audio.wav;type=audio/wav' \
+  -F 'model=openai/whisper-small' \
+  -F 'response_format=json' \
+  -F 'language=en'
+```
+
+### Supported Parameters
+
+| Parameter         | Description                                            |
+| ----------------- | ------------------------------------------------------ |
+| `file`            | Path to a `.wav` audio file                            |
+| `model`           | Whisper model to use (e.g., `openai/whisper-small`)    |
+| `prompt`          | *(Optional)* Text prompt to guide the transcription    |
+| `response_format` | One of `json`, `text`, `srt`, `verbose_json`, or `vtt` |
+| `temperature`     | *(Optional)* Sampling temperature as a float           |
+| `language`        | ISO 639-1 code (e.g., `en`, `fr`, `zh`)                |
+
+## 4. Sample Output
+
+```json
+{
+  "text": "Testing testing testing the whisper small model testing testing testing the audio transcription function testing testing testing the whisper small model"
+}
+```
+
+## 5. Notes
+
+* Router uses extended HTTPX timeouts to support long transcription jobs.
+* This implementation dynamically discovers valid transcription backends and routes requests accordingly.
+
+## 6. Resources
+
+* [PR #469 – Add Whisper Transcription API](https://github.com/vllm-project/production-stack/pull/469)
+* [OpenAI Whisper GitHub](https://github.com/openai/whisper)
+* [Blog: vLLM Whisper Transcription Walkthrough](https://davidgao7.github.io/posts/vllm-v1-whisper-transcription/)