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+# faster-whisper on DGX Spark / ThinkStation PGX
+
+This repository provides a minimal, reproducible setup to run `faster-whisper` with CUDA on an NVIDIA DGX Spark–equivalent environment (Lenovo ThinkStation PGX).  
+Even if the host already has CUDA 13 installed (and is hard to change), you can run GPU inference reliably by building CTranslate2 with CUDA 12.4 + cuDNN 9 inside Docker.
+
+Official faster-whisper repository: https://github.com/SYSTRAN/faster-whisper
+
+## Why this setup is needed
+- `faster-whisper` relies on CTranslate2, and the CUDA/cuDNN versions used at build time must match the runtime environment for the GPU build to work.
+- On ThinkStation PGX, CUDA 13 is installed on the host. A simple `pip install ctranslate2` may install a CPU-only or non-CUDA build.
+- Following only the official instructions sometimes results in `ValueError: This CTranslate2 package was not compiled with CUDA support`.
+- To ensure reproducibility, we explicitly build CTranslate2 from source with CUDA 12.4 + cuDNN 9 in Docker, and reinstall the Python bindings from that build.
+
+## What this repo provides
+- `docker-compose.yml` and `docker/Dockerfile` for a GPU-enabled container:
+  - Base image: `nvidia/cuda:12.4.1-cudnn-devel-ubuntu22.04`
+  - CTranslate2 v4.6.2 built from source with CUDA enabled
+  - The CPU/non-CUDA `ctranslate2` wheel installed via pip is uninstalled once, and then replaced with the locally built CUDA-enabled Python bindings
+- A minimal FastAPI + Uvicorn server (`docker/server.py`):
+  - `POST /transcribe`: receive audio and return Whisper transcription
+  - `GET /health`: return model name, device, and compute_type
+- Sample audio and test scripts (`test/`)
+
+## Key design points
+- CTranslate2 is built with CUDA 12.x + cuDNN 9, and the same version is guaranteed at runtime via the Docker image.
+- `entrypoint.sh` explicitly configures `LD_LIBRARY_PATH` and dynamically adds the paths to `nvidia.cublas.lib` and `nvidia.cudnn.lib`.
+- Docker is required to isolate from host CUDA 13; GPU access assumes NVIDIA Container Toolkit.
+
+## Prerequisites
+- NVIDIA Container Toolkit installed
+- GPU driver correctly installed on the host
+- Host CUDA can remain 13.x (no need to change)
+- Internet connection (for first-time model download)
+
+### Optional: Local CPU-only run (.venv)
+If you want to try CPU-only inference without Docker, you can use a local Python virtual environment:
+
+```bash
+python3 -m venv .venv
+source .venv/bin/activate
+pip install --upgrade pip
+pip install -r docker/requirements.txt
+
+# Run sample audio on CPU
+FW_DEVICE=cpu FW_COMPUTE_TYPE=int8 python3 test/transcribe_cpu.py
+```
+
+This path is intended just for quick CPU checks; for GPU we recommend using the Docker setup below.
+
+## Setup (Docker)
+
+```bash
+# Build
+docker compose build
+
+# Start (GPU required)
+docker compose up -d
+
+# Health check
+curl http://localhost:8002/health
+```
+
+## How to use the inference API
+
+```bash
+curl -X POST http://localhost:8002/transcribe \
+  -F "file=@test/003.wav" \
+  -F "language=ja" \
+  -F "beam_size=5" \
+  -F "vad_filter=true"
+```
+
+The response includes `text`, `segments` (start/end/text), `language`, and `language_probability`.
+
+You can change model and runtime settings via environment variables:
+- `FW_MODEL` (default: `large-v3`)
+- `FW_DEVICE` (default: `cuda`)
+- `FW_COMPUTE_TYPE` (default: `float16`)
+
+### Running `test/` scripts inside the Docker container
+
+If you prefer not to install Python on the host, you can run the test scripts inside the running container:
+
+```bash
+# 1) Enter the container (assuming docker compose is up)
+docker compose exec whisper bash
+
+# 2) GPU sample (default: large-v3 / float16)
+python3 test/transcribe.py
+
+# 3) CPU run (switch compute_type via env vars)
+FW_DEVICE=cpu FW_COMPUTE_TYPE=int8 python3 test/transcribe_cpu.py
+```
+
+Both scripts use `test/003.wav` as the default input and print the transcript and segments to stdout.  
+To test with your own audio file, pass the path as an argument:
+
+```bash
+python3 test/transcribe.py /path/to/your.wav
+```
+
+## Directory layout (partial)
+
+- `docker-compose.yml` — Service definition (including GPU resources)
+- `docker/Dockerfile` — Build CTranslate2 from source on CUDA 12.4 + cuDNN 9
+- `docker/entrypoint.sh` — Configure CUDA library paths and start uvicorn
+- `docker/server.py` — FastAPI server (`/health`, `/transcribe`)
+- `docker/requirements.txt` — Minimal dependencies: `faster-whisper`, `fastapi`, `uvicorn[standard]`, `python-multipart`
+- `test/` — Sample audio (`003.wav`) and local test scripts
+
+## Notes for reproducibility
+
+- A simple `pip install faster-whisper` does not guarantee a CUDA-enabled `ctranslate2` build.
+- Even if installation succeeds, a CUDA version mismatch may cause the GPU not to be used at runtime.
+- This Dockerfile explicitly uninstalls the CPU build once and reinstalls the locally built CUDA-enabled version to keep everything consistent.
+
+