diff --git a/docker/Dockerfile b/Dockerfile
similarity index 100%
rename from docker/Dockerfile
rename to Dockerfile
diff --git a/README.md b/README.md
index ecc2fc3..5ab5046 100644
--- a/README.md
+++ b/README.md
@@ -1,61 +1,67 @@
-# whisper-test (faster-whisper on DGX Spark/ThinkStation PGX)
+# faster-whisper on DGX Spark / ThinkStation PGX
 
-NVIDIA DGX Spark 相当環境（Lenovo ThinkStation PGX）で、`faster-whisper` を CUDA で確実に動かすための最小構成をまとめたリポジトリです。ホスト側 CUDA 13 が既に入っており変更しづらい研究用マシンでも、Docker 上で CUDA 12.4 + cuDNN 9 でビルドした CTranslate2 を用いて GPU 推論を再現できます。
+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.
 
-公式 faster-whisper リポジトリ: https://github.com/SYSTRAN/faster-whisper
+Official faster-whisper repository: https://github.com/SYSTRAN/faster-whisper
 
-## なぜこの構成が必要か
-- `faster-whisper` は CTranslate2 を利用し、ビルド時の CUDA/cuDNN と実行環境が一致していないと GPU 版が動作しない。
-- ThinkStation PGX ではホストに CUDA 13 が入っており、`pip install ctranslate2` すると CPU 版や CUDA 非対応ビルドが入ることがある。
-- 公式手順だけでは CUDA 版が入らず、`ValueError: This CTranslate2 package was not compiled with CUDA support` となるケースがあった。
-- そこで CUDA 12.4 + cuDNN 9 を明示した Docker で CTranslate2 をソースビルドし、Python バインディングを明示的に再インストールすることで再現性を確保。
+## 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.
 
-## 提供物
-- `docker-compose.yml` と `docker/Dockerfile` による GPU 対応コンテナ構成
-  - ベース: `nvidia/cuda:12.4.1-cudnn-devel-ubuntu22.04`
-  - CTranslate2 v4.6.2 を CUDA 有効でソースビルド
-  - `pip` で入る CPU 版 `ctranslate2` を一度アンインストールし、ビルド済み Python バインディングを再インストール
-- FastAPI + Uvicorn の簡易サーバ (`docker/server.py`)
-  - `POST /transcribe` で音声を受け取り、Whisper 推論結果を返却
-  - `GET /health` でモデル名・デバイス・compute_type を返却
-- サンプル音声とテストスクリプト（`test/`）
+## 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/`)
 
-## 仕組みのポイント
-- CUDA 12.x + cuDNN 9 で CTranslate2 をビルドし、実行時も同一バージョンを保証
-- `entrypoint.sh` で `LD_LIBRARY_PATH` を明示設定し、`nvidia.cublas.lib` / `nvidia.cudnn.lib` のパスを動的に追加
-- ホスト CUDA 13 と切り離すため Docker を必須とし、NVIDIA Container Toolkit を前提に GPU を渡す
+## 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.
 
-## 前提条件
-- NVIDIA Container Toolkit 導入済み
-- GPU ドライバがホストに正しく入っていること
-- ホスト側 CUDA は 13 系のままで可（変更不要）
+## 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:
 
-### ローカルCPU実行（任意）
-Docker を使わず手元の Python (.venv) で CPU 推論だけ試す場合の簡易手順:
 ```bash
 python3 -m venv .venv
 source .venv/bin/activate
 pip install --upgrade pip
 pip install -r docker/requirements.txt
 
-# CPU でサンプル音声を実行
+# Run sample audio on CPU
 FW_DEVICE=cpu FW_COMPUTE_TYPE=int8 python3 test/transcribe_cpu.py
 ```
-※ CPU のみの確認用。GPU を使う場合は Docker 構成を推奨。
 
-## セットアップ
+This path is intended just for quick CPU checks; for GPU we recommend using the Docker setup below.
+
+## Setup (Docker)
+
 ```bash
-# ビルド
+# Build
 docker compose build
 
-# 起動（GPU 必須）
+# Start (GPU required)
 docker compose up -d
 
-# ヘルスチェック
+# Health check
 curl http://localhost:8002/health
 ```
 
-## 推論APIの使い方
+## How to use the inference API
+
 ```bash
 curl -X POST http://localhost:8002/transcribe \
   -F "file=@test/003.wav" \
@@ -63,37 +69,49 @@ curl -X POST http://localhost:8002/transcribe \
   -F "beam_size=5" \
   -F "vad_filter=true"
 ```
-レスポンスには `text` と `segments`（start/end/text）、`language`, `language_probability` が含まれます。
 
-環境変数でモデルや実行設定を変更できます:
-- `FW_MODEL` (デフォルト `large-v3`)
-- `FW_DEVICE` (デフォルト `cuda`)
-- `FW_COMPUTE_TYPE` (デフォルト `float16`)
+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:
 
-### Docker コンテナ内で `test/` のスクリプトを実行する例
-ホストに Python を入れずに検証したい場合、起動済みコンテナ内でテストスクリプトを実行できます。
 ```bash
-# 1) コンテナに入る（compose が起動している前提）
+# 1) Enter the container (assuming docker compose is up)
 docker compose exec whisper bash
 
-# 2) GPU 版サンプル（デフォルトの large-v3 / float16）
+# 2) GPU sample (default: large-v3 / float16)
 python3 test/transcribe.py
 
-# 3) CPU で試したい場合（compute_type を切り替え）
+# 3) CPU run (switch compute_type via env vars)
 FW_DEVICE=cpu FW_COMPUTE_TYPE=int8 python3 test/transcribe_cpu.py
 ```
-いずれも `test/003.wav` を入力にし、標準出力へテキストと各セグメントを表示します。自分の音声で試す場合は `python3 test/transcribe.py /path/to/your.wav` のようにパスを指定してください。
 
-## ディレクトリ構成（抜粋）
-- `docker-compose.yml` — サービス定義（GPU リクエスト含む）
-- `docker/Dockerfile` — CUDA 12.4 + cuDNN 9 ベースで CTranslate2 をソースビルド
-- `docker/entrypoint.sh` — CUDA ライブラリパスを動的設定し uvicorn 起動
-- `docker/server.py` — FastAPI サーバ（/health, /transcribe）
-- `docker/requirements.txt` — 最小依存: `faster-whisper`, `fastapi`, `uvicorn[standard]`, `python-multipart`
-- `test/` — サンプル音声とローカル実行スクリプト
+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.
 
-## 再現のための注意点
-- `pip install faster-whisper` だけでは CUDA 版 `ctranslate2` は保証されない
-- CUDA バージョン不一致だとインストール成功しても実行時に GPU を掴まない
-- 本 Dockerfile は一度 CPU 版をアンインストールし、ビルド済み CUDA 版を再インストールして整合性を取る
 
diff --git a/README_EN.md b/README_EN.md
deleted file mode 100644
index 5ab5046..0000000
--- a/README_EN.md
+++ /dev/null
@@ -1,117 +0,0 @@
-# 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.
-
-