Add OpenAI-compatible API and Docker deployment

- Add FastAPI-based API in whisperx/api/ - Implement transcription endpoint compatible with OpenAI - Added Dockerfile and docker-compose.yml for easy deployment - Updated README with Docker instructions - Added new script whisperx-serve for running the API
Add reference to ROCm issue #5616
2026-05-13 01:37:47 +03:00 · 2025-12-27 04:16:29 +00:00 · 2025-12-27 04:10:59 +00:00 · 2025-12-27 03:59:25 +00:00 · 2025-10-21 09:13:50 -06:00 · 2025-10-21 15:57:26 +02:00
15 changed files with 701 additions and 182 deletions
@@ -0,0 +1,27 @@
+.git
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python
+.pytest_cache
+.coverage
+htmlcov
+.env
+.venv
+venv/
+ENV/
+env/
+
+# Docker
+Dockerfile*
+docker-compose*.yml
+.dockerignore
+
+# IDE
+.vscode
+.idea
+
+# OS
+.DS_Store
+*.log
@@ -0,0 +1,76 @@
+# Troubleshooting cuDNN Loading Errors
+
+This guide helps resolve common cuDNN-related errors when running WhisperX on GPU. These issues typically occur when the system can't locate cuDNN libraries or finds conflicting versions.
+
+## Unable to Load cuDNN Libraries
+
+If you encounter the following error when running WhisperX:
+
+`Unable to load any of {libcudnn_cnn.so.9.1.0, libcudnn_cnn.so.9.1, libcudnn_cnn.so.9, libcudnn_cnn.so}`
+
+This means the cuDNN libraries are installed (via whisperx dependencies) but aren't in a location where the system's dynamic linker can find them.
+
+### Solution 1: Add to LD_LIBRARY_PATH (Recommended)
+
+Add this at the start of your Python script or notebook:
+
+```python
+import os
+
+# Get current LD_LIBRARY_PATH
+original = os.environ.get("LD_LIBRARY_PATH", "")
+
+cudnn_path = "/usr/local/lib/python3.12/dist-packages/nvidia/cudnn/lib/"
+os.environ['LD_LIBRARY_PATH'] = original + ":" + cudnn_path
+```
+
+**Note:** Adjust the Python version (`python3.12`) to match your environment.
+
+### Solution 2: Symlink to LD_LIBRARY_PATH Directory
+
+If Solution 1 didn't work and you still get the "unable to load" error, symlink the libraries to a directory that's already in your `LD_LIBRARY_PATH`:
+
+1. Check what's in your LD_LIBRARY_PATH: `echo "$LD_LIBRARY_PATH"`
+2. Assuming that there is only one path set.  
+   Symlink the downloaded libcudnn files to that path:  
+   `ln -s /usr/local/lib/python3.12/dist-packages/nvidia/cudnn/lib/libcudnn* "$LD_LIBRARY_PATH"/`
+
+   **Note:** If `LD_LIBRARY_PATH` contains multiple paths (separated by `:`), pick one directory and use it instead of `"$LD_LIBRARY_PATH"`. For example: `/usr/lib/x86_64-linux-gnu/`
+
+## cuDNN Version Incompatibility
+
+If you encounter this error:
+
+```
+RuntimeError: cuDNN version incompatibility: PyTorch was compiled against (9, 10, 2) but found runtime version (9, 2, 1)
+```
+
+This means PyTorch is finding a different cuDNN version than the one it was compiled with. **PyTorch comes bundled with its own cuDNN**, but a conflicting cuDNN in `LD_LIBRARY_PATH` is taking precedence.
+
+### Solution: Remove Conflicting cuDNN from Path
+
+Check if there's a conflicting cuDNN path:
+
+```bash
+echo $LD_LIBRARY_PATH
+```
+
+If you see paths pointing to older cuDNN installations (e.g., system-installed cuDNN or manually downloaded), try one of these:
+
+**Option 1: Clear LD_LIBRARY_PATH temporarily**
+
+```python
+import os
+# Let PyTorch use its bundled cuDNN
+os.environ.pop('LD_LIBRARY_PATH', None)
+```
+
+**Option 2: Set LD_LIBRARY_PATH to only the correct version**
+
+```python
+import os
+# Point only to the cuDNN that matches PyTorch's compiled version
+os.environ['LD_LIBRARY_PATH'] = "/usr/local/lib/python3.12/dist-packages/nvidia/cudnn/lib/"
+```
+
+**Note:** This error is unlikely on a clean install. If it occurs anyway, [open an issue](https://github.com/m-bain/whisperX/issues). If you've modified system libraries or CUDA/cuDNN, the options above should help resolve most cases.
@@ -0,0 +1,36 @@
+# Use ROCm PyTorch base image
+FROM rocm/pytorch:latest
+
+# Set environment variables for ROCm and Python
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+ENV PYTHONPATH=/app
+ENV HF_HOME=/app/.cache/huggingface
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    ffmpeg \
+    libsndfile1 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Set working directory
+WORKDIR /app
+
+# Copy project files
+COPY . .
+
+# Install Python dependencies
+RUN pip install --upgrade pip
+RUN pip install -e .
+
+# Expose port
+EXPOSE 8000
+
+# Set default environment variables
+ENV WHISPERX_MODEL=turbo
+ENV WHISPERX_DEVICE=cuda
+ENV WHISPERX_COMPUTE_TYPE=float16
+
+# Start the server
+CMD ["python", "-m", "whisperx.api.serve"]
@@ -111,6 +111,46 @@ uv sync --all-extras --dev

 You may also need to install ffmpeg, rust etc. Follow openAI instructions here https://github.com/openai/whisper#setup.

+## Docker Deployment 🐳
+
+For easy deployment with GPU support, use Docker Compose:
+
+### Prerequisites
+- Docker and Docker Compose installed
+- ROCm compatible GPU (AMD) or NVIDIA GPU with CUDA
+- For AMD ROCm, ensure ROCm drivers are installed on host
+
+### Steps
+
+1. Clone the repository:
+```bash
+git clone https://github.com/m-bain/whisperX.git
+cd whisperX
+```
+
+2. Build and run the container:
+```bash
+docker-compose up --build
+```
+
+The API will be available at `http://localhost:8000`
+
+### Environment Variables
+- `WHISPERX_MODEL`: Model size (default: large-v2)
+- `WHISPERX_DEVICE`: cuda or cpu (default: cuda)
+- `WHISPERX_COMPUTE_TYPE`: float16 or float32 (default: float16)
+
+### API Usage
+The API is compatible with OpenAI's transcription endpoint:
+
+```bash
+curl -X POST http://localhost:8000/v1/audio/transcriptions \
+  -H "Content-Type: multipart/form-data" \
+  -F "file=@audio.wav" \
+  -F "model=whisper-1" \
+  -F "language=en"
+```
+
 ### Speaker Diarization

 To **enable Speaker Diarization**, include your Hugging Face access token (read) that you can generate from [Here](https://huggingface.co/settings/tokens) after the `--hf_token` argument and accept the user agreement for the following models: [Segmentation](https://huggingface.co/pyannote/segmentation-3.0) and [Speaker-Diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) (if you choose to use Speaker-Diarization 2.x, follow requirements [here](https://huggingface.co/pyannote/speaker-diarization) instead.)
@@ -0,0 +1,159 @@
+# WhisperX ROCm Fork
+
+This is a fork of [m-bain/whisperX](https://github.com/m-bain/whisperX) configured for AMD ROCm GPUs.
+
+## Tested Configuration
+
+- **OS**: Debian Sid (trixie/forky)
+- **GPU**: AMD Radeon RX 7700 XT (gfx1101, 12GB VRAM)
+- **ROCm**: 7.1.1
+- **Python**: 3.10
+- **PyTorch**: 2.11.0+rocm7.0 (nightly)
+- **CTranslate2**: 4.6.2 (ROCm build from [paralin/ctranslate2-rocm](https://github.com/paralin/ctranslate2-rocm))
+
+## Prerequisites
+
+1. ROCm 7.1+ installed at `/opt/rocm`
+2. CTranslate2 built with ROCm support (see [paralin/ctranslate2-rocm](https://github.com/paralin/ctranslate2-rocm))
+
+## Installation (Debian Sid)
+
+### 1. Clone and setup
+
+```bash
+git clone https://github.com/paralin/whisperX-rocm.git ~/whisperx
+cd ~/whisperx
+git checkout rocm
+```
+
+### 2. Create venv with uv
+
+```bash
+uv venv
+uv pip install -e .
+```
+
+### 3. Install ROCm PyTorch
+
+```bash
+uv pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm7.0
+```
+
+### 4. Install ROCm CTranslate2
+
+First build CTranslate2 with ROCm support (see [paralin/ctranslate2-rocm](https://github.com/paralin/ctranslate2-rocm)), then:
+
+```bash
+# Remove PyPI ctranslate2 (has CUDA binaries)
+rm -rf .venv/lib/python3.10/site-packages/ctranslate2*
+
+# Install ROCm build
+export CTRANSLATE2_ROOT=/usr/local
+export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
+uv pip install --reinstall pybind11 ~/ctranslate2/python
+```
+
+## Environment Variables
+
+These must be set before running whisperx:
+
+```bash
+export HSA_OVERRIDE_GFX_VERSION=11.0.1  # for gfx1101
+export AMDGPU_TARGETS=gfx1101
+export ROCM_PATH=/opt/rocm
+export HIP_VISIBLE_DEVICES=0
+export ROCR_VISIBLE_DEVICES=0
+export LD_LIBRARY_PATH=/usr/local/lib:/opt/rocm/lib:/opt/rocm/lib/llvm/lib:$LD_LIBRARY_PATH
+```
+
+## Usage
+
+```bash
+# Set environment (add to ~/.bashrc for convenience)
+export HSA_OVERRIDE_GFX_VERSION=11.0.1
+export ROCM_PATH=/opt/rocm
+export HIP_VISIBLE_DEVICES=0
+export LD_LIBRARY_PATH=/usr/local/lib:/opt/rocm/lib:/opt/rocm/lib/llvm/lib:$LD_LIBRARY_PATH
+
+cd ~/whisperx
+uv run whisperx audio.wav \
+  --language en \
+  --model large-v3 \
+  --compute_type float16 \
+  --device cuda \
+  --batch_size 8 \
+  --vad_method silero \
+  --output_dir ./output \
+  --output_format all
+```
+
+Note: We use `--device cuda` because ROCm's HIP layer translates CUDA API calls to AMD GPU.
+
+## Verify Installation
+
+```bash
+# Check PyTorch sees the GPU
+python -c "import torch; print(CUDA:, torch.cuda.is_available()); print(Device:, torch.cuda.get_device_name(0))"
+
+# Check CTranslate2
+python -c "import ctranslate2; print(ctranslate2.__version__); print(ctranslate2.get_supported_compute_types(cuda))"
+```
+
+Expected output:
+```
+CUDA: True
+Device: AMD Radeon RX 7700 XT
+
+4.6.2
+{int8_float16, int8_bfloat16, bfloat16, int8_float32, int8, float16, float32}
+```
+
+## GPU Architecture
+
+Set `HSA_OVERRIDE_GFX_VERSION` based on your GPU:
+
+| GPU | Architecture | HSA_OVERRIDE_GFX_VERSION |
+|-----|--------------|--------------------------|
+| RX 7900 XTX/XT | gfx1100 | 11.0.0 |
+| RX 7800 XT | gfx1101 | 11.0.1 |
+| RX 7700 XT | gfx1101 | 11.0.1 |
+| RX 7600 | gfx1102 | 11.0.2 |
+| RX 6900/6800/6700 | gfx1030 | 10.3.0 |
+| RX 6600 | gfx1032 | 10.3.2 |
+
+## Upstream
+
+- Original: [m-bain/whisperX](https://github.com/m-bain/whisperX)
+
+## Known Issues
+
+### Memory Access Fault on Long Audio
+
+When transcribing longer audio files (>60s), you may encounter:
+
+```
+Memory access fault by GPU node-1 (Agent handle: 0x...) on address 0x...
+Reason: Page not present or supervisor privilege.
+```
+
+**Status**: Under investigation. Short clips (~60s) work fine at ~28x realtime with small model.
+
+**Workaround**: Process audio in chunks, or use CPU mode for long files.
+
+**Working example** (first 60s):
+```python
+from faster_whisper import WhisperModel
+model = WhisperModel("small", device="cuda", compute_type="float16")
+segments, info = model.transcribe("audio.wav", language="en", clip_timestamps=[0, 60])
+```
+
+**Search terms for updates**:
+- `"Memory access fault by GPU node" "Page not present or supervisor privilege" ROCm 7.1 PyTorch site:github.com`
+- `"Memory access fault" ROCm CTranslate2 faster-whisper gfx1101`
+
+This may be related to:
+- ROCm 7.1.1 + PyTorch nightly (2.11.0+rocm7.0) incompatibility
+- GPU memory fragmentation with longer sequences
+- HIP/ROCm memory management issues with certain operations
+
+**Related issue**: https://github.com/ROCm/ROCm/issues/5616 (gfx1103, same error pattern)
@@ -0,0 +1,28 @@
+version: '3.8'
+
+services:
+  whisperx-api:
+    build:
+      context: .
+      dockerfile: Dockerfile
+    ports:
+      - "8000:8000"
+    environment:
+      - WHISPERX_MODEL=turbo
+      - WHISPERX_DEVICE=cuda
+      - WHISPERX_COMPUTE_TYPE=float16
+    volumes:
+      # Mount Hugging Face cache if needed
+      - hf_cache:/app/.cache/huggingface
+    devices:
+      # Allow access to all GPUs
+      - /dev/kfd:/dev/kfd
+      - /dev/dri:/dev/dri
+    cap_add:
+      - SYS_ADMIN
+    security_opt:
+      - seccomp:unconfined
+    # For AMD ROCm GPUs, use device passthrough
+
+volumes:
+  hf_cache:
@@ -2,7 +2,7 @@
 urls = { repository = "https://github.com/m-bain/whisperx" }
 authors = [{ name = "Max Bain" }]
 name = "whisperx"
-version = "3.7.3"
+version = "3.7.4"
 description = "Time-Accurate Automatic Speech Recognition using Whisper."
 readme = "README.md"
 requires-python = ">=3.9, <3.14"
@@ -19,15 +19,19 @@ dependencies = [
    "av<16.0.0",
    "numpy>=2.1.0,<2.3.0; python_version >='3.13'",
    "pyannote-audio>=3.3.2,<4.0.0",
-    "torch~=2.7.1",
-    "torchaudio~=2.7.1",
+    "torch~=2.8.0",
+    "torchaudio~=2.8.0",
    "transformers>=4.48.0",
-    "triton>=3.3.0; sys_platform == 'linux' and platform_machine == 'x86_64'" # only install triton on x86_64 Linux
+    "triton>=3.3.0; sys_platform == 'linux' and platform_machine == 'x86_64'", # only install triton on x86_64 Linux
+    "fastapi>=0.104.0",
+    "uvicorn[standard]>=0.24.0",
+    "python-multipart>=0.0.6",
 ]


 [project.scripts]
 whisperx = "whisperx.__main__:cli"
+whisperx-serve = "whisperx.api.serve:serve"

 [build-system]
 requires = ["setuptools"]
@@ -177,9 +177,9 @@ source = { registry = "https://pypi.org/simple" }
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    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.13'" },
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-    { name = "torch", version = "2.7.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "platform_machine != 'x86_64' and sys_platform != 'darwin'" },
-    { name = "torch", version = "2.7.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
+    { name = "torch", version = "2.8.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
+    { name = "torch", version = "2.8.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "platform_machine != 'x86_64' and sys_platform != 'darwin'" },
+    { name = "torch", version = "2.8.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
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+[[package]]
+name = "importlib-metadata"
+version = "8.7.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
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+]
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+wheels = [
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+]
+
 [[package]]
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@@ -1135,9 +1147,9 @@ name = "julius"
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-    { name = "torch", version = "2.7.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
+    { name = "torch", version = "2.8.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
+    { name = "torch", version = "2.8.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "platform_machine != 'x86_64' and sys_platform != 'darwin'" },
+    { name = "torch", version = "2.8.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
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@@ -1357,9 +1369,9 @@ dependencies = [
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    { name = "pytorch-lightning" },
    { name = "pyyaml" },
-    { name = "torch", version = "2.7.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
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-    { name = "torch", version = "2.7.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
+    { name = "torch", version = "2.8.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
+    { name = "torch", version = "2.8.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "platform_machine != 'x86_64' and sys_platform != 'darwin'" },
+    { name = "torch", version = "2.8.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
    { name = "torchmetrics" },
    { name = "tqdm" },
    { name = "typing-extensions" },
@@ -2009,77 +2021,77 @@ wheels = [

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+    { name = "torch", version = "2.8.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "platform_machine != 'x86_64' and sys_platform != 'darwin'" },
+    { name = "torch", version = "2.8.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
+    { name = "torchaudio", version = "2.8.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux') or sys_platform == 'darwin'" },
+    { name = "torchaudio", version = "2.8.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux') or (platform_machine == 'aarch64' and platform_python_implementation != 'CPython' and sys_platform == 'linux') or (platform_machine != 'x86_64' and sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "torchaudio", version = "2.8.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
    { name = "transformers" },
    { name = "triton", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
 ]
@@ -4019,12 +4039,12 @@ requires-dist = [
    { name = "onnxruntime", marker = "python_full_version < '3.10'", specifier = ">=1.19,<1.20.0" },
    { name = "pandas", specifier = ">=2.2.3,<2.3.0" },
    { name = "pyannote-audio", specifier = ">=3.3.2,<4.0.0" },
-    { name = "torch", marker = "sys_platform == 'darwin'", specifier = "~=2.7.1", index = "https://download.pytorch.org/whl/cpu" },
-    { name = "torch", marker = "platform_machine != 'x86_64' and sys_platform != 'darwin'", specifier = "~=2.7.1", index = "https://download.pytorch.org/whl/cpu" },
-    { name = "torch", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'", specifier = "~=2.7.1", index = "https://download.pytorch.org/whl/cu128" },
-    { name = "torchaudio", marker = "sys_platform == 'darwin'", specifier = "~=2.7.1", index = "https://download.pytorch.org/whl/cpu" },
-    { name = "torchaudio", marker = "platform_machine != 'x86_64' and sys_platform != 'darwin'", specifier = "~=2.7.1", index = "https://download.pytorch.org/whl/cpu" },
-    { name = "torchaudio", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'", specifier = "~=2.7.1", index = "https://download.pytorch.org/whl/cu128" },
+    { name = "torch", marker = "sys_platform == 'darwin'", specifier = "~=2.8.0", index = "https://download.pytorch.org/whl/cpu" },
+    { name = "torch", marker = "platform_machine != 'x86_64' and sys_platform != 'darwin'", specifier = "~=2.8.0", index = "https://download.pytorch.org/whl/cpu" },
+    { name = "torch", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'", specifier = "~=2.8.0", index = "https://download.pytorch.org/whl/cu128" },
+    { name = "torchaudio", marker = "sys_platform == 'darwin'", specifier = "~=2.8.0", index = "https://download.pytorch.org/whl/cpu" },
+    { name = "torchaudio", marker = "platform_machine != 'x86_64' and sys_platform != 'darwin'", specifier = "~=2.8.0", index = "https://download.pytorch.org/whl/cpu" },
+    { name = "torchaudio", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'", specifier = "~=2.8.0", index = "https://download.pytorch.org/whl/cu128" },
    { name = "transformers", specifier = ">=4.48.0" },
    { name = "triton", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'", specifier = ">=3.3.0", index = "https://download.pytorch.org/whl/cu128" },
 ]
@@ -58,6 +58,7 @@ def cli():
    parser.add_argument("--suppress_numerals", action="store_true", help="whether to suppress numeric symbols and currency symbols during sampling, since wav2vec2 cannot align them correctly")

    parser.add_argument("--initial_prompt", type=str, default=None, help="optional text to provide as a prompt for the first window.")
+    parser.add_argument("--hotwords", type=str, default=None, help="hotwords/hint phrases to the model (e.g. \"WhisperX, PyAnnote, GPU\"); improves recognition of rare/technical terms")
    parser.add_argument("--condition_on_previous_text", type=str2bool, default=False, help="if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop")
    parser.add_argument("--fp16", type=str2bool, default=True, help="whether to perform inference in fp16; True by default")

@@ -14,7 +14,7 @@ import torchaudio
 from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

 from whisperx.audio import SAMPLE_RATE, load_audio
-from whisperx.utils import interpolate_nans
+from whisperx.utils import interpolate_nans, PUNKT_LANGUAGES
 from whisperx.schema import (
    AlignedTranscriptionResult,
    SingleSegment,
@@ -192,11 +192,13 @@ def align(
                clean_wdx.append(wdx)


+        # Use language-specific Punkt model if available otherwise we fallback to English.
+        punkt_lang = PUNKT_LANGUAGES.get(model_lang, 'english')
        try:
-            sentence_splitter = nltk_load('tokenizers/punkt/english.pickle')
+            sentence_splitter = nltk_load(f'tokenizers/punkt_tab/{punkt_lang}.pickle')
        except LookupError:
            nltk.download('punkt_tab', quiet=True)
-            sentence_splitter = nltk_load('tokenizers/punkt/english.pickle')
+            sentence_splitter = nltk_load(f'tokenizers/punkt_tab/{punkt_lang}.pickle')
        sentence_spans = list(sentence_splitter.span_tokenize(text))

        segment_data[sdx] = {
@@ -0,0 +1,86 @@
+import os
+import tempfile
+import asyncio
+from contextlib import asynccontextmanager
+from fastapi import FastAPI, UploadFile, File, Form, HTTPException
+from fastapi.responses import JSONResponse
+import torch
+import whisperx
+from whisperx.schema import TranscriptionResult
+
+
+model = None
+align_model_metadata = None
+
+
+def load_transcription_model(model_name: str = "turbo", device: str = None, compute_type: str = "float16"):
+    global model, align_model_metadata
+    if device is None:
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+    print(f"Loading WhisperX model: {model_name} on {device} with {compute_type}")
+    model = whisperx.load_model(model_name, device, compute_type=compute_type)
+    # For alignment, load the metadata
+    align_model_metadata = whisperx.alignment.DEFAULT_ALIGN_MODELS_HF
+    print("Model loaded and ready.")
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    # Load the model at startup
+    model_name = os.getenv("WHISPERX_MODEL", "turbo")
+    device = os.getenv("WHISPERX_DEVICE", "cuda")
+    compute_type = os.getenv("WHISPERX_COMPUTE_TYPE", "float16")
+    load_transcription_model(model_name, device, compute_type)
+    yield
+    # Cleanup if needed
+    print("Shutting down API")
+
+
+app = FastAPI(
+    title="WhisperX API",
+    description="OpenAI-compatible API for speech transcription using WhisperX",
+    version="1.0.0",
+    lifespan=lifespan
+)
+
+
+@app.get("/")
+async def root():
+    return {"message": "WhisperX API is running"}
+
+
+@app.post("/v1/audio/transcriptions")
+async def transcribe_audio(
+    file: UploadFile = File(...),
+    model_name: str = Form("whisper-1"),  # OpenAI uses 'whisper-1', we ignore this
+    language: str = Form(None),
+    response_format: str = Form("json"),
+    temperature: float = Form(0.0),  # We don't use temperature for now
+    prompt: str = Form(None)  # Not used
+):
+    if model is None:
+        raise HTTPException(status_code=500, detail="Model not loaded")
+
+    if not file.filename.lower().endswith(('.wav', '.mp3', '.flac', '.m4a', '.webm', '.mp4', '.mpga', '.ogg', '.opus')):
+        raise HTTPException(status_code=400, detail="Unsupported audio format")
+
+    # Save uploaded file to temp file
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_file:
+        temp_file.write(await file.read())
+        audio_path = temp_file.name
+
+    try:
+        # Load audio
+        audio = whisperx.load_audio(audio_path)
+
+        # Transcribe
+        result = model(audio, batch_size=16, language=language)
+        text = " ".join([segment['text'] for segment in result["segments"]]).strip()
+
+        # If we have segments, might want to return more info, but for OpenAI compatibility, just text
+
+        return JSONResponse({"text": text})
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Transcription failed: {str(e)}")
+    finally:
+        os.unlink(audio_path)
@@ -0,0 +1,16 @@
+import uvicorn
+
+
+def serve(host: str = "0.0.0.0", port: int = 8000, workers: int = 1):
+    """Run the WhisperX API server"""
+    uvicorn.run(
+        "whisperx.api.main:app",
+        host=host,
+        port=port,
+        workers=workers,
+        reload=False  # No reload for production
+    )
+
+
+if __name__ == "__main__":
+    serve()
@@ -106,6 +106,7 @@ def transcribe_task(args: dict, parser: argparse.ArgumentParser):
        "no_speech_threshold": args.pop("no_speech_threshold"),
        "condition_on_previous_text": False,
        "initial_prompt": args.pop("initial_prompt"),
+        "hotwords": args.pop("hotwords"),
        "suppress_tokens": [int(x) for x in args.pop("suppress_tokens").split(",")],
        "suppress_numerals": args.pop("suppress_numerals"),
    }
@@ -126,6 +126,29 @@ TO_LANGUAGE_CODE = {

 LANGUAGES_WITHOUT_SPACES = ["ja", "zh"]

+# Mapping of language codes to NLTK Punkt tokenizer model names
+PUNKT_LANGUAGES = {
+    'cs': 'czech',
+    'da': 'danish', 
+    'de': 'german',
+    'el': 'greek',
+    'en': 'english',
+    'es': 'spanish',
+    'et': 'estonian',
+    'fi': 'finnish',
+    'fr': 'french',
+    'it': 'italian',
+    'nl': 'dutch',
+    'no': 'norwegian',
+    'pl': 'polish',
+    'pt': 'portuguese',
+    'sl': 'slovene',
+    'sv': 'swedish',
+    'tr': 'turkish',
+    "ml": "malayalam",
+    "ru": "russian",
+}
+
 system_encoding = sys.getdefaultencoding()

 if system_encoding != "utf-8":
@@ -410,7 +433,7 @@ class WriteJSON(ResultWriter):

 def get_writer(
    output_format: str, output_dir: str
-) -> Callable[[dict, TextIO, dict], None]:
+) -> Callable[[dict, str, dict], None]:
    writers = {
        "txt": WriteTXT,
        "vtt": WriteVTT,
@@ -425,7 +448,7 @@ def get_writer(
    if output_format == "all":
        all_writers = [writer(output_dir) for writer in writers.values()]

-        def write_all(result: dict, file: TextIO, options: dict):
+        def write_all(result: dict, file: str, options: dict):
            for writer in all_writers:
                writer(result, file, options)
Author	SHA1	Message	Date
vlad.os	c1fcb3f57c	Add OpenAI-compatible API and Docker deployment - Add FastAPI-based API in whisperx/api/ - Implement transcription endpoint compatible with OpenAI - Added Dockerfile and docker-compose.yml for easy deployment - Updated README with Docker instructions - Added new script whisperx-serve for running the API	2026-05-13 01:37:47 +03:00
cjs	d154f4b39b	Add reference to ROCm issue #5616	2025-12-27 04:16:29 +00:00
cjs	0c6bdd9fbe	Document memory access fault on long audio	2025-12-27 04:10:59 +00:00
cjs	7d70dcafb6	Add ROCM.md with Debian Sid setup instructions	2025-12-27 03:59:25 +00:00
Barabazs	d32ec3e301	fix: add missing comma	2025-10-21 09:13:50 -06:00
pplkit	db317c358b	feat: add language-aware sentence tokenization (#1269 ) * feat: add language-aware sentence tokenization * feat: add missing punkt languages --------- Co-authored-by: pulkit <129310466+p1kit@users.noreply.github.com> Co-authored-by: Barabazs <31799121+Barabazs@users.noreply.github.com>	2025-10-21 15:57:26 +02:00
JulianFP	6e1d1caaf4	fix: incorrect type annotation in get_writer return value The audio_path attribute that the __call__ method of the ResultWriter class takes is a str, not TextIO	2025-10-17 09:43:43 -06:00
Barabazs	c8f7597345	feat: add hotwords argument to CLI for improved recognition of rare terms	2025-10-17 09:21:56 -06:00
Barabazs	5925e5f8c7	docs: add cuDNN troubleshooting for common issues (#1266 ) * docs: add troubleshooting guide for cuDNN loading errors * docs: add cuDNN version incompatibility troubleshooting	2025-10-16 10:56:51 +02:00
Barabazs	617835dc27	chore: upgrade torch and torchaudio dependencies to 2.8.0	2025-10-16 07:41:45 +00:00