> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/openai/whisper/llms.txt
> Use this file to discover all available pages before exploring further.

# load_model()

> Load a Whisper ASR model

## Function Signature

```python theme={null}
def load_model(
    name: str,
    device: Optional[Union[str, torch.device]] = None,
    download_root: str = None,
    in_memory: bool = False,
) -> Whisper
```

## Parameters

<ParamField path="name" type="str" required>
  One of the official model names listed by `whisper.available_models()`, or path to a model checkpoint containing the model dimensions and the model state\_dict.

  Available official models:

  * `tiny.en`, `tiny`
  * `base.en`, `base`
  * `small.en`, `small`
  * `medium.en`, `medium`
  * `large-v1`, `large-v2`, `large-v3`, `large`
  * `large-v3-turbo`, `turbo`

  Models with `.en` suffix are English-only variants.
</ParamField>

<ParamField path="device" type="Union[str, torch.device]" default="None">
  The PyTorch device to put the model into. If not specified, automatically selects `"cuda"` if available, otherwise `"cpu"`.

  Common values: `"cuda"`, `"cpu"`, `"cuda:0"`, `torch.device("cuda")`
</ParamField>

<ParamField path="download_root" type="str" default="None">
  Path to download the model files. By default, uses `"~/.cache/whisper"` (or `$XDG_CACHE_HOME/whisper` if the environment variable is set).

  The function creates this directory if it doesn't exist.
</ParamField>

<ParamField path="in_memory" type="bool" default="False">
  Whether to preload the model weights into host memory. When `True`, the model checkpoint is kept in memory as bytes rather than being read from disk.

  This can be useful for deployment scenarios where filesystem access is limited.
</ParamField>

## Returns

<ResponseField name="model" type="Whisper">
  The Whisper ASR model instance, ready for inference. The model is already loaded onto the specified device and has its state\_dict loaded.

  The returned model has alignment heads set if it's an official model (used for word-level timestamps).
</ResponseField>

## Example

```python theme={null}
import whisper

# Load the base model (auto-detects CUDA)
model = whisper.load_model("base")

# Load the turbo model on CPU
model = whisper.load_model("turbo", device="cpu")

# Load from a custom checkpoint file
model = whisper.load_model("/path/to/custom-model.pt")

# Load with custom cache directory
model = whisper.load_model("small", download_root="/custom/cache/path")

# Load in-memory (useful for serverless deployments)
model = whisper.load_model("base", in_memory=True)
```

## Notes

### Model Download and Caching

* On first use, models are downloaded from Azure CDN to the cache directory
* Downloaded models are verified using SHA256 checksums
* Subsequent calls reuse the cached model files
* If checksum verification fails, the model is re-downloaded automatically

### Model Selection

Choose a model based on your requirements:

* **Speed**: `tiny` (fastest) → `turbo` → `base` → `small` → `medium` → `large` (slowest)
* **Accuracy**: `tiny` (lowest) → `base` → `small` → `medium` → `large` → `turbo` (highest)
* **English-only**: Use `.en` variants for better English performance
* **Multilingual**: Use non-`.en` models for 99+ languages

### Device Compatibility

* FP16 (half precision) is only supported on CUDA devices
* CPU inference automatically uses FP32 even if FP16 is requested
* The model returns a warning if performing inference on CPU when CUDA is available

### Error Handling

```python theme={null}
try:
    model = whisper.load_model("nonexistent-model")
except RuntimeError as e:
    # Raises: Model nonexistent-model not found; available models = [...]
    print(e)
```

### Alignment Heads

Official models automatically have alignment heads configured, which are used for:

* Word-level timestamp extraction
* Cross-attention pattern analysis
* Dynamic time warping for precise timing

Custom checkpoints loaded from file paths will not have alignment heads set unless they were included in the checkpoint.