
### `AutoModelForAudioClassification` Class
`transformers.AutoModelForAudioClassification`
- **Description**: This is a generic model class that is instantiated as one of the model classes of the library (with an audio classification head) when created with the `from_pretrained()` or `from_config()` class methods. It cannot be instantiated directly using `__init__()` (this will throw an error).
  
#### `from_config`
`from_config(**kwargs)`
- **Parameters**:
  - `config (PretrainedConfig)`: The model class to instantiate is selected based on the configuration class:
    - `HubertConfig`: `HubertForSequenceClassification` (Hubert model)
    - `SEWConfig`: `SEWForSequenceClassification` (SEW model)
    - `SEWDConfig`: `SEWDForSequenceClassification` (SEW-D model)
    - `UniSpeechConfig`: `UniSpeechForSequenceClassification` (UniSpeech model)
    - `UniSpeechSatConfig`: `UniSpeechSatForSequenceClassification` (UniSpeechSat model)
    - `Wav2Vec2Config`: `Wav2Vec2ForSequenceClassification` (Wav2Vec2 model)
  
    Instantiates one of the model classes of the library (with an audio classification head) from a configuration.
  
  - **Note**: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. To load the model weights, use the `from_pretrained()` method.

#### Example Usage:
```python
from transformers import AutoConfig, AutoModelForAudioClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained('bert-base-cased')
model = AutoModelForAudioClassification.from_config(config)
```

#### `from_pretrained`
`from_pretrained(*model_args, **kwargs)`
- **Parameters**:
  - `pretrained_model_name_or_path (str or os.PathLike)`: Can be either:
    - A string, the model ID of a pretrained model hosted inside a model repository on huggingface.co (e.g., 'bert-base-uncased', or namespaced under a user or organization name).
    - A path to a directory containing model weights saved using `save_pretrained()`.
    - A path or URL to a TensorFlow index checkpoint file (e.g., `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config`.
  - `model_args (additional positional arguments, optional)`: Will be passed to the underlying model `__init__()` method.
  - `config (PretrainedConfig, optional)`: Configuration for the model to use instead of an automatically loaded configuration.
  - `state_dict (Dict[str, torch.Tensor], optional)`: A state dictionary to use instead of a state dictionary loaded from saved weights file.
  - `cache_dir (str or os.PathLike, optional)`: Path to a directory in which a downloaded pretrained model configuration should be cached.
  - `from_tf (bool, optional, defaults to False)`: Load the model weights from a TensorFlow checkpoint save file.
  - `force_download (bool, optional, defaults to False)`: Whether or not to force (re-)download the model weights and configuration files.
  - `resume_download (bool, optional, defaults to False)`: Whether or not to delete incompletely received files.
  - `proxies (Dict[str, str], optional)`: A dictionary of proxy servers to use.
  - `output_loading_info (bool, optional, defaults to False)`: Whether to return a dictionary containing missing keys, unexpected keys, and error messages.
  - `local_files_only (bool, optional, defaults to False)`: Whether or not to only look at local files (e.g., not try downloading the model).
  - `revision (str, optional, defaults to "main")`: The specific model version to use.
  - `trust_remote_code (bool, optional, defaults to False)`: Whether or not to allow custom models defined on the Hub to run code on your local machine.

#### Example Usage:
```python
from transformers import AutoConfig, AutoModelForAudioClassification

# Download model and configuration from huggingface.co and cache.
model = AutoModelForAudioClassification.from_pretrained('bert-base-cased')

# Update configuration during loading
model = AutoModelForAudioClassification.from_pretrained('bert-base-cased', output_attentions=True)
print(model.config.output_attentions)  # True

# Loading from a TensorFlow checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained('./tf_model/bert_tf_model_config.json')
model = AutoModelForAudioClassification.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
```

### Model Class to Instantiate Based on Configuration:
The model class to instantiate is selected based on the `model_type` property of the config object or from pattern matching on `pretrained_model_name_or_path`:

- **hubert**: `HubertForSequenceClassification`
- **sew**: `SEWForSequenceClassification`
- **sew-d**: `SEWDForSequenceClassification`
- **unispeech**: `UniSpeechForSequenceClassification`
- **unispeech-sat**: `UniSpeechSatForSequenceClassification`
- **wav2vec2**: `Wav2Vec2ForSequenceClassification`

The model is set in evaluation mode by default using `model.eval()`. To train the model, you should first set it back to training mode with `model.train()`.