### Install SpeechBrain via PyPI or Local Install

Source: https://speechbrain.github.io/index

Instructions for installing the SpeechBrain toolkit. Users can choose between a quick installation using pip for PyPI access or a local installation by cloning the GitHub repository for deeper access to recipes and the toolkit.

```bash
# From PyPI
pip install speechbrain

# Local installation
git clone https://github.com/speechbrain/speechbrain.git
cd speechbrain
pip install -r requirements.txt
pip install --editable .
```

--------------------------------

### Custom ASR Brain Implementation in SpeechBrain

Source: https://speechbrain.github.io/index

An example of how to define a custom Automatic Speech Recognition (ASR) brain class in SpeechBrain. This snippet illustrates defining custom models, feature extraction, and data augmentation within the SpeechBrain framework.

```python
class ASR_Brain(sb.Brain):
  def compute_forward(self, batch, stage):

    # Compute features (mfcc, fbanks, etc.) on the fly
    features = self.hparams.compute_features(batch.wavs)

    # Improve robustness with pre-built augmentations
    features = self.hparams.augment(features)

    # Apply your custom model
    return self.modules.myCustomModel(features)
```

--------------------------------

### Train a SpeechBrain Model with a YAML Recipe

Source: https://speechbrain.github.io/index

Demonstrates how to train a SpeechBrain model using a Python script and a YAML hyperparameter file. This allows for easy configuration and modification of training parameters, including learning rate.

```python
cd recipes/{dataset}/{task}/train

# Train the model using the default recipe
python train.py hparams/train.yaml

# Train the model with a hyperparameter tweak
python train.py hparams/train.yaml --learning_rate=0.1
```

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