ASSISTANT
It seems like you're looking to define a Wav2Vec2 model for audio classification in TensorFlow. To provide a complete runnable code, I'll outline how you might structure the `TFWav2Vec2ForAudioClassification` class and the `mean_pool` function to achieve this, assuming you're importing the necessary components from TensorFlow and other relevant libraries.
Firstly, the `mean_pool` function would be for pooling the hidden states outputted by the Wav2Vec2 model to obtain a single vector representation for each audio clip. Here's a simple implementation of such a function:
```python
import tensorflow as tf
from tensorflow.keras import layers
# Function for Mean Pooling
def mean_pool(hidden_states, feature_lengths):
"""
hidden_states: A batch of hidden state vectors
feature_lengths: A batch that contains the length of hidden state sequences
"""
def lengths_to_mask(lengths):
maxlen = tf.reduce_max(lengths)
row_vector = tf.range(0, maxlen)
# Create a matrix with `len(lengths)` rows, each containing a row_vector
matrix = tf.expand_dims(row_vector, 0)
# Use broadcasting to compare to each length value which creates the mask
mask = tf.less(matrix, tf.expand_dims(lengths, -1))
return mask
mask = lengths_to_mask(feature_lengths)
mask = tf.cast(mask, tf.float32)
masked_hidden_states = hidden_states * mask[..., tf.newaxis]
summed = tf.reduce_sum(masked_hidden_states, axis=1)
counts = tf.reduce_sum(mask, axis=1, keepdims=True)
return summed / counts
```
Now let's define a sample `TFWav2Vec2ForAudioClassification` class. We would need a pre-trained Wav2Vec2 model as a backbone for our custom audio classification layer, assuming one exists:
```python
class TFWav2Vec2ForAudioClassification(layers.Layer):
def __init__(self, num_labels, pre_trained_model):
super(TFWav2Vec2ForAudioClassification, self).__init__()
self.wav2vec2 = pre_trained_model
self.dropout = layers.Dropout(0.1)
self.classifier = layers.Dense(num_labels, activation='softmax')
def call(self, input_values, training=False, mask=None, feature_lengths=None):
hidden_states = self.wav2vec2(input_values)[0]
# Apply mean pooling.
pooled_output = mean_pool(hidden_states, feature_lengths) if feature_lengths is not None else tf.reduce_mean(hidden_states, axis=1)
# Optionally apply dropout.
pooled_output = self.dropout(pooled_output, training=training)
# Classify the pooled outputs.
logits = self.classifier(pooled_output)
return logits
```
You'd need to replace `pre_trained_model` with an actual instance of a pre-trained Wav2Vec2 model. The forward pass calls the Wav2Vec2 model, applies the mean pooling, optionally applies dropout for regularization, and then passes the vector through a dense layer for classification.
When using this model, make sure to compile it with the appropriate optimizer, loss function, and metrics, and train it using TensorFlow's `.fit()` function on your prepared datasets:
```python
num_labels = label_mapper.vocabulary_size()
# Replace the following with the actual instantiation of the pre-trained Wav2Vec2 model.
pre_trained_wav2vec2 = ... # Initialize this with the actual pre-trained model.
model = TFWav2Vec2ForAudioClassification(num_labels, pre_trained_wav2vec2)
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
# Then you can train your model with .fit(), using your train_ds, val_ds, and possibly test_ds.
# model.fit(train_ds, validation_data=val_ds, epochs=5)
```
Please note, the code provided is a simplified example to guide you on structuring the model and you may need to adjust it according to your requirements and the specifics of the Wav2Vec2 model you are using.