Source: AI Technology Online
Just yesterday, OpenAI officially released the GPT-4o model, which supports real-time reasoning in audio, visual, and text multimodal scenarios. Besides eagerly wanting to use the GPT-4o model, everyone must also want to understand some of the implementation details of this model.
Before GPT-4o, you could interact with ChatGPT in voice mode, with an average delay of 2.8 seconds (GPT-3.5) and 5.4 seconds (GPT-4). To achieve this, the voice mode consists of a pipeline of three independent models: a simple model transcribes audio into text, GPT-3.5 or GPT-4 receives the text and outputs text, and a third simple model converts the text back into audio. This process means that the main source of intelligence, GPT-4, loses a lot of information—it cannot directly observe tone, multiple speakers, or background noise, nor can it output laughter, singing, or express emotions.
However, GPT-4o trains a new model end-to-end across text, visual, and audio, meaning that all inputs and outputs are processed by the same neural network. Because GPT-4o is the first model to combine all these modalities, we are currently only scratching the surface of what this model can do and its limitations.
Next, let’s talk about how to train a new model end-to-end across text, visual, and audio:
Training a new end-to-end model covering text, visual, and audio data is a complex and challenging task, roughly divided into the following steps:
1. Data Collection and Processing
Text Data: Collect a large amount of relevant text data and perform necessary preprocessing, such as tokenization, removing stop words, etc.
Visual Data: Collect images or videos related to the text data, and perform labeling and preprocessing.
Audio Data: If the model needs to process audio input, collect relevant audio files and perform necessary audio feature extraction.
2. Model Selection and Design
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Select a model architecture suitable for multimodal (text, visual, audio) input, such as a multimodal Transformer model. For details on the implementation of the Transformer model, refer to previous articles by Teacher Lion.
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Design the input layer of the model to accept different types of data (text, images, audio).
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Determine the output layer of the model to produce the required predictions or classification results.
Design Approaches for Various Modalities:
1. Text Data Input Layer Design
For text data, the usual approach is to convert text into numerical vectors, which can be achieved through methods such as word embeddings or TF-IDF vectors.
Word Embeddings: Use pre-trained word embedding models (like Word2Vec, GloVe, BERT, etc.) to convert text into fixed-dimensional vectors. These vectors capture the semantic information of words, making semantically similar words close in vector space.
Text Vectorization: Besides word embeddings, text can also be directly converted into sparse vectors, such as using the TF-IDF (Term Frequency-Inverse Document Frequency) method. This method focuses more on capturing the frequency and importance of words in documents.
In the model input layer, you can use the text vectors as input to pass to the subsequent neural network layers.
2. Image Data Input Layer Design
For image data, convolutional neural networks (CNNs) are typically used. When designing the input layer, consider the size of the images, the number of channels, and the preprocessing methods.
Image Size and Channel Count: Determine the image size (e.g., 224×224, 299×299, etc.) and the number of channels (RGB three-channel or grayscale single-channel) that the model will accept. This depends on your dataset and specific tasks.
Preprocessing: Perform appropriate preprocessing on the images, such as scaling, cropping, normalization, etc., to ensure the model can correctly process the image data.
In the model input layer, you can use the preprocessed image data as input and pass it to the CNN layer for feature extraction.
3. Audio Data Input Layer Design
For audio data, common processing methods include converting it into spectrograms or MFCC (Mel Frequency Cepstral Coefficients) audio features.
Spectrogram: Convert the audio signal into a time-frequency representation using Short-Time Fourier Transform (STFT) to obtain the spectrogram. The spectrogram can capture the frequency and time information of the audio signal.
MFCC: Extract Mel Frequency Cepstral Coefficients from the audio signal through a series of processing steps, capturing the perceptual characteristics of the audio.
In the model input layer, you can use these audio features as input to pass to the subsequent neural network layers for processing.
4. Multimodal Data Fusion
If you need to process text, image, and audio data simultaneously and wish to fuse them together for subsequent processing, consider the following methods:
Feature Concatenation: Directly concatenate the feature vectors of text, image, and audio to form a larger feature vector. This method is simple and direct but may not fully utilize the complementarity of different modality data.
Attention Mechanism: Use an attention mechanism to dynamically fuse data from different modalities. By calculating the correlation between different modality data, assign different weights to each modality to achieve more effective data fusion.
Multimodal Transformer: Utilize the multi-head self-attention mechanism of the Transformer model to process text, image, and audio data simultaneously. By establishing attention connections between different modalities, the model can learn the complex relationships between them.
3. Feature Extraction
For text data, you can use word embeddings (like Word2Vec, GloVe, or BERT embeddings) to extract features.
For visual data, you can use pre-trained convolutional neural networks (CNNs) to extract image features.
For audio data, you can use audio feature extraction techniques, such as MFCC (Mel Frequency Cepstral Coefficients).
4. Data Fusion
Determine how to fuse data from different modalities together. This can be achieved through various methods, such as feature concatenation, feature fusion networks (like multimodal Transformers), or attention-based fusion mechanisms.
In data fusion, we focus on integrating data from different sources, formats, and characteristics to provide a more comprehensive and accurate data view. The following is a detailed elaboration of the details in the data fusion process:
1. Data Preprocessing:
Data Cleaning: First, clean the data from each data source to remove duplicates, invalid, or erroneous data. This includes handling missing values, outliers, and noisy data.
Data Normalization: Since the data from different sources may use different measurement units or formats, normalization is necessary to ensure all data is compared and integrated on the same scale.
Data Transformation: Sometimes, to facilitate analysis and fusion, it may be necessary to transform the data, such as log transformation or Box-Cox transformation, to improve the normality, stability, and homogeneity of variance of the data.
2. Feature Extraction and Selection:
Feature Extraction: Extract meaningful information from the raw data to form new features. This can be achieved through statistical methods (like mean, variance, skewness, etc.), machine learning algorithms (like PCA, t-SNE, etc.), or other domain-specific techniques (like frequency analysis in signal processing).
Feature Selection: Select the most relevant features from the extracted features in relation to the task. This can be done through correlation analysis, mutual information, model-based feature selection, etc.
3. Data Alignment and Matching:
Time Alignment: If the data is time series data, align the timestamps of different data sources to ensure consistency in time.
Entity Matching: For the same entity (like customers, products, etc.) from different data sources, matching and identification are necessary to ensure the accuracy and consistency of the data.
4. Data Fusion Methods:
Rule-Based Fusion: Fuse data according to predefined rules. For example, for different attribute values of the same entity provided by two data sources, rules can be set based on the reliability of the data source, timestamps, etc., to select the final value.
Model-Based Fusion: Use machine learning models to fuse data. For example, ensemble learning methods (like random forests, gradient boosting trees, etc.) can be used to combine information from multiple sources to improve prediction accuracy.
Hybrid Methods: Combine rule-based and model-based methods for data fusion.
5. Evaluation and Optimization of Fusion Effects:
Effect Evaluation: Compare the data before and after fusion to evaluate the effectiveness of the fusion. This can be achieved by calculating correlation, accuracy, completeness, etc.
Optimization Iteration: Based on the evaluation results, adjust and optimize the fusion methods and parameters to improve the effectiveness of data fusion.
6. Post-Processing and Validation:
Data Verification: After data fusion, verification is necessary to ensure the accuracy and completeness of the data. This can be done by comparing with other reliable data sources, using business rules for validation, etc.
Outlier Detection and Handling: After fusion, outlier detection and handling are also necessary to identify and correct potential data anomalies.
By addressing these details, data fusion can provide a more comprehensive and accurate data foundation for subsequent data analysis and decision-making.
5. Training and Optimization
6. Evaluation and Testing
Evaluate the model’s performance on an independent test set.
Make necessary adjustments and optimizations to the model based on the evaluation results.
7. Deployment and Application
Deploy the trained model to the production environment and provide APIs for accessing the model.
Fine-tune and optimize the model according to the actual application scenario.
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