Speech Emotion Recognition With Openai Whisper Large V3
The Speech Emotion Recognition With Openai Whisper Large V3 model is designed to recognize emotions in speech. It's trained on a dataset with over 4,000 audio recordings labeled with various emotions, including happy, sad, angry, and surprised. Using the Whisper Large V3 model, it can classify audio recordings into different emotional categories with high accuracy. But what makes this model unique is its ability to handle a wide range of emotions and its efficiency in processing audio data. With a high F1 score of 0.9198, it's clear that this model is effective in identifying emotional states from speech data. So, how does it work? Simply put, it uses the Whisper Feature Extractor to preprocess audio data, and then it's fine-tuned for audio classification tasks. This model is not only accurate but also efficient, making it a practical choice for real-world applications.
Table of Contents
Model Overview
The Speech Emotion Recognition with Whisper model is a cutting-edge AI tool that can recognize emotions in speech. Imagine being able to understand how someone is feeling just by listening to their voice! This model can classify audio recordings into different emotional categories, such as Happy, Sad, Surprised, and more.
Capabilities
Capable of recognizing emotions in speech, this model can classify audio recordings into different emotional categories. It can identify emotional states from speech data with high accuracy.
Primary Tasks
- Classify audio recordings into emotional categories
- Identify emotional states from speech data
Strengths
- High accuracy in recognizing emotions (
0.9199) - Effective in identifying emotional states from speech data
- Trained on a diverse dataset with multiple emotions
Unique Features
- Utilizes the Whisper Feature Extractor to standardize and normalize audio features
- Fine-tuned for audio classification tasks
- Supports multiple emotions, including Happy, Sad, Surprised, and more
How it Works
So, how does this model work its magic? Here’s a simplified overview:
- Audio Loading: Loads audio files and converts them to numpy arrays using Librosa.
- Feature Extraction: Processes audio data using the Whisper Feature Extractor.
- Model Training: Trains the model using a dataset of labeled audio recordings.
- Emotion Recognition: Classifies audio recordings into emotional categories using the trained model.
Performance Metrics
But how well does it perform? Let’s take a look at some impressive performance metrics:
| Metric | Value |
|---|---|
| Loss | 0.5008 |
| Accuracy | 0.9199 |
| Precision | 0.9230 |
| Recall | 0.9199 |
| F1 Score | 0.9198 |
These metrics demonstrate the model’s ability to effectively identify emotional states from speech data.
Using the Model
To use the model, you can follow these steps:
- Install Required Libraries: Install the necessary libraries, including transformers, librosa, and torch.
- Load the Model: Load the pre-trained model using the
AutoModelForAudioClassificationclass. - Preprocess Audio: Preprocess the audio data using the
preprocess_audiofunction. - Make Predictions: Make predictions using the
predict_emotionfunction.
Here’s an example code snippet to get you started:
from transformers import AutoModelForAudioClassification, AutoFeatureExtractor
import librosa
import torch
import numpy as np
# Load the model and feature extractor
model_id = "firdhokk/speech-emotion-recognition-with-openai-whisper-large-v3"
model = AutoModelForAudioClassification.from_pretrained(model_id)
feature_extractor = AutoFeatureExtractor.from_pretrained(model_id, do_normalize=True)
# Preprocess audio and make predictions
audio_path = "/content/drive/MyDrive/Audio/Speech_URDU/Happy/SM5_F4_H058.wav"
predicted_emotion = predict_emotion(audio_path, model, feature_extractor)
print(f"Predicted Emotion: {predicted_emotion}")
Note that this is just a simplified example, and you may need to modify the code to suit your specific use case.
Limitations
While this model is powerful, it’s not perfect. Let’s take a closer look at some of its limitations:
Limited Emotion Coverage
The model is trained on a dataset with a specific set of emotions, which might not cover all possible emotional states. For example, the dataset only includes 8 emotions: Happy, Sad, Angry, Neutral, Disgust, Fearful, Surprised, and Calm. What about other emotions like Excited, Bored, or Confused?
Imbalanced Dataset
The dataset used to train the model is not perfectly balanced. Some emotions, like Happy and Sad, have more samples than others, like Disgust and Fearful. This imbalance might affect the model’s performance on underrepresented emotions.
Limited Audio Quality
The model is trained on audio recordings with a specific quality and format. What if the audio input is noisy, distorted, or in a different format? The model might struggle to recognize emotions in such cases.
Dependence on Feature Extraction
The model relies on the Whisper Feature Extractor to preprocess audio data. If the feature extractor is not accurate or robust, the model’s performance might suffer.
Limited Generalizability
The model is trained on a specific dataset and might not generalize well to other datasets or real-world scenarios. What if the audio recordings are from a different culture, language, or context?
Overfitting
The model is trained with a relatively small batch size and a large number of epochs, which might lead to overfitting. This means the model might perform well on the training data but not as well on new, unseen data.
Computational Requirements
The model requires significant computational resources, especially when dealing with large audio files or long recordings. This might limit its use in real-time applications or on devices with limited processing power.
By acknowledging these limitations, we can better understand the strengths and weaknesses of this model and work towards improving its performance and robustness.
Format
The model uses a specific format to process and classify audio recordings into different emotional categories. Let’s break it down:
Audio Format
The model accepts audio files in various formats, but they need to be preprocessed using the Whisper Feature Extractor. This extractor standardizes and normalizes the audio features for input to the model.
Input Requirements
- Audio files should be loaded using Librosa and converted to numpy arrays.
- The audio data should be processed using the Whisper Feature Extractor.
- The preprocessed audio data is then passed to the model as input.
Output Format
The model outputs emotion labels, which are mapped to numeric IDs. The supported emotion labels are:
| Emotion Label | Numeric ID |
|---|---|
| Angry | 0 |
| Disgust | 1 |
| Fearful | 2 |
| Happy | 3 |
| Neutral | 4 |
| Sad | 5 |
| Surprised | 6 |
Code Example
Here’s an example of how to preprocess audio data and pass it to the model:
from transformers import AutoModelForAudioClassification, AutoFeatureExtractor
import librosa
import torch
import numpy as np
# Load the audio file
audio_path = "/content/drive/MyDrive/Audio/Speech_URDU/Happy/SM5_F4_H058.wav"
audio_array, sampling_rate = librosa.load(audio_path, sr=feature_extractor.sampling_rate)
# Preprocess the audio data
max_length = int(feature_extractor.sampling_rate * 30.0)
if len(audio_array) > max_length:
audio_array = audio_array[:max_length]
else:
audio_array = np.pad(audio_array, (0, max_length - len(audio_array)))
inputs = feature_extractor(
audio_array,
sampling_rate=feature_extractor.sampling_rate,
max_length=max_length,
truncation=True,
return_tensors="pt",
)
# Pass the preprocessed audio data to the model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
inputs = {key: value.to(device) for key, value in inputs.items()}
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
predicted_id = torch.argmax(logits, dim=-1).item()
predicted_label = id2label[predicted_id]
print(f"Predicted Emotion: {predicted_label}")
Note that this code example assumes you have the transformers library installed and have loaded the pre-trained model.
