๐ Speech Emotion Recognition with Whisper
This project uses the Whisper model to recognize emotions in speech. It aims to classify audio recordings into different emotional categories, such as Happy, Sad, and Surprised.
๐ Quick Start
To use this speech emotion recognition model, you can follow these steps:
from transformers import AutoModelForAudioClassification, AutoFeatureExtractor
import librosa
import torch
import numpy as np
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)
id2label = model.config.id2label
def preprocess_audio(audio_path, feature_extractor, max_duration=30.0):
audio_array, sampling_rate = librosa.load(audio_path, sr=feature_extractor.sampling_rate)
max_length = int(feature_extractor.sampling_rate * max_duration)
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",
)
return inputs
def predict_emotion(audio_path, model, feature_extractor, id2label, max_duration=30.0):
inputs = preprocess_audio(audio_path, feature_extractor, max_duration)
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]
return predicted_label
audio_path = "/content/drive/MyDrive/Audio/Speech_URDU/Happy/SM5_F4_H058.wav"
predicted_emotion = predict_emotion(audio_path, model, feature_extractor, id2label)
print(f"Predicted Emotion: {predicted_emotion}")
โจ Features
- Leverage the Whisper model for speech emotion recognition.
- Classify audio recordings into multiple emotional categories.
- Use multiple datasets for training and evaluation to ensure model generalization.
๐ฆ Installation
The framework versions used in this project are as follows:
Property |
Details |
Transformers |
4.44.2 |
Pytorch |
2.4.1+cu121 |
Datasets |
3.0.0 |
Tokenizers |
0.19.1 |
๐ป Usage Examples
Basic Usage
from transformers import AutoModelForAudioClassification, AutoFeatureExtractor
import librosa
import torch
import numpy as np
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)
id2label = model.config.id2label
audio_path = "/content/drive/MyDrive/Audio/Speech_URDU/Happy/SM5_F4_H058.wav"
predicted_emotion = predict_emotion(audio_path, model, feature_extractor, id2label)
print(f"Predicted Emotion: {predicted_emotion}")
Advanced Usage
def preprocess_audio(audio_path, feature_extractor, max_duration=30.0):
audio_array, sampling_rate = librosa.load(audio_path, sr=feature_extractor.sampling_rate)
max_length = int(feature_extractor.sampling_rate * max_duration)
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",
)
return inputs
def predict_emotion(audio_path, model, feature_extractor, id2label, max_duration=30.0):
inputs = preprocess_audio(audio_path, feature_extractor, max_duration)
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]
return predicted_label
๐ Documentation
๐ Dataset
The dataset used for training and evaluation is sourced from multiple datasets, including:
The dataset contains recordings labeled with various emotions. The distribution of the emotions in the dataset is as follows:
Emotion |
Count |
sad |
752 |
happy |
752 |
angry |
752 |
neutral |
716 |
disgust |
652 |
fearful |
652 |
surprised |
652 |
calm |
192 |
The "calm" emotion was excluded during training due to its underrepresentation.
๐ค Preprocessing
- Audio Loading: Use Librosa to load the audio files and convert them to numpy arrays.
- Feature Extraction: Process the audio data using the Whisper Feature Extractor, which standardizes and normalizes the audio features for input to the model.
๐ง Model
The model used is the Whisper Large V3 model, fine - tuned for audio classification tasks:
- Model: openai/whisper-large-v3
- Output: Emotion labels (
Angry', 'Disgust', 'Fearful', 'Happy', 'Neutral', 'Sad', 'Surprised'
)
The emotion labels are mapped to numeric IDs for model training and evaluation.
โ๏ธ Training
The model is trained with the following parameters:
- Learning Rate:
5e-05
- Train Batch Size:
2
- Eval Batch Size:
2
- Random Seed:
42
- Gradient Accumulation Steps:
5
- Total Train Batch Size:
10
(effective batch size after gradient accumulation)
- Optimizer: Adam with parameters:
betas=(0.9, 0.999)
and epsilon=1e-08
- Learning Rate Scheduler:
linear
- Warmup Ratio for LR Scheduler:
0.1
- Number of Epochs:
25
- Mixed Precision Training: Native AMP (Automatic Mixed Precision)
The training uses Wandb for experiment tracking and monitoring.
๐ Metrics
The following evaluation metrics were obtained after training the model:
- Loss:
0.5008
- Accuracy:
0.9199
- Precision:
0.9230
- Recall:
0.9199
- F1 Score:
0.9198
These metrics demonstrate the model's performance on the speech emotion recognition task.
๐งช Results
After training, the model is evaluated on the test dataset, and the results are monitored using Wandb in this Link.
Training Loss |
Epoch |
Step |
Validation Loss |
Accuracy |
Precision |
Recall |
F1 |
0.4948 |
0.9995 |
394 |
0.4911 |
0.8286 |
0.8449 |
0.8286 |
0.8302 |
0.6271 |
1.9990 |
788 |
0.5307 |
0.8225 |
0.8559 |
0.8225 |
0.8277 |
0.2364 |
2.9985 |
1182 |
0.5076 |
0.8692 |
0.8727 |
0.8692 |
0.8684 |
0.0156 |
3.9980 |
1576 |
0.5669 |
0.8732 |
0.8868 |
0.8732 |
0.8745 |
0.2305 |
5.0 |
1971 |
0.4578 |
0.9108 |
0.9142 |
0.9108 |
0.9114 |
0.0112 |
5.9995 |
2365 |
0.4701 |
0.9108 |
0.9159 |
0.9108 |
0.9114 |
0.0013 |
6.9990 |
2759 |
0.5232 |
0.9138 |
0.9204 |
0.9138 |
0.9137 |
0.1894 |
7.9985 |
3153 |
0.5008 |
0.9199 |
0.9230 |
0.9199 |
0.9198 |
0.0877 |
8.9980 |
3547 |
0.5517 |
0.9138 |
0.9152 |
0.9138 |
0.9138 |
0.1471 |
10.0 |
3942 |
0.5856 |
0.8895 |
0.9002 |
0.8895 |
0.8915 |
0.0026 |
10.9995 |
4336 |
0.8334 |
0.8773 |
0.8949 |
0.8773 |
0.8770 |
๐ License
The project uses the Apache - 2.0 license.