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language:
- en
license: apache-2.0
tags:
- audio
- audio-classification
- emotion-recognition
- speech-emotion-recognition
- f1
- formula1
- wavlm
base_model: microsoft/wavlm-base-plus
pipeline_tag: audio-classification
metrics:
- accuracy
- f1
---
# F1 Driver Tone & Emotion Detector
An advanced Speech Emotion Recognition (SER) deep learning model designed to detect the emotional tone of Formula 1 driver team radio communications in real-time.
The model processes raw audio input from driver-to-pit-wall communications, extracts context-rich speech representations using a pretrained **WavLM** encoder (`microsoft/wavlm-base-plus`), and passes them through a downstream **BiLSTM with Temporal Attention mechanism** to classify driver emotions.
---
## Model Overview
- **Base Audio Encoder:** [microsoft/wavlm-base-plus](https://huggingface.co/microsoft/wavlm-base-plus) (768-dimensional frozen embeddings)
- **Downstream Architecture:** Bidirectional LSTM (128 hidden size) + Temporal Attention Module + Linear Classifier
- **Target Emotions (6 Classes):**
- `0`: **Anger**
- `1`: **Disgust**
- `2`: **Fear**
- `3`: **Happy**
- `4`: **Neutral**
- `5`: **Sad**
---
## Model Architecture
```text
RAW DRIVER AUDIO (.wav, .mp3, .flac)
|
v
Audio Preprocessing Pipeline
- Convert Multi-channel to Mono
- Resample to 16,000 Hz (16 kHz)
- Peak Amplitude Normalization
|
v
Frozen WavLM Encoder
(microsoft/wavlm-base-plus)
|
v
Frame Embeddings [B, T, 768]
|
v
Bidirectional LSTM
(hidden_size = 128 x 2 = 256)
|
v
Temporal Attention
(Aggregates sequence into fixed context vector [B, 256])
|
v
Linear Classifier
(256 -> 6 Logits)
|
v
Softmax Probabilities & Emotion Output
```
---
## Model Summary
| Property | Value |
| :--- | :--- |
| **Model Name** | F1 Driver Tone & Emotion Detector |
| **Architecture Type** | WavLM + BiLSTM + Temporal Attention |
| **Base Speech Encoder** | [microsoft/wavlm-base-plus](https://huggingface.co/microsoft/wavlm-base-plus) |
| **Total Parameters** | ~95.2M |
| **Trainable Parameters** | ~462K (Downstream Classifier Head) |
| **Frozen Parameters** | ~94.7M (Frozen Base Encoder) |
| **Encoder Transformer Layers** | 12 Layers |
| **Encoder Hidden Dimension** | 768 |
| **Encoder Attention Heads** | 12 |
| **Downstream Feature Extractor** | Bidirectional LSTM (`hidden_size` = 128) |
| **BiLSTM Output Dimension** | 256 (`128 x 2`) |
| **Aggregation Mechanism** | Frame-level Temporal Attention |
| **Classifier Head** | Linear Layer (`256 -> 6` Logits) |
| **Number of Classes** | 6 (`Anger`, `Disgust`, `Fear`, `Happy`, `Neutral`, `Sad`) |
| **Target Sampling Rate** | 16,000 Hz (16 kHz Mono) |
| **Inference Chunking** | 2.5s Sliding Window (1.5s Hop / 1.0s Overlap) |
| **Modality** | Audio |
| **Output Format** | Emotion Label, Confidence %, Probability Distribution |
| **Training Loss Function** | Cross-Entropy Loss |
| **Optimizer** | AdamW (`lr` = 1e-3, `weight_decay` = 1e-4) |
---
---
## Input & Output Specifications
### Direct PyTorch Model I/O (`models.py` + `best_model.pt`)
For users directly loading the PyTorch `BiLSTMAttentionClassifier` model and `.pt` weights:
- **Model Input:**
- `x`: PyTorch Tensor of WavLM frame embeddings with shape **`[Batch_Size, Sequence_Length, 768]`**
- `mask`: Timestep mask tensor with shape **`[Batch_Size, Sequence_Length]`** (Optional: 1 for valid frame, 0 for padding)
- **Model Output:**
- `logits`: Tensor of shape **`[Batch_Size, 6]`** (Unnormalized raw class logits)
- `attn_weights`: Attention weight tensor of shape **`[Batch_Size, Sequence_Length]`** (Frame-level temporal attention scores)
---
### Full Pipeline I/O (Using `modeling_f1tone.py`)
For users using the complete audio-to-prediction helper script:
- **Input Requirements:**
- Raw Audio File (`.wav`, `.mp3`, `.flac`, `.ogg`, `.m4a`)
- Automatically converted to 16 kHz Mono and peak-normalized.
- **Output Return Format:**
Structured Python dictionary:
```json
{
"audio_file": "driver_radio_01.wav",
"predicted_emotion": "Anger",
"confidence": 94.25,
"probabilities": {
"Anger": 94.25,
"Neutral": 3.10,
"Disgust": 1.45,
"Fear": 0.70,
"Happy": 0.30,
"Sad": 0.20
},
"attention_weights": [0.012, 0.045, 0.180, ...]
}
```
---
## How to Run Inference using `modeling_f1tone.py`
### Directory Setup (`input/` folder)
To run predictions on audio files:
1. Create an `input/` folder in the directory where `modeling_f1tone.py` is located.
2. Place your team radio `.wav` or `.mp3` files inside `input/`.
```text
ToneDetectorF1/
├── best_model.pt
├── modeling_f1tone.py
├── config.json
└── input/
├── driver_radio_1.wav
└── pit_communication_2.mp3
```
### Running via Command Line
**Option A: Process all files in `input/` folder**
```bash
python modeling_f1tone.py
```
**Option B: Pass an explicit audio file path**
```bash
python modeling_f1tone.py --audio_path path/to/my_audio.wav
```
### Python Code Integration Example
```python
from modeling_f1tone import FinalPredictor
# Initialize predictor with model checkpoint
predictor = FinalPredictor(checkpoint_path="best_model.pt")
# Perform inference on an audio clip
result = predictor.predict_single("input/driver_radio_1.wav")
print(f"Predicted Emotion : {result['predicted_emotion']}")
print(f"Confidence : {result['confidence']:.2f}%")
print("Full Probabilities:", result['probabilities'])
```
---
## Verified Model Evaluation Results
Evaluation performed on holdout test set (1,117 audio evaluation samples):
| Metric | Score |
| :--- | :--- |
| **Test Set Accuracy** | **67.86%** |
| **Test Set Macro F1-Score** | **0.6794** |
| **Validation Accuracy (Epoch 8)** | **69.89%** |
| **Validation Macro F1-Score** | **0.7006** |
| **Feature Encoder** | `microsoft/wavlm-base-plus` (768-dim, Frozen) |
| **Classifier Head** | BiLSTM (128) + Temporal Attention + Linear (6) |
### Per-Class Performance Breakdown (Test Set)
| Emotion Class | Precision | Recall | F1-Score | Evaluation Support |
| :--- | :--- | :--- | :--- | :--- |
| **Anger** | **76.71%** | **87.96%** | **0.8195** | 191 samples |
| **Neutral** | **77.85%** | **75.46%** | **0.7664** | 163 samples |
| **Happy** | **79.05%** | **61.26%** | **0.6903** | 191 samples |
| **Disgust** | **64.65%** | **67.02%** | **0.6581** | 191 samples |
| **Fear** | **52.05%** | **66.84%** | **0.5853** | 190 samples |
| **Sad** | **63.33%** | **49.74%** | **0.5572** | 191 samples |
---
## Citation & License
- **License:** Apache 2.0
- **Base Encoder Paper:** *WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing* (Chen et al., 2022)
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