DiSER
Fine-tuned microsoft/wavlm-base-plus for 7-class speech emotion recognition, achieving 74.10% accuracy and 72.92% macro-F1 on a combined RAVDESS + TESS + CREMA-D + SAVEE corpus.
Fonte do modelo
- Tipo
- Other
- Tamanho do arquivo
- 1,59 MB–1,03 GB
- Versão
- vRevision 7e42d5f
- Atualizado
- 22 de set. de 2026
- Downloads
- 4
- Fonte
- Hugging Face
Descrição da fonte
DiSER v2.0 — Speech Emotion Recognition
Fine-tuned microsoft/wavlm-base-plus for
7-class speech emotion recognition, achieving 74.10% accuracy and
72.92% macro-F1 on a combined RAVDESS + TESS + CREMA-D + SAVEE corpus.
Architecture
Raw waveform (4s @ 16kHz)
→ WavLM Feature Extractor CNN (frozen)
→ WavLM Transformer × 12 (fine-tuned, discriminative LR)
→ Weighted Layer Aggregation [Yang et al. 2021, SUPERB]
→ Attentive Statistics Pooling [Okabe et al. 2018] → μ ⊕ σ
→ Classification Head (Linear → GELU → Dropout → LayerNorm × 2 → Linear)
→ 7-class emotion logits
Performance
| Metric | Score |
|---|---|
| Test Accuracy | 74.10% |
| Macro F1 | 72.92% |
| Weighted F1 | 73.23% |
| ONNX CPU latency | 844 ms / sample |
Per-class recall
| angry | 84.1% | | disgust | 77.7% | | fear | 82.4% | | happy | 30.6% | | neutral | 74.3% | | sad | 82.1% | | surprise | 95.7% |
Training Details
- Training set: 11,491 samples (stratified speaker-independent split)
- Test set: 4,911 samples (no speaker overlap with train)
- Optimizer: AdamW with discriminative LRs (backbone=5e-06, head=3e-04)
- Scheduler: Cosine annealing with 8% linear warmup
- Loss: Focal Loss (γ=2.0) + effective-number class weights
- Augmentation: Gaussian noise, time stretching, pitch shift, Mixup
- Epochs trained: 4 (early stopping on macro-F1)
Scientific References
| Method | Reference |
|---|---|
| WavLM backbone | Chen et al. (2022), IEEE JSTSP |
| Weighted layer aggregation | Yang et al. (2021), INTERSPEECH |
| Attentive statistics pooling | Okabe et al. (2018), INTERSPEECH |
| Focal Loss | Lin et al. (2017), ICCV |
| Effective-number weighting | Cui et al. (2019), CVPR |
| Mixup augmentation | Zhang et al. (2018), ICLR |
Usage
import torch, librosa
from transformers import AutoFeatureExtractor
# Load
feature_extractor = AutoFeatureExtractor.from_pretrained("shrey416/DiSER")
model = ... # load from best_model.pt
# Infer
y, sr = librosa.load("audio.wav", sr=16000)
inputs = feature_extractor(y, sampling_rate=16000, return_tensors="pt",
padding="max_length", max_length=64000, truncation=True)
with torch.no_grad():
logits = model(inputs["input_values"])
emotions = ["angry","disgust","fear","happy","neutral","sad","surprise"]
print(emotions[logits.argmax(-1)])
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Trechos de fonte
2 trechos--- language: en license: apache-2.0 tags: - speech-emotion-recognition - audio-classification - wavlm - pytorch - onnx datasets: - RAVDESS - TESS - CREMA-D - SAVEE metrics: - accuracy - f1 --- # DiSER v2.0 — Speech Emotion Recognition Fine-tuned [`microsoft/wavlm-base-plus`](https://huggingface.co/microsoft/wavlm-base-plus) for 7-class speech emotion recognition, achieving **74.10% accuracy** and **72.92% macro-F1** on a combined RAVDESS + TESS + CREMA-D + SAVEE corpus. ## Architecture ``` Raw waveform (4s @ 16kHz) → WavLM Feature Extractor CNN (frozen) → WavLM Transformer × 12 (fine-tuned, discriminative LR) → Weighted Layer Aggregation [Yang et al. 2021, SUPERB] → Attentive Statistics Pooling [Okabe et al. 2018] → μ ⊕ σ → Classification Head (Linear → GELU → Dropout → LayerNorm × 2 → Linear) → 7-class emotion logits ``` ## Performance | Metric | Score | |---|---| | Test Accuracy | **74.10%** | | Macro F1 | **72.92%** | | Weighted F1 | **73.23%** | | ONNX CPU latency | 844 ms / sample | ### Per-class recall | angry | 84.1% | | disgust | 77.7% | | fear | 82.4% | | happy | 30.6% | | neutral | 74.3% | | sad | 82.1% | | surprise | 95.7% | ## Training Details - **Training set**: 11,491 samples (stratified speaker-independent split) - **Test set**: 4,911 samples (no speaker overlap with train) - **Optimizer**: AdamW with discriminative LRs (backbone=5e-06, head=3e-04) - **Scheduler**: Cosine annealing with 8% linear warmup - **Loss**: Focal Loss (γ=2.0) + effective-number class weights - **Augmentation**: Gaussian noise, time stretching, pitch shift, Mixup - **Epochs trained**: 4 (early stopping on macro-F1) ## Scientific References | Method | Reference | |---|---| | WavLM backbone | Chen et al. (2022), *IEEE JSTSP* | | Weighted layer aggregation | Yang et al. (2021), *INTERSPEECH* | | Attentive statistics pooling | Okabe et al. (2018), *INTERSPEECH* | | Focal Loss | Lin et al. (2017), *ICCV* | | Effective-number weighting | Cui et al. (2019), *CVPR* | | Mixup augmentation | Zhang et al. (2018), *ICLR* | ## Usage ```python import torch, librosa from transformers import AutoFeatureExtractor # Load feature_extractor = AutoFeatureExtractor.from_pretrained("shrey416/DiSER") model = ... # load from best_model.pt # Infer y, sr = librosa.load("audio.wav", sr=16000) inputs = feature_extractor(y, sampling_rate=16000, return_tensors="pt", pa...
Source context: 4 downloads · 0 likes · Pipeline audio-classification · Repo shrey416/DiSER
