SER_wav2vec

SER Wav2Vec2 — Speech Emotion Recognition

Model Summary

• Architecture: Wav2Vec2 encoder with a sequence classification head (Wav2Vec2ForSequenceClassification).
• Sampling rate: 16 kHz mono inputs.
• Label set: angry, calm, disgust, fear, happy, neutral, sad, surprise.
• Base: facebook/wav2vec2-base-960h fine‑tuned for emotion classification.

Intended Use

• Input: short audio clips of speech.
• Output: per‑class probabilities for the above emotions and a dominant label.
• Use cases: UX feedback, call‑center analytics, demo apps, research prototypes.

Out‑of‑Scope

• Clinical or safety‑critical decisions.
• Non‑speech audio (music, noise) or multilingual speech without adaptation.

Data and Training

• Dataset: CREMA‑D (via confit/cremad-parquet).
• Preprocessing: resample to 16 kHz, convert to mono, optional padding for very short clips.
• Fine‑tuning objective: cross‑entropy classification.

Configuration Snapshot

• Hidden size: 768
• Attention heads: 12
• Hidden layers: 12
• Classifier projection: 256
• Final dropout: 0.0
• Feature extractor norm: group

Metrics

• Reported metrics: accuracy, F1‑score.
• Benchmark numbers depend on split and preprocessing; fill in with your evaluation results.

Usage

Python (Transformers)

import torch, numpy as np, soundfile as sf, librosa
from transformers import Wav2Vec2ForSequenceClassification, AutoFeatureExtractor

model_dir = "path/to/best_model"  # local directory or Hub repo id
model = Wav2Vec2ForSequenceClassification.from_pretrained(model_dir)
fe = AutoFeatureExtractor.from_pretrained(model_dir)
model.eval()

def load_audio(path, sr=fe.sampling_rate):
    y, s = sf.read(path, always_2d=False)
    if isinstance(y, np.ndarray):
        if y.ndim > 1:
            y = np.mean(y, axis=1)
        if s != sr:
            y = librosa.resample(y.astype(np.float32), orig_sr=s, target_sr=sr)
        y = y.astype(np.float32)
    else:
        y = np.array(y, dtype=np.float32)
    if y.size < sr // 10:
        y = np.pad(y, (0, max(0, sr - y.size)))
    return y

audio = load_audio("sample.wav")
inputs = fe(audio, sampling_rate=fe.sampling_rate, return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits
probs = torch.softmax(logits, dim=-1)[0].cpu().numpy()
labels = [model.config.id2label[str(i)] if isinstance(list(model.config.id2label.keys())[0], str) else model.config.id2label[i] for i in range(len(probs))]
pairs = sorted(zip(labels, probs), key=lambda x: x[1], reverse=True)
print(pairs[:3])

FastAPI Integration

This repository includes a FastAPI server that exposes POST /predict and returns sorted label probabilities and the dominant emotion.

Input and Output Schema

• Input: audio file (wav, mp3, m4a, etc.). Internally converted to 16 kHz mono.
• Output JSON:

{
  "results": [{ "label": "happy", "score": 0.81 }, ...],
  "dominant": { "label": "happy", "score": 0.81 }
}

Limitations and Bias

• Emotion labels are subjective; datasets may reflect staged emotions.
• Performance can degrade with strong noise, reverberation, or accents not present in training.
• Not suitable for sensitive decision‑making without rigorous validation.

Ethical Considerations

• Obtain consent where required.
• Be transparent about the model’s limitations and intended use.
• Avoid deployment in contexts where misclassification can cause harm.

Citation

If you use this model, please cite:

• Baevski et al., "wav2vec 2.0: A Framework for Self‑Supervised Learning of Speech Representations", NeurIPS 2020.
• CREMA‑D: Cao et al., "CREMA-D: Crowd-sourced Emotional Multimodal Actors Dataset", IEEE Transactions on Affective Computing.

License

MIT