Wav2vec2 Base Drum Kit

--- license: mit language: en pipeline_tag: audio-classification tags: - audio - audio-classification - wav2vec2 - drum - percussion datasets: - airasoul/drum-kit metrics: - accuracy - f1 --- # Wav2Vec2 for drum-kit classification Fine-tuned [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) for **audio classification** of single drum/percussion sounds into 10 classes. ## Classes - clap, conga, crash, cymbal, hat, kick, ride, rim, snare, tom ## Usage ```python import torch import librosa from transformers import AutoFeatureExtractor, AutoModelForAudioClassification model_id = "airasoul/wav2vec2-base-drum-kit" # e.g. username/wav2vec2-base-drum-kit feature_extractor = AutoFeatureExtractor.from_pretrained(model_id) model = AutoModelForAudioClassification.from_pretrained(model_id) model.eval() # Load a WAV (16 kHz mono) audio, sr = librosa.load("path/to/audio.wav", sr=16000, mono=True) inputs = feature_extractor( audio, sampling_rate=16000, max_length=48000, # 3 s at 16 kHz truncation=True, return_tensors="pt", padding=True ) with torch.no_grad(): logits = model(**inputs).logits pred_id = logits.argmax(dim=-1).item() label = model.config.id2label.get(pred_id) or model.config.id2label.get(str(pred_id)) print(label) # e.g. "kick" ``` ## Training - **Base:** facebook/wav2vec2-base - **Task:** Single-label classification over 10 drum classes - **Data:** Custom drum-kit dataset with augmentation (time stretch, noise, gain) - **Input:** 16 kHz mono, up to 3 s (truncated or padded) ## Results - **Validation accuracy:** 95.7% (epoch 10) - **Test accuracy:** 97.0% (300 samples, held-out) | Class | Precision | Recall | F1-score | |--------|-----------|--------|----------| | clap | 1.00 | 1.00 | 1.00 | | conga | 0.96 | 0.93 | 0.95 | | crash | 0.97 | 0.97 | 0.97 | | cymbal | 1.00 | 0.91 | 0.95 | | hat | 1.00 | 0.97 | 0.98 | | kick | 1.00 | 0.94 | 0.97 | | ride | 0.94 | 1.00 | 0.97 | | rim | 1.00 | 1.00 | 1.00 | | snare | 0.89 | 0.96 | 0.93 | | tom | 0.92 | 1.00 | 0.96 | ## Limitations - Trained on short, single-hit drum sounds. Performance may drop on long mixes, multiple overlapping sounds, or very different recording conditions.

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