---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wav2vec2-base-one-shot-hip-hop-drums-clf
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# wav2vec2-base-one-shot-hip-hop-drums-clf

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on [yojul/one-shot-hip-hop-drums](https://huggingface.co/datasets/yojul/one-shot-hip-hop-drums).
It achieves the following results on the evaluation set:
- Loss: 0.2463
- Accuracy: 0.9243

## Model description

This a model is a classifier of one-shot drum sample, it has been trained on 17k hip-hop drum samples. It is able to classify samples within 7 classes : Kicks, Snares, Cymbals, Open-hats, Hi-hats, 808s, Claps.

## Intended uses & limitations

It might be used to automatically sort large number of drum samples when there are no prior knowledge on metadata. The model can take any audio file as input, but note that it has been trained on audio files downsampled at 16kHz.

## Training and evaluation data

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8432        | 1.0   | 123  | 0.7449          | 0.8523   |
| 0.4692        | 2.0   | 246  | 0.4199          | 0.8894   |
| 0.3478        | 3.0   | 369  | 0.3122          | 0.9148   |
| 0.3054        | 4.0   | 492  | 0.2771          | 0.9156   |
| 0.2522        | 5.0   | 615  | 0.2676          | 0.9217   |
| 0.2221        | 6.0   | 738  | 0.2495          | 0.9217   |
| 0.2256        | 7.0   | 861  | 0.2588          | 0.9184   |
| 0.1949        | 8.0   | 984  | 0.2525          | 0.9232   |
| 0.1837        | 9.0   | 1107 | 0.2505          | 0.9237   |
| 0.1644        | 10.0  | 1230 | 0.2463          | 0.9243   |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1