# Audio Classification

Audio classification is the task of assigning a label or class to a given audio. It can be used for recognizing which command a user is giving or the emotion of a statement, as well as identifying a speaker.

Inputs
Audio Classification Model
Output
Up
0.200
Down
0.800

## Use Cases

### Command Recognition

Command recognition or keyword spotting classifies utterances into a predefined set of commands. This is often done on-device for fast response time.

As an example, using the Google Speech Commands dataset, given an input, a model can classify which of the following commands the user is typing:

'yes', 'no', 'up', 'down', 'left', 'right', 'on', 'off', 'stop', 'go', 'unknown', 'silence'


Speechbrain models can easily perform this task with just a couple of lines of code!

from speechbrain.pretrained import EncoderClassifier
model = EncoderClassifier.from_hparams(
)
model.classify_file("file.wav")


### Language Identification

Datasets such as VoxLingua107 allow anyone to train language identification models for up to 107 languages! This can be extremely useful as a preprocessing step for other systems. Here's an example modeltrained on VoxLingua107.

### Emotion recognition

Emotion recognition is self explanatory. In addition to trying the widgets, you can use the Inference API to perform audio classification. Here is a simple example that uses a HuBERT model fine-tuned for this task.

import json
import requests

API_URL = "https://api-inference.huggingface.co/models/superb/hubert-large-superb-er"

def query(filename):
with open(filename, "rb") as f:

data = query("sample1.flac")
# [{'label': 'neu', 'score': 0.60},
# {'label': 'hap', 'score': 0.20},
# {'label': 'ang', 'score': 0.13},


### Speaker Identification

Speaker Identification is classifying the audio of the person speaking. Speakers are usually predefined. You can try out this task with this model. A useful dataset for this task is VoxCeleb1.

## Solving audio classification for your own data

We have some great news! You can do fine-tuning (transfer learning) to train a well-performing model without requiring as much data. Pretrained models such as Wav2Vec2 and HuBERT exist. Facebook's Wav2Vec2 XLS-R model is a large multilingual model trained on 128 languages and with 436K hours of speech.

We suggest checking out the following example (Colab Notebook) to learn how to fine-tune a model for audio classification with a single or multiple GPUs and share it on the Hub.

## Compatible libraries

speechbrain Transformers
Audio Classification demo
Audio Classification
Examples
Examples
or
This model can be loaded on the Inference API on-demand.
Models for Audio Classification
Datasets for Audio Classification

Note A benchmark of 10 different audio tasks.

Metrics for Audio Classification
accuracy
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with: Accuracy = (TP + TN) / (TP + TN + FP + FN) Where: TP: True positive TN: True negative FP: False positive FN: False negative
recall
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation: Recall = TP / (TP + FN) Where TP is the true positives and FN is the false negatives.
precision
Precision is the fraction of correctly labeled positive examples out of all of the examples that were labeled as positive. It is computed via the equation: Precision = TP / (TP + FP) where TP is the True positives (i.e. the examples correctly labeled as positive) and FP is the False positive examples (i.e. the examples incorrectly labeled as positive).
f1
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation: F1 = 2 * (precision * recall) / (precision + recall)