Audio Classification

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(
  "speechbrain/google_speech_command_xvector"
)
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

headers = {"Authorization": f"Bearer {API_TOKEN}"}
API_URL = "https://api-inference.huggingface.co/models/superb/hubert-large-superb-er"

def query(filename):
    with open(filename, "rb") as f:
        data = f.read()
    response = requests.request("POST", API_URL, headers=headers, data=data)
    return json.loads(response.content.decode("utf-8"))

data = query("sample1.flac")
# [{'label': 'neu', 'score': 0.60},
# {'label': 'hap', 'score': 0.20},
# {'label': 'ang', 'score': 0.13},
# {'label': 'sad', 'score': 0.07}]

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.