Keyword Spotting (KS) is an exciting area in AI that allows devices to detect specific words or phrases from spoken language. With the introduction of the Wav2Vec2 model, this process has become even more efficient. In this article, we will guide you on how to utilize the Wav2Vec2-Large model for Keyword Spotting. We will cover everything from the model description and task explanation to practical usage examples.

Model Description

The Wav2Vec2 model we are using here is a modified version specifically designed for the SUPERB Keyword Spotting task. Think of it as a carefully designed listening device that is trained to recognize certain commands. The foundation of this model is wav2vec2-large-lv60, pre-trained on a 16kHz sampled speech audio. It is essential to input speech audio at the same sampling rate of 16kHz for the model to perform optimally. For further details, refer to SUPERB: Speech processing Universal PERformance Benchmark.

Task and Dataset Description

Keyword Spotting is akin to a keen listener categorizing known words in a conversation. The Wav2Vec2 model uses the widely recognized Speech Commands dataset v1.0, which comprises ten classes of keywords, a silence class, and an unknown class to account for false positives. This careful structuring helps in creating an efficient model by ensuring it has a clear understanding of what to listen for.

Usage Examples

Now that we’ve set the stage, let’s dive into how to use the model. Below are two methods you can use:

Method 1: Using the Audio Classification Pipeline

The easiest way to get started is by leveraging the Audio Classification pipeline provided by the Transformers library. Here’s a quick snippet:

from datasets import load_dataset
from transformers import pipeline

dataset = load_dataset("anton-l/superb_demo", "ks", split="test")
classifier = pipeline("audio-classification", model="superb/wav2vec2-large-superb-ks")
labels = classifier(dataset[0]["file"], top_k=5)

Method 2: Using the Model Directly

If you wish to have more control over the model, you can use it directly. Here is how:

import torch
from datasets import load_dataset
from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2FeatureExtractor
from torchaudio.sox_effects import apply_effects_file

effects = [[“channels”, 1], [“rate”, 16000], [“gain”, -3.0]]

def map_to_array(example):
    speech, _ = apply_effects_file(example["file"], effects)
    example["speech"] = speech.squeeze(0).numpy()
    return example

# load a demo dataset and read audio files
dataset = load_dataset("anton-l/superb_demo", "ks", split="test")
dataset = dataset.map(map_to_array)

model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-large-superb-ks")
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-large-superb-ks")

# compute attention masks and normalize the waveform if needed
inputs = feature_extractor(dataset[:4]["speech"], sampling_rate=16000, padding=True, return_tensors="pt")
logits = model(**inputs).logits
predicted_ids = torch.argmax(logits, dim=-1)
labels = [model.config.id2label[_id] for _id in predicted_ids.tolist()]

Troubleshooting

While using the Wav2Vec2 model, you may encounter some challenges. Here are a few troubleshooting tips:

• Issue with Sampling Rate: Ensure that your audio input is consistently sampled at 16kHz; otherwise, the model may fail to recognize keywords.
• Low Accuracy: Review your dataset structure. Make sure it aligns with the expected format to optimize model performance.
• Performance Drops: If your available system resources are limited, consider reducing the batch size during inference.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.