Several pre-trained models are available for audio search applications, each designed to handle different aspects of audio processing. These models typically focus on tasks like generating audio embeddings, speech-to-text conversion, or cross-modal retrieval. Popular options include VGGish, Wav2Vec, Whisper, and CLAP. VGGish, developed by Google, generates compact embeddings from audio spectrograms, making it useful for similarity searches. Wav2Vec and its variants (e.g., Wav2Vec 2.0) from Meta are self-supervised models trained on raw audio, excelling at speech recognition tasks. OpenAI’s Whisper extends this with multilingual support and robust noise tolerance. CLAP (Contrastive Language-Audio Pretraining) links text and audio by training on paired data, enabling cross-modal searches (e.g., finding audio clips matching a text query).
To implement audio search, developers often use these models to convert audio into searchable representations. For example, VGGish embeddings can be indexed in a vector database like FAISS, allowing fast similarity searches. Whisper transcribes spoken content into text, which can then be processed using traditional text search engines like Elasticsearch. CLAP’s dual-encoder architecture allows direct comparison between text queries and audio embeddings, enabling scenarios like finding sound effects described in natural language. Real-world applications include identifying music tracks by humming (using VGGish embeddings) or searching podcast episodes via transcribed keywords (using Whisper). These models reduce the need for manual feature engineering, as they capture high-level patterns from raw audio data.
Developers can access these models through open-source libraries. TensorFlow Hub hosts VGGish, while Hugging Face Transformers provides implementations of Wav2Vec 2.0, Whisper, and CLAP. For scalable deployments, tools like Annoy or FAISS optimize vector search performance. Fine-tuning is often necessary for domain-specific tasks—for instance, adapting Whisper for medical terminology or CLAP for niche audio genres. APIs like Google’s AudioSet or Spotify’s ANN search demonstrate practical integrations. When choosing a model, consider factors like latency (Whisper is larger but more accurate), language support (CLAP handles multilingual text queries), and hardware constraints (Wav2Vec 2.0 can run on edge devices with quantization). Combining these models—such as using Whisper for transcription and CLAP for cross-modal retrieval—can create powerful hybrid search systems.