VoxLingua107 ECAPA-TDNN Language Identification Model
| Property | Value |
|---|---|
| Architecture | ECAPA-TDNN |
| Training Data | VoxLingua107 (6628 hours) |
| Languages Supported | 107 |
| Accuracy | 93% on development set |
| Paper | VoxLingua107: a Dataset for Spoken Language Recognition (2021) |
What is langid?
Langid is a sophisticated spoken language recognition model that leverages the ECAPA-TDNN architecture, traditionally used in speaker recognition, to identify the language being spoken in audio content. This model represents a significant advancement in multilingual speech processing, capable of distinguishing between 107 different languages, from widely-spoken languages like English and Mandarin to less common ones like Manx and Breton.
Implementation Details
The model is implemented using SpeechBrain and trained on the VoxLingua107 dataset, which comprises 6,628 hours of speech data automatically collected from YouTube. The architecture employs ECAPA-TDNN (Emphasized Channel Attention, Propagation and Aggregation Time Delay Neural Network), which has proven highly effective in speech processing tasks.
- Utilizes utterance-level feature extraction for language identification
- Provides cosine similarity scores for language matching
- Supports batch processing of audio signals
- Outputs 256-dimensional embeddings for custom applications
Core Capabilities
- Direct language identification across 107 languages
- Embedding extraction for custom language ID models
- Processing of various audio formats and lengths
- Real-time language detection capabilities
- Support for both common and rare languages
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to handle 107 languages, combined with its use of the ECAPA-TDNN architecture, makes it one of the most comprehensive language identification systems available. The extensive training on YouTube data provides real-world robustness, though with some inherent biases.
Q: What are the recommended use cases?
The model is ideal for automated language identification in speech processing pipelines, content categorization, and as a feature extractor for building custom language identification systems. It's particularly useful for applications requiring multilingual audio processing at scale.
