Lang-ID-VoxLingua107-ECAPA Language Identification Model
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Research Paper | SpeechBrain Paper |
| Downloads | 271,484 |
| Supported Languages | 107 |
What is lang-id-voxlingua107-ecapa?
This is a sophisticated spoken language recognition model developed using SpeechBrain, trained on the extensive VoxLingua107 dataset. It employs the ECAPA-TDNN architecture, previously successful in speaker recognition, but enhanced with additional fully connected hidden layers post-embedding. The model processes 16kHz sampled audio and can accurately identify speech from 107 different languages.
Implementation Details
The model utilizes the ECAPA-TDNN architecture with cross-entropy loss training. It automatically handles audio normalization, including resampling and mono channel selection, making it highly adaptable to various input formats. The system can be used both for direct language identification and as an utterance-level feature extractor for custom language identification models.
- Trained on 6,628 hours of speech data
- Processes 16kHz single-channel audio
- Generates 256-dimensional embeddings
- Achieves 93.3% accuracy on the VoxLingua107 development dataset
Core Capabilities
- Direct language identification across 107 languages
- Utterance-level embedding extraction
- Automatic audio normalization
- GPU-compatible inference
- Batch processing support
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to handle 107 different languages and its use of the ECAPA-TDNN architecture with enhanced fully connected layers makes it particularly powerful for language identification tasks. The automatic audio normalization and dual-use capability (both for direct identification and embedding extraction) add to its versatility.
Q: What are the recommended use cases?
The model is ideal for automated language identification in speech recordings, content categorization, and as a feature extractor for building custom language identification systems. It's particularly useful for applications requiring multilingual speech processing or content organization.
