wav2vec2-large-xlsr-kazakh
| Property | Value |
|---|---|
| Parameter Count | 315M |
| License | Apache 2.0 |
| Tensor Type | F32 |
| Test WER | 19.65% |
What is wav2vec2-large-xlsr-kazakh?
wav2vec2-large-xlsr-kazakh is a specialized speech recognition model fine-tuned from facebook's wav2vec2-large-xlsr-53 base model, specifically adapted for the Kazakh language. This model represents a significant advancement in Kazakh language speech processing, utilizing the powerful XLSR architecture to achieve high-quality automatic speech recognition (ASR) capabilities.
Implementation Details
The model is built upon the wav2vec2-large-xlsr-53 architecture and has been fine-tuned using the Kazakh Speech Corpus v1.1. It operates on audio input sampled at 16kHz and employs the CTC (Connectionist Temporal Classification) approach for speech recognition tasks.
- Architecture: Wav2Vec2-Large-XLSR-53 base model
- Training Data: Kazakh Speech Corpus v1.1
- Input Requirements: 16kHz audio sampling rate
- Performance Metric: 19.65% Word Error Rate (WER) on test set
Core Capabilities
- Direct speech-to-text transcription for Kazakh language
- Robust performance without requiring a language model
- Batch processing support for multiple audio inputs
- Compatible with PyTorch and Transformers library
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Kazakh language speech recognition, based on the powerful wav2vec2-large-xlsr-53 architecture. It achieves a competitive WER of 19.65% without requiring an additional language model, making it particularly valuable for Kazakh ASR applications.
Q: What are the recommended use cases?
The model is ideal for automatic speech recognition tasks involving Kazakh language content, including transcription services, voice command systems, and speech analytics applications. It's particularly suitable for scenarios requiring 16kHz audio input and where direct speech-to-text conversion is needed.
