wav2vec2-large-xlsr-53-esperanto
| Property | Value |
|---|---|
| Parameter Count | 315M |
| License | Apache 2.0 |
| WER Score | 12.31% |
| Framework | PyTorch |
What is wav2vec2-large-xlsr-53-esperanto?
This model is a fine-tuned version of Facebook's wav2vec2-large-xlsr-53 specifically adapted for Esperanto speech recognition. It represents a significant achievement in bringing advanced speech recognition capabilities to the Esperanto-speaking community, utilizing the Common Voice dataset for training.
Implementation Details
The model is built on the wav2vec2 architecture and requires 16kHz audio input for optimal performance. It uses a CTC (Connectionist Temporal Classification) approach for speech recognition and has been specifically optimized for Esperanto phonetics and language patterns.
- 315M trainable parameters for robust speech recognition
- Built on Facebook's proven wav2vec2-large-xlsr-53 architecture
- Optimized for 16kHz audio input
- Trained on the Common Voice Esperanto dataset
Core Capabilities
- Direct speech-to-text transcription without requiring a language model
- Achieves 12.31% Word Error Rate on test data
- Handles various Esperanto speech patterns and accents
- Efficient batch processing for multiple audio inputs
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Esperanto speech recognition, which is relatively rare in the speech recognition landscape. It achieves impressive accuracy with a 12.31% WER without requiring additional language models.
Q: What are the recommended use cases?
The model is ideal for Esperanto speech transcription tasks, educational tools, accessibility applications, and any scenario requiring automatic speech recognition for Esperanto content. It's particularly suitable for applications requiring 16kHz audio processing.
