Wav2Vec2-Conformer-Large-960h with Rotary Position Embeddings
| Property | Value |
|---|---|
| Parameter Count | 593M |
| License | Apache 2.0 |
| Paper | fairseq S2T: Fast Speech-to-Text Modeling |
| Word Error Rate (Clean) | 1.96% |
| Word Error Rate (Other) | 3.98% |
What is wav2vec2-conformer-rope-large-960h-ft?
This is a state-of-the-art speech recognition model developed by Facebook that combines the Wav2Vec2 architecture with Conformer and rotary position embeddings. It's specifically designed for high-accuracy speech-to-text conversion, trained on 960 hours of LibriSpeech audio data at 16kHz sampling rate.
Implementation Details
The model utilizes a sophisticated architecture that incorporates rotary position embeddings into the Conformer framework, enabling better handling of sequential speech data. It's implemented using PyTorch and supports F32 tensor operations.
- Pre-trained and fine-tuned on LibriSpeech 960h dataset
- Optimized for 16kHz sampled speech input
- Implements CTC (Connectionist Temporal Classification) for sequence modeling
- Utilizes attention masks for improved performance
Core Capabilities
- Achieves 1.96% WER on clean speech test sets
- Handles varied speech conditions with 3.98% WER on other test sets
- Supports batch processing for efficient inference
- Provides easy integration through the Transformers library
Frequently Asked Questions
Q: What makes this model unique?
The combination of Wav2Vec2 architecture with Conformer and rotary position embeddings makes it particularly effective for speech recognition tasks, achieving state-of-the-art WER rates on LibriSpeech benchmarks.
Q: What are the recommended use cases?
This model is ideal for English speech recognition tasks requiring high accuracy, particularly in clean audio conditions. It's well-suited for transcription services, voice assistants, and audio content analysis where 16kHz audio input is available.
