wav2vec2-large-robust-ft-libri-960h
| Property | Value |
|---|---|
| Parameter Count | 315M |
| License | Apache 2.0 |
| Paper | Research Paper |
| Tensor Type | F32 |
| Author |
What is wav2vec2-large-robust-ft-libri-960h?
This is Facebook's advanced speech recognition model that builds upon the wav2vec2-large-robust architecture. It's specifically designed for robust speech processing across multiple domains, having been pre-trained on diverse datasets including LibriLight, CommonVoice, Switchboard, and Fisher, followed by fine-tuning on 960 hours of LibriSpeech data.
Implementation Details
The model utilizes a sophisticated transformer-based architecture optimized for speech recognition tasks. It processes audio input at 16kHz sampling rate and employs CTC (Connectionist Temporal Classification) for speech-to-text conversion.
- Multi-domain pre-training architecture
- 315M trainable parameters
- Supports batch processing of audio inputs
- Implements robust feature extraction for varied acoustic conditions
Core Capabilities
- High-accuracy speech recognition across multiple domains
- Robust performance on telephone and clean audio data
- Effective handling of domain adaptation challenges
- Support for both single and batch audio processing
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its robust training approach across multiple domains, making it particularly effective at handling various audio conditions and sources. It demonstrates strong performance even when the domain of unlabeled pre-training data differs from the fine-tuning domain.
Q: What are the recommended use cases?
The model is ideal for automatic speech recognition tasks, particularly in scenarios requiring robust performance across different audio domains. It's especially suitable for applications involving audiobook transcription, telephone speech processing, and general speech-to-text conversion tasks.
