wav2vec2-lv-60-espeak-cv-ft
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Paper | Simple and Effective Zero-shot Cross-lingual Phoneme Recognition |
| Author | |
| Downloads | 39,937 |
What is wav2vec2-lv-60-espeak-cv-ft?
This is a sophisticated speech recognition model that builds upon the wav2vec2-large-lv60 architecture and is specifically fine-tuned for multilingual phoneme recognition using the CommonVoice dataset. The model is designed to process audio input at 16kHz and outputs phonetic labels that can be mapped to words using a phonetic dictionary.
Implementation Details
The model leverages the Transformers architecture and PyTorch framework, implementing a cross-lingual transfer learning approach by mapping phonemes of training languages to target languages using articulatory features. It's built on the wav2vec 2.0 framework, which has demonstrated significant success in self-supervised learning for speech recognition.
- Built on wav2vec2-large-lv60 pre-trained model
- Requires 16kHz audio input sampling
- Outputs phonetic labels for multilingual speech recognition
- Implements CTC (Connectionist Temporal Classification) for sequence modeling
Core Capabilities
- Multilingual phoneme recognition
- Zero-shot cross-lingual transfer learning
- Acoustic model functionality
- Direct phonetic transcription
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its ability to perform zero-shot cross-lingual phoneme recognition without requiring task-specific architectures. It uses a simple yet effective approach of mapping phonemes across languages using articulatory features, outperforming previous methods that relied on specialized architectures.
Q: What are the recommended use cases?
The model is ideal for multilingual speech recognition tasks, particularly when dealing with unseen languages. It's especially useful for phonetic transcription tasks and can serve as a standalone acoustic model in larger speech recognition systems.
