wav2vec2-xlsr-53-espeak-cv-ft
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Paper | Simple and Effective Zero-shot Cross-lingual Phoneme Recognition |
| Downloads | 352,764 |
| Task | Automatic Speech Recognition |
What is wav2vec2-xlsr-53-espeak-cv-ft?
This is a sophisticated multilingual speech recognition model that builds upon the wav2vec2-large-xlsr-53 architecture and has been specifically fine-tuned on the CommonVoice dataset for phoneme recognition across multiple languages. The model is designed to process audio input sampled at 16kHz and outputs phonetic labels that can be mapped to words using a phonetic dictionary.
Implementation Details
The model utilizes 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 employs the CTC (Connectionist Temporal Classification) loss function for training and inference.
- Built on wav2vec2-large-xlsr-53 pre-trained model
- Fine-tuned on CommonVoice dataset
- Supports multiple languages through zero-shot cross-lingual transfer
- Requires 16kHz audio input sampling rate
Core Capabilities
- Multilingual phoneme recognition
- Zero-shot cross-lingual transfer learning
- Direct phonetic transcription output
- High-accuracy speech recognition across unseen languages
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 leverages multilingual pretraining and articulatory feature mapping to achieve superior performance compared to previous approaches.
Q: What are the recommended use cases?
The model is ideal for multilingual speech recognition tasks, particularly when dealing with low-resource languages or when phonetic transcription is needed. It's especially useful in scenarios where traditional word-based ASR systems might struggle with unseen languages.
