xm_transformer_s2ut_hk-en
| Property | Value |
|---|---|
| License | CC-BY-NC-4.0 |
| Framework | Fairseq |
| Task Type | Speech-to-Speech Translation |
| Primary Languages | Hokkien to English |
What is xm_transformer_s2ut_hk-en?
xm_transformer_s2ut_hk-en is a sophisticated speech-to-speech translation model designed specifically for converting Hokkien speech to English. It employs a single-pass decoder (S2UT) architecture and is built on Facebook's fairseq framework. The model has been trained on a diverse dataset including TED talks, drama content, and the TAT corpus, incorporating both supervised and weakly supervised learning approaches.
Implementation Details
The model implements a transformer-based architecture with direct speech-to-speech translation capabilities. It integrates with the Unit HiFiGAN vocoder for high-quality speech synthesis and requires 16000Hz mono channel audio input. The implementation includes sophisticated processing pipelines for both speech recognition and synthesis stages.
- Built on the fairseq framework with transformer architecture
- Utilizes single-pass decoder (S2UT) technology
- Integrates with Unit HiFiGAN vocoder for speech synthesis
- Supports multiple domains including TED talks and drama content
Core Capabilities
- Direct Hokkien to English speech translation
- High-quality speech synthesis using Unit HiFiGAN
- Support for multiple domain adaptations
- Handles both supervised and weakly supervised training data
- Compatible with 16kHz mono audio input
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its ability to perform direct speech-to-speech translation between Hokkien and English, utilizing a single-pass decoder architecture. It's particularly notable for incorporating both supervised and weakly supervised training data from various domains.
Q: What are the recommended use cases?
The model is ideal for applications requiring Hokkien to English speech translation, particularly in contexts similar to TED talks and dramatic content. It's well-suited for applications in educational settings, media translation, and cross-cultural communication tools.
