MMS-300M-1130 Forced Aligner
| Property | Value |
|---|---|
| Parameter Count | 315M |
| License | CC-BY-NC-4.0 |
| Tensor Type | F32 |
| Language Support | 158 languages |
What is mms-300m-1130-forced-aligner?
This model is a sophisticated forced alignment tool that synchronizes text with audio across 158 languages. It's a conversion from TorchAudio to HF Transformers of the MMS-300M checkpoint, specifically trained for forced alignment tasks. The model implements an efficient memory-optimized approach compared to traditional TorchAudio forced alignment APIs.
Implementation Details
The model utilizes PyTorch and Transformers architecture, implementing wav2vec2 technology for audio processing. It features a memory-efficient implementation that processes audio in batches and generates precise alignments between text and speech.
- Supports batch processing with customizable batch sizes
- Compatible with both CPU and GPU (CUDA) environments
- Implements efficient memory management techniques
- Provides romanization support for text preprocessing
Core Capabilities
- Multi-language forced alignment across 158 languages
- Efficient emission generation for audio processing
- Text preprocessing with romanization support
- Precise timestamp generation for word-level alignment
- Flexible deployment options with CPU/GPU support
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its extensive language support (158 languages) and memory-efficient implementation. It's particularly valuable for projects requiring precise audio-text alignment across multiple languages while maintaining reasonable computational requirements.
Q: What are the recommended use cases?
The model is ideal for applications such as subtitle generation, speech recognition verification, language learning materials creation, and any scenario requiring precise synchronization between text and audio across multiple languages.
