viwav2vec2-base-3k
| Property | Value |
|---|---|
| Parameter Count | 95M |
| Tensor Type | F32 |
| License | cc-by-sa-4.0 |
| Language | Vietnamese |
| Paper | View Paper |
What is viwav2vec2-base-3k?
viwav2vec2-base-3k is a specialized speech recognition model pre-trained on 3,000 hours of Vietnamese speech data. Built on Facebook's wav2vec2 architecture, this model is specifically designed to process 16kHz sampled speech audio from various sources including spontaneous conversations, reading sessions, and broadcast content.
Implementation Details
The model utilizes the transformer-based wav2vec2 architecture and is implemented using PyTorch. It's distributed in the Safetensors format and requires 16kHz audio input for optimal performance. While the model comes pre-trained, it requires fine-tuning for specific downstream tasks like automatic speech recognition.
- Pre-trained on 3K hours of diverse Vietnamese speech
- Supports 16kHz audio input processing
- Implements the wav2vec2 architecture
- Uses PyTorch framework
Core Capabilities
- Processing raw Vietnamese speech audio
- Feature extraction from audio signals
- Support for various speech types (spontaneous, reading, broadcasting)
- Ready for fine-tuning on downstream tasks
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically trained on Vietnamese speech data, making it particularly effective for Vietnamese speech processing tasks. Its training on 3,000 hours of diverse speech data provides robust feature extraction capabilities for Vietnamese language applications.
Q: What are the recommended use cases?
The model is best suited for Vietnamese speech recognition tasks after proper fine-tuning. It can be used as a foundation for building automatic speech recognition systems, speech analysis tools, and other audio processing applications specifically for Vietnamese language content.
