chinese-hubert-base
| Property | Value |
|---|---|
| License | MIT |
| Author | TencentGameMate |
| Downloads | 4,450 |
| Framework | PyTorch |
What is chinese-hubert-base?
chinese-hubert-base is a specialized speech processing model pretrained on the WenetSpeech L subset, comprising 10,000 hours of Chinese speech data. Developed by TencentGameMate, this model implements the HuBERT architecture for speech feature extraction and processing, specifically optimized for Chinese language audio.
Implementation Details
The model is built using the Transformers library (version 4.16.2) and PyTorch framework. It utilizes the Wav2Vec2FeatureExtractor for processing input audio and the HubertModel architecture for feature extraction. The model operates directly on raw audio input without requiring a tokenizer, as it was pretrained solely on audio data.
- Supports half-precision (FP16) inference for improved performance
- Processes raw audio input using Wav2Vec2FeatureExtractor
- Returns hidden state representations of audio features
- Compatible with PyTorch ecosystem
Core Capabilities
- Audio feature extraction from raw waveforms
- Specialized processing of Chinese speech
- Support for downstream speech recognition tasks after fine-tuning
- Efficient inference with half-precision support
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically pretrained on a large-scale Chinese speech dataset (WenetSpeech), making it particularly effective for Chinese speech processing tasks. Its architecture is based on the proven HuBERT approach, optimized for feature extraction from audio signals.
Q: What are the recommended use cases?
The model is best suited for speech feature extraction tasks and can be fine-tuned for speech recognition applications. It requires additional training with a tokenizer for text-based tasks, making it ideal for researchers and developers working on Chinese speech processing applications.
