wav2vec2-dogri-stt
| Property | Value |
|---|---|
| Author | addy88 |
| Model Type | Speech Recognition |
| Framework | Wav2Vec2 + CTC |
| Model URL | Hugging Face |
What is wav2vec2-dogri-stt?
wav2vec2-dogri-stt is a specialized speech recognition model designed specifically for the Dogri language. Built on Facebook's wav2vec2 architecture, this model enables direct speech-to-text transcription without requiring an additional language model. It represents a significant step forward in making automatic speech recognition accessible for the Dogri-speaking community.
Implementation Details
The model leverages the Wav2Vec2ForCTC architecture combined with a specialized processor for handling Dogri audio inputs. It processes audio files through a straightforward pipeline that includes audio loading, preprocessing, and direct transcription using CTC (Connectionist Temporal Classification) decoding.
- Utilizes the Transformers library from Hugging Face
- Implements direct inference without language model dependency
- Supports standard audio input formats through soundfile library
- Features automatic padding and tensor conversion
Core Capabilities
- Direct audio-to-text transcription for Dogri language
- Batch processing support through PyTorch tensors
- Efficient inference with automatic feature extraction
- Skip special tokens functionality for clean transcription output
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically trained for Dogri language speech recognition, making it one of the few available solutions for automated Dogri transcription. Its direct implementation without requiring a separate language model makes it particularly practical for real-world applications.
Q: What are the recommended use cases?
The model is ideal for Dogri speech transcription tasks, including automated subtitling, voice command systems, and speech documentation. It's particularly suitable for applications requiring real-time or batch processing of Dogri audio content.
