Wav2Vec2-large-xlsr-hindi
| Property | Value |
|---|---|
| Parameter Count | 316M |
| Model Type | Speech Recognition |
| Framework | PyTorch |
| Tensor Type | F32 |
| Downloads | 450,907 |
What is Wav2Vec2-large-xlsr-hindi?
Wav2Vec2-large-xlsr-hindi is a specialized speech recognition model fine-tuned for the Hindi language, based on Facebook's wav2vec2-large-xlsr-53 architecture. This model has been specifically adapted using the Multilingual and code-switching ASR challenges dataset for low resource Indian languages, making it particularly effective for Hindi speech recognition tasks.
Implementation Details
The model operates on 16kHz audio input and utilizes the powerful Wav2Vec2 architecture with 316M parameters. It implements CTC (Connectionist Temporal Classification) for speech recognition and can be deployed without requiring an additional language model. The model achieved a Word Error Rate (WER) of 72.62% on the Common Voice Hindi test set.
- Requires 16kHz audio input sampling rate
- Built on PyTorch framework
- Implements CTC-based speech recognition
- Supports batch processing for efficient inference
Core Capabilities
- Direct speech-to-text transcription for Hindi audio
- Batch processing support for multiple audio files
- Integration with popular audio processing libraries like torchaudio
- Compatible with Hugging Face's transformers library
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Hindi speech recognition, leveraging the powerful XLSR-53 architecture while being fine-tuned on Indian language datasets. Its integration with popular frameworks makes it easily accessible for production deployment.
Q: What are the recommended use cases?
The model is ideal for Hindi speech transcription tasks, automated subtitling, voice command systems, and any application requiring Hindi speech-to-text conversion. It's particularly suitable for scenarios where 16kHz audio input can be guaranteed.
