wav2vec2-large-xlsr-malayalam
| Property | Value |
|---|---|
| Base Model | facebook/wav2vec2-large-xlsr-53 |
| Task | Speech Recognition |
| Language | Malayalam |
| WER | 28.43% |
| Author | gvs |
What is wav2vec2-large-xlsr-malayalam?
This is a specialized speech recognition model fine-tuned specifically for the Malayalam language. It builds upon Facebook's wav2vec2-large-xlsr-53 architecture and has been trained on a comprehensive collection of Malayalam speech datasets including Indic TTS Malayalam Speech Corpus, Openslr Malayalam Speech Corpus, SMC Malayalam Speech Corpus, and IIIT-H Indic Speech Databases.
Implementation Details
The model operates at a 16kHz sampling rate and implements CTC (Connectionist Temporal Classification) for speech recognition. It processes raw audio input and outputs Malayalam text transcriptions without requiring an external language model.
- Built on wav2vec2-large-xlsr-53 architecture
- Trained on multiple high-quality Malayalam speech corpora
- Supports 16kHz audio input
- Direct transcription without language model dependency
Core Capabilities
- Accurate Malayalam speech recognition
- Handles various Malayalam dialects and accents
- Robust performance across multiple datasets
- Easy integration with HuggingFace Transformers library
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Malayalam language speech recognition, trained on a diverse range of Malayalam speech datasets, making it particularly effective for real-world applications in Malayalam-speaking contexts.
Q: What are the recommended use cases?
The model is ideal for Malayalam speech transcription tasks, voice assistants, automated subtitling, and any application requiring Malayalam speech-to-text conversion. It's particularly suitable for applications requiring 16kHz audio processing.
