Quds-v4-sherpa-onnx

🚨 Non Commercial Usage Only

💡 Fine-tuned specifically for the domain of Islamic lectures and specialized Howzah courses, such as Tafsir, Fiqh, Usul, and Rijal.

🔔 FOR NeMo version (includes both rnnt and ctc decoders and word timestamps) send a request in Community

Model Details

• Model Name: Quds-v4-sherpa-onnx
• Model Type: Automatic Speech Recognition (ASR) / Speech-to-Text
• Architecture: NeMo FastConformer Large Hybrid (RNN-T variant)
• Format: ONNX (Open Neural Network Exchange)
• Language: Persian (Farsi)

Model Description

Quds-v4-sherpa-onnx is a highly accurate Persian Automatic Speech Recognition model. It is based on the robust NeMo FastConformer Large Hybrid architecture and is specifically exported in the ONNX format to enable fast, cross-platform inference. This release features an RNN-T (Recurrent Neural Network Transducer) decoder.

Performance & Accuracy

• The model achieves very high accuracy on standard, formal Persian speech.

Training Details

• Training Dataset: The model was trained on a high-quality, non-public (private) dataset.
• Dataset Size: 600 hours of audio data.

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Usage

This is sherpa version of model: Usage Docs.

For using without sherpa, check this model.

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Limitations

1. Dialect Sensitivity: Due to training on standard Persian audio, the model struggles with heavy regional accents, colloquialisms, and non-standard dialects of Persian.
2. Audio Quality Sensitivity: As with most ASR models, performance degrades in highly noisy environments.
3. Domain Specificity: The model may not be perfectly suited for highly specialized technical or medical domains.