whisper-large-fa-v1-ONNX

whisper-large-fa-v1 (ONNX)#

This is an ONNX version of vhdm/whisper-large-fa-v1. It was automatically converted and uploaded using this Hugging Face Space.

Usage with Transformers.js#

See the pipeline documentation for automatic-speech-recognition: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.AutomaticSpeechRecognitionPipeline

📢 vhdm/whisper-large-fa-v1#

🎧 Fine-tuned Whisper Large V3 Turbo for Persian Speech Recognition

This model is a fine-tuned version of openai/whisper-large-v3-turbo trained specifically on high-quality Persian speech data from the vhdm/persian-voice-v1 dataset.

🧪 Evaluation Results#

The model shows consistent improvement over training and reaches a solid WER of ~14% on clean Persian speech data.

🧠 Model Description#

This model aims to bring high-accuracy automatic speech recognition (ASR) to Persian language using the Whisper architecture. By leveraging OpenAI's powerful Whisper Large V3 Turbo backbone and carefully curated Persian data, it can transcribe Persian audio with high fidelity.

✅ Intended Use#

This model is best suited for:

• 📱 Transcribing Persian voice notes
• 🗣️ Real-time or batch ASR for Persian podcasts, videos, and interviews
• 🔍 Creating searchable transcripts of Persian audio content
• 🧩 Fine-tuning or domain adaptation for Persian speech tasks

🚫 Limitations#

• The model is fine-tuned on clean audio from specific sources and may perform poorly on noisy, accented, or dialectal speech.
• Not optimized for real-time streaming ASR (though inference is fast).
• It may occasionally produce hallucinations (incorrect but plausible words), a common issue in Whisper models.

📚 Training Data#

The model was trained on the vhdm/persian-voice-v1 dataset, a curated collection of Persian speech recordings with high-quality transcriptions.

⚙️ Training Procedure#

• Optimizer: AdamW (betas=(0.9, 0.999), eps=1e-08)
• Learning Rate: 1e-5
• Batch Sizes: Train - 16 | Eval - 8
• Scheduler: Linear with 500 warmup steps
• Mixed Precision: Native AMP (automatic mixed precision)
• Seed: 42
• Training Steps: 5000

⏱️ Training Time & Hardware#

The model was trained using an NVIDIA H100 GPU, and the full fine-tuning process took approximately 20 hours.

📈 Training Progress#

🧰 Framework Versions#

• transformers: 4.52.4
• torch: 2.7.1+cu118
• datasets: 3.6.0
• tokenizers: 0.21.1

🚀 Try it out#

You can load and test the model using 🤗 Transformers:

from transformers import pipeline

pipe = pipeline("automatic-speech-recognition", model="vhdm/whisper-large-fa-v1")
result = pipe("path_to_persian_audio.wav")
print(result["text"])