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--- |
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library_name: transformers |
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language: |
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- fa |
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license: mit |
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base_model: openai/whisper-large-v3-turbo |
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tags: |
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- whisper |
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- whisper-large-v3 |
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- persian |
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- farsi |
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- speech-recognition |
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- asr |
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- automatic-speech-recognition |
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- audio |
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- transformers |
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- generated_from_trainer |
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- h100 |
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- huggingface |
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- vhdm |
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datasets: |
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- vhdm/persian-voice-v1.1 |
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metrics: |
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- wer |
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model-index: |
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- name: vhdm/whisper-large-fa-v1 |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: vhdm/persian-voice-v1 |
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type: vhdm/persian-voice-v1.1 |
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args: 'config: fa, split: test' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 14.065335753176045 |
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--- |
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# 📢 vhdm/whisper-large-fa-v1 |
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🎧 **Fine-tuned Whisper Large V3 Turbo for Persian Speech Recognition** |
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This model is a fine-tuned version of [`openai/whisper-large-v3-turbo`](https://huggingface.co/openai/whisper-large-v3-turbo) trained specifically on high-quality Persian speech data from the [`vhdm/persian-voice-v1`](https://huggingface.co/datasets/vhdm/persian-voice-v1) dataset. |
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--- |
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## 🧪 Evaluation Results |
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| Metric | Value | |
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|--------|-------| |
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| **Final Validation Loss** | 0.1445 | |
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| **Word Error Rate (WER)** | **14.07%** | |
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The model shows consistent improvement over training and reaches a solid WER of ~14% on clean Persian speech data. |
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## 🧠 Model Description |
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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. |
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--- |
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## ✅ Intended Use |
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This model is best suited for: |
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- 📱 Transcribing Persian voice notes |
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- 🗣️ Real-time or batch ASR for Persian podcasts, videos, and interviews |
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- 🔍 Creating searchable transcripts of Persian audio content |
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- 🧩 Fine-tuning or domain adaptation for Persian speech tasks |
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### 🚫 Limitations |
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- The model is fine-tuned on clean audio from specific sources and may perform poorly on noisy, accented, or dialectal speech. |
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- Not optimized for real-time streaming ASR (though inference is fast). |
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- It may occasionally produce hallucinations (incorrect but plausible words), a common issue in Whisper models. |
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## 📚 Training Data |
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The model was trained on the [`vhdm/persian-voice-v1`](https://huggingface.co/datasets/vhdm/persian-voice-v1) dataset, a curated collection of Persian speech recordings with high-quality transcriptions. |
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--- |
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## ⚙️ Training Procedure |
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- **Optimizer**: AdamW (`betas=(0.9, 0.999)`, `eps=1e-08`) |
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- **Learning Rate**: 1e-5 |
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- **Batch Sizes**: Train - 16 | Eval - 8 |
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- **Scheduler**: Linear with 500 warmup steps |
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- **Mixed Precision**: Native AMP (automatic mixed precision) |
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- **Seed**: 42 |
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- **Training Steps**: 5000 |
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## ⏱️ Training Time & Hardware |
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The model was trained using an **NVIDIA H100 GPU**, and the full fine-tuning process took approximately **20 hours**. |
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## 📈 Training Progress |
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| Step | Training Loss | Validation Loss | WER (%) | |
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|------|----------------|-----------------|----------| |
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| 1000 | 0.2190 | 0.2093 | 22.07 | |
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| 2000 | 0.1191 | 0.1698 | 17.85 | |
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| 3000 | 0.1051 | 0.1485 | 15.79 | |
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| 4000 | 0.0644 | 0.1530 | 16.03 | |
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| 5000 | 0.0289 | 0.1445 | **14.07** | |
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--- |
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## 🧰 Framework Versions |
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- `transformers`: 4.52.4 |
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- `torch`: 2.7.1+cu118 |
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- `datasets`: 3.6.0 |
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- `tokenizers`: 0.21.1 |
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--- |
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## 🚀 Try it out |
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You can load and test the model using 🤗 Transformers: |
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```python |
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from transformers import pipeline |
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pipe = pipeline("automatic-speech-recognition", model="vhdm/whisper-large-fa-v1") |
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result = pipe("path_to_persian_audio.wav") |
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print(result["text"]) |
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