DevMehdip
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whisper-small-fa-lora

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 base_model: openai/whisper-small
 library_name: peft
 tags:
-- base_model:adapter:openai/whisper-small
 - lora
 - transformers
 ---
-# Model Card for Model ID
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-## Model Details
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-### Framework versions
-- PEFT 0.17.1

 base_model: openai/whisper-small
 library_name: peft
 tags:
+- whisper
 - lora
 - transformers
+- speech-to-text
+- persian
+- stt
+- fine-tune
+- adapter
+datasets:
+- vhdm/persian-voice-v1
+language:
+- fa
+metrics:
+- cer
+- wer
 ---
+# Whisper-Small Persian STT — LoRA Fine-Tuned
+A fine-tuned version of **openai/whisper-small** for **Persian speech-to-text (ASR)** using **LoRA**.
+This model is optimized for persian conversational speech and dataset-quality audio.
+---
+## Model Details
+### Model Description
+This model is a **LoRA fine-tuned Whisper Small** focused on **Persian (fa)** speech recognition.
+It improves transcription accuracy on standard Persian audio segments (16kHz, mono, normalized WAV).
+- **Developed by:** *Mehdi Pouladrag*
+- **Model type:** Speech-to-Text (ASR) — Whisper Small (Seq2Seq Transformer)
+- **Language(s):** Persian (fa)
+- **License:** MIT (or your preferred license)
+- **Finetuned from:** `openai/whisper-small`
+- **Dataset:** `persian-voice-v1` (single dataset)
+- **Training technique:** LoRA (Low-Rank Adaptation)
+### Model Sources
+- **Repository:** https://github.com/Mehdipoladrag/Fine-tuning-Whisper-Model
+---
+## Uses
+### Direct Use
+- Convert Persian speech to text
+- Subtitle generation for Persian audio
+- Conversational ASR
+- Podcast / video transcription
+- General Persian content recognition
+### Downstream Use
+- Integrate into ASR pipelines
+- Use in real-time Persian voice applications
+- Further fine-tuning on custom Persian domains (medical, legal, etc.)
+### Out-of-Scope Use
+- Non-Persian audio
+- Low-quality/noisy multi-speaker overlapping speech
+- Misuse for surveillance or unethical monitoring
+---
+## Bias, Risks, and Limitations
+- Whisper may still struggle with dialect-heavy, noisy, or low-quality audio.
+- The dataset used is relatively limited (~6099 audio–subtitle pairs), so:
+  - Certain accents may be underrepresented.
+  - Model may hallucinate or mis-transcribe in rare cases.
+### Recommendations
+Users should:
+- Provide clean 16kHz mono WAV audio
+- Use domain-specific fine-tuning if necessary
+- Validate outputs before critical use
+---
+## How to Get Started
+```python
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+from peft import PeftModel
+processor = WhisperProcessor.from_pretrained("Mehdipoladrag/REPO_NAME")
+model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
+# Load LoRA adapter
+model = PeftModel.from_pretrained(model, "Mehdipoladrag/REPO_NAME")
+audio = ...  # your 16kHz mono waveform
+inputs = processor(audio, sampling_rate=16000, return_tensors="pt")
+pred_ids = model.generate(inputs["input_features"])
+text = processor.batch_decode(pred_ids, skip_special_tokens=True)[0]
+print(text)