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This is an ONNX version of vhdm/whisper-large-fa-v1. It was automatically converted and uploaded using this Hugging Face Space.
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Quellenbeschreibung
This is an ONNX version of vhdm/whisper-large-fa-v1. It was automatically converted and uploaded using this Hugging Face Space.
See the pipeline documentation for :
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1 QuelleVerifiziert 22. Sept.
Modellartefakte
2 Artefakteonnx/decoder_model_q4.onnx
onnx · Q4 · 358 MB · SHA-256 532fa9adcbf5…84b5 · Hugging Face
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automatic-speech-recognition🎧 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.
| Metric | Value |
|---|---|
| Final Validation Loss | 0.1445 |
| Word Error Rate (WER) | 14.07% |
The model shows consistent improvement over training and reaches a solid WER of ~14% on clean Persian speech data.
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.
This model is best suited for:
The model was trained on the vhdm/persian-voice-v1 dataset, a curated collection of Persian speech recordings with high-quality transcriptions.
betas=(0.9, 0.999), eps=1e-08)The model was trained using an NVIDIA H100 GPU, and the full fine-tuning process took approximately 20 hours.
| Step | Training Loss | Validation Loss | WER (%) |
|---|---|---|---|
| 1000 | 0.2190 | 0.2093 | 22.07 |
| 2000 | 0.1191 | 0.1698 | 17.85 |
| 3000 | 0.1051 | 0.1485 | 15.79 |
| 4000 | 0.0644 | 0.1530 | 16.03 |
| 5000 | 0.0289 | 0.1445 | 14.07 |
transformers: 4.52.4torch: 2.7.1+cu118datasets: 3.6.0tokenizers: 0.21.1You 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"])
--- library_name: transformers.js language: - fa license: mit base_model: - vhdm/whisper-large-fa-v1 tags: - whisper - whisper-large-v3 - persian - farsi - speech-recognition - asr - automatic-speech-recognition - audio - transformers - generated_from_trainer - h100 - huggingface - vhdm datasets: - vhdm/persian-voice-v1.1 metrics: - wer model-index: - name: vhdm/whisper-large-fa-v1 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: vhdm/persian-voice-v1 type: vhdm/persian-voice-v1.1 args: 'config: fa, split: test' metrics: - name: Wer type: wer value: 14.065335753176045 pipeline_tag: automatic-speech-recognition --- # whisper-large-fa-v1 (ONNX) This is an ONNX version of [vhdm/whisper-large-fa-v1](https://huggingface.co/vhdm/whisper-large-fa-v1). It was automatically converted and uploaded using [this Hugging Face Space](https://huggingface.co/spaces/onnx-community/convert-to-onnx). ## 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`](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. --- ## 🧪 Evaluation Results | Metric | Value | |--------|-------| | **Final Validation Loss** | 0.1445 | | **Word Error Rate (WER)** | **14.07%** | 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 dom...
Source context: 19 downloads · 0 likes · Pipeline automatic-speech-recognition · Library transformers.js · Repo onnx-community/whisper-large-fa-v1-ONNX