Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Persian
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use nezamisafa/whisper-persian-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nezamisafa/whisper-persian-v4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="nezamisafa/whisper-persian-v4")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("nezamisafa/whisper-persian-v4") model = AutoModelForSpeechSeq2Seq.from_pretrained("nezamisafa/whisper-persian-v4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 06e1512cdde7cafd50e0fb0ee65659b7557606ae84e1e576d629e4839dc83089
- Size of remote file:
- 1.18 GB
- SHA256:
- 1e393d0f267d0c11166aeeb5fd2eb1b6aa17003f9112d7e031c3872ab3198715
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.