This is a custom packaged version of OpenAI's Whisper Large V3 model, converted to safetensors format for safer and faster loading. This repo includes both the model weights and tokenizer files required for ASR (Automatic Speech Recognition) tasks.

Files Included

model.safetensors: Model weights in safetensors format
tokenizer_config.json: Tokenizer configuration
vocab.json: Vocabulary file
merges.txt: BPE merges
special_tokens_map.json: Special token mapping

Usage (Python)

python

from transformers import WhisperForConditionalGeneration, WhisperTokenizer

model = WhisperForConditionalGeneration.from_pretrained("Zvatlov/whisper-large-v3")
tokenizer = WhisperTokenizer.from_pretrained("Zvatlov/whisper-large-v3")

Example Inference

python

import torch
from transformers import WhisperProcessor, WhisperForConditionalGeneration

processor = WhisperProcessor.from_pretrained("Zvatlov/whisper-large-v3")
model = WhisperForConditionalGeneration.from_pretrained("Zvatlov/whisper-large-v3")

# Load audio
from datasets import load_dataset
ds = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
input_audio = ds[0]["audio"]["array"]

# Prepare input
inputs = processor(input_audio, return_tensors="pt")
with torch.no_grad():
    generated_ids = model.generate(inputs["input_features"])

# Decode output
transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)
print(transcription[0])

Notes

Tensor type: FP16
Total parameters: ~1.5B
This model is not fine-tuned; original Whisper weights were packaged for safe and fast loading.

suiyaradant/whisper-large-v3

Whisper Large V3 — Safetensors Version

Files Included

Usage (Python)

Example Inference

Notes

suiyaradant/
whisper-large-v3