whisper large v3 FP8 dynamic EU AI Act Compliance Profile

whisper-large-v3-FP8-Dynamic

Model Overview

• Model Architecture: whisper-large-v3
  • Input: Audio-Text
  • Output: Text
• Model Optimizations:
  • Weight quantization: FP8
  • Activation quantization: FP8
• Release Date: 04/16/2025
• Version: 1.0
• Model Developers: Neural Magic

Quantized version of openai/whisper-large-v3.

Model Optimizations

This model was obtained by quantizing the weights of openai/whisper-large-v3 to FP8 data type, ready for inference with vLLM >= 0.5.2.

Deployment

Use with vLLM

This model can be deployed efficiently using the vLLM backend, as shown in the example below.

from vllm.assets.audio import AudioAsset
from vllm import LLM, SamplingParams

# prepare model
llm = LLM(
    model="neuralmagic/whisper-large-v3-FP8-Dynamic",
    max_model_len=448,
    max_num_seqs=400,
    limit_mm_per_prompt={"audio": 1},
)

# prepare inputs
inputs = {  # Test explicit encoder/decoder prompt
    "encoder_prompt": {
        "prompt": "",
        "multi_modal_data": {
            "audio": AudioAsset("winning_call").audio_and_sample_rate,
        },
    },
    "decoder_prompt": "<|startoftranscript|>",
}

# generate response
print("========== SAMPLE GENERATION ==============")
outputs = llm.generate(inputs, SamplingParams(temperature=0.0, max_tokens=64))
print(f"PROMPT  : {outputs[0].prompt}")
print(f"RESPONSE: {outputs[0].outputs[0].text}")
print("==========================================")

vLLM also supports OpenAI-compatible serving. See the documentation for more details.

Creation

This model was created with llm-compressor by running the code snippet below.

python quantize.py \
    --model_path ope

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