whisper-large-v2-quantized.w8a8

Model Overview

• Model Architecture: whisper-large-v2
  • Input: Audio-Text
  • Output: Text
• Model Optimizations:
  • Weight quantization: INT8
  • Activation quantization: INT8
• Release Date: 04/16/2025
• Version: 1.0
• Model Developers: Neural Magic

Quantized version of openai/whisper-large-v2.

Model Optimizations

This model was obtained by quantizing the weights of openai/whisper-large-v2 to INT8 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-v2-quantized.w8a8",
    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.

Model Creation Code

python quantize.py --model_path openai/whisper-large-v2 --quant_path "output_dir/whisper-large-v2-quantized.w8a8" --calib_size 1024 --dampening_frac 0.01

import torch
import argparse
from datasets import load_dataset
from transformers import WhisperProcessor
from llmcompressor import oneshot
from llmcompressor.modifiers.quantization import GPTQModifier
from llmcompressor.transformers.tracing import TraceableWhisperForConditionalGeneration
import os
from compressed_tensors.quantization import QuantizationArgs, QuantizationType, QuantizationStrategy, ActivationOrdering, QuantizationScheme
from llmcompressor.modifiers.smoothquant import SmoothQuantModifier

parser = argparse.ArgumentParser()
parser.add_argument('--model_path', type=str)
parser.add_argument('--quant_path', type=str)
parser.add_argument('--calib_size', type=int, default=256)
parser.add_argument('--dampening_frac', type=float, default=0.1) 
parser.add_argument('--observer', type=str, default="minmax")
parser.add_argument('--save_dir', type=str, required=True)


args = parser.parse_args()
model_id = args.model_path

model = TraceableWhisperForConditionalGeneration.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="auto",
)
model.config.forced_decoder_ids = None
processor = WhisperProcessor.from_pretrained(model_id)

# Configure processor the dataset task.
processor.tokenizer.set_prefix_tokens(language="en", task="transcribe")

# Select calibration dataset.
DATASET_ID = "MLCommons/peoples_speech"
DATASET_SUBSET = "test"
DATASET_SPLIT = "test"

# Select number of samples for calibration. 512 samples is a good place to start.
# Increasing the number of samples can improve accuracy.

NUM_CALIBRATION_SAMPLES = args.calib_size
MAX_SEQUENCE_LENGTH = 2048
dampening_frac=args.dampening_frac
actorder_arg=args.actorder
group_size=args.group_size

# Load dataset and preprocess.
ds = load_dataset(
    DATASET_ID,
    DATASET_SUBSET,
    split=f"{DATASET_SPLIT}[:{NUM_CALIBRATION_SAMPLES}]",
    trust_remote_code=True,
)

def preprocess(example):
    return {
        "array": example["audio"]["array"],
        "sampling_rate": example["audio"]["sampling_rate"],
        "text": " " + example["text"].capitalize(),
    }

ds = ds.map(preprocess, remove_columns=ds.column_names)

# Process inputs.
def process(sample):
    inputs = processor(
        audio=sample["array"],
        sampling_rate=sample["sampling_rate"],
        text=sample["text"],
        add_special_tokens=True,
        return_tensors="pt",
    )

    inputs["input_features"] = inputs["input_features"].to(dtype=model.dtype)
    inputs["decoder_input_ids"] = inputs["labels"]
    del inputs["labels"]

    return inputs

ds = ds.map(process, remove_columns=ds.column_names)

# Define a oneshot data collator for multimodal inputs.
def data_collator(batch):
    assert len(batch) == 1
    return {key: torch.tensor(value) for key, value in batch[0].items()}

ignore=["lm_head"]

#Recipe
recipe = [
    GPTQModifier(
        targets="Linear",
        scheme="W8A8",
        sequential_targets=["WhisperEncoderLayer", "WhisperDecoderLayer"],
        ignore=ignore,
    )
]

# Apply algorithms.
oneshot(
    model=model,
    dataset=ds,
    recipe=recipe,
    max_seq_length=MAX_SEQUENCE_LENGTH,
    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
    data_collator=data_collator,
)


# Save to disk compressed.
save_name = f"{model_id.split('/')[-1]}-quantized.w8a8"
save_path = os.path.join(args.save_dir, save_name)
print("Saving model:", save_path)
model.save_pretrained(save_path, save_compressed=True)
processor.save_pretrained(save_path)

Evaluation

The model was evaluated on LibriSpeech and Fleurs datasets using lmms-eval, via the following commands:

Evaluation Commands

Librispeech:

lmms-eval \
    --model=whisper_vllm \
    --model_args="pretrained=neuralmagic-ent/whisper-large-v2-quantized.w8a8" \
    --batch_size 64 \
    --output_path <output_file_path> \
    --tasks librispeech

Fleurs:

lmms-eval \
    --model=whisper_vllm \
    --model_args="pretrained=neuralmagic-ent/whisper-large-v2-quantized.w8a8" \
    --batch_size 64 \
    --output_path <output_file_path> \
    --tasks fleurs

Accuracy

Benchmark	Split	BF16	w8a8	Recovery (%)
LibriSpeech (WER)	test-clean	3.1437	3.1343	100.30%
	test-other	5.2362	5.2021	100.66%
Fleurs (X→en, WER)	cmn_hans_cn	15.2148	15.4498	98.48%
	en	4.0717	4.0717	100.00%
	yue_hant_hk	8.5106	8.3830	101.52%