whisper-large-v3-turbo-quantized.w4a16

Model Overview

• Model Architecture: whisper-large-v3-turbo
  • Input: Audio-Text
  • Output: Text
• Model Optimizations:
  • Weight quantization: INT4
• Release Date: 04/16/2025
• Version: 1.0
• Model Developers: Open AI

Quantized version of openai/whisper-large-v3-turbo.

Model Optimizations

This model was obtained by quantizing the weights of openai/whisper-large-v3-turbo to INT4 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-turbo-quantized.w4a16",
    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.

Deploy on Red Hat AI Inference Server

podman run --rm -it --device nvidia.com/gpu=all -p 8000:8000 \
 --ipc=host \
--env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
--env "HF_HUB_OFFLINE=0" -v ~/.cache/vllm:/home/vllm/.cache \
--name=vllm \
registry.access.redhat.com/rhaiis/rh-vllm-cuda \
vllm serve \
--tensor-parallel-size 8 \
--max-model-len 32768  \
--enforce-eager --model RedHatAI/whisper-large-v3-turbo-quantized.w4a16

Deploy on Red Hat Openshift AI

# Setting up vllm server with ServingRuntime
# Save as: vllm-servingruntime.yaml
apiVersion: serving.kserve.io/v1alpha1
kind: ServingRuntime
metadata:
 name: vllm-cuda-runtime # OPTIONAL CHANGE: set a unique name
 annotations:
   openshift.io/display-name: vLLM NVIDIA GPU ServingRuntime for KServe
   opendatahub.io/recommended-accelerators: '["nvidia.com/gpu"]'
 labels:
   opendatahub.io/dashboard: 'true'
spec:
 annotations:
   prometheus.io/port: '8080'
   prometheus.io/path: '/metrics'
 multiModel: false
 supportedModelFormats:
   - autoSelect: true
     name: vLLM
 containers:
   - name: kserve-container
     image: quay.io/modh/vllm:rhoai-2.25-cuda # CHANGE if needed. If AMD: quay.io/modh/vllm:rhoai-2.25-rocm
     command:
       - python
       - -m
       - vllm.entrypoints.openai.api_server
     args:
       - "--port=8080"
       - "--model=/mnt/models"
       - "--served-model-name={{.Name}}"
     env:
       - name: HF_HOME
         value: /tmp/hf_home
     ports:
       - containerPort: 8080
         protocol: TCP

# Attach model to vllm server. This is an NVIDIA template
# Save as: inferenceservice.yaml
apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
  annotations:
    openshift.io/display-name: whisper-large-v3-turbo-quantized.w4a16 # OPTIONAL CHANGE
    serving.kserve.io/deploymentMode: RawDeployment
  name: whisper-large-v3-turbo-quantized.w4a16          # specify model name. This value will be used to invoke the model in the payload
  labels:
    opendatahub.io/dashboard: 'true'
spec:
  predictor:
    maxReplicas: 1
    minReplicas: 1
    model:
      modelFormat:
        name: vLLM
      name: ''
      resources:
        limits:
          cpu: '2'			# this is model specific
          memory: 8Gi		# this is model specific
          nvidia.com/gpu: '1'	# this is accelerator specific
        requests:			# same comment for this block
          cpu: '1'
          memory: 4Gi
          nvidia.com/gpu: '1'
      runtime: vllm-cuda-runtime	# must match the ServingRuntime name above
      storageUri: oci://registry.redhat.io/rhelai1/modelcar-whisper-large-v3-turbo-quantized-w4a16:1.5
    tolerations:
    - effect: NoSchedule
      key: nvidia.com/gpu
      operator: Exists

# make sure first to be in the project where you want to deploy the model
# oc project <project-name>

# apply both resources to run model

# Apply the ServingRuntime
oc apply -f vllm-servingruntime.yaml

# Replace <inference-service-name> and <cluster-ingress-domain> below:
# - Run `oc get inferenceservice` to find your URL if unsure.

# Call the server using curl:
curl https://<inference-service-name>-predictor-default.<domain>/v1/chat/completions
        -H "Content-Type: application/json" \
        -d '{
    "model": "whisper-large-v3-turbo-quantized.w4a16",
    "stream": true,
    "stream_options": {
        "include_usage": true
    },
    "max_tokens": 1,
    "messages": [
        {
            "role": "user",
            "content": "How can a bee fly when its wings are so small?"
        }
    ]
}'

See Red Hat Openshift AI 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-v3-turbo --quant_path "output_dir/whisper-large-v3-turbo-quantized.w4a16" --calib_size 1024 --group_size 64 --dampening_frac 0.01 --actorder weight

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('--actorder', type=str, default="dynamic")
parser.add_argument('--group_size', type=int, default=128)
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",
    config_groups={
        "config_group": QuantizationScheme(
            targets=["Linear"],
            weights=QuantizationArgs(
                num_bits=4,
                type=QuantizationType.INT,
                strategy=QuantizationStrategy.GROUP,
                group_size=group_size,
                symmetric=True,
                dynamic=False,
                actorder=getattr(ActivationOrdering, actorder_arg.upper()),
            ),
        ),
    },
    sequential_targets=["WhisperEncoderLayer", "WhisperDecoderLayer"],
    ignore=["re:.*lm_head"],
    update_size=NUM_CALIBRATION_SAMPLES,
    dampening_frac=dampening_frac
)

# 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.w4a16"
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-v3-turbo-quantized.w4a16" \
    --batch_size 64 \
    --output_path <output_file_path> \
    --tasks librispeech

Fleurs:

lmms-eval \
    --model=whisper_vllm \
    --model_args="pretrained=neuralmagic-ent/whisper-large-v3-turbo-quantized.w4a16" \
    --batch_size 64 \
    --output_path <output_file_path> \
    --tasks fleurs

Benchmark	Split	BF16	W4A16	Recovery (%)
LibriSpeech (WER)	test-clean	2.1876	2.1951	99.66%
	test-other	3.8992	4.0411	96.49%
Fleurs (X→en, WER)	cmn_hans_cn	7.8019	8.3448	93.49%
	en	4.0236	4.0580	99.15
	yue_hant_hk	9.4210	11.8108	97.77%