Whisper Large Onnx Int4 Inc

🚀 INT4 Whisper large ONNX Model

Whisper is a pre - trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models can generalize well to many datasets and domains without fine - tuning. This repository contains the INT4 weight - only quantization for the Whisper large model in ONNX format, powered by Intel® Neural Compressor and Intel® Extension for Transformers.

This INT4 ONNX model is generated by the weight - only quantization method of Intel® Neural Compressor.

📚 Documentation

Model Details

Intended Use

📦 Installation

Export to ONNX Model

The FP32 model is exported with openai/whisper - large:

optimum-cli export onnx --model openai/whisper-large whisper-large-with-past/ --task automatic-speech-recognition-with-past --opset 13

Install ONNX Runtime

Install onnxruntime>=1.16.0 to support MatMulFpQ4 operator.

💻 Usage Examples

Run Quantization

Build Intel® Neural Compressor from master branch and run INT4 weight - only quantization.

The weight - only quantization configuration is as below:

We provide the key code below. For the complete script, please refer to whisper example.

from neural_compressor import quantization, PostTrainingQuantConfig
from neural_compressor.utils.constant import FP32

model_list = ['encoder_model.onnx', 'decoder_model.onnx', 'decoder_with_past_model.onnx']
for model in model_list:
    config = PostTrainingQuantConfig(
        approach="weight_only",
        calibration_sampling_size=[8],
        op_type_dict={".*": {"weight": {"bits": 4, 
                                        "algorithm": ["RTN"], 
                                        "scheme": ["sym"], 
                                        "group_size": 32}}},)
    q_model = quantization.fit(
        os.path.join("/path/to/whisper-large-with-past", model), # FP32 model path
        config,
        calib_dataloader=dataloader)
    q_model.save(os.path.join("/path/to/whisper-large-onnx-int4", model)) # INT4 model path

Evaluation

Operator Statistics

Below shows the operator statistics in the INT4 ONNX model:

Evaluation of wer

Evaluate the model on librispeech_asr dataset with below code:

import os
from evaluate import load
from datasets import load_dataset
from transformers import WhisperForConditionalGeneration, WhisperProcessor, AutoConfig
model_name = 'openai/whisper-large'
model_path = 'whisper-large-onnx-int4'
processor = WhisperProcessor.from_pretrained(model_name)
model = WhisperForConditionalGeneration.from_pretrained(model_name)
config = AutoConfig.from_pretrained(model_name)
wer = load("wer")
librispeech_test_clean = load_dataset("librispeech_asr", "clean", split="test")

from optimum.onnxruntime import ORTModelForSpeechSeq2Seq
from transformers import PretrainedConfig
model_config = PretrainedConfig.from_pretrained(model_name)
predictions = []
references = []
sessions = ORTModelForSpeechSeq2Seq.load_model(
            os.path.join(model_path, 'encoder_model.onnx'),
            os.path.join(model_path, 'decoder_model.onnx'),
            os.path.join(model_path, 'decoder_with_past_model.onnx'))
model = ORTModelForSpeechSeq2Seq(sessions[0], sessions[1], model_config, model_path, sessions[2])
for idx, batch in enumerate(librispeech_test_clean):
    audio = batch["audio"]
    input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features
    reference = processor.tokenizer._normalize(batch['text'])
    references.append(reference)
    predicted_ids = model.generate(input_features)[0]
    transcription = processor.decode(predicted_ids)
    prediction = processor.tokenizer._normalize(transcription)
    predictions.append(prediction)
wer_result = wer.compute(references=references, predictions=predictions)
print(f"Result wer: {wer_result * 100}")

📊 Metrics (Model Performance)

📄 License

This model is licensed under the Apache 2.0 license.