Quantize Speech Recognition Models using NNCF PTQ API

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Google Colab Github

This tutorial demonstrates how to use the NNCF (Neural Network Compression Framework) 8-bit quantization in post-training mode (without the fine-tuning pipeline) to optimize the speech recognition model, known as Data2Vec for the high-speed inference via OpenVINO™ Toolkit. This notebook uses a fine-tuned data2vec-audio-base-960h PyTorch model trained on the LibriSpeech ASR corpus. The tutorial is designed to be extendable to custom models and datasets. It consists of the following steps:

  • Download and prepare model.

  • Define data loading and accuracy validation functionality.

  • Prepare the model for quantization and quantize.

  • Compare performance of the original and quantized models.

  • Compare Accuracy of the Original and Quantized Models.

Table of contents:

Download and prepare model

data2vec is a framework for self-supervised representation learning for images, speech, and text as described in data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language (Baevski et al., 2022). The algorithm uses the same learning mechanism for different modalities.

pre-trained pipeline

pre-trained pipeline

In our case, we will use data2vec-audio-base-960h model, which was finetuned on 960 hours of audio from LibriSpeech Automatic Speech Recognition corpus and distributed as part of HuggingFace transformers.

Obtain Pytorch model representation

For instantiating PyTorch model class, we should use Data2VecAudioForCTC.from_pretrained method with providing model ID for downloading from HuggingFace hub. Model weights and configuration files will be downloaded automatically in first time usage. Keep in mind that downloading the files can take several minutes and depends on your internet connection.

Additionally, we can create processor class which is responsible for model specific pre- and post-processing steps.

!pip install -q "openvino==2023.1.0.dev20230811" "nncf>=2.5.0"
!pip install -q datasets "torchmetrics>=0.11.0"
!pip install -q soundfile librosa transformers onnx

from transformers import Wav2Vec2Processor, Data2VecAudioForCTC

processor = Wav2Vec2Processor.from_pretrained("facebook/data2vec-audio-base-960h")
model = Data2VecAudioForCTC.from_pretrained("facebook/data2vec-audio-base-960h")

Convert model to OpenVINO Intermediate Representation

from pathlib import Path
# Set model directory
MODEL_DIR = Path("model")
MODEL_DIR.mkdir(exist_ok=True)

import openvino as ov
import torch

core = ov.Core()

BATCH_SIZE = 1
MAX_SEQ_LENGTH = 30480


def export_model_to_onnx(model, path):
    # switch model to evaluation mode
    model.eval()
    # disallow gradient propagation for reducing memory during export
    with torch.no_grad():
        # define dummy input with specific shape
        default_input = torch.zeros([1, MAX_SEQ_LENGTH], dtype=torch.float)
        inputs = {
            "inputs": default_input
        }

        # define names for dynamic dimentions
        symbolic_names = {0: "batch_size", 1: "sequence_len"}
        # export model
        torch.onnx.export(
            model,
            (inputs["inputs"]),
            path,
            opset_version=11,
            input_names=["inputs"],
            output_names=["logits"],
            dynamic_axes={
                "inputs": symbolic_names,
                "logits": symbolic_names,
            },
        )
        print("ONNX model saved to {}".format(path))


onnx_model_path = MODEL_DIR / "data2vec-audo-base.onnx"
ir_model_path = onnx_model_path.with_suffix('.xml')

if not ir_model_path.exists():
    if not onnx_model_path.exists():
        export_model_to_onnx(model, onnx_model_path)
    ov_model = ov.convert_model(onnx_model_path)
    ov.save_model(ov_model, str(ir_model_path))
    print("IR model saved to {}".format(ir_model_path))
else:
    print("Read IR model from {}".format(ir_model_path))
    ov_model = core.read_model(ir_model_path)

Read IR model from model/data2vec-audo-base.xml

Prepare inference data

For demonstration purposes, we will use short dummy version of LibriSpeech dataset - patrickvonplaten/librispeech_asr_dummy to speed up model evaluation. Model accuracy can be different from reported in the paper. For reproducing original accuracy, use librispeech_asr dataset.

from datasets import load_dataset

ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")


# define preprocessing function for converting audio to input values for model
def map_to_input(batch):
    preprocessed_signal = processor(batch["audio"]["array"], return_tensors="pt", padding="longest", sampling_rate=batch['audio']['sampling_rate'])
    input_values = preprocessed_signal.input_values
    batch['input_values'] = input_values
    return batch


# apply preprocessing function to dataset and remove audio column, to save memory as we do not need it anymore
dataset = ds.map(map_to_input, batched=False, remove_columns=["audio"])

test_sample = ds[0]["audio"]

Check model inference result

The code below is used for running model inference on a single sample from the dataset. It contains the following steps:

  • Get the input_values tensor as model input.

  • Run model inference and obtain logits.

  • Find logits ids with highest probability, using argmax.

  • Decode predicted token ids, using processor.

For reference, see the same function provided for OpenVINO model.

import numpy as np


# inference function for pytorch
def torch_infer(model, sample):
    logits = model(torch.Tensor(sample['input_values'])).logits
    # take argmax and decode
    predicted_ids = torch.argmax(logits, dim=-1)
    transcription = processor.batch_decode(predicted_ids)
    return transcription


# inference function for openvino
def ov_infer(model, sample):
    output = model.output(0)
    logits = model(np.array(sample['input_values']))[output]
    predicted_ids = np.argmax(logits, axis=-1)
    transcription = processor.batch_decode(torch.from_numpy(predicted_ids))
    return transcription

core = ov.Core()

pt_transcription = torch_infer(model, dataset[0])
compiled_model = core.compile_model(ov_model)
ov_transcription = ov_infer(compiled_model, dataset[0])

import IPython.display as ipd

print(f"[Reference]:     {dataset[0]['text']}")
print(f"[PyTorch]:       {pt_transcription[0]}")
print(f"[OpenVINO FP16]: {ov_transcription[0]}")
ipd.Audio(test_sample["array"], rate=16000)

[Reference]:     MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL
[PyTorch]:       MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL
[OpenVINO FP16]: MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL

Validate model accuracy on dataset

For model accuracy evaluation, Word Error Rate metric can be used. Word Error Rate or WER is the ratio of errors in a transcript to the total words spoken. A lower WER in speech-to-text means better accuracy in recognizing speech.

For WER calculation, we will use `torchmetrics <https://torchmetrics.readthedocs.io/en/stable/text/word_error_rate.html>`__ library.

from torchmetrics import WordErrorRate
from tqdm.notebook import tqdm


def compute_wer(dataset, model, infer_fn):
    wer = WordErrorRate()
    for sample in tqdm(dataset):
        # run infer function on sample
        transcription = infer_fn(model, sample)
        # update metric on sample result
        wer.update(transcription, [sample['text']])
    # finalize metric calculation
    result = wer.compute()
    return result

pt_result = compute_wer(dataset, model, torch_infer)
ov_result = compute_wer(dataset, compiled_model, ov_infer)

0%|          | 0/73 [00:00<?, ?it/s]

0%|          | 0/73 [00:00<?, ?it/s]

print(f'[PyTorch]   Word Error Rate: {pt_result:.4f}')
print(f'[OpenVino]  Word Error Rate: {ov_result:.4f}')

[PyTorch]   Word Error Rate: 0.0383
[OpenVino]  Word Error Rate: 0.0383

Quantization

NNCF provides a suite of advanced algorithms for Neural Networks inference optimization in OpenVINO with minimal accuracy drop.

Create a quantized model from the pre-trained FP16 model and the calibration dataset. The optimization process contains the following steps:

1. Create a Dataset for quantization.
2. Run `nncf.quantize` for getting an optimized model. The `nncf.quantize` function provides an interface for model quantization. It requires an instance of the OpenVINO Model and quantization dataset. Optionally, some additional parameters for the configuration quantization process (number of samples for quantization, preset, ignored scope, etc.) can be provided. For more accurate results, we should keep the operation in the postprocessing subgraph in floating point precision, using the `ignored_scope` parameter. `advanced_parameters` can be used to specify advanced quantization parameters for fine-tuning the quantization algorithm. In this tutorial we pass range estimator parameters for activations. For more information see [Tune quantization parameters](https://docs.openvino.ai/2023.0/basic_quantization_flow.html#tune-quantization-parameters).
3. Serialize OpenVINO IR model using `ov.save_model` function.

import nncf
from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters
from nncf.quantization.range_estimator import AggregatorType
from nncf.quantization.range_estimator import RangeEstimatorParameters
from nncf.quantization.range_estimator import StatisticsCollectorParameters
from nncf.quantization.range_estimator import StatisticsType
from nncf.parameters import ModelType


def transform_fn(data_item):
    """
    Extract the model's input from the data item.
    The data item here is the data item that is returned from the data source per iteration.
    This function should be passed when the data item cannot be used as model's input.
    """
    return np.array(data_item["input_values"])


calibration_dataset = nncf.Dataset(dataset, transform_fn)


quantized_model = nncf.quantize(
    ov_model,
    calibration_dataset,
    model_type=ModelType.TRANSFORMER,  # specify additional transformer patterns in the model
    subset_size=len(dataset),
    ignored_scope=nncf.IgnoredScope(
        names=[
            "/data2vec_audio/encoder/layers.3/feed_forward/intermediate_dense/MatMul",
            "/data2vec_audio/feature_extractor/conv_layers.2/conv/Conv",
            "/data2vec_audio/encoder/layers.3/Add_1",
            "/data2vec_audio/encoder/layers.2/feed_forward/intermediate_dense/MatMul",
            "/data2vec_audio/feature_extractor/conv_layers.0/conv/Conv",
            "/data2vec_audio/encoder/layers.4/Add_1",
            "/data2vec_audio/encoder/layers.4/feed_forward/intermediate_dense/MatMul",
            "/data2vec_audio/encoder/layers.4/final_layer_norm/Div",
            "/data2vec_audio/encoder/layers.4/feed_forward/output_dense/MatMul",
            "/data2vec_audio/encoder/layers.8/attention/MatMul_1",
            "/data2vec_audio/feature_extractor/conv_layers.1/conv/Conv",
            "/data2vec_audio/encoder/layers.2/Add_1",
            "/data2vec_audio/feature_extractor/conv_layers.0/layer_norm/Div",
            "/data2vec_audio/encoder/layers.1/feed_forward/intermediate_dense/MatMul",
            "/data2vec_audio/encoder/layers.1/Add_1",
            "/data2vec_audio/feature_extractor/conv_layers.3/layer_norm/Div"
        ],
    ),
    advanced_parameters=AdvancedQuantizationParameters(
        activations_range_estimator_params=RangeEstimatorParameters(
            min=StatisticsCollectorParameters(
                statistics_type=StatisticsType.MIN,
                aggregator_type=AggregatorType.MIN
            ),
            max=StatisticsCollectorParameters(
                statistics_type=StatisticsType.QUANTILE,
                aggregator_type=AggregatorType.MEAN,
                quantile_outlier_prob=0.0001
            ),
        )
    )
)

INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
INFO:nncf:16 ignored nodes was found by name in the NNCFGraph
INFO:nncf:220 ignored nodes was found by types in the NNCFGraph
INFO:nncf:24 ignored nodes was found by name in the NNCFGraph
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Statistics collection: 100%|██████████| 73/73 [00:37<00:00,  1.93it/s]
Biases correction: 100%|██████████| 74/74 [00:16<00:00,  4.60it/s]

After quantization is finished, compressed model representation can be saved using serialize function.

MODEL_NAME = 'quantized_data2vec_base'
quantized_model_path = Path(f"{MODEL_NAME}_openvino_model/{MODEL_NAME}_quantized.xml")
ov.save_model(quantized_model, str(quantized_model_path))

Check INT8 model inference result

INT8 model is the same in usage like the original one. We need to read it, using the core.read_model method and load on the device, using core.compile_model. After that, we can reuse the same ov_infer function for getting model inference result on test sample.

int8_compiled_model = core.compile_model(quantized_model)

transcription = ov_infer(int8_compiled_model, dataset[0])
print(f"[Reference]:     {dataset[0]['text']}")
print(f"[OpenVINO INT8]: {transcription[0]}")
ipd.Audio(test_sample["array"], rate=16000)

[Reference]:     MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL
[OpenVINO INT8]: MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL