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HuggingFace-WavLM-Base-Plus

Automatic Speech Recognition
PyTorch
LiteRT
backbone
android
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HuggingFace-WavLM-Base-Plus: Optimized for Mobile Deployment

Real-time Speech processing

HuggingFaceWavLMBasePlus is a real time speech processing backbone based on Microsoft's WavLM model.

This model is an implementation of HuggingFace-WavLM-Base-Plus found here.

This repository provides scripts to run HuggingFace-WavLM-Base-Plus on Qualcomm® devices. More details on model performance across various devices, can be found here.

Model Details

  • Model Type: Model_use_case.speech_recognition
  • Model Stats:
    • Model checkpoint: wavlm-libri-clean-100h-base-plus
    • Input resolution: 1x320000
    • Number of parameters: 95.1M
    • Model size (float): 363 MB
Model Precision Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Primary Compute Unit Target Model
HuggingFace-WavLM-Base-Plus float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 2847.928 ms 125 - 908 MB CPU HuggingFace-WavLM-Base-Plus.tflite
HuggingFace-WavLM-Base-Plus float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 2452.462 ms 125 - 1358 MB CPU HuggingFace-WavLM-Base-Plus.tflite
HuggingFace-WavLM-Base-Plus float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 1621.496 ms 125 - 191 MB CPU HuggingFace-WavLM-Base-Plus.tflite
HuggingFace-WavLM-Base-Plus float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) ONNX 290.065 ms 1 - 48 MB NPU HuggingFace-WavLM-Base-Plus.onnx.zip
HuggingFace-WavLM-Base-Plus float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 2254.766 ms 125 - 907 MB CPU HuggingFace-WavLM-Base-Plus.tflite
HuggingFace-WavLM-Base-Plus float SA7255P ADP Qualcomm® SA7255P TFLITE 2847.928 ms 125 - 908 MB CPU HuggingFace-WavLM-Base-Plus.tflite
HuggingFace-WavLM-Base-Plus float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 1750.923 ms 125 - 129 MB CPU HuggingFace-WavLM-Base-Plus.tflite
HuggingFace-WavLM-Base-Plus float SA8295P ADP Qualcomm® SA8295P TFLITE 1989.042 ms 124 - 1052 MB CPU HuggingFace-WavLM-Base-Plus.tflite
HuggingFace-WavLM-Base-Plus float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 1576.959 ms 125 - 130 MB CPU HuggingFace-WavLM-Base-Plus.tflite
HuggingFace-WavLM-Base-Plus float SA8775P ADP Qualcomm® SA8775P TFLITE 2254.766 ms 125 - 907 MB CPU HuggingFace-WavLM-Base-Plus.tflite
HuggingFace-WavLM-Base-Plus float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 1458.456 ms 114 - 1162 MB CPU HuggingFace-WavLM-Base-Plus.tflite
HuggingFace-WavLM-Base-Plus float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 218.835 ms 0 - 855 MB NPU HuggingFace-WavLM-Base-Plus.onnx.zip
HuggingFace-WavLM-Base-Plus float Samsung Galaxy S25 Snapdragon® 8 Elite For Galaxy Mobile TFLITE 1092.305 ms 125 - 908 MB CPU HuggingFace-WavLM-Base-Plus.tflite
HuggingFace-WavLM-Base-Plus float Samsung Galaxy S25 Snapdragon® 8 Elite For Galaxy Mobile ONNX 189.459 ms 0 - 663 MB NPU HuggingFace-WavLM-Base-Plus.onnx.zip
HuggingFace-WavLM-Base-Plus float Snapdragon 8 Elite Gen 5 QRD Snapdragon® 8 Elite Gen5 Mobile TFLITE 877.898 ms 123 - 901 MB CPU HuggingFace-WavLM-Base-Plus.tflite
HuggingFace-WavLM-Base-Plus float Snapdragon 8 Elite Gen 5 QRD Snapdragon® 8 Elite Gen5 Mobile ONNX 155.058 ms 0 - 729 MB NPU HuggingFace-WavLM-Base-Plus.onnx.zip
HuggingFace-WavLM-Base-Plus float Snapdragon X Elite CRD Snapdragon® X Elite ONNX 295.23 ms 202 - 202 MB NPU HuggingFace-WavLM-Base-Plus.onnx.zip

Installation

Install the package via pip:

# NOTE: 3.10 <= PYTHON_VERSION < 3.14 is supported.
pip install "qai-hub-models[huggingface-wavlm-base-plus]"

Configure Qualcomm® AI Hub Workbench to run this model on a cloud-hosted device

Sign-in to Qualcomm® AI Hub Workbench with your Qualcomm® ID. Once signed in navigate to Account -> Settings -> API Token.

With this API token, you can configure your client to run models on the cloud hosted devices.

qai-hub configure --api_token API_TOKEN

Navigate to docs for more information.

Demo off target

The package contains a simple end-to-end demo that downloads pre-trained weights and runs this model on a sample input.

python -m qai_hub_models.models.huggingface_wavlm_base_plus.demo

The above demo runs a reference implementation of pre-processing, model inference, and post processing.

NOTE: If you want running in a Jupyter Notebook or Google Colab like environment, please add the following to your cell (instead of the above).

%run -m qai_hub_models.models.huggingface_wavlm_base_plus.demo

Run model on a cloud-hosted device

In addition to the demo, you can also run the model on a cloud-hosted Qualcomm® device. This script does the following:

  • Performance check on-device on a cloud-hosted device
  • Downloads compiled assets that can be deployed on-device for Android.
  • Accuracy check between PyTorch and on-device outputs.
python -m qai_hub_models.models.huggingface_wavlm_base_plus.export

How does this work?

This export script leverages Qualcomm® AI Hub to optimize, validate, and deploy this model on-device. Lets go through each step below in detail:

Step 1: Compile model for on-device deployment

To compile a PyTorch model for on-device deployment, we first trace the model in memory using the jit.trace and then call the submit_compile_job API.

import torch

import qai_hub as hub
from qai_hub_models.models.huggingface_wavlm_base_plus import Model

# Load the model
torch_model = Model.from_pretrained()

# Device
device = hub.Device("Samsung Galaxy S25")

# Trace model
input_shape = torch_model.get_input_spec()
sample_inputs = torch_model.sample_inputs()

pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])

# Compile model on a specific device
compile_job = hub.submit_compile_job(
    model=pt_model,
    device=device,
    input_specs=torch_model.get_input_spec(),
)

# Get target model to run on-device
target_model = compile_job.get_target_model()

Step 2: Performance profiling on cloud-hosted device

After compiling models from step 1. Models can be profiled model on-device using the target_model. Note that this scripts runs the model on a device automatically provisioned in the cloud. Once the job is submitted, you can navigate to a provided job URL to view a variety of on-device performance metrics.

profile_job = hub.submit_profile_job(
    model=target_model,
    device=device,
)

Step 3: Verify on-device accuracy

To verify the accuracy of the model on-device, you can run on-device inference on sample input data on the same cloud hosted device.

input_data = torch_model.sample_inputs()
inference_job = hub.submit_inference_job(
    model=target_model,
    device=device,
    inputs=input_data,
)
    on_device_output = inference_job.download_output_data()

With the output of the model, you can compute like PSNR, relative errors or spot check the output with expected output.

Note: This on-device profiling and inference requires access to Qualcomm® AI Hub Workbench. Sign up for access.

Run demo on a cloud-hosted device

You can also run the demo on-device.

python -m qai_hub_models.models.huggingface_wavlm_base_plus.demo --eval-mode on-device

NOTE: If you want running in a Jupyter Notebook or Google Colab like environment, please add the following to your cell (instead of the above).

%run -m qai_hub_models.models.huggingface_wavlm_base_plus.demo -- --eval-mode on-device

Deploying compiled model to Android

The models can be deployed using multiple runtimes:

  • TensorFlow Lite (.tflite export): This tutorial provides a guide to deploy the .tflite model in an Android application.

  • QNN (.so export ): This sample app provides instructions on how to use the .so shared library in an Android application.

View on Qualcomm® AI Hub

Get more details on HuggingFace-WavLM-Base-Plus's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

  • The license for the original implementation of HuggingFace-WavLM-Base-Plus can be found here.
  • The license for the compiled assets for on-device deployment can be found here

References

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