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Whisper-Base-En

Automatic Speech Recognition
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Whisper-Base-En: Optimized for Mobile Deployment

Transformer-based automatic speech recognition (ASR) model for multilingual transcription and translation available on HuggingFace

HuggingFace Whisper-Small ASR (Automatic Speech Recognition) model is a state-of-the-art system designed for transcribing spoken language into written text. This model is based on the transformer architecture and has been optimized for edge inference by replacing Multi-Head Attention (MHA) with Single-Head Attention (SHA) and linear layers with convolutional (conv) layers. It exhibits robust performance in realistic, noisy environments, making it highly reliable for real-world applications. Specifically, it excels in long-form transcription, capable of accurately transcribing audio clips up to 30 seconds long. Time to the first token is the encoder's latency, while time to each additional token is decoder's latency, where we assume a max decoded length specified below.

This model is an implementation of Whisper-Base-En found here.

This repository provides scripts to run Whisper-Base-En 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: openai/whisper-base
    • Input resolution: 80x3000 (30 seconds audio)
    • Max decoded sequence length: 200 tokens
    • Number of parameters (HfWhisperEncoder): 23.7M
    • Model size (HfWhisperEncoder) (float): 90.7 MB
    • Number of parameters (HfWhisperDecoder): 48.9M
    • Model size (HfWhisperDecoder) (float): 187 MB
Model Precision Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Primary Compute Unit Target Model
HfWhisperEncoder float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 863.839 ms 38 - 66 MB GPU Whisper-Base-En.tflite
HfWhisperEncoder float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN_DLC 130.073 ms 0 - 130 MB NPU Whisper-Base-En.dlc
HfWhisperEncoder float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 292.604 ms 38 - 111 MB GPU Whisper-Base-En.tflite
HfWhisperEncoder float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN_DLC 112.638 ms 1 - 130 MB NPU Whisper-Base-En.dlc
HfWhisperEncoder float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 234.299 ms 0 - 69 MB GPU Whisper-Base-En.tflite
HfWhisperEncoder float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN_DLC 43.537 ms 0 - 45 MB NPU Whisper-Base-En.dlc
HfWhisperEncoder float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 400.795 ms 38 - 67 MB GPU Whisper-Base-En.tflite
HfWhisperEncoder float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN_DLC 50.197 ms 1 - 133 MB NPU Whisper-Base-En.dlc
HfWhisperEncoder float SA7255P ADP Qualcomm® SA7255P TFLITE 863.839 ms 38 - 66 MB GPU Whisper-Base-En.tflite
HfWhisperEncoder float SA7255P ADP Qualcomm® SA7255P QNN_DLC 130.073 ms 0 - 130 MB NPU Whisper-Base-En.dlc
HfWhisperEncoder float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 257.222 ms 0 - 71 MB GPU Whisper-Base-En.tflite
HfWhisperEncoder float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN_DLC 43.566 ms 0 - 43 MB NPU Whisper-Base-En.dlc
HfWhisperEncoder float SA8295P ADP Qualcomm® SA8295P TFLITE 229.608 ms 38 - 74 MB GPU Whisper-Base-En.tflite
HfWhisperEncoder float SA8295P ADP Qualcomm® SA8295P QNN_DLC 92.405 ms 0 - 139 MB NPU Whisper-Base-En.dlc
HfWhisperEncoder float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 229.942 ms 0 - 68 MB GPU Whisper-Base-En.tflite
HfWhisperEncoder float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN_DLC 43.746 ms 0 - 42 MB NPU Whisper-Base-En.dlc
HfWhisperEncoder float SA8775P ADP Qualcomm® SA8775P TFLITE 400.795 ms 38 - 67 MB GPU Whisper-Base-En.tflite
HfWhisperEncoder float SA8775P ADP Qualcomm® SA8775P QNN_DLC 50.197 ms 1 - 133 MB NPU Whisper-Base-En.dlc
HfWhisperEncoder float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 230.509 ms 38 - 114 MB GPU Whisper-Base-En.tflite
HfWhisperEncoder float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN_DLC 43.232 ms 1 - 41 MB NPU Whisper-Base-En.dlc
HfWhisperEncoder float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 174.142 ms 38 - 105 MB GPU Whisper-Base-En.tflite
HfWhisperEncoder float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN_DLC 34.16 ms 0 - 135 MB NPU Whisper-Base-En.dlc
HfWhisperEncoder float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 171.96 ms 37 - 67 MB GPU Whisper-Base-En.tflite
HfWhisperEncoder float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN_DLC 27.427 ms 0 - 129 MB NPU Whisper-Base-En.dlc
HfWhisperEncoder float Snapdragon X Elite CRD Snapdragon® X Elite QNN_DLC 42.083 ms 33 - 33 MB NPU Whisper-Base-En.dlc
HfWhisperDecoder float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 9.663 ms 5 - 153 MB NPU Whisper-Base-En.tflite
HfWhisperDecoder float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN_DLC 6.259 ms 9 - 62 MB NPU Whisper-Base-En.dlc
HfWhisperDecoder float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 7.142 ms 5 - 195 MB NPU Whisper-Base-En.tflite
HfWhisperDecoder float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN_DLC 5.165 ms 17 - 71 MB NPU Whisper-Base-En.dlc
HfWhisperDecoder float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 6.046 ms 2 - 388 MB NPU Whisper-Base-En.tflite
HfWhisperDecoder float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN_DLC 3.874 ms 20 - 32 MB NPU Whisper-Base-En.dlc
HfWhisperDecoder float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 6.999 ms 5 - 154 MB NPU Whisper-Base-En.tflite
HfWhisperDecoder float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN_DLC 4.701 ms 19 - 69 MB NPU Whisper-Base-En.dlc
HfWhisperDecoder float SA7255P ADP Qualcomm® SA7255P TFLITE 9.663 ms 5 - 153 MB NPU Whisper-Base-En.tflite
HfWhisperDecoder float SA7255P ADP Qualcomm® SA7255P QNN_DLC 6.259 ms 9 - 62 MB NPU Whisper-Base-En.dlc
HfWhisperDecoder float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 5.937 ms 2 - 373 MB NPU Whisper-Base-En.tflite
HfWhisperDecoder float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN_DLC 4.004 ms 20 - 33 MB NPU Whisper-Base-En.dlc
HfWhisperDecoder float SA8295P ADP Qualcomm® SA8295P TFLITE 7.372 ms 5 - 151 MB NPU Whisper-Base-En.tflite
HfWhisperDecoder float SA8295P ADP Qualcomm® SA8295P QNN_DLC 5.244 ms 15 - 113 MB NPU Whisper-Base-En.dlc
HfWhisperDecoder float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 5.978 ms 2 - 328 MB NPU Whisper-Base-En.tflite
HfWhisperDecoder float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN_DLC 3.905 ms 20 - 32 MB NPU Whisper-Base-En.dlc
HfWhisperDecoder float SA8775P ADP Qualcomm® SA8775P TFLITE 6.999 ms 5 - 154 MB NPU Whisper-Base-En.tflite
HfWhisperDecoder float SA8775P ADP Qualcomm® SA8775P QNN_DLC 4.701 ms 19 - 69 MB NPU Whisper-Base-En.dlc
HfWhisperDecoder float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 5.929 ms 2 - 249 MB NPU Whisper-Base-En.tflite
HfWhisperDecoder float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN_DLC 3.978 ms 20 - 32 MB NPU Whisper-Base-En.dlc
HfWhisperDecoder float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 4.517 ms 3 - 190 MB NPU Whisper-Base-En.tflite
HfWhisperDecoder float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN_DLC 3.178 ms 5 - 62 MB NPU Whisper-Base-En.dlc
HfWhisperDecoder float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 4.061 ms 3 - 154 MB NPU Whisper-Base-En.tflite
HfWhisperDecoder float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN_DLC 2.622 ms 20 - 80 MB NPU Whisper-Base-En.dlc
HfWhisperDecoder float Snapdragon X Elite CRD Snapdragon® X Elite QNN_DLC 3.57 ms 150 - 150 MB NPU Whisper-Base-En.dlc

Installation

Install the package via pip:

pip install "qai-hub-models[whisper-base-en]"

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

Sign-in to Qualcomm® AI Hub 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.whisper_base_en.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.whisper_base_en.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.whisper_base_en.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.whisper_base_en import Model

# Load the model
torch_model = Model.from_pretrained()

# Device
device = hub.Device("Samsung Galaxy S24")

# 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. Sign up for access.

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 Whisper-Base-En's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

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

References

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