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Whisper-Small-En

Automatic Speech Recognition
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Whisper-Small-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-Small-En found here.

This repository provides scripts to run Whisper-Small-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-small
    • Input resolution: 80x3000 (30 seconds audio)
    • Max decoded sequence length: 200 tokens
    • Number of parameters (HfWhisperEncoder): 102M
    • Model size (HfWhisperEncoder) (float): 391 MB
    • Number of parameters (HfWhisperDecoder): 139M
    • Model size (HfWhisperDecoder) (float): 533 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 3333.928 ms 109 - 159 MB GPU Whisper-Small-En.tflite
HfWhisperEncoder float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN_DLC 424.425 ms 0 - 443 MB NPU Whisper-Small-En.dlc
HfWhisperEncoder float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 1115.67 ms 18 - 223 MB GPU Whisper-Small-En.tflite
HfWhisperEncoder float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN_DLC 135.465 ms 1 - 28 MB NPU Whisper-Small-En.dlc
HfWhisperEncoder float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 1416.508 ms 106 - 155 MB GPU Whisper-Small-En.tflite
HfWhisperEncoder float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN_DLC 154.567 ms 0 - 442 MB NPU Whisper-Small-En.dlc
HfWhisperEncoder float SA7255P ADP Qualcomm® SA7255P TFLITE 3333.928 ms 109 - 159 MB GPU Whisper-Small-En.tflite
HfWhisperEncoder float SA7255P ADP Qualcomm® SA7255P QNN_DLC 424.425 ms 0 - 443 MB NPU Whisper-Small-En.dlc
HfWhisperEncoder float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 882.114 ms 18 - 186 MB GPU Whisper-Small-En.tflite
HfWhisperEncoder float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN_DLC 137.649 ms 0 - 33 MB NPU Whisper-Small-En.dlc
HfWhisperEncoder float SA8295P ADP Qualcomm® SA8295P TFLITE 798.622 ms 109 - 158 MB GPU Whisper-Small-En.tflite
HfWhisperEncoder float SA8295P ADP Qualcomm® SA8295P QNN_DLC 241.302 ms 0 - 440 MB NPU Whisper-Small-En.dlc
HfWhisperEncoder float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 768.865 ms 18 - 222 MB GPU Whisper-Small-En.tflite
HfWhisperEncoder float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN_DLC 138.695 ms 0 - 32 MB NPU Whisper-Small-En.dlc
HfWhisperEncoder float SA8775P ADP Qualcomm® SA8775P TFLITE 1416.508 ms 106 - 155 MB GPU Whisper-Small-En.tflite
HfWhisperEncoder float SA8775P ADP Qualcomm® SA8775P QNN_DLC 154.567 ms 0 - 442 MB NPU Whisper-Small-En.dlc
HfWhisperEncoder float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 775.503 ms 110 - 130 MB GPU Whisper-Small-En.tflite
HfWhisperEncoder float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN_DLC 137.037 ms 0 - 30 MB NPU Whisper-Small-En.dlc
HfWhisperEncoder float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 569.222 ms 111 - 300 MB GPU Whisper-Small-En.tflite
HfWhisperEncoder float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN_DLC 106.478 ms 1 - 449 MB NPU Whisper-Small-En.dlc
HfWhisperEncoder float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 622.531 ms 110 - 156 MB GPU Whisper-Small-En.tflite
HfWhisperEncoder float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN_DLC 87.001 ms 0 - 426 MB NPU Whisper-Small-En.dlc
HfWhisperEncoder float Snapdragon X Elite CRD Snapdragon® X Elite QNN_DLC 130.938 ms 158 - 158 MB NPU Whisper-Small-En.dlc
HfWhisperDecoder float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 28.37 ms 14 - 506 MB NPU Whisper-Small-En.tflite
HfWhisperDecoder float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN_DLC 18.043 ms 38 - 335 MB NPU Whisper-Small-En.dlc
HfWhisperDecoder float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 19.146 ms 14 - 59 MB NPU Whisper-Small-En.tflite
HfWhisperDecoder float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN_DLC 12.048 ms 60 - 74 MB NPU Whisper-Small-En.dlc
HfWhisperDecoder float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 21.142 ms 14 - 505 MB NPU Whisper-Small-En.tflite
HfWhisperDecoder float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN_DLC 13.304 ms 55 - 346 MB NPU Whisper-Small-En.dlc
HfWhisperDecoder float SA7255P ADP Qualcomm® SA7255P TFLITE 28.37 ms 14 - 506 MB NPU Whisper-Small-En.tflite
HfWhisperDecoder float SA7255P ADP Qualcomm® SA7255P QNN_DLC 18.043 ms 38 - 335 MB NPU Whisper-Small-En.dlc
HfWhisperDecoder float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 19.112 ms 14 - 40 MB NPU Whisper-Small-En.tflite
HfWhisperDecoder float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN_DLC 11.743 ms 57 - 83 MB NPU Whisper-Small-En.dlc
HfWhisperDecoder float SA8295P ADP Qualcomm® SA8295P TFLITE 20.478 ms 14 - 429 MB NPU Whisper-Small-En.tflite
HfWhisperDecoder float SA8295P ADP Qualcomm® SA8295P QNN_DLC 14.571 ms 47 - 328 MB NPU Whisper-Small-En.dlc
HfWhisperDecoder float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 19.092 ms 0 - 57 MB NPU Whisper-Small-En.tflite
HfWhisperDecoder float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN_DLC 11.972 ms 43 - 67 MB NPU Whisper-Small-En.dlc
HfWhisperDecoder float SA8775P ADP Qualcomm® SA8775P TFLITE 21.142 ms 14 - 505 MB NPU Whisper-Small-En.tflite
HfWhisperDecoder float SA8775P ADP Qualcomm® SA8775P QNN_DLC 13.304 ms 55 - 346 MB NPU Whisper-Small-En.dlc
HfWhisperDecoder float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 18.961 ms 14 - 66 MB NPU Whisper-Small-En.tflite
HfWhisperDecoder float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN_DLC 11.942 ms 60 - 85 MB NPU Whisper-Small-En.dlc
HfWhisperDecoder float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 15.165 ms 14 - 668 MB NPU Whisper-Small-En.tflite
HfWhisperDecoder float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN_DLC 9.579 ms 0 - 308 MB NPU Whisper-Small-En.dlc
HfWhisperDecoder float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 13.094 ms 14 - 459 MB NPU Whisper-Small-En.tflite
HfWhisperDecoder float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN_DLC 8.145 ms 58 - 169 MB NPU Whisper-Small-En.dlc
HfWhisperDecoder float Snapdragon X Elite CRD Snapdragon® X Elite QNN_DLC 10.743 ms 392 - 392 MB NPU Whisper-Small-En.dlc

Installation

Install the package via pip:

pip install "qai-hub-models[whisper-small-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_small_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_small_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_small_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_small_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-Small-En's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

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

References

Community

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