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Whisper-Tiny-En

Automatic Speech Recognition
PyTorch
TF Lite
foundation
android
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Whisper-Tiny-En: Optimized for Mobile Deployment

Automatic speech recognition (ASR) model for English transcription as well as translation

OpenAI’s Whisper ASR (Automatic Speech Recognition) model is a state-of-the-art system designed for transcribing spoken language into written text. 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 mean decoded length specified below.

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

This repository provides scripts to run Whisper-Tiny-En on Qualcomm® devices. More details on model performance across various devices, can be found here.

Model Details

  • Model Type: Speech recognition
  • Model Stats:
    • Model checkpoint: tiny.en
    • Input resolution: 80x3000 (30 seconds audio)
    • Mean decoded sequence length: 112 tokens
    • Number of parameters (WhisperEncoder): 9.39M
    • Model size (WhisperEncoder): 35.9 MB
    • Number of parameters (WhisperDecoder): 28.2M
    • Model size (WhisperDecoder): 108 MB
Model Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Precision Primary Compute Unit Target Model
WhisperEncoder Samsung Galaxy S23 Snapdragon® 8 Gen 2 TFLITE 98.056 ms 15 - 132 MB FP16 GPU Whisper-Tiny-En.tflite
WhisperEncoder Samsung Galaxy S23 Snapdragon® 8 Gen 2 QNN 141.405 ms 0 - 51 MB FP16 NPU Whisper-Tiny-En.so
WhisperEncoder Samsung Galaxy S24 Snapdragon® 8 Gen 3 TFLITE 76.661 ms 18 - 47 MB FP16 GPU Whisper-Tiny-En.tflite
WhisperEncoder Samsung Galaxy S24 Snapdragon® 8 Gen 3 QNN 112.275 ms 0 - 190 MB FP16 NPU Whisper-Tiny-En.so
WhisperEncoder Snapdragon 8 Elite QRD Snapdragon® 8 Elite TFLITE 78.773 ms 18 - 38 MB FP16 GPU Whisper-Tiny-En.tflite
WhisperEncoder Snapdragon 8 Elite QRD Snapdragon® 8 Elite QNN 101.227 ms 0 - 194 MB FP16 NPU Use Export Script
WhisperEncoder QCS8550 (Proxy) QCS8550 Proxy TFLITE 102.8 ms 16 - 60 MB FP16 GPU Whisper-Tiny-En.tflite
WhisperEncoder QCS8550 (Proxy) QCS8550 Proxy QNN 102.554 ms 0 - 6 MB FP16 NPU Use Export Script
WhisperEncoder SA8255 (Proxy) SA8255P Proxy TFLITE 122.669 ms 15 - 121 MB FP16 GPU Whisper-Tiny-En.tflite
WhisperEncoder SA8255 (Proxy) SA8255P Proxy QNN 104.998 ms 1 - 2 MB FP16 NPU Use Export Script
WhisperEncoder SA8775 (Proxy) SA8775P Proxy TFLITE 99.384 ms 18 - 130 MB FP16 GPU Whisper-Tiny-En.tflite
WhisperEncoder SA8775 (Proxy) SA8775P Proxy QNN 106.066 ms 0 - 11 MB FP16 NPU Use Export Script
WhisperEncoder SA8650 (Proxy) SA8650P Proxy TFLITE 101.119 ms 15 - 62 MB FP16 GPU Whisper-Tiny-En.tflite
WhisperEncoder SA8650 (Proxy) SA8650P Proxy QNN 104.287 ms 0 - 6 MB FP16 NPU Use Export Script
WhisperEncoder SA8295P ADP SA8295P TFLITE 104.321 ms 20 - 40 MB FP16 GPU Whisper-Tiny-En.tflite
WhisperEncoder SA8295P ADP SA8295P QNN 127.364 ms 1 - 6 MB FP16 NPU Use Export Script
WhisperEncoder QCS8450 (Proxy) QCS8450 Proxy TFLITE 155.23 ms 20 - 56 MB FP16 GPU Whisper-Tiny-En.tflite
WhisperEncoder QCS8450 (Proxy) QCS8450 Proxy QNN 162.925 ms 0 - 197 MB FP16 NPU Use Export Script
WhisperEncoder Snapdragon X Elite CRD Snapdragon® X Elite QNN 95.469 ms 0 - 0 MB FP16 NPU Use Export Script
WhisperDecoder Samsung Galaxy S23 Snapdragon® 8 Gen 2 TFLITE 4.304 ms 3 - 5 MB FP16 NPU Whisper-Tiny-En.tflite
WhisperDecoder Samsung Galaxy S23 Snapdragon® 8 Gen 2 QNN 2.254 ms 2 - 153 MB FP16 NPU Whisper-Tiny-En.so
WhisperDecoder Samsung Galaxy S24 Snapdragon® 8 Gen 3 TFLITE 2.841 ms 1 - 221 MB FP16 NPU Whisper-Tiny-En.tflite
WhisperDecoder Samsung Galaxy S24 Snapdragon® 8 Gen 3 QNN 1.652 ms 4 - 25 MB FP16 NPU Whisper-Tiny-En.so
WhisperDecoder Snapdragon 8 Elite QRD Snapdragon® 8 Elite TFLITE 2.482 ms 0 - 31 MB FP16 NPU Whisper-Tiny-En.tflite
WhisperDecoder Snapdragon 8 Elite QRD Snapdragon® 8 Elite QNN 1.524 ms 4 - 28 MB FP16 NPU Use Export Script
WhisperDecoder QCS8550 (Proxy) QCS8550 Proxy TFLITE 3.771 ms 3 - 5 MB FP16 NPU Whisper-Tiny-En.tflite
WhisperDecoder QCS8550 (Proxy) QCS8550 Proxy QNN 2.363 ms 5 - 6 MB FP16 NPU Use Export Script
WhisperDecoder SA8255 (Proxy) SA8255P Proxy TFLITE 3.667 ms 3 - 5 MB FP16 NPU Whisper-Tiny-En.tflite
WhisperDecoder SA8255 (Proxy) SA8255P Proxy QNN 2.217 ms 4 - 6 MB FP16 NPU Use Export Script
WhisperDecoder SA8775 (Proxy) SA8775P Proxy TFLITE 3.725 ms 3 - 6 MB FP16 NPU Whisper-Tiny-En.tflite
WhisperDecoder SA8775 (Proxy) SA8775P Proxy QNN 2.262 ms 9 - 12 MB FP16 NPU Use Export Script
WhisperDecoder SA8650 (Proxy) SA8650P Proxy TFLITE 3.68 ms 3 - 5 MB FP16 NPU Whisper-Tiny-En.tflite
WhisperDecoder SA8650 (Proxy) SA8650P Proxy QNN 2.266 ms 3 - 4 MB FP16 NPU Use Export Script
WhisperDecoder SA8295P ADP SA8295P TFLITE 4.786 ms 3 - 29 MB FP16 NPU Whisper-Tiny-En.tflite
WhisperDecoder SA8295P ADP SA8295P QNN 3.334 ms 9 - 15 MB FP16 NPU Use Export Script
WhisperDecoder QCS8450 (Proxy) QCS8450 Proxy TFLITE 4.332 ms 3 - 217 MB FP16 NPU Whisper-Tiny-En.tflite
WhisperDecoder QCS8450 (Proxy) QCS8450 Proxy QNN 2.71 ms 4 - 28 MB FP16 NPU Use Export Script
WhisperDecoder Snapdragon X Elite CRD Snapdragon® X Elite QNN 2.097 ms 10 - 10 MB FP16 NPU Use Export Script

Installation

This model can be installed as a Python package via pip.

pip install "qai-hub-models[whisper_tiny_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_tiny_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_tiny_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_tiny_en.export

Profiling Results
------------------------------------------------------------
WhisperEncoder
Device                          : Samsung Galaxy S23 (13)   
Runtime                         : TFLITE                    
Estimated inference time (ms)   : 98.1                      
Estimated peak memory usage (MB): [15, 132]                 
Total # Ops                     : 271                       
Compute Unit(s)                 : GPU (260 ops) CPU (11 ops)

------------------------------------------------------------
WhisperDecoder
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 4.3                    
Estimated peak memory usage (MB): [3, 5]                 
Total # Ops                     : 557                    
Compute Unit(s)                 : NPU (557 ops)

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_tiny_en import WhisperEncoder,WhisperDecoder

# Load the model
encoder_model = WhisperEncoder.from_pretrained()
decoder_model = WhisperDecoder.from_pretrained()

# Device
device = hub.Device("Samsung Galaxy S23")

# Trace model
encoder_input_shape = encoder_model.get_input_spec()
encoder_sample_inputs = encoder_model.sample_inputs()

traced_encoder_model = torch.jit.trace(encoder_model, [torch.tensor(data[0]) for _, data in encoder_sample_inputs.items()])

# Compile model on a specific device
encoder_compile_job = hub.submit_compile_job(
    model=traced_encoder_model ,
    device=device,
    input_specs=encoder_model.get_input_spec(),
)

# Get target model to run on-device
encoder_target_model = encoder_compile_job.get_target_model()
# Trace model
decoder_input_shape = decoder_model.get_input_spec()
decoder_sample_inputs = decoder_model.sample_inputs()

traced_decoder_model = torch.jit.trace(decoder_model, [torch.tensor(data[0]) for _, data in decoder_sample_inputs.items()])

# Compile model on a specific device
decoder_compile_job = hub.submit_compile_job(
    model=traced_decoder_model ,
    device=device,
    input_specs=decoder_model.get_input_spec(),
)

# Get target model to run on-device
decoder_target_model = decoder_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.

encoder_profile_job = hub.submit_profile_job(
    model=encoder_target_model,
    device=device,
)
decoder_profile_job = hub.submit_profile_job(
    model=decoder_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.

encoder_input_data = encoder_model.sample_inputs()
encoder_inference_job = hub.submit_inference_job(
    model=encoder_target_model,
    device=device,
    inputs=encoder_input_data,
)
encoder_inference_job.download_output_data()
decoder_input_data = decoder_model.sample_inputs()
decoder_inference_job = hub.submit_inference_job(
    model=decoder_target_model,
    device=device,
    inputs=decoder_input_data,
)
decoder_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-Tiny-En's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

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

References

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