---
library_name: pytorch
license: mit
tags:
- foundation
- android
pipeline_tag: automatic-speech-recognition
---
# Whisper-Base-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-Base-En found [here](https://github.com/openai/whisper/tree/main).
This repository provides scripts to run Whisper-Base-En on Qualcomm® devices.
More details on model performance across various devices, can be found
[here](https://aihub.qualcomm.com/models/whisper_base_en).
### Model Details
- **Model Type:** Speech recognition
- **Model Stats:**
- Model checkpoint: base.en
- Input resolution: 80x3000 (30 seconds audio)
- Mean decoded sequence length: 112 tokens
- Number of parameters (WhisperEncoder): 23.7M
- Model size (WhisperEncoder): 90.6 MB
- Number of parameters (WhisperDecoder): 48.6M
- Model size (WhisperDecoder): 186 MB
| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
|---|---|---|---|---|---|---|---|---|
| WhisperDecoder | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 9.829 ms | 5 - 26 MB | FP16 | NPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.tflite) |
| WhisperDecoder | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 4.542 ms | 18 - 20 MB | FP16 | NPU | [Whisper-Base-En.so](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.so) |
| WhisperDecoder | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 13.868 ms | 16 - 294 MB | FP16 | NPU | [Whisper-Base-En.onnx](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.onnx) |
| WhisperDecoder | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 7.654 ms | 13 - 126 MB | FP16 | NPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.tflite) |
| WhisperDecoder | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 3.355 ms | 41 - 61 MB | FP16 | NPU | [Whisper-Base-En.so](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.so) |
| WhisperDecoder | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 10.817 ms | 52 - 122 MB | FP16 | NPU | [Whisper-Base-En.onnx](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.onnx) |
| WhisperDecoder | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 7.277 ms | 5 - 115 MB | FP16 | NPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.tflite) |
| WhisperDecoder | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 3.485 ms | 20 - 87 MB | FP16 | NPU | Use Export Script |
| WhisperDecoder | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 10.054 ms | 52 - 114 MB | FP16 | NPU | [Whisper-Base-En.onnx](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.onnx) |
| WhisperDecoder | SA7255P ADP | SA7255P | TFLITE | 36.431 ms | 6 - 112 MB | FP16 | NPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.tflite) |
| WhisperDecoder | SA7255P ADP | SA7255P | QNN | 26.407 ms | 18 - 27 MB | FP16 | NPU | Use Export Script |
| WhisperDecoder | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 9.875 ms | 5 - 27 MB | FP16 | NPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.tflite) |
| WhisperDecoder | SA8255 (Proxy) | SA8255P Proxy | QNN | 4.395 ms | 20 - 22 MB | FP16 | NPU | Use Export Script |
| WhisperDecoder | SA8295P ADP | SA8295P | TFLITE | 12.265 ms | 0 - 98 MB | FP16 | NPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.tflite) |
| WhisperDecoder | SA8295P ADP | SA8295P | QNN | 5.712 ms | 18 - 35 MB | FP16 | NPU | Use Export Script |
| WhisperDecoder | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 9.887 ms | 5 - 28 MB | FP16 | NPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.tflite) |
| WhisperDecoder | SA8650 (Proxy) | SA8650P Proxy | QNN | 4.199 ms | 20 - 23 MB | FP16 | NPU | Use Export Script |
| WhisperDecoder | SA8775P ADP | SA8775P | TFLITE | 12.431 ms | 5 - 112 MB | FP16 | NPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.tflite) |
| WhisperDecoder | SA8775P ADP | SA8775P | QNN | 5.327 ms | 18 - 27 MB | FP16 | NPU | Use Export Script |
| WhisperDecoder | QCS8275 (Proxy) | QCS8275 Proxy | TFLITE | 36.431 ms | 6 - 112 MB | FP16 | NPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.tflite) |
| WhisperDecoder | QCS8275 (Proxy) | QCS8275 Proxy | QNN | 26.407 ms | 18 - 27 MB | FP16 | NPU | Use Export Script |
| WhisperDecoder | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 9.824 ms | 5 - 30 MB | FP16 | NPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.tflite) |
| WhisperDecoder | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 4.267 ms | 20 - 22 MB | FP16 | NPU | Use Export Script |
| WhisperDecoder | QCS9075 (Proxy) | QCS9075 Proxy | TFLITE | 12.431 ms | 5 - 112 MB | FP16 | NPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.tflite) |
| WhisperDecoder | QCS9075 (Proxy) | QCS9075 Proxy | QNN | 5.327 ms | 18 - 27 MB | FP16 | NPU | Use Export Script |
| WhisperDecoder | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 12.142 ms | 5 - 116 MB | FP16 | NPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.tflite) |
| WhisperDecoder | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 6.874 ms | 20 - 89 MB | FP16 | NPU | Use Export Script |
| WhisperDecoder | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 3.765 ms | 20 - 20 MB | FP16 | NPU | Use Export Script |
| WhisperDecoder | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 12.093 ms | 107 - 107 MB | FP16 | NPU | [Whisper-Base-En.onnx](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.onnx) |
| WhisperEncoder | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 199.056 ms | 0 - 75 MB | FP16 | GPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.tflite) |
| WhisperEncoder | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 252.347 ms | 1 - 3 MB | FP16 | NPU | [Whisper-Base-En.so](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.so) |
| WhisperEncoder | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 295.512 ms | 54 - 564 MB | FP16 | NPU | [Whisper-Base-En.onnx](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.onnx) |
| WhisperEncoder | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 176.369 ms | 39 - 81 MB | FP16 | GPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.tflite) |
| WhisperEncoder | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 160.252 ms | 1 - 19 MB | FP16 | NPU | [Whisper-Base-En.so](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.so) |
| WhisperEncoder | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 244.67 ms | 71 - 1525 MB | FP16 | NPU | [Whisper-Base-En.onnx](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.onnx) |
| WhisperEncoder | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 157.268 ms | 39 - 67 MB | FP16 | GPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.tflite) |
| WhisperEncoder | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 165.673 ms | 0 - 1390 MB | FP16 | NPU | Use Export Script |
| WhisperEncoder | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 331.668 ms | 73 - 1532 MB | FP16 | NPU | [Whisper-Base-En.onnx](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.onnx) |
| WhisperEncoder | SA7255P ADP | SA7255P | TFLITE | 1163.651 ms | 30 - 54 MB | FP16 | GPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.tflite) |
| WhisperEncoder | SA7255P ADP | SA7255P | QNN | 1009.943 ms | 1 - 10 MB | FP16 | NPU | Use Export Script |
| WhisperEncoder | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 198.905 ms | 0 - 65 MB | FP16 | GPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.tflite) |
| WhisperEncoder | SA8255 (Proxy) | SA8255P Proxy | QNN | 249.979 ms | 1 - 3 MB | FP16 | NPU | Use Export Script |
| WhisperEncoder | SA8295P ADP | SA8295P | TFLITE | 206.932 ms | 38 - 69 MB | FP16 | GPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.tflite) |
| WhisperEncoder | SA8295P ADP | SA8295P | QNN | 219.218 ms | 1 - 17 MB | FP16 | NPU | Use Export Script |
| WhisperEncoder | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 205.514 ms | 0 - 57 MB | FP16 | GPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.tflite) |
| WhisperEncoder | SA8650 (Proxy) | SA8650P Proxy | QNN | 208.878 ms | 1 - 3 MB | FP16 | NPU | Use Export Script |
| WhisperEncoder | SA8775P ADP | SA8775P | TFLITE | 365.876 ms | 38 - 63 MB | FP16 | GPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.tflite) |
| WhisperEncoder | SA8775P ADP | SA8775P | QNN | 216.546 ms | 1 - 11 MB | FP16 | NPU | Use Export Script |
| WhisperEncoder | QCS8275 (Proxy) | QCS8275 Proxy | TFLITE | 1163.651 ms | 30 - 54 MB | FP16 | GPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.tflite) |
| WhisperEncoder | QCS8275 (Proxy) | QCS8275 Proxy | QNN | 1009.943 ms | 1 - 10 MB | FP16 | NPU | Use Export Script |
| WhisperEncoder | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 205.363 ms | 0 - 33 MB | FP16 | GPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.tflite) |
| WhisperEncoder | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 225.859 ms | 1 - 3 MB | FP16 | NPU | Use Export Script |
| WhisperEncoder | QCS9075 (Proxy) | QCS9075 Proxy | TFLITE | 365.876 ms | 38 - 63 MB | FP16 | GPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.tflite) |
| WhisperEncoder | QCS9075 (Proxy) | QCS9075 Proxy | QNN | 216.546 ms | 1 - 11 MB | FP16 | NPU | Use Export Script |
| WhisperEncoder | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 609.548 ms | 38 - 86 MB | FP16 | GPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.tflite) |
| WhisperEncoder | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 353.514 ms | 1 - 1440 MB | FP16 | NPU | Use Export Script |
| WhisperEncoder | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 174.657 ms | 0 - 0 MB | FP16 | NPU | Use Export Script |
| WhisperEncoder | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 284.386 ms | 133 - 133 MB | FP16 | NPU | [Whisper-Base-En.onnx](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.onnx) |
## Installation
Install the package via pip:
```bash
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](https://app.aihub.qualcomm.com/) 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.
```bash
qai-hub configure --api_token API_TOKEN
```
Navigate to [docs](https://app.aihub.qualcomm.com/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.
```bash
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.
```bash
python -m qai_hub_models.models.whisper_base_en.export
```
```
Profiling Results
------------------------------------------------------------
WhisperDecoder
Device : Samsung Galaxy S23 (13)
Runtime : TFLITE
Estimated inference time (ms) : 9.8
Estimated peak memory usage (MB): [5, 26]
Total # Ops : 983
Compute Unit(s) : NPU (983 ops)
------------------------------------------------------------
WhisperEncoder
Device : Samsung Galaxy S23 (13)
Runtime : TFLITE
Estimated inference time (ms) : 199.1
Estimated peak memory usage (MB): [0, 75]
Total # Ops : 419
Compute Unit(s) : GPU (408 ops) CPU (11 ops)
```
## How does this work?
This [export script](https://aihub.qualcomm.com/models/whisper_base_en/qai_hub_models/models/Whisper-Base-En/export.py)
leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) 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.
```python
import torch
import qai_hub as hub
from qai_hub_models.models.whisper_base_en import Model
# Load the model
model = Model.from_pretrained()
decoder_model = model.decoder
encoder_model = model.encoder
# Device
device = hub.Device("Samsung Galaxy S23")
# 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()
# 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()
```
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.
```python
decoder_profile_job = hub.submit_profile_job(
model=decoder_target_model,
device=device,
)
encoder_profile_job = hub.submit_profile_job(
model=encoder_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.
```python
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()
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()
```
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](https://myaccount.qualcomm.com/signup).
## Deploying compiled model to Android
The models can be deployed using multiple runtimes:
- TensorFlow Lite (`.tflite` export): [This
tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a
guide to deploy the .tflite model in an Android application.
- QNN (`.so` export ): This [sample
app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
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](https://aihub.qualcomm.com/models/whisper_base_en).
Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
## License
* The license for the original implementation of Whisper-Base-En can be found
[here](https://github.com/openai/whisper/blob/main/LICENSE).
* The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)
## References
* [Robust Speech Recognition via Large-Scale Weak Supervision](https://cdn.openai.com/papers/whisper.pdf)
* [Source Model Implementation](https://github.com/openai/whisper/tree/main)
## Community
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).