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README.md

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+---
+library_name: pytorch
+license: mit
+tags:
+- foundation
+- android
+pipeline_tag: automatic-speech-recognition
+
+---
+
+![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/whisper_medium_en/web-assets/model_demo.png)
+
+# Whisper-Medium-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-Medium-En found [here](https://github.com/openai/whisper/tree/main).
+
+
+This repository provides scripts to run Whisper-Medium-En on Qualcomm® devices.
+More details on model performance across various devices, can be found
+[here](https://aihub.qualcomm.com/models/whisper_medium_en).
+
+
+### Model Details
+
+- **Model Type:** Speech recognition
+- **Model Stats:**
+  - Model checkpoint: medium.en
+  - Input resolution: 80x3000 (30 seconds audio)
+  - Mean decoded sequence length: 224 tokens
+  - Number of parameters: 769 M
+  - Model size (WhisperEncoder): 769 MB
+  - Model size (WhisperDecoder): 726 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 | QNN | 38.334 ms | 160 - 167 MB | FP16 | NPU | [Whisper-Medium-En.so](https://huggingface.co/qualcomm/Whisper-Medium-En/blob/main/WhisperDecoder.so) |
+| WhisperDecoder | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 30.874 ms | 161 - 178 MB | FP16 | NPU | [Whisper-Medium-En.so](https://huggingface.co/qualcomm/Whisper-Medium-En/blob/main/WhisperDecoder.so) |
+| WhisperDecoder | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 30.408 ms | 141 - 544 MB | FP16 | NPU | Use Export Script |
+| WhisperDecoder | SA7255P ADP | SA7255P | QNN | 212.509 ms | 154 - 163 MB | FP16 | NPU | Use Export Script |
+| WhisperDecoder | SA8255 (Proxy) | SA8255P Proxy | QNN | 38.11 ms | 162 - 165 MB | FP16 | NPU | Use Export Script |
+| WhisperDecoder | SA8295P ADP | SA8295P | QNN | 39.889 ms | 160 - 170 MB | FP16 | NPU | Use Export Script |
+| WhisperDecoder | SA8650 (Proxy) | SA8650P Proxy | QNN | 38.703 ms | 162 - 165 MB | FP16 | NPU | Use Export Script |
+| WhisperDecoder | SA8775P ADP | SA8775P | QNN | 40.488 ms | 161 - 169 MB | FP16 | NPU | Use Export Script |
+| WhisperDecoder | QCS8275 (Proxy) | QCS8275 Proxy | QNN | 212.509 ms | 154 - 163 MB | FP16 | NPU | Use Export Script |
+| WhisperDecoder | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 38.285 ms | 162 - 165 MB | FP16 | NPU | Use Export Script |
+| WhisperDecoder | QCS9075 (Proxy) | QCS9075 Proxy | QNN | 40.488 ms | 161 - 169 MB | FP16 | NPU | Use Export Script |
+| WhisperDecoder | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 33.066 ms | 162 - 162 MB | FP16 | NPU | Use Export Script |
+| WhisperEncoder | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 1812.235 ms | 1 - 8 MB | FP16 | NPU | [Whisper-Medium-En.so](https://huggingface.co/qualcomm/Whisper-Medium-En/blob/main/WhisperEncoder.so) |
+| WhisperEncoder | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 1342.833 ms | 1 - 17 MB | FP16 | NPU | [Whisper-Medium-En.so](https://huggingface.co/qualcomm/Whisper-Medium-En/blob/main/WhisperEncoder.so) |
+| WhisperEncoder | SA7255P ADP | SA7255P | QNN | 9877.481 ms | 0 - 7 MB | FP16 | NPU | Use Export Script |
+| WhisperEncoder | SA8295P ADP | SA8295P | QNN | 1808.045 ms | 0 - 10 MB | FP16 | NPU | Use Export Script |
+| WhisperEncoder | SA8650 (Proxy) | SA8650P Proxy | QNN | 1798.114 ms | 1 - 3 MB | FP16 | NPU | Use Export Script |
+| WhisperEncoder | SA8775P ADP | SA8775P | QNN | 1678.664 ms | 0 - 7 MB | FP16 | NPU | Use Export Script |
+| WhisperEncoder | QCS8275 (Proxy) | QCS8275 Proxy | QNN | 9877.481 ms | 0 - 7 MB | FP16 | NPU | Use Export Script |
+| WhisperEncoder | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 1770.444 ms | 1 - 3 MB | FP16 | NPU | Use Export Script |
+| WhisperEncoder | QCS9075 (Proxy) | QCS9075 Proxy | QNN | 1678.664 ms | 0 - 7 MB | FP16 | NPU | Use Export Script |
+| WhisperEncoder | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 1356.093 ms | 0 - 0 MB | FP16 | NPU | Use Export Script |
+
+
+
+
+## Installation
+
+
+Install the package via pip:
+```bash
+pip install "qai-hub-models[whisper-medium-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_medium_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_medium_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_medium_en.export
+```
+```
+Profiling Results
+------------------------------------------------------------
+WhisperDecoder
+Device                          : Samsung Galaxy S23 (13)
+Runtime                         : QNN                    
+Estimated inference time (ms)   : 38.3                   
+Estimated peak memory usage (MB): [160, 167]             
+Total # Ops                     : 5747                   
+Compute Unit(s)                 : NPU (5747 ops)         
+
+------------------------------------------------------------
+WhisperEncoder
+Device                          : Samsung Galaxy S23 (13)
+Runtime                         : QNN                    
+Estimated inference time (ms)   : 1812.2                 
+Estimated peak memory usage (MB): [1, 8]                 
+Total # Ops                     : 3213                   
+Compute Unit(s)                 : NPU (3213 ops)         
+```
+
+
+## How does this work?
+
+This [export script](https://aihub.qualcomm.com/models/whisper_medium_en/qai_hub_models/models/Whisper-Medium-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_medium_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-Medium-En's performance across various devices [here](https://aihub.qualcomm.com/models/whisper_medium_en).
+Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
+
+
+## License
+* The license for the original implementation of Whisper-Medium-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:[email protected]).
+
+