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--- |
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library_name: pytorch |
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license: mit |
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pipeline_tag: automatic-speech-recognition |
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tags: |
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- foundation |
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- android |
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--- |
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# Whisper-Base-En: Optimized for Mobile Deployment |
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## Automatic speech recognition (ASR) model for English transcription as well as translation |
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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. |
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This model is an implementation of Whisper-Base-En found [here](https://github.com/openai/whisper/tree/main). |
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This repository provides scripts to run Whisper-Base-En on Qualcomm® devices. |
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More details on model performance across various devices, can be found |
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[here](https://aihub.qualcomm.com/models/whisper_base_en). |
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### Model Details |
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- **Model Type:** Speech recognition |
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- **Model Stats:** |
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- Model checkpoint: base.en |
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- Input resolution: 80x3000 (30 seconds audio) |
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- Mean decoded sequence length: 112 tokens |
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- Number of parameters (WhisperEncoder): 23.7M |
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- Model size (WhisperEncoder): 90.6 MB |
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- Number of parameters (WhisperDecoder): 48.6M |
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- Model size (WhisperDecoder): 186 MB |
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| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model |
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|---|---|---|---|---|---|---|---|---| |
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| WhisperDecoder | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 38.913 ms | 2 - 40 MB | FP16 | NPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.tflite) | |
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| WhisperDecoder | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 4.2 ms | 20 - 63 MB | FP16 | NPU | [Whisper-Base-En.so](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.so) | |
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| WhisperDecoder | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 16.043 ms | 17 - 204 MB | FP16 | NPU | [Whisper-Base-En.onnx](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.onnx) | |
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| WhisperDecoder | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 32.737 ms | 5 - 85 MB | FP16 | NPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.tflite) | |
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| WhisperDecoder | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 3.241 ms | 21 - 75 MB | FP16 | NPU | [Whisper-Base-En.so](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.so) | |
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| WhisperDecoder | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 13.836 ms | 48 - 123 MB | FP16 | NPU | [Whisper-Base-En.onnx](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.onnx) | |
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| WhisperDecoder | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 28.728 ms | 4 - 77 MB | FP16 | NPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.tflite) | |
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| WhisperDecoder | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 2.575 ms | 9 - 61 MB | FP16 | NPU | Use Export Script | |
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| WhisperDecoder | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 12.41 ms | 52 - 123 MB | FP16 | NPU | [Whisper-Base-En.onnx](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.onnx) | |
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| WhisperDecoder | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 38.898 ms | 5 - 41 MB | FP16 | NPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.tflite) | |
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| WhisperDecoder | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 4.204 ms | 20 - 23 MB | FP16 | NPU | Use Export Script | |
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| WhisperDecoder | SA7255P ADP | SA7255P | TFLITE | 69.303 ms | 6 - 77 MB | FP16 | NPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.tflite) | |
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| WhisperDecoder | SA7255P ADP | SA7255P | QNN | 26.928 ms | 18 - 27 MB | FP16 | NPU | Use Export Script | |
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| WhisperDecoder | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 39.507 ms | 4 - 40 MB | FP16 | NPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.tflite) | |
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| WhisperDecoder | SA8255 (Proxy) | SA8255P Proxy | QNN | 4.275 ms | 19 - 21 MB | FP16 | NPU | Use Export Script | |
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| WhisperDecoder | SA8295P ADP | SA8295P | TFLITE | 41.075 ms | 6 - 73 MB | FP16 | NPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.tflite) | |
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| WhisperDecoder | SA8295P ADP | SA8295P | QNN | 5.685 ms | 18 - 32 MB | FP16 | NPU | Use Export Script | |
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| WhisperDecoder | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 39.484 ms | 5 - 41 MB | FP16 | NPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.tflite) | |
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| WhisperDecoder | SA8650 (Proxy) | SA8650P Proxy | QNN | 4.293 ms | 20 - 22 MB | FP16 | NPU | Use Export Script | |
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| WhisperDecoder | SA8775P ADP | SA8775P | TFLITE | 39.427 ms | 6 - 77 MB | FP16 | NPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.tflite) | |
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| WhisperDecoder | SA8775P ADP | SA8775P | QNN | 5.544 ms | 18 - 27 MB | FP16 | NPU | Use Export Script | |
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| WhisperDecoder | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 42.323 ms | 0 - 76 MB | FP16 | NPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.tflite) | |
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| WhisperDecoder | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 5.017 ms | 20 - 75 MB | FP16 | NPU | Use Export Script | |
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| WhisperDecoder | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 3.852 ms | 20 - 20 MB | FP16 | NPU | Use Export Script | |
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| WhisperDecoder | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 14.43 ms | 108 - 108 MB | FP16 | NPU | [Whisper-Base-En.onnx](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperDecoder.onnx) | |
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| WhisperEncoder | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 337.754 ms | 0 - 82 MB | FP16 | GPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.tflite) | |
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| WhisperEncoder | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 273.538 ms | 0 - 84 MB | FP16 | NPU | [Whisper-Base-En.so](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.so) | |
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| WhisperEncoder | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 281.449 ms | 71 - 273 MB | FP16 | NPU | [Whisper-Base-En.onnx](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.onnx) | |
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| WhisperEncoder | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 159.488 ms | 37 - 81 MB | FP16 | GPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.tflite) | |
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| WhisperEncoder | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 185.633 ms | 13 - 300 MB | FP16 | NPU | [Whisper-Base-En.so](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.so) | |
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| WhisperEncoder | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 205.979 ms | 66 - 395 MB | FP16 | NPU | [Whisper-Base-En.onnx](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.onnx) | |
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| WhisperEncoder | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 150.003 ms | 39 - 64 MB | FP16 | GPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.tflite) | |
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| WhisperEncoder | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 152.344 ms | 0 - 304 MB | FP16 | NPU | Use Export Script | |
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| WhisperEncoder | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 164.022 ms | 71 - 403 MB | FP16 | NPU | [Whisper-Base-En.onnx](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.onnx) | |
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| WhisperEncoder | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 237.381 ms | 0 - 61 MB | FP16 | GPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.tflite) | |
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| WhisperEncoder | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 220.272 ms | 1 - 2 MB | FP16 | NPU | Use Export Script | |
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| WhisperEncoder | SA7255P ADP | SA7255P | TFLITE | 1155.427 ms | 37 - 59 MB | FP16 | GPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.tflite) | |
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| WhisperEncoder | SA7255P ADP | SA7255P | QNN | 937.355 ms | 1 - 10 MB | FP16 | NPU | Use Export Script | |
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| WhisperEncoder | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 236.135 ms | 0 - 64 MB | FP16 | GPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.tflite) | |
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| WhisperEncoder | SA8255 (Proxy) | SA8255P Proxy | QNN | 209.326 ms | 1 - 3 MB | FP16 | NPU | Use Export Script | |
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| WhisperEncoder | SA8295P ADP | SA8295P | TFLITE | 204.514 ms | 38 - 69 MB | FP16 | GPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.tflite) | |
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| WhisperEncoder | SA8295P ADP | SA8295P | QNN | 219.035 ms | 1 - 15 MB | FP16 | NPU | Use Export Script | |
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| WhisperEncoder | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 203.287 ms | 0 - 56 MB | FP16 | GPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.tflite) | |
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| WhisperEncoder | SA8650 (Proxy) | SA8650P Proxy | QNN | 208.865 ms | 1 - 3 MB | FP16 | NPU | Use Export Script | |
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| WhisperEncoder | SA8775P ADP | SA8775P | TFLITE | 365.818 ms | 38 - 63 MB | FP16 | GPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.tflite) | |
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| WhisperEncoder | SA8775P ADP | SA8775P | QNN | 194.677 ms | 1 - 10 MB | FP16 | NPU | Use Export Script | |
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| WhisperEncoder | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 268.324 ms | 38 - 88 MB | FP16 | GPU | [Whisper-Base-En.tflite](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.tflite) | |
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| WhisperEncoder | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 322.888 ms | 1 - 298 MB | FP16 | NPU | Use Export Script | |
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| WhisperEncoder | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 160.084 ms | 0 - 0 MB | FP16 | NPU | Use Export Script | |
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| WhisperEncoder | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 297.755 ms | 133 - 133 MB | FP16 | NPU | [Whisper-Base-En.onnx](https://huggingface.co/qualcomm/Whisper-Base-En/blob/main/WhisperEncoder.onnx) | |
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## Installation |
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This model can be installed as a Python package via pip. |
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```bash |
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pip install "qai-hub-models[whisper_base_en]" |
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``` |
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## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device |
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Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your |
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Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`. |
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With this API token, you can configure your client to run models on the cloud |
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hosted devices. |
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```bash |
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qai-hub configure --api_token API_TOKEN |
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``` |
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Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information. |
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## Demo off target |
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The package contains a simple end-to-end demo that downloads pre-trained |
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weights and runs this model on a sample input. |
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```bash |
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python -m qai_hub_models.models.whisper_base_en.demo |
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``` |
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The above demo runs a reference implementation of pre-processing, model |
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inference, and post processing. |
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**NOTE**: If you want running in a Jupyter Notebook or Google Colab like |
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environment, please add the following to your cell (instead of the above). |
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``` |
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%run -m qai_hub_models.models.whisper_base_en.demo |
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``` |
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### Run model on a cloud-hosted device |
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In addition to the demo, you can also run the model on a cloud-hosted Qualcomm® |
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device. This script does the following: |
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* Performance check on-device on a cloud-hosted device |
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* Downloads compiled assets that can be deployed on-device for Android. |
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* Accuracy check between PyTorch and on-device outputs. |
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```bash |
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python -m qai_hub_models.models.whisper_base_en.export |
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``` |
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``` |
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Profiling Results |
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------------------------------------------------------------ |
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WhisperDecoder |
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Device : Samsung Galaxy S23 (13) |
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Runtime : TFLITE |
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Estimated inference time (ms) : 38.9 |
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Estimated peak memory usage (MB): [2, 40] |
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Total # Ops : 983 |
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Compute Unit(s) : NPU (983 ops) |
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------------------------------------------------------------ |
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WhisperEncoder |
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Device : Samsung Galaxy S23 (13) |
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Runtime : TFLITE |
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Estimated inference time (ms) : 337.8 |
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Estimated peak memory usage (MB): [0, 82] |
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Total # Ops : 419 |
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Compute Unit(s) : GPU (408 ops) CPU (11 ops) |
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``` |
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## How does this work? |
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This [export script](https://aihub.qualcomm.com/models/whisper_base_en/qai_hub_models/models/Whisper-Base-En/export.py) |
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leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model |
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on-device. Lets go through each step below in detail: |
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Step 1: **Compile model for on-device deployment** |
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To compile a PyTorch model for on-device deployment, we first trace the model |
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in memory using the `jit.trace` and then call the `submit_compile_job` API. |
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```python |
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import torch |
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import qai_hub as hub |
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from qai_hub_models.models.whisper_base_en import Model |
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# Load the model |
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model = Model.from_pretrained() |
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decoder_model = model.decoder |
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encoder_model = model.encoder |
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# Device |
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device = hub.Device("Samsung Galaxy S23") |
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# Trace model |
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decoder_input_shape = decoder_model.get_input_spec() |
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decoder_sample_inputs = decoder_model.sample_inputs() |
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traced_decoder_model = torch.jit.trace(decoder_model, [torch.tensor(data[0]) for _, data in decoder_sample_inputs.items()]) |
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# Compile model on a specific device |
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decoder_compile_job = hub.submit_compile_job( |
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model=traced_decoder_model , |
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device=device, |
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input_specs=decoder_model.get_input_spec(), |
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) |
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# Get target model to run on-device |
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decoder_target_model = decoder_compile_job.get_target_model() |
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# Trace model |
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encoder_input_shape = encoder_model.get_input_spec() |
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encoder_sample_inputs = encoder_model.sample_inputs() |
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traced_encoder_model = torch.jit.trace(encoder_model, [torch.tensor(data[0]) for _, data in encoder_sample_inputs.items()]) |
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# Compile model on a specific device |
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encoder_compile_job = hub.submit_compile_job( |
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model=traced_encoder_model , |
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device=device, |
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input_specs=encoder_model.get_input_spec(), |
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) |
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# Get target model to run on-device |
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encoder_target_model = encoder_compile_job.get_target_model() |
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``` |
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Step 2: **Performance profiling on cloud-hosted device** |
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After compiling models from step 1. Models can be profiled model on-device using the |
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`target_model`. Note that this scripts runs the model on a device automatically |
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provisioned in the cloud. Once the job is submitted, you can navigate to a |
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provided job URL to view a variety of on-device performance metrics. |
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```python |
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decoder_profile_job = hub.submit_profile_job( |
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model=decoder_target_model, |
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device=device, |
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) |
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encoder_profile_job = hub.submit_profile_job( |
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model=encoder_target_model, |
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device=device, |
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) |
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``` |
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Step 3: **Verify on-device accuracy** |
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To verify the accuracy of the model on-device, you can run on-device inference |
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on sample input data on the same cloud hosted device. |
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```python |
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decoder_input_data = decoder_model.sample_inputs() |
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decoder_inference_job = hub.submit_inference_job( |
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model=decoder_target_model, |
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device=device, |
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inputs=decoder_input_data, |
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) |
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decoder_inference_job.download_output_data() |
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encoder_input_data = encoder_model.sample_inputs() |
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encoder_inference_job = hub.submit_inference_job( |
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model=encoder_target_model, |
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device=device, |
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inputs=encoder_input_data, |
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) |
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encoder_inference_job.download_output_data() |
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``` |
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With the output of the model, you can compute like PSNR, relative errors or |
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spot check the output with expected output. |
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**Note**: This on-device profiling and inference requires access to Qualcomm® |
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AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup). |
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## Deploying compiled model to Android |
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The models can be deployed using multiple runtimes: |
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- TensorFlow Lite (`.tflite` export): [This |
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tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a |
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guide to deploy the .tflite model in an Android application. |
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- QNN (`.so` export ): This [sample |
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app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html) |
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provides instructions on how to use the `.so` shared library in an Android application. |
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## View on Qualcomm® AI Hub |
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Get more details on Whisper-Base-En's performance across various devices [here](https://aihub.qualcomm.com/models/whisper_base_en). |
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Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/) |
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## License |
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* The license for the original implementation of Whisper-Base-En can be found [here](https://github.com/openai/whisper/blob/main/LICENSE). |
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* 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) |
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## References |
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* [Robust Speech Recognition via Large-Scale Weak Supervision](https://cdn.openai.com/papers/whisper.pdf) |
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* [Source Model Implementation](https://github.com/openai/whisper/tree/main) |
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## Community |
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* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI. |
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* For questions or feedback please [reach out to us](mailto:[email protected]). |
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