v0.42.0

Deprecation notice. Please refer to the new model here: https://huggingface.co/qualcomm/Whisper-Small

DEPLOYMENT_MODEL_LICENSE.pdf

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LICENSE

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-The license of the original trained model can be found at https://github.com/huggingface/transformers/blob/v4.42.3/LICENSE.
-The license for the deployable model files (.tflite, .onnx, .dlc, .bin, etc.) can be found in DEPLOYMENT_MODEL_LICENSE.pdf.

README.md

@@ -1,247 +1,10 @@
+
 ---
 library_name: pytorch
 license: other
 tags:
-- foundation
-- android
-pipeline_tag: automatic-speech-recognition
+- deprecated
+pipeline_tag: other
 
 ---
-
-![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/whisper_small_v2/web-assets/model_demo.png)
-
-# Whisper-Small-V2: 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-V2 found [here](https://github.com/huggingface/transformers/tree/v4.42.3/src/transformers/models/whisper).
-
-
-This repository provides scripts to run Whisper-Small-V2 on Qualcomm® devices.
-More details on model performance across various devices, can be found
-[here](https://aihub.qualcomm.com/models/whisper_small_v2).
-
-
-
-### 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
-
-| 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) | QNN_CONTEXT_BINARY | 426.871 ms | 0 - 9 MB | NPU | Use Export Script |
-| HfWhisperEncoder | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_CONTEXT_BINARY | 308.724 ms | 0 - 23 MB | NPU | Use Export Script |
-| HfWhisperEncoder | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_CONTEXT_BINARY | 137.833 ms | 0 - 2 MB | NPU | Use Export Script |
-| HfWhisperEncoder | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_CONTEXT_BINARY | 157.263 ms | 1 - 11 MB | NPU | Use Export Script |
-| HfWhisperEncoder | float | SA7255P ADP | Qualcomm® SA7255P | QNN_CONTEXT_BINARY | 426.871 ms | 0 - 9 MB | NPU | Use Export Script |
-| HfWhisperEncoder | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_CONTEXT_BINARY | 136.564 ms | 0 - 3 MB | NPU | Use Export Script |
-| HfWhisperEncoder | float | SA8295P ADP | Qualcomm® SA8295P | QNN_CONTEXT_BINARY | 234.197 ms | 0 - 15 MB | NPU | Use Export Script |
-| HfWhisperEncoder | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_CONTEXT_BINARY | 135.525 ms | 1 - 4 MB | NPU | Use Export Script |
-| HfWhisperEncoder | float | SA8775P ADP | Qualcomm® SA8775P | QNN_CONTEXT_BINARY | 157.263 ms | 1 - 11 MB | NPU | Use Export Script |
-| HfWhisperEncoder | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_CONTEXT_BINARY | 137.433 ms | 0 - 4 MB | NPU | Use Export Script |
-| HfWhisperEncoder | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | PRECOMPILED_QNN_ONNX | 138.874 ms | 0 - 258 MB | NPU | Use Export Script |
-| HfWhisperEncoder | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_CONTEXT_BINARY | 105.031 ms | 0 - 19 MB | NPU | Use Export Script |
-| HfWhisperEncoder | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | PRECOMPILED_QNN_ONNX | 106.239 ms | 122 - 141 MB | NPU | Use Export Script |
-| HfWhisperEncoder | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_CONTEXT_BINARY | 88.885 ms | 0 - 15 MB | NPU | Use Export Script |
-| HfWhisperEncoder | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | PRECOMPILED_QNN_ONNX | 90.593 ms | 149 - 163 MB | NPU | Use Export Script |
-| HfWhisperEncoder | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_CONTEXT_BINARY | 132.398 ms | 0 - 0 MB | NPU | Use Export Script |
-| HfWhisperEncoder | float | Snapdragon X Elite CRD | Snapdragon® X Elite | PRECOMPILED_QNN_ONNX | 135.347 ms | 226 - 226 MB | NPU | Use Export Script |
-| HfWhisperDecoder | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_CONTEXT_BINARY | 17.58 ms | 47 - 55 MB | NPU | Use Export Script |
-| HfWhisperDecoder | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_CONTEXT_BINARY | 18.304 ms | 53 - 68 MB | NPU | Use Export Script |
-| HfWhisperDecoder | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_CONTEXT_BINARY | 12.261 ms | 60 - 62 MB | NPU | Use Export Script |
-| HfWhisperDecoder | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_CONTEXT_BINARY | 13.14 ms | 57 - 66 MB | NPU | Use Export Script |
-| HfWhisperDecoder | float | SA7255P ADP | Qualcomm® SA7255P | QNN_CONTEXT_BINARY | 17.58 ms | 47 - 55 MB | NPU | Use Export Script |
-| HfWhisperDecoder | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_CONTEXT_BINARY | 11.926 ms | 55 - 57 MB | NPU | Use Export Script |
-| HfWhisperDecoder | float | SA8295P ADP | Qualcomm® SA8295P | QNN_CONTEXT_BINARY | 14.17 ms | 56 - 70 MB | NPU | Use Export Script |
-| HfWhisperDecoder | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_CONTEXT_BINARY | 11.95 ms | 60 - 65 MB | NPU | Use Export Script |
-| HfWhisperDecoder | float | SA8775P ADP | Qualcomm® SA8775P | QNN_CONTEXT_BINARY | 13.14 ms | 57 - 66 MB | NPU | Use Export Script |
-| HfWhisperDecoder | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_CONTEXT_BINARY | 12.158 ms | 57 - 60 MB | NPU | Use Export Script |
-| HfWhisperDecoder | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | PRECOMPILED_QNN_ONNX | 12.922 ms | 58 - 60 MB | NPU | Use Export Script |
-| HfWhisperDecoder | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_CONTEXT_BINARY | 9.384 ms | 60 - 79 MB | NPU | Use Export Script |
-| HfWhisperDecoder | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | PRECOMPILED_QNN_ONNX | 10.145 ms | 75 - 96 MB | NPU | Use Export Script |
-| HfWhisperDecoder | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_CONTEXT_BINARY | 8.102 ms | 60 - 74 MB | NPU | Use Export Script |
-| HfWhisperDecoder | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | PRECOMPILED_QNN_ONNX | 8.78 ms | 73 - 87 MB | NPU | Use Export Script |
-| HfWhisperDecoder | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_CONTEXT_BINARY | 10.312 ms | 60 - 60 MB | NPU | Use Export Script |
-| HfWhisperDecoder | float | Snapdragon X Elite CRD | Snapdragon® X Elite | PRECOMPILED_QNN_ONNX | 10.069 ms | 286 - 286 MB | NPU | Use Export Script |
-
-
-
-
-## Installation
-
-
-Install the package via pip:
-```bash
-pip install "qai-hub-models[whisper-small-v2]"
-```
-
-
-## 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_small_v2.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_v2.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_small_v2.export
-```
-
-
-
-## How does this work?
-
-This [export script](https://aihub.qualcomm.com/models/whisper_small_v2/qai_hub_models/models/Whisper-Small-V2/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_small_v2 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.
-```python
-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.
-```python
-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](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-Small-V2's performance across various devices [here](https://aihub.qualcomm.com/models/whisper_small_v2).
-Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
-
-
-## License
-* The license for the original implementation of Whisper-Small-V2 can be found
-  [here](https://github.com/huggingface/transformers/blob/v4.42.3/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/huggingface/transformers/tree/v4.42.3/src/transformers/models/whisper)
-
-
-
-## 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).
-
-
+This model is deprecated. Please refer to the new version of the model here: https://huggingface.co/qualcomm/Whisper-Small

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