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Update README with mobile demo app details (#15308)
Added information about mobile demo apps for Android and iOS. ### Summary [PLEASE REMOVE] See [CONTRIBUTING.md's Pull Requests](https://github.com/pytorch/executorch/blob/main/CONTRIBUTING.md#pull-requests) for ExecuTorch PR guidelines. [PLEASE REMOVE] If this PR closes an issue, please add a `Fixes #<issue-id>` line. [PLEASE REMOVE] If this PR introduces a fix or feature that should be the upcoming release notes, please add a "Release notes: <area>" label. For a list of available release notes labels, check out [CONTRIBUTING.md's Pull Requests](https://github.com/pytorch/executorch/blob/main/CONTRIBUTING.md#pull-requests). ### Test plan [PLEASE REMOVE] How did you test this PR? Please write down any manual commands you used and note down tests that you have written if applicable.
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examples/models/voxtral/README.md

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@@ -198,7 +198,7 @@ Add the `--data_path` argument to provide the appropriate data blob to the comma
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- For CUDA: `--data_path path/to/aoti_cuda_blob.ptd`
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- For Metal: `--data_path path/to/aoti_metal_blob.ptd`
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# Example output:
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### Example output:
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```
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The speaker in this audio seems to be talking about their concerns about a device called the model or maybe they're just talking about the model in general. They mention that the model was trained with the speaker for inference, which suggests that
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the model was trained based on the speaker's data or instructions. They also mention that the volume is quite small, which could imply that the speaker is trying to control the volume of the model's output, likely because they are concerned about how loud the model's responses might
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I 00:00:24.036828 executorch:stats.h:153] Sampling time over 487 tokens: 0.099000 (seconds)
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```
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# Generating audio input
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## Generating audio input
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You can easily produce an `.bin` for the audio input in Python like this:
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```
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# t = some torch.Tensor
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# Convert to .wav format
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afconvert -f WAVE -d LEI16 call_samantha_hall.aiff call_samantha_hall.wav
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```
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## Android and iOS mobile demo apps
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We have example mobile demo apps for Android and iOS (using XNNPACK) [here](https://github.com/meta-pytorch/executorch-examples/tree/main/llm)

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