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Georgi Gerganov
8de452c18b
Improve decoding (#291)
* whisper : prepare infra for new decoding strategies
* whisper : apply logit filters and compute logprobs
* whisper : add whisper_get_logits()
* whisper : separate self and cross attention memory
Initial step needed for supporting parallel decoders
* whisper : move probs_id buffer to whisper_context
* whisper : refactor kv cache into separate struct
* whisper : move self-attention kv cache to whisper_decoder
* whisper : wip decoding parameters + strategies
* whisper : wip decoding parameters + strategies (part 2)
* whisper : wip decoding parameters + strategies (part 3)
* whisper : wip decoding parameters + strategies (part 4)
* whisper : fix prompt_past update to not include prompt_init
* whisper : temperature + best_of support
* whisper : support for compression_ration_threshold
We actually use entropy, but it is similar
* command : fix example to use logits instead of obsolete probs
* whisper : handle empty sequence ranking
* whisper : add WHISPER_DEBUG + diagnostic prints + new main args
* whisper : minor fixes
* whisper : add beam-search support
* whisper : bug fix when there no previous context
* whisper : add comments
* stream : disable temperature fallback
For real-time processing, we always want a single decoder running at T=0
* whisper.swiftui : update example - fix paths + add empty folders
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2 years ago |
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whisper.cpp.swift
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whisper : add loader class to allow loading from buffer and others (#353)
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2 years ago |
whisper.swiftui.demo
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Improve decoding (#291)
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2 years ago |
whisper.swiftui.xcodeproj
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Improve decoding (#291)
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2 years ago |
README.md
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A sample SwiftUI app using whisper.cpp to do voice-to-text transcriptions.
See also: whisper.objc.
To use:
- Select a model from the whisper.cpp repository.1
- Add the model to "whisper.swiftui.demo/Resources/models" via Xcode.
- Select a sample audio file (for example, jfk.wav).
- Add the model to "whisper.swiftui.demo/Resources/samples" via Xcode.
- Select the "release" build configuration under "Run", then deploy and run to your device.
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I recommend the tiny, base or small models for running on an iOS device. ↩︎