Fabio
c36510c21e
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48 lines
1.6 KiB
C++
48 lines
1.6 KiB
C++
#pragma once
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#include "engine/framework/assets/tensor_source.h"
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#include "engine/framework/core/execution_context.h"
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#include <memory>
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#include <vector>
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namespace engine::models::auk {
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class VAEEncoderRuntime {
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public:
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VAEEncoderRuntime(core::ExecutionContext & execution, const assets::TensorSource & source, int64_t samples);
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~VAEEncoderRuntime();
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VAEEncoderRuntime(const VAEEncoderRuntime &) = delete;
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VAEEncoderRuntime & operator=(const VAEEncoderRuntime &) = delete;
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void prepare(int64_t samples);
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int64_t frames() const;
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// Mono 24 kHz input, noise in [1, 64, frames]; normalized output is [1, frames, 64].
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// Optional statistics capture is [1, 128, frames], before sampling.
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std::vector<float> encode(const std::vector<float> & audio, const std::vector<float> & noise,
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std::vector<float> * statistics = nullptr);
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private:
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struct State;
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std::unique_ptr<State> state_;
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};
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class VAEDecoderRuntime {
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public:
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VAEDecoderRuntime(core::ExecutionContext & execution, const assets::TensorSource & source, int64_t frames);
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~VAEDecoderRuntime();
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VAEDecoderRuntime(const VAEDecoderRuntime &) = delete;
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VAEDecoderRuntime & operator=(const VAEDecoderRuntime &) = delete;
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// Reuses the prepared shape; a different shape replaces only the graph workspace.
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void prepare(int64_t frames);
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// Normalized FM output in [1, frames, 64] order; returns mono 24 kHz samples.
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std::vector<float> decode(const std::vector<float> & latents);
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private:
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struct State;
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std::unique_ptr<State> state_;
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};
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} // namespace engine::models::auk
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