llama.cpp/examples/tts/CMakeLists.txt

set(TARGET llama-tts)
add_executable(${TARGET} tts.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE llama common ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_17)
tts : add OuteTTS support (#10784) * server : add "tokens" output ggml-ci * server : output embeddings for all tokens when pooling = none ggml-ci * server : be explicit about the pooling type in the tests ggml-ci * server : do not normalize embeddings when there is no pooling ggml-ci * llama : add OuteTTS support (wip) * wip * extract features * first conv * group norm * resnet conv * resnet * attn * pos net * layer norm * convnext * head * hann window * fix n_embd + remove llama.cpp hacks * compute hann window * fft * spectrum processing * clean-up * tts : receive input text and generate codes * clip : fix new conv name * tts : minor fix * tts : add header + minor fixes ggml-ci * tts : add matchematical constant ggml-ci * tts : fix sampling + cut initial noise * tts : fixes * tts : update default samplers ggml-ci * tts : text pre-processing * tts : outetts-voc -> wavtokenizer-dec * tts : remove hardcoded constants ggml-ci * tts : fix tensor shapes * llama : refactor wavtokenizer tensors ggml-ci * cont ggml-ci * cont [no ci] * llama : update WavTokenizer to non-causal attn * llama : handle no-vocab detokenization * tts : add Python example for OuteTTS (wip) * tts : extend python example to generate spectrogram ggml-ci * server : fix rebase artifacts * tts : enable "return_tokens" in Python example ggml-ci * tts : minor fixes * common : support HF download for vocoder 2024-12-18 18:27:21 +01:00			`set(TARGET llama-tts)`
			`add_executable(${TARGET} tts.cpp)`
			`install(TARGETS ${TARGET} RUNTIME)`
			`target_link_libraries(${TARGET} PRIVATE llama common ${CMAKE_THREAD_LIBS_INIT})`
			`target_compile_features(${TARGET} PRIVATE cxx_std_17)`