cleanup
This commit is contained in:
parent
82e8592f2a
commit
8516bab15c
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@ -6,7 +6,7 @@ At the moment it's ***very*** work in progress.
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## Build
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Populate `./include/` with the `llama.cpp` and `encodec.cpp` headers.
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Populate `./include/` with the `ggml`, `llama.cpp`, and `encodec.cpp` headers.
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Populate `./libs/` with the compiled libraries of `llama.cpp` and `encodec.cpp`.
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@ -35,10 +35,14 @@ Run `make`.
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* [x] sum embeddings for the `prom` and prior `resp`s
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* [x] working `AR` output
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* [x] `AR` sampling
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* [ ] working `NAR-len` output
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* [x] working `NAR-len` output
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* [x] `NAR-len` sampling
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* [ ] working `NAR` output
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* [x] working `NAR` output
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* [x] `NAR` sampling
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* [x] decode audio to disk
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* [ ] a functional CLI
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* [ ] actually make it work
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* [x] actually make it work
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* [ ] clean up to make the code usable
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* [ ] feature parity with the PyTorch version
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* [ ] vocos
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* [ ] additional tasks (`stt`, `ns`, `sr`, samplers)
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@ -49,7 +49,7 @@ std::vector<float> VALL_E_API read_2d_tensor( struct ggml_tensor* tensor ) {
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return res;
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}
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/*
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ggml_tensor* VALL_E_API view_2d_tensor( struct ggml_tensor* tensor, int32_t start, int32_t end, int32_t dim ) {
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// to-do: implement other dim
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if ( start < 0 ) start = tensor->ne[1] + start;
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@ -70,6 +70,7 @@ ggml_tensor* VALL_E_API view_2d_tensor( struct ggml_tensor* tensor, int32_t star
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return res;
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}
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*/
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ggml_tensor* VALL_E_API view_2d_tensor( struct ggml_context* ctx, struct ggml_tensor* tensor, int32_t start, int32_t end, int32_t dim ) {
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// to-do: implement other dim
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if ( start < 0 ) start = tensor->ne[1] + start;
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@ -80,7 +81,7 @@ ggml_tensor* VALL_E_API view_2d_tensor( struct ggml_context* ctx, struct ggml_te
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return res;
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}
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void VALL_E_API print_tokens( const std::vector<llama_token>& tokens, const std::string& prefix ) {
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void VALL_E_API print_tokens( const std::vector<token_t>& tokens, const std::string& prefix ) {
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printf("%s[", prefix.c_str());
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for ( auto i = 0; i < tokens.size(); ++i ) {
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printf("%i%s", tokens[i], i + 1 < tokens.size() ? ", " : "");
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@ -171,7 +172,7 @@ void VALL_E_API vall_e_inputs_map_init( io_map_t& io_map, llama_model* model ) {
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}
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// maps embeddings easily
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std::vector<std::vector<float>> VALL_E_API map_embeddings( const std::vector<llama_token>& tokens, int n_embd, const float* embds ) {
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std::vector<std::vector<float>> VALL_E_API map_embeddings( const std::vector<token_t>& tokens, int n_embd, const float* embds ) {
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std::vector<std::vector<float>> embedded( tokens.size() );
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for ( auto i = 0; i < tokens.size(); ++i ) {
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embedded[i].insert( embedded[i].end(), embds + (tokens[i] * n_embd), embds + ((tokens[i]+1) * n_embd) );
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@ -181,7 +182,7 @@ std::vector<std::vector<float>> VALL_E_API map_embeddings( const std::vector<lla
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// handles adding either a token OR the embedding of that token into the batch
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// this really, really helps avoid needing to abuse the tokenizer
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void VALL_E_API batch_add( llama_batch& batch, llama_token id, int n_embd, const float* embds, llama_pos pos, bool output, const std::vector<llama_seq_id> & seq_ids ) {
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void VALL_E_API batch_add( llama_batch& batch, token_t id, int n_embd, const float* embds, llama_pos pos, bool output, const std::vector<llama_seq_id> & seq_ids ) {
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GGML_ASSERT(batch.seq_id[batch.n_tokens] && "llama_batch size exceeded");
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// insert raw embedding instead
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@ -203,35 +204,36 @@ void VALL_E_API batch_add( llama_batch& batch, llama_token id, int n_embd, const
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batch.n_tokens++;
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}
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// reads a waveform from disk
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bool VALL_E_API read_wav_from_disk(std::string in_path, std::vector<float> & audio_arr) {
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std::vector<float> VALL_E_API read_audio_from_disk( const std::string& path ) {
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std::vector<float> res;
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uint32_t channels;
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uint32_t sample_rate;
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drwav_uint64 total_frame_count;
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float * raw_audio = drwav_open_file_and_read_pcm_frames_f32(
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in_path.c_str(), &channels, &sample_rate, &total_frame_count, NULL);
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float * raw_audio = drwav_open_file_and_read_pcm_frames_f32(path.c_str(), &channels, &sample_rate, &total_frame_count, NULL);
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if (raw_audio == NULL) {
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fprintf(stderr, "%s: could not read wav file\n", __func__);
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return false;
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return res;
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}
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if (sample_rate != 24000) {
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fprintf(stderr, "%s: wav file is wrong sample rate\n", __func__);
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return false;
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return res;
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}
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fprintf(stderr, "\n%s: Number of frames read = %lld.\n", __func__, total_frame_count);
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audio_arr.resize(total_frame_count);
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memcpy(audio_arr.data(), raw_audio, total_frame_count * sizeof(float));
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res.resize(total_frame_count);
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memcpy(res.data(), raw_audio, total_frame_count * sizeof(float));
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drwav_free(raw_audio, NULL);
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return true;
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return res;
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}
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// writes a waveform to disk
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void VALL_E_API write_wav_on_disk(std::vector<float> & audio_arr, std::string dest_path) {
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void VALL_E_API write_audio_to_disk( const std::vector<float>& wavform, const std::string& path ) {
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drwav_data_format format;
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format.bitsPerSample = 32;
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format.sampleRate = 24000;
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@ -240,22 +242,14 @@ void VALL_E_API write_wav_on_disk(std::vector<float> & audio_arr, std::string de
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format.format = DR_WAVE_FORMAT_IEEE_FLOAT;
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drwav wav;
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drwav_init_file_write(&wav, dest_path.c_str(), &format, NULL);
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drwav_uint64 frames = drwav_write_pcm_frames(&wav, audio_arr.size(), audio_arr.data());
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drwav_init_file_write(&wav, path.c_str(), &format, NULL);
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drwav_uint64 frames = drwav_write_pcm_frames(&wav, wavform.size(), wavform.data());
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drwav_uninit(&wav);
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fprintf(stderr, "%s: Number of frames written = %lld.\n", __func__, frames);
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}
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// reads a waveform from disk then encodes it
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std::vector<std::vector<int32_t>> VALL_E_API encode_audio_from_disk( struct encodec_context* ectx, const std::string& path ) {
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// read audio from disk
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std::vector<float> wavform;
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if(!read_wav_from_disk(path, wavform)) {
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fprintf(stderr, "%s: error during reading wav file\n", __func__);
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return {};
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}
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std::vector<std::vector<int32_t>> VALL_E_API encode_audio( struct encodec_context* ectx, const std::vector<float>& wavform ) {
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// compress audio
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if (!encodec_compress_audio(ectx, wavform.data(), wavform.size(), 1)) {
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fprintf(stderr, "%s: error during compression \n", __func__);
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@ -301,23 +295,22 @@ std::vector<float> VALL_E_API decode_audio( struct encodec_context* ectx, const
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}
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// sums embeddings over a 2D "tensor"
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std::vector<std::vector<float>> VALL_E_API sum_embeddings( const std::vector<std::vector<llama_token>>& input, int n_embd, int rvq_l, const float** embds, int mode ) {
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auto n_tokens = input[0].size();
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//auto n_embd = input[0].size();
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std::vector<std::vector<float>> VALL_E_API sum_embeddings( const std::vector<std::vector<token_t>>& inputs, int n_embd, int rvq_l, const float** embds, int mode ) {
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auto n_tokens = inputs[0].size();
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std::vector<std::vector<float>> res( n_tokens, std::vector<float>( n_embd, 0.0 ) );
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// iterate through rvq levels (only up to inclusive the target rvq level)
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for ( auto l = 0; l < input.size() && l <= rvq_l; ++l ) {
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for ( auto l = 0; l < inputs.size() && l <= rvq_l; ++l ) {
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int offset = 0;
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// handles the cringe logic I have
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if ( mode == EMBEDDING_MODE_RESP_AR_NAR ) {
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offset = input.size() == 1 ? 0 : 1;
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offset = inputs.size() == 1 ? 0 : 1;
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} else if ( mode == EMBEDDING_MODE_RESP_NAR_LEN ) {
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offset = input.size() == 1 ? 8 : 1;
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offset = inputs.size() == 1 ? 8 : 1;
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}
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// embed the current level's tokens
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auto embedded = map_embeddings( input[l], n_embd, embds[l + offset] );
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auto embedded = map_embeddings( inputs[l], n_embd, embds[l + offset] );
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for ( auto idx = 0; idx < n_tokens; ++idx ) {
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for ( auto embd_idx = 0; embd_idx < n_embd; ++embd_idx ) {
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@ -360,7 +353,7 @@ std::vector<float> VALL_E_API log_soft_max( int n_logits, const float* logits )
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return res;
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}
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void VALL_E_API fill_batch( llama_batch& batch, input_t& input, io_map_t& io_map, int mode ) {
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void VALL_E_API fill_batch( llama_batch& batch, vall_e_inputs_t& inputs, io_map_t& io_map, int mode ) {
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// keeps track of the position for each sequence
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size_t pos = 0;
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auto n_embd = io_map.n_embd;
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@ -395,34 +388,34 @@ void VALL_E_API fill_batch( llama_batch& batch, input_t& input, io_map_t& io_map
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};
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// insert text tokens
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for ( auto& id : input.phn ) batch_add( batch, id, n_embd, text_embds, pos++, false );
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for ( auto& id : inputs.phn ) batch_add( batch, id, n_embd, text_embds, pos++, false );
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batch_add( batch, 0, n_embd, sep_embds, pos++, false );
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pos = 0;
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// insert lang token
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batch_add( batch, input.lang, n_embd, lang_embds, pos++, false );
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batch_add( batch, inputs.lang, n_embd, lang_embds, pos++, false );
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batch_add( batch, 0, n_embd, sep_embds, pos++, false );
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pos = 0;
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// insert rvq level token
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batch_add( batch, input.rvq_l, n_embd, rvq_l_embds, pos++, false );
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batch_add( batch, inputs.rvq_l, n_embd, rvq_l_embds, pos++, false );
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batch_add( batch, 0, n_embd, sep_embds, pos++, false );
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pos = 0;
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// insert prom tokens
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auto summed_proms_embds = sum_embeddings( input.prom, n_embd, input.rvq_l, prom_embds );
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auto summed_proms_embds = sum_embeddings( inputs.prom, n_embd, inputs.rvq_l, prom_embds );
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for ( auto i = 0; i < summed_proms_embds.size(); ++i ) {
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batch_add( batch, -1, n_embd, summed_proms_embds[i].data(), pos++, false );
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}
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batch_add( batch, 0, n_embd, sep_embds, pos++, mode == INFERENCE_MODE_AR ); // set as the last logit if AR
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pos = 0;
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// input starting len token
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if ( input.task == "len" ) {
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// inputs starting len token
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if ( inputs.task == "len" ) {
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batch_add( batch, 0, n_embd, len_embds, pos++, true );
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pos = 0;
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}
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// insert resp tokens
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if ( !input.resp.empty() ) {
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auto summed_resps_embds = sum_embeddings( input.resp, n_embd, input.rvq_l, resp_embds, mode == INFERENCE_MODE_AR ? EMBEDDING_MODE_RESP_AR_NAR : EMBEDDING_MODE_RESP_NAR_LEN );
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if ( !inputs.resp.empty() ) {
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auto summed_resps_embds = sum_embeddings( inputs.resp, n_embd, inputs.rvq_l, resp_embds, mode == INFERENCE_MODE_AR ? EMBEDDING_MODE_RESP_AR_NAR : EMBEDDING_MODE_RESP_NAR_LEN );
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for ( auto i = 0; i < summed_resps_embds.size(); ++i ) {
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batch_add( batch, -1, n_embd, &summed_resps_embds[i][0], pos++, true );
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}
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@ -431,13 +424,13 @@ void VALL_E_API fill_batch( llama_batch& batch, input_t& input, io_map_t& io_map
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}
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// generation code, should handle all modalities easily
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std::vector<llama_token> VALL_E_API generate( llama_context* ctx, llama_model* model, input_t& input, io_map_t& io_map, int max_tokens, int mode, bool verbose ) {
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std::vector<token_t> VALL_E_API generate( vall_e_context_t* ctx, vall_e_inputs_t& inputs, int max_tokens, int mode, bool verbose ) {
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bool causal = true; // sample autoregressively or not
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int n_outputs = 0; // number of output tokens to expect
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// create batch (targetting embeddings instead of tokens)
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llama_batch batch = llama_batch_init( CTX_SIZE, io_map.n_embd, CTX_SIZE );
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fill_batch( batch, input, io_map, mode );
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llama_batch batch = llama_batch_init( ctx->params.ctx_size, ctx->io_map.n_embd, ctx->params.ctx_size );
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fill_batch( batch, inputs, ctx->io_map, mode );
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// determine how many outputs we need
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for ( auto i = 0; i < batch.n_tokens; ++i ) {
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@ -468,7 +461,7 @@ std::vector<llama_token> VALL_E_API generate( llama_context* ctx, llama_model* m
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"resps|NAR:5:6",
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"resps|NAR:6:7",
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};
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embd_name = k_embds[input.rvq_l];
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embd_name = k_embds[inputs.rvq_l];
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// duration inferencing mode
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} else if ( mode == INFERENCE_MODE_LEN ) {
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embd_name = "len";
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@ -477,24 +470,24 @@ std::vector<llama_token> VALL_E_API generate( llama_context* ctx, llama_model* m
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embd_name = "resps|NAR:0:0";
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}
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auto& io = vall_e_inputs_map_get(io_map, embd_name);
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auto& io = vall_e_inputs_map_get(ctx->io_map, embd_name);
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const float* embds = io.embds.data();
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int32_t n_embd = io.n_embd;
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int32_t n_vocab = io.n_vocab;
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llama_token stop_token = io.end - io.start - 1;
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token_t stop_token = io.end - io.start - 1;
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if ( verbose ) printf("Generating in %s (%i) mode (%i:%i) (%i)\n", embd_name.c_str(), io.head_idx, io.start, io.end, stop_token);
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// update model's output heads / causal mode
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llama_set_output_head( model, io.head );
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llama_set_output_head( ctx->llama.model, io.head );
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// to-do: figure this out......
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{
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llama_set_causal_attn( ctx, causal ); // to-do: fix GGML_ASSERT(mask->ne[0] == a->ne[0])
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llama_set_causal_attn( ctx->llama.ctx, causal ); // to-do: fix GGML_ASSERT(mask->ne[0] == a->ne[0])
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// *const_cast<bool*>(&model->hparams.causal_attn) = true; // force set this
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}
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std::vector<llama_token> output_tokens;
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std::vector<token_t> output_tokens;
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const auto t_main_start = ggml_time_us();
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// if INFERENCE_MODE_AR || INFERENCE_MODE_LEN
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@ -510,14 +503,14 @@ std::vector<llama_token> VALL_E_API generate( llama_context* ctx, llama_model* m
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output_tokens.reserve(max_tokens);
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while ( output_tokens.size() < max_tokens ) {
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if ( llama_decode(ctx, batch) ) {
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if ( llama_decode(ctx->llama.ctx, batch) ) {
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fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1);
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return output_tokens;
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}
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llama_kv_cache_clear(ctx); // necessary for many reasons
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llama_kv_cache_clear(ctx->llama.ctx); // necessary for many reasons
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// sample token
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auto t = llama_sampler_sample(smpl, ctx, -1);
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auto t = llama_sampler_sample(smpl, ctx->llama.ctx, -1);
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// is stop token
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if ( t == stop_token ) {
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@ -527,15 +520,15 @@ std::vector<llama_token> VALL_E_API generate( llama_context* ctx, llama_model* m
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// store token
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output_tokens.emplace_back(t);
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// update batch with token
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batch_add( batch, t, io_map.n_embd, embds, output_tokens.size(), true );
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batch_add( batch, t, ctx->io_map.n_embd, embds, output_tokens.size(), true );
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if ( verbose ) print_tokens( output_tokens );
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}
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llama_sampler_free(smpl);
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} else if ( mode == INFERENCE_MODE_NAR_DEMASK ) {
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// to-do: assert n_outputs == input.resp[rvq_l-1].size()
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const llama_token MASK_TOKEN = 1024; // token value for masking
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// to-do: assert n_outputs == inputs.resp[rvq_l-1].size()
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const token_t MASK_TOKEN = 1024; // token value for masking
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const float PI = 3.141592653589793f;
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// to-do: derive from sampling arguments
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int32_t steps = 10; // number of demasking steps
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@ -548,11 +541,11 @@ std::vector<llama_token> VALL_E_API generate( llama_context* ctx, llama_model* m
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output_tokens.resize(n_outputs, MASK_TOKEN);
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// for CFG
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input_t null_input{};
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vall_e_inputs_t null_input{};
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null_input.phn = {1, 2}; // <bos></eos>
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null_input.resp.resize(1);
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llama_batch null_batch = llama_batch_init( CTX_SIZE, io_map.n_embd, CTX_SIZE );
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llama_batch null_batch = llama_batch_init( ctx->params.ctx_size, ctx->io_map.n_embd, ctx->params.ctx_size );
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// token scores to reference for masking
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std::vector<float> scores(n_outputs, 1.0);
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|
|
@ -598,29 +591,29 @@ std::vector<llama_token> VALL_E_API generate( llama_context* ctx, llama_model* m
|
|||
// update batch
|
||||
// to-do: only update the embeddings instead
|
||||
batch.n_tokens = 0;
|
||||
input.resp[0] = output_tokens;
|
||||
fill_batch( batch, input, io_map, mode );
|
||||
inputs.resp[0] = output_tokens;
|
||||
fill_batch( batch, inputs, ctx->io_map, mode );
|
||||
// update null batch
|
||||
null_input.resp[0] = output_tokens;
|
||||
null_batch.n_tokens = 0;
|
||||
fill_batch( null_batch, input, io_map, mode );
|
||||
fill_batch( null_batch, inputs, ctx->io_map, mode );
|
||||
|
||||
// cfg decode
|
||||
if ( llama_decode(ctx, null_batch) ) {
|
||||
if ( llama_decode(ctx->llama.ctx, null_batch) ) {
|
||||
fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1);
|
||||
return output_tokens;
|
||||
}
|
||||
llama_kv_cache_clear(ctx); // necessary for many reasons
|
||||
llama_kv_cache_clear(ctx->llama.ctx); // necessary for many reasons
|
||||
// copy null probabilities
|
||||
std::vector<float> null_logits(n_outputs * n_vocab, 0.0f);
|
||||
memcpy( null_logits.data(), llama_get_logits( ctx ), sizeof(float) * n_vocab * n_outputs );
|
||||
memcpy( null_logits.data(), llama_get_logits( ctx->llama.ctx ), sizeof(float) * n_vocab * n_outputs );
|
||||
|
||||
// decode
|
||||
if ( llama_decode(ctx, batch) ) {
|
||||
if ( llama_decode(ctx->llama.ctx, batch) ) {
|
||||
fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1);
|
||||
return output_tokens;
|
||||
}
|
||||
llama_kv_cache_clear(ctx); // necessary for many reasons
|
||||
llama_kv_cache_clear(ctx->llama.ctx); // necessary for many reasons
|
||||
|
||||
auto sparams = llama_sampler_chain_default_params();
|
||||
sparams.no_perf = false;
|
||||
|
|
@ -631,15 +624,7 @@ std::vector<llama_token> VALL_E_API generate( llama_context* ctx, llama_model* m
|
|||
llama_sampler_chain_add(smpl, llama_sampler_init_temp (sampling_temperature));
|
||||
llama_sampler_chain_add(smpl, llama_sampler_init_dist (LLAMA_DEFAULT_SEED));
|
||||
|
||||
auto* logits = llama_get_logits( ctx );
|
||||
|
||||
/*
|
||||
// perform CFG sampling
|
||||
for ( auto i = 0; i < n_vocab * n_outputs; ++i ) {
|
||||
logits[i] = null_logit[i] + (logits[i] - null_logit[i]) * cfg_strength;
|
||||
}
|
||||
*/
|
||||
|
||||
auto* logits = llama_get_logits( ctx->llama.ctx );
|
||||
for ( auto idx = 0; idx < n_outputs; ++idx ) {
|
||||
// skip if not masked
|
||||
if ( !is_masked[idx] ) {
|
||||
|
|
@ -657,7 +642,7 @@ std::vector<llama_token> VALL_E_API generate( llama_context* ctx, llama_model* m
|
|||
}
|
||||
|
||||
// sample ith token
|
||||
auto t = llama_sampler_sample(smpl, ctx, batch.n_tokens - n_outputs + idx );
|
||||
auto t = llama_sampler_sample(smpl, ctx->llama.ctx, batch.n_tokens - n_outputs + idx );
|
||||
// store token if it was masked
|
||||
output_tokens[idx] = t;
|
||||
// update score if it was masked
|
||||
|
|
@ -669,15 +654,15 @@ std::vector<llama_token> VALL_E_API generate( llama_context* ctx, llama_model* m
|
|||
if ( verbose ) print_tokens( output_tokens );
|
||||
}
|
||||
} else if ( mode == INFERENCE_MODE_NAR ) {
|
||||
// to-do: assert n_outputs == input.resp[rvq_l-1].size()
|
||||
// to-do: assert n_outputs == inputs.resp[rvq_l-1].size()
|
||||
output_tokens.clear();
|
||||
output_tokens.resize(n_outputs);
|
||||
// do one step on many tokens
|
||||
if ( llama_decode(ctx, batch) ) {
|
||||
if ( llama_decode(ctx->llama.ctx, batch) ) {
|
||||
fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1);
|
||||
return output_tokens;
|
||||
}
|
||||
llama_kv_cache_clear(ctx); // necessary for many reasons
|
||||
llama_kv_cache_clear(ctx->llama.ctx); // necessary for many reasons
|
||||
|
||||
auto sparams = llama_sampler_chain_default_params();
|
||||
sparams.no_perf = false;
|
||||
|
|
@ -690,7 +675,7 @@ std::vector<llama_token> VALL_E_API generate( llama_context* ctx, llama_model* m
|
|||
|
||||
for ( auto idx = 0; idx < n_outputs; ++idx ) {
|
||||
// sample ith token
|
||||
auto t = llama_sampler_sample(smpl, ctx, batch.n_tokens - n_outputs + idx);
|
||||
auto t = llama_sampler_sample(smpl, ctx->llama.ctx, batch.n_tokens - n_outputs + idx);
|
||||
|
||||
// store token
|
||||
output_tokens[idx] = t;
|
||||
|
|
@ -708,7 +693,7 @@ std::vector<llama_token> VALL_E_API generate( llama_context* ctx, llama_model* m
|
|||
__func__, output_tokens.size(), (t_main_end - t_main_start) / 1000000.0f, output_tokens.size() / ((t_main_end - t_main_start) / 1000000.0f));
|
||||
|
||||
fprintf(stderr, "\n");
|
||||
llama_perf_context_print(ctx);
|
||||
llama_perf_context_print(ctx->llama.ctx);
|
||||
fprintf(stderr, "\n");
|
||||
}
|
||||
|
||||
|
|
@ -717,137 +702,93 @@ std::vector<llama_token> VALL_E_API generate( llama_context* ctx, llama_model* m
|
|||
return output_tokens;
|
||||
}
|
||||
|
||||
int main( int argc, char** argv ) {
|
||||
// to-do: replace all of this with proper loading code
|
||||
int32_t ngl = 0;
|
||||
int modality = MODALITY_NAR_LEN;
|
||||
input_t input{};
|
||||
io_map_t io_map{};
|
||||
std::vector<token_t> VALL_E_API phonemize( vall_e_context_t* ctx, const std::string& text, const std::string& language ) {
|
||||
return {1,22,111,100,4,37,115,169,11,2}; // <bos>hˈɛloː ʋˈɔrlt</eos>
|
||||
/*
|
||||
const int n_prompt = -llama_tokenize(model, inputs.phonemes.c_str(), inputs.phonemes.size(), NULL, 0, true, true);
|
||||
// allocate space for the tokens and tokenize the inputs.phonemes
|
||||
inputs.phn.resize(n_prompt);
|
||||
if (llama_tokenize(model, inputs.phonemes.c_str(), inputs.phonemes.size(), inputs.phn.data(), inputs.phn.size(), true, true) < 0) {
|
||||
fprintf(stderr, "%s: error: failed to tokenize: %s\n", __func__, inputs.phonemes.c_str());
|
||||
return 1;
|
||||
}
|
||||
|
||||
// input.phonemes = "hˈɛloː ʋˈɔrlt";
|
||||
input.phn = {1,22,111,100,4,37,115,169,11,2}; // <bos>hˈɛloː ʋˈɔrlt</eos>
|
||||
input.prom = {
|
||||
{62,835,835,835,339,395,798,537,537,537,537,222,76,989,548,65,705,375,261,375,297,503,529,571,707,346,266,862,148,496,574,115,115,438,934,339,865,876,63,40,779,461,602,794,10,220,507,869,639,705,869,917,705,893,917,705,869,938,439,175,139,506,375,529,297,705,651,238,962,461,195,441,377,581,473,795,644,626,459,981,767,670,696,73,779,257,738,1017,1019,133,133,1017,835,604,699,626,67,92,707,92,179,179,772,869,441,799,630,238,745,904,904,904,106,133,133,1017,1017,395,883,87,519,594,1002,682,996,540,186,855,430,202,347,889,61,92,542,297,67,669,571,707,346,67,359,571,707,669,604,395,1008,810,35,621,67,600,333,123,284,568,817,243,778,464,638,610,359,538,464,975,321,700,377,484,179,284,284,621,538,464,745,171,171,159,744,744,287,461,69,15,529,67,92,669,464,515,605,24,822,865,293,865,172,638,359,562,138,839,846,775,556,775,1006,917,346,312,148,331,496,646,67,314,15,705,131,855,662,287,172,85,107,519,374,450,391,609,643,778,80,287,794,794,115,785,794,461,699,519,932,522,652,262,508,902,932,932,391,769,18,507,90,442,762,610,610,669,605,35,855,56,989,863,195,464,604,257,904,632,786,951,461,239,195,878,771,146,481,146,481,434,643,917,280,67,464,115,744,744,115,115,115,819,709,63,907,359,519,996,616,682,996,616,519,762,917,841,772,568,954,600,422,893,592,464,626,86,143,615,171,744,744,196,115,821,415,521,799,654,839,644,473,592,953,523,855,738,855,855,876,1017,63,329},
|
||||
{913,859,740,740,937,601,961,961,877,747,747,559,474,618,20,316,58,316,180,112,290,869,610,869,869,943,127,153,236,794,282,857,984,196,875,648,993,913,860,616,38,833,620,133,123,992,247,367,252,50,298,27,27,631,163,784,271,20,843,514,869,258,180,66,803,281,123,493,831,102,556,992,385,122,31,251,990,827,26,347,460,43,43,460,228,43,841,913,302,544,544,277,859,404,646,775,315,848,726,185,203,314,203,174,252,174,378,954,214,993,924,809,277,765,363,544,363,518,791,185,454,193,193,193,193,193,573,977,924,76,434,56,193,962,610,24,954,459,396,112,903,137,398,474,506,791,839,399,102,25,205,792,459,474,526,817,869,192,792,593,878,506,24,410,539,788,522,667,566,584,588,992,444,24,869,925,635,393,903,742,320,1023,833,136,216,924,220,24,563,630,968,96,708,24,708,127,399,364,67,740,381,981,203,248,607,744,252,996,474,582,248,527,423,25,387,94,229,775,122,474,792,367,650,371,413,448,448,784,506,795,848,298,27,526,96,905,70,693,956,1002,1002,37,747,857,993,124,193,193,193,193,732,732,732,992,447,792,929,291,289,524,451,27,27,524,202,693,374,1002,125,732,585,367,317,679,395,413,189,493,386,650,110,912,505,384,399,851,367,367,27,230,988,810,975,842,956,1002,4,551,729,956,1002,750,648,231,950,193,96,912,410,732,539,103,193,904,491,213,792,792,998,193,399,151,410,96,673,497,1002,241,833,956,630,43,399,775,732,792,792,792,792,917,750,185,812,812,700,859,841,363,833,630},
|
||||
{786,36,821,937,1000,705,1016,345,345,470,165,581,95,404,95,95,1006,477,95,95,691,254,997,657,176,124,95,673,489,326,218,437,907,590,752,541,1016,821,445,563,181,555,181,345,576,190,987,0,265,997,488,12,598,687,152,108,52,95,95,71,87,945,95,997,754,488,955,694,925,82,18,1020,1006,542,788,441,325,532,246,132,560,532,947,655,653,842,732,36,36,829,36,937,989,989,752,651,87,489,677,260,789,462,95,227,986,955,95,810,624,435,280,868,832,879,863,821,829,937,168,270,489,544,909,562,957,0,593,714,675,690,626,227,794,489,489,563,489,298,269,741,249,516,360,240,516,336,93,808,1022,682,555,737,147,405,476,895,323,694,412,689,963,72,193,298,181,521,741,193,93,153,773,677,689,495,30,564,719,1020,559,940,53,53,53,929,360,971,403,1012,997,919,957,433,919,787,401,401,355,276,370,414,690,697,330,629,552,930,720,259,579,221,62,945,135,1020,626,663,401,153,997,381,830,185,587,853,207,126,66,529,410,113,997,488,431,563,488,488,719,746,790,296,843,752,790,23,984,292,41,27,120,249,124,900,358,801,227,978,95,997,997,997,371,561,86,388,52,667,601,894,545,997,498,900,494,365,852,986,95,841,664,256,18,1020,963,901,447,498,262,388,691,997,646,651,757,468,114,601,437,940,212,655,541,970,870,521,237,957,563,794,563,564,620,489,351,489,489,257,733,629,489,227,622,962,7,598,374,470,114,159,211,298,363,843,818,153,59,452,529,258,419,605,689,526,39,982,829,982,752,678,1005,312},
|
||||
{673,673,919,866,762,961,52,674,528,528,675,526,12,753,297,967,661,845,482,303,338,1021,506,445,247,214,206,94,434,799,210,885,552,695,853,1022,916,762,764,721,445,434,529,999,771,708,767,498,282,736,227,150,299,12,536,767,321,561,12,530,147,530,262,325,196,990,874,997,944,875,426,12,282,571,571,282,365,534,365,424,89,388,563,222,31,1019,624,74,215,651,1018,74,956,1022,74,18,633,350,72,448,454,769,267,938,12,534,929,723,829,614,505,364,1018,1014,838,673,919,74,223,761,266,78,177,736,20,718,425,1001,366,58,874,58,153,627,312,197,801,530,767,674,196,633,327,425,376,413,1019,209,594,383,744,458,468,711,282,885,640,435,655,571,556,1020,310,116,273,116,504,633,15,736,633,448,662,612,487,345,19,612,665,556,198,778,705,403,706,31,196,197,536,805,427,339,161,241,116,504,58,945,853,734,670,424,807,19,397,175,144,419,19,221,697,68,321,800,210,824,972,712,911,362,427,694,182,651,972,863,684,887,548,806,27,627,639,432,193,103,198,436,837,366,212,125,1001,493,874,808,17,17,127,204,530,300,345,425,246,240,640,906,340,310,633,246,774,114,633,522,777,874,494,577,353,939,571,693,857,722,530,521,354,492,735,214,806,483,736,530,118,234,536,177,132,522,349,259,436,973,528,414,224,762,212,854,744,271,568,127,323,736,304,499,499,78,536,736,805,232,126,468,566,611,52,339,450,258,157,602,594,854,602,599,82,124,472,563,666,174,936,818,66,758,627,52,350,999,734,215,919,1018,874,885},
|
||||
{528,448,646,190,222,884,939,907,907,673,413,786,527,517,710,449,119,531,565,762,531,501,522,246,162,871,8,594,206,937,462,712,862,151,103,261,882,990,1007,314,683,864,693,812,319,786,107,531,31,342,632,460,269,429,531,531,717,417,321,671,1015,152,467,863,285,875,941,417,475,825,596,957,117,460,162,162,117,630,735,527,272,558,38,39,605,375,39,900,862,646,712,804,622,963,407,93,828,796,306,415,70,667,371,531,1000,411,710,162,812,381,673,498,691,884,928,712,528,48,630,24,593,901,973,579,722,75,139,909,919,328,764,393,777,753,512,577,175,577,512,922,834,863,30,69,94,68,616,691,835,335,486,345,306,374,732,938,580,311,715,495,527,1008,306,369,663,512,369,320,360,80,42,1021,1021,1021,175,568,526,362,320,317,488,613,937,548,966,545,596,177,306,480,522,577,512,512,638,1008,82,100,696,89,714,531,639,460,679,718,492,509,492,624,460,572,531,306,19,473,915,558,285,319,713,1018,381,877,667,425,905,43,437,632,634,324,306,207,324,303,48,69,467,39,902,599,3,617,465,78,918,459,1009,427,751,145,531,349,356,1021,157,507,780,624,165,507,144,270,94,414,899,379,947,994,853,107,586,652,877,92,19,91,188,544,624,470,503,513,13,192,563,145,531,618,743,470,62,701,499,436,679,505,198,959,3,766,839,437,491,395,1021,512,306,512,356,851,1021,1021,78,690,856,735,286,280,4,1008,369,359,309,651,864,561,170,692,952,877,520,959,306,37,1021,31,236,162,773,522,254,446,606,691,804,882,58,974},
|
||||
{1011,939,881,881,140,937,724,724,937,1011,381,229,965,251,745,69,305,206,566,813,503,116,940,127,353,621,57,779,595,744,755,530,701,862,760,443,293,768,156,281,960,504,327,979,55,790,545,953,830,759,667,485,861,63,485,55,898,581,520,49,99,651,940,945,685,621,728,487,650,530,934,378,522,522,522,996,534,522,739,534,378,543,94,602,390,948,692,692,41,41,768,412,982,692,692,774,176,791,526,497,57,940,542,685,694,916,813,890,357,193,430,863,929,412,412,903,140,763,465,707,569,925,859,985,24,411,835,298,293,791,837,460,182,296,137,474,809,111,376,1021,111,490,111,938,542,578,477,506,57,385,300,873,240,104,667,204,515,834,24,125,113,980,111,997,859,997,376,193,490,824,511,799,719,575,451,575,251,222,630,429,920,788,300,993,641,154,816,940,618,130,940,462,823,955,1001,569,508,632,2,903,399,333,709,489,726,932,725,777,970,843,717,940,211,534,274,161,392,103,31,462,813,985,638,213,352,219,236,381,287,111,87,818,953,112,336,980,1016,72,960,426,238,60,9,487,665,129,24,24,162,312,411,111,157,473,466,222,940,341,55,457,712,179,451,111,831,918,826,814,940,30,468,240,207,389,923,186,95,300,876,679,576,543,582,111,227,312,112,545,747,378,165,158,610,601,425,238,704,630,124,644,949,982,297,868,569,24,57,465,24,859,111,24,752,775,24,647,465,495,57,24,57,227,907,296,581,843,1013,514,555,319,937,347,478,186,684,15,241,534,369,381,846,578,314,711,814,435,41,986,673,991},
|
||||
{485,748,562,562,485,380,834,997,78,963,755,142,978,135,362,421,217,79,530,1012,972,946,127,587,838,818,456,548,424,479,944,650,694,447,391,616,938,908,206,259,998,292,818,128,353,273,566,796,333,146,110,986,571,451,166,229,421,300,911,689,329,145,287,273,542,808,301,491,0,278,825,442,0,100,818,826,66,904,642,566,135,305,999,993,905,485,755,782,365,977,485,1015,570,1002,755,169,967,36,721,1019,273,931,273,166,216,31,346,946,32,290,362,828,464,748,782,1002,1015,755,1014,100,315,777,549,177,882,110,603,975,531,608,67,1011,950,465,368,416,798,941,635,602,553,300,200,644,498,325,786,734,342,222,403,1,716,175,899,273,40,333,999,74,54,644,408,976,407,631,577,338,435,612,333,273,162,709,882,555,384,995,173,459,442,72,72,200,72,711,219,282,716,442,431,801,976,130,622,72,582,384,516,772,0,440,1001,249,1,953,65,945,438,249,511,561,205,507,821,998,427,746,290,544,426,693,999,190,214,167,219,534,166,325,975,414,326,326,268,679,991,418,868,445,632,160,380,890,346,315,806,258,806,486,326,797,471,18,790,33,66,63,66,224,38,599,599,110,801,761,18,936,230,253,171,393,774,887,887,403,466,495,524,261,666,256,687,759,263,713,185,454,242,988,185,161,911,430,86,550,439,327,527,671,782,383,916,590,315,806,583,465,785,321,315,421,856,66,352,0,634,540,362,948,185,16,224,372,694,259,648,87,733,659,603,67,269,901,66,566,173,705,746,566,911,10,743,860,78,782,1002,755,389,175},
|
||||
{948,948,975,975,948,322,672,639,902,55,916,439,498,389,407,682,451,401,386,440,499,348,736,891,603,762,783,407,886,76,543,699,137,458,639,253,63,475,55,436,502,888,542,131,524,167,738,131,907,29,378,545,227,382,478,399,218,872,917,202,330,2,371,264,667,355,1016,768,590,408,463,542,214,202,715,891,840,297,509,689,290,439,672,714,528,940,1019,534,975,475,1019,835,975,558,975,981,330,635,96,858,606,627,367,191,191,669,40,873,359,267,701,426,210,1012,899,975,475,1012,610,6,300,749,231,616,877,631,720,574,551,398,503,789,684,664,390,277,150,990,823,190,971,903,175,863,316,965,988,988,800,612,336,506,242,847,389,939,415,202,83,317,2,153,365,363,57,2,891,965,300,754,763,426,555,621,303,415,367,902,829,741,119,380,902,25,884,439,822,49,76,760,566,316,249,555,774,955,834,309,859,173,935,812,682,586,141,606,197,131,644,631,913,586,202,117,810,884,76,592,754,531,586,925,649,583,145,816,821,283,871,1017,316,377,646,339,201,76,780,76,976,217,38,598,977,617,825,833,49,231,749,749,633,205,231,271,50,249,684,555,982,526,895,288,22,57,722,996,260,1018,110,833,644,738,648,468,798,297,769,282,197,402,465,510,194,930,182,909,749,986,187,187,917,38,38,985,985,988,815,878,814,459,237,768,781,649,683,749,934,729,463,181,625,231,917,96,499,839,720,439,842,205,808,338,617,681,326,446,905,346,647,533,49,728,147,432,846,536,586,611,49,879,872,893,859,859,961,989,975,701,495,65},
|
||||
};
|
||||
input.resp = {};
|
||||
for ( auto& token : inputs.phn ) printf("%i ", token );
|
||||
printf("\n");
|
||||
*/
|
||||
}
|
||||
|
||||
std::string vall_e_model_path = "./data/vall_e.gguf";
|
||||
std::string encodec_model_path = "./data/encodec.bin";
|
||||
std::string input_prompt_path = "./data/prom.wav";
|
||||
std::string output_response_path = "./data/resp.wav";
|
||||
vall_e_context_t* VALL_E_API vall_e_load( const vall_e_context_params_t& params ) {
|
||||
vall_e_context_t* ctx = new vall_e_context_t();
|
||||
ctx->params = params;
|
||||
|
||||
// load dynamic backends
|
||||
// setup ggml
|
||||
ggml_backend_load_all();
|
||||
|
||||
// initialize the models
|
||||
// setup llama.cpp
|
||||
llama_model_params model_params = llama_model_default_params();
|
||||
model_params.n_gpu_layers = ngl;
|
||||
model_params.n_gpu_layers = params.gpu_layers;
|
||||
|
||||
llama_model* model = llama_load_model_from_file(vall_e_model_path.c_str(), model_params);
|
||||
if (model == NULL) {
|
||||
ctx->llama.model = llama_load_model_from_file(params.model_path.c_str(), model_params);
|
||||
if ( !ctx->llama.model ) {
|
||||
fprintf(stderr , "%s: error: unable to load model\n" , __func__);
|
||||
return 1;
|
||||
return ctx;
|
||||
}
|
||||
|
||||
// initialize the context
|
||||
llama_context_params ctx_params = llama_context_default_params();
|
||||
ctx_params.n_ctx = CTX_SIZE;
|
||||
ctx_params.n_batch = CTX_SIZE;
|
||||
ctx_params.n_ubatch = CTX_SIZE;
|
||||
ctx_params.n_threads = N_THREADS;
|
||||
ctx_params.n_threads_batch = N_THREADS;
|
||||
ctx_params.n_ctx = params.ctx_size;
|
||||
ctx_params.n_batch = params.ctx_size;
|
||||
ctx_params.n_ubatch = params.ctx_size;
|
||||
ctx_params.n_threads = params.cpu_threads;
|
||||
ctx_params.n_threads_batch = params.cpu_threads;
|
||||
ctx_params.no_perf = false;
|
||||
ctx_params.attention_type = LLAMA_ATTENTION_TYPE_CAUSAL;
|
||||
|
||||
llama_context* ctx = llama_new_context_with_model(model, ctx_params);
|
||||
if (ctx == NULL) {
|
||||
ctx->llama.ctx = llama_new_context_with_model(ctx->llama.model, ctx_params);
|
||||
if ( !ctx->llama.ctx ) {
|
||||
fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
|
||||
return 1;
|
||||
return ctx;
|
||||
}
|
||||
|
||||
// initialize the sampler
|
||||
/*
|
||||
auto sparams = llama_sampler_chain_default_params();
|
||||
sparams.no_perf = false;
|
||||
llama_sampler * smpl = llama_sampler_chain_init(sparams);
|
||||
|
||||
llama_sampler_chain_add(smpl, llama_sampler_init_top_k(0));
|
||||
llama_sampler_chain_add(smpl, llama_sampler_init_top_p(1.0, 1));
|
||||
llama_sampler_chain_add(smpl, llama_sampler_init_temp (1.0));
|
||||
llama_sampler_chain_add(smpl, llama_sampler_init_dist (LLAMA_DEFAULT_SEED));
|
||||
*/
|
||||
|
||||
|
||||
struct encodec_context* ectx = encodec_load_model(encodec_model_path.c_str(), 0, ngl);
|
||||
if (!ectx) {
|
||||
// setup encodec.cpp
|
||||
ctx->encodec.ctx = encodec_load_model(params.encodec_path.c_str(), 0, params.gpu_layers);
|
||||
if ( !ctx->encodec.ctx ) {
|
||||
fprintf(stderr, "%s: error during loading model\n", __func__);
|
||||
return 1;
|
||||
return ctx;
|
||||
}
|
||||
encodec_set_target_bandwidth(ctx->encodec.ctx, 6);
|
||||
encodec_set_sample_rate(ctx->encodec.ctx, 24000);
|
||||
|
||||
// setup vall_e.cpp
|
||||
vall_e_inputs_map_init( ctx->io_map, ctx->llama.model );
|
||||
|
||||
return ctx;
|
||||
}
|
||||
vall_e_inputs_t vall_e_prepare_inputs( vall_e_context_t* ctx, const std::string& text, const std::string& prompt_path, const std::string& language ) {
|
||||
vall_e_inputs_t inputs;
|
||||
|
||||
encodec_set_target_bandwidth(ectx, 6);
|
||||
encodec_set_sample_rate(ectx, 24000);
|
||||
inputs.phn = phonemize( ctx, text, language );
|
||||
inputs.prom = encode_audio( ctx->encodec.ctx, read_audio_from_disk( prompt_path ) );
|
||||
if ( language == "en" ) inputs.lang = 0;
|
||||
else if ( language == "ja" ) inputs.lang = 1;
|
||||
else if ( language == "de" ) inputs.lang = 2;
|
||||
else if ( language == "fr" ) inputs.lang = 3;
|
||||
else if ( language == "zh" ) inputs.lang = 4;
|
||||
else if ( language == "ko" ) inputs.lang = 5;
|
||||
|
||||
// load wavform
|
||||
if ( input.prom.empty() ) {
|
||||
input.prom = encode_audio_from_disk(ectx, input_prompt_path);
|
||||
}
|
||||
//input.resp = encode_audio_from_disk(ectx, output_response_path);
|
||||
|
||||
|
||||
// grab input embeddings
|
||||
vall_e_inputs_map_init( io_map, model );
|
||||
|
||||
// tokenize phonemes
|
||||
// to-do: make this work, the vocab does not work
|
||||
if ( input.phonemes != "" ) {
|
||||
const int n_prompt = -llama_tokenize(model, input.phonemes.c_str(), input.phonemes.size(), NULL, 0, true, true);
|
||||
// allocate space for the tokens and tokenize the input.phonemes
|
||||
input.phn.resize(n_prompt);
|
||||
if (llama_tokenize(model, input.phonemes.c_str(), input.phonemes.size(), input.phn.data(), input.phn.size(), true, true) < 0) {
|
||||
fprintf(stderr, "%s: error: failed to tokenize: %s\n", __func__, input.phonemes.c_str());
|
||||
return 1;
|
||||
}
|
||||
|
||||
for ( auto& token : input.phn ) printf("%i ", token );
|
||||
printf("\n");
|
||||
}
|
||||
|
||||
// check for embds
|
||||
/*
|
||||
{
|
||||
input.task = "len";
|
||||
printf("batch init\n");
|
||||
llama_batch batch = llama_batch_init( CTX_SIZE, io_map.n_embd, CTX_SIZE );
|
||||
printf("fill init\n");
|
||||
fill_batch( batch, input, io_map, INFERENCE_MODE_LEN );
|
||||
printf("filled init\n");
|
||||
|
||||
for ( auto i = 0; i < batch.n_tokens; ++i ) {
|
||||
float summed = 0;
|
||||
for ( auto j = 0; j < 1024; ++j ) {
|
||||
summed += batch.embd[i * 1024 + j];
|
||||
}
|
||||
printf("%i: \t%i \t%f\n", i, batch.pos[i], summed);
|
||||
}
|
||||
}
|
||||
*/
|
||||
|
||||
// inference
|
||||
std::vector<llama_token> output_tokens;
|
||||
return inputs;
|
||||
}
|
||||
// to-do: provide sampling params
|
||||
vall_e_audio_codes_t vall_e_generate( vall_e_context_t* ctx, vall_e_inputs_t& inputs, int modality ) {
|
||||
// NAR-len demasking
|
||||
std::vector<token_t> output_tokens;
|
||||
if ( modality == MODALITY_NAR_LEN ) {
|
||||
// inference len
|
||||
int len = 0;
|
||||
int len = 75;
|
||||
if ( !len ) {
|
||||
input.task = "len";
|
||||
output_tokens = generate( ctx, model, input, io_map, 5, INFERENCE_MODE_LEN );
|
||||
inputs.task = "len";
|
||||
output_tokens = generate( ctx, inputs, 5, INFERENCE_MODE_LEN );
|
||||
{
|
||||
int digit = 1;
|
||||
for (auto it = output_tokens.rbegin(); it < output_tokens.rend(); ++it) {
|
||||
|
|
@ -859,47 +800,59 @@ int main( int argc, char** argv ) {
|
|||
if ( len <= 0 || len > MAX_DURATION ) len = MAX_DURATION;
|
||||
}
|
||||
// fill with mask tokens
|
||||
input.resp.resize(1);
|
||||
inputs.resp.resize(1);
|
||||
for ( auto i = 0; i < len; ++i ) {
|
||||
input.resp[0].emplace_back( 1024 ); // fill with masked tokens
|
||||
inputs.resp[0].emplace_back( 1024 ); // fill with masked tokens
|
||||
}
|
||||
/*
|
||||
input.resp = {
|
||||
{922,738,461,341,341,10,416,416,416,416,346,346,346,346,346,484,484,484,484,484,484,333,442,442,359,359,359,459,459,975,975,626,626,626,626,626,610,359,359,359,359,359,359,359,359,359,610,610,442,90,90,90,90,90,90,90,90,90,90,90,90,90,90,90,90,638,638,638,638,975,975,672,875,63,144},
|
||||
// {993,700,384,213,794,10,305,778,58,225,118,260,768,768,260,474,903,732,70,992,447,70,1000,665,848,379,485,934,181,795,438,298,688,324,934,756,395,795,110,328,343,172,768,871,593,355,396,783,24,24,911,20,27,562,697,616,668,27,27,755,20,505,248,79,822,461,197,156,27,492,151,1013,669,669,562},
|
||||
// {626,989,936,488,511,624,997,112,112,648,210,650,563,650,41,41,490,920,977,986,920,927,131,167,167,968,346,168,167,168,120,355,766,599,712,390,558,810,948,332,332,867,994,346,955,392,920,452,576,346,52,254,52,307,897,307,968,920,167,563,167,167,167,968,167,488,968,488,1001,938,563,741,432,566,758},
|
||||
// {916,874,798,212,496,751,620,616,982,745,975,890,890,141,141,321,321,214,899,42,151,722,310,971,774,35,627,995,27,43,248,248,595,774,942,352,810,35,384,340,654,639,89,214,737,197,657,45,622,321,337,19,483,679,938,938,682,938,938,141,938,310,114,724,116,327,372,607,607,310,204,713,762,853,853},
|
||||
};
|
||||
*/
|
||||
|
||||
// inference NAR-len 0
|
||||
input.task = "tts";
|
||||
inputs.task = "tts";
|
||||
for ( auto l = 0; l < 8; ++l ) {
|
||||
input.rvq_l = l;
|
||||
output_tokens = generate( ctx, model, input, io_map, 5, l == 0 ? INFERENCE_MODE_NAR_DEMASK : INFERENCE_MODE_NAR );
|
||||
if ( l == 0 ) input.resp.clear();
|
||||
input.resp.emplace_back( output_tokens );
|
||||
inputs.rvq_l = l;
|
||||
output_tokens = generate( ctx, inputs, 5, l == 0 ? INFERENCE_MODE_NAR_DEMASK : INFERENCE_MODE_NAR );
|
||||
if ( l == 0 ) inputs.resp.clear();
|
||||
inputs.resp.emplace_back( output_tokens );
|
||||
}
|
||||
// AR+NAR
|
||||
} else if ( modality == MODALITY_AR_NAR ){
|
||||
input.task = "tts";
|
||||
inputs.task = "tts";
|
||||
for ( auto l = 0; l < 8; ++l ) {
|
||||
input.rvq_l = l;
|
||||
output_tokens = generate( ctx, model, input, io_map, l == 0 ? MAX_DURATION : 1, l == 0 ? INFERENCE_MODE_AR : INFERENCE_MODE_NAR );
|
||||
input.resp.emplace_back( output_tokens );
|
||||
inputs.rvq_l = l;
|
||||
output_tokens = generate( ctx, inputs, l == 0 ? MAX_DURATION : 1, l == 0 ? INFERENCE_MODE_AR : INFERENCE_MODE_NAR );
|
||||
inputs.resp.emplace_back( output_tokens );
|
||||
}
|
||||
}
|
||||
|
||||
// write audio to disk
|
||||
auto waveform = decode_audio( ectx, input.resp );
|
||||
write_wav_on_disk( waveform, output_response_path );
|
||||
return inputs.resp;
|
||||
}
|
||||
void VALL_E_API vall_e_free( vall_e_context_t* ctx ) {
|
||||
encodec_free(ctx->encodec.ctx);
|
||||
llama_free(ctx->llama.ctx);
|
||||
llama_free_model(ctx->llama.model);
|
||||
ggml_free(ctx->io_map.ctx);
|
||||
delete ctx;
|
||||
}
|
||||
|
||||
// cleanup
|
||||
encodec_free(ectx);
|
||||
int main( int argc, char** argv ) {
|
||||
// to-do: parse CLI args
|
||||
|
||||
llama_free(ctx);
|
||||
|
||||
llama_free_model(model);
|
||||
vall_e_context_params_t params;
|
||||
params.model_path = "./data/vall_e.gguf";
|
||||
params.encodec_path = "./data/encodec.bin";
|
||||
params.gpu_layers = N_GPU_LAYERS;
|
||||
params.cpu_threads = N_THREADS;
|
||||
vall_e_context_t* ctx = vall_e_load( params );
|
||||
|
||||
std::string prompt_path = "./data/prom.wav";
|
||||
std::string output_path = "./data/resp.wav";
|
||||
std::string language = "en";
|
||||
int modality = MODALITY_NAR_LEN;
|
||||
|
||||
auto inputs = vall_e_prepare_inputs( ctx, "Hello world.", prompt_path, language );
|
||||
auto output_audio_codes = vall_e_generate( ctx, inputs, modality );
|
||||
write_audio_to_disk( decode_audio( ctx->encodec.ctx, output_audio_codes ), output_path );
|
||||
|
||||
vall_e_free( ctx );
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
|
@ -35,46 +35,10 @@ const int MODALITY_NAR_LEN = 1;
|
|||
const int MAX_DURATION = 75 * 12;
|
||||
const int CTX_SIZE = 2048;
|
||||
const int N_THREADS = 8;
|
||||
const int N_GPU_LAYERS = 0;
|
||||
|
||||
// stores the raw inputs to be fed
|
||||
struct input_t {
|
||||
std::string task = "tts";
|
||||
|
||||
std::string phonemes = "";
|
||||
std::vector<llama_token> phn = {};
|
||||
llama_token lang = 0;
|
||||
llama_token rvq_l = 0;
|
||||
std::vector<std::vector<llama_token>> prom = {};
|
||||
std::vector<std::vector<llama_token>> resp = {};
|
||||
};
|
||||
|
||||
// reference mapping from vall_e.export.py
|
||||
/*
|
||||
[(0, 256), 'text_emb.weight', 'classifiers.proj.9.weight', None],
|
||||
[(256, 264), 'rvq_l_emb.weight', None, '<|RVQ:{l}|>'],
|
||||
[(264, 270), 'langs_emb.weight', None, '<|lang:{lang}|>'],
|
||||
[(270, 279), 'tasks_emb.weight', None, '<|task:{task}|>'],
|
||||
[(279, 290), 'len_emb.weight', 'classifiers.proj.10.weight', '<|len:{id}|>'],
|
||||
[(290, 291), 'tones_emb.weight', None, '<|tone:{tone}|>'],
|
||||
[(291, 292), 'sep', None, '<|sep|>'],
|
||||
[(292, 1316), 'proms_emb.embeddings.0.weight', None, '<|P|0|{id}|>'],
|
||||
[(1316, 2340), 'proms_emb.embeddings.1.weight', None, '<|P|1|{id}|>'],
|
||||
[(2340, 3364), 'proms_emb.embeddings.2.weight', None, '<|P|2|{id}|>'],
|
||||
[(3364, 4388), 'proms_emb.embeddings.3.weight', None, '<|P|3|{id}|>'],
|
||||
[(4388, 5412), 'proms_emb.embeddings.4.weight', None, '<|P|4|{id}|>'],
|
||||
[(5412, 6436), 'proms_emb.embeddings.5.weight', None, '<|P|5|{id}|>'],
|
||||
[(6436, 7460), 'proms_emb.embeddings.6.weight', None, '<|P|6|{id}|>'],
|
||||
[(7460, 8484), 'proms_emb.embeddings.7.weight', None, '<|P|7|{id}|>'],
|
||||
[(8484, 9509), 'resps_emb.embeddings.0.weight', 'classifiers.proj.0.weight', '<|R|AR|0:0|{id}|>'],
|
||||
[(9509, 10533), 'resps_emb.embeddings.1.weight', 'classifiers.proj.1.weight', '<|R|NAR|0:1|{id}|>'],
|
||||
[(10533, 11557), 'resps_emb.embeddings.2.weight', 'classifiers.proj.2.weight', '<|R|NAR|1:2|{id}|>'],
|
||||
[(11557, 12581), 'resps_emb.embeddings.3.weight', 'classifiers.proj.3.weight', '<|R|NAR|2:3|{id}|>'],
|
||||
[(12581, 13605), 'resps_emb.embeddings.4.weight', 'classifiers.proj.4.weight', '<|R|NAR|3:4|{id}|>'],
|
||||
[(13605, 14629), 'resps_emb.embeddings.5.weight', 'classifiers.proj.5.weight', '<|R|NAR|4:5|{id}|>'],
|
||||
[(14629, 15653), 'resps_emb.embeddings.6.weight', 'classifiers.proj.6.weight', '<|R|NAR|5:6|{id}|>'],
|
||||
[(15653, 16677), 'resps_emb.embeddings.7.weight', 'classifiers.proj.7.weight', '<|R|NAR|6:7|{id}|>'],
|
||||
[(16677, 17702), 'resps_emb.embeddings.8.weight', 'classifiers.proj.8.weight', '<|R|NAR|0:0|{id}|>']
|
||||
*/
|
||||
typedef llama_token token_t;
|
||||
typedef std::vector<std::vector<token_t>> vall_e_audio_codes_t;
|
||||
|
||||
// stores embeddings + metadata for an embedding range
|
||||
struct io_t {
|
||||
|
|
@ -109,29 +73,73 @@ struct score_t {
|
|||
bool operator<( const score_t& that ) const { return this->value < that.value; }
|
||||
};
|
||||
|
||||
struct vall_e_context_params_t {
|
||||
std::string model_path;
|
||||
std::string encodec_path;
|
||||
int32_t gpu_layers = N_GPU_LAYERS;
|
||||
int32_t cpu_threads = N_THREADS;
|
||||
int32_t ctx_size = CTX_SIZE;
|
||||
bool verbose = false;
|
||||
};
|
||||
// stores everything needed for vall_e.cpp
|
||||
struct vall_e_context_t {
|
||||
vall_e_context_params_t params;
|
||||
|
||||
io_map_t io_map;
|
||||
|
||||
struct {
|
||||
llama_model* model = NULL;
|
||||
llama_context* ctx = NULL;
|
||||
} llama;
|
||||
|
||||
struct {
|
||||
encodec_context* ctx;
|
||||
} encodec;
|
||||
};
|
||||
// stores the raw inputs to be fed
|
||||
struct vall_e_inputs_t {
|
||||
std::string task = "tts";
|
||||
|
||||
std::vector<token_t> phn = {};
|
||||
token_t lang = 0;
|
||||
token_t rvq_l = 0;
|
||||
vall_e_audio_codes_t prom = {};
|
||||
vall_e_audio_codes_t resp = {};
|
||||
};
|
||||
|
||||
// helper tensor functions
|
||||
std::vector<float> VALL_E_API read_2d_tensor( struct ggml_tensor* tensor );
|
||||
ggml_tensor* VALL_E_API view_2d_tensor( ggml_tensor* tensor, int32_t start, int32_t end, int32_t dim = 0 ); // cringe method to keep in my pocket
|
||||
//ggml_tensor* VALL_E_API view_2d_tensor( ggml_tensor* tensor, int32_t start, int32_t end, int32_t dim = 0 ); // cringe method to keep in my pocket
|
||||
ggml_tensor* VALL_E_API view_2d_tensor( ggml_context* ctx, ggml_tensor* tensor, int32_t start, int32_t end, int32_t dim = 0 );
|
||||
void VALL_E_API print_tokens( const std::vector<llama_token>& tokens, const std::string& prefix = "Tokens: " );
|
||||
void VALL_E_API print_tokens( const std::vector<token_t>& tokens, const std::string& prefix = "Tokens: " );
|
||||
|
||||
std::vector<std::vector<float>> VALL_E_API map_embeddings( const std::vector<llama_token>& tokens, int n_embd, const float* embds );
|
||||
std::vector<std::vector<float>> VALL_E_API sum_embeddings( const std::vector<std::vector<llama_token>>& input, int n_embd, int rvq_l, const float** embds, int mode = EMBEDDING_MODE_PROM );
|
||||
std::vector<std::vector<float>> VALL_E_API map_embeddings( const std::vector<token_t>& tokens, int n_embd, const float* embds );
|
||||
std::vector<std::vector<float>> VALL_E_API sum_embeddings( const vall_e_audio_codes_t& input, int n_embd, int rvq_l, const float** embds, int mode = EMBEDDING_MODE_PROM );
|
||||
std::vector<float> VALL_E_API soft_max( int n_logits, const float* logits );
|
||||
|
||||
// batch and inferencing
|
||||
void VALL_E_API batch_add( llama_batch& batch, llama_token id, int n_embd, const float* embds, llama_pos pos, bool output, const std::vector<llama_seq_id> & seq_ids = {0} );
|
||||
void VALL_E_API fill_batch( llama_batch& batch, input_t& input, io_map_t& inputs_map, int mode );
|
||||
std::vector<llama_token> VALL_E_API generate( llama_context* ctx, llama_model* model, input_t& input, io_map_t& inputs_map, int max_tokens, int mode, bool verbose = true );
|
||||
void VALL_E_API batch_add( llama_batch& batch, token_t id, int n_embd, const float* embds, llama_pos pos, bool output, const std::vector<llama_seq_id> & seq_ids = {0} );
|
||||
void VALL_E_API fill_batch( llama_batch& batch, vall_e_inputs_t& input, io_map_t& inputs_map, int mode );
|
||||
std::vector<token_t> VALL_E_API generate( vall_e_context_t* ctx, vall_e_inputs_t& input, int max_tokens, int mode, bool verbose = true );
|
||||
|
||||
//
|
||||
std::vector<token_t> VALL_E_API phonemize( vall_e_context_t* ctx, const std::string& text, const std::string& language = "auto" );
|
||||
|
||||
// encodec helpers
|
||||
bool VALL_E_API read_wav_from_disk( std::string in_path, std::vector<float>& audio_arr );
|
||||
void VALL_E_API write_wav_on_disk( std::vector<float>& audio_arr, std::string dest_path );
|
||||
std::vector<std::vector<int32_t>> VALL_E_API encode_audio_from_disk( struct encodec_context* ectx, const std::string& path );
|
||||
std::vector<float> VALL_E_API read_audio_from_disk( const std::string& path );
|
||||
void VALL_E_API write_audio_to_disk( const std::vector<float>& waveform, const std::string& path );
|
||||
|
||||
std::vector<std::vector<int32_t>> VALL_E_API encode_audio( struct encodec_context* ectx, const std::vector<float>& waveform );
|
||||
std::vector<float> VALL_E_API decode_audio( struct encodec_context* ectx, const std::vector<std::vector<int32_t>>& codes_2d );
|
||||
|
||||
// model-accessing helpers
|
||||
const io_t& VALL_E_API vall_e_inputs_map_get_embeddings( io_map_t& inputs_map, const std::string& name );
|
||||
const float* VALL_E_API vall_e_inputs_map_get_embeddings_p( io_map_t& inputs_map, const std::string& name );
|
||||
int32_t VALL_E_API vall_e_inputs_map_get_classifier_idx( io_map_t& inputs_map, const std::string& name );
|
||||
void VALL_E_API vall_e_inputs_map_init( io_map_t&, llama_model* model );
|
||||
void VALL_E_API vall_e_inputs_map_init( io_map_t&, llama_model* model );
|
||||
|
||||
// context management
|
||||
vall_e_context_t* VALL_E_API vall_e_load( const vall_e_context_params_t& params );
|
||||
vall_e_inputs_t vall_e_prepare_inputs( vall_e_context_t* ctx, const std::string& text, const std::string& prompt_path, const std::string& lang );
|
||||
vall_e_audio_codes_t vall_e_generate( vall_e_context_t* ctx, vall_e_inputs_t& inputs, int modality = MODALITY_NAR_LEN );
|
||||
void VALL_E_API vall_e_free( vall_e_context_t* ctx );
|
||||
Loading…
Reference in New Issue
Block a user