mirror of
https://github.com/ggerganov/whisper.cpp.git
synced 2024-12-18 20:27:53 +00:00
diarization : try conv and self-attention embeddings
This commit is contained in:
Georgi Gerganov
parent
d11f35920e
commit
ec44ad0a75
75
ggml.c
75
ggml.c
@ -8529,28 +8529,30 @@ void ggml_svd_reduce_dims(
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float * A0 = (float *) malloc(n * m * sizeof(float));
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// average vector
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float * M = (float *) malloc(m * sizeof(float));
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//float * M = (float *) malloc(m * sizeof(float));
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{
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for (int j = 0; j < m; ++j) {
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M[j] = 0.0f;
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}
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for (int i = 0; i < n; ++i) {
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for (int j = 0; j < m; ++j) {
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M[j] += A[i * m + j];
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}
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}
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for (int j = 0; j < m; ++j) {
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M[j] /= (float) n;
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}
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}
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//{
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// for (int j = 0; j < m; ++j) {
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// M[j] = 0.0f;
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// }
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// for (int i = 0; i < n; ++i) {
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// for (int j = 0; j < m; ++j) {
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// M[j] += A[i * m + j];
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// }
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// }
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// for (int j = 0; j < m; ++j) {
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// M[j] /= (float) n;
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// }
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//}
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// subtract average vector
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for (int i = 0; i < n; ++i) {
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for (int j = 0; j < m; ++j) {
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A[i * m + j] -= M[j];
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}
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}
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//// subtract average vector
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//for (int i = 0; i < n; ++i) {
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// for (int j = 0; j < m; ++j) {
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// A[i * m + j] -= M[j];
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// }
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//}
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//free(M);
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memcpy(A0, A, n * m * sizeof(float));
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@ -8616,11 +8618,11 @@ void ggml_svd_reduce_dims(
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}
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// print S
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//printf("S:\n");
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//for (int i = 0; i < n; ++i) {
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// printf("- %d = %9.5f\n", i, S[i]);
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//}
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//printf("\n");
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printf("S:\n");
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for (int i = 0; i < n; ++i) {
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printf("- %d = %9.5f\n", i, S[i]);
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}
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printf("\n");
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// print V
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//printf("V:\n");
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@ -8652,16 +8654,16 @@ void ggml_svd_reduce_dims(
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}
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// normalize U
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//for (int i = 0; i < n; ++i) {
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// double sum = 0.0;
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// for (int j = 0; j < m; ++j) {
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// sum += U[i * m + j] * U[i * m + j];
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// }
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// sum = sqrt(sum);
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// for (int j = 0; j < m; ++j) {
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// U[i * m + j] /= sum*sqrt((double) m);
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// }
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//}
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for (int i = 0; i < n; ++i) {
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double sum = 0.0;
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for (int j = 0; j < m; ++j) {
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sum += U[i * m + j] * U[i * m + j];
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}
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sum = sqrt(sum);
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for (int j = 0; j < m; ++j) {
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U[i * m + j] /= sum*sqrt((double) m);
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}
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}
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// print U
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//printf("U:\n");
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@ -8674,12 +8676,11 @@ void ggml_svd_reduce_dims(
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//}
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//printf("\n");
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printf("n = %d, m = %d, nd = %d\n", n, m, nd);
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// project A0 onto U
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for (int i = 0; i < n; ++i) {
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for (int j = 0; j < nd; ++j) {
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A[i * nd + j] = 0.0f;
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//if (j == 0) continue;
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for (int k = 0; k < m; ++k) {
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A[i * nd + j] += A0[i * m + k] * U[j * m + k];
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}
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248
whisper.cpp
248
whisper.cpp
@ -268,6 +268,14 @@ static const std::map<e_model, size_t> MEM_REQ_KV_SELF = {
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{ MODEL_LARGE, 71ull*MB },
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};
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static const std::map<e_model, size_t> MEM_REQ_KV_ENC_SELF = {
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{ MODEL_TINY, 23ull*MB },
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{ MODEL_BASE, 26ull*MB },
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{ MODEL_SMALL, 216ull*MB },
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{ MODEL_MEDIUM, 243ull*MB },
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{ MODEL_LARGE, 271ull*MB },
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};
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static const std::map<e_model, size_t> MEM_REQ_KV_CROSS = {
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{ MODEL_TINY, 9ull*MB },
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{ MODEL_BASE, 18ull*MB },
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@ -571,6 +579,7 @@ struct whisper_context {
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// cross-attention KV cache for the decoders
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// shared between all decoders
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whisper_kv_cache kv_cross;
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whisper_kv_cache kv_enc_self;
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whisper_decoder decoders[WHISPER_MAX_DECODERS] = {};
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@ -807,7 +816,7 @@ static bool whisper_model_load(struct whisper_model_loader * loader, whisper_con
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MEM_REQ_SCRATCH3.at (model.type) +
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scale*MEM_REQ_MODEL.at (model.type) +
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scale*MEM_REQ_KV_CROSS.at(model.type) +
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scale*std::max(MEM_REQ_ENCODE.at(model.type), MEM_REQ_DECODE.at(model.type));
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scale*std::max(MEM_REQ_ENCODE.at(model.type), MEM_REQ_DECODE.at(model.type));
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// this is the memory required by one decoder
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const size_t mem_required_decoder =
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@ -838,6 +847,11 @@ static bool whisper_model_load(struct whisper_model_loader * loader, whisper_con
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return false;
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}
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if (!kv_cache_init(model.hparams, scale*MEM_REQ_KV_ENC_SELF.at(model.type), wctx.kv_enc_self, wctx.wtype, model.hparams.n_audio_ctx)) {
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fprintf(stderr, "%s: kv_cache_init() failed for cross-attention cache\n", __func__);
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return false;
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}
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{
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const size_t memory_size = ggml_nbytes(wctx.kv_cross.k) + ggml_nbytes(wctx.kv_cross.v);
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fprintf(stderr, "%s: kv cross size = %7.2f MB\n", __func__, memory_size/1024.0/1024.0);
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@ -1415,6 +1429,9 @@ static bool whisper_encode(
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}
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}
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struct ggml_cgraph gf = {};
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gf.n_threads = n_threads;
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struct ggml_tensor * cur;
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// convolution + gelu
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@ -1442,6 +1459,18 @@ static bool whisper_encode(
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cur = ggml_gelu(ctx0, cur);
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}
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//{
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// //printf("cur: %d %d %d %d, size element = %d\n", cur->ne[0], cur->ne[1], cur->ne[2], cur->ne[3], ggml_element_size(cur));
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// wctx.use_buf(ctx0, -1);
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// struct ggml_tensor * k = ggml_view_1d(ctx0, wctx.kv_enc_self.k, n_state*n_ctx, (ggml_element_size(wctx.kv_enc_self.k)*n_state)*(0*n_ctx));
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// //struct ggml_tensor * v = ggml_view_1d(ctx0, wctx.kv_enc_self.v, n_state*n_ctx, (ggml_element_size(wctx.kv_enc_self.v)*n_state)*(il*n_ctx));
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// ggml_build_forward_expand(&gf, ggml_cpy(ctx0, cur, k));
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// //ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
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//}
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wctx.use_buf(ctx0, 3);
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// ===================================================================
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@ -1522,6 +1551,18 @@ static bool whisper_encode(
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Vcur),
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Vcur);
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//{
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// //printf("Kcur: %d %d %d %d, size element = %d\n", Kcur->ne[0], Kcur->ne[1], Kcur->ne[2], Kcur->ne[3], ggml_element_size(Kcur));
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// wctx.use_buf(ctx0, -1);
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// struct ggml_tensor * k = ggml_view_1d(ctx0, wctx.kv_enc_self.k, n_state*n_ctx, (ggml_element_size(wctx.kv_enc_self.k)*n_state)*(il*n_ctx));
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// struct ggml_tensor * v = ggml_view_1d(ctx0, wctx.kv_enc_self.v, n_state*n_ctx, (ggml_element_size(wctx.kv_enc_self.v)*n_state)*(il*n_ctx));
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// ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
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// ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
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//}
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// ------
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wctx.use_buf(ctx0, 0);
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@ -1606,6 +1647,18 @@ static bool whisper_encode(
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cur = ggml_cpy(ctx0,
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KQV_merged,
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ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_state, n_ctx));
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{
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//printf("cur: %d %d %d %d, size element = %d\n", cur->ne[0], cur->ne[1], cur->ne[2], cur->ne[3], ggml_element_size(cur));
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wctx.use_buf(ctx0, -1);
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struct ggml_tensor * k = ggml_view_1d(ctx0, wctx.kv_enc_self.k, n_state*n_ctx, (ggml_element_size(wctx.kv_enc_self.k)*n_state)*(il*n_ctx));
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//struct ggml_tensor * v = ggml_view_1d(ctx0, wctx.kv_enc_self.v, n_state*n_ctx, (ggml_element_size(wctx.kv_enc_self.v)*n_state)*(il*n_ctx));
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ggml_build_forward_expand(&gf, ggml_cpy(ctx0, cur, k));
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//ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
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}
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}
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// projection
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@ -1715,8 +1768,6 @@ static bool whisper_encode(
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// run the computation
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{
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struct ggml_cgraph gf = {};
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gf.n_threads = n_threads;
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ggml_build_forward_expand(&gf, cur);
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ggml_graph_compute (ctx0, &gf);
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@ -4858,7 +4909,7 @@ void whisper_full_cluster_segments(struct whisper_context * ctx) {
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const int n_state = ctx->model.hparams.n_audio_state;
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const int n_layer = ctx->model.hparams.n_audio_layer;
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#if 1
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#if 0
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// use the last layer of the encoder
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{
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std::vector<float> embd(n_segments*n_state);
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@ -4878,7 +4929,7 @@ void whisper_full_cluster_segments(struct whisper_context * ctx) {
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const int n_features = std::min(4, n_segments);
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ggml_svd_reduce_dims(n_state, n_segments, embd.data(), n_features);
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#else
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#elif 0
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// use cross kv cache of various layers
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for (int il = 0; il < n_layer; ++il) {
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std::vector<float> embd(n_segments*n_ctx*n_state);
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@ -4900,10 +4951,56 @@ void whisper_full_cluster_segments(struct whisper_context * ctx) {
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const int n_features = std::min(4, n_segments);
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ggml_svd_reduce_dims(n_ctx*n_state, n_segments, embd.data(), n_features);
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#elif 0
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// use conv embedding
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for (int il = 0; il < 1; ++il) {
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std::vector<float> embd(n_segments*n_ctx*n_state);
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for (int i = 0; i < n_segments; ++i) {
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const auto & segment_i = ctx->result_all[i];
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printf("%s: layer %2d, segment %3d: t0 = %7d, t1 = %7d, text = %s\n", __func__, il, i, (int) segment_i.t0, (int) segment_i.t1, segment_i.text.c_str());
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ctx->mel.n_len = segment_i.t1;
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whisper_encode(*ctx, segment_i.t0, 7, true);
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const size_t offs = ggml_element_size(ctx->kv_enc_self.k)*(il*n_ctx*n_state);
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const ggml_fp16_t * f = (const ggml_fp16_t * )((const char *) ctx->kv_enc_self.k->data + offs);
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for (int j = 0; j < n_ctx*n_state; ++j) {
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embd[i*n_ctx*n_state + j] = ggml_fp16_to_fp32(f[j]);
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}
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}
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const int n_features = std::min(3, n_segments);
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ggml_svd_reduce_dims(n_ctx*n_state, n_segments, embd.data(), n_features);
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#else
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// use enc self kv cache of various layers
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for (int il = 0; il < n_layer; ++il) {
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std::vector<float> embd(n_segments*n_ctx*n_state);
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for (int i = 0; i < n_segments; ++i) {
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const auto & segment_i = ctx->result_all[i];
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printf("%s: layer %2d, segment %3d: t0 = %7d, t1 = %7d, text = %s\n", __func__, il, i, (int) segment_i.t0, (int) segment_i.t1, segment_i.text.c_str());
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ctx->mel.n_len = segment_i.t1;
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whisper_encode(*ctx, segment_i.t0, 7, true);
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const size_t offs = ggml_element_size(ctx->kv_enc_self.k)*(il*n_ctx*n_state);
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const ggml_fp16_t * f = (const ggml_fp16_t * )((const char *) ctx->kv_enc_self.k->data + offs);
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for (int j = 0; j < n_ctx*n_state; ++j) {
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embd[i*n_ctx*n_state + j] = ggml_fp16_to_fp32(f[j]);
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}
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}
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const int n_features = std::min(4, n_segments);
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ggml_svd_reduce_dims(n_ctx*n_state, n_segments, embd.data(), n_features);
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#endif
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std::vector<std::vector<float>> features(n_segments);
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std::vector<std::vector<double>> features(n_segments);
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for (int i = 0; i < n_segments; ++i) {
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features[i].resize(n_features);
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@ -4915,8 +5012,8 @@ void whisper_full_cluster_segments(struct whisper_context * ctx) {
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// fuzzy c-means clustering
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const int n_clusters = 2;
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std::vector<std::vector<float>> centroids(n_clusters, std::vector<float>(n_features, 0.0));
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std::vector<std::vector<float>> membership(n_segments, std::vector<float>(n_clusters, 0.0));
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std::vector<std::vector<double>> centroids(n_clusters, std::vector<double>(n_features, 0.0));
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std::vector<std::vector<double>> membership(n_segments, std::vector<double>(n_clusters, 0.0));
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// initialize the centroids
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for (int i = 0; i < n_clusters; ++i) {
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@ -4928,8 +5025,11 @@ void whisper_full_cluster_segments(struct whisper_context * ctx) {
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// initialize the membership
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for (int i = 0; i < n_segments; ++i) {
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//membership[i][i % n_clusters] = 1.0;
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//for (int j = 0; j < n_clusters; ++j) {
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// membership[i][j] = rand() / (float) RAND_MAX;
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//}
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for (int j = 0; j < n_clusters; ++j) {
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membership[i][j] = rand() / (float) RAND_MAX;
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membership[i][j] = 1.0 / n_clusters;
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}
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}
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@ -4937,42 +5037,47 @@ void whisper_full_cluster_segments(struct whisper_context * ctx) {
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// iterate
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for (int i = 0; i < niter; ++i) {
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// update the centroids
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for (int j = 0; j < n_clusters; ++j) {
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for (int k = 0; k < n_features; ++k) {
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centroids[j][k] = 0.0;
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}
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}
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for (int j = 0; j < n_segments; ++j) {
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for (int k = 0; k < n_clusters; ++k) {
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for (int l = 0; l < n_features; ++l) {
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centroids[k][l] += membership[j][k]*features[j][l];
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// print the membership
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if (i == niter - 1) {
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//{
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for (int i = 0; i < n_segments; ++i) {
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#if 1
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printf("%s: membership %3d: ", __func__, i);
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for (int j = 0; j < n_clusters; ++j) {
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printf("%.1f ", membership[i][j]);
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}
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printf(" '%s'\n", ctx->result_all[i].text.c_str());
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#else
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printf("%s: features : ", __func__);
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for (int j = 0; j < n_features; ++j) {
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printf("%8.3f ", features[i][j]);
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}
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printf(" '%s'\n", ctx->result_all[i].text.c_str());
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#endif
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}
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}
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printf("----------------\n");
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for (int j = 0; j < n_clusters; ++j) {
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float sum = 0.0;
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for (int k = 0; k < n_segments; ++k) {
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sum += membership[k][j];
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}
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for (int k = 0; k < n_features; ++k) {
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centroids[j][k] /= sum;
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// print the centroids
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for (int i = 0; i < n_clusters; ++i) {
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printf("%s: centroid %d: ", __func__, i);
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for (int j = 0; j < n_features; ++j) {
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printf("%f ", centroids[i][j]);
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}
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printf("\n");
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}
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}
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// update the membership
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for (int j = 0; j < n_segments; ++j) {
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for (int k = 0; k < n_clusters; ++k) {
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float sum = 0.0;
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||||
double sum = 0.0;
|
||||
for (int l = 0; l < n_clusters; ++l) {
|
||||
//sum += std::pow(whisper_distance(features[j], centroids[k])/whisper_distance(features[j], centroids[l]), 2.0/(2.0 - 1.0));
|
||||
|
||||
double d0 = 0.0;
|
||||
double d1 = 0.0;
|
||||
|
||||
#if 1
|
||||
// use the euclidean distance
|
||||
{
|
||||
for (int m = 0; m < n_features; ++m) {
|
||||
@ -4985,68 +5090,59 @@ void whisper_full_cluster_segments(struct whisper_context * ctx) {
|
||||
}
|
||||
d1 = std::sqrt(d1);
|
||||
}
|
||||
|
||||
#else
|
||||
// use the cosine distance
|
||||
//{
|
||||
// double dot = 0.0;
|
||||
// double norm0 = 0.0;
|
||||
// double norm1 = 0.0;
|
||||
{
|
||||
double dot = 0.0;
|
||||
double norm0 = 0.0;
|
||||
double norm1 = 0.0;
|
||||
|
||||
// for (int m = 0; m < n_features; ++m) {
|
||||
// dot += features[j][m]*centroids[k][m];
|
||||
// norm0 += std::pow(features[j][m], 2.0);
|
||||
// norm1 += std::pow(centroids[k][m], 2.0);
|
||||
// }
|
||||
for (int m = 0; m < n_features; ++m) {
|
||||
dot += features[j][m]*centroids[k][m];
|
||||
norm0 += std::pow(features[j][m], 2.0);
|
||||
norm1 += std::pow(centroids[k][m], 2.0);
|
||||
}
|
||||
|
||||
// d0 = 1.0 - dot/(std::sqrt(norm0)*std::sqrt(norm1));
|
||||
d0 = 1.0 - dot/(std::sqrt(norm0)*std::sqrt(norm1));
|
||||
|
||||
// dot = 0.0;
|
||||
// norm0 = 0.0;
|
||||
// norm1 = 0.0;
|
||||
dot = 0.0;
|
||||
norm0 = 0.0;
|
||||
norm1 = 0.0;
|
||||
|
||||
// for (int m = 0; m < n_features; ++m) {
|
||||
// dot += features[j][m]*centroids[l][m];
|
||||
// norm0 += std::pow(features[j][m], 2.0);
|
||||
// norm1 += std::pow(centroids[l][m], 2.0);
|
||||
// }
|
||||
for (int m = 0; m < n_features; ++m) {
|
||||
dot += features[j][m]*centroids[l][m];
|
||||
norm0 += std::pow(features[j][m], 2.0);
|
||||
norm1 += std::pow(centroids[l][m], 2.0);
|
||||
}
|
||||
|
||||
// d1 = 1.0 - dot/(std::sqrt(norm0)*std::sqrt(norm1));
|
||||
//}
|
||||
d1 = 1.0 - dot/(std::sqrt(norm0)*std::sqrt(norm1));
|
||||
}
|
||||
#endif
|
||||
|
||||
sum += std::pow(d0/d1, 2.0/(1.15 - 1.0));
|
||||
if (d1 > 0.0) {
|
||||
sum += std::pow(d0/d1, 2.0/(1.20 - 1.0));
|
||||
} else {
|
||||
sum += 1.0;
|
||||
}
|
||||
}
|
||||
|
||||
membership[j][k] = sum == 0.0 ? 0.0 : 1.0/sum;
|
||||
membership[j][k] = sum == 0.0 ? 1.0 : 1.0/sum;
|
||||
}
|
||||
}
|
||||
|
||||
// print the membership
|
||||
if (i == niter - 1) {
|
||||
//{
|
||||
for (int i = 0; i < n_segments; ++i) {
|
||||
printf("%s: membership %3d: ", __func__, i);
|
||||
for (int j = 0; j < n_clusters; ++j) {
|
||||
printf("%f ", membership[i][j]);
|
||||
// update the centroids
|
||||
for (int j = 0; j < n_clusters; ++j) {
|
||||
for (int k = 0; k < n_features; ++k) {
|
||||
double sum = 0.0;
|
||||
double sum2 = 0.0;
|
||||
for (int l = 0; l < n_segments; ++l) {
|
||||
sum += membership[l][j]*features[l][k];
|
||||
sum2 += membership[l][j];
|
||||
}
|
||||
printf(" '%s'\n", ctx->result_all[i].text.c_str());
|
||||
//printf("%s: features : ", __func__);
|
||||
//for (int j = 0; j < n_features; ++j) {
|
||||
// printf("%8.3f ", features[i][j]);
|
||||
//}
|
||||
//printf(" '%s'\n", ctx->result_all[i].text.c_str());
|
||||
centroids[j][k] = sum2 == 0.0 ? 0.0 : sum/sum2;
|
||||
}
|
||||
printf("----------------\n");
|
||||
}
|
||||
}
|
||||
|
||||
// print the centroids
|
||||
for (int i = 0; i < n_clusters; ++i) {
|
||||
printf("%s: centroid %d: ", __func__, i);
|
||||
for (int j = 0; j < n_features; ++j) {
|
||||
printf("%f ", centroids[i][j]);
|
||||
}
|
||||
printf("\n");
|
||||
}
|
||||
}
|
||||
|
||||
// restore the mel length
|
||||
|
||||
Loading…
Reference in New Issue
Block a user