release : v1.2.1

Just in case

.gitignore

@@ -10,6 +10,7 @@ build-em/
 build-debug/
 build-release/
 build-static/
+build-no-accel/
 build-sanitize-addr/
 build-sanitize-thread/
 

CMakeLists.txt

@@ -1,6 +1,6 @@
 cmake_minimum_required (VERSION 3.0)
 
-project(whisper.cpp VERSION 1.2.0)
+project(whisper.cpp VERSION 1.2.1)
 
 # Add path to modules
 list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/")

Makefile

@@ -141,6 +141,8 @@ ifdef WHISPER_GPROF
 	CXXFLAGS += -pg
 endif
 ifneq ($(filter aarch64%,$(UNAME_M)),)
+	CFLAGS += -mcpu=native
+	CXXFLAGS += -mcpu=native
 endif
 ifneq ($(filter armv6%,$(UNAME_M)),)
 	# Raspberry Pi 1, 2, 3

README.md

@@ -4,7 +4,7 @@
 [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
 [![npm](https://img.shields.io/npm/v/whisper.cpp.svg)](https://www.npmjs.com/package/whisper.cpp/)
 
-Stable: [v1.2.0](https://github.com/ggerganov/whisper.cpp/releases/tag/v1.2.0) / [Roadmap | F.A.Q.](https://github.com/ggerganov/whisper.cpp/discussions/126)
+Stable: [v1.2.1](https://github.com/ggerganov/whisper.cpp/releases/tag/v1.2.1) / [Roadmap | F.A.Q.](https://github.com/ggerganov/whisper.cpp/discussions/126)
 
 High-performance inference of [OpenAI's Whisper](https://github.com/openai/whisper) automatic speech recognition (ASR) model:
 
@@ -469,7 +469,9 @@ in [models](models).
 - [X] .NET: | [#422](https://github.com/ggerganov/whisper.cpp/discussions/422)
   - [sandrohanea/whisper.net](https://github.com/sandrohanea/whisper.net)
   - [NickDarvey/whisper](https://github.com/NickDarvey/whisper)
-- [ ] Python: soon | [WIP](https://github.com/ggerganov/whisper.cpp/issues/9)
+- [X] Python: | [#9](https://github.com/ggerganov/whisper.cpp/issues/9)
+  - [stlukey/whispercpp.py](https://github.com/stlukey/whispercpp.py) (Cython)
+  - [aarnphm/whispercpp](https://github.com/aarnphm/whispercpp) (Pybind11)
 
 ## Examples
 

bindings/javascript/package.json

@@ -1,6 +1,6 @@
 {
   "name": "whisper.cpp",
-  "version": "1.2.0",
+  "version": "1.2.1",
   "description": "Whisper speech recognition",
   "main": "whisper.js",
   "scripts": {

examples/main/main.cpp

@@ -618,8 +618,6 @@ int main(int argc, char ** argv) {
                 fprintf(stderr, "%s: failed to process audio\n", argv[0]);
                 return 10;
             }
-
-            whisper_full_cluster_segments(ctx);
         }
 
         // output stuff

ggml.c

@@ -8517,195 +8517,6 @@ enum ggml_opt_result ggml_opt(
 
 ////////////////////////////////////////////////////////////////////////////////
 
-void ggml_svd_reduce_dims(
-        int ne0,
-        int ne1,
-        float * a,
-        int nd) {
-    int n = ne1;
-    int m = ne0;
-
-    float * A = a;
-    float * A0 = (float *) malloc(n * m * sizeof(float));
-
-    // average vector
-    //float * M = (float *) malloc(m * sizeof(float));
-
-    //{
-    //    for (int j = 0; j < m; ++j) {
-    //        M[j] = 0.0f;
-    //    }
-    //    for (int i = 0; i < n; ++i) {
-    //        for (int j = 0; j < m; ++j) {
-    //            M[j] += A[i * m + j];
-    //        }
-    //    }
-    //    for (int j = 0; j < m; ++j) {
-    //        M[j] /= (float) n;
-    //    }
-    //}
-
-    //// subtract average vector
-    //for (int i = 0; i < n; ++i) {
-    //    for (int j = 0; j < m; ++j) {
-    //        A[i * m + j] -= M[j];
-    //    }
-    //}
-
-    //free(M);
-
-    memcpy(A0, A, n * m * sizeof(float));
-
-    // print A
-    //printf("A:\n");
-    //for (int i = 0; i < n; ++i) {
-    //    printf("col %d : ", i);
-    //    for (int j = 0; j < m; ++j) {
-    //        printf("%9.5f ", A[i * m + j]);
-    //    }
-    //    printf("\n");
-    //}
-    //printf("\n");
-
-    // SVD
-    // A = U * S * V^T
-
-    float * U = (float *) malloc(n * m * sizeof(float));
-    float * S = (float *) malloc(n * sizeof(float));
-    float * V = (float *) malloc(n * n * sizeof(float));
-
-    int lda = m;
-    int ldu = m;
-    int ldvt = n;
-
-    float work_size;
-    int lwork = -1;
-    int info = 0;
-
-    sgesvd_("S", "S", &m, &n, A, &lda, S, U, &ldu, V, &ldvt, &work_size, &lwork, &info);
-
-    lwork = (int) work_size;
-
-    //printf("work_size = %f, info = %d, lwork = %d\n", work_size, info, lwork);
-
-    float * work = (float *) malloc(lwork * sizeof(float));
-
-    sgesvd_("S", "S", &m, &n, A, &lda, S, U, &ldu, V, &ldvt, work, &lwork, &info);
-
-    free(work);
-
-    // print U
-    //printf("U:\n");
-    //for (int i = 0; i < n; ++i) {
-    //    printf("col %d : ", i);
-    //    for (int j = 0; j < m; ++j) {
-    //        printf("%9.5f ", U[i * m + j]);
-    //    }
-    //    printf("\n");
-    //}
-    //printf("\n");
-
-    // normalize S
-    {
-        double sum = 0.0;
-        for (int i = 0; i < n; ++i) {
-            sum += S[i];
-        }
-        sum *= sqrt((double) m);
-        for (int i = 0; i < n; ++i) {
-            S[i] /= sum;
-        }
-    }
-
-    // print S
-    printf("S:\n");
-    for (int i = 0; i < n; ++i) {
-        printf("- %d = %9.5f\n", i, S[i]);
-    }
-    printf("\n");
-
-    // print V
-    //printf("V:\n");
-    //for (int i = 0; i < n; ++i) {
-    //    printf("col %d : ", i);
-    //    for (int j = 0; j < n; ++j) {
-    //        printf("%9.5f ", V[i * n + j]);
-    //    }
-    //    printf("\n");
-    //}
-    //printf("\n");
-
-    // print A
-    //printf("A:\n");
-    //for (int i = 0; i < n; ++i) {
-    //    printf("col %d : ", i);
-    //    for (int j = 0; j < m; ++j) {
-    //        printf("%9.5f ", A[i * m + j]);
-    //    }
-    //    printf("\n");
-    //}
-    //printf("\n");
-
-    // compute singular vectors in U
-    for (int i = 0; i < n; ++i) {
-        for (int j = 0; j < m; ++j) {
-            U[i * m + j] *= S[i];
-        }
-    }
-
-    // normalize U
-    for (int i = 0; i < n; ++i) {
-        double sum = 0.0;
-        for (int j = 0; j < m; ++j) {
-            sum += U[i * m + j] * U[i * m + j];
-        }
-        sum = sqrt(sum);
-        for (int j = 0; j < m; ++j) {
-            U[i * m + j] /= sum*sqrt((double) m);
-        }
-    }
-
-    // print U
-    //printf("U:\n");
-    //for (int i = 0; i < n; ++i) {
-    //    printf("col %d : ", i);
-    //    for (int j = 0; j < m; ++j) {
-    //        printf("%9.5f ", U[i * m + j]);
-    //    }
-    //    printf("\n");
-    //}
-    //printf("\n");
-
-    // project A0 onto U
-    for (int i = 0; i < n; ++i) {
-        for (int j = 0; j < nd; ++j) {
-            A[i * nd + j] = 0.0f;
-            //if (j == 0) continue;
-            for (int k = 0; k < m; ++k) {
-                A[i * nd + j] += A0[i * m + k] * U[j * m + k];
-            }
-        }
-    }
-
-    // print A
-    //printf("A:\n");
-    //for (int i = 0; i < n; ++i) {
-    //    printf("col %d : ", i);
-    //    for (int j = 0; j < n; ++j) {
-    //        printf("%9.5f ", A[i * n + j]);
-    //    }
-    //    printf("\n");
-    //}
-    //printf("\n");
-
-    free(U);
-    free(S);
-    free(V);
-    free(A0);
-}
-
-////////////////////////////////////////////////////////////////////////////////
-
 int ggml_cpu_has_avx(void) {
 #if defined(__AVX__)
     return 1;

ggml.h

@@ -726,16 +726,6 @@ enum ggml_opt_result ggml_opt(
         struct ggml_opt_params params,
         struct ggml_tensor * f);
 
-//
-// Temp stuff
-//
-
-void ggml_svd_reduce_dims(
-        int ne0,
-        int ne1,
-        float * a,
-        int nd);
-
 //
 // system info
 //

whisper.cpp

@@ -268,14 +268,6 @@ static const std::map<e_model, size_t> MEM_REQ_KV_SELF = {
     { MODEL_LARGE,    71ull*MB },
 };
 
-static const std::map<e_model, size_t> MEM_REQ_KV_ENC_SELF = {
-    { MODEL_TINY,     23ull*MB },
-    { MODEL_BASE,     26ull*MB },
-    { MODEL_SMALL,   216ull*MB },
-    { MODEL_MEDIUM,  243ull*MB },
-    { MODEL_LARGE,   271ull*MB },
-};
-
 static const std::map<e_model, size_t> MEM_REQ_KV_CROSS = {
     { MODEL_TINY,      9ull*MB },
     { MODEL_BASE,     18ull*MB },
@@ -579,7 +571,6 @@ struct whisper_context {
     // cross-attention KV cache for the decoders
     // shared between all decoders
     whisper_kv_cache kv_cross;
-    whisper_kv_cache kv_enc_self;
 
     whisper_decoder decoders[WHISPER_MAX_DECODERS] = {};
 
@@ -601,18 +592,16 @@ struct whisper_context {
 
     mutable std::mt19937 rng; // used for sampling at t > 0.0
 
-    int lang_id;
+    int lang_id = 0; // english by default
 
     // [EXPERIMENTAL] token-level timestamps data
-    int64_t t_beg;
-    int64_t t_last;
+    int64_t t_beg = 0;
+    int64_t t_last = 0;
     whisper_token tid_last;
     std::vector<float> energy; // PCM signal energy
 
     // [EXPERIMENTAL] speed-up techniques
-    int32_t exp_n_audio_ctx; // 0 - use default
-
-    std::vector<float> audio_embd;
+    int32_t exp_n_audio_ctx = 0; // 0 - use default
 
     void use_buf(struct ggml_context * ctx, int i) {
 #if defined(WHISPER_USE_SCRATCH)
@@ -847,11 +836,6 @@ static bool whisper_model_load(struct whisper_model_loader * loader, whisper_con
             return false;
         }
 
-        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)) {
-            fprintf(stderr, "%s: kv_cache_init() failed for cross-attention cache\n", __func__);
-            return false;
-        }
-
         {
             const size_t memory_size = ggml_nbytes(wctx.kv_cross.k) + ggml_nbytes(wctx.kv_cross.v);
             fprintf(stderr, "%s: kv cross size = %7.2f MB\n", __func__, memory_size/1024.0/1024.0);
@@ -1374,8 +1358,7 @@ static bool whisper_model_load(struct whisper_model_loader * loader, whisper_con
 static bool whisper_encode(
         whisper_context & wctx,
               const int   mel_offset,
-              const int   n_threads,
-              bool repeat = false) {
+              const int   n_threads) {
     const int64_t t_start_us = ggml_time_us();
 
     const auto & model   = wctx.model;
@@ -1407,31 +1390,13 @@ static bool whisper_encode(
         const int i0 = std::min(mel_offset, mel_inp.n_len);
         const int i1 = std::min(mel_offset + 2*n_ctx, mel_inp.n_len);
 
-        if (repeat == false) {
-            for (int j = 0; j < mel_inp.n_mel; ++j) {
-                for (int i = i0; i < i1; ++i) {
-                    dst[j*2*n_ctx + (i - i0)] = mel_inp.data[j*mel_inp.n_len + i];
-                }
-            }
-        } else {
-            for (int j = 0; j < mel_inp.n_mel; ++j) {
-                int k = 0;
-                while (k < 2*n_ctx) {
-                    for (int i = i0; i < i1; ++i) {
-                        dst[j*2*n_ctx + k] = mel_inp.data[j*mel_inp.n_len + i];
-                        k++;
-                        if (k >= 2*n_ctx) {
-                            break;
-                        }
-                    }
-                }
+        for (int j = 0; j < mel_inp.n_mel; ++j) {
+            for (int i = i0; i < i1; ++i) {
+                dst[j*2*n_ctx + (i - i0)] = mel_inp.data[j*mel_inp.n_len + i];
             }
         }
     }
 
-    struct ggml_cgraph gf = {};
-    gf.n_threads = n_threads;
-
     struct ggml_tensor * cur;
 
     // convolution + gelu
@@ -1459,18 +1424,6 @@ static bool whisper_encode(
         cur = ggml_gelu(ctx0, cur);
     }
 
-    //{
-    //    //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));
-
-    //    wctx.use_buf(ctx0, -1);
-
-    //    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));
-    //    //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));
-
-    //    ggml_build_forward_expand(&gf, ggml_cpy(ctx0, cur, k));
-    //    //ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
-    //}
-
     wctx.use_buf(ctx0, 3);
 
     // ===================================================================
@@ -1551,18 +1504,6 @@ static bool whisper_encode(
                         Vcur),
                     Vcur);
 
-            //{
-            //    //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));
-
-            //    wctx.use_buf(ctx0, -1);
-
-            //    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));
-            //    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));
-
-            //    ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
-            //    ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
-            //}
-
             // ------
 
             wctx.use_buf(ctx0, 0);
@@ -1647,18 +1588,6 @@ static bool whisper_encode(
             cur = ggml_cpy(ctx0,
                     KQV_merged,
                     ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_state, n_ctx));
-
-            {
-                //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));
-
-                wctx.use_buf(ctx0, -1);
-
-                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));
-                //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));
-
-                ggml_build_forward_expand(&gf, ggml_cpy(ctx0, cur, k));
-                //ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
-            }
         }
 
         // projection
@@ -1768,6 +1697,8 @@ static bool whisper_encode(
 
     // run the computation
     {
+        struct ggml_cgraph gf = {};
+        gf.n_threads = n_threads;
 
         ggml_build_forward_expand(&gf, cur);
         ggml_graph_compute       (ctx0, &gf);
@@ -1789,24 +1720,6 @@ static bool whisper_encode(
     //    printf("\n");
     //}
 
-    {
-        //const int i0 = std::min(mel_offset, mel_inp.n_len);
-        //const int i1 = std::min(mel_offset + 2*n_ctx, mel_inp.n_len);
-        const int i0 = 0;
-        const int i1 = cur->ne[1];
-
-        //printf("i0 = %d, i1 = %d, (i1 - i0) = %d, embd size = %d\n", i0, i1, i1 - i0, cur->ne[0]);
-
-        wctx.audio_embd.clear();
-        wctx.audio_embd.resize(cur->ne[0], 0.0f);
-        for (int j = 0; j < cur->ne[0]; ++j) {
-            for (int i = i0; i < i1; ++i) {
-                wctx.audio_embd[j] += ((float *)(cur->data))[(i - i0)*cur->ne[0] + j];
-            }
-            wctx.audio_embd[j] /= (i1 - i0);
-        }
-    }
-
     // pre-compute cross-attention memory
     {
         struct ggml_cgraph gf = {};
@@ -3049,6 +2962,9 @@ struct whisper_full_params whisper_full_default_params(enum whisper_sampling_str
 
         /*.encoder_begin_callback           =*/ nullptr,
         /*.encoder_begin_callback_user_data =*/ nullptr,
+
+        /*.logits_filter_callback           =*/ nullptr,
+        /*.logits_filter_callback_user_data =*/ nullptr,
     };
 
     switch (strategy) {
@@ -3176,7 +3092,7 @@ static const std::vector<std::string> non_speech_tokens = {
 // - applies logit filters
 // - computes logprobs and probs
 static void whisper_process_logits(
-        const struct whisper_context & ctx,
+              struct whisper_context & ctx,
     const struct whisper_full_params   params,
               struct whisper_decoder & decoder,
                                float   temperature) {
@@ -3232,6 +3148,9 @@ static void whisper_process_logits(
         logits[vocab.token_translate]  = -INFINITY;
         logits[vocab.token_transcribe] = -INFINITY;
 
+        if (params.logits_filter_callback) {
+            params.logits_filter_callback(&ctx, tokens_cur.data(), tokens_cur.size(), logits.data(), params.logits_filter_callback_user_data);
+        }
 
         // suppress non-speech tokens
         // ref: https://github.com/openai/whisper/blob/7858aa9c08d98f75575035ecd6481f462d66ca27/whisper/tokenizer.py#L224-L253
@@ -3935,7 +3854,7 @@ int whisper_full(
                         return a.sequence.sum_logprobs_all > b.sequence.sum_logprobs_all;
                     });
 
-                    unsigned int cur_c = 0;
+                    uint32_t cur_c = 0;
 
                     for (int j = 0; j < n_decoders_cur; ++j) {
                         auto & decoder = ctx->decoders[j];
@@ -4420,7 +4339,7 @@ int whisper_full_n_segments(struct whisper_context * ctx) {
 }
 
 int whisper_full_lang_id(struct whisper_context * ctx) {
-    return ctx->lang_id; 
+    return ctx->lang_id;
 }
 
 int64_t whisper_full_get_segment_t0(struct whisper_context * ctx, int i_segment) {
@@ -4893,258 +4812,3 @@ static void whisper_exp_compute_token_level_timestamps(
     //    }
     //}
 }
-
-//
-// diarization stuff
-//
-
-void whisper_full_cluster_segments(struct whisper_context * ctx) {
-    const int n_segments = ctx->result_all.size();
-    printf("%s: clustering %d segments\n", __func__, n_segments);
-
-    const auto mel_len_save = ctx->mel.n_len;
-    printf("%s: mel_len_save = %d\n", __func__, mel_len_save);
-
-    const int n_ctx   = ctx->model.hparams.n_audio_ctx;
-    const int n_state = ctx->model.hparams.n_audio_state;
-    const int n_layer = ctx->model.hparams.n_audio_layer;
-
-#if 0
-    // use the last layer of the encoder
-    {
-        std::vector<float> embd(n_segments*n_state);
-
-        for (int i = 0; i < n_segments; ++i) {
-            const auto & segment_i = ctx->result_all[i];
-            printf("%s: segment %3d: t0 = %7d, t1 = %7d, text = %s\n", __func__, i, (int) segment_i.t0, (int) segment_i.t1, segment_i.text.c_str());
-
-            ctx->mel.n_len = segment_i.t1;
-            whisper_encode(*ctx, segment_i.t0, 7, true);
-
-            for (int j = 0; j < n_state; ++j) {
-                embd[i*n_state + j] = ctx->audio_embd[j];
-            }
-        }
-
-        const int n_features = std::min(4, n_segments);
-
-        ggml_svd_reduce_dims(n_state, n_segments, embd.data(), n_features);
-#elif 0
-    // use cross kv cache of various layers
-    for (int il = 0; il < n_layer; ++il) {
-        std::vector<float> embd(n_segments*n_ctx*n_state);
-
-        for (int i = 0; i < n_segments; ++i) {
-            const auto & segment_i = ctx->result_all[i];
-            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());
-
-            ctx->mel.n_len = segment_i.t1;
-            whisper_encode(*ctx, segment_i.t0, 7, true);
-
-            const size_t offs = ggml_element_size(ctx->kv_cross.k)*(il*n_ctx*n_state);
-            const ggml_fp16_t * f = (const ggml_fp16_t * )((const char *) ctx->kv_cross.k->data + offs);
-
-            for (int j = 0; j < n_ctx*n_state; ++j) {
-                embd[i*n_ctx*n_state + j] = ggml_fp16_to_fp32(f[j]);
-            }
-        }
-
-        const int n_features = std::min(4, n_segments);
-
-        ggml_svd_reduce_dims(n_ctx*n_state, n_segments, embd.data(), n_features);
-#elif 0
-    // use conv embedding
-    for (int il = 0; il < 1; ++il) {
-        std::vector<float> embd(n_segments*n_ctx*n_state);
-
-        for (int i = 0; i < n_segments; ++i) {
-            const auto & segment_i = ctx->result_all[i];
-            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());
-
-            ctx->mel.n_len = segment_i.t1;
-            whisper_encode(*ctx, segment_i.t0, 7, true);
-
-            const size_t offs = ggml_element_size(ctx->kv_enc_self.k)*(il*n_ctx*n_state);
-            const ggml_fp16_t * f = (const ggml_fp16_t * )((const char *) ctx->kv_enc_self.k->data + offs);
-
-            for (int j = 0; j < n_ctx*n_state; ++j) {
-                embd[i*n_ctx*n_state + j] = ggml_fp16_to_fp32(f[j]);
-            }
-        }
-
-        const int n_features = std::min(3, n_segments);
-
-        ggml_svd_reduce_dims(n_ctx*n_state, n_segments, embd.data(), n_features);
-#else
-    // use enc self kv cache of various layers
-    for (int il = 0; il < n_layer; ++il) {
-        std::vector<float> embd(n_segments*n_ctx*n_state);
-
-        for (int i = 0; i < n_segments; ++i) {
-            const auto & segment_i = ctx->result_all[i];
-            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());
-
-            ctx->mel.n_len = segment_i.t1;
-            whisper_encode(*ctx, segment_i.t0, 7, true);
-
-            const size_t offs = ggml_element_size(ctx->kv_enc_self.k)*(il*n_ctx*n_state);
-            const ggml_fp16_t * f = (const ggml_fp16_t * )((const char *) ctx->kv_enc_self.k->data + offs);
-
-            for (int j = 0; j < n_ctx*n_state; ++j) {
-                embd[i*n_ctx*n_state + j] = ggml_fp16_to_fp32(f[j]);
-            }
-        }
-
-        const int n_features = std::min(4, n_segments);
-
-        ggml_svd_reduce_dims(n_ctx*n_state, n_segments, embd.data(), n_features);
-#endif
-
-        std::vector<std::vector<double>> features(n_segments);
-
-        for (int i = 0; i < n_segments; ++i) {
-            features[i].resize(n_features);
-            for (int j = 0; j < n_features; ++j) {
-                features[i][j] = embd[i*n_features + j];
-            }
-        }
-
-        // fuzzy c-means clustering
-        const int n_clusters = 2;
-
-        std::vector<std::vector<double>> centroids(n_clusters,  std::vector<double>(n_features, 0.0));
-        std::vector<std::vector<double>> membership(n_segments, std::vector<double>(n_clusters, 0.0));
-
-        // initialize the centroids
-        for (int i = 0; i < n_clusters; ++i) {
-            for (int j = 0; j < n_features; ++j) {
-                centroids[i][j] = features[i][j];
-            }
-        }
-
-        // initialize the membership
-        for (int i = 0; i < n_segments; ++i) {
-            //membership[i][i % n_clusters] = 1.0;
-            //for (int j = 0; j < n_clusters; ++j) {
-            //    membership[i][j] = rand() / (float) RAND_MAX;
-            //}
-            for (int j = 0; j < n_clusters; ++j) {
-                membership[i][j] = 1.0 / n_clusters;
-            }
-        }
-
-        const int niter = 10000;
-
-        // iterate
-        for (int i = 0; i < niter; ++i) {
-            // print the membership
-            if (i == niter - 1) {
-            //{
-                for (int i = 0; i < n_segments; ++i) {
-#if 1
-                    printf("%s: membership %3d: ", __func__, i);
-                    for (int j = 0; j < n_clusters; ++j) {
-                        printf("%.1f ", membership[i][j]);
-                    }
-                    printf(" '%s'\n", ctx->result_all[i].text.c_str());
-#else
-                    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());
-#endif
-                }
-                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");
-                }
-            }
-
-            // update the membership
-            for (int j = 0; j < n_segments; ++j) {
-                for (int k = 0; k < n_clusters; ++k) {
-                    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) {
-                                d0 += std::pow(features[j][m] - centroids[k][m], 2.0);
-                            }
-                            d0 = std::sqrt(d0);
-
-                            for (int m = 0; m < n_features; ++m) {
-                                d1 += std::pow(features[j][m] - centroids[l][m], 2.0);
-                            }
-                            d1 = std::sqrt(d1);
-                        }
-#else
-                        // use the cosine distance
-                        {
-                            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);
-                            }
-
-                            d0 = 1.0 - dot/(std::sqrt(norm0)*std::sqrt(norm1));
-
-                            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);
-                            }
-
-                            d1 = 1.0 - dot/(std::sqrt(norm0)*std::sqrt(norm1));
-                        }
-#endif
-
-                        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 ? 1.0 : 1.0/sum;
-                }
-            }
-
-            // 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];
-                    }
-                    centroids[j][k] = sum2 == 0.0 ? 0.0 : sum/sum2;
-                }
-            }
-        }
-    }
-
-    // restore the mel length
-    ctx->mel.n_len = mel_len_save;
-}

whisper.h

@@ -243,6 +243,16 @@ extern "C" {
     // If it returns false, the computation is aborted
     typedef bool (*whisper_encoder_begin_callback)(struct whisper_context * ctx, void * user_data);
 
+    // Logits filter callback
+    // Can be used to modify the logits before sampling
+    // If not NULL, called after applying temperature to logits
+    typedef void (*whisper_logits_filter_callback)(
+            struct whisper_context * ctx,
+          const whisper_token_data * tokens,
+                               int   n_tokens,
+                             float * logits,
+                              void * user_data);
+
     // Parameters for the whisper_full() function
     // If you chnage the order or add new parameters, make sure to update the default values in whisper.cpp:
     // whisper_full_default_params()
@@ -315,6 +325,10 @@ extern "C" {
         // called each time before the encoder starts
         whisper_encoder_begin_callback encoder_begin_callback;
         void * encoder_begin_callback_user_data;
+
+        // called by each decoder to filter obtained logits
+        whisper_logits_filter_callback logits_filter_callback;
+        void * logits_filter_callback_user_data;
     };
 
     WHISPER_API struct whisper_full_params whisper_full_default_params(enum whisper_sampling_strategy strategy);
@@ -372,10 +386,6 @@ extern "C" {
     WHISPER_API int whisper_bench_memcpy(int n_threads);
     WHISPER_API int whisper_bench_ggml_mul_mat(int n_threads);
 
-    // Temporary experimental API
-
-    WHISPER_API void whisper_full_cluster_segments(struct whisper_context * ctx);
-
 #ifdef __cplusplus
 }
 #endif