Merge branch 'master' into avx512

This turned out pretty good overall. The algorithm has been moved from
main.cpp to whisper.cpp and can be reused for all subtitles types. This
means that now you can specify the maximum length of the generated
lines. Simply provide the "-ml" argument specifying the max length in
number of characters

CMakeLists.txt

@@ -47,7 +47,7 @@ else()
     option(WHISPER_SUPPORT_OPENBLAS    "whisper: support for OpenBLAS" OFF)
 endif()
 
-option(WHISPER_PERF                    "whisper: enable perf timings"          OFF)
+option(WHISPER_PERF                    "whisper: enable perf timings" OFF)
 
 # sanitizers
 
@@ -151,6 +151,10 @@ else()
     endif()
 endif()
 
+if (WHISPER_PERF)
+    set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DGGML_PERF)
+endif()
+
 #
 # whisper - this is the main library of the project
 #

Makefile

@@ -1,6 +1,14 @@
+ifndef UNAME_S
 UNAME_S := $(shell uname -s)
+endif
+
+ifndef UNAME_P
 UNAME_P := $(shell uname -p)
+endif
+
+ifndef UNAME_M
 UNAME_M := $(shell uname -m)
+endif
 
 # Mac OS + Arm can report x86_64
 # ref: https://github.com/ggerganov/whisper.cpp/issues/66#issuecomment-1282546789
@@ -8,8 +16,8 @@ ifeq ($(UNAME_S),Darwin)
 	ifneq ($(UNAME_P),arm)
 		SYSCTL_M := $(shell sysctl -n hw.optional.arm64)
 		ifeq ($(SYSCTL_M),1)
-			UNAME_P := arm
-			UNAME_M := arm64
+			# UNAME_P := arm
+			# UNAME_M := arm64
 			warn := $(warning Your arch is announced as x86_64, but it seems to actually be ARM64. Not fixing that can lead to bad performance. For more info see: https://github.com/ggerganov/whisper.cpp/issues/66\#issuecomment-1282546789)
 		endif
 	endif
@@ -42,12 +50,16 @@ endif
 # TODO: probably these flags need to be tweaked on some architectures
 #       feel free to update the Makefile for your architecture and send a pull request or issue
 ifeq ($(UNAME_M),x86_64)
-	CFLAGS += -mavx -mavx2 -mfma -mf16c
+	# AVX 512
+	CFLAGS += -mavx512f -mfma -mf16c
+
+	# AVX 256
+	#CFLAGS += -mavx -mavx2 -mfma -mf16c
 endif
 ifeq ($(UNAME_M),amd64)
 	CFLAGS += -mavx -mavx2 -mfma -mf16c
 endif
-ifneq ($(filter arm%,$(UNAME_M)),)
+ifndef WHISPER_NO_ACCELERATE
 	# Mac M1 - include Accelerate framework
 	ifeq ($(UNAME_S),Darwin)
 		CFLAGS  += -DGGML_USE_ACCELERATE
@@ -78,13 +90,13 @@ main: examples/main/main.cpp ggml.o whisper.o
 	./main -h
 
 ggml.o: ggml.c ggml.h
-	$(CC)  $(CFLAGS)   -c ggml.c
+	$(CC)  $(CFLAGS)   -c ggml.c -o ggml.o
 
 whisper.o: whisper.cpp whisper.h
-	$(CXX) $(CXXFLAGS) -c whisper.cpp
+	$(CXX) $(CXXFLAGS) -c whisper.cpp -o whisper.o
 
 libwhisper.a: ggml.o whisper.o
-	ar rcs libwhisper.a ggml.o whisper.o
+	$(AR) rcs libwhisper.a ggml.o whisper.o
 
 clean:
 	rm -f *.o main stream bench libwhisper.a

README.md

@@ -26,14 +26,41 @@ Supported platforms:
 
 The entire implementation of the model is contained in 2 source files:
 
-- [ggml.h](ggml.h) / [ggml.c](ggml.c)
-- [whisper.h](whisper.h) / [whisper.cpp](whisper.cpp)
+- Tensor operations: [ggml.h](ggml.h) / [ggml.c](ggml.c)
+- Transformer inference: [whisper.h](whisper.h) / [whisper.cpp](whisper.cpp)
 
 Having such a lightweight implementation of the model allows to easily integrate it in different platforms and applications.
 As an example, here is a video of running the model on an iPhone 13 device - fully offline, on-device:
 
 https://user-images.githubusercontent.com/1991296/197385372-962a6dea-bca1-4d50-bf96-1d8c27b98c81.mp4
 
+## Implementation details
+
+- The core tensor operations are implemented in C ([ggml.h](ggml.h) / [ggml.c](ggml.c))
+- The transformer model and the high-level C-style API are implemented in C++ ([whisper.h](whisper.h) / [whisper.cpp](whisper.cpp))
+- Sample usage is demonstrated in [main.cpp](examples/main)
+- Sample real-time audio transcription from the microphone is demonstrated in [stream.cpp](examples/stream)
+- Various other examples are available in the [examples](examples) folder
+
+The tensor operators are optimized heavily for Apple silicon CPUs. Depending on the computation size, Arm Neon SIMD
+instrisics or CBLAS Accelerate framework routines are used. The latter are especially effective for bigger sizes since
+the Accelerate framework utilizes the special-purpose AMX coprocessor available in modern Apple products.
+
+## Limitations
+
+- Inference only
+- No GPU support
+- Very basic greedy sampling scheme - always pick up the token with highest probability.
+  This should be similar to the [GreedyDecoder](https://github.com/openai/whisper/blob/main/whisper/decoding.py#L249-L274)
+  from the original python implementation, so in order to make a fair comparison between the 2 implementations, make sure
+  to run the python code with the following parameters:
+
+  ```
+  whisper --best_of None --beam_size None ...
+  ```
+
+  In the future, `whisper.cpp` will support more sampling strategies.
+
 ## Quick start
 
 First, download one of the Whisper models converted in [ggml format](models). For example:
@@ -59,8 +86,8 @@ For a quick demo, simply run `make base.en`:
 ```java
 $ make base.en
 
-cc  -I.              -O3 -std=c11   -pthread -DGGML_USE_ACCELERATE   -c ggml.c
-c++ -I. -I./examples -O3 -std=c++11 -pthread -c whisper.cpp
+cc  -I.              -O3 -std=c11   -pthread -DGGML_USE_ACCELERATE   -c ggml.c -o ggml.o
+c++ -I. -I./examples -O3 -std=c++11 -pthread -c whisper.cpp -o whisper.o
 c++ -I. -I./examples -O3 -std=c++11 -pthread examples/main/main.cpp whisper.o ggml.o -o main  -framework Accelerate
 ./main -h
 
@@ -70,13 +97,18 @@ options:
   -h,       --help           show this help message and exit
   -s SEED,  --seed SEED      RNG seed (default: -1)
   -t N,     --threads N      number of threads to use during computation (default: 4)
+  -p N,     --processors N   number of processors to use during computation (default: 1)
   -ot N,    --offset-t N     time offset in milliseconds (default: 0)
   -on N,    --offset-n N     segment index offset (default: 0)
+  -mc N,    --max-context N  maximum number of text context tokens to store (default: max)
+  -ml N,    --max-len N      maximum segment length in characters (default: 0)
+  -wt N,    --word-thold N   word timestamp probability threshold (default: 0.010000)
   -v,       --verbose        verbose output
             --translate      translate from source language to english
   -otxt,    --output-txt     output result in a text file
   -ovtt,    --output-vtt     output result in a vtt file
   -osrt,    --output-srt     output result in a srt file
+  -owts,    --output-words   output script for generating karaoke video
   -ps,      --print_special  print special tokens
   -pc,      --print_colors   print colors
   -nt,      --no_timestamps  do not print timestamps
@@ -86,7 +118,7 @@ options:
 
 bash ./models/download-ggml-model.sh base.en
 Downloading ggml model base.en ...
-ggml-base.en.bin               100%[========================>] 141.11M  6.34MB/s    in 24s     
+ggml-base.en.bin               100%[========================>] 141.11M  6.34MB/s    in 24s
 Done! Model 'base.en' saved in 'models/ggml-base.en.bin'
 You can now use it like this:
 
@@ -114,23 +146,26 @@ whisper_model_load: n_text_layer  = 6
 whisper_model_load: n_mels        = 80
 whisper_model_load: f16           = 1
 whisper_model_load: type          = 2
-whisper_model_load: mem_required  = 505.00 MB
+whisper_model_load: mem_required  = 670.00 MB
 whisper_model_load: adding 1607 extra tokens
-whisper_model_load: ggml ctx size = 163.43 MB
+whisper_model_load: ggml ctx size = 140.60 MB
 whisper_model_load: memory size =    22.83 MB
 whisper_model_load: model size  =   140.54 MB
 
-main: processing 'samples/jfk.wav' (176000 samples, 11.0 sec), 4 threads, lang = en, task = transcribe, timestamps = 1 ...
+system_info: n_threads = 4 / 10 | AVX2 = 0 | AVX512 = 0 | NEON = 1 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 |
 
-[00:00.000 --> 00:11.000]   And so my fellow Americans, ask not what your country can do for you, ask what you can do for your country.
+main: processing 'samples/jfk.wav' (176000 samples, 11.0 sec), 4 threads, 1 processors, lang = en, task = transcribe, timestamps = 1 ...
 
 
-whisper_print_timings:     load time =    87.21 ms
-whisper_print_timings:      mel time =    24.26 ms
-whisper_print_timings:   sample time =     3.87 ms
-whisper_print_timings:   encode time =   323.67 ms / 53.94 ms per layer
-whisper_print_timings:   decode time =    83.25 ms / 13.87 ms per layer
-whisper_print_timings:    total time =   522.66 ms
+[00:00:00.000 --> 00:00:11.000]   And so my fellow Americans, ask not what your country can do for you, ask what you can do for your country.
+
+
+whisper_print_timings:     load time =   105.91 ms
+whisper_print_timings:      mel time =    24.62 ms
+whisper_print_timings:   sample time =     3.63 ms
+whisper_print_timings:   encode time =   324.71 ms / 54.12 ms per layer
+whisper_print_timings:   decode time =    83.58 ms / 13.93 ms per layer
+whisper_print_timings:    total time =   542.81 ms
 ```
 
 The command downloads the `base.en` model converted to custom `ggml` format and runs the inference on all `.wav` samples in the folder `samples`.
@@ -172,8 +207,8 @@ make large
 
 | Model  | Disk   | Mem     | SHA                                        |
 | ---    | ---    | ---     | ---                                        |
-| tiny   |  75 MB | ~280 MB | `bd577a113a864445d4c299885e0cb97d4ba92b5f` |
-| base   | 142 MB | ~430 MB | `465707469ff3a37a2b9b8d8f89f2f99de7299dac` |
+| tiny   |  75 MB | ~390 MB | `bd577a113a864445d4c299885e0cb97d4ba92b5f` |
+| base   | 142 MB | ~500 MB | `465707469ff3a37a2b9b8d8f89f2f99de7299dac` |
 | small  | 466 MB | ~1.0 GB | `55356645c2b361a969dfd0ef2c5a50d530afd8d5` |
 | medium | 1.5 GB | ~2.6 GB | `fd9727b6e1217c2f614f9b698455c4ffd82463b4` |
 | large  | 2.9 GB | ~4.7 GB | `b1caaf735c4cc1429223d5a74f0f4d0b9b59a299` |
@@ -185,7 +220,7 @@ in about half a minute on a MacBook M1 Pro, using `medium.en` model:
 
 <details>
   <summary>Expand to see the result</summary>
-  
+
 ```java
 $ ./main -m models/ggml-medium.en.bin -f samples/gb1.wav -t 8
 
@@ -273,32 +308,108 @@ to highlight words with high or low confidence:
 
 <img width="965" alt="image" src="https://user-images.githubusercontent.com/1991296/197356445-311c8643-9397-4e5e-b46e-0b4b4daa2530.png">
 
-## Implementation details
+## Controlling the length of the generated text segments (experimental)
 
-- The core tensor operations are implemented in C ([ggml.h](ggml.h) / [ggml.c](ggml.c))
-- The high-level C-style API is implemented in C++ ([whisper.h](whisper.h) / [whisper.cpp](whisper.cpp))
-- Sample usage is demonstrated in [main.cpp](examples/main)
-- Sample real-time audio transcription from the microphone is demonstrated in [stream.cpp](examples/stream)
-- Various other examples are available in the [examples](examples) folder
+For example, to limit the line length to a maximum of 16 characters, simply add `-ml 16`: 
 
-The tensor operators are optimized heavily for Apple silicon CPUs. Depending on the computation size, Arm Neon SIMD
-instrisics or CBLAS Accelerate framework routines are used. The latter are especially effective for bigger sizes since
-the Accelerate framework utilizes the special-purpose AMX coprocessor available in modern Apple products.
+```java
+./main -m ./models/ggml-base.en.bin -f ./samples/jfk.wav -ml 16
 
-## Limitations
+whisper_model_load: loading model from './models/ggml-base.en.bin'
+...
+system_info: n_threads = 4 / 10 | AVX2 = 0 | AVX512 = 0 | NEON = 1 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | 
 
-- Inference only
-- No GPU support
-- Very basic greedy sampling scheme - always pick up the token with highest probability.
-  This should be similar to the [GreedyDecoder](https://github.com/openai/whisper/blob/main/whisper/decoding.py#L249-L274)
-  from the original python implementation, so in order to make a fair comparison between the 2 implementations, make sure
-  to run the python code with the following parameters:
+main: processing './samples/jfk.wav' (176000 samples, 11.0 sec), 4 threads, 1 processors, lang = en, task = transcribe, timestamps = 1 ...
 
-  ```
-  whisper --best_of None --beam_size None ...
-  ```
+[00:00:00.000 --> 00:00:00.850]   And so my
+[00:00:00.850 --> 00:00:01.590]   fellow
+[00:00:01.590 --> 00:00:04.140]   Americans, ask
+[00:00:04.140 --> 00:00:05.660]   not what your
+[00:00:05.660 --> 00:00:06.840]   country can do
+[00:00:06.840 --> 00:00:08.430]   for you, ask
+[00:00:08.430 --> 00:00:09.440]   what you can do
+[00:00:09.440 --> 00:00:10.020]   for your
+[00:00:10.020 --> 00:00:11.000]   country.
+```
 
-  In the future, `whisper.cpp` will support more sampling strategies.
+## Word-level timestamp
+
+The `--max-len` argument can be used to obtain word-level timestamps. Simply use `-ml 1`:
+
+```java
+./main -m ./models/ggml-base.en.bin -f ./samples/jfk.wav -ml 1
+
+whisper_model_load: loading model from './models/ggml-base.en.bin'
+...
+system_info: n_threads = 4 / 10 | AVX2 = 0 | AVX512 = 0 | NEON = 1 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | 
+
+main: processing './samples/jfk.wav' (176000 samples, 11.0 sec), 4 threads, 1 processors, lang = en, task = transcribe, timestamps = 1 ...
+
+[00:00:00.000 --> 00:00:00.320]  
+[00:00:00.320 --> 00:00:00.370]   And
+[00:00:00.370 --> 00:00:00.690]   so
+[00:00:00.690 --> 00:00:00.850]   my
+[00:00:00.850 --> 00:00:01.590]   fellow
+[00:00:01.590 --> 00:00:02.850]   Americans
+[00:00:02.850 --> 00:00:03.300]  ,
+[00:00:03.300 --> 00:00:04.140]   ask
+[00:00:04.140 --> 00:00:04.990]   not
+[00:00:04.990 --> 00:00:05.410]   what
+[00:00:05.410 --> 00:00:05.660]   your
+[00:00:05.660 --> 00:00:06.260]   country
+[00:00:06.260 --> 00:00:06.600]   can
+[00:00:06.600 --> 00:00:06.840]   do
+[00:00:06.840 --> 00:00:07.010]   for
+[00:00:07.010 --> 00:00:08.170]   you
+[00:00:08.170 --> 00:00:08.190]  ,
+[00:00:08.190 --> 00:00:08.430]   ask
+[00:00:08.430 --> 00:00:08.910]   what
+[00:00:08.910 --> 00:00:09.040]   you
+[00:00:09.040 --> 00:00:09.320]   can
+[00:00:09.320 --> 00:00:09.440]   do
+[00:00:09.440 --> 00:00:09.760]   for
+[00:00:09.760 --> 00:00:10.020]   your
+[00:00:10.020 --> 00:00:10.510]   country
+[00:00:10.510 --> 00:00:11.000]  .
+```
+
+## Karaoke-style movie generation (experimental)
+
+The [main](examples/main) example provides support for output of karaoke-style movies, where the
+currently pronounced word is highlighted. Use the `-wts` argument and run the generated bash script.
+This requires to have `ffmpeg` installed.
+
+Here are a few *"typical"* examples:
+
+```java
+./main -m ./models/ggml-base.en.bin -f ./samples/jfk.wav -owts
+source ./samples/jfk.wav.wts
+ffplay ./samples/jfk.wav.mp4
+```
+
+https://user-images.githubusercontent.com/1991296/199337465-dbee4b5e-9aeb-48a3-b1c6-323ac4db5b2c.mp4
+
+---
+
+```java
+./main -m ./models/ggml-base.en.bin -f ./samples/mm0.wav -owts
+source ./samples/mm0.wav.wts
+ffplay ./samples/mm0.wav.mp4
+```
+
+https://user-images.githubusercontent.com/1991296/199337504-cc8fd233-0cb7-4920-95f9-4227de3570aa.mp4
+
+---
+
+```java
+./main -m ./models/ggml-base.en.bin -f ./samples/gb0.wav -owts
+source ./samples/gb0.wav.wts
+ffplay ./samples/gb0.wav.mp4
+```
+
+https://user-images.githubusercontent.com/1991296/199337538-b7b0c7a3-2753-4a88-a0cd-f28a317987ba.mp4
+
+---
 
 ## Benchmarks
 

examples/main/README.md

@@ -6,21 +6,29 @@ It can be used as a reference for using the `whisper.cpp` library in other proje
 ```
 ./main -h
 
-usage: ./main [options] file0.wav file1.wav ...
+usage: ./bin/main [options] file0.wav file1.wav ...
 
-options:
   -h,       --help           show this help message and exit
   -s SEED,  --seed SEED      RNG seed (default: -1)
   -t N,     --threads N      number of threads to use during computation (default: 4)
-  -o N,     --offset N       offset in milliseconds (default: 0)
+  -p N,     --processors N   number of processors to use during computation (default: 1)
+  -ot N,    --offset-t N     time offset in milliseconds (default: 0)
+  -on N,    --offset-n N     segment index offset (default: 0)
+  -mc N,    --max-context N  maximum number of text context tokens to store (default: max)
+  -ml N,    --max-len N      maximum segment length in characters (default: 0)
+  -wt N,    --word-thold N   word timestamp probability threshold (default: 0.010000)
   -v,       --verbose        verbose output
             --translate      translate from source language to english
   -otxt,    --output-txt     output result in a text file
   -ovtt,    --output-vtt     output result in a vtt file
   -osrt,    --output-srt     output result in a srt file
+  -owts,    --output-words   output script for generating karaoke video
   -ps,      --print_special  print special tokens
+  -pc,      --print_colors   print colors
   -nt,      --no_timestamps  do not print timestamps
   -l LANG,  --language LANG  spoken language (default: en)
   -m FNAME, --model FNAME    model path (default: models/ggml-base.en.bin)
   -f FNAME, --file FNAME     input WAV file path
+  -h,       --help           show this help message and exit
+
 ```

examples/main/main.cpp

@@ -36,6 +36,7 @@ std::string to_timestamp(int64_t t, bool comma = false) {
     return std::string(buf);
 }
 
+// helper function to replace substrings
 void replace_all(std::string & s, const std::string & search, const std::string & replace) {
     for (size_t pos = 0; ; pos += replace.length()) {
         pos = s.find(search, pos);
@@ -45,29 +46,6 @@ void replace_all(std::string & s, const std::string & search, const std::string
     }
 }
 
-// a cost-function that is high for text that takes longer to pronounce
-float voice_length(const std::string & text) {
-    float res = 0.0f;
-
-    // letters - add 1
-    // digits - add 3
-    // else - 0
-    for (size_t i = 0; i < text.size(); ++i) {
-        if (text[i] >= '0' && text[i] <= '9') {
-            res += 3.0f;
-        } else if (text[i] >= 'a' && text[i] <= 'z') {
-            res += 1.0f;
-        } else if (text[i] >= 'A' && text[i] <= 'Z') {
-            res += 1.0f;
-        } else {
-            res += 0.01f;
-        }
-        // TODO: support unicode
-    }
-
-    return res;
-}
-
 // command-line parameters
 struct whisper_params {
     int32_t seed         = -1; // RNG seed, not used currently
@@ -76,8 +54,9 @@ struct whisper_params {
     int32_t offset_t_ms  = 0;
     int32_t offset_n     = 0;
     int32_t max_context  = -1;
+    int32_t max_len      = 0;
 
-    float word_thold = 0.1f;
+    float word_thold = 0.01f;
 
     bool verbose              = false;
     bool translate            = false;
@@ -118,6 +97,8 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
             params.offset_n = std::stoi(argv[++i]);
         } else if (arg == "-mc" || arg == "--max-context") {
             params.max_context = std::stoi(argv[++i]);
+        } else if (arg == "-ml" || arg == "--max-len") {
+            params.max_len = std::stoi(argv[++i]);
         } else if (arg == "-wt" || arg == "--word-thold") {
             params.word_thold = std::stof(argv[++i]);
         } else if (arg == "-v" || arg == "--verbose") {
@@ -174,13 +155,14 @@ void whisper_print_usage(int argc, char ** argv, const whisper_params & params)
     fprintf(stderr, "  -ot N,    --offset-t N     time offset in milliseconds (default: %d)\n", params.offset_t_ms);
     fprintf(stderr, "  -on N,    --offset-n N     segment index offset (default: %d)\n", params.offset_n);
     fprintf(stderr, "  -mc N,    --max-context N  maximum number of text context tokens to store (default: max)\n");
+    fprintf(stderr, "  -ml N,    --max-len N      maximum segment length in characters (default: %d)\n", params.max_len);
     fprintf(stderr, "  -wt N,    --word-thold N   word timestamp probability threshold (default: %f)\n", params.word_thold);
     fprintf(stderr, "  -v,       --verbose        verbose output\n");
     fprintf(stderr, "            --translate      translate from source language to english\n");
     fprintf(stderr, "  -otxt,    --output-txt     output result in a text file\n");
     fprintf(stderr, "  -ovtt,    --output-vtt     output result in a vtt file\n");
     fprintf(stderr, "  -osrt,    --output-srt     output result in a srt file\n");
-    fprintf(stderr, "  -owts,    --output-words   output word-level timestamps to a text file\n");
+    fprintf(stderr, "  -owts,    --output-words   output script for generating karaoke video\n");
     fprintf(stderr, "  -ps,      --print_special  print special tokens\n");
     fprintf(stderr, "  -pc,      --print_colors   print colors\n");
     fprintf(stderr, "  -nt,      --no_timestamps  do not print timestamps\n");
@@ -190,65 +172,67 @@ void whisper_print_usage(int argc, char ** argv, const whisper_params & params)
     fprintf(stderr, "\n");
 }
 
-void whisper_print_segment_callback(struct whisper_context * ctx, void * user_data) {
+void whisper_print_segment_callback(struct whisper_context * ctx, int n_new, void * user_data) {
     const whisper_params & params = *(whisper_params *) user_data;
 
     const int n_segments = whisper_full_n_segments(ctx);
 
-    // print the last segment
-    const int i = n_segments - 1;
-    if (i == 0) {
+    // print the last n_new segments
+    const int s0 = n_segments - n_new;
+    if (s0 == 0) {
         printf("\n");
     }
 
-    if (params.no_timestamps) {
-        if (params.print_colors) {
-            for (int j = 0; j < whisper_full_n_tokens(ctx, i); ++j) {
-                if (params.print_special_tokens == false) {
-                    const whisper_token id = whisper_full_get_token_id(ctx, i, j);
-                    if (id >= whisper_token_eot(ctx)) {
-                        continue;
+    for (int i = s0; i < n_segments; i++) {
+        if (params.no_timestamps) {
+            if (params.print_colors) {
+                for (int j = 0; j < whisper_full_n_tokens(ctx, i); ++j) {
+                    if (params.print_special_tokens == false) {
+                        const whisper_token id = whisper_full_get_token_id(ctx, i, j);
+                        if (id >= whisper_token_eot(ctx)) {
+                            continue;
+                        }
                     }
+
+                    const char * text = whisper_full_get_token_text(ctx, i, j);
+                    const float  p    = whisper_full_get_token_p   (ctx, i, j);
+
+                    const int col = std::max(0, std::min((int) k_colors.size(), (int) (std::pow(p, 3)*float(k_colors.size()))));
+
+                    printf("%s%s%s", k_colors[col].c_str(), text, "\033[0m");
                 }
-
-                const char * text = whisper_full_get_token_text(ctx, i, j);
-                const float  p    = whisper_full_get_token_p   (ctx, i, j);
-
-                const int col = std::max(0, std::min((int) k_colors.size(), (int) (std::pow(p, 3)*float(k_colors.size()))));
-
-                printf("%s%s%s", k_colors[col].c_str(), text, "\033[0m");
+            } else {
+                const char * text = whisper_full_get_segment_text(ctx, i);
+                printf("%s", text);
             }
+            fflush(stdout);
         } else {
-            const char * text = whisper_full_get_segment_text(ctx, i);
-            printf("%s", text);
-        }
-        fflush(stdout);
-    } else {
-        const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
-        const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
+            const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
+            const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
 
-        if (params.print_colors) {
-            printf("[%s --> %s]  ", to_timestamp(t0).c_str(), to_timestamp(t1).c_str());
-            for (int j = 0; j < whisper_full_n_tokens(ctx, i); ++j) {
-                if (params.print_special_tokens == false) {
-                    const whisper_token id = whisper_full_get_token_id(ctx, i, j);
-                    if (id >= whisper_token_eot(ctx)) {
-                        continue;
+            if (params.print_colors) {
+                printf("[%s --> %s]  ", to_timestamp(t0).c_str(), to_timestamp(t1).c_str());
+                for (int j = 0; j < whisper_full_n_tokens(ctx, i); ++j) {
+                    if (params.print_special_tokens == false) {
+                        const whisper_token id = whisper_full_get_token_id(ctx, i, j);
+                        if (id >= whisper_token_eot(ctx)) {
+                            continue;
+                        }
                     }
+
+                    const char * text = whisper_full_get_token_text(ctx, i, j);
+                    const float  p    = whisper_full_get_token_p   (ctx, i, j);
+
+                    const int col = std::max(0, std::min((int) k_colors.size(), (int) (std::pow(p, 3)*float(k_colors.size()))));
+
+                    printf("%s%s%s", k_colors[col].c_str(), text, "\033[0m");
                 }
+                printf("\n");
+            } else {
+                const char * text = whisper_full_get_segment_text(ctx, i);
 
-                const char * text = whisper_full_get_token_text(ctx, i, j);
-                const float  p    = whisper_full_get_token_p   (ctx, i, j);
-
-                const int col = std::max(0, std::min((int) k_colors.size(), (int) (std::pow(p, 3)*float(k_colors.size()))));
-
-                printf("%s%s%s", k_colors[col].c_str(), text, "\033[0m");
+                printf("[%s --> %s]  %s\n", to_timestamp(t0).c_str(), to_timestamp(t1).c_str(), text);
             }
-            printf("\n");
-        } else {
-            const char * text = whisper_full_get_segment_text(ctx, i);
-
-            printf("[%s --> %s]  %s\n", to_timestamp(t0).c_str(), to_timestamp(t1).c_str(), text);
         }
     }
 }
@@ -318,566 +302,41 @@ bool output_srt(struct whisper_context * ctx, const char * fname, const whisper_
     return true;
 }
 
-struct Interval {
-    int x0;
-    int x1;
-    int type;
-};
-
-struct IntervalArray : public std::vector<Interval> {
-    int F = -1;
-};
-
-std::vector<IntervalArray> fit_text_to_audio(const IntervalArray & input, int N, float alpha = 2.0f) {
-    const int x_max = input.back().x1;
-
-    std::vector<int> ls;
-    std::vector<int> rs;
-    std::vector<int> xs;
-    std::vector<int> gs;
-
-    int G_max = 0;
-
-    for (const auto & ii : input) {
-        if (ii.type == 0) {
-            continue;
-        }
-
-        ls.push_back(ii.x0);
-        rs.push_back(ii.x1);
-        xs.push_back(ii.x0);
-        xs.push_back(ii.x1);
-
-        gs.push_back(G_max);
-        G_max += ii.x1 - ii.x0;
-        gs.push_back(G_max);
-    }
-
-    const int inf = 100*G_max;
-
-    struct Cell {
-        int fval  = -1;
-        int xprev = -1;
-        int w     = -1;
-    };
-
-    // Function F + initial conditions
-    std::vector<std::vector<Cell>> F(xs.size());
-    for (auto & Fx : F) {
-        Fx.resize(N + 1);
-        for (auto & f : Fx) f.fval = inf;
-        Fx[0].fval = alpha*G_max;
-    }
-
-    // DP core
-    for (int n = 1; n <= N; ++n) {
-        for (int ix = 0; ix < (int) xs.size(); ++ix) {
-            const int x = xs[ix];
-
-            int best_fval  = inf;
-            int best_xprev = -1;
-            int best_w     = -1;
-
-            for (int il = 0; il < (int) ls.size(); ++il) {
-                const int l = ls[il];
-
-                if (l < n) continue;
-                if (l >= x) break;
-
-                for (int ir = il; ir < (int) rs.size(); ++ir) {
-                    const int r = rs[ir];
-
-                    if (r < l + 1) continue;
-                    if (r > x) break;
-
-                    const int cur_fval = F[2*il][n - 1].fval + (r - l) - (alpha + 1)*(gs[2*ir + 1] - gs[2*il]);
-
-                    if (cur_fval < best_fval) {
-                        best_fval  = cur_fval;
-                        best_xprev = 2*il;
-                        best_w     = r - l;
-                    }
-                }
-            }
-
-            F[ix][n].fval  = best_fval;
-            F[ix][n].xprev = best_xprev;
-            F[ix][n].w     = best_w;
-        }
-    }
-
-    // generate output
-    std::vector<IntervalArray> res(N + 1);
-    {
-        for (int i = 1; i <= N; ++i) {
-            IntervalArray resCur;
-
-            int n = i;
-
-            std::vector<int> grid(x_max + 1, 0); // initally, everything is background
-
-            int best_ix   = 0;
-            int best_fval = F[0][n].fval;
-            for (int ix = 1; ix < (int) xs.size(); ++ix) {
-                if (F[ix][n].fval < best_fval) {
-                    best_ix   = ix;
-                    best_fval = F[ix][n].fval;
-                }
-            }
-
-            resCur.F = (i == input.size()/2) ? 0 : best_fval;
-            while (true) {
-                const int ix = F[best_ix][n].xprev;
-                const int w  = F[best_ix][n].w;
-                for (int x = xs[ix]; x < xs[ix] + w; ++x) {
-                    grid[x] = 1; // i.e. green
-                }
-                best_ix = F[best_ix][n].xprev;
-                if (--n == 0) break;
-            }
-
-            int x0   = 0;
-            int type = grid[0];
-            for (int x1 = 1; x1 <= x_max; ++x1) {
-                if (grid[x1] != grid[x1 - 1] || x1 == x_max) {
-                    if (type == 1) {
-                        resCur.push_back({x0, x1, 1});
-                    }
-                    x0   = x1;
-                    type = grid[x1];
-                }
-            }
-
-            res[i] = std::move(resCur);
-        }
-    }
-
-    return res;
-}
-
-
-// word-level timestamps (experimental)
-// TODO: probably still has bugs, needs refactoring, etc..
-// TODO: auto threshold
-// TODO: extra pass to detect unused speech and assign to tokens
+// karaoke video generation
+// outputs a bash script that uses ffmpeg to generate a video with the subtitles
 // TODO: font parameter adjustments
-bool output_wts(struct whisper_context * ctx, const char * fname, const char * fname_inp, const whisper_params & params, const std::vector<float> & pcmf32) {
-    std::vector<float> pcm_avg(pcmf32.size(), 0);
-
-    // average the fabs of the signal
-    {
-        const int hw = 32;
-
-        for (int i = 0; i < pcmf32.size(); i++) {
-            float sum = 0;
-            for (int j = -hw; j <= hw; j++) {
-                if (i + j >= 0 && i + j < pcmf32.size()) {
-                    sum += fabs(pcmf32[i + j]);
-                }
-            }
-            pcm_avg[i] = sum/(2*hw + 1);
-        }
-    }
-
-    struct token_info {
-        int64_t t0 = -1;
-        int64_t t1 = -1;
-
-        int64_t tt0 = -1;
-        int64_t tt1 = -1;
-
-        whisper_token id;
-        whisper_token tid;
-
-        float p     = 0.0f;
-        float pt    = 0.0f;
-        float ptsum = 0.0f;
-
-        std::string text;
-        float vlen = 0.0f; // voice length of this token
-
-        void calc_vlen(struct whisper_context * ctx) {
-            if (id >= whisper_token_eot(ctx)) {
-                vlen = 0.1f;
-                return;
-            }
-
-            vlen = voice_length(text);
-        }
-
-        bool is_voice() const {
-            return vlen > 0.5f;
-        }
-    };
-
-    int64_t t_beg  = 0;
-    int64_t t_last = 0;
-
-    whisper_token tid_last = 0;
-
+bool output_wts(struct whisper_context * ctx, const char * fname, const char * fname_inp, const whisper_params & params, float t_sec) {
     std::ofstream fout(fname);
 
     fprintf(stderr, "%s: saving output to '%s'\n", __func__, fname);
 
-    fout << "!/bin/bash" << "\n";
+    // TODO: become parameter
+    static const char * font = "/System/Library/Fonts/Supplemental/Courier New Bold.ttf";
+
+    fout << "#!/bin/bash" << "\n";
     fout << "\n";
 
-    fout << "ffmpeg -i " << fname_inp << " -f lavfi -i color=size=1200x120:duration=" << float(pcm_avg.size() + 1000)/WHISPER_SAMPLE_RATE << ":rate=25:color=black -vf \"";
-
-    bool is_first = true;
+    fout << "ffmpeg -i " << fname_inp << " -f lavfi -i color=size=1200x120:duration=" << t_sec << ":rate=25:color=black -vf \"";
 
     for (int i = 0; i < whisper_full_n_segments(ctx); i++) {
         const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
         const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
 
-        const char *text = whisper_full_get_segment_text(ctx, i);
-
-        const int s0 = std::max(0,                   (int) (t0*WHISPER_SAMPLE_RATE/100));
-        const int s1 = std::min((int) pcm_avg.size(), (int) (t1*WHISPER_SAMPLE_RATE/100));
-
         const int n = whisper_full_n_tokens(ctx, i);
 
-        std::vector<token_info> tokens(n);
-
-        if (n <= 1) {
-            continue;
-        }
-
+        std::vector<whisper_token_data> tokens(n);
         for (int j = 0; j < n; ++j) {
-            struct whisper_token_data token = whisper_full_get_token_data(ctx, i, j);
-
-            if (j == 0) {
-                if (token.id == whisper_token_beg(ctx)) {
-                    tokens[j    ].t0 = t0;
-                    tokens[j    ].t1 = t0;
-                    tokens[j + 1].t0 = t0;
-
-                    t_beg  = t0;
-                    t_last = t0;
-                    tid_last = whisper_token_beg(ctx);
-                } else {
-                    tokens[j    ].t0 = t_last;
-                }
-            }
-
-            const int64_t tt = t_beg + 2*(token.tid - whisper_token_beg(ctx));
-
-            tokens[j].id    = token.id;
-            tokens[j].tid   = token.tid;
-            tokens[j].p     = token.p;
-            tokens[j].pt    = token.pt;
-            tokens[j].ptsum = token.ptsum;
-
-            tokens[j].text = whisper_token_to_str(ctx, token.id);
-            tokens[j].calc_vlen(ctx);
-
-            if (token.pt > params.word_thold && token.ptsum > 0.01 && token.tid > tid_last) {
-                if (j > 0) {
-                    tokens[j - 1].t1 = tt;
-                }
-                tokens[j].t0 = tt;
-                tid_last = token.tid;
-            }
+            tokens[j] = whisper_full_get_token_data(ctx, i, j);
         }
 
-        tokens[n - 2].t1 = t1;
-        tokens[n - 1].t0 = t1;
-        tokens[n - 1].t1 = t1;
-
-        t_last = t1;
-
-        {
-            {
-                int p0 = 0;
-                int p1 = 0;
-
-                while (true) {
-                    while (p1 < n && tokens[p1].t1 < 0) {
-                        p1++;
-                    }
-
-                    if (p1 >= n) {
-                        p1--;
-                    }
-
-                    if (p1 > p0) {
-                        IntervalArray arr;
-
-                        const int s0 = std::max(0,                        (int) (tokens[p0].t0*WHISPER_SAMPLE_RATE/100));
-                        const int s1 = std::min((int) pcm_avg.size() - 1, (int) (tokens[p1].t1*WHISPER_SAMPLE_RATE/100));
-
-                        const int ns = s1 - s0;
-
-                        float sum = 0.0f;
-                        for (int k = s0; k < s1; k++) {
-                            sum += pcm_avg[k];
-                        }
-
-                        const float thold = sum/ns;
-
-                        printf("segment %4d: s0 = %6d, s1 = %6d, ns = %6d, thold = %f\n", i, s0, s1, ns, thold);
-
-                        {
-                            int last_s    = -1;
-                            int last_type = -1;
-                            for (int k = s0; k < s1; k++) {
-                                const int type = pcm_avg[k] > thold ? 1 : 0;
-
-                                if (type != last_type) {
-                                    if (last_type != -1) {
-                                        arr.push_back({ last_s, k, last_type });
-                                    }
-                                    last_s    = k;
-                                    last_type = type;
-                                }
-                            }
-                        }
-
-                        //for (int k = 0; k < arr.size(); k++) {
-                        //    printf("  %4d: %6d, %6d, %d\n", k, arr[k].x0, arr[k].x1, arr[k].type);
-                        //}
-
-                        int n_voice = 0;
-
-                        for (int j = p0; j <= p1; ++j) {
-                            if (tokens[j].is_voice()) {
-                                n_voice++;
-                            }
-                        }
-
-                        if (n_voice > 0 && arr.size() > n_voice) {
-                            printf("xxxxxxxx n = %d, n_voice = %d, arr.size() = %d\n", n, n_voice, (int) arr.size());
-                            auto res = fit_text_to_audio(arr, n_voice, 2.0f);
-                            printf("done fit_text_to_audio, F = %d\n", res[n_voice].F);
-
-                            {
-                                int tid = p0;
-                                for (int k = 0; k < (int) res[n_voice].size(); ++k) {
-                                    while (!tokens[tid].is_voice() && tid <= p1) {
-                                        //if (tokens[tid].t0 < 0) {
-                                        //    tokens[tid].t0 = (int64_t) (100*res[n_voice][k].x0/WHISPER_SAMPLE_RATE);
-                                        //}
-                                        //if (tokens[tid].t1 < 0) {
-                                        //    tokens[tid].t1 = tokens[tid].t0;
-                                        //}
-
-                                        tid++;
-                                    }
-
-                                    if (tid > p1) {
-                                        break;
-                                    }
-
-                                    if (tokens[tid].t0 < 0) {
-                                        tokens[tid].t0 = (int64_t) (100*res[n_voice][k].x0/WHISPER_SAMPLE_RATE);
-                                        if (tid > 0) {
-                                            tokens[tid - 1].t1 = tokens[tid].t0;
-                                        }
-                                    }
-
-                                    if (tokens[tid].t1 < 0) {
-                                        tokens[tid].t1 = (int64_t) (100*res[n_voice][k].x1/WHISPER_SAMPLE_RATE);
-                                    }
-
-                                    tid++;
-                                }
-
-                                printf("xxxxxxxx n = %d, tid = %d\n", n, tid);
-                            }
-                        }
-                    }
-
-                    p1++;
-                    p0 = p1;
-                    if (p1 >= n) {
-                        break;
-                    }
-                }
-            }
-
-            {
-                int p0 = 0;
-                int p1 = 0;
-                while (true) {
-                    while (p1 < n && tokens[p1].t1 < 0) {
-                        p1++;
-                    }
-
-                    if (p1 >= n) {
-                        p1--;
-                    }
-
-                    if (p1 > p0) {
-                        double psum = 0.0;
-                        for (int j = p0; j <= p1; j++) {
-                            psum += tokens[j].vlen;
-                        }
-
-                        //printf("analyzing %d - %d, psum = %f\n", p0, p1, psum);
-
-                        const double dt = tokens[p1].t1 - tokens[p0].t0;
-
-                        for (int j = p0 + 1; j <= p1; j++) {
-                            const double ct = tokens[j - 1].t0 + dt*tokens[j - 1].vlen/psum;
-                            //const double ct = tokens[j - 1].t0 + (dt*(j - p0))/(p1 - p0 + 1);
-                            //const double ct = tokens[p0].t0 + (dt*(j - p0))/(p1 - p0 + 1);
-
-                            tokens[j - 1].t1 = ct;
-                            tokens[j    ].t0 = ct;
-                        }
-                    }
-
-                    p1++;
-                    p0 = p1;
-                    if (p1 >= n) {
-                        break;
-                    }
-                }
-            }
-
-            for (int j = 0; j < n - 1; j++) {
-                if (tokens[j + 1].t0 < 0) {
-                    tokens[j + 1].t0 = tokens[j].t1;
-                }
-
-                tokens[j].tt0 = tokens[j].t0;
-                tokens[j].tt1 = tokens[j].t1;
-
-                if (j < n - 2) {
-                    tokens[j].tt1 = std::max(tokens[j].tt1, tokens[j + 1].t0);
-                }
-            }
-
-            // VAD
-            {
-                const int hw = WHISPER_SAMPLE_RATE; // take one second of audio around the token
-
-                for (int j = 0; j < n; j++) {
-                    const int64_t t0 = tokens[j].t0;
-                    const int64_t t1 = tokens[j].t1;
-
-                    int s0 = std::max(0,                        (int) (t0*WHISPER_SAMPLE_RATE/100));
-                    int s1 = std::min((int) pcm_avg.size() - 1, (int) (t1*WHISPER_SAMPLE_RATE/100));
-
-                    const int ss0 = std::max(0,                        (int) (t0*WHISPER_SAMPLE_RATE/100) - hw);
-                    const int ss1 = std::min((int) pcm_avg.size() - 1, (int) (t1*WHISPER_SAMPLE_RATE/100) + hw);
-
-                    const int n = ss1 - ss0;
-
-                    float sum = 0.0f;
-                    for (int k = ss0; k < ss1; k++) {
-                        sum += pcm_avg[k];
-                    }
-
-                    const float avg = sum/n;
-
-                    const float thold = 0.5*avg;
-
-                    {
-                        int k = s0;
-                        if (pcm_avg[k] > thold && j > 0) {
-                            while (k > 0 && pcm_avg[k] > thold) {
-                                k--;
-                            }
-                            tokens[j].t0 = (int64_t) (100*k/WHISPER_SAMPLE_RATE);
-                            if (tokens[j].t0 < tokens[j - 1].t1) {
-                                tokens[j].t0 = tokens[j - 1].t1;
-                            } else {
-                                s0 = k;
-                            }
-                        } else {
-                            while (pcm_avg[k] < thold && k < s1) {
-                                k++;
-                            }
-                            s0 = k;
-                            tokens[j].t0 = 100*k/WHISPER_SAMPLE_RATE;
-                        }
-                    }
-
-                    {
-                        int k = s1;
-                        if (pcm_avg[k] > thold) {
-                            while (k < (int) pcm_avg.size() - 1 && pcm_avg[k] > thold) {
-                                k++;
-                            }
-                            tokens[j].t1 = 100*k/WHISPER_SAMPLE_RATE;
-                            if (j < n - 1 && tokens[j].t1 > tokens[j + 1].t0) {
-                                tokens[j].t1 = tokens[j + 1].t0;
-                            } else {
-                                s1 = k;
-                            }
-                        } else {
-                            while (pcm_avg[k] < thold && k > s0) {
-                                k--;
-                            }
-                            s1 = k;
-                            tokens[j].t1 = 100*k/WHISPER_SAMPLE_RATE;
-                        }
-                    }
-                }
-            }
-
-            const int t_expand = 0;
-
-            for (int j = 0; j < n; j++) {
-                if (j > 0) {
-                    tokens[j].t0 = std::max(0, (int) (tokens[j].t0 - t_expand));
-                }
-                if (j < n - 1) {
-                    tokens[j].t1 = tokens[j].t1 + t_expand;
-                }
-            }
-        }
-
-        {
-            const std::string fname_tokens = "tokens-" + std::to_string(i) + ".txt";
-
-            std::ofstream fout(fname_tokens);
-
-            int s0 = std::max(0,                        (int) (t0*WHISPER_SAMPLE_RATE/100));
-            int s1 = std::min((int) pcm_avg.size() - 1, (int) (t1*WHISPER_SAMPLE_RATE/100));
-
-            for (int j = s0; j < s1; j++) {
-                int k = -1;
-                for (int r = 0; r < n; r++) {
-                    if (j >= (int) (tokens[r].t0*WHISPER_SAMPLE_RATE/100) && j < (int) (tokens[r].t1*WHISPER_SAMPLE_RATE/100)) {
-                        k = r;
-                        break;
-                    }
-                }
-
-                fout << j << " " << pcm_avg[j] << " " << float(k%3 + 1)/30.0 << std::endl;
-            }
-
-            fout.close();
-        }
-
-        for (int j = 0; j < n; ++j) {
-            const auto & token = tokens[j];
-            const auto tt = token.pt > params.word_thold && token.ptsum > 0.01 ? whisper_token_to_str(ctx, token.tid) : "[?]";
-            printf("%s: %10s %6.3f %6.3f %6.3f %6.3f %5d %5d '%s'\n", __func__,
-                    tt, token.p, token.pt, token.ptsum, token.vlen, (int) token.t0, (int) token.t1, token.text.c_str());
-
-            if (tokens[j].id >= whisper_token_eot(ctx)) {
-                continue;
-            }
-
-            //printf("[%s --> %s] %s\n", to_timestamp(token.t0).c_str(), to_timestamp(token.t1).c_str(), whisper_token_to_str(ctx, token.id));
-
-            //fout << "# " << to_timestamp(token.t0) << " --> " << to_timestamp(token.t1) << " " << whisper_token_to_str(ctx, token.id) << "\n";
-        }
-
-        static const int line_wrap = 60;
-        static const char * font = "/System/Library/Fonts/Supplemental/Courier New Bold.ttf";
-
-        if (!is_first) {
+        if (i > 0) {
             fout << ",";
         }
 
         // background text
         fout << "drawtext=fontfile='" << font << "':fontsize=24:fontcolor=gray:x=(w-text_w)/2:y=h/2:text='':enable='between(t," << t0/100.0 << "," << t0/100.0 << ")'";
 
-        is_first = false;
+        bool is_first = true;
 
         for (int j = 0; j < n; ++j) {
             const auto & token = tokens[j];
@@ -886,10 +345,6 @@ bool output_wts(struct whisper_context * ctx, const char * fname, const char * f
                 continue;
             }
 
-            //if (!tokens[j].is_voice()) {
-            //    continue;
-            //}
-
             std::string txt_bg;
             std::string txt_fg; // highlight token
             std::string txt_ul; // underline
@@ -925,17 +380,6 @@ bool output_wts(struct whisper_context * ctx, const char * fname, const char * f
                     }
 
                     ncnt += txt.size();
-
-                    if (ncnt > line_wrap) {
-                        if (k < j) {
-                            txt_bg = "> ";
-                            txt_fg = "> ";
-                            txt_ul = "\\ \\ ";
-                            ncnt = 0;
-                        } else {
-                            break;
-                        }
-                    }
                 }
 
                 ::replace_all(txt_bg, "'", "’");
@@ -944,8 +388,11 @@ bool output_wts(struct whisper_context * ctx, const char * fname, const char * f
                 ::replace_all(txt_fg, "\"", "\\\"");
             }
 
-            // background text
-            fout << ",drawtext=fontfile='" << font << "':fontsize=24:fontcolor=gray:x=(w-text_w)/2:y=h/2:text='" << txt_bg << "':enable='between(t," << token.tt0/100.0 << "," << token.tt1/100.0 << ")'";
+            if (is_first) {
+                // background text
+                fout << ",drawtext=fontfile='" << font << "':fontsize=24:fontcolor=gray:x=(w-text_w)/2:y=h/2:text='" << txt_bg << "':enable='between(t," << t0/100.0 << "," << t1/100.0 << ")'";
+                is_first = false;
+            }
 
             // foreground text
             fout << ",drawtext=fontfile='" << font << "':fontsize=24:fontcolor=lightgreen:x=(w-text_w)/2+8:y=h/2:text='" << txt_fg << "':enable='between(t," << token.t0/100.0 << "," << token.t1/100.0 << ")'";
@@ -1086,6 +533,10 @@ int main(int argc, char ** argv) {
             wparams.n_max_text_ctx       = params.max_context >= 0 ? params.max_context : wparams.n_max_text_ctx;
             wparams.offset_ms            = params.offset_t_ms;
 
+            wparams.token_timestamps     = params.output_wts || params.max_len > 0;
+            wparams.thold_pt             = params.word_thold;
+            wparams.max_len              = params.output_wts && params.max_len == 0 ? 60 : params.max_len;
+
             // this callback is called on each new segment
             if (!wparams.print_realtime) {
                 wparams.new_segment_callback           = whisper_print_segment_callback;
@@ -1123,7 +574,7 @@ int main(int argc, char ** argv) {
             // output to WTS file
             if (params.output_wts) {
                 const auto fname_wts = fname_inp + ".wts";
-                output_wts(ctx, fname_wts.c_str(), fname_inp.c_str(), params, pcmf32);
+                output_wts(ctx, fname_wts.c_str(), fname_inp.c_str(), params, float(pcmf32.size() + 1000)/WHISPER_SAMPLE_RATE);
             }
         }
     }

examples/whisper.nvim/README.md

@@ -78,6 +78,14 @@ There are a lot of ways to improve this idea and I don't have much experience wi
   *"optimize by sorting the data first"*
   
   The plugin would then make an appropriate query using the selected text and code context to Copilot or GPT-3 and return the result.
+  
+  Here is a proof-of-concept:
+  
+  https://user-images.githubusercontent.com/1991296/199078847-0278fcde-5667-4748-ba0d-7d55381d6047.mp4
+    
+  https://user-images.githubusercontent.com/1991296/200067939-f98d2ac2-7519-438a-85f9-79db0841ba4f.mp4
+  
+  For explanation how this works see: https://twitter.com/ggerganov/status/1587168771789258756
 
 ## Discussion
 

extra/sha-all.sh

@@ -0,0 +1,7 @@
+#!/bin/bash
+
+# Compute the SHA1 of all model files in ./models/ggml-*.bin
+
+for f in ./models/ggml-*.bin; do
+    shasum "$f" -a 1
+done

ggml.c

@@ -14,7 +14,7 @@
 #include <stdint.h>
 #include <stdio.h>
 
-#if defined _MSC_VER
+#if defined _MSC_VER || defined(__MINGW32__)
 #include <Windows.h>
 
 typedef volatile LONG atomic_int;
@@ -44,6 +44,11 @@ static int pthread_create(pthread_t* out, void* unused, thread_ret_t(*func)(void
 static int pthread_join(pthread_t thread, void* unused) {
     return (int) WaitForSingleObject(thread, INFINITE);
 }
+
+static int sched_yield (void) {
+    Sleep (0);
+    return 0;
+}
 #else
 #include <pthread.h>
 #include <stdatomic.h>
@@ -193,7 +198,7 @@ static ggml_fp16_t table_exp_f16[1 << 16];
 // timing
 //
 
-#if defined(_MSC_VER)
+#if defined(_MSC_VER) || defined(__MINGW32__)
 static int64_t timer_freq;
 void ggml_time_init(void) {
     LARGE_INTEGER frequency;
@@ -322,6 +327,45 @@ inline static void ggml_vec_dot_f32(const int n, float * restrict s, const float
     for (int i = n16; i < n; ++i) {
         sumf += x[i]*y[i];
     }
+#elif defined(__AVX512F__)
+    const int n64 = (n & ~63);
+
+    __m512 sum0 = _mm512_setzero_ps();
+    __m512 sum1 = _mm512_setzero_ps();
+    __m512 sum2 = _mm512_setzero_ps();
+    __m512 sum3 = _mm512_setzero_ps();
+
+    __m512 x0, x1, x2, x3;
+    __m512 y0, y1, y2, y3;
+
+    for (int i = 0; i < n64; i += 64) {
+        x0 = _mm512_loadu_ps(x + i + 0);
+        x1 = _mm512_loadu_ps(x + i + 16);
+        x2 = _mm512_loadu_ps(x + i + 32);
+        x3 = _mm512_loadu_ps(x + i + 48);
+
+        y0 = _mm512_loadu_ps(y + i + 0);
+        y1 = _mm512_loadu_ps(y + i + 16);
+        y2 = _mm512_loadu_ps(y + i + 32);
+        y3 = _mm512_loadu_ps(y + i + 48);
+
+        sum0 = _mm512_fmadd_ps(x0, y0, sum0);
+        sum1 = _mm512_fmadd_ps(x1, y1, sum1);
+        sum2 = _mm512_fmadd_ps(x2, y2, sum2);
+        sum3 = _mm512_fmadd_ps(x3, y3, sum3);
+    }
+
+    sum0 = _mm512_add_ps(sum0, sum1);
+    sum2 = _mm512_add_ps(sum2, sum3);
+    sum0 = _mm512_add_ps(sum0, sum2);
+
+    sumf = sum0[0] + sum0[1] + sum0[2] + sum0[3] + sum0[4] + sum0[5] + sum0[6] + sum0[7] +
+           sum0[8] + sum0[9] + sum0[10] + sum0[11] + sum0[12] + sum0[13] + sum0[14] + sum0[15];
+
+    // leftovers
+    for (int i = n64; i < n; ++i) {
+        sumf += x[i]*y[i];
+    }
 #elif defined(__AVX2__)
     // AVX 256-bit
     const int n32 = (n & ~31);
@@ -519,6 +563,47 @@ inline static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t
     for (int i = n32; i < n; ++i) {
         sumf += ggml_fp16_to_fp32(x[i])*ggml_fp16_to_fp32(y[i]);
     }
+#elif defined(__AVX512F__)
+    // AVX 512-bit
+    const int n64 = (n & ~63);
+
+    __m512 sum0 = _mm512_setzero_ps();
+    __m512 sum1 = _mm512_setzero_ps();
+    __m512 sum2 = _mm512_setzero_ps();
+    __m512 sum3 = _mm512_setzero_ps();
+
+    __m512 x0, x1, x2, x3;
+    __m512 y0, y1, y2, y3;
+
+    for (int i = 0; i < n64; i += 64) {
+        x0 = _mm512_cvtph_ps(_mm256_loadu_si256((__m256i*)(x + i + 0 )));
+        x1 = _mm512_cvtph_ps(_mm256_loadu_si256((__m256i*)(x + i + 16)));
+        x2 = _mm512_cvtph_ps(_mm256_loadu_si256((__m256i*)(x + i + 32)));
+        x3 = _mm512_cvtph_ps(_mm256_loadu_si256((__m256i*)(x + i + 48)));
+
+        y0 = _mm512_cvtph_ps(_mm256_loadu_si256((__m256i*)(y + i + 0 )));
+        y1 = _mm512_cvtph_ps(_mm256_loadu_si256((__m256i*)(y + i + 16)));
+        y2 = _mm512_cvtph_ps(_mm256_loadu_si256((__m256i*)(y + i + 32)));
+        y3 = _mm512_cvtph_ps(_mm256_loadu_si256((__m256i*)(y + i + 48)));
+
+        sum0 = _mm512_fmadd_ps(x0, y0, sum0);
+        sum1 = _mm512_fmadd_ps(x1, y1, sum1);
+        sum2 = _mm512_fmadd_ps(x2, y2, sum2);
+        sum3 = _mm512_fmadd_ps(x3, y3, sum3);
+    }
+
+    const __m512 sum01 = _mm512_add_ps(sum0, sum1);
+    const __m512 sum23 = _mm512_add_ps(sum2, sum3);
+    const __m512 sum0123 = _mm512_add_ps(sum01, sum23);
+
+    sumf = sum0123[0] + sum0123[1] + sum0123[2]  + sum0123[3]  + sum0123[4]  + sum0123[5]  + sum0123[6]  + sum0123[7] +
+           sum0123[8] + sum0123[9] + sum0123[10] + sum0123[11] + sum0123[12] + sum0123[13] + sum0123[14] + sum0123[15];
+
+    // leftovers
+    for (int i = n64; i < n; ++i) {
+        //GGML_ASSERT(false);
+        sumf += ggml_fp16_to_fp32(x[i])*ggml_fp16_to_fp32(y[i]);
+    }
 #elif defined(__AVX2__)
     // AVX 256-bit
     const int n32 = (n & ~31);
@@ -625,7 +710,7 @@ inline static void ggml_vec_mad_f32(const int n, float * restrict y, const float
     // NEON 128-bit
     const int n16 = (n & ~15);
 
-    const float32x4_t v4 = vdupq_n_f32(v);
+    const float32x4_t v0 = vdupq_n_f32(v);
 
     float32x4_t x0, x1, x2, x3;
     float32x4_t y0, y1, y2, y3;
@@ -641,14 +726,14 @@ inline static void ggml_vec_mad_f32(const int n, float * restrict y, const float
         y2 = vld1q_f32(y + i + 8);
         y3 = vld1q_f32(y + i + 12);
 
-        y0 = vfmaq_f32(y0, x0, v4);
-        y1 = vfmaq_f32(y1, x1, v4);
-        y2 = vfmaq_f32(y2, x2, v4);
-        y3 = vfmaq_f32(y3, x3, v4);
+        y0 = vfmaq_f32(y0, x0, v0);
+        y1 = vfmaq_f32(y1, x1, v0);
+        y2 = vfmaq_f32(y2, x2, v0);
+        y3 = vfmaq_f32(y3, x3, v0);
 
-        vst1q_f32(y + i + 0, y0);
-        vst1q_f32(y + i + 4, y1);
-        vst1q_f32(y + i + 8, y2);
+        vst1q_f32(y + i + 0,  y0);
+        vst1q_f32(y + i + 4,  y1);
+        vst1q_f32(y + i + 8,  y2);
         vst1q_f32(y + i + 12, y3);
     }
 
@@ -656,11 +741,46 @@ inline static void ggml_vec_mad_f32(const int n, float * restrict y, const float
     for (int i = n16; i < n; ++i) {
         y[i] += x[i]*v;
     }
+#elif defined(__AVX512F__)
+    // AVX512 512-bit
+    const int n64 = (n & ~63);
+
+    const __m512 v0 = _mm512_set1_ps(v);
+
+    __m512 x0, x1, x2, x3;
+    __m512 y0, y1, y2, y3;
+
+    for (int i = 0; i < n64; i += 64) {
+        x0 = _mm512_loadu_ps(x + i + 0);
+        x1 = _mm512_loadu_ps(x + i + 16);
+        x2 = _mm512_loadu_ps(x + i + 32);
+        x3 = _mm512_loadu_ps(x + i + 48);
+
+        y0 = _mm512_loadu_ps(y + i + 0);
+        y1 = _mm512_loadu_ps(y + i + 16);
+        y2 = _mm512_loadu_ps(y + i + 32);
+        y3 = _mm512_loadu_ps(y + i + 48);
+
+        y0 = _mm512_fmadd_ps(x0, v0, y0);
+        y1 = _mm512_fmadd_ps(x1, v0, y1);
+        y2 = _mm512_fmadd_ps(x2, v0, y2);
+        y3 = _mm512_fmadd_ps(x3, v0, y3);
+
+        _mm512_storeu_ps(y + i + 0,  y0);
+        _mm512_storeu_ps(y + i + 16, y1);
+        _mm512_storeu_ps(y + i + 32, y2);
+        _mm512_storeu_ps(y + i + 48, y3);
+    }
+
+    // leftovers
+    for (int i = n64; i < n; ++i) {
+        y[i] += x[i]*v;
+    }
 #elif defined(__AVX2__)
     // AVX 256-bit
     const int n32 = (n & ~31);
 
-    const __m256 v4 = _mm256_set1_ps(v);
+    const __m256 v0 = _mm256_set1_ps(v);
 
     __m256 x0, x1, x2, x3;
     __m256 y0, y1, y2, y3;
@@ -676,13 +796,13 @@ inline static void ggml_vec_mad_f32(const int n, float * restrict y, const float
         y2 = _mm256_loadu_ps(y + i + 16);
         y3 = _mm256_loadu_ps(y + i + 24);
 
-        y0 = _mm256_fmadd_ps(x0, v4, y0);
-        y1 = _mm256_fmadd_ps(x1, v4, y1);
-        y2 = _mm256_fmadd_ps(x2, v4, y2);
-        y3 = _mm256_fmadd_ps(x3, v4, y3);
+        y0 = _mm256_fmadd_ps(x0, v0, y0);
+        y1 = _mm256_fmadd_ps(x1, v0, y1);
+        y2 = _mm256_fmadd_ps(x2, v0, y2);
+        y3 = _mm256_fmadd_ps(x3, v0, y3);
 
-        _mm256_storeu_ps(y + i + 0, y0);
-        _mm256_storeu_ps(y + i + 8, y1);
+        _mm256_storeu_ps(y + i + 0,  y0);
+        _mm256_storeu_ps(y + i + 8,  y1);
         _mm256_storeu_ps(y + i + 16, y2);
         _mm256_storeu_ps(y + i + 24, y3);
     }
@@ -695,7 +815,7 @@ inline static void ggml_vec_mad_f32(const int n, float * restrict y, const float
     // WASM SIMD 128-bit
     const int n16 = (n & ~15);
 
-    const v128_t v4 = wasm_f32x4_splat(v);
+    const v128_t v0 = wasm_f32x4_splat(v);
 
     v128_t x0, x1, x2, x3;
     v128_t y0, y1, y2, y3;
@@ -711,10 +831,10 @@ inline static void ggml_vec_mad_f32(const int n, float * restrict y, const float
         y2 = wasm_v128_load(y + i + 8);
         y3 = wasm_v128_load(y + i + 12);
 
-        y0 = wasm_f32x4_add(y0, wasm_f32x4_mul(x0, v4));
-        y1 = wasm_f32x4_add(y1, wasm_f32x4_mul(x1, v4));
-        y2 = wasm_f32x4_add(y2, wasm_f32x4_mul(x2, v4));
-        y3 = wasm_f32x4_add(y3, wasm_f32x4_mul(x3, v4));
+        y0 = wasm_f32x4_add(y0, wasm_f32x4_mul(x0, v0));
+        y1 = wasm_f32x4_add(y1, wasm_f32x4_mul(x1, v0));
+        y2 = wasm_f32x4_add(y2, wasm_f32x4_mul(x2, v0));
+        y3 = wasm_f32x4_add(y3, wasm_f32x4_mul(x3, v0));
 
         wasm_v128_store(y + i + 0, y0);
         wasm_v128_store(y + i + 4, y1);
@@ -740,7 +860,7 @@ inline static void ggml_vec_mad_f16(const int n, ggml_fp16_t * restrict y, ggml_
     const int n32 = (n & ~31);
 
 #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
-    const float16x8_t v8 = vdupq_n_f16(v);
+    const float16x8_t v0 = vdupq_n_f16(v);
 
     float16x8_t x0, x1, x2, x3;
     float16x8_t y0, y1, y2, y3;
@@ -756,10 +876,10 @@ inline static void ggml_vec_mad_f16(const int n, ggml_fp16_t * restrict y, ggml_
         x2 = vld1q_f16(x + i + 16);
         x3 = vld1q_f16(x + i + 24);
 
-        y0 = vfmaq_f16(y0, x0, v8);
-        y1 = vfmaq_f16(y1, x1, v8);
-        y2 = vfmaq_f16(y2, x2, v8);
-        y3 = vfmaq_f16(y3, x3, v8);
+        y0 = vfmaq_f16(y0, x0, v0);
+        y1 = vfmaq_f16(y1, x1, v0);
+        y2 = vfmaq_f16(y2, x2, v0);
+        y3 = vfmaq_f16(y3, x3, v0);
 
         vst1q_f16(y + i + 0 , y0);
         vst1q_f16(y + i + 8 , y1);
@@ -767,8 +887,7 @@ inline static void ggml_vec_mad_f16(const int n, ggml_fp16_t * restrict y, ggml_
         vst1q_f16(y + i + 24, y3);
     }
 #else
-    const float32x4_t v40 = vdupq_n_f32(v);
-    const float32x4_t v41 = vdupq_n_f32(v);
+    const float32x4_t v0 = vdupq_n_f32(v);
 
     float32x4_t x0, x1, x2, x3, x4, x5, x6, x7;
     float32x4_t y0, y1, y2, y3, y4, y5, y6, y7;
@@ -792,14 +911,14 @@ inline static void ggml_vec_mad_f16(const int n, ggml_fp16_t * restrict y, ggml_
         x6 = vcvt_f32_f16(vld1_f16(x + i + 24));
         x7 = vcvt_f32_f16(vld1_f16(x + i + 28));
 
-        y0 = vfmaq_f32(y0, x0, v40);
-        y1 = vfmaq_f32(y1, x1, v40);
-        y2 = vfmaq_f32(y2, x2, v40);
-        y3 = vfmaq_f32(y3, x3, v40);
-        y4 = vfmaq_f32(y4, x4, v41);
-        y5 = vfmaq_f32(y5, x5, v41);
-        y6 = vfmaq_f32(y6, x6, v41);
-        y7 = vfmaq_f32(y7, x7, v41);
+        y0 = vfmaq_f32(y0, x0, v0);
+        y1 = vfmaq_f32(y1, x1, v0);
+        y2 = vfmaq_f32(y2, x2, v0);
+        y3 = vfmaq_f32(y3, x3, v0);
+        y4 = vfmaq_f32(y4, x4, v0);
+        y5 = vfmaq_f32(y5, x5, v0);
+        y6 = vfmaq_f32(y6, x6, v0);
+        y7 = vfmaq_f32(y7, x7, v0);
 
         vst1_f16(y + i + 0 , vcvt_f16_f32(y0));
         vst1_f16(y + i + 4 , vcvt_f16_f32(y1));
@@ -817,11 +936,47 @@ inline static void ggml_vec_mad_f16(const int n, ggml_fp16_t * restrict y, ggml_
         GGML_ASSERT(false);
         y[i] = ggml_fp32_to_fp16(ggml_fp16_to_fp32(y[i]) + ggml_fp16_to_fp32(x[i])*v);
     }
+#elif defined(__AVX512F__)
+    // AVX 512-bit
+    const int n64 = (n & ~63);
+
+    const __m512 v0 = _mm512_set1_ps(v);
+
+    __m512 x0, x1, x2, x3;
+    __m512 y0, y1, y2, y3;
+
+    for (int i = 0; i < n64; i += 64) {
+        x0 = _mm512_cvtph_ps(_mm256_loadu_si256((__m256i*)(x + i + 0 )));
+        x1 = _mm512_cvtph_ps(_mm256_loadu_si256((__m256i*)(x + i + 16)));
+        x2 = _mm512_cvtph_ps(_mm256_loadu_si256((__m256i*)(x + i + 32)));
+        x3 = _mm512_cvtph_ps(_mm256_loadu_si256((__m256i*)(x + i + 48)));
+
+        y0 = _mm512_cvtph_ps(_mm256_loadu_si256((__m256i*)(y + i + 0 )));
+        y1 = _mm512_cvtph_ps(_mm256_loadu_si256((__m256i*)(y + i + 16)));
+        y2 = _mm512_cvtph_ps(_mm256_loadu_si256((__m256i*)(y + i + 32)));
+        y3 = _mm512_cvtph_ps(_mm256_loadu_si256((__m256i*)(y + i + 48)));
+
+        y0 = _mm512_fmadd_ps(x0, v0, y0);
+        y1 = _mm512_fmadd_ps(x1, v0, y1);
+        y2 = _mm512_fmadd_ps(x2, v0, y2);
+        y3 = _mm512_fmadd_ps(x3, v0, y3);
+
+        _mm256_storeu_si256((__m256i*)(y + i + 0 ), _mm512_cvtps_ph(y0, 0));
+        _mm256_storeu_si256((__m256i*)(y + i + 16), _mm512_cvtps_ph(y1, 0));
+        _mm256_storeu_si256((__m256i*)(y + i + 32), _mm512_cvtps_ph(y2, 0));
+        _mm256_storeu_si256((__m256i*)(y + i + 48), _mm512_cvtps_ph(y3, 0));
+    }
+
+    // leftovers
+    for (int i = n64; i < n; ++i) {
+        GGML_ASSERT(false);
+        y[i] = ggml_fp32_to_fp16(ggml_fp16_to_fp32(y[i]) + ggml_fp16_to_fp32(x[i])*v);
+    }
 #elif defined(__AVX2__)
     // AVX 256-bit
     const int n32 = (n & ~31);
 
-    const __m256 v8 = _mm256_set1_ps(v);
+    const __m256 v0 = _mm256_set1_ps(v);
 
     __m256 x0, x1, x2, x3;
     __m256 y0, y1, y2, y3;
@@ -837,10 +992,10 @@ inline static void ggml_vec_mad_f16(const int n, ggml_fp16_t * restrict y, ggml_
         x2 = _mm256_cvtph_ps(_mm_loadu_si128((__m128i*)(x + i + 16)));
         x3 = _mm256_cvtph_ps(_mm_loadu_si128((__m128i*)(x + i + 24)));
 
-        y0 = _mm256_fmadd_ps(x0, v8, y0);
-        y1 = _mm256_fmadd_ps(x1, v8, y1);
-        y2 = _mm256_fmadd_ps(x2, v8, y2);
-        y3 = _mm256_fmadd_ps(x3, v8, y3);
+        y0 = _mm256_fmadd_ps(x0, v0, y0);
+        y1 = _mm256_fmadd_ps(x1, v0, y1);
+        y2 = _mm256_fmadd_ps(x2, v0, y2);
+        y3 = _mm256_fmadd_ps(x3, v0, y3);
 
         _mm_storeu_si128((__m128i*)(y + i + 0 ), _mm256_cvtps_ph(y0, 0));
         _mm_storeu_si128((__m128i*)(y + i + 8 ), _mm256_cvtps_ph(y1, 0));
@@ -857,7 +1012,7 @@ inline static void ggml_vec_mad_f16(const int n, ggml_fp16_t * restrict y, ggml_
     // WASM SIMD 128-bit
     const int n16 = (n & ~15);
 
-    const v128_t v4 = wasm_f32x4_splat(v);
+    const v128_t v0 = wasm_f32x4_splat(v);
 
     v128_t x0, x1, x2, x3;
     v128_t y0, y1, y2, y3;
@@ -881,10 +1036,10 @@ inline static void ggml_vec_mad_f16(const int n, ggml_fp16_t * restrict y, ggml_
         y2 = wasm_v128_load(ty + 8);
         y3 = wasm_v128_load(ty + 12);
 
-        y0 = wasm_f32x4_add(y0, wasm_f32x4_mul(x0, v4));
-        y1 = wasm_f32x4_add(y1, wasm_f32x4_mul(x1, v4));
-        y2 = wasm_f32x4_add(y2, wasm_f32x4_mul(x2, v4));
-        y3 = wasm_f32x4_add(y3, wasm_f32x4_mul(x3, v4));
+        y0 = wasm_f32x4_add(y0, wasm_f32x4_mul(x0, v0));
+        y1 = wasm_f32x4_add(y1, wasm_f32x4_mul(x1, v0));
+        y2 = wasm_f32x4_add(y2, wasm_f32x4_mul(x2, v0));
+        y3 = wasm_f32x4_add(y3, wasm_f32x4_mul(x3, v0));
 
         wasm_v128_store(ty + 0, y0);
         wasm_v128_store(ty + 4, y1);
@@ -3145,7 +3300,10 @@ void ggml_compute_forward_add_f32(
     GGML_ASSERT(nb00 == sizeof(float));
 
     if (nb10 == sizeof(float)) {
-        for (int j = ith; j < n; j += nth) {
+        const int j0 = (n/nth)*ith;
+        const int j1 = ith == nth - 1 ? n : (n/nth)*(ith + 1);
+
+        for (int j = j0; j < j1; j++) {
             ggml_vec_add_f32(nc,
                     (float *) ((char *) dst->data  + j*nb1),
                     (float *) ((char *) src0->data + j*nb01),
@@ -6852,7 +7010,7 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
                     } break;
                 case GGML_OP_ADD:
                     {
-                        node->n_tasks = 1;
+                        node->n_tasks = n_threads;
                     } break;
                 case GGML_OP_SUB:
                 case GGML_OP_MUL:
@@ -8084,7 +8242,7 @@ int ggml_cpu_has_avx512(void) {
 }
 
 int ggml_cpu_has_neon(void) {
-#if defined(__ARM_NEON__)
+#if defined(__ARM_NEON)
     return 1;
 #else
     return 0;

models/README.md

@@ -22,6 +22,20 @@ A third option to obtain the model files is to download them from Hugging Face:
 
 https://huggingface.co/datasets/ggerganov/whisper.cpp/tree/main
 
+## Available models
+
+| Model     | Disk   | Mem     | SHA                                        |
+| ---       | ---    | ---     | ---                                        |
+| tiny      |  75 MB | ~390 MB | `bd577a113a864445d4c299885e0cb97d4ba92b5f` |
+| tiny.en   |  75 MB | ~390 MB | `c78c86eb1a8faa21b369bcd33207cc90d64ae9df` |
+| base      | 142 MB | ~500 MB | `465707469ff3a37a2b9b8d8f89f2f99de7299dac` |
+| base.en   | 142 MB | ~500 MB | `137c40403d78fd54d454da0f9bd998f78703390c` |
+| small     | 466 MB | ~1.0 GB | `55356645c2b361a969dfd0ef2c5a50d530afd8d5` |
+| small.en  | 466 MB | ~1.0 GB | `db8a495a91d927739e50b3fc1cc4c6b8f6c2d022` |
+| medium    | 1.5 GB | ~2.6 GB | `fd9727b6e1217c2f614f9b698455c4ffd82463b4` |
+| medium.en | 1.5 GB | ~2.6 GB | `8c30f0e44ce9560643ebd10bbe50cd20eafd3723` |
+| large     | 2.9 GB | ~4.7 GB | `b1caaf735c4cc1429223d5a74f0f4d0b9b59a299` |
+
 ## Model files for testing purposes
 
 The model files pefixed with `for-tests-` are empty (i.e. do not contain any weights) and are used by the CI for testing purposes.

models/download-ggml-model.cmd

@@ -0,0 +1,63 @@
+@echo off
+
+pushd %~dp0
+set models_path=%CD%
+popd
+
+set argc=0
+for %%x in (%*) do set /A argc+=1
+
+set models=tiny.en tiny base.en base small.en small medium.en medium large
+
+if %argc% neq 1 (
+  echo.
+  echo Usage: download-ggml-model.cmd model
+  CALL :list_models
+  goto :eof
+)
+
+set model=%1
+
+for %%b in (%models%) do ( 
+  if "%%b"=="%model%" (
+    CALL :download_model
+    goto :eof
+  )
+)
+
+echo Invalid model: %model%
+CALL :list_models
+goto :eof
+
+:download_model
+echo Downloading ggml model %model%...
+
+cd %models_path%
+
+if exist "ggml-%model%.bin" (
+  echo Model %model% already exists. Skipping download.
+  goto :eof
+)
+
+PowerShell -NoProfile -ExecutionPolicy Bypass -Command "Invoke-WebRequest -Uri https://ggml.ggerganov.com/ggml-model-whisper-%model%.bin -OutFile ggml-%model%.bin"
+
+if %ERRORLEVEL% neq 0 ( 
+  echo Failed to download ggml model %model%
+  echo Please try again later or download the original Whisper model files and convert them yourself.
+  goto :eof
+)
+
+echo Done! Model %model% saved in %models_path%\models\ggml-%model%.bin
+echo You can now use it like this:
+echo main.exe -m %models_path%\models\ggml-%model%.bin -f %models_path%\samples\jfk.wav
+
+goto :eof
+
+:list_models
+  echo.
+  echo Available models:
+  (for %%a in (%models%) do ( 
+    echo %%a 
+  ))
+  echo.
+  exit /b

whisper.cpp

@@ -133,11 +133,19 @@ static const std::map<std::string, std::pair<int, std::string>> g_lang = {
 static const size_t MB = 1024*1024;
 
 static const std::map<e_model, size_t> MEM_REQ_MODEL = {
-    { MODEL_TINY,     86ull*MB },
-    { MODEL_BASE,    165ull*MB },
-    { MODEL_SMALL,   540ull*MB },
-    { MODEL_MEDIUM, 1650ull*MB },
-    { MODEL_LARGE,  3260ull*MB },
+    { MODEL_TINY,     74ull*MB },
+    { MODEL_BASE,    142ull*MB },
+    { MODEL_SMALL,   466ull*MB },
+    { MODEL_MEDIUM, 1464ull*MB },
+    { MODEL_LARGE,  2952ull*MB },
+};
+
+static const std::map<e_model, size_t> MEM_REQ_MEMORY = {
+    { MODEL_TINY,     12ull*MB },
+    { MODEL_BASE,     24ull*MB },
+    { MODEL_SMALL,    70ull*MB },
+    { MODEL_MEDIUM,  184ull*MB },
+    { MODEL_LARGE,   306ull*MB },
 };
 
 static const std::map<e_model, size_t> MEM_REQ_ENCODE = {
@@ -410,6 +418,12 @@ struct whisper_context {
     std::vector<whisper_segment> result_all;
 
     std::vector<whisper_token> prompt_past;
+
+    // [EXPERIMENTAL] token-level timestamps data
+    int64_t t_beg;
+    int64_t t_last;
+    whisper_token tid_last;
+    std::vector<float> energy; // PCM signal energy
 };
 
 // load the model from a ggml file
@@ -423,7 +437,7 @@ struct whisper_context {
 //
 // see the convert-pt-to-ggml.py script for details
 //
-bool whisper_model_load(const std::string & fname, whisper_context & wctx) {
+static bool whisper_model_load(const std::string & fname, whisper_context & wctx) {
     fprintf(stderr, "%s: loading model from '%s'\n", __func__, fname.c_str());
 
     auto & model = wctx.model;
@@ -498,7 +512,7 @@ bool whisper_model_load(const std::string & fname, whisper_context & wctx) {
 
         wctx.buf_model = new std::vector<uint8_t>();
         wctx.buf_model->resize(MEM_REQ_MODEL.at(model.type));
-        wctx.buf_memory.resize(std::max(MEM_REQ_MODEL.at(model.type), MEM_REQ_MODEL.at(model.type))); // TODO: TMP !!!
+        wctx.buf_memory.resize(MEM_REQ_MEMORY.at(model.type));
         wctx.buf_compute.resize(std::max(MEM_REQ_ENCODE.at(model.type), MEM_REQ_DECODE.at(model.type)));
         wctx.buf_compute_layer.resize(std::max(MEM_REQ_ENCODE_LAYER.at(model.type), MEM_REQ_DECODE_LAYER.at(model.type)));
 
@@ -722,20 +736,6 @@ bool whisper_model_load(const std::string & fname, whisper_context & wctx) {
         }
     }
 
-    // create the ggml memory context
-    {
-        struct ggml_init_params params = {
-            .mem_size   = wctx.buf_memory.size(),
-            .mem_buffer = wctx.buf_memory.data(),
-        };
-
-        model.ctx_mem = ggml_init(params);
-        if (!model.ctx_mem) {
-            fprintf(stderr, "%s: ggml_init() failed\n", __func__);
-            return false;
-        }
-    }
-
     // prepare memory for the weights
     {
         auto & ctx = model.ctx;
@@ -932,6 +932,20 @@ bool whisper_model_load(const std::string & fname, whisper_context & wctx) {
         }
     }
 
+    // create the ggml memory context
+    {
+        struct ggml_init_params params = {
+            .mem_size   = wctx.buf_memory.size(),
+            .mem_buffer = wctx.buf_memory.data(),
+        };
+
+        model.ctx_mem = ggml_init(params);
+        if (!model.ctx_mem) {
+            fprintf(stderr, "%s: ggml_init() failed\n", __func__);
+            return false;
+        }
+    }
+
     // key + value memory
     {
         auto & ctx = model.ctx_mem;
@@ -1054,7 +1068,7 @@ bool whisper_model_load(const std::string & fname, whisper_context & wctx) {
 //   - n_threads:  number of threads to use
 //   - mel_offset: offset in the mel spectrogram (i.e. audio offset)
 //
-bool whisper_encode(
+static bool whisper_encode(
               whisper_context & wctx,
         const int n_threads,
         const int mel_offset) {
@@ -1440,7 +1454,7 @@ bool whisper_encode(
 //   - n_tokens:   number of tokens in the prompt
 //   - n_past:     number of past tokens to prefix the prompt with
 //
-bool whisper_decode(
+static bool whisper_decode(
               whisper_context & wctx,
         const int n_threads,
         const whisper_token * tokens,
@@ -1803,10 +1817,12 @@ bool whisper_decode(
 }
 
 // the most basic sampling scheme - select the top token
-whisper_token_data whisper_sample_best(
+static whisper_token_data whisper_sample_best(
         const whisper_vocab & vocab,
         const float * probs) {
-    whisper_token_data result;
+    whisper_token_data result = {
+        0, 0, 0.0f, 0.0f, 0.0f, -1, -1, 0.0f,
+    };
 
     int n_logits = vocab.id_to_token.size();
 
@@ -1879,7 +1895,7 @@ whisper_token_data whisper_sample_best(
 }
 
 // samples only from the timestamps tokens
-whisper_vocab::id whisper_sample_timestamp(
+static whisper_vocab::id whisper_sample_timestamp(
         const whisper_vocab & vocab,
         const float * probs) {
     int n_logits = vocab.id_to_token.size();
@@ -1931,7 +1947,7 @@ static std::string to_timestamp(int64_t t, bool comma = false) {
 // naive Discrete Fourier Transform
 // input is real-valued
 // output is complex-valued
-void dft(const std::vector<float> & in, std::vector<float> & out) {
+static void dft(const std::vector<float> & in, std::vector<float> & out) {
     int N = in.size();
 
     out.resize(N*2);
@@ -1955,7 +1971,7 @@ void dft(const std::vector<float> & in, std::vector<float> & out) {
 // poor man's implementation - use something better
 // input is real-valued
 // output is complex-valued
-void fft(const std::vector<float> & in, std::vector<float> & out) {
+static void fft(const std::vector<float> & in, std::vector<float> & out) {
     out.resize(in.size()*2);
 
     int N = in.size();
@@ -2006,7 +2022,7 @@ void fft(const std::vector<float> & in, std::vector<float> & out) {
 }
 
 // ref: https://github.com/openai/whisper/blob/main/whisper/audio.py#L92-L124
-bool log_mel_spectrogram(
+static bool log_mel_spectrogram(
     const float * samples,
     const int n_samples,
     const int sample_rate,
@@ -2331,6 +2347,11 @@ struct whisper_full_params whisper_full_default_params(enum whisper_sampling_str
                     /*.print_realtime       =*/ false,
                     /*.print_timestamps     =*/ true,
 
+                    /*.token_timestamps     =*/ false,
+                    /*.thold_pt             =*/ 0.01f,
+                    /*.thold_ptsum          =*/ 0.01f,
+                    /*.max_len              =*/ 0,
+
                     /*.language             =*/ "en",
 
                     /*.greedy               =*/ {
@@ -2363,6 +2384,11 @@ struct whisper_full_params whisper_full_default_params(enum whisper_sampling_str
                     /*.print_realtime       =*/ false,
                     /*.print_timestamps     =*/ true,
 
+                    /*.token_timestamps     =*/ false,
+                    /*.thold_pt             =*/ 0.01f,
+                    /*.thold_ptsum          =*/ 0.01f,
+                    /*.max_len              =*/ 0,
+
                     /*.language             =*/ "en",
 
                     /*.greedy               =*/ {
@@ -2384,6 +2410,68 @@ struct whisper_full_params whisper_full_default_params(enum whisper_sampling_str
     return result;
 }
 
+// forward declarations
+static std::vector<float> get_signal_energy(const float * signal, int n_samples, int n_samples_per_half_window);
+static void whisper_exp_compute_token_level_timestamps(
+        struct whisper_context * ctx,
+        int   i_segment,
+        float thold_pt,
+        float thold_ptsum);
+
+// wrap the last segment to max_len characters
+// returns the number of new segments
+static int whisper_wrap_segment(struct whisper_context * ctx, int max_len) {
+    auto segment = ctx->result_all.back();
+
+    int res = 1;
+    int acc = 0;
+
+    std::string text;
+
+    for (int i = 0; i < (int) segment.tokens.size(); i++) {
+        const auto & token = segment.tokens[i];
+        if (token.id >= whisper_token_eot(ctx)) {
+            continue;
+        }
+
+        const auto txt = whisper_token_to_str(ctx, token.id);
+
+        const int cur = strlen(txt);
+
+        if (acc + cur > max_len && i > 0) {
+            // split here
+            ctx->result_all.back().text = std::move(text);
+            ctx->result_all.back().t1 = token.t0;
+            ctx->result_all.back().tokens.resize(i);
+
+            ctx->result_all.push_back({});
+            ctx->result_all.back().t0 = token.t0;
+            ctx->result_all.back().t1 = segment.t1;
+
+            // add tokens [i, end] to the new segment
+            ctx->result_all.back().tokens.insert(
+                    ctx->result_all.back().tokens.end(),
+                    segment.tokens.begin() + i,
+                    segment.tokens.end());
+
+            acc = 0;
+            text = "";
+
+            segment = ctx->result_all.back();
+            i = -1;
+
+            res++;
+        } else {
+            acc += cur;
+            text += txt;
+        }
+    }
+
+    ctx->result_all.back().text = std::move(text);
+
+    return res;
+}
+
 int whisper_full(
         struct whisper_context * ctx,
         struct whisper_full_params params,
@@ -2400,6 +2488,13 @@ int whisper_full(
         return -1;
     }
 
+    if (params.token_timestamps) {
+        ctx->t_beg = 0;
+        ctx->t_last = 0;
+        ctx->tid_last = 0;
+        ctx->energy = get_signal_energy(samples, n_samples, 32);
+    }
+
     const int seek_start = params.offset_ms/10;
 
     // if length of spectrogram is less than 1s (100 samples), then return
@@ -2549,6 +2644,7 @@ int whisper_full(
             }
         }
 
+        // shrink down to result_len
         tokens_cur.resize(result_len);
 
         for (const auto & r : tokens_cur) {
@@ -2587,8 +2683,19 @@ int whisper_full(
                         for (int j = i0; j <= i; j++) {
                             result_all.back().tokens.push_back(tokens_cur[j]);
                         }
+
+                        int n_new = 1;
+
+                        if (params.token_timestamps) {
+                            whisper_exp_compute_token_level_timestamps(
+                                    ctx, result_all.size() - 1, params.thold_pt, params.thold_ptsum);
+
+                            if (params.max_len > 0) {
+                                n_new = whisper_wrap_segment(ctx, params.max_len);
+                            }
+                        }
                         if (params.new_segment_callback) {
-                            params.new_segment_callback(ctx, params.new_segment_callback_user_data);
+                            params.new_segment_callback(ctx, n_new, params.new_segment_callback_user_data);
                         }
                     }
                     text = "";
@@ -2617,8 +2724,19 @@ int whisper_full(
                 for (int j = i0; j < (int) tokens_cur.size(); j++) {
                     result_all.back().tokens.push_back(tokens_cur[j]);
                 }
+
+                int n_new = 1;
+
+                if (params.token_timestamps) {
+                    whisper_exp_compute_token_level_timestamps(
+                            ctx, result_all.size() - 1, params.thold_pt, params.thold_ptsum);
+
+                    if (params.max_len > 0) {
+                        n_new = whisper_wrap_segment(ctx, params.max_len);
+                    }
+                }
                 if (params.new_segment_callback) {
-                    params.new_segment_callback(ctx, params.new_segment_callback_user_data);
+                    params.new_segment_callback(ctx, n_new, params.new_segment_callback_user_data);
                 }
             }
         }
@@ -2752,7 +2870,7 @@ int whisper_full_parallel(
 
             // call the new_segment_callback for each segment
             if (params.new_segment_callback) {
-                params.new_segment_callback(ctx, params.new_segment_callback_user_data);
+                params.new_segment_callback(ctx, 1, params.new_segment_callback_user_data);
             }
         }
 
@@ -2828,3 +2946,304 @@ const char * whisper_print_system_info() {
 
     return s.c_str();
 }
+
+// =================================================================================================
+
+//
+// Experimental stuff below
+//
+// Not sure if these should be part of the library at all, because the quality of the results is not
+// guaranteed. Might get removed at some point unless a robust algorithm implementation is found
+//
+
+// =================================================================================================
+
+//
+// token-level timestamps
+//
+
+static int timestamp_to_sample(int64_t t, int n_samples) {
+    return std::max(0, std::min((int) n_samples - 1, (int) ((t*WHISPER_SAMPLE_RATE)/100)));
+}
+
+static int64_t sample_to_timestamp(int i_sample) {
+    return (100*i_sample)/WHISPER_SAMPLE_RATE;
+}
+
+// a cost-function / heuristic that is high for text that takes longer to pronounce
+// obviously, can be improved
+static float voice_length(const std::string & text) {
+    float res = 0.0f;
+
+    for (size_t i = 0; i < text.size(); ++i) {
+        if (text[i] == ' ') {
+            res += 0.01f;
+        } else if (text[i] == ',') {
+            res += 2.00f;
+        } else if (text[i] == '.') {
+            res += 3.00f;
+        } else if (text[i] == '!') {
+            res += 3.00f;
+        } else if (text[i] == '?') {
+            res += 3.00f;
+        } else if (text[i] >= '0' && text[i] <= '9') {
+            res += 3.00f;
+        } else {
+            res += 1.00f;
+        }
+    }
+
+    return res;
+}
+
+// average the fabs of the signal
+static std::vector<float> get_signal_energy(const float * signal, int n_samples, int n_samples_per_half_window) {
+    const int hw = n_samples_per_half_window;
+
+    std::vector<float> result(n_samples);
+
+    for (int i = 0; i < n_samples; i++) {
+        float sum = 0;
+        for (int j = -hw; j <= hw; j++) {
+            if (i + j >= 0 && i + j < n_samples) {
+                sum += fabs(signal[i + j]);
+            }
+        }
+        result[i] = sum/(2*hw + 1);
+    }
+
+    return result;
+}
+
+static void whisper_exp_compute_token_level_timestamps(
+        struct whisper_context * ctx,
+        int   i_segment,
+        float thold_pt,
+        float thold_ptsum) {
+    auto & segment = ctx->result_all[i_segment];
+    auto & tokens  = segment.tokens;
+
+    const int n_samples = ctx->energy.size();
+
+    if (n_samples == 0) {
+        fprintf(stderr, "%s: no signal data available\n", __func__);
+        return;
+    }
+
+    const int64_t t0 = segment.t0;
+    const int64_t t1 = segment.t1;
+
+    const int s0 = timestamp_to_sample(t0, n_samples);
+    const int s1 = timestamp_to_sample(t1, n_samples);
+
+    const int n = tokens.size();
+
+    if (n == 0) {
+        return;
+    }
+
+    if (n == 1) {
+        tokens[0].t0 = t0;
+        tokens[0].t1 = t1;
+
+        return;
+    }
+
+    auto & t_beg    = ctx->t_beg;
+    auto & t_last   = ctx->t_last;
+    auto & tid_last = ctx->tid_last;
+
+    for (int j = 0; j < n; ++j) {
+        auto & token = tokens[j];
+
+        if (j == 0) {
+            if (token.id == whisper_token_beg(ctx)) {
+                tokens[j    ].t0 = t0;
+                tokens[j    ].t1 = t0;
+                tokens[j + 1].t0 = t0;
+
+                t_beg    = t0;
+                t_last   = t0;
+                tid_last = whisper_token_beg(ctx);
+            } else {
+                tokens[j    ].t0 = t_last;
+            }
+        }
+
+        const int64_t tt = t_beg + 2*(token.tid - whisper_token_beg(ctx));
+
+        tokens[j].id    = token.id;
+        tokens[j].tid   = token.tid;
+        tokens[j].p     = token.p;
+        tokens[j].pt    = token.pt;
+        tokens[j].ptsum = token.ptsum;
+
+        tokens[j].vlen = voice_length(whisper_token_to_str(ctx, token.id));
+
+        if (token.pt > thold_pt && token.ptsum > thold_ptsum && token.tid > tid_last && tt <= t1) {
+            if (j > 0) {
+                tokens[j - 1].t1 = tt;
+            }
+            tokens[j].t0 = tt;
+            tid_last = token.tid;
+        }
+    }
+
+    tokens[n - 2].t1 = t1;
+    tokens[n - 1].t0 = t1;
+    tokens[n - 1].t1 = t1;
+
+    t_last = t1;
+
+    // find intervals of tokens with unknown timestamps
+    // fill the timestamps by proportionally splitting the interval based on the token voice lengths
+    {
+        int p0 = 0;
+        int p1 = 0;
+
+        while (true) {
+            while (p1 < n && tokens[p1].t1 < 0) {
+                p1++;
+            }
+
+            if (p1 >= n) {
+                p1--;
+            }
+
+            if (p1 > p0) {
+                double psum = 0.0;
+                for (int j = p0; j <= p1; j++) {
+                    psum += tokens[j].vlen;
+                }
+
+                //printf("analyzing %d - %d, psum = %f\n", p0, p1, psum);
+
+                const double dt = tokens[p1].t1 - tokens[p0].t0;
+
+                // split the time proportionally to the voice length
+                for (int j = p0 + 1; j <= p1; j++) {
+                    const double ct = tokens[j - 1].t0 + dt*tokens[j - 1].vlen/psum;
+
+                    tokens[j - 1].t1 = ct;
+                    tokens[j    ].t0 = ct;
+                }
+            }
+
+            p1++;
+            p0 = p1;
+            if (p1 >= n) {
+                break;
+            }
+        }
+    }
+
+    // fix up (just in case)
+    for (int j = 0; j < n - 1; j++) {
+        if (tokens[j].t1 < 0) {
+            tokens[j + 1].t0 = tokens[j].t1;
+        }
+
+        if (j > 0) {
+            if (tokens[j - 1].t1 > tokens[j].t0) {
+                tokens[j].t0 = tokens[j - 1].t1;
+                tokens[j].t1 = std::max(tokens[j].t0, tokens[j].t1);
+            }
+        }
+    }
+
+    // VAD
+    // expand or contract tokens based on voice activity
+    {
+        const int hw = WHISPER_SAMPLE_RATE/8;
+
+        for (int j = 0; j < n; j++) {
+            if (tokens[j].id >= whisper_token_eot(ctx)) {
+                continue;
+            }
+
+            int s0 = timestamp_to_sample(tokens[j].t0, n_samples);
+            int s1 = timestamp_to_sample(tokens[j].t1, n_samples);
+
+            const int ss0 = std::max(s0 - hw, 0);
+            const int ss1 = std::min(s1 + hw, n_samples);
+
+            const int ns = ss1 - ss0;
+
+            float sum = 0.0f;
+
+            for (int k = ss0; k < ss1; k++) {
+                sum += ctx->energy[k];
+            }
+
+            const float thold = 0.5*sum/ns;
+
+            {
+                int k = s0;
+                if (ctx->energy[k] > thold && j > 0) {
+                    while (k > 0 && ctx->energy[k] > thold) {
+                        k--;
+                    }
+                    tokens[j].t0 = sample_to_timestamp(k);
+                    if (tokens[j].t0 < tokens[j - 1].t1) {
+                        tokens[j].t0 = tokens[j - 1].t1;
+                    } else {
+                        s0 = k;
+                    }
+                } else {
+                    while (ctx->energy[k] < thold && k < s1) {
+                        k++;
+                    }
+                    s0 = k;
+                    tokens[j].t0 = sample_to_timestamp(k);
+                }
+            }
+
+            {
+                int k = s1;
+                if (ctx->energy[k] > thold) {
+                    while (k < n_samples - 1 && ctx->energy[k] > thold) {
+                        k++;
+                    }
+                    tokens[j].t1 = sample_to_timestamp(k);
+                    if (j < ns - 1 && tokens[j].t1 > tokens[j + 1].t0) {
+                        tokens[j].t1 = tokens[j + 1].t0;
+                    } else {
+                        s1 = k;
+                    }
+                } else {
+                    while (ctx->energy[k] < thold && k > s0) {
+                        k--;
+                    }
+                    s1 = k;
+                    tokens[j].t1 = sample_to_timestamp(k);
+                }
+            }
+        }
+    }
+
+    // fixed token expand (optional)
+    //{
+    //    const int t_expand = 0;
+
+    //    for (int j = 0; j < n; j++) {
+    //        if (j > 0) {
+    //            tokens[j].t0 = std::max(0, (int) (tokens[j].t0 - t_expand));
+    //        }
+    //        if (j < n - 1) {
+    //            tokens[j].t1 = tokens[j].t1 + t_expand;
+    //        }
+    //    }
+    //}
+
+    // debug info
+    //for (int j = 0; j < n; ++j) {
+    //    const auto & token = tokens[j];
+    //    const auto tt = token.pt > thold_pt && token.ptsum > 0.01 ? whisper_token_to_str(ctx, token.tid) : "[?]";
+    //    printf("%s: %10s %6.3f %6.3f %6.3f %6.3f %5d %5d '%s'\n", __func__,
+    //            tt, token.p, token.pt, token.ptsum, token.vlen, (int) token.t0, (int) token.t1, whisper_token_to_str(ctx, token.id));
+
+    //    if (tokens[j].id >= whisper_token_eot(ctx)) {
+    //        continue;
+    //    }
+    //}
+}

whisper.h

@@ -68,14 +68,21 @@ extern "C" {
 
     typedef int whisper_token;
 
-    struct whisper_token_data {
+    typedef struct whisper_token_data {
         whisper_token id;  // token id
         whisper_token tid; // forced timestamp token id
 
         float p;     // probability of the token
         float pt;    // probability of the timestamp token
         float ptsum; // sum of probabilities of all timestamp tokens
-    };
+
+        // token-level timestamp data
+        // do not use if you haven't computed token-level timestamps
+        int64_t t0; // start time of the token
+        int64_t t1; //   end time of the token
+
+        float vlen; // voice length of the token
+    } whisper_token_data;
 
     // Allocates all memory needed for the model and loads the model from the given file.
     // Returns NULL on failure.
@@ -129,7 +136,7 @@ extern "C" {
     // You can also implement your own sampling method using the whisper_get_probs() function.
     // whisper_sample_best() returns the token with the highest probability
     // whisper_sample_timestamp() returns the most probable timestamp token
-    WHISPER_API struct whisper_token_data whisper_sample_best(struct whisper_context * ctx);
+    WHISPER_API whisper_token_data whisper_sample_best(struct whisper_context * ctx);
     WHISPER_API whisper_token whisper_sample_timestamp(struct whisper_context * ctx);
 
     // Return the id of the specified language, returns -1 if not found
@@ -172,7 +179,7 @@ extern "C" {
     // Text segment callback
     // Called on every newly generated text segment
     // Use the whisper_full_...() functions to obtain the text segments
-    typedef void (*whisper_new_segment_callback)(struct whisper_context * ctx, void * user_data);
+    typedef void (*whisper_new_segment_callback)(struct whisper_context * ctx, int n_new, void * user_data);
 
     struct whisper_full_params {
         enum whisper_sampling_strategy strategy;
@@ -188,6 +195,12 @@ extern "C" {
         bool print_realtime;
         bool print_timestamps;
 
+        // [EXPERIMENTAL] token-level timestamps
+        bool  token_timestamps; // enable token-level timestamps
+        float thold_pt;         // timestamp token probability threshold (~0.01)
+        float thold_ptsum;      // timestamp token sum probability threshold (~0.01)
+        int   max_len;          // max segment length in characters
+
         const char * language;
 
         struct {
@@ -244,7 +257,7 @@ extern "C" {
 
     // Get token data for the specified token in the specified segment.
     // This contains probabilities, timestamps, etc.
-    WHISPER_API struct whisper_token_data whisper_full_get_token_data(struct whisper_context * ctx, int i_segment, int i_token);
+    WHISPER_API whisper_token_data whisper_full_get_token_data(struct whisper_context * ctx, int i_segment, int i_token);
 
     // Get the probability of the specified token in the specified segment.
     WHISPER_API float whisper_full_get_token_p(struct whisper_context * ctx, int i_segment, int i_token);