8de452c18b
* whisper : prepare infra for new decoding strategies * whisper : apply logit filters and compute logprobs * whisper : add whisper_get_logits() * whisper : separate self and cross attention memory Initial step needed for supporting parallel decoders * whisper : move probs_id buffer to whisper_context * whisper : refactor kv cache into separate struct * whisper : move self-attention kv cache to whisper_decoder * whisper : wip decoding parameters + strategies * whisper : wip decoding parameters + strategies (part 2) * whisper : wip decoding parameters + strategies (part 3) * whisper : wip decoding parameters + strategies (part 4) * whisper : fix prompt_past update to not include prompt_init * whisper : temperature + best_of support * whisper : support for compression_ration_threshold We actually use entropy, but it is similar * command : fix example to use logits instead of obsolete probs * whisper : handle empty sequence ranking * whisper : add WHISPER_DEBUG + diagnostic prints + new main args * whisper : minor fixes * whisper : add beam-search support * whisper : bug fix when there no previous context * whisper : add comments * stream : disable temperature fallback For real-time processing, we always want a single decoder running at T=0 * whisper.swiftui : update example - fix paths + add empty folders |
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| .. | ||
| CMakeLists.txt | ||
| README.md | ||
| main.cpp | ||
README.md
main
This is the main example demonstrating most of the functionality of the Whisper model.
It can be used as a reference for using the whisper.cpp library in other projects.
./main -h
usage: ./main [options] file0.wav file1.wav ...
options:
-h, --help [default] show this help message and exit
-t N, --threads N [4 ] number of threads to use during computation
-p N, --processors N [1 ] number of processors to use during computation
-ot N, --offset-t N [0 ] time offset in milliseconds
-on N, --offset-n N [0 ] segment index offset
-d N, --duration N [0 ] duration of audio to process in milliseconds
-mc N, --max-context N [-1 ] maximum number of text context tokens to store
-ml N, --max-len N [0 ] maximum segment length in characters
-wt N, --word-thold N [0.01 ] word timestamp probability threshold
-su, --speed-up [false ] speed up audio by x2 (reduced accuracy)
-tr, --translate [false ] translate from source language to english
-otxt, --output-txt [false ] output result in a text file
-ovtt, --output-vtt [false ] output result in a vtt file
-osrt, --output-srt [false ] output result in a srt file
-owts, --output-words [false ] output script for generating karaoke video
-ps, --print-special [false ] print special tokens
-pc, --print-colors [false ] print colors
-nt, --no-timestamps [true ] do not print timestamps
-l LANG, --language LANG [en ] spoken language
-m FNAME, --model FNAME [models/ggml-base.en.bin] model path
-f FNAME, --file FNAME [ ] input WAV file path