initial_prompt: Optional text to provide as a prompt for the first window. initial_prompt: Optional text string or iterable of token ids to provide as a prompt for the first window. prefix: Optional text to provide as a prefix for the first window. suppress_blank: Suppress blank outputs at...
initial_prompt_zh=Please break sentences correctly and keep punctuation. The maximum length of the clip should not exceed 12 seconds. initial_prompt_zh=Please break sentences correctly and retain punctuation. ;字幕识别时,cpu进程 ;cpu process during subtitle recognition 0 comments on commit 7e900c7 ...
Accept an iterable of token IDs for the argument initial_prompt (useful to include timestamp tokens in the prompt) Avoid computing higher temperatures when no_speech_threshold is met (same as https://github.com/openai/whisper/commit/e334ff141d5444fbf6904edaaf408e5b0b416fe8) Fix truncated ou...
As an initial step, the Open Whisper-style Speech Model (OWSM) reproduced OpenAI's Whisper using publicly available data and open-source toolkits. With the aim of reproducing Whisper, the previous OWSM v1 through v3 models were still based on Transformer, which might lead to inferior ...
JSON_PROMPT_MODE = 3 @staticmethod def from_string(s: str): normalized = s.lower() if s is not None else None if normalized == "prepend_all_segments": return VadInitialPromptMode.PREPEND_ALL_SEGMENTS elif normalized == "prepend_first_segment": return VadInitialPromptMode.PREPREND_FIRST_...
-initial_prompt : None -prefix : None -suppress_blank : True -suppress_tokens : [-1] -without_timestamps : False -max_initial_timestamp : 1.0 -word_timestamps : False -prepend_punctuations : "'“¿([{- -append_punctuations : "'.。,,!!??::”)]}、 ...
NOTE: emprically, condition_on_previous_text=True will degrade the performance of faster-distil-whisper for long audio. Degradation on the first chunk was observed with initial_prompt too.Word-level timestampssegments, _ = model.transcribe("audio.mp3", word_timestamps=True) for segment in ...
-1.0 -no_speech_threshold : 0.95 -condition_on_previous_text : False -initial_prompt : None -prefix : None -suppress_blank : False -suppress_tokens : [-1] -without_timestamps : False -max_initial_timestamp : 1.0 -word_timestamps : True -prepend_punctuations : -append_punctuations : ...
Degradation on the first chunk was observed with initial_prompt too.Word-level timestampssegments, _ = model.transcribe("audio.mp3", word_timestamps=True) for segment in segments: for word in segment.words: print("[%.2fs -> %.2fs] %s" % (word.start, word.end, word.word))...
-initial_prompt : None -prefix : None -suppress_blank : True -suppress_tokens : [-1] -without_timestamps : False -max_initial_timestamp : 1.0 -word_timestamps : False -prepend_punctuations : "'“¿([{- -append_punctuations : "'.。,,!!??::”)]}、 ...