Where can I find API docs for Whisper?

[blackears]

Is there any documentation for Whisper's API? All I can find is the example code on the main page.

[phineas-pta]

u mean openai sppech to text api ?

[blackears]

Yes. Something that lists all the Python object methods.

[phineas-pta]

that's the opposite of what i said

anyway see purfview comment

[Purfview]

Read here:

whisper/whisper/transcribe.py

Lines 59 to 114 in ba3f3cd

	Parameters
	----------
	model: Whisper
	The Whisper model instance
	audio: Union[str, np.ndarray, torch.Tensor]
	The path to the audio file to open, or the audio waveform
	verbose: bool
	Whether to display the text being decoded to the console. If True, displays all the details,
	If False, displays minimal details. If None, does not display anything
	temperature: Union[float, Tuple[float, ...]]
	Temperature for sampling. It can be a tuple of temperatures, which will be successively used
	upon failures according to either `compression_ratio_threshold` or `logprob_threshold`.
	compression_ratio_threshold: float
	If the gzip compression ratio is above this value, treat as failed
	logprob_threshold: float
	If the average log probability over sampled tokens is below this value, treat as failed
	no_speech_threshold: float
	If the no_speech probability is higher than this value AND the average log probability
	over sampled tokens is below `logprob_threshold`, consider the segment as silent
	condition_on_previous_text: bool
	if True, the previous output of the model is provided as a prompt for the next window;
	disabling may make the text inconsistent across windows, but the model becomes less prone to
	getting stuck in a failure loop, such as repetition looping or timestamps going out of sync.
	word_timestamps: bool
	Extract word-level timestamps using the cross-attention pattern and dynamic time warping,
	and include the timestamps for each word in each segment.
	prepend_punctuations: str
	If word_timestamps is True, merge these punctuation symbols with the next word
	append_punctuations: str
	If word_timestamps is True, merge these punctuation symbols with the previous word
	initial_prompt: Optional[str]
	Optional text to provide as a prompt for the first window. This can be used to provide, or
	"prompt-engineer" a context for transcription, e.g. custom vocabularies or proper nouns
	to make it more likely to predict those word correctly.
	decode_options: dict
	Keyword arguments to construct `DecodingOptions` instances
	clip_timestamps: Union[str, List[float]]
	Comma-separated list start,end,start,end,... timestamps (in seconds) of clips to process.
	The last end timestamp defaults to the end of the file.
	hallucination_silence_threshold: Optional[float]
	When word_timestamps is True, skip silent periods longer than this threshold (in seconds)
	when a possible hallucination is detected

And here:

whisper/whisper/transcribe.py

Lines 513 to 548 in ba3f3cd

	parser.add_argument("audio", nargs="+", type=str, help="audio file(s) to transcribe")
	parser.add_argument("--model", default="small", type=valid_model_name, help="name of the Whisper model to use")
	parser.add_argument("--model_dir", type=str, default=None, help="the path to save model files; uses ~/.cache/whisper by default")
	parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu", help="device to use for PyTorch inference")
	parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs")
	parser.add_argument("--output_format", "-f", type=str, default="all", choices=["txt", "vtt", "srt", "tsv", "json", "all"], help="format of the output file; if not specified, all available formats will be produced")
	parser.add_argument("--verbose", type=str2bool, default=True, help="whether to print out the progress and debug messages")
	parser.add_argument("--task", type=str, default="transcribe", choices=["transcribe", "translate"], help="whether to perform X->X speech recognition ('transcribe') or X->English translation ('translate')")
	parser.add_argument("--language", type=str, default=None, choices=sorted(LANGUAGES.keys()) + sorted([k.title() for k in TO_LANGUAGE_CODE.keys()]), help="language spoken in the audio, specify None to perform language detection")
	parser.add_argument("--temperature", type=float, default=0, help="temperature to use for sampling")
	parser.add_argument("--best_of", type=optional_int, default=5, help="number of candidates when sampling with non-zero temperature")
	parser.add_argument("--beam_size", type=optional_int, default=5, help="number of beams in beam search, only applicable when temperature is zero")
	parser.add_argument("--patience", type=float, default=None, help="optional patience value to use in beam decoding, as in https://arxiv.org/abs/2204.05424, the default (1.0) is equivalent to conventional beam search")
	parser.add_argument("--length_penalty", type=float, default=None, help="optional token length penalty coefficient (alpha) as in https://arxiv.org/abs/1609.08144, uses simple length normalization by default")
	parser.add_argument("--suppress_tokens", type=str, default="-1", help="comma-separated list of token ids to suppress during sampling; '-1' will suppress most special characters except common punctuations")
	parser.add_argument("--initial_prompt", type=str, default=None, help="optional text to provide as a prompt for the first window.")
	parser.add_argument("--condition_on_previous_text", type=str2bool, default=True, help="if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop")
	parser.add_argument("--fp16", type=str2bool, default=True, help="whether to perform inference in fp16; True by default")
	parser.add_argument("--temperature_increment_on_fallback", type=optional_float, default=0.2, help="temperature to increase when falling back when the decoding fails to meet either of the thresholds below")
	parser.add_argument("--compression_ratio_threshold", type=optional_float, default=2.4, help="if the gzip compression ratio is higher than this value, treat the decoding as failed")
	parser.add_argument("--logprob_threshold", type=optional_float, default=-1.0, help="if the average log probability is lower than this value, treat the decoding as failed")
	parser.add_argument("--no_speech_threshold", type=optional_float, default=0.6, help="if the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence")
	parser.add_argument("--word_timestamps", type=str2bool, default=False, help="(experimental) extract word-level timestamps and refine the results based on them")
	parser.add_argument("--prepend_punctuations", type=str, default="\"\'“¿([{-", help="if word_timestamps is True, merge these punctuation symbols with the next word")
	parser.add_argument("--append_punctuations", type=str, default="\"\'.。,，!！?？:：”)]}、", help="if word_timestamps is True, merge these punctuation symbols with the previous word")
	parser.add_argument("--highlight_words", type=str2bool, default=False, help="(requires --word_timestamps True) underline each word as it is spoken in srt and vtt")
	parser.add_argument("--max_line_width", type=optional_int, default=None, help="(requires --word_timestamps True) the maximum number of characters in a line before breaking the line")
	parser.add_argument("--max_line_count", type=optional_int, default=None, help="(requires --word_timestamps True) the maximum number of lines in a segment")
	parser.add_argument("--max_words_per_line", type=optional_int, default=None, help="(requires --word_timestamps True, no effect with --max_line_width) the maximum number of words in a segment")
	parser.add_argument("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS")
	parser.add_argument("--clip_timestamps", type=str, default="0", help="comma-separated list start,end,start,end,... timestamps (in seconds) of clips to process, where the last end timestamp defaults to the end of the file")
	parser.add_argument("--hallucination_silence_threshold", type=optional_float, help="(requires --word_timestamps True) skip silent periods longer than this threshold (in seconds) when a possible hallucination is detected")