openvino_genai.WhisperPipeline#

class openvino_genai.WhisperPipeline#

Bases: pybind11_object

Automatic speech recognition pipeline

__init__(self: openvino_genai.py_openvino_genai.WhisperPipeline, models_path: os.PathLike, device: str, **kwargs) → None#

WhisperPipeline class constructor. models_path (os.PathLike): Path to the model file. device (str): Device to run the model on (e.g., CPU, GPU).

Methods

__delattr__(name, /)

Implement delattr(self, name).

__dir__()

Default dir() implementation.

__eq__(value, /)

Return self==value.

__format__(format_spec, /)

Default object formatter.

__ge__(value, /)

Return self>=value.

__getattribute__(name, /)

Return getattr(self, name).

__gt__(value, /)

Return self>value.

__hash__()

Return hash(self).

__init__(self, models_path, device, **kwargs)

WhisperPipeline class constructor.

__init_subclass__

This method is called when a class is subclassed.

__le__(value, /)

Return self<=value.

__lt__(value, /)

Return self<value.

__ne__(value, /)

Return self!=value.

__new__(**kwargs)

__reduce__()

Helper for pickle.

__reduce_ex__(protocol, /)

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value, /)

Implement setattr(self, name, value).

__sizeof__()

Size of object in memory, in bytes.

__str__()

Return str(self).

__subclasshook__

Abstract classes can override this to customize issubclass().

generate(self, raw_speech_input[, ...])

High level generate that receives raw speech as a vector of floats and returns decoded output.

get_generation_config(self)

get_tokenizer(self)

set_generation_config(self, config)

__class__#

alias of pybind11_type

__delattr__(name, /)#

Implement delattr(self, name).

__dir__()#

Default dir() implementation.

__eq__(value, /)#

Return self==value.

__format__(format_spec, /)#

Default object formatter.

__ge__(value, /)#

Return self>=value.

__getattribute__(name, /)#

Return getattr(self, name).

__gt__(value, /)#

Return self>value.

__hash__()#

Return hash(self).

__init__(self: openvino_genai.py_openvino_genai.WhisperPipeline, models_path: os.PathLike, device: str, **kwargs) → None#

WhisperPipeline class constructor. models_path (os.PathLike): Path to the model file. device (str): Device to run the model on (e.g., CPU, GPU).

__init_subclass__()#

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__le__(value, /)#

Return self<=value.

__lt__(value, /)#

Return self<value.

__ne__(value, /)#

Return self!=value.

__new__(**kwargs)#
__reduce__()#

Helper for pickle.

__reduce_ex__(protocol, /)#

Helper for pickle.

__repr__()#

Return repr(self).

__setattr__(name, value, /)#

Implement setattr(self, name, value).

__sizeof__()#

Size of object in memory, in bytes.

__str__()#

Return str(self).

__subclasshook__()#

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

generate(self: openvino_genai.py_openvino_genai.WhisperPipeline, raw_speech_input: list[float], generation_config: openvino_genai.py_openvino_genai.WhisperGenerationConfig | None = None, streamer: Callable[[str], bool] | openvino_genai.py_openvino_genai.ChunkStreamerBase | None = None, **kwargs) → openvino_genai.py_openvino_genai.WhisperDecodedResults#

High level generate that receives raw speech as a vector of floats and returns decoded output.

Parameters:

  • raw_speech_input (List[float]) – inputs in the form of list of floats. Required to be normalized to near [-1, 1] range and have 16k Hz sampling rate.

  • generation_config (WhisperGenerationConfig or a Dict) – generation_config

  • streamer – streamer either as a lambda with a boolean returning flag whether generation should be stopped. Streamer supported for short-form audio (< 30 seconds) with return_timestamps=False only

:type : Callable[[str], bool], ov.genai.StreamerBase

Parameters:

kwargs – arbitrary keyword arguments with keys corresponding to WhisperGenerationConfig fields.

:type : Dict

Returns:

return results in decoded form

Return type:

WhisperDecodedResults

WhisperGenerationConfig :param max_length: the maximum length the generated tokens can have. Corresponds to the length of the input prompt +

max_new_tokens. Its effect is overridden by max_new_tokens, if also set.

Parameters:

  • max_new_tokens (int) – the maximum numbers of tokens to generate, excluding the number of tokens in the prompt. max_new_tokens has priority over max_length.

  • eos_token_id (int) – End of stream token id.

Whisper specific parameters:

Parameters:

  • decoder_start_token_id (int) – Corresponds to the ”<|startoftranscript|>” token.

  • pad_token_id (int) – Padding token id.

  • translate_token_id (int) – Translate token id.

  • transcribe_token_id (int) – Transcribe token id.

  • no_timestamps_token_id (int) – No timestamps token id.

  • prev_sot_token_id (int) – Corresponds to the ”<|startofprev|>” token.

  • is_multilingual (bool)

  • begin_suppress_tokens (list[int]) – A list containing tokens that will be suppressed at the beginning of the sampling process.

  • suppress_tokens (list[int]) – A list containing the non-speech tokens that will be suppressed during generation.

  • language (Optional[str]) – Language token to use for generation in the form of <|en|>. You can find all the possible language tokens in the generation_config.json lang_to_id dictionary.

  • lang_to_id (Dict[str, int]) – Language token to token_id map. Initialized from the generation_config.json lang_to_id dictionary.

  • task (int) – Task to use for generation, either “translate” or “transcribe”

  • return_timestamps (bool) –

    If true the pipeline will return timestamps along the text for segments of words in the text. For instance, if you get WhisperDecodedResultChunk

    start_ts = 0.5 end_ts = 1.5 text = “ Hi there!”

    then it means the model predicts that the segment “Hi there!” was spoken after 0.5 and before 1.5 seconds. Note that a segment of text refers to a sequence of one or more words, rather than individual words.

  • initial_prompt – Initial prompt tokens passed as a previous transcription (after <|startofprev|> token) to the first processing

window. Can be used to steer the model to use particular spellings or styles.

Example:

auto result = pipeline.generate(raw_speech); // He has gone and gone for good answered Paul Icrom who…

auto result = pipeline.generate(raw_speech, ov::genai::initial_prompt(“Polychrome”)); // He has gone and gone for good answered Polychrome who…

Parameters:

hotwords – Hotwords tokens passed as a previous transcription (after <|startofprev|> token) to the all processing windows.

Can be used to steer the model to use particular spellings or styles.

Example:

auto result = pipeline.generate(raw_speech); // He has gone and gone for good answered Paul Icrom who…

auto result = pipeline.generate(raw_speech, ov::genai::hotwords(“Polychrome”)); // He has gone and gone for good answered Polychrome who…

get_generation_config(self: openvino_genai.py_openvino_genai.WhisperPipeline) → openvino_genai.py_openvino_genai.WhisperGenerationConfig#
get_tokenizer(self: openvino_genai.py_openvino_genai.WhisperPipeline) → openvino_genai.py_openvino_genai.Tokenizer#
set_generation_config(self: openvino_genai.py_openvino_genai.WhisperPipeline, config: openvino_genai.py_openvino_genai.WhisperGenerationConfig) → None#