Class swarmauri_standard.stt.WhisperLargeSTT.WhisperLargeSTT
swarmauri_standard.stt.WhisperLargeSTT.WhisperLargeSTT
WhisperLargeSTT(**data)
Bases: STTBase
A class implementing OpenAI's Whisper Large V3 model using HuggingFace's Inference API.
This class provides both synchronous and asynchronous methods for transcribing or translating audio files using the Whisper Large V3 model. It supports both single file processing and batch processing with controlled concurrency.
ATTRIBUTE | DESCRIPTION |
---|---|
allowed_models |
List of supported model identifiers.
TYPE:
|
name |
The name/identifier of the model being used.
TYPE:
|
type |
Type identifier for the model.
TYPE:
|
api_key |
HuggingFace API key for authentication.
TYPE:
|
Link to API KEY: https://huggingface.co/login?next=%2Fsettings%2Ftokens
Example
model = WhisperLargeSTT(api_key="your-api-key") text = model.predict("audio.mp3", task="transcription") print(text)
Initialize the WhisperLargeSTT instance.
PARAMETER | DESCRIPTION |
---|---|
**data
|
Keyword arguments containing model configuration. Must include 'api_key' for HuggingFace API authentication.
TYPE:
|
RAISES | DESCRIPTION |
---|---|
ValueError
|
If required configuration parameters are missing. |
Source code in swarmauri_standard/stt/WhisperLargeSTT.py
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type
class-attribute
instance-attribute
type = 'WhisperLargeSTT'
api_key
instance-attribute
api_key
allowed_models
class-attribute
instance-attribute
allowed_models = allowed_models or get_allowed_models()
name
class-attribute
instance-attribute
name = allowed_models[0]
model_config
class-attribute
instance-attribute
model_config = ConfigDict(
extra="forbid", arbitrary_types_allowed=True
)
id
class-attribute
instance-attribute
id = Field(default_factory=generate_id)
members
class-attribute
instance-attribute
members = None
owners
class-attribute
instance-attribute
owners = None
host
class-attribute
instance-attribute
host = None
default_logger
class-attribute
default_logger = None
logger
class-attribute
instance-attribute
logger = None
version
class-attribute
instance-attribute
version = '0.1.0'
predict
predict(audio_path, task='transcription')
Process a single audio file using the Hugging Face Inference API.
PARAMETER | DESCRIPTION |
---|---|
audio_path
|
Path to the audio file to be processed.
TYPE:
|
task
|
Task to perform. 'transcription': Transcribe audio in its original language. 'translation': Translate audio to English.
TYPE:
|
RETURNS | DESCRIPTION |
---|---|
str
|
Transcribed or translated text from the audio file.
TYPE:
|
RAISES | DESCRIPTION |
---|---|
ValueError
|
If the specified task is not supported. |
Exception
|
If the API response format is unexpected. |
HTTPError
|
If the API request fails. |
Source code in swarmauri_standard/stt/WhisperLargeSTT.py
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|
apredict
async
apredict(audio_path, task='transcription')
Asynchronously process a single audio file.
This method provides the same functionality as predict()
but operates
asynchronously for better performance in async contexts.
PARAMETER | DESCRIPTION |
---|---|
audio_path
|
Path to the audio file to be processed.
TYPE:
|
task
|
Task to perform. 'transcription': Transcribe audio in its original language. 'translation': Translate audio to English.
TYPE:
|
RETURNS | DESCRIPTION |
---|---|
str
|
Transcribed or translated text from the audio file.
TYPE:
|
RAISES | DESCRIPTION |
---|---|
ValueError
|
If the specified task is not supported. |
Exception
|
If the API response format is unexpected. |
HTTPError
|
If the API request fails. |
Source code in swarmauri_standard/stt/WhisperLargeSTT.py
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|
batch
batch(path_task_dict)
Synchronously process multiple audio files.
PARAMETER | DESCRIPTION |
---|---|
path_task_dict
|
Dictionary mapping file paths to their respective tasks. Key: Path to audio file. Value: Task to perform ("transcription" or "translation").
TYPE:
|
RETURNS | DESCRIPTION |
---|---|
List[str]
|
List[str]: List of processed texts, maintaining the order of input files. |
Example
files = { ... "file1.mp3": "transcription", ... "file2.mp3": "translation" ... } results = model.batch(files)
Source code in swarmauri_standard/stt/WhisperLargeSTT.py
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|
abatch
async
abatch(path_task_dict, max_concurrent=5)
Process multiple audio files in parallel with controlled concurrency.
This method provides the same functionality as batch()
but operates
asynchronously with controlled concurrency to prevent overwhelming
the API or local resources.
PARAMETER | DESCRIPTION |
---|---|
path_task_dict
|
Dictionary mapping file paths to their respective tasks. Key: Path to audio file. Value: Task to perform ("transcription" or "translation").
TYPE:
|
max_concurrent
|
Maximum number of concurrent requests. Defaults to 5.
TYPE:
|
RETURNS | DESCRIPTION |
---|---|
List[str]
|
List[str]: List of processed texts, maintaining the order of input files. |
Example
files = { ... "file1.mp3": "transcription", ... "file2.mp3": "translation" ... } results = await model.abatch(files, max_concurrent=3)
Source code in swarmauri_standard/stt/WhisperLargeSTT.py
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get_allowed_models
get_allowed_models()
Queries the LLMProvider API endpoint to retrieve the list of allowed models.
RETURNS | DESCRIPTION |
---|---|
List[str]
|
List[str]: List of allowed model names. |
Source code in swarmauri_standard/stt/WhisperLargeSTT.py
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|
stream
stream(audio_path, task='transcription')
Source code in swarmauri_standard/stt/WhisperLargeSTT.py
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|
astream
async
astream(audio_path, task='transcription')
Source code in swarmauri_standard/stt/WhisperLargeSTT.py
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register_model
classmethod
register_model()
Decorator to register a base model in the unified registry.
RETURNS | DESCRIPTION |
---|---|
Callable
|
A decorator function that registers the model class.
TYPE:
|
Source code in swarmauri_base/DynamicBase.py
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register_type
classmethod
register_type(resource_type=None, type_name=None)
Decorator to register a subtype under one or more base models in the unified registry.
PARAMETER | DESCRIPTION |
---|---|
resource_type
|
The base model(s) under which to register the subtype. If None, all direct base classes (except DynamicBase) are used.
TYPE:
|
type_name
|
An optional custom type name for the subtype.
TYPE:
|
RETURNS | DESCRIPTION |
---|---|
Callable
|
A decorator function that registers the subtype.
TYPE:
|
Source code in swarmauri_base/DynamicBase.py
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model_validate_toml
classmethod
model_validate_toml(toml_data)
Validate a model from a TOML string.
Source code in swarmauri_base/TomlMixin.py
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model_dump_toml
model_dump_toml(
fields_to_exclude=None, api_key_placeholder=None
)
Return a TOML representation of the model.
Source code in swarmauri_base/TomlMixin.py
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model_validate_yaml
classmethod
model_validate_yaml(yaml_data)
Validate a model from a YAML string.
Source code in swarmauri_base/YamlMixin.py
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model_dump_yaml
model_dump_yaml(
fields_to_exclude=None, api_key_placeholder=None
)
Return a YAML representation of the model.
Source code in swarmauri_base/YamlMixin.py
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model_post_init
model_post_init(logger=None)
Assign a logger instance after model initialization.
Source code in swarmauri_base/LoggerMixin.py
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add_allowed_model
add_allowed_model(model)
Add a new model to the list of allowed models.
RAISES | DESCRIPTION |
---|---|
ValueError
|
If the model is already in the allowed models list. |
Source code in swarmauri_base/stt/STTBase.py
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remove_allowed_model
remove_allowed_model(model)
Remove a model from the list of allowed models.
RAISES | DESCRIPTION |
---|---|
ValueError
|
If the model is not in the allowed models list. |
Source code in swarmauri_base/stt/STTBase.py
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