qiJ d Z ddlZddlZddlmZ ddlmZ ddlmZm Z ddl
m Z ddl m
Z
mZmZmZmZmZ d d
lmZ d d lmZmZmZmZ ej4 e Z eg d Z eee Z d
e fdZ dde ejB z de ejB z dz de"de#e e f dz de"e z dz de dz de"fdZ$ G d d Z%ddgZ&y)zAutoFeatureExtractor class. N) OrderedDict )PreTrainedConfig) get_class_from_dynamic_moduleresolve_trust_remote_code)FeatureExtractionMixin) CONFIG_NAMEFEATURE_EXTRACTOR_NAMEPROCESSOR_NAME cached_fileloggingsafe_load_json_file )_LazyAutoMapping)CONFIG_MAPPING_NAMES
AutoConfigmodel_type_to_module_name!replace_list_option_in_docstrings)3)z audio-spectrogram-transformerASTFeatureExtractor)audioflamingo3WhisperFeatureExtractor)clapClapFeatureExtractor)clvpClvpFeatureExtractor)csmEncodecFeatureExtractor)dacDacFeatureExtractor)zdata2vec-audioWav2Vec2FeatureExtractor)diaDiaFeatureExtractor)encodecr )gemma3n Gemma3nAudioFeatureExtractor)glmasrr )granite_speech GraniteSpeechFeatureExtractor)higgs_audio_v2_tokenizerr )hubertr )kyutai_speech_to_text"KyutaiSpeechToTextFeatureExtractor)lasr_ctcLasrFeatureExtractor) lasr_encoderr. )markuplmMarkupLMFeatureExtractor)mimir ) moonshiner )moshir )musicgenr )musicgen_melody MusicgenMelodyFeatureExtractor) parakeet_ctcParakeetFeatureExtractor)parakeet_encoderr9 )pe_audioPeAudioFeatureExtractor)pe_audio_videor< )phi4_multimodal Phi4MultimodalFeatureExtractor) pop2pianoPop2PianoFeatureExtractor) qwen2_5_omnir ) qwen2_audior )qwen3_omni_moer ) seamless_m4tSeamlessM4TFeatureExtractor)seamless_m4t_v2rF )sewr )zsew-dr )speech_to_textSpeech2TextFeatureExtractor)speecht5SpeechT5FeatureExtractor) unispeechr )z
unispeech-satr )univnetUnivNetFeatureExtractor) vibevoice_acoustic_tokenizer*VibeVoiceAcousticTokenizerFeatureExtractor)
vibevoice_asrrQ )voxtralr )voxtral_realtime VoxtralRealtimeFeatureExtractor)wav2vec2r )z
wav2vec2-bertr )zwav2vec2-conformerr )wavlmr )whisperr )xcodecr
class_namec t j D ]< \ }}| |v s t | }t j d| d } t || c S t j j D ] }t |dd | k( s|c S t j d }t || r t || S y # t $ r Y w xY w)N.ztransformers.models__name__ transformers) FEATURE_EXTRACTOR_MAPPING_NAMESitemsr importlib
import_modulegetattrAttributeErrorFEATURE_EXTRACTOR_MAPPING_extra_contentvalueshasattr)rZ module_name
extractorsmodule extractor main_modules b/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/auto/feature_extraction_auto.py!feature_extractor_class_from_namero _ s #B#H#H#J Z #3K@K ,,q
->@UVF
vz22
/==DDF 9j$ /: = )).9K{J'{J// "
s B99 CC pretrained_model_name_or_path cache_dirforce_downloadproxiestokenrevisionlocal_files_onlyc t | t ||||||dd
}t | t ||||||dd
} | s|st j d i S i }
|t | } d| v r| d }
|
|
t | }
|
S )a
Loads the feature extractor configuration from a pretrained model feature extractor configuration.
Args:
pretrained_model_name_or_path (`str` or `os.PathLike`):
This can be either:
- a string, the *model id* of a pretrained model configuration hosted inside a model repo on
huggingface.co.
- a path to a *directory* containing a configuration file saved using the
[`~FeatureExtractionMixin.save_pretrained`] method, e.g., `./my_model_directory/`.
cache_dir (`str` or `os.PathLike`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the standard
cache should not be used.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force to (re-)download the configuration files and override the cached versions if they
exist.
proxies (`dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `hf auth login` (stored in `~/.huggingface`).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
local_files_only (`bool`, *optional*, defaults to `False`):
If `True`, will only try to load the feature extractor configuration from local files.
Passing `token=True` is required when you want to use a private model.
Returns:
`Dict`: The configuration of the feature extractor.
Examples:
```python
# Download configuration from huggingface.co and cache.
feature_extractor_config = get_feature_extractor_config("facebook/wav2vec2-base-960h")
# This model does not have a feature extractor config so the result will be an empty dict.
feature_extractor_config = get_feature_extractor_config("FacebookAI/xlm-roberta-base")
# Save a pretrained feature extractor locally and you can reload its config
from transformers import AutoFeatureExtractor
feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h")
feature_extractor.save_pretrained("feature-extractor-test")
feature_extractor_config = get_feature_extractor_config("feature-extractor-test")
```F) filenamerq rr rs rt ru rv _raise_exceptions_for_gated_repo%_raise_exceptions_for_missing_entriesz:Could not locate the feature extractor configuration file.feature_extractor)r r r
loggerinfor ) rp rq rr rs rt ru rv kwargsresolved_processor_file resolved_feature_extractor_filefeature_extractor_dictprocessor_dicts rn get_feature_extractor_configr w s D *% %))..3 '2%'%))..3 '# +3J PQ
*,-DE . 0%34G%H "&27M7U!45T!U !! c N e Zd ZdZd Ze ee d Ze dd Z
y)AutoFeatureExtractora+
This is a generic feature extractor class that will be instantiated as one of the feature extractor classes of the
library when created with the [`AutoFeatureExtractor.from_pretrained`] class method.
This class cannot be instantiated directly using `__init__()` (throws an error).
c t d )NzAutoFeatureExtractor is designed to be instantiated using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.)OSError)selfs rn __init__z AutoFeatureExtractor.__init__ s
f
r c |j dd }|j dd }d|d< t j |fi |\ }}|j dd }d}d|j di v r|d d }|`|^t |t
st
j |fd|i|}t |dd }t |d r d|j v r|j d }| t | }|du} |duxs t | t v }
| r*d |v r|j d d
} nd} t |||
| | }| rD|rBt! ||fi |}|j d d }|j# |j |fi |S | |j |fi |S t | t v r%t t | } |j |fi |S t% d | d
t& dt( dt( ddj+ d t, D
)a
Instantiate one of the feature extractor classes of the library from a pretrained model vocabulary.
The feature extractor class to instantiate is selected based on the `model_type` property of the config object
(either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it's
missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:
List options
Params:
pretrained_model_name_or_path (`str` or `os.PathLike`):
This can be either:
- a string, the *model id* of a pretrained feature_extractor hosted inside a model repo on
huggingface.co.
- a path to a *directory* containing a feature extractor file saved using the
[`~feature_extraction_utils.FeatureExtractionMixin.save_pretrained`] method, e.g.,
`./my_model_directory/`.
- a path or url to a saved feature extractor JSON *file*, e.g.,
`./my_model_directory/preprocessor_config.json`.
cache_dir (`str` or `os.PathLike`, *optional*):
Path to a directory in which a downloaded pretrained model feature extractor should be cached if the
standard cache should not be used.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force to (re-)download the feature extractor files and override the cached versions
if they exist.
proxies (`dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `hf auth login` (stored in `~/.huggingface`).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
return_unused_kwargs (`bool`, *optional*, defaults to `False`):
If `False`, then this function returns just the final feature extractor object. If `True`, then this
functions returns a `Tuple(feature_extractor, unused_kwargs)` where *unused_kwargs* is a dictionary
consisting of the key/value pairs whose keys are not feature extractor attributes: i.e., the part of
`kwargs` which has not been used to update `feature_extractor` and is otherwise ignored.
trust_remote_code (`bool`, *optional*, defaults to `False`):
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to `True` for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.
kwargs (`dict[str, Any]`, *optional*):
The values in kwargs of any keys which are feature extractor attributes will be used to override the
loaded values. Behavior concerning key/value pairs whose keys are *not* feature extractor attributes is
controlled by the `return_unused_kwargs` keyword parameter.
Passing `token=True` is required when you want to use a private model.
Examples:
```python
>>> from transformers import AutoFeatureExtractor
>>> # Download feature extractor from huggingface.co and cache.
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h")
>>> # If feature extractor files are in a directory (e.g. feature extractor was saved using *save_pretrained('./test/saved_model/')*)
>>> # feature_extractor = AutoFeatureExtractor.from_pretrained("./test/saved_model/")
```configNtrust_remote_codeT
_from_autofeature_extractor_typer auto_mapz--r
code_revisionz"Unrecognized feature extractor in z4. Should have a `feature_extractor_type` key in its z of z3, or one of the following `model_type` keys in its z: z, c 3 K | ] }| y w)N ).0cs rn z7AutoFeatureExtractor.from_pretrained..m s @lq@ls )popr get_feature_extractor_dictget
isinstancer r from_pretrainedrc rh r ro typere splitr r register_for_auto_class
ValueErrorr
r joinr_ ) clsrp r~ r r config_dict_feature_extractor_classfeature_extractor_auto_maphas_remote_codehas_local_code
upstream_repos rn r z$AutoFeatureExtractor.from_pretrained ss L Hd+ "JJ':D A #| /JJKhslrs Q"-//2JD"Q %)" ![__Z%D D)4Z)@AW)X & # */I/Q f&67#331EVZ` '.f6NPT&U #vz*/E /X-3__=S-T* " .&GH_&` #4D@0<iV Pi@i 11 : @ @ Fq I
$
9!#@.Racp! 0&C*,I'MS' #
?D1A # ; ; =:*::;Xc\bc c
$
0:*::;Xc\bc c
&\ 6
6&?V &M #:*::;Xc\bc c01N0O P33I2J${m \((3}Btyy@lLk@l7l6m
o
r c 4 t j | || y)a0
Register a new feature extractor for this class.
Args:
config_class ([`PreTrainedConfig`]):
The configuration corresponding to the model to register.
feature_extractor_class ([`FeatureExtractorMixin`]): The feature extractor to register.
)exist_okN)re register) config_classr r s rn r z AutoFeatureExtractor.registerp s "**<9P[c*dr N)F) r]
__module__ __qualname____doc__r classmethodr r_ r staticmethodr r r rn r r sH
&'FGy
H y
v e er r re )NFNNNF)'r ra os collectionsr configuration_utilsr dynamic_module_utilsr r feature_extraction_utilsr utilsr r
r r r
r auto_factoryr configuration_autor r r r
get_loggerr] r| r_ re strro PathLikebooldictr r __all__r r rn r s$ " # 4 \ > s s *
H %"-46# p --ACb c # 4 +/ %) # "k"#k"R[[ 4'k" k" #s(^d
" k"
#: k" Dj
k" k"\Ue Uep '(>
?r