Exemplos de DummyWaterfallDimensionalityReducer em Python

Exemplo n.º 1

Arquivo: test_examples.py Projeto: Redict/rg_text_to_sound

class TestDummyWaterfallDimensionalityReducer(AbstractTestWaterfallDimensionalityReducer):
    @pytest.fixture(params=[
        DummyWaterfallDimensionalityReducer()
    ])
    def model(self, request):
        return request.param

        

Exemplo n.º 2

Arquivo: tts_pipeline.py Projeto: Mirco-Nani/rg_text_to_sound

def get_pipeline():
    return WaterfallPipeline(
        keyword_extractor=UnifiedKeywordPairsExtractorV2(
            words_pairs=[("Bright", "Dark"), ("Full", "Hollow"),
                         ("Smooth", "Rough"), ("Warm", "Metallic"),
                         ("Clear", "Muddy"), ("Thin", "Thick"),
                         ("Pure", "Noisy"), ("Rich", "Sparse"),
                         ("Soft", "Hard")],
            ner_model_path=os.path.join(assets_folder, "ner_model"),
            verbose=True),
        embedder=ZeroEmbedder(),
        dimensionality_reducer=DummyWaterfallDimensionalityReducer())

Exemplo n.º 3

def get_pipeline():
    return WaterfallPipeline(
        keyword_extractor=UnifiedKeywordExtractor(target_words=[
            "Bright", "Dark", "Full", "Hollow", "Smooth", "Rough", "Warm",
            "Metallic", "Clear", "Muddy", "Thin", "thick", "Pure", "Noisy",
            "Rich", "Sparse", "Soft", "Hard"
        ],
                                                  ner_model_path=os.path.join(
                                                      assets_folder,
                                                      "ner_model")),
        embedder=GNewsWaterfallEmbedder(
        ),  # this is very small, so it runs fast
        dimensionality_reducer=DummyWaterfallDimensionalityReducer())

Exemplo n.º 4

)
from tts_pipeline.pipelines.waterfall.models.gnews_models import GNewsWaterfallEmbedder
from tts_pipeline.pipelines.waterfall.models.ner_model import NERKeywordExtractor
from tts_pipeline.pipelines.waterfall.models.UnifiedKeywordExtractor import UnifiedKeywordExtractor

PIPELINES_TO_TEST = [
    #WaterfallPipeline(
    #    DummyWaterfallKeywordExtractor(),
    #    BERTWaterfallEmbedder(tf_hub_url = "https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-4_H-128_A-2/1"),
    #    DummyWaterfallDimensionalityReducer()
    #),
    WaterfallPipeline(
        NERKeywordExtractor(),
        #DummyWaterfallKeywordExtractor(),
        GNewsWaterfallEmbedder(),
        DummyWaterfallDimensionalityReducer()
    ),
    WaterfallPipeline(
        UnifiedKeywordExtractor(["Bright","Dark","Full","Hollow","Smooth","Rough","Warm","Metallic","Smooth","Rough","Clear","Muddy","Thin","thick","Pure","Noisy","Rich","Sparse","Soft","Hard"]),
        #DummyWaterfallKeywordExtractor(),
        GNewsWaterfallEmbedder(),
        DummyWaterfallDimensionalityReducer()
    )
]

DEFAULT_SENTENCE="give me a bright guitar"

class TestWaterfallPipeline(AbstractTestInferencePipeline):
    @pytest.fixture(params=PIPELINES_TO_TEST)
    def pipeline(self, request):
        return request.param

Exemplo n.º 5

def get_pipeline():
    return WaterfallPipeline(
        keyword_extractor=DummyWaterfallKeywordExtractor(),
        embedder=BERTWaterfallEmbedder(),
        dimensionality_reducer=DummyWaterfallDimensionalityReducer())