86 lines
1.9 KiB
Python
86 lines
1.9 KiB
Python
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from ._hdemucs import HDemucs, hdemucs_high, hdemucs_low, hdemucs_medium
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from .conformer import Conformer
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from .conv_tasnet import conv_tasnet_base, ConvTasNet
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from .deepspeech import DeepSpeech
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from .emformer import Emformer
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from .rnnt import emformer_rnnt_base, emformer_rnnt_model, RNNT
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from .rnnt_decoder import Hypothesis, RNNTBeamSearch
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from .squim import (
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squim_objective_base,
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squim_objective_model,
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squim_subjective_base,
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squim_subjective_model,
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SquimObjective,
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SquimSubjective,
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)
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from .tacotron2 import Tacotron2
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from .wav2letter import Wav2Letter
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from .wav2vec2 import (
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hubert_base,
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hubert_large,
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hubert_pretrain_base,
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hubert_pretrain_large,
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hubert_pretrain_model,
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hubert_pretrain_xlarge,
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hubert_xlarge,
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HuBERTPretrainModel,
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wav2vec2_base,
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wav2vec2_large,
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wav2vec2_large_lv60k,
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wav2vec2_model,
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wav2vec2_xlsr_1b,
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wav2vec2_xlsr_2b,
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wav2vec2_xlsr_300m,
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Wav2Vec2Model,
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wavlm_base,
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wavlm_large,
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wavlm_model,
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)
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from .wavernn import WaveRNN
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__all__ = [
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"Wav2Letter",
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"WaveRNN",
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"ConvTasNet",
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"conv_tasnet_base",
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"DeepSpeech",
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"Wav2Vec2Model",
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"HuBERTPretrainModel",
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"wavlm_model",
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"wavlm_base",
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"wavlm_large",
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"wav2vec2_model",
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"wav2vec2_base",
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"wav2vec2_large",
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"wav2vec2_large_lv60k",
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"hubert_base",
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"hubert_large",
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"hubert_xlarge",
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"hubert_pretrain_model",
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"hubert_pretrain_base",
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"hubert_pretrain_large",
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"hubert_pretrain_xlarge",
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"wav2vec2_xlsr_300m",
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"wav2vec2_xlsr_1b",
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"wav2vec2_xlsr_2b",
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"Tacotron2",
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"Conformer",
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"Emformer",
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"Hypothesis",
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"RNNT",
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"RNNTBeamSearch",
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"emformer_rnnt_base",
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"emformer_rnnt_model",
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"HDemucs",
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"hdemucs_low",
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"hdemucs_medium",
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"hdemucs_high",
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"squim_objective_base",
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"squim_objective_model",
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"squim_subjective_base",
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"squim_subjective_model",
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"SquimObjective",
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"SquimSubjective",
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]
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