import logging
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import torch
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import torch.nn as nn
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from funasr.export.utils.torch_function import MakePadMask
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from funasr.export.utils.torch_function import sequence_mask
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from funasr.models.encoder.sanm_encoder import SANMEncoder
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from funasr.models.encoder.conformer_encoder import ConformerEncoder
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from funasr.export.models.encoder.sanm_encoder import SANMEncoder as SANMEncoder_export
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from funasr.export.models.encoder.conformer_encoder import ConformerEncoder as ConformerEncoder_export
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from funasr.models.predictor.cif import CifPredictorV2, CifPredictorV3
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from funasr.export.models.predictor.cif import CifPredictorV2 as CifPredictorV2_export
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from funasr.export.models.predictor.cif import CifPredictorV3 as CifPredictorV3_export
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from funasr.models.decoder.sanm_decoder import ParaformerSANMDecoder
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from funasr.models.decoder.transformer_decoder import ParaformerDecoderSAN
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from funasr.export.models.decoder.sanm_decoder import ParaformerSANMDecoder as ParaformerSANMDecoder_export
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from funasr.export.models.decoder.transformer_decoder import ParaformerDecoderSAN as ParaformerDecoderSAN_export
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class Paraformer(nn.Module):
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"""
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Author: Speech Lab, Alibaba Group, China
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Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
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https://arxiv.org/abs/2206.08317
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"""
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def __init__(
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self,
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model,
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max_seq_len=512,
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feats_dim=560,
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model_name='model',
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**kwargs,
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):
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super().__init__()
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onnx = False
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if "onnx" in kwargs:
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onnx = kwargs["onnx"]
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if isinstance(model.encoder, SANMEncoder):
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self.encoder = SANMEncoder_export(model.encoder, onnx=onnx)
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elif isinstance(model.encoder, ConformerEncoder):
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self.encoder = ConformerEncoder_export(model.encoder, onnx=onnx)
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if isinstance(model.predictor, CifPredictorV2):
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self.predictor = CifPredictorV2_export(model.predictor)
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if isinstance(model.decoder, ParaformerSANMDecoder):
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self.decoder = ParaformerSANMDecoder_export(model.decoder, onnx=onnx)
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elif isinstance(model.decoder, ParaformerDecoderSAN):
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self.decoder = ParaformerDecoderSAN_export(model.decoder, onnx=onnx)
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self.feats_dim = feats_dim
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self.model_name = model_name
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if onnx:
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self.make_pad_mask = MakePadMask(max_seq_len, flip=False)
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else:
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self.make_pad_mask = sequence_mask(max_seq_len, flip=False)
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def forward(
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self,
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speech: torch.Tensor,
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speech_lengths: torch.Tensor,
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):
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# a. To device
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batch = {"speech": speech, "speech_lengths": speech_lengths}
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# batch = to_device(batch, device=self.device)
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enc, enc_len = self.encoder(**batch)
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mask = self.make_pad_mask(enc_len)[:, None, :]
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pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = self.predictor(enc, mask)
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pre_token_length = pre_token_length.floor().type(torch.int32)
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decoder_out, _ = self.decoder(enc, enc_len, pre_acoustic_embeds, pre_token_length)
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decoder_out = torch.log_softmax(decoder_out, dim=-1)
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# sample_ids = decoder_out.argmax(dim=-1)
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return decoder_out, pre_token_length
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def get_dummy_inputs(self):
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speech = torch.randn(2, 30, self.feats_dim)
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speech_lengths = torch.tensor([6, 30], dtype=torch.int32)
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return (speech, speech_lengths)
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def get_dummy_inputs_txt(self, txt_file: str = "/mnt/workspace/data_fbank/0207/12345.wav.fea.txt"):
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import numpy as np
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fbank = np.loadtxt(txt_file)
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fbank_lengths = np.array([fbank.shape[0], ], dtype=np.int32)
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speech = torch.from_numpy(fbank[None, :, :].astype(np.float32))
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speech_lengths = torch.from_numpy(fbank_lengths.astype(np.int32))
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return (speech, speech_lengths)
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def get_input_names(self):
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return ['speech', 'speech_lengths']
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def get_output_names(self):
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return ['logits', 'token_num']
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def get_dynamic_axes(self):
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return {
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'speech': {
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0: 'batch_size',
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1: 'feats_length'
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},
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'speech_lengths': {
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0: 'batch_size',
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},
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'logits': {
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0: 'batch_size',
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1: 'logits_length'
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},
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}
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class BiCifParaformer(nn.Module):
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"""
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Author: Speech Lab, Alibaba Group, China
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Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
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https://arxiv.org/abs/2206.08317
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"""
|
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def __init__(
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self,
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model,
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max_seq_len=512,
|
feats_dim=560,
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model_name='model',
|
**kwargs,
|
):
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super().__init__()
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onnx = False
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if "onnx" in kwargs:
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onnx = kwargs["onnx"]
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if isinstance(model.encoder, SANMEncoder):
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self.encoder = SANMEncoder_export(model.encoder, onnx=onnx)
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elif isinstance(model.encoder, ConformerEncoder):
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self.encoder = ConformerEncoder_export(model.encoder, onnx=onnx)
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else:
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logging.warning("Unsupported encoder type to export.")
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if isinstance(model.predictor, CifPredictorV3):
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self.predictor = CifPredictorV3_export(model.predictor)
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else:
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logging.warning("Wrong predictor type to export.")
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if isinstance(model.decoder, ParaformerSANMDecoder):
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self.decoder = ParaformerSANMDecoder_export(model.decoder, onnx=onnx)
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elif isinstance(model.decoder, ParaformerDecoderSAN):
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self.decoder = ParaformerDecoderSAN_export(model.decoder, onnx=onnx)
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else:
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logging.warning("Unsupported decoder type to export.")
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self.feats_dim = feats_dim
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self.model_name = model_name
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if onnx:
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self.make_pad_mask = MakePadMask(max_seq_len, flip=False)
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else:
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self.make_pad_mask = sequence_mask(max_seq_len, flip=False)
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def forward(
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self,
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speech: torch.Tensor,
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speech_lengths: torch.Tensor,
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):
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# a. To device
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batch = {"speech": speech, "speech_lengths": speech_lengths}
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# batch = to_device(batch, device=self.device)
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enc, enc_len = self.encoder(**batch)
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mask = self.make_pad_mask(enc_len)[:, None, :]
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pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = self.predictor(enc, mask)
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pre_token_length = pre_token_length.round().type(torch.int32)
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decoder_out, _ = self.decoder(enc, enc_len, pre_acoustic_embeds, pre_token_length)
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decoder_out = torch.log_softmax(decoder_out, dim=-1)
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# get predicted timestamps
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us_alphas, us_cif_peak = self.predictor.get_upsample_timestmap(enc, mask, pre_token_length)
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return decoder_out, pre_token_length, us_alphas, us_cif_peak
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def get_dummy_inputs(self):
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speech = torch.randn(2, 30, self.feats_dim)
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speech_lengths = torch.tensor([6, 30], dtype=torch.int32)
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return (speech, speech_lengths)
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def get_dummy_inputs_txt(self, txt_file: str = "/mnt/workspace/data_fbank/0207/12345.wav.fea.txt"):
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import numpy as np
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fbank = np.loadtxt(txt_file)
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fbank_lengths = np.array([fbank.shape[0], ], dtype=np.int32)
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speech = torch.from_numpy(fbank[None, :, :].astype(np.float32))
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speech_lengths = torch.from_numpy(fbank_lengths.astype(np.int32))
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return (speech, speech_lengths)
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def get_input_names(self):
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return ['speech', 'speech_lengths']
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def get_output_names(self):
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return ['logits', 'token_num', 'us_alphas', 'us_cif_peak']
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def get_dynamic_axes(self):
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return {
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'speech': {
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0: 'batch_size',
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1: 'feats_length'
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},
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'speech_lengths': {
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0: 'batch_size',
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},
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'logits': {
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0: 'batch_size',
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1: 'logits_length'
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},
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'us_alphas': {
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0: 'batch_size',
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1: 'alphas_length'
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},
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'us_cif_peak': {
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0: 'batch_size',
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1: 'alphas_length'
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},
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}
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