repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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espnet | espnet-master/test/espnet2/asr/encoder/test_hubert_encoder.py | import pytest
import torch
from packaging.version import parse as V
from espnet2.asr.encoder.hubert_encoder import TorchAudioHuBERTPretrainEncoder
is_torch_1_12_1_plus = V(torch.__version__) >= V("1.12.1")
@pytest.mark.parametrize(
"finetuning, eval, freeze_encoder_updates",
[
(False, False, 0),
... | 2,678 | 25.009709 | 78 | py |
espnet | espnet-master/test/espnet2/asr/encoder/test_contextual_block_transformer_encoder.py | import pytest
import torch
from espnet2.asr.encoder.contextual_block_transformer_encoder import ( # noqa: H301
ContextualBlockTransformerEncoder,
)
@pytest.mark.parametrize("input_layer", ["linear", "conv2d", "embed", None])
@pytest.mark.parametrize("positionwise_layer_type", ["conv1d", "conv1d-linear"])
def te... | 1,574 | 28.716981 | 84 | py |
espnet | espnet-master/test/espnet2/asr/encoder/test_whisper_encoder.py | import sys
import pytest
import torch
from packaging.version import parse as V
from espnet2.asr.encoder.whisper_encoder import OpenAIWhisperEncoder
pytest.importorskip("whisper")
# NOTE(Shih-Lun): needed for `return_complex` param in torch.stft()
is_torch_1_7_plus = V(torch.__version__) >= V("1.7.0")
is_python_3_8_... | 2,635 | 27.967033 | 81 | py |
espnet | espnet-master/test/espnet2/asr/encoder/test_longformer_encoder.py | import pytest
import torch
from espnet2.asr.encoder.longformer_encoder import LongformerEncoder
pytest.importorskip("longformer")
@pytest.mark.parametrize(
"input_layer",
["linear", "conv2d", "conv2d1", "conv2d2", "conv2d6", "conv2d8", "embed"],
)
@pytest.mark.parametrize("positionwise_layer_type", ["conv1d... | 2,575 | 28.953488 | 80 | py |
espnet | espnet-master/test/espnet2/asr/encoder/test_e_branchformer_encoder.py | import pytest
import torch
from espnet2.asr.ctc import CTC
from espnet2.asr.encoder.e_branchformer_encoder import EBranchformerEncoder
@pytest.mark.parametrize(
"input_layer",
[
"linear",
"conv1d2",
"conv2d",
"conv2d1",
"conv2d2",
"conv2d6",
"conv2d8",
... | 4,375 | 29.17931 | 76 | py |
espnet | espnet-master/test/espnet2/asr/encoder/test_conformer_encoder.py | import pytest
import torch
from espnet2.asr.ctc import CTC
from espnet2.asr.encoder.conformer_encoder import ConformerEncoder
@pytest.mark.parametrize(
"input_layer",
["linear", "conv2d", "conv2d1", "conv2d2", "conv2d6", "conv2d8", "embed"],
)
@pytest.mark.parametrize("positionwise_layer_type", ["conv1d", "c... | 4,431 | 28.546667 | 81 | py |
espnet | espnet-master/test/espnet2/asr/encoder/test_transformer_encoder_multispkr.py | import pytest
import torch
from espnet2.asr.encoder.transformer_encoder_multispkr import TransformerEncoder
@pytest.mark.parametrize("input_layer", ["conv2d"])
@pytest.mark.parametrize("positionwise_layer_type", ["conv1d"])
@pytest.mark.parametrize("num_inf", [1, 2, 3])
def test_Encoder_forward_backward(
input_l... | 1,231 | 24.666667 | 80 | py |
espnet | espnet-master/test/espnet2/asr/encoder/test_branchformer_encoder.py | import pytest
import torch
from espnet2.asr.encoder.branchformer_encoder import BranchformerEncoder
@pytest.mark.parametrize(
"input_layer",
["linear", "conv2d", "conv2d1", "conv2d2", "conv2d6", "conv2d8", "embed"],
)
@pytest.mark.parametrize("use_linear_after_conv", [True, False])
@pytest.mark.parametrize(
... | 5,048 | 27.206704 | 78 | py |
espnet | espnet-master/test/espnet2/asr/encoder/test_vgg_rnn_encoder.py | import pytest
import torch
from espnet2.asr.encoder.vgg_rnn_encoder import VGGRNNEncoder
@pytest.mark.parametrize("rnn_type", ["lstm", "gru"])
@pytest.mark.parametrize("bidirectional", [True, False])
@pytest.mark.parametrize("use_projection", [True, False])
def test_Encoder_forward_backward(rnn_type, bidirectional, ... | 838 | 28.964286 | 88 | py |
espnet | espnet-master/test/espnet2/asr/encoder/test_transformer_encoder.py | import pytest
import torch
from espnet2.asr.ctc import CTC
from espnet2.asr.encoder.transformer_encoder import TransformerEncoder
@pytest.mark.parametrize("input_layer", ["linear", "conv2d", "embed", None])
@pytest.mark.parametrize("positionwise_layer_type", ["conv1d", "conv1d-linear"])
@pytest.mark.parametrize(
... | 2,110 | 27.527027 | 81 | py |
espnet | espnet-master/test/espnet2/asr/decoder/test_mlm_decoder.py | import pytest
import torch
from espnet2.asr.decoder.mlm_decoder import MLMDecoder
@pytest.mark.parametrize("input_layer", ["linear", "embed"])
@pytest.mark.parametrize("normalize_before", [True, False])
@pytest.mark.parametrize("use_output_layer", [True, False])
def test_MLMDecoder_backward(input_layer, normalize_be... | 1,088 | 30.114286 | 78 | py |
espnet | espnet-master/test/espnet2/asr/decoder/test_hugging_face_transformers_decoder.py | import pytest
import torch
from espnet2.asr.decoder.hugging_face_transformers_decoder import (
HuggingFaceTransformersDecoder,
)
@pytest.mark.parametrize(
"model_name_or_path",
[
"akreal/tiny-random-t5",
"akreal/tiny-random-mbart",
],
)
@pytest.mark.parametrize("encoder_output_size", ... | 1,481 | 31.217391 | 82 | py |
espnet | espnet-master/test/espnet2/asr/decoder/test_whisper_decoder.py | import sys
import pytest
import torch
from packaging.version import parse as V
from espnet2.asr.decoder.whisper_decoder import OpenAIWhisperDecoder
VOCAB_SIZE_WHISPER_MULTILINGUAL = 51865
pytest.importorskip("whisper")
# NOTE(Shih-Lun): needed for `persistent` param in
# torch.nn.Module.register_bu... | 3,391 | 29.836364 | 87 | py |
espnet | espnet-master/test/espnet2/asr/decoder/test_s4_decoder.py | import pytest
import torch
from packaging.version import parse as V
from espnet2.asr.decoder.s4_decoder import S4Decoder
from espnet.nets.batch_beam_search import BatchBeamSearch
# Check to have torch.linalg
is_torch_1_10_plus = V(torch.__version__) >= V("1.10.0")
@pytest.mark.parametrize("input_layer", ["embed"])
... | 3,230 | 30.676471 | 87 | py |
espnet | espnet-master/test/espnet2/asr/decoder/test_transducer_decoder.py | import pytest
import torch
from espnet2.asr.decoder.transducer_decoder import TransducerDecoder
from espnet2.asr.transducer.beam_search_transducer import Hypothesis
@pytest.mark.parametrize("rnn_type", ["lstm", "gru"])
def test_TransducerDecoder_forward(rnn_type):
ys = torch.randint(0, 10, [4, 10], dtype=torch.l... | 1,576 | 28.203704 | 87 | py |
espnet | espnet-master/test/espnet2/asr/decoder/test_transformer_decoder.py | import pytest
import torch
from espnet2.asr.ctc import CTC
from espnet2.asr.decoder.transformer_decoder import ( # noqa: H301
DynamicConvolution2DTransformerDecoder,
DynamicConvolutionTransformerDecoder,
LightweightConvolution2DTransformerDecoder,
LightweightConvolutionTransformerDecoder,
Transfor... | 8,092 | 30.490272 | 86 | py |
espnet | espnet-master/test/espnet2/asr/decoder/test_rnn_decoder.py | import pytest
import torch
from espnet2.asr.decoder.rnn_decoder import RNNDecoder
from espnet.nets.beam_search import BeamSearch
@pytest.mark.parametrize("context_residual", [True, False])
@pytest.mark.parametrize("rnn_type", ["lstm", "gru"])
def test_RNNDecoder_backward(context_residual, rnn_type):
decoder = RN... | 2,206 | 31.940299 | 86 | py |
espnet | espnet-master/test/espnet2/lm/test_seq_rnn_lm.py | import pytest
import torch
from espnet2.lm.seq_rnn_lm import SequentialRNNLM
from espnet.nets.batch_beam_search import BatchBeamSearch
from espnet.nets.beam_search import BeamSearch
@pytest.mark.parametrize("rnn_type", ["LSTM", "GRU", "RNN_TANH", "RNN_RELU"])
@pytest.mark.parametrize("tie_weights", [True, False])
de... | 3,110 | 30.424242 | 77 | py |
espnet | espnet-master/test/espnet2/lm/test_transformer_lm.py | import pytest
import torch
from espnet2.lm.transformer_lm import TransformerLM
from espnet.nets.batch_beam_search import BatchBeamSearch
from espnet.nets.beam_search import BeamSearch
@pytest.mark.parametrize("pos_enc", ["sinusoidal", None])
def test_TransformerLM_backward(pos_enc):
model = TransformerLM(10, pos... | 2,457 | 27.581395 | 65 | py |
espnet | espnet-master/test/espnet2/iterators/test_sequence_iter_factory.py | import pytest
import torch
from espnet2.iterators.sequence_iter_factory import SequenceIterFactory
class Dataset:
def __getitem__(self, item):
return item
def collate_func(x):
return torch.tensor(x)
@pytest.mark.parametrize("collate", [None, collate_func])
def test_SequenceIterFactory_larger_than... | 2,206 | 30.528571 | 86 | py |
espnet | espnet-master/test/espnet2/tts/test_prodiff.py | import pytest
import torch
from packaging.version import parse as V
from espnet2.tts.prodiff import ProDiff
from espnet2.tts.prodiff.loss import SSimLoss
is_torch_1_7_plus = V(torch.__version__) >= V("1.7.0")
@pytest.mark.parametrize("reduction_factor", [1])
@pytest.mark.parametrize(
"spk_embed_dim, spk_embed_i... | 4,549 | 32.211679 | 86 | py |
espnet | espnet-master/test/espnet2/tts/test_fastspeech.py | import pytest
import torch
from espnet2.tts.fastspeech import FastSpeech
@pytest.mark.parametrize("reduction_factor", [1, 3])
@pytest.mark.parametrize(
"spk_embed_dim, spk_embed_integration_type",
[(None, "add"), (2, "add"), (2, "concat")],
)
@pytest.mark.parametrize("encoder_type", ["transformer", "conforme... | 2,871 | 28.608247 | 80 | py |
espnet | espnet-master/test/espnet2/tts/test_tacotron2.py | import pytest
import torch
from espnet2.tts.tacotron2 import Tacotron2
@pytest.mark.parametrize("prenet_layers", [0, 1])
@pytest.mark.parametrize("postnet_layers", [0, 1])
@pytest.mark.parametrize("reduction_factor", [1, 3])
@pytest.mark.parametrize(
"spk_embed_dim, spk_embed_integration_type",
[(None, "add"... | 2,798 | 26.99 | 63 | py |
espnet | espnet-master/test/espnet2/tts/test_fastspeech2.py | import pytest
import torch
from espnet2.tts.fastspeech2 import FastSpeech2
@pytest.mark.parametrize("reduction_factor", [1, 3])
@pytest.mark.parametrize(
"spk_embed_dim, spk_embed_integration_type",
[(None, "add"), (2, "add"), (2, "concat")],
)
@pytest.mark.parametrize("encoder_type", ["transformer", "confor... | 3,903 | 32.084746 | 86 | py |
espnet | espnet-master/test/espnet2/tts/test_transformer.py | import pytest
import torch
from espnet2.tts.transformer import Transformer
@pytest.mark.parametrize("eprenet_conv_layers", [0, 1])
@pytest.mark.parametrize("dprenet_layers", [0, 1])
@pytest.mark.parametrize("postnet_layers", [0, 1])
@pytest.mark.parametrize("reduction_factor", [1, 3])
@pytest.mark.parametrize(
"... | 3,311 | 28.052632 | 64 | py |
espnet | espnet-master/test/espnet2/tts/feats_extract/test_dio.py | import pytest
import torch
from espnet2.tts.feats_extract.dio import Dio
@pytest.mark.parametrize("use_continuous_f0", [False, True])
@pytest.mark.parametrize("use_log_f0", [False, True])
@pytest.mark.parametrize(
"use_token_averaged_f0, reduction_factor", [(False, 1), (True, 1), (True, 3)]
)
def test_forward(
... | 1,928 | 27.367647 | 83 | py |
espnet | espnet-master/test/espnet2/tts/feats_extract/test_linear_spectrogram.py | import numpy as np
import torch
from espnet2.tts.feats_extract.linear_spectrogram import LinearSpectrogram
from espnet2.tts.feats_extract.log_mel_fbank import LogMelFbank
def test_forward():
layer = LinearSpectrogram(n_fft=4, hop_length=1)
x = torch.randn(2, 4, 9)
y, _ = layer(x, torch.LongTensor([4, 3])... | 1,430 | 27.058824 | 74 | py |
espnet | espnet-master/test/espnet2/tts/feats_extract/test_log_mel_fbank.py | import numpy as np
import torch
from espnet2.tts.feats_extract.log_mel_fbank import LogMelFbank
from espnet.transform.spectrogram import logmelspectrogram
def test_forward():
layer = LogMelFbank(n_fft=4, hop_length=1, n_mels=2)
x = torch.randn(2, 4, 9)
y, _ = layer(x, torch.LongTensor([4, 3]))
assert... | 1,422 | 28.040816 | 87 | py |
espnet | espnet-master/test/espnet2/tts/feats_extract/test_energy.py | import pytest
import torch
from espnet2.tts.feats_extract.energy import Energy
@pytest.mark.parametrize(
"use_token_averaged_energy, reduction_factor", [(False, None), (True, 1), (True, 3)]
)
def test_forward(use_token_averaged_energy, reduction_factor):
layer = Energy(
n_fft=128,
hop_length=... | 1,718 | 29.157895 | 88 | py |
espnet | espnet-master/test/espnet2/tts/feats_extract/test_log_spectrogram.py | import numpy as np
import torch
from espnet2.tts.feats_extract.log_spectrogram import LogSpectrogram
from espnet.transform.spectrogram import spectrogram
def test_forward():
layer = LogSpectrogram(n_fft=4, hop_length=1)
x = torch.randn(2, 4, 9)
y, _ = layer(x, torch.LongTensor([4, 3]))
assert y.shape... | 1,279 | 26.234043 | 68 | py |
espnet | espnet-master/test/espnet2/optimizers/test_sgd.py | import torch
from espnet2.optimizers.sgd import SGD
def test_SGD():
linear = torch.nn.Linear(1, 1)
opt = SGD(linear.parameters())
x = torch.randn(1, 1)
linear(x).sum().backward()
opt.step()
| 213 | 16.833333 | 38 | py |
espnet | espnet-master/test/espnet2/asr_transducer/test_activation.py | import pytest
import torch
from espnet2.asr_transducer.decoder.stateless_decoder import StatelessDecoder
from espnet2.asr_transducer.encoder.encoder import Encoder
from espnet2.asr_transducer.espnet_transducer_model import ESPnetASRTransducerModel
from espnet2.asr_transducer.joint_network import JointNetwork
def pre... | 2,492 | 27.329545 | 83 | py |
espnet | espnet-master/test/espnet2/asr_transducer/test_error_calculator_transducer.py | import pytest
import torch
from espnet2.asr_transducer.decoder.rnn_decoder import RNNDecoder
from espnet2.asr_transducer.decoder.stateless_decoder import StatelessDecoder
from espnet2.asr_transducer.error_calculator import ErrorCalculator
from espnet2.asr_transducer.joint_network import JointNetwork
@pytest.mark.par... | 1,462 | 33.023256 | 85 | py |
espnet | espnet-master/test/espnet2/asr_transducer/test_beam_search_transducer.py | import numpy as np
import pytest
import torch
from espnet2.asr_transducer.beam_search_transducer import (
BeamSearchTransducer,
Hypothesis,
)
from espnet2.asr_transducer.decoder.mega_decoder import MEGADecoder
from espnet2.asr_transducer.decoder.rnn_decoder import RNNDecoder
from espnet2.asr_transducer.decoder... | 5,704 | 34.880503 | 85 | py |
espnet | espnet-master/test/espnet2/asr_transducer/test_decoder.py | import pytest
import torch
from espnet2.asr_transducer.decoder.mega_decoder import MEGADecoder
from espnet2.asr_transducer.decoder.rnn_decoder import RNNDecoder
from espnet2.asr_transducer.decoder.rwkv_decoder import RWKVDecoder
from espnet2.asr_transducer.decoder.stateless_decoder import StatelessDecoder
def prepar... | 2,615 | 22.781818 | 82 | py |
espnet | espnet-master/test/espnet2/asr_transducer/test_encoder.py | import pytest
import torch
from espnet2.asr_transducer.encoder.encoder import Encoder
from espnet2.asr_transducer.utils import TooShortUttError
@pytest.mark.parametrize(
"input_conf, body_conf, main_conf",
[
(
{"vgg_like": True, "susbsampling_factor": 4, "conv_size": 8},
[
... | 12,111 | 27.838095 | 88 | py |
espnet | espnet-master/test/espnet2/asr_transducer/test_espnet_transducer_model.py | from pathlib import Path
import numpy as np
import pytest
import torch
from espnet2.asr.specaug.specaug import SpecAug
from espnet2.asr_transducer.decoder.mega_decoder import MEGADecoder
from espnet2.asr_transducer.decoder.rnn_decoder import RNNDecoder
from espnet2.asr_transducer.decoder.rwkv_decoder import RWKVDecod... | 13,503 | 27.670913 | 85 | py |
espnet | espnet-master/test/espnet2/schedulers/test_warmup_lr.py | import numpy as np
import torch
from espnet2.schedulers.noam_lr import NoamLR
from espnet2.schedulers.warmup_lr import WarmupLR
def test_WarumupLR():
linear = torch.nn.Linear(2, 2)
opt = torch.optim.SGD(linear.parameters(), 0.1)
sch = WarmupLR(opt)
lr = opt.param_groups[0]["lr"]
opt.step()
s... | 1,134 | 24.222222 | 77 | py |
espnet | espnet-master/test/espnet2/schedulers/test_warmup_reducelronplateau.py | import numpy as np
import torch
from espnet2.schedulers.warmup_reducelronplateau import WarmupReduceLROnPlateau
def test_WarmupReduceLROnPlateau():
linear = torch.nn.Linear(2, 2)
opt = torch.optim.SGD(linear.parameters(), 0.1)
sch = WarmupReduceLROnPlateau(opt, mode="min", factor=0.1, patience=1, cooldow... | 770 | 22.363636 | 86 | py |
espnet | espnet-master/test/espnet2/schedulers/test_warmup_step_lr.py | import numpy as np
import torch
from espnet2.schedulers.warmup_step_lr import WarmupStepLR
def test_WarmupStepLR():
linear = torch.nn.Linear(2, 2)
opt = torch.optim.SGD(linear.parameters(), 0.1)
sch = WarmupStepLR(opt)
lr = opt.param_groups[0]["lr"]
opt.step()
sch.step()
lr2 = opt.param_... | 486 | 20.173913 | 58 | py |
espnet | espnet-master/test/espnet2/schedulers/test_noam_lr.py | import torch
from espnet2.schedulers.noam_lr import NoamLR
def test_NoamLR():
linear = torch.nn.Linear(2, 2)
opt = torch.optim.SGD(linear.parameters(), 0.1)
sch = NoamLR(opt)
lr = opt.param_groups[0]["lr"]
opt.step()
sch.step()
lr2 = opt.param_groups[0]["lr"]
assert lr != lr2
| 313 | 18.625 | 51 | py |
espnet | espnet-master/test/espnet2/utils/test_sized_dict.py | import multiprocessing
import sys
import numpy as np
import pytest
import torch.multiprocessing
from espnet2.utils.sized_dict import SizedDict, get_size
def test_get_size():
d = {}
x = np.random.randn(10)
d["a"] = x
size1 = sys.getsizeof(d)
assert size1 + get_size(x) + get_size("a") == get_size(... | 1,525 | 20.194444 | 88 | py |
espnet | espnet-master/test/espnet2/tasks/test_abs_task.py | import configargparse
import pytest
import torch
from espnet2.tasks.abs_task import AbsTask
from espnet2.torch_utils.device_funcs import force_gatherable
from espnet2.train.abs_espnet_model import AbsESPnetModel
from espnet2.train.collate_fn import CommonCollateFn
class DummyModel(AbsESPnetModel):
def __init__(s... | 3,469 | 24.703704 | 75 | py |
espnet | espnet-master/espnet/scheduler/pytorch.py | """PyTorch optimizer schdulers."""
from typing import List
from torch.optim import Optimizer
from espnet.scheduler.scheduler import SchedulerInterface
class PyTorchScheduler:
"""PyTorch optimizer scheduler."""
def __init__(self, schedulers: List[SchedulerInterface], optimizer: Optimizer):
"""Initi... | 801 | 29.846154 | 83 | py |
espnet | espnet-master/espnet/vc/pytorch_backend/vc.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2020 Nagoya University (Wen-Chin Huang)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""E2E VC training / decoding functions."""
import copy
import json
import logging
import math
import os
import time
import chainer
import kaldiio
import num... | 26,226 | 34.346361 | 88 | py |
espnet | espnet-master/espnet/nets/st_interface.py | """ST Interface module."""
from espnet.nets.asr_interface import ASRInterface
from espnet.utils.dynamic_import import dynamic_import
class STInterface(ASRInterface):
"""ST Interface for ESPnet model implementation.
NOTE: This class is inherited from ASRInterface to enable joint translation
and recogniti... | 2,271 | 32.411765 | 87 | py |
espnet | espnet-master/espnet/nets/transducer_decoder_interface.py | """Transducer decoder interface module."""
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
@dataclass
class Hypothesis:
"""Default hypothesis definition for Transducer search algorithms."""
score: float
yseq: List[int]
dec_state: Union[
... | 4,282 | 26.632258 | 85 | py |
espnet | espnet-master/espnet/nets/batch_beam_search_online_sim.py | """Parallel beam search module for online simulation."""
import logging
from pathlib import Path
from typing import List
import torch
import yaml
from espnet.nets.batch_beam_search import BatchBeamSearch
from espnet.nets.beam_search import Hypothesis
from espnet.nets.e2e_asr_common import end_detect
class BatchBea... | 10,567 | 36.742857 | 87 | py |
espnet | espnet-master/espnet/nets/e2e_asr_common.py | #!/usr/bin/env python3
# encoding: utf-8
# Copyright 2017 Johns Hopkins University (Shinji Watanabe)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Common functions for ASR."""
import json
import logging
import sys
from itertools import groupby
import numpy as np
def end_detect(ended_hyps, i, M=3,... | 8,773 | 33.543307 | 85 | py |
espnet | espnet-master/espnet/nets/ctc_prefix_score.py | #!/usr/bin/env python3
# Copyright 2018 Mitsubishi Electric Research Labs (Takaaki Hori)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import numpy as np
import torch
class CTCPrefixScoreTH(object):
"""Batch processing of CTCPrefixScore
which is based on Algorithm 2 in WATANABE et al.
"HY... | 13,899 | 37.826816 | 87 | py |
espnet | espnet-master/espnet/nets/beam_search_transducer.py | """Search algorithms for Transducer models."""
import logging
from typing import List, Union
import numpy as np
import torch
from espnet.nets.pytorch_backend.transducer.custom_decoder import CustomDecoder
from espnet.nets.pytorch_backend.transducer.joint_network import JointNetwork
from espnet.nets.pytorch_backend.t... | 30,731 | 33.26087 | 88 | py |
espnet | espnet-master/espnet/nets/batch_beam_search.py | """Parallel beam search module."""
import logging
from typing import Any, Dict, List, NamedTuple, Tuple
import torch
from packaging.version import parse as V
from torch.nn.utils.rnn import pad_sequence
from espnet.nets.beam_search import BeamSearch, Hypothesis
is_torch_1_9_plus = V(torch.__version__) >= V("1.9.0")
... | 13,532 | 37.228814 | 88 | py |
espnet | espnet-master/espnet/nets/beam_search.py | """Beam search module."""
import logging
from itertools import chain
from typing import Any, Dict, List, NamedTuple, Tuple, Union
import torch
from espnet.nets.e2e_asr_common import end_detect
from espnet.nets.scorer_interface import PartialScorerInterface, ScorerInterface
class Hypothesis(NamedTuple):
"""Hypo... | 20,199 | 36.269373 | 88 | py |
espnet | espnet-master/espnet/nets/e2e_mt_common.py | #!/usr/bin/env python3
# encoding: utf-8
# Copyright 2019 Kyoto University (Hirofumi Inaguma)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Common functions for ST and MT."""
import nltk
import numpy as np
class ErrorCalculator(object):
"""Calculate BLEU for ST and MT models during training.
... | 2,731 | 35.426667 | 85 | py |
espnet | espnet-master/espnet/nets/lm_interface.py | """Language model interface."""
import argparse
from espnet.nets.scorer_interface import ScorerInterface
from espnet.utils.dynamic_import import dynamic_import
from espnet.utils.fill_missing_args import fill_missing_args
class LMInterface(ScorerInterface):
"""LM Interface for ESPnet model implementation."""
... | 2,590 | 28.781609 | 82 | py |
espnet | espnet-master/espnet/nets/scorer_interface.py | """Scorer interface module."""
import warnings
from typing import Any, List, Tuple
import torch
class ScorerInterface:
"""Scorer interface for beam search.
The scorer performs scoring of the all tokens in vocabulary.
Examples:
* Search heuristics
* :class:`espnet.nets.scorers.lengt... | 5,902 | 30.566845 | 87 | py |
espnet | espnet-master/espnet/nets/asr_interface.py | """ASR Interface module."""
import argparse
from espnet.bin.asr_train import get_parser
from espnet.utils.dynamic_import import dynamic_import
from espnet.utils.fill_missing_args import fill_missing_args
class ASRInterface:
"""ASR Interface for ESPnet model implementation."""
@staticmethod
def add_argum... | 5,957 | 33.439306 | 88 | py |
espnet | espnet-master/espnet/nets/batch_beam_search_online.py | """Parallel beam search module for online simulation."""
import logging
from typing import Any # noqa: H301
from typing import Dict # noqa: H301
from typing import List # noqa: H301
from typing import Tuple # noqa: H301
import torch
from espnet.nets.batch_beam_search import BatchBeamSearch # noqa: H301
from esp... | 11,320 | 35.519355 | 88 | py |
espnet | espnet-master/espnet/nets/mt_interface.py | """MT Interface module."""
import argparse
from espnet.bin.asr_train import get_parser
from espnet.utils.fill_missing_args import fill_missing_args
class MTInterface:
"""MT Interface for ESPnet model implementation."""
@staticmethod
def add_arguments(parser):
"""Add arguments to parser."""
... | 3,428 | 35.094737 | 87 | py |
espnet | espnet-master/espnet/nets/tts_interface.py | # -*- coding: utf-8 -*-
# Copyright 2018 Nagoya University (Tomoki Hayashi)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""TTS Interface realted modules."""
from espnet.asr.asr_utils import torch_load
try:
import chainer
except ImportError:
Reporter = None
else:
class Reporter(chainer.C... | 2,582 | 27.076087 | 87 | py |
espnet | espnet-master/espnet/nets/beam_search_timesync.py | """
Time Synchronous One-Pass Beam Search.
Implements joint CTC/attention decoding where
hypotheses are expanded along the time (input) axis,
as described in https://arxiv.org/abs/2210.05200.
Supports CPU and GPU inference.
References: https://arxiv.org/abs/1408.2873 for CTC beam search
Author: Brian Yan
"""
import l... | 8,986 | 36.445833 | 88 | py |
espnet | espnet-master/espnet/nets/chainer_backend/e2e_asr.py | # Copyright 2017 Johns Hopkins University (Shinji Watanabe)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""RNN sequence-to-sequence speech recognition model (chainer)."""
import logging
import math
import chainer
import numpy as np
from chainer import reporter
from espnet.nets.chainer_backend.asr_in... | 7,619 | 32.568282 | 87 | py |
espnet | espnet-master/espnet/nets/chainer_backend/e2e_asr_transformer.py | # encoding: utf-8
"""Transformer-based model for End-to-end ASR."""
import logging
import math
from argparse import Namespace
from distutils.util import strtobool
import chainer
import chainer.functions as F
import numpy as np
from chainer import reporter
from espnet.nets.chainer_backend.asr_interface import Chainer... | 23,226 | 36.282504 | 88 | py |
espnet | espnet-master/espnet/nets/chainer_backend/rnn/training.py | # Copyright 2017 Johns Hopkins University (Shinji Watanabe)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import collections
import logging
import math
import numpy as np
# chainer related
from chainer import Variable, cuda, training
from chainer.training.updaters.multiprocess_parallel_updater import ... | 9,245 | 34.561538 | 86 | py |
espnet | espnet-master/espnet/nets/chainer_backend/transformer/training.py | # Copyright 2017 Johns Hopkins University (Shinji Watanabe)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Class Declaration of Transformer's Training Subprocess."""
import collections
import logging
import math
import numpy as np
from chainer import cuda
from chainer import functions as F
from chainer... | 11,798 | 35.642857 | 88 | py |
espnet | espnet-master/espnet/nets/scorers/length_bonus.py | """Length bonus module."""
from typing import Any, List, Tuple
import torch
from espnet.nets.scorer_interface import BatchScorerInterface
class LengthBonus(BatchScorerInterface):
"""Length bonus in beam search."""
def __init__(self, n_vocab: int):
"""Initialize class.
Args:
n_v... | 1,740 | 28.016667 | 87 | py |
espnet | espnet-master/espnet/nets/scorers/ctc.py | """ScorerInterface implementation for CTC."""
import numpy as np
import torch
from espnet.nets.ctc_prefix_score import CTCPrefixScore, CTCPrefixScoreTH
from espnet.nets.scorer_interface import BatchPartialScorerInterface
class CTCPrefixScorer(BatchPartialScorerInterface):
"""Decoder interface wrapper for CTCPre... | 4,953 | 30.35443 | 85 | py |
espnet | espnet-master/espnet/nets/scorers/ngram.py | """Ngram lm implement."""
from abc import ABC
import kenlm
import torch
from espnet.nets.scorer_interface import BatchScorerInterface, PartialScorerInterface
class Ngrambase(ABC):
"""Ngram base implemented through ScorerInterface."""
def __init__(self, ngram_model, token_list):
"""Initialize Ngram... | 3,080 | 29.205882 | 85 | py |
espnet | espnet-master/espnet/nets/scorers/uasr.py | """ScorerInterface implementation for UASR."""
import numpy as np
import torch
from espnet.nets.ctc_prefix_score import CTCPrefixScore, CTCPrefixScoreTH
from espnet.nets.scorers.ctc import CTCPrefixScorer
class UASRPrefixScorer(CTCPrefixScorer):
"""Decoder interface wrapper for CTCPrefixScore."""
def __ini... | 1,469 | 28.4 | 87 | py |
espnet | espnet-master/espnet/nets/pytorch_backend/e2e_asr.py | # Copyright 2017 Johns Hopkins University (Shinji Watanabe)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""RNN sequence-to-sequence speech recognition model (pytorch)."""
import argparse
import logging
import math
import os
from itertools import groupby
import chainer
import numpy as np
import torch
... | 19,358 | 34.456044 | 87 | py |
espnet | espnet-master/espnet/nets/pytorch_backend/e2e_tts_fastspeech.py | # Copyright 2019 Tomoki Hayashi
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""FastSpeech related modules."""
import logging
import torch
import torch.nn.functional as F
from espnet.asr.asr_utils import get_model_conf, torch_load
from espnet.nets.pytorch_backend.fastspeech.duration_calculator import... | 34,348 | 37.165556 | 88 | py |
espnet | espnet-master/espnet/nets/pytorch_backend/e2e_mt.py | # Copyright 2019 Kyoto University (Hirofumi Inaguma)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""RNN sequence-to-sequence text translation model (pytorch)."""
import argparse
import logging
import math
import os
import chainer
import nltk
import numpy as np
import torch
from chainer import reporte... | 13,630 | 35.642473 | 88 | py |
espnet | espnet-master/espnet/nets/pytorch_backend/e2e_vc_transformer.py | # Copyright 2020 Nagoya University (Wen-Chin Huang)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Voice Transformer Network (Transformer-VC) related modules."""
import logging
import torch
import torch.nn.functional as F
from espnet.nets.pytorch_backend.e2e_asr_transformer import subsequent_mask
fr... | 46,109 | 38.647463 | 88 | py |
espnet | espnet-master/espnet/nets/pytorch_backend/e2e_vc_tacotron2.py | # Copyright 2020 Nagoya University (Wen-Chin Huang)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Tacotron2-VC related modules."""
import logging
from distutils.util import strtobool
import numpy as np
import torch
import torch.nn.functional as F
from espnet.nets.pytorch_backend.e2e_tts_tacotron2 i... | 30,409 | 37.788265 | 88 | py |
espnet | espnet-master/espnet/nets/pytorch_backend/e2e_asr_conformer.py | # Copyright 2020 Johns Hopkins University (Shinji Watanabe)
# Northwestern Polytechnical University (Pengcheng Guo)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""
Conformer speech recognition model (pytorch).
It is a fusion of `e2e_asr_transformer.py`
Refer to: https://arxiv.org/abs/20... | 2,955 | 35.493827 | 82 | py |
espnet | espnet-master/espnet/nets/pytorch_backend/e2e_tts_transformer.py | # Copyright 2019 Tomoki Hayashi
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""TTS-Transformer related modules."""
import logging
import torch
import torch.nn.functional as F
from espnet.nets.pytorch_backend.e2e_tts_tacotron2 import GuidedAttentionLoss
from espnet.nets.pytorch_backend.e2e_tts_tacotr... | 45,641 | 38.27883 | 88 | py |
espnet | espnet-master/espnet/nets/pytorch_backend/gtn_ctc.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""GTN CTC implementation."""
import gtn
import torch
class GTNCTCLossFunction(torch.autograd.Function):
"""GTN CTC module."""
# Copied from FB's GTN example implementation:
# https://github.com/facebookresearch/gtn_applications/blob/master/utils.py#L251
... | 3,974 | 32.403361 | 88 | py |
espnet | espnet-master/espnet/nets/pytorch_backend/e2e_tts_tacotron2.py | # Copyright 2018 Nagoya University (Tomoki Hayashi)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Tacotron 2 related modules."""
import logging
import numpy as np
import torch
import torch.nn.functional as F
from espnet.nets.pytorch_backend.nets_utils import make_non_pad_mask
from espnet.nets.pytor... | 34,041 | 36.993304 | 88 | py |
espnet | espnet-master/espnet/nets/pytorch_backend/ctc.py | import logging
import numpy as np
import torch
import torch.nn.functional as F
from packaging.version import parse as V
from espnet.nets.pytorch_backend.nets_utils import to_device
class CTC(torch.nn.Module):
"""CTC module
:param int odim: dimension of outputs
:param int eprojs: number of encoder proje... | 9,877 | 35.72119 | 87 | py |
espnet | espnet-master/espnet/nets/pytorch_backend/e2e_st.py | # Copyright 2019 Kyoto University (Hirofumi Inaguma)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""RNN sequence-to-sequence speech translation model (pytorch)."""
import argparse
import copy
import logging
import math
import os
from itertools import groupby
import chainer
import nltk
import numpy as... | 24,036 | 35.037481 | 88 | py |
espnet | espnet-master/espnet/nets/pytorch_backend/wavenet.py | # -*- coding: utf-8 -*-
# Copyright 2019 Tomoki Hayashi (Nagoya University)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""This code is based on https://github.com/kan-bayashi/PytorchWaveNetVocoder."""
import logging
import sys
import time
import numpy as np
import torch
import torch.nn.functional a... | 13,938 | 30.113839 | 88 | py |
espnet | espnet-master/espnet/nets/pytorch_backend/e2e_asr_mix_transformer.py | #!/usr/bin/env python3
# encoding: utf-8
# Copyright 2020 Johns Hopkins University (Xuankai Chang)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""
Transformer speech recognition model for single-channel multi-speaker mixture speech.
It is a fusion of `e2e_asr_mix.py` and `e2e_asr_transformer.py`. Ref... | 18,143 | 38.529412 | 88 | py |
espnet | espnet-master/espnet/nets/pytorch_backend/e2e_st_conformer.py | # Copyright 2020 Kyoto University (Hirofumi Inaguma)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""
Conformer speech translation model (pytorch).
It is a fusion of `e2e_st_transformer.py`
Refer to: https://arxiv.org/abs/2005.08100
"""
from espnet.nets.pytorch_backend.conformer.argument import ( # ... | 2,594 | 33.6 | 82 | py |
espnet | espnet-master/espnet/nets/pytorch_backend/initialization.py | #!/usr/bin/env python
# Copyright 2019 Kyoto University (Hirofumi Inaguma)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Initialization functions for RNN sequence-to-sequence models."""
import math
def lecun_normal_init_parameters(module):
"""Initialize parameters in the LeCun's manner."""
... | 1,561 | 26.892857 | 67 | py |
espnet | espnet-master/espnet/nets/pytorch_backend/e2e_mt_transformer.py | # Copyright 2019 Kyoto University (Hirofumi Inaguma)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Transformer text translation model (pytorch)."""
import logging
import math
from argparse import Namespace
import numpy as np
import torch
from espnet.nets.e2e_asr_common import end_detect
from espnet... | 16,007 | 37.854369 | 88 | py |
espnet | espnet-master/espnet/nets/pytorch_backend/nets_utils.py | # -*- coding: utf-8 -*-
"""Network related utility tools."""
import logging
from typing import Dict
import numpy as np
import torch
def to_device(m, x):
"""Send tensor into the device of the module.
Args:
m (torch.nn.Module): Torch module.
x (Tensor): Torch tensor.
Returns:
Te... | 16,551 | 31.84127 | 87 | py |
espnet | espnet-master/espnet/nets/pytorch_backend/e2e_asr_maskctc.py | # Copyright 2020 Johns Hopkins University (Shinji Watanabe)
# Waseda University (Yosuke Higuchi)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""
Mask CTC based non-autoregressive speech recognition model (pytorch).
See https://arxiv.org/abs/2005.08700 for the detail.
"""
import loggin... | 10,739 | 37.633094 | 88 | py |
espnet | espnet-master/espnet/nets/pytorch_backend/e2e_asr_transducer.py | """Transducer speech recognition model (pytorch)."""
import logging
import math
from argparse import ArgumentParser, Namespace
from dataclasses import asdict
from typing import List
import chainer
import numpy
import torch
from espnet.nets.asr_interface import ASRInterface
from espnet.nets.beam_search_transducer imp... | 18,123 | 32.316176 | 88 | py |
espnet | espnet-master/espnet/nets/pytorch_backend/e2e_asr_mulenc.py | # Copyright 2017 Johns Hopkins University (Shinji Watanabe)
# Copyright 2017 Johns Hopkins University (Ruizhi Li)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Define e2e module for multi-encoder network. https://arxiv.org/pdf/1811.04903.pdf."""
import argparse
import logging
import math
import os
fr... | 31,487 | 34.379775 | 88 | py |
espnet | espnet-master/espnet/nets/pytorch_backend/e2e_asr_transformer.py | # Copyright 2019 Shigeki Karita
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Transformer speech recognition model (pytorch)."""
import logging
import math
from argparse import Namespace
import numpy
import torch
from espnet.nets.asr_interface import ASRInterface
from espnet.nets.ctc_prefix_score i... | 22,522 | 38.863717 | 88 | py |
espnet | espnet-master/espnet/nets/pytorch_backend/e2e_asr_mix.py | #!/usr/bin/env python3
"""
This script is used to construct End-to-End models of multi-speaker ASR.
Copyright 2017 Johns Hopkins University (Shinji Watanabe)
Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""
import argparse
import logging
import math
import os
import sys
from itertools import groupby
im... | 30,819 | 36.13253 | 88 | py |
espnet | espnet-master/espnet/nets/pytorch_backend/e2e_st_transformer.py | # Copyright 2019 Kyoto University (Hirofumi Inaguma)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Transformer speech recognition model (pytorch)."""
import logging
import math
from argparse import Namespace
import numpy
import torch
from espnet.nets.e2e_asr_common import ErrorCalculator as ASRErro... | 22,657 | 37.534014 | 88 | py |
espnet | espnet-master/espnet/nets/pytorch_backend/streaming/window.py | import torch
# TODO(pzelasko): Currently allows half-streaming only;
# needs streaming attention decoder implementation
class WindowStreamingE2E(object):
"""WindowStreamingE2E constructor.
:param E2E e2e: E2E ASR object
:param recog_args: arguments for "recognize" method of E2E
"""
def __init__... | 2,768 | 32.768293 | 84 | py |
espnet | espnet-master/espnet/nets/pytorch_backend/streaming/segment.py | import numpy as np
import torch
class SegmentStreamingE2E(object):
"""SegmentStreamingE2E constructor.
:param E2E e2e: E2E ASR object
:param recog_args: arguments for "recognize" method of E2E
"""
def __init__(self, e2e, recog_args, rnnlm=None):
self._e2e = e2e
self._recog_args =... | 4,774 | 35.730769 | 88 | py |
espnet | espnet-master/espnet/nets/pytorch_backend/transducer/joint_network.py | """Transducer joint network implementation."""
import torch
from espnet.nets.pytorch_backend.nets_utils import get_activation
class JointNetwork(torch.nn.Module):
"""Transducer joint network module.
Args:
joint_output_size: Joint network output dimension
encoder_output_size: Encoder output ... | 2,306 | 30.175676 | 87 | py |
espnet | espnet-master/espnet/nets/pytorch_backend/transducer/rnn_encoder.py | """RNN encoder implementation for Transducer model.
These classes are based on the ones in espnet.nets.pytorch_backend.rnn.encoders,
and modified to output intermediate representation based given list of layers as input.
To do so, RNN class rely on a stack of 1-layer LSTM instead of a multi-layer LSTM.
The additional ... | 18,464 | 31.225131 | 88 | py |
espnet | espnet-master/espnet/nets/pytorch_backend/transducer/rnn_decoder.py | """RNN decoder definition for Transducer model."""
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from espnet.nets.transducer_decoder_interface import (
ExtendedHypothesis,
Hypothesis,
TransducerDecoderInterface,
)
class RNNDecoder(TransducerDecoderInterface, torch.nn.Module):
... | 9,028 | 29.503378 | 88 | py |
espnet | espnet-master/espnet/nets/pytorch_backend/transducer/vgg2l.py | """VGG2L module definition for custom encoder."""
from typing import Tuple, Union
import torch
class VGG2L(torch.nn.Module):
"""VGG2L module for custom encoder.
Args:
idim: Input dimension.
odim: Output dimension.
pos_enc: Positional encoding class.
"""
def __init__(self, ... | 2,782 | 28.924731 | 81 | py |
espnet | espnet-master/espnet/nets/pytorch_backend/transducer/utils.py | """Utility functions for Transducer models."""
import os
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from espnet.nets.pytorch_backend.nets_utils import pad_list
from espnet.nets.transducer_decoder_interface import ExtendedHypothesis, Hypothesis
def get_decoder_input(
lab... | 10,508 | 24.884236 | 87 | py |
espnet | espnet-master/espnet/nets/pytorch_backend/transducer/custom_encoder.py | """Cutom encoder definition for transducer models."""
from typing import List, Tuple, Union
import torch
from espnet.nets.pytorch_backend.transducer.blocks import build_blocks
from espnet.nets.pytorch_backend.transducer.vgg2l import VGG2L
from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm
from ... | 4,661 | 34.861538 | 88 | py |
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