change LooseVersion to parse

ci/install.sh

@@ -48,7 +48,7 @@ python3 -m pip freeze
 # Check pytorch version
 python3 <<EOF
 import torch
-from distutils.version import LooseVersion as L
+from packaging.version import parse as L
 version = '$TH_VERSION'.split(".")
 next_version = f"{version[0]}.{version[1]}.{int(version[2]) + 1}"
 

ci/test_integration_espnet2.sh

@@ -40,13 +40,13 @@ echo "==== use_streaming, feats_type=raw, token_types=bpe, model_conf.extract_fe
     --feats_normalize "utterance_mvn" --lm-args "--max_epoch=1" --python "${python}" \
     --asr-args "--model_conf extract_feats_in_collect_stats=false --max_epoch=1 --encoder=contextual_block_transformer --decoder=transformer
                 --encoder_conf block_size=40 --encoder_conf hop_size=16 --encoder_conf look_ahead=16"
-    
+
 if python3 -c "import k2" &> /dev/null; then
     echo "==== use_k2, num_paths > nll_batch_size, feats_type=raw, token_types=bpe, model_conf.extract_feats_in_collect_stats=False, normalize=utt_mvn ==="
     ./run.sh --num_paths 500 --nll_batch_size 20 --use_k2 true --ngpu 0 --stage 12 --stop-stage 13 --skip-upload false --feats-type "raw" --token-type "bpe" \
         --feats_normalize "utterance_mvn" --lm-args "--max_epoch=1" --python "${python}" \
         --asr-args "--model_conf extract_feats_in_collect_stats=false --max_epoch=1"
-    
+
     echo "==== use_k2, num_paths == nll_batch_size, feats_type=raw, token_types=bpe, model_conf.extract_feats_in_collect_stats=False, normalize=utt_mvn ==="
     ./run.sh --num_paths 20 --nll_batch_size 20 --use_k2 true --ngpu 0 --stage 12 --stop-stage 13 --skip-upload false --feats-type "raw" --token-type "bpe" \
        --feats_normalize "utterance_mvn" --lm-args "--max_epoch=1" --python "${python}" \
@@ -68,15 +68,15 @@ rm -rf exp dump data
 # NOTE(kan-bayashi): pytorch 1.4 - 1.6 works but 1.6 has a problem with CPU,
 #   so we test this recipe using only pytorch > 1.6 here.
 #   See also: https://github.com/pytorch/pytorch/issues/42446
-if python3 -c 'import torch as t; from distutils.version import LooseVersion as L; assert L(t.__version__) > L("1.6")' &> /dev/null; then
+if python3 -c 'import torch as t; from packaging.version import parse as L; assert L(t.__version__) > L("1.6")' &> /dev/null; then
     ./run.sh --fs 22050 --tts_task gan_tts --feats_extract linear_spectrogram --feats_normalize none --inference_model latest.pth \
         --ngpu 0 --stop-stage 8 --skip-upload false --train-args "--num_iters_per_epoch 1 --max_epoch 1" --python "${python}"
     rm -rf exp dump data
 fi
 cd "${cwd}"
 
 # [ESPnet2] test enh recipe
-if python -c 'import torch as t; from distutils.version import LooseVersion as L; assert L(t.__version__) >= L("1.2.0")' &> /dev/null;  then
+if python -c 'import torch as t; from packaging.version import parse as L; assert L(t.__version__) >= L("1.2.0")' &> /dev/null;  then
     cd ./egs2/mini_an4/enh1
     echo "==== [ESPnet2] ENH ==="
     ./run.sh --stage 1 --stop-stage 1 --python "${python}"
@@ -101,7 +101,7 @@ if python3 -c "import fairseq" &> /dev/null; then
 fi
 
 # [ESPnet2] test enh_asr1 recipe
-if python -c 'import torch as t; from distutils.version import LooseVersion as L; assert L(t.__version__) >= L("1.2.0")' &> /dev/null;  then
+if python -c 'import torch as t; from packaging.version import parse as L; assert L(t.__version__) >= L("1.2.0")' &> /dev/null;  then
     cd ./egs2/mini_an4/enh_asr1
     echo "==== [ESPnet2] ENH_ASR ==="
     ./run.sh --ngpu 0 --stage 0 --stop-stage 15 --skip-upload_hf false --feats-type "raw" --spk-num 1 --enh_asr_args "--max_epoch=1 --enh_separator_conf num_spk=1" --python "${python}"
@@ -122,7 +122,7 @@ done
 for t in ${token_types}; do
     ./run.sh --stage 5 --stop-stage 5 --tgt_token_type "${t}" --src_token_type "${t}" --python "${python}"
 done
-for t in ${feats_types}; do 
+for t in ${feats_types}; do
     for t2 in ${token_types}; do
         echo "==== feats_type=${t}, token_types=${t2} ==="
         ./run.sh --ngpu 0 --stage 6 --stop-stage 13 --skip-upload false --feats-type "${t}" --tgt_token_type "${t2}" --src_token_type "${t2}" \
@@ -147,7 +147,7 @@ cd "${cwd}"
 # [ESPnet2] Validate configuration files
 echo "<blank>" > dummy_token_list
 echo "==== [ESPnet2] Validation configuration files ==="
-if python3 -c 'import torch as t; from distutils.version import LooseVersion as L; assert L(t.__version__) >= L("1.8.0")' &> /dev/null;  then
+if python3 -c 'import torch as t; from packaging.version import parse as L; assert L(t.__version__) >= L("1.8.0")' &> /dev/null;  then
     for f in egs2/*/asr1/conf/train_asr*.yaml; do
         if [ "$f" == "egs2/fsc/asr1/conf/train_asr.yaml" ]; then
             if ! python3 -c "import s3prl" > /dev/null; then

egs2/aishell4/enh1/local/generate_fe_trainingdata.py.patch

@@ -2,9 +2,9 @@
 +++ generate_fe_trainingdata.new.py
 @@ -1,8 +1,8 @@
  #!/usr/bin/env python
- 
+
 -import io
-+from distutils.version import LooseVersion
++from packaging.version import parse as V
  import os
 -import subprocess
 +import sys
@@ -14,27 +14,27 @@
 @@ -12,6 +12,10 @@
  import librosa
  import argparse
- 
+
 +
-+is_py_3_3_plus = LooseVersion(sys.version) > LooseVersion("3.3")
++is_py_3_3_plus = V(sys.version) > V("3.3")
 +
 +
  def get_line_context(file_path, line_number):
      return linecache.getline(file_path, line_number).strip()
- 
+
 @@ -119,7 +123,7 @@
          return data / max_val
- 
+
  def add_noise(clean, noise, rir, snr):
 -    random.seed(time.clock())
 +    random.seed(time.perf_counter() if is_py_3_3_plus else time.clock())
      if len(noise.shape) == 1 and len(clean.shape) > 1:
          noise = add_reverb(noise, rir[:, 16:24])
          noise = noise[:-7999]
 @@ -189,7 +193,7 @@
- 
+
      for i in range(args.wavnum):
- 
+
 -        random.seed(time.clock())
 +        random.seed(time.perf_counter() if is_py_3_3_plus else time.clock())
          wav1idx = random.randint(0, len(open(wavlist1,'r').readlines())-1)

egs2/fsc/asr1/run.sh

@@ -9,7 +9,7 @@ train_set="train"
 valid_set="valid"
 test_sets="test valid"
 
-if python3 -c 'import torch as t; from distutils.version import LooseVersion as L; assert L(t.__version__) >= L("1.7.0")' &> /dev/null;  then
+if python3 -c 'import torch as t; from packaging.version import parse as L; assert L(t.__version__) >= L("1.7.0")' &> /dev/null;  then
 	asr_config=conf/train_asr.yaml
 else
 	asr_config=conf/tuning/train_asr_transformer_adam_specaug.yaml #s3prl is installed when pytorch > 1.7. Hence using default frontend

egs2/fsc_challenge/asr1/run.sh

@@ -9,7 +9,7 @@ train_set="train"
 valid_set="valid"
 test_sets="test valid"
 
-if python3 -c 'import torch as t; from distutils.version import LooseVersion as L; assert L(t.__version__) >= L("1.7.0")' &> /dev/null;  then
+if python3 -c 'import torch as t; from packaging.version import parse as L; assert L(t.__version__) >= L("1.7.0")' &> /dev/null;  then
 	asr_config=conf/train_asr.yaml
 else
 	asr_config=conf/tuning/train_asr_transformer_adam_specaug.yaml #s3prl is installed when pytorch > 1.7. Hence using default frontend

egs2/fsc_unseen/asr1/run.sh

@@ -9,7 +9,7 @@ train_set="train"
 valid_set="valid"
 test_sets="test valid"
 
-if python3 -c 'import torch as t; from distutils.version import LooseVersion as L; assert L(t.__version__) >= L("1.7.0")' &> /dev/null;  then
+if python3 -c 'import torch as t; from packaging.version import parse as L; assert L(t.__version__) >= L("1.7.0")' &> /dev/null;  then
 	asr_config=conf/train_asr.yaml
 else
 	asr_config=conf/tuning/train_asr_transformer_adam_specaug.yaml #s3prl is installed when pytorch > 1.7. Hence using default frontend

espnet/asr/pytorch_backend/asr.py

@@ -4,7 +4,7 @@
 """Training/decoding definition for the speech recognition task."""
 
 import copy
-from distutils.version import LooseVersion
+from packaging.version import parse as V
 import itertools
 import json
 import logging
@@ -989,7 +989,7 @@ def recog(args):
         # It seems quantized LSTM only supports non-packed sequence before torch 1.4.0.
         # Reference issue: https://github.com/pytorch/pytorch/issues/27963
         if (
-            torch.__version__ < LooseVersion("1.4.0")
+            torch.__version__ < V("1.4.0")
             and "lstm" in train_args.etype
             and torch.nn.LSTM in q_config
         ):
@@ -999,7 +999,7 @@ def recog(args):
 
         # Dunno why but weight_observer from dynamic quantized module must have
         # dtype=torch.qint8 with torch < 1.5 although dtype=torch.float16 is supported.
-        if args.quantize_dtype == "float16" and torch.__version__ < LooseVersion(
+        if args.quantize_dtype == "float16" and torch.__version__ < V(
             "1.5.0"
         ):
             raise ValueError(

espnet/asr/pytorch_backend/recog.py

@@ -1,6 +1,6 @@
 """V2 backend for `asr_recog.py` using py:class:`espnet.nets.beam_search.BeamSearch`."""
 
-from distutils.version import LooseVersion
+from packaging.version import parse as V
 import json
 import logging
 
@@ -54,15 +54,15 @@ def recog_v2(args):
 
         # See https://github.com/espnet/espnet/pull/3616 for more information.
         if (
-            torch.__version__ < LooseVersion("1.4.0")
+            torch.__version__ < V("1.4.0")
             and "lstm" in train_args.etype
             and torch.nn.LSTM in q_config
         ):
             raise ValueError(
                 "Quantized LSTM in ESPnet is only supported with torch 1.4+."
             )
 
-        if args.quantize_dtype == "float16" and torch.__version__ < LooseVersion(
+        if args.quantize_dtype == "float16" and torch.__version__ < V(
             "1.5.0"
         ):
             raise ValueError(

espnet/nets/pytorch_backend/ctc.py

@@ -1,4 +1,4 @@
-from distutils.version import LooseVersion
+from packaging.version import parse as V
 import logging
 
 import numpy as np
@@ -30,7 +30,7 @@ def __init__(self, odim, eprojs, dropout_rate, ctc_type="warpctc", reduce=True):
         # In case of Pytorch >= 1.7.0, CTC will be always builtin
         self.ctc_type = (
             ctc_type
-            if LooseVersion(torch.__version__) < LooseVersion("1.7.0")
+            if V(torch.__version__) < V("1.7.0")
             else "builtin"
         )
 

espnet/nets/pytorch_backend/e2e_tts_transformer.py

@@ -714,7 +714,7 @@ def forward(self, xs, ilens, ys, labels, olens, spembs=None, *args, **kwargs):
             labels = labels[:, :max_olen]
 
         # forward encoder
-        x_masks = self._source_mask(ilens)
+        x_masks = self._source_mask(ilens).to(xs.device)
         hs, h_masks = self.encoder(xs, x_masks)
 
         # integrate speaker embedding
@@ -732,7 +732,7 @@ def forward(self, xs, ilens, ys, labels, olens, spembs=None, *args, **kwargs):
         ys_in = self._add_first_frame_and_remove_last_frame(ys_in)
 
         # forward decoder
-        y_masks = self._target_mask(olens_in)
+        y_masks = self._target_mask(olens_in).to(xs.device)
         zs, _ = self.decoder(ys_in, y_masks, hs, h_masks)
         # (B, Lmax//r, odim * r) -> (B, Lmax//r * r, odim)
         before_outs = self.feat_out(zs).view(zs.size(0), -1, self.odim)
@@ -975,7 +975,7 @@ def calculate_all_attentions(
         self.eval()
         with torch.no_grad():
             # forward encoder
-            x_masks = self._source_mask(ilens)
+            x_masks = self._source_mask(ilens).to(xs.device)
             hs, h_masks = self.encoder(xs, x_masks)
 
             # integrate speaker embedding
@@ -994,7 +994,7 @@ def calculate_all_attentions(
             ys_in = self._add_first_frame_and_remove_last_frame(ys_in)
 
             # forward decoder
-            y_masks = self._target_mask(olens_in)
+            y_masks = self._target_mask(olens_in).to(xs.device)
             zs, _ = self.decoder(ys_in, y_masks, hs, h_masks)
 
             # calculate final outputs
@@ -1097,7 +1097,7 @@ def _source_mask(self, ilens):
                     [[1, 1, 1, 0, 0]]], dtype=torch.uint8)
 
         """
-        x_masks = make_non_pad_mask(ilens).to(next(self.parameters()).device)
+        x_masks = make_non_pad_mask(ilens)
         return x_masks.unsqueeze(-2)
 
     def _target_mask(self, olens):
@@ -1126,7 +1126,7 @@ def _target_mask(self, olens):
                      [1, 1, 1, 0, 0]]], dtype=torch.uint8)
 
         """
-        y_masks = make_non_pad_mask(olens).to(next(self.parameters()).device)
+        y_masks = make_non_pad_mask(olens)
         s_masks = subsequent_mask(y_masks.size(-1), device=y_masks.device).unsqueeze(0)
         return y_masks.unsqueeze(-2) & s_masks
 

espnet2/asr/espnet_model.py

@@ -1,5 +1,5 @@
 from contextlib import contextmanager
-from distutils.version import LooseVersion
+from packaging.version import parse as V
 import logging
 from typing import Dict
 from typing import List
@@ -29,7 +29,7 @@
 from espnet2.torch_utils.device_funcs import force_gatherable
 from espnet2.train.abs_espnet_model import AbsESPnetModel
 
-if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
+if V(torch.__version__) >= V("1.6.0"):
     from torch.cuda.amp import autocast
 else:
     # Nothing to do if torch<1.6.0

espnet2/asr/maskctc_model.py

@@ -1,5 +1,5 @@
 from contextlib import contextmanager
-from distutils.version import LooseVersion
+from packaging.version import parse as V
 from itertools import groupby
 import logging
 from typing import Dict
@@ -31,7 +31,7 @@
 from espnet2.text.token_id_converter import TokenIDConverter
 from espnet2.torch_utils.device_funcs import force_gatherable
 
-if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
+if V(torch.__version__) >= V("1.6.0"):
     from torch.cuda.amp import autocast
 else:
     # Nothing to do if torch<1.6.0

espnet2/bin/tts_inference.py

@@ -8,7 +8,7 @@
 import sys
 import time
 
-from distutils.version import LooseVersion
+from packaging.version import parse as V
 from pathlib import Path
 from typing import Any
 from typing import Dict
@@ -300,7 +300,7 @@ def from_pretrained(
                 from parallel_wavegan import __version__
 
                 # NOTE(kan-bayashi): Filelock download is supported from 0.5.2
-                assert LooseVersion(__version__) > LooseVersion("0.5.1"), (
+                assert V(__version__) > V("0.5.1"), (
                     "Please install the latest parallel_wavegan "
                     "via `pip install -U parallel_wavegan`."
                 )

espnet2/diar/espnet_model.py

@@ -2,7 +2,7 @@
 #  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
 
 from contextlib import contextmanager
-from distutils.version import LooseVersion
+from packaging.version import parse as V
 from itertools import permutations
 from typing import Dict
 from typing import Optional
@@ -22,7 +22,7 @@
 from espnet2.torch_utils.device_funcs import force_gatherable
 from espnet2.train.abs_espnet_model import AbsESPnetModel
 
-if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
+if V(torch.__version__) >= V("1.6.0"):
     from torch.cuda.amp import autocast
 else:
     # Nothing to do if torch<1.6.0

espnet2/enh/decoder/stft_decoder.py

@@ -1,11 +1,11 @@
-from distutils.version import LooseVersion
+from packaging.version import parse as V
 import torch
 from torch_complex.tensor import ComplexTensor
 
 from espnet2.enh.decoder.abs_decoder import AbsDecoder
 from espnet2.layers.stft import Stft
 
-is_torch_1_9_plus = LooseVersion(torch.__version__) >= LooseVersion("1.9.0")
+is_torch_1_9_plus = V(torch.__version__) >= V("1.9.0")
 
 
 class STFTDecoder(AbsDecoder):

espnet2/enh/encoder/stft_encoder.py

@@ -1,11 +1,11 @@
-from distutils.version import LooseVersion
+from packaging.version import parse as V
 import torch
 from torch_complex.tensor import ComplexTensor
 
 from espnet2.enh.encoder.abs_encoder import AbsEncoder
 from espnet2.layers.stft import Stft
 
-is_torch_1_9_plus = LooseVersion(torch.__version__) >= LooseVersion("1.9.0")
+is_torch_1_9_plus = V(torch.__version__) >= V("1.9.0")
 
 
 class STFTEncoder(AbsEncoder):

espnet2/enh/espnet_enh_s2t_model.py

@@ -1,5 +1,5 @@
 from contextlib import contextmanager
-from distutils.version import LooseVersion
+from packaging.version import parse as V
 import logging
 import random
 from typing import Dict
@@ -16,7 +16,7 @@
 from espnet2.torch_utils.device_funcs import force_gatherable
 from espnet2.train.abs_espnet_model import AbsESPnetModel
 
-if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
+if V(torch.__version__) >= V("1.6.0"):
     from torch.cuda.amp import autocast
 else:
     # Nothing to do if torch<1.6.0

espnet2/enh/espnet_model.py

@@ -1,5 +1,5 @@
 """Enhancement model module."""
-from distutils.version import LooseVersion
+from packaging.version import parse as V
 from typing import Dict
 from typing import List
 from typing import Optional
@@ -20,7 +20,7 @@
 from espnet2.train.abs_espnet_model import AbsESPnetModel
 
 
-is_torch_1_9_plus = LooseVersion(torch.__version__) >= LooseVersion("1.9.0")
+is_torch_1_9_plus = V(torch.__version__) >= V("1.9.0")
 
 EPS = torch.finfo(torch.get_default_dtype()).eps