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Minor fixes

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This commit is contained in:
pkufool 2022-06-07 15:59:58 +08:00
parent d7be9bd9c5
commit aebe9c22dd
12 changed files with 460 additions and 21 deletions
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16
egs/librispeech/ASR/pruned_transducer_stateless/decode.py
View File

@ -303,6 +303,15 @@ def get_parser():
""",
)
parser.add_argument(
"--causal-convolution",
type=str2bool,
default=False,
help="""Whether to use causal convolution, this requires to be True when
using dynamic_chunk_training.
""",
)
parser.add_argument(
"--decode-chunk-size",
type=int,
@ -368,7 +377,7 @@ def decode_one_batch(
feature_lens = supervisions["num_frames"].to(device)
if params.simulate_streaming:
encoder_out, encoder_out_lens = model.encoder.streaming_forward(
encoder_out, encoder_out_lens, _ = model.encoder.streaming_forward(
x=feature,
x_lens=feature_lens,
states=[],
@ -639,8 +648,9 @@ def main():
params.vocab_size = sp.get_piece_size()
if params.simulate_streaming:
# Decoding in streaming requires causal convolution"
params.causal_convolution = True
assert (
params.causal_convolution
), "Decoding in streaming requires causal convolution"
logging.info(params)

11
egs/librispeech/ASR/pruned_transducer_stateless/export.py
View File

@ -150,6 +150,15 @@ def get_parser():
""",
)
parser.add_argument(
"--causal-convolution",
type=str2bool,
default=False,
help="""Whether to use causal convolution, this requires to be True when
exporting a streaming model.
""",
)
return parser
@ -175,7 +184,7 @@ def main():
params.vocab_size = sp.get_piece_size()
if params.streaming_model:
params.causal_convolution = True
assert params.causal_convolution
logging.info(params)

15
egs/librispeech/ASR/pruned_transducer_stateless/train.py
View File

@ -37,6 +37,7 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
--exp-dir pruned_transducer_stateless/exp \
--full-libri 1 \
--dynamic-chunk-training 1 \
--causal-convolution 1 \
--short-chunk-size 25 \
--num-left-chunks 4 \
--max-duration 300
@ -243,6 +244,15 @@ def get_parser():
""",
)
parser.add_argument(
"--causal-convolution",
type=str2bool,
default=False,
help="""Whether to use causal convolution, this requires to be True when
using dynamic_chunk_training.
""",
)
parser.add_argument(
"--short-chunk-size",
type=int,
@ -804,8 +814,9 @@ def run(rank, world_size, args):
params.vocab_size = sp.get_piece_size()
if params.dynamic_chunk_training:
# dynamic_chunk_training requires causal convolution
params.causal_convolution = True
assert (
params.causal_convolution
), "dynamic_chunk_training requires causal convolution"
logging.info(params)

15
egs/librispeech/ASR/pruned_transducer_stateless2/decode.py
View File

@ -59,6 +59,7 @@ Usage:
--epoch 28 \
--avg 15 \
--simulate-streaming 1 \
--causal-convolution 1 \
--decode-chunk-size 16 \
--left-context 64 \
--exp-dir ./pruned_transducer_stateless2/exp \
@ -255,6 +256,15 @@ def get_parser():
""",
)
parser.add_argument(
"--causal-convolution",
type=str2bool,
default=False,
help="""Whether to use causal convolution, this requires to be True when
using dynamic_chunk_training.
""",
)
parser.add_argument(
"--decode-chunk-size",
type=int,
@ -584,8 +594,9 @@ def main():
params.vocab_size = sp.get_piece_size()
if params.simulate_streaming:
# Decoding in streaming requires causal convolution
params.causal_convolution = True
assert (
params.causal_convolution
), "Decoding in streaming requires causal convolution"
logging.info(params)

11
egs/librispeech/ASR/pruned_transducer_stateless2/export.py
View File

@ -165,6 +165,15 @@ def get_parser():
""",
)
parser.add_argument(
"--causal-convolution",
type=str2bool,
default=False,
help="""Whether to use causal convolution, this requires to be True when
exporting a streaming model.
""",
)
return parser
@ -189,7 +198,7 @@ def main():
params.vocab_size = sp.get_piece_size()
if params.streaming_model:
params.causal_convolution = True
assert params.causal_convolution
logging.info(params)

15
egs/librispeech/ASR/pruned_transducer_stateless2/train.py
View File

@ -48,6 +48,7 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
--exp-dir pruned_transducer_stateless/exp \
--full-libri 1 \
--dynamic-chunk-training 1 \
--causal_convolution 1 \
--short-chunk-size 25 \
--num-left-chunks 4 \
--max-duration 300
@ -283,6 +284,15 @@ def get_parser():
""",
)
parser.add_argument(
"--causal-convolution",
type=str2bool,
default=False,
help="""Whether to use causal convolution, this requires to be True when
using dynamic_chunk_training.
""",
)
parser.add_argument(
"--short-chunk-size",
type=int,
@ -847,8 +857,9 @@ def run(rank, world_size, args):
params.vocab_size = sp.get_piece_size()
if params.dynamic_chunk_training:
# dynamic_chunk_training requires causal convolution
params.causal_convolution = True
assert (
params.causal_convolution
), "dynamic_chunk_training requires causal convolution"
logging.info(params)

14
egs/librispeech/ASR/pruned_transducer_stateless3/decode.py
View File

@ -265,6 +265,15 @@ def get_parser():
""",
)
parser.add_argument(
"--causal-convolution",
type=str2bool,
default=False,
help="""Whether to use causal convolution, this requires to be True when
using dynamic_chunk_training.
""",
)
parser.add_argument(
"--decode-chunk-size",
type=int,
@ -626,8 +635,9 @@ def main():
params.vocab_size = sp.get_piece_size()
if params.simulate_streaming:
# Decoding in streaming requires causal convolution
params.causal_convolution = True
assert (
params.causal_convolution
), "Decoding in streaming requires causal convolution"
logging.info(params)

11
egs/librispeech/ASR/pruned_transducer_stateless3/export.py
View File

@ -166,6 +166,15 @@ def get_parser():
""",
)
parser.add_argument(
"--causal-convolution",
type=str2bool,
default=False,
help="""Whether to use causal convolution, this requires to be True when
exporting a streaming model.
""",
)
return parser
@ -190,7 +199,7 @@ def main():
params.vocab_size = sp.get_piece_size()
if params.streaming_model:
params.causal_convolution = True
assert params.causal_convolution
logging.info(params)

14
egs/librispeech/ASR/pruned_transducer_stateless3/train.py
View File

@ -295,6 +295,15 @@ def get_parser():
""",
)
parser.add_argument(
"--causal-convolution",
type=str2bool,
default=False,
help="""Whether to use causal convolution, this requires to be True when
using dynamic_chunk_training.
""",
)
parser.add_argument(
"--short-chunk-size",
type=int,
@ -935,8 +944,9 @@ def run(rank, world_size, args):
params.vocab_size = sp.get_piece_size()
if params.dynamic_chunk_training:
# dynamic_chunk_training requires causal convolution
params.causal_convolution = True
assert (
params.causal_convolution
), "dynamic_chunk_training requires causal convolution"
logging.info(params)

15
egs/librispeech/ASR/pruned_transducer_stateless4/decode.py
View File

@ -60,6 +60,7 @@ Usage:
--epoch 30 \
--avg 15 \
--simulate-streaming 1 \
--causal-convolution 1 \
--decode-chunk-size 16 \
--left-context 64 \
--exp-dir ./pruned_transducer_stateless4/exp \
@ -267,6 +268,15 @@ def get_parser():
""",
)
parser.add_argument(
"--causal-convolution",
type=str2bool,
default=False,
help="""Whether to use causal convolution, this requires to be True when
using dynamic_chunk_training.
""",
)
parser.add_argument(
"--decode-chunk-size",
type=int,
@ -599,8 +609,9 @@ def main():
params.vocab_size = sp.get_piece_size()
if params.simulate_streaming:
# Decoding in streaming requires causal convolution
params.causal_convolution = True
assert (
params.causal_convolution
), "Decoding in streaming requires causal convolution"
logging.info(params)

1
egs/librispeech/ASR/pruned_transducer_stateless4/export.py
View File

@ -1 +0,0 @@
../pruned_transducer_stateless2/export.py

328
egs/librispeech/ASR/pruned_transducer_stateless4/export.py Executable file
View File

@ -0,0 +1,328 @@
#!/usr/bin/env python3
#
# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This script converts several saved checkpoints
# to a single one using model averaging.
"""
Usage:
./pruned_transducer_stateless4/export.py \
--exp-dir ./pruned_transducer_stateless4/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--epoch 20 \
--avg 10
It will generate a file exp_dir/pretrained.pt
To use the generated file with `pruned_transducer_stateless4/decode.py`,
you can do:
cd /path/to/exp_dir
ln -s pretrained.pt epoch-9999.pt
cd /path/to/egs/librispeech/ASR
./pruned_transducer_stateless4/decode.py \
--exp-dir ./pruned_transducer_stateless4/exp \
--epoch 9999 \
--avg 1 \
--max-duration 100 \
--bpe-model data/lang_bpe_500/bpe.model
"""
import argparse
import logging
from pathlib import Path
import sentencepiece as spm
import torch
from train import get_params, get_transducer_model
from icefall.checkpoint import (
average_checkpoints,
average_checkpoints_with_averaged_model,
find_checkpoints,
load_checkpoint,
)
from icefall.utils import str2bool
def get_parser():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument(
"--epoch",
type=int,
default=28,
help="""It specifies the checkpoint to use for averaging.
Note: Epoch counts from 0.
You can specify --avg to use more checkpoints for model averaging.""",
)
parser.add_argument(
"--iter",
type=int,
default=0,
help="""If positive, --epoch is ignored and it
will use the checkpoint exp_dir/checkpoint-iter.pt.
You can specify --avg to use more checkpoints for model averaging.
""",
)
parser.add_argument(
"--avg",
type=int,
default=15,
help="Number of checkpoints to average. Automatically select "
"consecutive checkpoints before the checkpoint specified by "
"'--epoch' and '--iter'",
)
parser.add_argument(
"--use-averaged-model",
type=str2bool,
default=True,
help="Whether to load averaged model. Currently it only supports "
"using --epoch. If True, it would decode with the averaged model "
"over the epoch range from `epoch-avg` (excluded) to `epoch`."
"Actually only the models with epoch number of `epoch-avg` and "
"`epoch` are loaded for averaging. ",
)
parser.add_argument(
"--exp-dir",
type=str,
default="pruned_transducer_stateless2/exp",
help="""It specifies the directory where all training related
files, e.g., checkpoints, log, etc, are saved
""",
)
parser.add_argument(
"--bpe-model",
type=str,
default="data/lang_bpe_500/bpe.model",
help="Path to the BPE model",
)
parser.add_argument(
"--jit",
type=str2bool,
default=False,
help="""True to save a model after applying torch.jit.script.
""",
)
parser.add_argument(
"--context-size",
type=int,
default=2,
help="The context size in the decoder. 1 means bigram; "
"2 means tri-gram",
)
parser.add_argument(
"--dynamic-chunk-training",
type=str2bool,
default=False,
help="""Whether to use dynamic_chunk_training, if you want a streaming
model, this requires to be True.
Note: not needed here, adding it here to construct transducer model,
as we reuse the code in train.py.
""",
)
parser.add_argument(
"--short-chunk-size",
type=int,
default=25,
help="""Chunk length of dynamic training, the chunk size would be either
max sequence length of current batch or uniformly sampled from (1, short_chunk_size).
Note: not needed for here, adding it here to construct transducer model,
as we reuse the code in train.py.
""",
)
parser.add_argument(
"--num-left-chunks",
type=int,
default=4,
help="""How many left context can be seen in chunks when calculating attention.
Note: not needed here, adding it here to construct transducer model,
as we reuse the code in train.py.
""",
)
parser.add_argument(
"--streaming-model",
type=str2bool,
default=False,
help="""Whether to export a streaming model, if the models in exp-dir
are streaming model, this should be True.
""",
)
parser.add_argument(
"--causal-convolution",
type=str2bool,
default=False,
help="""Whether to use causal convolution, this requires to be True when
exporting a streaming model.
""",
)
return parser
def main():
args = get_parser().parse_args()
args.exp_dir = Path(args.exp_dir)
params = get_params()
params.update(vars(args))
device = torch.device("cpu")
if torch.cuda.is_available():
device = torch.device("cuda", 0)
logging.info(f"device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()
if params.streaming_model:
assert params.causal_convolution
logging.info(params)
logging.info("About to create model")
model = get_transducer_model(params)
model.to(device)
if not params.use_averaged_model:
if params.iter > 0:
filenames = find_checkpoints(
params.exp_dir, iteration=-params.iter
)[: params.avg]
if len(filenames) == 0:
raise ValueError(
f"No checkpoints found for"
f" --iter {params.iter}, --avg {params.avg}"
)
elif len(filenames) < params.avg:
raise ValueError(
f"Not enough checkpoints ({len(filenames)}) found for"
f" --iter {params.iter}, --avg {params.avg}"
)
logging.info(f"averaging {filenames}")
model.to(device)
model.load_state_dict(average_checkpoints(filenames, device=device))
elif params.avg == 1:
load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
else:
start = params.epoch - params.avg + 1
filenames = []
for i in range(start, params.epoch + 1):
if i >= 1:
filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
logging.info(f"averaging {filenames}")
model.to(device)
model.load_state_dict(average_checkpoints(filenames, device=device))
else:
if params.iter > 0:
filenames = find_checkpoints(
params.exp_dir, iteration=-params.iter
)[: params.avg + 1]
if len(filenames) == 0:
raise ValueError(
f"No checkpoints found for"
f" --iter {params.iter}, --avg {params.avg}"
)
elif len(filenames) < params.avg + 1:
raise ValueError(
f"Not enough checkpoints ({len(filenames)}) found for"
f" --iter {params.iter}, --avg {params.avg}"
)
filename_start = filenames[-1]
filename_end = filenames[0]
logging.info(
"Calculating the averaged model over iteration checkpoints"
f" from {filename_start} (excluded) to {filename_end}"
)
model.to(device)
model.load_state_dict(
average_checkpoints_with_averaged_model(
filename_start=filename_start,
filename_end=filename_end,
device=device,
)
)
else:
assert params.avg > 0, params.avg
start = params.epoch - params.avg
assert start >= 1, start
filename_start = f"{params.exp_dir}/epoch-{start}.pt"
filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
logging.info(
f"Calculating the averaged model over epoch range from "
f"{start} (excluded) to {params.epoch}"
)
model.to(device)
model.load_state_dict(
average_checkpoints_with_averaged_model(
filename_start=filename_start,
filename_end=filename_end,
device=device,
)
)
model.to("cpu")
model.eval()
if params.jit:
# We won't use the forward() method of the model in C++, so just ignore
# it here.
# Otherwise, one of its arguments is a ragged tensor and is not
# torch scriptabe.
model.__class__.forward = torch.jit.ignore(model.__class__.forward)
logging.info("Using torch.jit.script")
model = torch.jit.script(model)
filename = params.exp_dir / "cpu_jit.pt"
model.save(str(filename))
logging.info(f"Saved to {filename}")
else:
logging.info("Not using torch.jit.script")
# Save it using a format so that it can be loaded
# by :func:`load_checkpoint`
filename = params.exp_dir / "pretrained.pt"
torch.save({"model": model.state_dict()}, str(filename))
logging.info(f"Saved to {filename}")
if __name__ == "__main__":
formatter = (
"%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
)
logging.basicConfig(format=formatter, level=logging.INFO)
main()

15
egs/librispeech/ASR/pruned_transducer_stateless4/train.py
View File

@ -49,6 +49,7 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
--exp-dir pruned_transducer_stateless4/exp \
--full-libri 1 \
--dynamic-chunk-training 1 \
--causal-convolution 1 \
--short-chunk-size 25 \
--num-left-chunks 4 \
--max-duration 300
@ -301,6 +302,15 @@ def get_parser():
""",
)
parser.add_argument(
"--causal-convolution",
type=str2bool,
default=False,
help="""Whether to use causal convolution, this requires to be True when
using dynamic_chunk_training.
""",
)
parser.add_argument(
"--short-chunk-size",
type=int,
@ -888,8 +898,9 @@ def run(rank, world_size, args):
params.vocab_size = sp.get_piece_size()
if params.dynamic_chunk_training:
# dynamic_chunk_training requires causal convolution
params.causal_convolution = True
assert (
params.causal_convolution
), "dynamic_chunk_training requires causal convolution"
logging.info(params)