mirror of
https://github.com/k2-fsa/icefall.git
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Merge 5ea4d94ac0920eb84762939b827a9ef45c7723a4 into abd9437e6d5419a497707748eb935e50976c3b7b
This commit is contained in:
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742610ca3b
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../pruned_transducer_stateless2/asr_datamodule.py
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egs/gigaspeech/ASR/pruned_transducer_stateless5/beam_search.py
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egs/gigaspeech/ASR/pruned_transducer_stateless5/beam_search.py
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../../../librispeech/ASR/pruned_transducer_stateless5/beam_search.py
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egs/gigaspeech/ASR/pruned_transducer_stateless5/conformer.py
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egs/gigaspeech/ASR/pruned_transducer_stateless5/conformer.py
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../../../librispeech/ASR/pruned_transducer_stateless5/conformer.py
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egs/gigaspeech/ASR/pruned_transducer_stateless5/decode.py
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egs/gigaspeech/ASR/pruned_transducer_stateless5/decode.py
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|
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#!/usr/bin/env python3
|
||||
#
|
||||
# Copyright 2021-2022 Xiaomi Corporation (Author: Fangjun Kuang,
|
||||
# Zengwei Yao)
|
||||
#
|
||||
# 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.
|
||||
"""
|
||||
Usage:
|
||||
(1) greedy search
|
||||
./pruned_transducer_stateless5/decode.py \
|
||||
--epoch 28 \
|
||||
--avg 15 \
|
||||
--exp-dir ./pruned_transducer_stateless5/exp \
|
||||
--max-duration 600 \
|
||||
--decoding-method greedy_search
|
||||
|
||||
(2) beam search (not recommended)
|
||||
./pruned_transducer_stateless5/decode.py \
|
||||
--epoch 28 \
|
||||
--avg 15 \
|
||||
--exp-dir ./pruned_transducer_stateless5/exp \
|
||||
--max-duration 600 \
|
||||
--decoding-method beam_search \
|
||||
--beam-size 4
|
||||
|
||||
(3) modified beam search
|
||||
./pruned_transducer_stateless5/decode.py \
|
||||
--epoch 28 \
|
||||
--avg 15 \
|
||||
--exp-dir ./pruned_transducer_stateless5/exp \
|
||||
--max-duration 600 \
|
||||
--decoding-method modified_beam_search \
|
||||
--beam-size 4
|
||||
|
||||
(4) fast beam search (one best)
|
||||
./pruned_transducer_stateless5/decode.py \
|
||||
--epoch 28 \
|
||||
--avg 15 \
|
||||
--exp-dir ./pruned_transducer_stateless5/exp \
|
||||
--max-duration 600 \
|
||||
--decoding-method fast_beam_search \
|
||||
--beam 20.0 \
|
||||
--max-contexts 8 \
|
||||
--max-states 64
|
||||
|
||||
(5) fast beam search (nbest)
|
||||
./pruned_transducer_stateless5/decode.py \
|
||||
--epoch 28 \
|
||||
--avg 15 \
|
||||
--exp-dir ./pruned_transducer_stateless5/exp \
|
||||
--max-duration 600 \
|
||||
--decoding-method fast_beam_search_nbest \
|
||||
--beam 20.0 \
|
||||
--max-contexts 8 \
|
||||
--max-states 64 \
|
||||
--num-paths 200 \
|
||||
--nbest-scale 0.5
|
||||
|
||||
(6) fast beam search (nbest oracle WER)
|
||||
./pruned_transducer_stateless5/decode.py \
|
||||
--epoch 28 \
|
||||
--avg 15 \
|
||||
--exp-dir ./pruned_transducer_stateless5/exp \
|
||||
--max-duration 600 \
|
||||
--decoding-method fast_beam_search_nbest_oracle \
|
||||
--beam 20.0 \
|
||||
--max-contexts 8 \
|
||||
--max-states 64 \
|
||||
--num-paths 200 \
|
||||
--nbest-scale 0.5
|
||||
|
||||
(7) fast beam search (with LG)
|
||||
./pruned_transducer_stateless5/decode.py \
|
||||
--epoch 28 \
|
||||
--avg 15 \
|
||||
--exp-dir ./pruned_transducer_stateless5/exp \
|
||||
--max-duration 600 \
|
||||
--decoding-method fast_beam_search_nbest_LG \
|
||||
--beam 20.0 \
|
||||
--max-contexts 8 \
|
||||
--max-states 64
|
||||
"""
|
||||
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
import math
|
||||
from collections import defaultdict
|
||||
from pathlib import Path
|
||||
from typing import Dict, List, Optional, Tuple
|
||||
|
||||
import k2
|
||||
import sentencepiece as spm
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from asr_datamodule import GigaSpeechAsrDataModule
|
||||
from beam_search import (
|
||||
beam_search,
|
||||
fast_beam_search_nbest,
|
||||
fast_beam_search_nbest_LG,
|
||||
fast_beam_search_nbest_oracle,
|
||||
fast_beam_search_one_best,
|
||||
greedy_search,
|
||||
greedy_search_batch,
|
||||
modified_beam_search,
|
||||
)
|
||||
from gigaspeech_scoring import asr_text_post_processing
|
||||
from train import add_model_arguments, get_params, get_transducer_model
|
||||
|
||||
from icefall.checkpoint import (
|
||||
average_checkpoints,
|
||||
average_checkpoints_with_averaged_model,
|
||||
find_checkpoints,
|
||||
load_checkpoint,
|
||||
)
|
||||
from icefall.lexicon import Lexicon
|
||||
from icefall.utils import (
|
||||
AttributeDict,
|
||||
setup_logger,
|
||||
store_transcripts,
|
||||
str2bool,
|
||||
write_error_stats,
|
||||
)
|
||||
|
||||
LOG_EPS = math.log(1e-10)
|
||||
|
||||
|
||||
def get_parser():
|
||||
parser = argparse.ArgumentParser(
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--epoch",
|
||||
type=int,
|
||||
default=30,
|
||||
help="""It specifies the checkpoint to use for decoding.
|
||||
Note: Epoch counts from 1.
|
||||
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_stateless5/exp",
|
||||
help="The experiment dir",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--bpe-model",
|
||||
type=str,
|
||||
default="data/lang_bpe_500/bpe.model",
|
||||
help="Path to the BPE model",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--lang-dir",
|
||||
type=Path,
|
||||
default="data/lang_bpe_500",
|
||||
help="The lang dir containing word table and LG graph",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--decoding-method",
|
||||
type=str,
|
||||
default="greedy_search",
|
||||
help="""Possible values are:
|
||||
- greedy_search
|
||||
- beam_search
|
||||
- modified_beam_search
|
||||
- fast_beam_search
|
||||
- fast_beam_search_nbest
|
||||
- fast_beam_search_nbest_oracle
|
||||
- fast_beam_search_nbest_LG
|
||||
If you use fast_beam_search_nbest_LG, you have to specify
|
||||
`--lang-dir`, which should contain `LG.pt`.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--beam-size",
|
||||
type=int,
|
||||
default=4,
|
||||
help="""An integer indicating how many candidates we will keep for each
|
||||
frame. Used only when --decoding-method is beam_search or
|
||||
modified_beam_search.""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--beam",
|
||||
type=float,
|
||||
default=20.0,
|
||||
help="""A floating point value to calculate the cutoff score during beam
|
||||
search (i.e., `cutoff = max-score - beam`), which is the same as the
|
||||
`beam` in Kaldi.
|
||||
Used only when --decoding-method is fast_beam_search,
|
||||
fast_beam_search_nbest, fast_beam_search_nbest_LG,
|
||||
and fast_beam_search_nbest_oracle
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--ngram-lm-scale",
|
||||
type=float,
|
||||
default=0.01,
|
||||
help="""
|
||||
Used only when --decoding_method is fast_beam_search_nbest_LG.
|
||||
It specifies the scale for n-gram LM scores.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--max-contexts",
|
||||
type=int,
|
||||
default=8,
|
||||
help="""Used only when --decoding-method is
|
||||
fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG,
|
||||
and fast_beam_search_nbest_oracle""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--max-states",
|
||||
type=int,
|
||||
default=64,
|
||||
help="""Used only when --decoding-method is
|
||||
fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG,
|
||||
and fast_beam_search_nbest_oracle""",
|
||||
)
|
||||
|
||||
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(
|
||||
"--max-sym-per-frame",
|
||||
type=int,
|
||||
default=1,
|
||||
help="""Maximum number of symbols per frame.
|
||||
Used only when --decoding_method is greedy_search""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--num-paths",
|
||||
type=int,
|
||||
default=200,
|
||||
help="""Number of paths for nbest decoding.
|
||||
Used only when the decoding method is fast_beam_search_nbest,
|
||||
fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--nbest-scale",
|
||||
type=float,
|
||||
default=0.5,
|
||||
help="""Scale applied to lattice scores when computing nbest paths.
|
||||
Used only when the decoding method is fast_beam_search_nbest,
|
||||
fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--simulate-streaming",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help="""Whether to simulate streaming in decoding, this is a good way to
|
||||
test a streaming model.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--decode-chunk-size",
|
||||
type=int,
|
||||
default=16,
|
||||
help="The chunk size for decoding (in frames after subsampling)",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--left-context",
|
||||
type=int,
|
||||
default=64,
|
||||
help="left context can be seen during decoding (in frames after subsampling)",
|
||||
)
|
||||
|
||||
add_model_arguments(parser)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
def post_processing(
|
||||
results: List[Tuple[List[str], List[str]]],
|
||||
) -> List[Tuple[List[str], List[str]]]:
|
||||
new_results = []
|
||||
for ref, hyp in results:
|
||||
new_ref = asr_text_post_processing(" ".join(ref)).split()
|
||||
new_hyp = asr_text_post_processing(" ".join(hyp)).split()
|
||||
new_results.append((new_ref, new_hyp))
|
||||
return new_results
|
||||
|
||||
|
||||
def decode_one_batch(
|
||||
params: AttributeDict,
|
||||
model: nn.Module,
|
||||
sp: spm.SentencePieceProcessor,
|
||||
batch: dict,
|
||||
word_table: Optional[k2.SymbolTable] = None,
|
||||
decoding_graph: Optional[k2.Fsa] = None,
|
||||
) -> Dict[str, List[List[str]]]:
|
||||
"""Decode one batch and return the result in a dict. The dict has the
|
||||
following format:
|
||||
|
||||
- key: It indicates the setting used for decoding. For example,
|
||||
if greedy_search is used, it would be "greedy_search"
|
||||
If beam search with a beam size of 7 is used, it would be
|
||||
"beam_7"
|
||||
- value: It contains the decoding result. `len(value)` equals to
|
||||
batch size. `value[i]` is the decoding result for the i-th
|
||||
utterance in the given batch.
|
||||
Args:
|
||||
params:
|
||||
It's the return value of :func:`get_params`.
|
||||
model:
|
||||
The neural model.
|
||||
sp:
|
||||
The BPE model.
|
||||
batch:
|
||||
It is the return value from iterating
|
||||
`lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
|
||||
for the format of the `batch`.
|
||||
word_table:
|
||||
The word symbol table.
|
||||
decoding_graph:
|
||||
The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
|
||||
only when --decoding_method is fast_beam_search, fast_beam_search_nbest,
|
||||
fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
|
||||
Returns:
|
||||
Return the decoding result. See above description for the format of
|
||||
the returned dict.
|
||||
"""
|
||||
device = next(model.parameters()).device
|
||||
feature = batch["inputs"]
|
||||
assert feature.ndim == 3
|
||||
|
||||
feature = feature.to(device)
|
||||
# at entry, feature is (N, T, C)
|
||||
|
||||
supervisions = batch["supervisions"]
|
||||
feature_lens = supervisions["num_frames"].to(device)
|
||||
|
||||
feature_lens += params.left_context
|
||||
feature = torch.nn.functional.pad(
|
||||
feature,
|
||||
pad=(0, 0, 0, params.left_context),
|
||||
value=LOG_EPS,
|
||||
)
|
||||
|
||||
if params.simulate_streaming:
|
||||
encoder_out, encoder_out_lens, _ = model.encoder.streaming_forward(
|
||||
x=feature,
|
||||
x_lens=feature_lens,
|
||||
chunk_size=params.decode_chunk_size,
|
||||
left_context=params.left_context,
|
||||
simulate_streaming=True,
|
||||
)
|
||||
else:
|
||||
encoder_out, encoder_out_lens = model.encoder(
|
||||
x=feature, x_lens=feature_lens
|
||||
)
|
||||
|
||||
hyps = []
|
||||
|
||||
if params.decoding_method == "fast_beam_search":
|
||||
hyp_tokens = fast_beam_search_one_best(
|
||||
model=model,
|
||||
decoding_graph=decoding_graph,
|
||||
encoder_out=encoder_out,
|
||||
encoder_out_lens=encoder_out_lens,
|
||||
beam=params.beam,
|
||||
max_contexts=params.max_contexts,
|
||||
max_states=params.max_states,
|
||||
)
|
||||
for hyp in sp.decode(hyp_tokens):
|
||||
hyps.append(hyp.split())
|
||||
elif params.decoding_method == "fast_beam_search_nbest_LG":
|
||||
hyp_tokens = fast_beam_search_nbest_LG(
|
||||
model=model,
|
||||
decoding_graph=decoding_graph,
|
||||
encoder_out=encoder_out,
|
||||
encoder_out_lens=encoder_out_lens,
|
||||
beam=params.beam,
|
||||
max_contexts=params.max_contexts,
|
||||
max_states=params.max_states,
|
||||
num_paths=params.num_paths,
|
||||
nbest_scale=params.nbest_scale,
|
||||
)
|
||||
for hyp in hyp_tokens:
|
||||
hyps.append([word_table[i] for i in hyp])
|
||||
elif params.decoding_method == "fast_beam_search_nbest":
|
||||
hyp_tokens = fast_beam_search_nbest(
|
||||
model=model,
|
||||
decoding_graph=decoding_graph,
|
||||
encoder_out=encoder_out,
|
||||
encoder_out_lens=encoder_out_lens,
|
||||
beam=params.beam,
|
||||
max_contexts=params.max_contexts,
|
||||
max_states=params.max_states,
|
||||
num_paths=params.num_paths,
|
||||
nbest_scale=params.nbest_scale,
|
||||
)
|
||||
for hyp in sp.decode(hyp_tokens):
|
||||
hyps.append(hyp.split())
|
||||
elif params.decoding_method == "fast_beam_search_nbest_oracle":
|
||||
hyp_tokens = fast_beam_search_nbest_oracle(
|
||||
model=model,
|
||||
decoding_graph=decoding_graph,
|
||||
encoder_out=encoder_out,
|
||||
encoder_out_lens=encoder_out_lens,
|
||||
beam=params.beam,
|
||||
max_contexts=params.max_contexts,
|
||||
max_states=params.max_states,
|
||||
num_paths=params.num_paths,
|
||||
ref_texts=sp.encode(supervisions["text"]),
|
||||
nbest_scale=params.nbest_scale,
|
||||
)
|
||||
for hyp in sp.decode(hyp_tokens):
|
||||
hyps.append(hyp.split())
|
||||
elif (
|
||||
params.decoding_method == "greedy_search"
|
||||
and params.max_sym_per_frame == 1
|
||||
):
|
||||
hyp_tokens = greedy_search_batch(
|
||||
model=model,
|
||||
encoder_out=encoder_out,
|
||||
encoder_out_lens=encoder_out_lens,
|
||||
)
|
||||
for hyp in sp.decode(hyp_tokens):
|
||||
hyps.append(hyp.split())
|
||||
elif params.decoding_method == "modified_beam_search":
|
||||
hyp_tokens = modified_beam_search(
|
||||
model=model,
|
||||
encoder_out=encoder_out,
|
||||
encoder_out_lens=encoder_out_lens,
|
||||
beam=params.beam_size,
|
||||
)
|
||||
for hyp in sp.decode(hyp_tokens):
|
||||
hyps.append(hyp.split())
|
||||
else:
|
||||
batch_size = encoder_out.size(0)
|
||||
|
||||
for i in range(batch_size):
|
||||
# fmt: off
|
||||
encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
|
||||
# fmt: on
|
||||
if params.decoding_method == "greedy_search":
|
||||
hyp = greedy_search(
|
||||
model=model,
|
||||
encoder_out=encoder_out_i,
|
||||
max_sym_per_frame=params.max_sym_per_frame,
|
||||
)
|
||||
elif params.decoding_method == "beam_search":
|
||||
hyp = beam_search(
|
||||
model=model,
|
||||
encoder_out=encoder_out_i,
|
||||
beam=params.beam_size,
|
||||
)
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Unsupported decoding method: {params.decoding_method}"
|
||||
)
|
||||
hyps.append(sp.decode(hyp).split())
|
||||
|
||||
if params.decoding_method == "greedy_search":
|
||||
return {"greedy_search": hyps}
|
||||
elif "fast_beam_search" in params.decoding_method:
|
||||
key = f"beam_{params.beam}_"
|
||||
key += f"max_contexts_{params.max_contexts}_"
|
||||
key += f"max_states_{params.max_states}"
|
||||
if "nbest" in params.decoding_method:
|
||||
key += f"_num_paths_{params.num_paths}_"
|
||||
key += f"nbest_scale_{params.nbest_scale}"
|
||||
if "LG" in params.decoding_method:
|
||||
key += f"_ngram_lm_scale_{params.ngram_lm_scale}"
|
||||
|
||||
return {key: hyps}
|
||||
else:
|
||||
return {f"beam_size_{params.beam_size}": hyps}
|
||||
|
||||
|
||||
def decode_dataset(
|
||||
dl: torch.utils.data.DataLoader,
|
||||
params: AttributeDict,
|
||||
model: nn.Module,
|
||||
sp: spm.SentencePieceProcessor,
|
||||
word_table: Optional[k2.SymbolTable] = None,
|
||||
decoding_graph: Optional[k2.Fsa] = None,
|
||||
) -> Dict[str, List[Tuple[List[str], List[str]]]]:
|
||||
"""Decode dataset.
|
||||
|
||||
Args:
|
||||
dl:
|
||||
PyTorch's dataloader containing the dataset to decode.
|
||||
params:
|
||||
It is returned by :func:`get_params`.
|
||||
model:
|
||||
The neural model.
|
||||
sp:
|
||||
The BPE model.
|
||||
word_table:
|
||||
The word symbol table.
|
||||
decoding_graph:
|
||||
The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
|
||||
only when --decoding_method is fast_beam_search, fast_beam_search_nbest,
|
||||
fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
|
||||
Returns:
|
||||
Return a dict, whose key may be "greedy_search" if greedy search
|
||||
is used, or it may be "beam_7" if beam size of 7 is used.
|
||||
Its value is a list of tuples. Each tuple contains two elements:
|
||||
The first is the reference transcript, and the second is the
|
||||
predicted result.
|
||||
"""
|
||||
num_cuts = 0
|
||||
|
||||
try:
|
||||
num_batches = len(dl)
|
||||
except TypeError:
|
||||
num_batches = "?"
|
||||
|
||||
log_interval = 20
|
||||
|
||||
results = defaultdict(list)
|
||||
for batch_idx, batch in enumerate(dl):
|
||||
texts = batch["supervisions"]["text"]
|
||||
|
||||
hyps_dict = decode_one_batch(
|
||||
params=params,
|
||||
model=model,
|
||||
sp=sp,
|
||||
decoding_graph=decoding_graph,
|
||||
word_table=word_table,
|
||||
batch=batch,
|
||||
)
|
||||
|
||||
for name, hyps in hyps_dict.items():
|
||||
this_batch = []
|
||||
assert len(hyps) == len(texts)
|
||||
for hyp_words, ref_text in zip(hyps, texts):
|
||||
ref_words = ref_text.split()
|
||||
this_batch.append((ref_words, hyp_words))
|
||||
|
||||
results[name].extend(this_batch)
|
||||
|
||||
num_cuts += len(texts)
|
||||
|
||||
if batch_idx % log_interval == 0:
|
||||
batch_str = f"{batch_idx}/{num_batches}"
|
||||
|
||||
logging.info(
|
||||
f"batch {batch_str}, cuts processed until now is {num_cuts}"
|
||||
)
|
||||
return results
|
||||
|
||||
|
||||
def save_results(
|
||||
params: AttributeDict,
|
||||
test_set_name: str,
|
||||
results_dict: Dict[str, List[Tuple[List[int], List[int]]]],
|
||||
):
|
||||
test_set_wers = dict()
|
||||
for key, results in results_dict.items():
|
||||
recog_path = (
|
||||
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
|
||||
)
|
||||
results = post_processing(results)
|
||||
store_transcripts(filename=recog_path, texts=results)
|
||||
logging.info(f"The transcripts are stored in {recog_path}")
|
||||
|
||||
# The following prints out WERs, per-word error statistics and aligned
|
||||
# ref/hyp pairs.
|
||||
errs_filename = (
|
||||
params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
|
||||
)
|
||||
with open(errs_filename, "w") as f:
|
||||
wer = write_error_stats(
|
||||
f, f"{test_set_name}-{key}", results, enable_log=True
|
||||
)
|
||||
test_set_wers[key] = wer
|
||||
|
||||
logging.info("Wrote detailed error stats to {}".format(errs_filename))
|
||||
|
||||
test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
|
||||
errs_info = (
|
||||
params.res_dir
|
||||
/ f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
|
||||
)
|
||||
with open(errs_info, "w") as f:
|
||||
print("settings\tWER", file=f)
|
||||
for key, val in test_set_wers:
|
||||
print("{}\t{}".format(key, val), file=f)
|
||||
|
||||
s = "\nFor {}, WER of different settings are:\n".format(test_set_name)
|
||||
note = "\tbest for {}".format(test_set_name)
|
||||
for key, val in test_set_wers:
|
||||
s += "{}\t{}{}\n".format(key, val, note)
|
||||
note = ""
|
||||
logging.info(s)
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def main():
|
||||
parser = get_parser()
|
||||
GigaSpeechAsrDataModule.add_arguments(parser)
|
||||
args = parser.parse_args()
|
||||
args.exp_dir = Path(args.exp_dir)
|
||||
|
||||
params = get_params()
|
||||
params.update(vars(args))
|
||||
|
||||
assert params.decoding_method in (
|
||||
"greedy_search",
|
||||
"beam_search",
|
||||
"fast_beam_search",
|
||||
"fast_beam_search_nbest",
|
||||
"fast_beam_search_nbest_LG",
|
||||
"fast_beam_search_nbest_oracle",
|
||||
"modified_beam_search",
|
||||
)
|
||||
params.res_dir = params.exp_dir / params.decoding_method
|
||||
|
||||
if params.iter > 0:
|
||||
params.suffix = f"iter-{params.iter}-avg-{params.avg}"
|
||||
else:
|
||||
params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
|
||||
|
||||
if params.simulate_streaming:
|
||||
params.suffix += f"-streaming-chunk-size-{params.decode_chunk_size}"
|
||||
params.suffix += f"-left-context-{params.left_context}"
|
||||
|
||||
if "fast_beam_search" in params.decoding_method:
|
||||
params.suffix += f"-beam-{params.beam}"
|
||||
params.suffix += f"-max-contexts-{params.max_contexts}"
|
||||
params.suffix += f"-max-states-{params.max_states}"
|
||||
if "nbest" in params.decoding_method:
|
||||
params.suffix += f"-nbest-scale-{params.nbest_scale}"
|
||||
params.suffix += f"-num-paths-{params.num_paths}"
|
||||
if "LG" in params.decoding_method:
|
||||
params.suffix += f"-ngram-lm-scale-{params.ngram_lm_scale}"
|
||||
elif "beam_search" in params.decoding_method:
|
||||
params.suffix += (
|
||||
f"-{params.decoding_method}-beam-size-{params.beam_size}"
|
||||
)
|
||||
else:
|
||||
params.suffix += f"-context-{params.context_size}"
|
||||
params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
|
||||
|
||||
if params.use_averaged_model:
|
||||
params.suffix += "-use-averaged-model"
|
||||
|
||||
setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")
|
||||
logging.info("Decoding started")
|
||||
|
||||
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> and <unk> are defined in local/train_bpe_model.py
|
||||
params.blank_id = sp.piece_to_id("<blk>")
|
||||
params.unk_id = sp.piece_to_id("<unk>")
|
||||
params.vocab_size = sp.get_piece_size()
|
||||
|
||||
if params.simulate_streaming:
|
||||
assert (
|
||||
params.causal_convolution
|
||||
), "Decoding in streaming requires causal convolution"
|
||||
|
||||
logging.info(params)
|
||||
|
||||
logging.info("About to create model")
|
||||
model = get_transducer_model(params)
|
||||
|
||||
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(device)
|
||||
model.eval()
|
||||
|
||||
if "fast_beam_search" in params.decoding_method:
|
||||
if params.decoding_method == "fast_beam_search_nbest_LG":
|
||||
lexicon = Lexicon(params.lang_dir)
|
||||
word_table = lexicon.word_table
|
||||
lg_filename = params.lang_dir / "LG.pt"
|
||||
logging.info(f"Loading {lg_filename}")
|
||||
decoding_graph = k2.Fsa.from_dict(
|
||||
torch.load(lg_filename, map_location=device)
|
||||
)
|
||||
decoding_graph.scores *= params.ngram_lm_scale
|
||||
else:
|
||||
word_table = None
|
||||
decoding_graph = k2.trivial_graph(
|
||||
params.vocab_size - 1, device=device
|
||||
)
|
||||
else:
|
||||
decoding_graph = None
|
||||
word_table = None
|
||||
|
||||
num_param = sum([p.numel() for p in model.parameters()])
|
||||
logging.info(f"Number of model parameters: {num_param}")
|
||||
|
||||
gigaspeech = GigaSpeechAsrDataModule(args)
|
||||
|
||||
dev_cuts = gigaspeech.dev_cuts()
|
||||
test_cuts = gigaspeech.test_cuts()
|
||||
|
||||
dev_dl = gigaspeech.test_dataloaders(dev_cuts)
|
||||
test_dl = gigaspeech.test_dataloaders(test_cuts)
|
||||
|
||||
test_sets = ["dev", "test"]
|
||||
test_dls = [dev_dl, test_dl]
|
||||
|
||||
for test_set, test_dl in zip(test_sets, test_dls):
|
||||
results_dict = decode_dataset(
|
||||
dl=test_dl,
|
||||
params=params,
|
||||
model=model,
|
||||
sp=sp,
|
||||
word_table=word_table,
|
||||
decoding_graph=decoding_graph,
|
||||
)
|
||||
|
||||
save_results(
|
||||
params=params,
|
||||
test_set_name=test_set,
|
||||
results_dict=results_dict,
|
||||
)
|
||||
|
||||
logging.info("Done!")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
1
egs/gigaspeech/ASR/pruned_transducer_stateless5/decode_stream.py
Symbolic link
1
egs/gigaspeech/ASR/pruned_transducer_stateless5/decode_stream.py
Symbolic link
@ -0,0 +1 @@
|
||||
../../../librispeech/ASR/pruned_transducer_stateless5/decode_stream.py
|
1
egs/gigaspeech/ASR/pruned_transducer_stateless5/decoder.py
Symbolic link
1
egs/gigaspeech/ASR/pruned_transducer_stateless5/decoder.py
Symbolic link
@ -0,0 +1 @@
|
||||
../../../librispeech/ASR/pruned_transducer_stateless5/decoder.py
|
@ -0,0 +1 @@
|
||||
../../../librispeech/ASR/pruned_transducer_stateless5/encoder_interface.py
|
1
egs/gigaspeech/ASR/pruned_transducer_stateless5/export.py
Symbolic link
1
egs/gigaspeech/ASR/pruned_transducer_stateless5/export.py
Symbolic link
@ -0,0 +1 @@
|
||||
../../../librispeech/ASR/pruned_transducer_stateless5/export.py
|
@ -0,0 +1 @@
|
||||
../conformer_ctc/gigaspeech_scoring.py
|
1
egs/gigaspeech/ASR/pruned_transducer_stateless5/joiner.py
Symbolic link
1
egs/gigaspeech/ASR/pruned_transducer_stateless5/joiner.py
Symbolic link
@ -0,0 +1 @@
|
||||
../../../librispeech/ASR/pruned_transducer_stateless5/joiner.py
|
1
egs/gigaspeech/ASR/pruned_transducer_stateless5/model.py
Symbolic link
1
egs/gigaspeech/ASR/pruned_transducer_stateless5/model.py
Symbolic link
@ -0,0 +1 @@
|
||||
../../../librispeech/ASR/pruned_transducer_stateless5/model.py
|
1
egs/gigaspeech/ASR/pruned_transducer_stateless5/optim.py
Symbolic link
1
egs/gigaspeech/ASR/pruned_transducer_stateless5/optim.py
Symbolic link
@ -0,0 +1 @@
|
||||
../../../librispeech/ASR/pruned_transducer_stateless5/optim.py
|
1
egs/gigaspeech/ASR/pruned_transducer_stateless5/scaling.py
Symbolic link
1
egs/gigaspeech/ASR/pruned_transducer_stateless5/scaling.py
Symbolic link
@ -0,0 +1 @@
|
||||
../../../librispeech/ASR/pruned_transducer_stateless5/scaling.py
|
@ -0,0 +1 @@
|
||||
../../../librispeech/ASR/pruned_transducer_stateless5/streaming_beam_search.py
|
673
egs/gigaspeech/ASR/pruned_transducer_stateless5/streaming_decode.py
Executable file
673
egs/gigaspeech/ASR/pruned_transducer_stateless5/streaming_decode.py
Executable file
@ -0,0 +1,673 @@
|
||||
#!/usr/bin/env python3
|
||||
# Copyright 2022 Xiaomi Corporation (Authors: Wei Kang, 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.
|
||||
|
||||
"""
|
||||
Usage:
|
||||
./pruned_transducer_stateless5/streaming_decode.py \
|
||||
--epoch 28 \
|
||||
--avg 15 \
|
||||
--left-context 32 \
|
||||
--decode-chunk-size 8 \
|
||||
--right-context 0 \
|
||||
--exp-dir ./pruned_transducer_stateless5/exp \
|
||||
--decoding_method greedy_search \
|
||||
--num-decode-streams 200
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
import math
|
||||
from pathlib import Path
|
||||
from typing import Dict, List, Optional, Tuple
|
||||
|
||||
import k2
|
||||
import numpy as np
|
||||
import sentencepiece as spm
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from asr_datamodule import GigaSpeechAsrDataModule
|
||||
from decode_stream import DecodeStream
|
||||
from gigaspeech_scoring import asr_text_post_processing
|
||||
from kaldifeat import Fbank, FbankOptions
|
||||
from lhotse import CutSet
|
||||
from streaming_beam_search import (
|
||||
fast_beam_search_one_best,
|
||||
greedy_search,
|
||||
modified_beam_search,
|
||||
)
|
||||
from torch.nn.utils.rnn import pad_sequence
|
||||
from train import add_model_arguments, get_params, get_transducer_model
|
||||
|
||||
from icefall.checkpoint import (
|
||||
average_checkpoints,
|
||||
average_checkpoints_with_averaged_model,
|
||||
find_checkpoints,
|
||||
load_checkpoint,
|
||||
)
|
||||
from icefall.utils import (
|
||||
AttributeDict,
|
||||
setup_logger,
|
||||
store_transcripts,
|
||||
str2bool,
|
||||
write_error_stats,
|
||||
)
|
||||
|
||||
LOG_EPS = math.log(1e-10)
|
||||
|
||||
|
||||
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 decoding.
|
||||
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_stateless5/exp",
|
||||
help="The experiment dir",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--bpe-model",
|
||||
type=str,
|
||||
default="data/lang_bpe_500/bpe.model",
|
||||
help="Path to the BPE model",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--decoding-method",
|
||||
type=str,
|
||||
default="greedy_search",
|
||||
help="""Supported decoding methods are:
|
||||
greedy_search
|
||||
modified_beam_search
|
||||
fast_beam_search
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--num_active_paths",
|
||||
type=int,
|
||||
default=4,
|
||||
help="""An interger indicating how many candidates we will keep for each
|
||||
frame. Used only when --decoding-method is modified_beam_search.""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--beam",
|
||||
type=float,
|
||||
default=4,
|
||||
help="""A floating point value to calculate the cutoff score during beam
|
||||
search (i.e., `cutoff = max-score - beam`), which is the same as the
|
||||
`beam` in Kaldi.
|
||||
Used only when --decoding-method is fast_beam_search""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--max-contexts",
|
||||
type=int,
|
||||
default=4,
|
||||
help="""Used only when --decoding-method is
|
||||
fast_beam_search""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--max-states",
|
||||
type=int,
|
||||
default=32,
|
||||
help="""Used only when --decoding-method is
|
||||
fast_beam_search""",
|
||||
)
|
||||
|
||||
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(
|
||||
"--decode-chunk-size",
|
||||
type=int,
|
||||
default=16,
|
||||
help="The chunk size for decoding (in frames after subsampling)",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--left-context",
|
||||
type=int,
|
||||
default=64,
|
||||
help="left context can be seen during decoding (in frames after subsampling)",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--right-context",
|
||||
type=int,
|
||||
default=0,
|
||||
help="right context can be seen during decoding (in frames after subsampling)",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--num-decode-streams",
|
||||
type=int,
|
||||
default=2000,
|
||||
help="The number of streams that can be decoded parallel.",
|
||||
)
|
||||
|
||||
add_model_arguments(parser)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
def post_processing(
|
||||
results: List[Tuple[List[str], List[str]]],
|
||||
) -> List[Tuple[List[str], List[str]]]:
|
||||
new_results = []
|
||||
for ref, hyp in results:
|
||||
new_ref = asr_text_post_processing(" ".join(ref)).split()
|
||||
new_hyp = asr_text_post_processing(" ".join(hyp)).split()
|
||||
new_results.append((new_ref, new_hyp))
|
||||
return new_results
|
||||
|
||||
|
||||
def decode_one_chunk(
|
||||
params: AttributeDict,
|
||||
model: nn.Module,
|
||||
decode_streams: List[DecodeStream],
|
||||
) -> List[int]:
|
||||
"""Decode one chunk frames of features for each decode_streams and
|
||||
return the indexes of finished streams in a List.
|
||||
|
||||
Args:
|
||||
params:
|
||||
It's the return value of :func:`get_params`.
|
||||
model:
|
||||
The neural model.
|
||||
decode_streams:
|
||||
A List of DecodeStream, each belonging to a utterance.
|
||||
Returns:
|
||||
Return a List containing which DecodeStreams are finished.
|
||||
"""
|
||||
device = model.device
|
||||
|
||||
features = []
|
||||
feature_lens = []
|
||||
states = []
|
||||
|
||||
processed_lens = []
|
||||
|
||||
for stream in decode_streams:
|
||||
feat, feat_len = stream.get_feature_frames(
|
||||
params.decode_chunk_size * params.subsampling_factor
|
||||
)
|
||||
features.append(feat)
|
||||
feature_lens.append(feat_len)
|
||||
states.append(stream.states)
|
||||
processed_lens.append(stream.done_frames)
|
||||
|
||||
feature_lens = torch.tensor(feature_lens, device=device)
|
||||
features = pad_sequence(features, batch_first=True, padding_value=LOG_EPS)
|
||||
|
||||
# if T is less than 7 there will be an error in time reduction layer,
|
||||
# because we subsample features with ((x_len - 1) // 2 - 1) // 2
|
||||
# we plus 2 here because we will cut off one frame on each size of
|
||||
# encoder_embed output as they see invalid paddings. so we need extra 2
|
||||
# frames.
|
||||
tail_length = 7 + (2 + params.right_context) * params.subsampling_factor
|
||||
if features.size(1) < tail_length:
|
||||
pad_length = tail_length - features.size(1)
|
||||
feature_lens += pad_length
|
||||
features = torch.nn.functional.pad(
|
||||
features,
|
||||
(0, 0, 0, pad_length),
|
||||
mode="constant",
|
||||
value=LOG_EPS,
|
||||
)
|
||||
|
||||
states = [
|
||||
torch.stack([x[0] for x in states], dim=2),
|
||||
torch.stack([x[1] for x in states], dim=2),
|
||||
]
|
||||
processed_lens = torch.tensor(processed_lens, device=device)
|
||||
|
||||
encoder_out, encoder_out_lens, states = model.encoder.streaming_forward(
|
||||
x=features,
|
||||
x_lens=feature_lens,
|
||||
states=states,
|
||||
left_context=params.left_context,
|
||||
right_context=params.right_context,
|
||||
processed_lens=processed_lens,
|
||||
)
|
||||
|
||||
encoder_out = model.joiner.encoder_proj(encoder_out)
|
||||
|
||||
if params.decoding_method == "greedy_search":
|
||||
greedy_search(
|
||||
model=model, encoder_out=encoder_out, streams=decode_streams
|
||||
)
|
||||
elif params.decoding_method == "fast_beam_search":
|
||||
processed_lens = processed_lens + encoder_out_lens
|
||||
fast_beam_search_one_best(
|
||||
model=model,
|
||||
encoder_out=encoder_out,
|
||||
processed_lens=processed_lens,
|
||||
streams=decode_streams,
|
||||
beam=params.beam,
|
||||
max_states=params.max_states,
|
||||
max_contexts=params.max_contexts,
|
||||
)
|
||||
elif params.decoding_method == "modified_beam_search":
|
||||
modified_beam_search(
|
||||
model=model,
|
||||
streams=decode_streams,
|
||||
encoder_out=encoder_out,
|
||||
num_active_paths=params.num_active_paths,
|
||||
)
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Unsupported decoding method: {params.decoding_method}"
|
||||
)
|
||||
|
||||
states = [torch.unbind(states[0], dim=2), torch.unbind(states[1], dim=2)]
|
||||
|
||||
finished_streams = []
|
||||
for i in range(len(decode_streams)):
|
||||
decode_streams[i].states = [states[0][i], states[1][i]]
|
||||
decode_streams[i].done_frames += encoder_out_lens[i]
|
||||
if decode_streams[i].done:
|
||||
finished_streams.append(i)
|
||||
|
||||
return finished_streams
|
||||
|
||||
|
||||
def decode_dataset(
|
||||
cuts: CutSet,
|
||||
params: AttributeDict,
|
||||
model: nn.Module,
|
||||
sp: spm.SentencePieceProcessor,
|
||||
decoding_graph: Optional[k2.Fsa] = None,
|
||||
) -> Dict[str, List[Tuple[List[str], List[str]]]]:
|
||||
"""Decode dataset.
|
||||
|
||||
Args:
|
||||
cuts:
|
||||
Lhotse Cutset containing the dataset to decode.
|
||||
params:
|
||||
It is returned by :func:`get_params`.
|
||||
model:
|
||||
The neural model.
|
||||
sp:
|
||||
The BPE model.
|
||||
decoding_graph:
|
||||
The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
|
||||
only when --decoding_method is fast_beam_search.
|
||||
Returns:
|
||||
Return a dict, whose key may be "greedy_search" if greedy search
|
||||
is used, or it may be "beam_7" if beam size of 7 is used.
|
||||
Its value is a list of tuples. Each tuple contains two elements:
|
||||
The first is the reference transcript, and the second is the
|
||||
predicted result.
|
||||
"""
|
||||
device = model.device
|
||||
|
||||
opts = FbankOptions()
|
||||
opts.device = device
|
||||
opts.frame_opts.dither = 0
|
||||
opts.frame_opts.snip_edges = False
|
||||
opts.frame_opts.samp_freq = 16000
|
||||
opts.mel_opts.num_bins = 80
|
||||
|
||||
log_interval = 20
|
||||
|
||||
decode_results = []
|
||||
# Contain decode streams currently running.
|
||||
decode_streams = []
|
||||
initial_states = model.encoder.get_init_state(
|
||||
params.left_context, device=device
|
||||
)
|
||||
for num, cut in enumerate(cuts):
|
||||
# each utterance has a DecodeStream.
|
||||
decode_stream = DecodeStream(
|
||||
params=params,
|
||||
initial_states=initial_states,
|
||||
decoding_graph=decoding_graph,
|
||||
device=device,
|
||||
)
|
||||
|
||||
audio: np.ndarray = cut.load_audio()
|
||||
# audio.shape: (1, num_samples)
|
||||
assert len(audio.shape) == 2
|
||||
assert audio.shape[0] == 1, "Should be single channel"
|
||||
assert audio.dtype == np.float32, audio.dtype
|
||||
|
||||
# The trained model is using normalized samples
|
||||
assert audio.max() <= 1, "Should be normalized to [-1, 1])"
|
||||
|
||||
samples = torch.from_numpy(audio).squeeze(0)
|
||||
|
||||
fbank = Fbank(opts)
|
||||
feature = fbank(samples.to(device))
|
||||
decode_stream.set_features(feature)
|
||||
decode_stream.ground_truth = cut.supervisions[0].text
|
||||
|
||||
decode_streams.append(decode_stream)
|
||||
|
||||
while len(decode_streams) >= params.num_decode_streams:
|
||||
finished_streams = decode_one_chunk(
|
||||
params=params, model=model, decode_streams=decode_streams
|
||||
)
|
||||
for i in sorted(finished_streams, reverse=True):
|
||||
decode_results.append(
|
||||
(
|
||||
decode_streams[i].ground_truth.split(),
|
||||
sp.decode(decode_streams[i].decoding_result()).split(),
|
||||
)
|
||||
)
|
||||
del decode_streams[i]
|
||||
|
||||
if num % log_interval == 0:
|
||||
logging.info(f"Cuts processed until now is {num}.")
|
||||
|
||||
# decode final chunks of last sequences
|
||||
while len(decode_streams):
|
||||
finished_streams = decode_one_chunk(
|
||||
params=params, model=model, decode_streams=decode_streams
|
||||
)
|
||||
for i in sorted(finished_streams, reverse=True):
|
||||
decode_results.append(
|
||||
(
|
||||
decode_streams[i].ground_truth.split(),
|
||||
sp.decode(decode_streams[i].decoding_result()).split(),
|
||||
)
|
||||
)
|
||||
del decode_streams[i]
|
||||
|
||||
if params.decoding_method == "greedy_search":
|
||||
key = "greedy_search"
|
||||
elif params.decoding_method == "fast_beam_search":
|
||||
key = (
|
||||
f"beam_{params.beam}_"
|
||||
f"max_contexts_{params.max_contexts}_"
|
||||
f"max_states_{params.max_states}"
|
||||
)
|
||||
elif params.decoding_method == "modified_beam_search":
|
||||
key = f"num_active_paths_{params.num_active_paths}"
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Unsupported decoding method: {params.decoding_method}"
|
||||
)
|
||||
return {key: decode_results}
|
||||
|
||||
|
||||
def save_results(
|
||||
params: AttributeDict,
|
||||
test_set_name: str,
|
||||
results_dict: Dict[str, List[Tuple[List[str], List[str]]]],
|
||||
):
|
||||
test_set_wers = dict()
|
||||
for key, results in results_dict.items():
|
||||
recog_path = (
|
||||
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
|
||||
)
|
||||
results = sorted(results)
|
||||
results = post_processing(results)
|
||||
store_transcripts(filename=recog_path, texts=results)
|
||||
logging.info(f"The transcripts are stored in {recog_path}")
|
||||
|
||||
# The following prints out WERs, per-word error statistics and aligned
|
||||
# ref/hyp pairs.
|
||||
errs_filename = (
|
||||
params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
|
||||
)
|
||||
with open(errs_filename, "w") as f:
|
||||
wer = write_error_stats(
|
||||
f, f"{test_set_name}-{key}", results, enable_log=True
|
||||
)
|
||||
test_set_wers[key] = wer
|
||||
|
||||
logging.info("Wrote detailed error stats to {}".format(errs_filename))
|
||||
|
||||
test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
|
||||
errs_info = (
|
||||
params.res_dir
|
||||
/ f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
|
||||
)
|
||||
with open(errs_info, "w") as f:
|
||||
print("settings\tWER", file=f)
|
||||
for key, val in test_set_wers:
|
||||
print("{}\t{}".format(key, val), file=f)
|
||||
|
||||
s = "\nFor {}, WER of different settings are:\n".format(test_set_name)
|
||||
note = "\tbest for {}".format(test_set_name)
|
||||
for key, val in test_set_wers:
|
||||
s += "{}\t{}{}\n".format(key, val, note)
|
||||
note = ""
|
||||
logging.info(s)
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def main():
|
||||
parser = get_parser()
|
||||
GigaSpeechAsrDataModule.add_arguments(parser)
|
||||
args = parser.parse_args()
|
||||
args.exp_dir = Path(args.exp_dir)
|
||||
|
||||
params = get_params()
|
||||
params.update(vars(args))
|
||||
|
||||
params.res_dir = params.exp_dir / "streaming" / params.decoding_method
|
||||
|
||||
if params.iter > 0:
|
||||
params.suffix = f"iter-{params.iter}-avg-{params.avg}"
|
||||
else:
|
||||
params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
|
||||
|
||||
# for streaming
|
||||
params.suffix += f"-streaming-chunk-size-{params.decode_chunk_size}"
|
||||
params.suffix += f"-left-context-{params.left_context}"
|
||||
params.suffix += f"-right-context-{params.right_context}"
|
||||
|
||||
# for fast_beam_search
|
||||
if params.decoding_method == "fast_beam_search":
|
||||
params.suffix += f"-beam-{params.beam}"
|
||||
params.suffix += f"-max-contexts-{params.max_contexts}"
|
||||
params.suffix += f"-max-states-{params.max_states}"
|
||||
|
||||
if params.use_averaged_model:
|
||||
params.suffix += "-use-averaged-model"
|
||||
|
||||
setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")
|
||||
logging.info("Decoding started")
|
||||
|
||||
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> and <unk> is defined in local/train_bpe_model.py
|
||||
params.blank_id = sp.piece_to_id("<blk>")
|
||||
params.unk_id = sp.piece_to_id("<unk>")
|
||||
params.vocab_size = sp.get_piece_size()
|
||||
|
||||
# Decoding in streaming requires causal convolution
|
||||
params.causal_convolution = True
|
||||
|
||||
logging.info(params)
|
||||
|
||||
logging.info("About to create model")
|
||||
model = get_transducer_model(params)
|
||||
|
||||
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 start >= 0:
|
||||
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(device)
|
||||
model.eval()
|
||||
model.device = device
|
||||
|
||||
decoding_graph = None
|
||||
if params.decoding_method == "fast_beam_search":
|
||||
decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
|
||||
|
||||
num_param = sum([p.numel() for p in model.parameters()])
|
||||
logging.info(f"Number of model parameters: {num_param}")
|
||||
|
||||
gigaspeech = GigaSpeechAsrDataModule(args)
|
||||
|
||||
dev_cuts = gigaspeech.dev_cuts()
|
||||
test_cuts = gigaspeech.test_cuts()
|
||||
|
||||
test_sets = ["dev", "test"]
|
||||
test_cuts = [dev_cuts, test_cuts]
|
||||
|
||||
for test_set, test_cut in zip(test_sets, test_cuts):
|
||||
results_dict = decode_dataset(
|
||||
cuts=test_cut,
|
||||
params=params,
|
||||
model=model,
|
||||
sp=sp,
|
||||
decoding_graph=decoding_graph,
|
||||
)
|
||||
|
||||
save_results(
|
||||
params=params,
|
||||
test_set_name=test_set,
|
||||
results_dict=results_dict,
|
||||
)
|
||||
|
||||
logging.info("Done!")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
1160
egs/gigaspeech/ASR/pruned_transducer_stateless5/train.py
Executable file
1160
egs/gigaspeech/ASR/pruned_transducer_stateless5/train.py
Executable file
File diff suppressed because it is too large
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Reference in New Issue
Block a user