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Merge branch 'k2-fsa:master' into master

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Zengwei Yao 2022-08-29 15:18:11 +08:00 committed by GitHub
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29 changed files with 186 additions and 123 deletions
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9
egs/aidatatang_200zh/ASR/local/compute_fbank_aidatatang_200zh.py
View File

@ -43,7 +43,7 @@ torch.set_num_interop_threads(1)
def compute_fbank_aidatatang_200zh(num_mel_bins: int = 80):
src_dir = Path("data/manifests")
src_dir = Path("data/manifests/aidatatang_200zh")
output_dir = Path("data/fbank")
num_jobs = min(15, os.cpu_count())
@ -62,6 +62,13 @@ def compute_fbank_aidatatang_200zh(num_mel_bins: int = 80):
)
assert manifests is not None
assert len(manifests) == len(dataset_parts), (
len(manifests),
len(dataset_parts),
list(manifests.keys()),
dataset_parts,
)
extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
with get_executor() as ex: # Initialize the executor only once.

62
egs/aidatatang_200zh/ASR/prepare.sh
View File

@ -50,28 +50,19 @@ if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
fi
if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
log "Stage 2: Process aidatatang_200zh"
if [ ! -f data/fbank/aidatatang_200zh/.fbank.done ]; then
mkdir -p data/fbank/aidatatang_200zh
lhotse prepare aidatatang-200zh $dl_dir data/manifests/aidatatang_200zh
touch data/fbank/aidatatang_200zh/.fbank.done
log "Stage 2: Prepare musan manifest"
# We assume that you have downloaded the musan corpus
# to data/musan
if [ ! -f data/manifests/.manifests.done ]; then
log "It may take 6 minutes"
mkdir -p data/manifests/
lhotse prepare musan $dl_dir/musan data/manifests/
touch data/manifests/.manifests.done
fi
fi
if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
log "Stage 3: Prepare musan manifest"
# We assume that you have downloaded the musan corpus
# to data/musan
if [ ! -f data/manifests/.musan_manifests.done ]; then
log "It may take 6 minutes"
mkdir -p data/manifests
lhotse prepare musan $dl_dir/musan data/manifests
touch data/manifests/.musan_manifests.done
fi
fi
if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
log "Stage 4: Compute fbank for musan"
log "Stage 3: Compute fbank for musan"
if [ ! -f data/fbank/.msuan.done ]; then
mkdir -p data/fbank
./local/compute_fbank_musan.py
@ -79,8 +70,8 @@ if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
fi
fi
if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
log "Stage 5: Compute fbank for aidatatang_200zh"
if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
log "Stage 4: Compute fbank for aidatatang_200zh"
if [ ! -f data/fbank/.aidatatang_200zh.done ]; then
mkdir -p data/fbank
./local/compute_fbank_aidatatang_200zh.py
@ -88,31 +79,38 @@ if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
fi
fi
if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
log "Stage 6: Prepare char based lang"
if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
log "Stage 5: Prepare char based lang"
lang_char_dir=data/lang_char
mkdir -p $lang_char_dir
# Prepare text.
grep "\"text\":" data/manifests/aidatatang_200zh/supervisions_train.json \
| sed -e 's/["text:\t ]*//g' | sed 's/,//g' \
| ./local/text2token.py -t "char" > $lang_char_dir/text
# Note: in Linux, you can install jq with the following command:
# 1. wget -O jq https://github.com/stedolan/jq/releases/download/jq-1.6/jq-linux64
# 2. chmod +x ./jq
# 3. cp jq /usr/bin
if [ ! -f $lang_char_dir/text ]; then
gunzip -c data/manifests/aidatatang_200zh/aidatatang_supervisions_train.jsonl.gz \
|jq '.text' |sed -e 's/["text:\t ]*//g' | sed 's/"//g' \
| ./local/text2token.py -t "char" > $lang_char_dir/text
fi
# Prepare words.txt
grep "\"text\":" data/manifests/aidatatang_200zh/supervisions_train.json \
| sed -e 's/["text:\t]*//g' | sed 's/,//g' \
| ./local/text2token.py -t "char" > $lang_char_dir/text_words
if [ ! -f $lang_char_dir/text_words ]; then
gunzip -c data/manifests/aidatatang_200zh/aidatatang_supervisions_train.jsonl.gz \
| jq '.text' | sed -e 's/["text:\t]*//g' | sed 's/"//g' \
| ./local/text2token.py -t "char" > $lang_char_dir/text_words
fi
cat $lang_char_dir/text_words | sed 's/ /\n/g' | sort -u | sed '/^$/d' \
| uniq > $lang_char_dir/words_no_ids.txt
if [ ! -f $lang_char_dir/words.txt ]; then
./local/prepare_words.py \
--input-file $lang_char_dir/words_no_ids.txt
--output-file $lang_char_dir/words.txt
--input-file $lang_char_dir/words_no_ids.txt \
--output-file $lang_char_dir/words.txt
fi
if [ ! -f $lang_char_dir/L_disambig.pt ]; then
./local/prepare_char.py
fi
fi

57
egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/decode.py
View File

@ -522,63 +522,14 @@ def main():
num_param = sum([p.numel() for p in model.parameters()])
logging.info(f"Number of model parameters: {num_param}")
# Note: Please use "pip install webdataset==0.1.103"
# for installing the webdataset.
import glob
import os
from lhotse import CutSet
from lhotse.dataset.webdataset import export_to_webdataset
# we need cut ids to display recognition results.
args.return_cuts = True
aidatatang_200zh = Aidatatang_200zhAsrDataModule(args)
dev = "dev"
test = "test"
if not os.path.exists(f"{dev}/shared-0.tar"):
os.makedirs(dev)
dev_cuts = aidatatang_200zh.valid_cuts()
export_to_webdataset(
dev_cuts,
output_path=f"{dev}/shared-%d.tar",
shard_size=300,
)
if not os.path.exists(f"{test}/shared-0.tar"):
os.makedirs(test)
test_cuts = aidatatang_200zh.test_cuts()
export_to_webdataset(
test_cuts,
output_path=f"{test}/shared-%d.tar",
shard_size=300,
)
dev_shards = [
str(path)
for path in sorted(glob.glob(os.path.join(dev, "shared-*.tar")))
]
cuts_dev_webdataset = CutSet.from_webdataset(
dev_shards,
split_by_worker=True,
split_by_node=True,
shuffle_shards=True,
)
test_shards = [
str(path)
for path in sorted(glob.glob(os.path.join(test, "shared-*.tar")))
]
cuts_test_webdataset = CutSet.from_webdataset(
test_shards,
split_by_worker=True,
split_by_node=True,
shuffle_shards=True,
)
dev_dl = aidatatang_200zh.valid_dataloaders(cuts_dev_webdataset)
test_dl = aidatatang_200zh.test_dataloaders(cuts_test_webdataset)
dev_cuts = aidatatang_200zh.valid_cuts()
test_cuts = aidatatang_200zh.test_cuts()
dev_dl = aidatatang_200zh.valid_dataloaders(dev_cuts)
test_dl = aidatatang_200zh.test_dataloaders(test_cuts)
test_sets = ["dev", "test"]
test_dl = [dev_dl, test_dl]

7
egs/aishell/ASR/local/compute_fbank_aidatatang_200zh.py
View File

@ -62,6 +62,13 @@ def compute_fbank_aidatatang_200zh(num_mel_bins: int = 80):
)
assert manifests is not None
assert len(manifests) == len(dataset_parts), (
len(manifests),
len(dataset_parts),
list(manifests.keys()),
dataset_parts,
)
extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
with get_executor() as ex: # Initialize the executor only once.

7
egs/aishell/ASR/local/compute_fbank_aishell.py
View File

@ -62,6 +62,13 @@ def compute_fbank_aishell(num_mel_bins: int = 80):
)
assert manifests is not None
assert len(manifests) == len(dataset_parts), (
len(manifests),
len(dataset_parts),
list(manifests.keys()),
dataset_parts,
)
extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
with get_executor() as ex: # Initialize the executor only once.

7
egs/aishell2/ASR/local/compute_fbank_aishell2.py
View File

@ -62,6 +62,13 @@ def compute_fbank_aishell2(num_mel_bins: int = 80):
)
assert manifests is not None
assert len(manifests) == len(dataset_parts), (
len(manifests),
len(dataset_parts),
list(manifests.keys()),
dataset_parts,
)
extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
with get_executor() as ex: # Initialize the executor only once.

7
egs/aishell4/ASR/local/compute_fbank_aishell4.py
View File

@ -63,6 +63,13 @@ def compute_fbank_aishell4(num_mel_bins: int = 80):
)
assert manifests is not None
assert len(manifests) == len(dataset_parts), (
len(manifests),
len(dataset_parts),
list(manifests.keys()),
dataset_parts,
)
extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
with get_executor() as ex: # Initialize the executor only once.

9
egs/alimeeting/ASR/local/compute_fbank_alimeeting.py
View File

@ -43,7 +43,7 @@ torch.set_num_interop_threads(1)
def compute_fbank_alimeeting(num_mel_bins: int = 80):
src_dir = Path("data/manifests")
src_dir = Path("data/manifests/alimeeting")
output_dir = Path("data/fbank")
num_jobs = min(15, os.cpu_count())
@ -63,6 +63,13 @@ def compute_fbank_alimeeting(num_mel_bins: int = 80):
)
assert manifests is not None
assert len(manifests) == len(dataset_parts), (
len(manifests),
len(dataset_parts),
list(manifests.keys()),
dataset_parts,
)
extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
with get_executor() as ex: # Initialize the executor only once.

2
egs/alimeeting/ASR/local/text2segments.py
View File

@ -30,9 +30,11 @@ with word segmenting:
import argparse
import paddle
import jieba
from tqdm import tqdm
paddle.enable_static()
jieba.enable_paddle()

2
egs/alimeeting/ASR/prepare.sh
View File

@ -107,7 +107,7 @@ if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
# Prepare text.
# Note: in Linux, you can install jq with the following command:
# wget -O jq https://github.com/stedolan/jq/releases/download/jq-1.6/jq-linux64
gunzip -c data/manifests/alimeeting/supervisions_train.jsonl.gz \
gunzip -c data/manifests/alimeeting/alimeeting_supervisions_train.jsonl.gz \
| jq ".text" | sed 's/"//g' \
| ./local/text2token.py -t "char" > $lang_char_dir/text

7
egs/gigaspeech/ASR/local/preprocess_gigaspeech.py
View File

@ -62,6 +62,13 @@ def preprocess_giga_speech():
)
assert manifests is not None
assert len(manifests) == len(dataset_parts), (
len(manifests),
len(dataset_parts),
list(manifests.keys()),
dataset_parts,
)
for partition, m in manifests.items():
logging.info(f"Processing {partition}")
raw_cuts_path = output_dir / f"cuts_{partition}_raw.jsonl.gz"

4
egs/librispeech/ASR/distillation_with_hubert.sh
View File

@ -81,9 +81,9 @@ if [ $stage -le 0 ] && [ $stop_stage -ge 0 ] && [ ! "$use_extracted_codebook" ==
# or
# pip install multi_quantization
has_quantization=$(python3 -c "import importlib; print(importlib.util.find_spec('quantization') is not None)")
has_quantization=$(python3 -c "import importlib; print(importlib.util.find_spec('multi_quantization') is not None)")
if [ $has_quantization == 'False' ]; then
log "Please install quantization before running following stages"
log "Please install multi_quantization before running following stages"
exit 1
fi

7
egs/librispeech/ASR/local/compute_fbank_librispeech.py
View File

@ -66,6 +66,13 @@ def compute_fbank_librispeech():
)
assert manifests is not None
assert len(manifests) == len(dataset_parts), (
len(manifests),
len(dataset_parts),
list(manifests.keys()),
dataset_parts,
)
extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
with get_executor() as ex: # Initialize the executor only once.

2
egs/librispeech/ASR/local/compute_fbank_musan.py
View File

@ -65,6 +65,8 @@ def compute_fbank_musan():
assert len(manifests) == len(dataset_parts), (
len(manifests),
len(dataset_parts),
list(manifests.keys()),
dataset_parts,
)
musan_cuts_path = output_dir / "musan_cuts.jsonl.gz"

7
egs/librispeech/ASR/local/preprocess_gigaspeech.py
View File

@ -68,6 +68,13 @@ def preprocess_giga_speech():
)
assert manifests is not None
assert len(manifests) == len(dataset_parts), (
len(manifests),
len(dataset_parts),
list(manifests.keys()),
dataset_parts,
)
for partition, m in manifests.items():
logging.info(f"Processing {partition}")
raw_cuts_path = output_dir / f"{prefix}_cuts_{partition}_raw.{suffix}"

4
egs/librispeech/ASR/pruned_transducer_stateless2/optim.py
View File

@ -164,6 +164,10 @@ class Eve(Optimizer):
p.mul_(1 - (weight_decay * is_above_target_rms))
p.addcdiv_(exp_avg, denom, value=-step_size)
# Constrain the range of scalar weights
if p.numel() == 1:
p.clamp_(min=-10, max=2)
return loss

6
egs/librispeech/ASR/pruned_transducer_stateless3/export.py
View File

@ -652,13 +652,13 @@ def main():
# Also export encoder/decoder/joiner separately
encoder_filename = params.exp_dir / "encoder_jit_script.pt"
export_encoder_model_jit_trace(model.encoder, encoder_filename)
export_encoder_model_jit_script(model.encoder, encoder_filename)
decoder_filename = params.exp_dir / "decoder_jit_script.pt"
export_decoder_model_jit_trace(model.decoder, decoder_filename)
export_decoder_model_jit_script(model.decoder, decoder_filename)
joiner_filename = params.exp_dir / "joiner_jit_script.pt"
export_joiner_model_jit_trace(model.joiner, joiner_filename)
export_joiner_model_jit_script(model.joiner, joiner_filename)
elif params.jit_trace is True:
convert_scaled_to_non_scaled(model, inplace=True)

2
egs/librispeech/ASR/pruned_transducer_stateless3/test_scaling_converter.py
View File

@ -181,7 +181,7 @@ def test_convert_scaled_to_non_scaled():
y = torch.randint(low=1, high=vocab_size - 1, size=(N, U))
d1 = model.decoder(y)
d2 = model.decoder(y)
d2 = converted_model.decoder(y)
assert torch.allclose(d1, d2)

11
egs/librispeech/ASR/pruned_transducer_stateless6/hubert_decode.py
View File

@ -81,18 +81,17 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
# hyps is a list, every element is decode result of a sentence.
hyps = hubert_model.ctc_greedy_search(batch)
texts = batch["supervisions"]["text"]
assert len(hyps) == len(texts)
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
this_batch = []
for hyp_text, ref_text in zip(hyps, texts):
assert len(hyps) == len(texts)
for cut_id, hyp_text, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
hyp_words = hyp_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results["ctc_greedy_search"].extend(this_batch)
num_cuts += len(texts)

2
egs/librispeech/ASR/pruned_transducer_stateless6/vq_utils.py
View File

@ -28,7 +28,7 @@ from typing import List, Tuple
import numpy as np
import torch
import torch.multiprocessing as mp
import quantization
import multi_quantization as quantization
from asr_datamodule import LibriSpeechAsrDataModule
from hubert_xlarge import HubertXlargeFineTuned

7
egs/spgispeech/ASR/local/compute_fbank_musan.py
View File

@ -69,6 +69,13 @@ def compute_fbank_musan():
)
assert manifests is not None
assert len(manifests) == len(dataset_parts), (
len(manifests),
len(dataset_parts),
list(manifests.keys()),
dataset_parts,
)
musan_cuts_path = src_dir / "cuts_musan.jsonl.gz"
if musan_cuts_path.is_file():

7
egs/tal_csasr/ASR/local/compute_fbank_tal_csasr.py
View File

@ -62,6 +62,13 @@ def compute_fbank_tal_csasr(num_mel_bins: int = 80):
)
assert manifests is not None
assert len(manifests) == len(dataset_parts), (
len(manifests),
len(dataset_parts),
list(manifests.keys()),
dataset_parts,
)
extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
with get_executor() as ex: # Initialize the executor only once.

7
egs/tedlium3/ASR/local/compute_fbank_tedlium.py
View File

@ -62,6 +62,13 @@ def compute_fbank_tedlium():
)
assert manifests is not None
assert len(manifests) == len(dataset_parts), (
len(manifests),
len(dataset_parts),
list(manifests.keys()),
dataset_parts,
)
extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
with get_executor() as ex: # Initialize the executor only once.

7
egs/timit/ASR/local/compute_fbank_timit.py
View File

@ -63,6 +63,13 @@ def compute_fbank_timit():
)
assert manifests is not None
assert len(manifests) == len(dataset_parts), (
len(manifests),
len(dataset_parts),
list(manifests.keys()),
dataset_parts,
)
extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
with get_executor() as ex: # Initialize the executor only once.

32
egs/wenetspeech/ASR/local/preprocess_wenetspeech.py
View File

@ -23,6 +23,8 @@ from pathlib import Path
from lhotse import CutSet, SupervisionSegment
from lhotse.recipes.utils import read_manifests_if_cached
from icefall import setup_logger
# Similar text filtering and normalization procedure as in:
# https://github.com/SpeechColab/WenetSpeech/blob/main/toolkits/kaldi/wenetspeech_data_prep.sh
@ -48,13 +50,17 @@ def preprocess_wenet_speech():
output_dir = Path("data/fbank")
output_dir.mkdir(exist_ok=True)
# Note: By default, we preprocess all sub-parts.
# You can delete those that you don't need.
# For instance, if you don't want to use the L subpart, just remove
# the line below containing "L"
dataset_parts = (
"L",
"M",
"S",
"DEV",
"TEST_NET",
"TEST_MEETING",
"S",
"M",
"L",
)
logging.info("Loading manifest (may take 10 minutes)")
@ -66,6 +72,13 @@ def preprocess_wenet_speech():
)
assert manifests is not None
assert len(manifests) == len(dataset_parts), (
len(manifests),
len(dataset_parts),
list(manifests.keys()),
dataset_parts,
)
for partition, m in manifests.items():
logging.info(f"Processing {partition}")
raw_cuts_path = output_dir / f"cuts_{partition}_raw.jsonl.gz"
@ -81,10 +94,13 @@ def preprocess_wenet_speech():
logging.info(f"Normalizing text in {partition}")
for sup in m["supervisions"]:
text = str(sup.text)
logging.info(f"Original text: {text}")
orig_text = text
sup.text = normalize_text(sup.text)
text = str(sup.text)
logging.info(f"Normalize text: {text}")
if len(orig_text) != len(text):
logging.info(
f"\nOriginal text vs normalized text:\n{orig_text}\n{text}"
)
# Create long-recording cut manifests.
logging.info(f"Processing {partition}")
@ -109,12 +125,10 @@ def preprocess_wenet_speech():
def main():
formatter = (
"%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
)
logging.basicConfig(format=formatter, level=logging.INFO)
setup_logger(log_filename="./log-preprocess-wenetspeech")
preprocess_wenet_speech()
logging.info("Done")
if __name__ == "__main__":

14
egs/wenetspeech/ASR/pruned_transducer_stateless2/train.py
View File

@ -81,7 +81,6 @@ For training with the S subset:
import argparse
import logging
import os
import warnings
from pathlib import Path
from shutil import copyfile
@ -120,8 +119,6 @@ LRSchedulerType = Union[
torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler
]
os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
def get_parser():
parser = argparse.ArgumentParser(
@ -162,7 +159,7 @@ def get_parser():
default=0,
help="""Resume training from from this epoch.
If it is positive, it will load checkpoint from
transducer_stateless2/exp/epoch-{start_epoch-1}.pt
pruned_transducer_stateless2/exp/epoch-{start_epoch-1}.pt
""",
)
@ -361,8 +358,8 @@ def get_params() -> AttributeDict:
"best_valid_loss": float("inf"),
"best_train_epoch": -1,
"best_valid_epoch": -1,
"batch_idx_train": 10,
"log_interval": 1,
"batch_idx_train": 0,
"log_interval": 50,
"reset_interval": 200,
# parameters for conformer
"feature_dim": 80,
@ -545,7 +542,7 @@ def compute_loss(
warmup: float = 1.0,
) -> Tuple[Tensor, MetricsTracker]:
"""
Compute CTC loss given the model and its inputs.
Compute RNN-T loss given the model and its inputs.
Args:
params:
Parameters for training. See :func:`get_params`.
@ -573,7 +570,7 @@ def compute_loss(
texts = batch["supervisions"]["text"]
y = graph_compiler.texts_to_ids(texts)
if type(y) == list:
if isinstance(y, list):
y = k2.RaggedTensor(y).to(device)
else:
y = y.to(device)
@ -697,7 +694,6 @@ def train_one_epoch(
tot_loss = MetricsTracker()
for batch_idx, batch in enumerate(train_dl):
params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"])

5
egs/wenetspeech/ASR/pruned_transducer_stateless5/train.py
View File

@ -61,7 +61,6 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
import argparse
import copy
import logging
import os
import warnings
from pathlib import Path
from shutil import copyfile
@ -103,8 +102,6 @@ LRSchedulerType = Union[
torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler
]
os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
def add_model_arguments(parser: argparse.ArgumentParser):
parser.add_argument(
@ -684,7 +681,7 @@ def compute_loss(
texts = batch["supervisions"]["text"]
y = graph_compiler.texts_to_ids(texts)
if type(y) == list:
if isinstance(y, list):
y = k2.RaggedTensor(y).to(device)
else:
y = y.to(device)

7
egs/yesno/ASR/local/compute_fbank_yesno.py
View File

@ -47,6 +47,13 @@ def compute_fbank_yesno():
)
assert manifests is not None
assert len(manifests) == len(dataset_parts), (
len(manifests),
len(dataset_parts),
list(manifests.keys()),
dataset_parts,
)
extractor = Fbank(
FbankConfig(sampling_rate=8000, num_mel_bins=num_mel_bins)
)

2
icefall/diagnostics.py
View File

@ -130,6 +130,8 @@ class TensorDiagnostic(object):
x = x[0]
if not isinstance(x, Tensor):
return
if x.numel() == 0: # for empty tensor
return
x = x.detach().clone()
if x.ndim == 0:
x = x.unsqueeze(0)