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add wenetspeech fine-tune scripts

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Yuekai Zhang 2024-01-25 13:53:46 +08:00
parent baa7c5fb8d
commit e1a55b945b
7 changed files with 2488 additions and 165 deletions
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4
egs/wenetspeech/ASR/local/compute_fbank_wenetspeech_splits.py
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@ -26,8 +26,8 @@ from lhotse import (
CutSet,
WhisperFbank,
WhisperFbankConfig,
KaldifeatWhisperFbank,
KaldifeatWhisperFbankConfig,
# KaldifeatWhisperFbank,
# KaldifeatWhisperFbankConfig,
KaldifeatFbank,
KaldifeatFbankConfig,
LilcomChunkyWriter,

26
egs/wenetspeech/ASR/prepare.sh
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@ -211,29 +211,13 @@ if [ $stage -le 130 ] && [ $stop_stage -ge 130 ]; then
fi
if [ $stage -le 131 ] && [ $stop_stage -ge 131 ]; then
log "Stage 131: test"
python3 ./local/compute_fbank_wenetspeech_splits.py \
--training-subset L \
--num-workers 8 \
--batch-duration 1000 \
--start 48 \
--stop 68 \
--num-mel-bins ${whisper_mel_bins} --whisper-fbank true \
--num-splits $num_splits
log "Stage 131: concat feats into train set"
if [ ! -f data/fbank/cuts_L.jsonl.gz ]; then
pieces=$(find data/fbank/L_split_1000 -name "cuts_L.*.jsonl.gz")
lhotse combine $pieces data/fbank/cuts_L.jsonl.gz
fi
fi
if [ $stage -le 132 ] && [ $stop_stage -ge 132 ]; then
log "Stage 132: test"
python3 ./local/compute_fbank_wenetspeech_splits.py \
--training-subset L \
--num-workers 8 \
--batch-duration 1000 \
--start 68 \
--num-mel-bins ${whisper_mel_bins} --whisper-fbank true \
--num-splits $num_splits
fi
if [ $stage -le 14 ] && [ $stop_stage -ge 14 ]; then
log "Stage 14: Compute fbank for musan"

304
egs/wenetspeech/ASR/whisper/decode.py
View File

@ -2,6 +2,7 @@
# Copyright 2021 Xiaomi Corporation (Author: Liyong Guo,
# Fangjun Kuang,
# Wei Kang)
# 2024 Yuekai Zhang
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
@ -16,47 +17,64 @@
# 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:
# Command for decoding using fine-tuned models:
git lfs install
git clone https://huggingface.co/yuekai/icefall_asr_aishell_whisper
ln -s icefall_asr_aishell_whisper/exp_large_v2/epoch-10-avg6.pt whisper/exp_large_v2/epoch-999.pt
python3 ./whisper/decode.py \
--exp-dir whisper/exp_large_v2 \
--model-name large-v2 \
--epoch 999 --avg 1 \
--beam-size 10 --max-duration 50
# Command for decoding using pretrained models (before fine-tuning):
python3 ./whisper/decode.py \
--exp-dir whisper/exp_large_v2 \
--model-name large-v2 \
--epoch -1 --avg 1 \
--remove-whisper-encoder-input-length-restriction False \
--beam-size 10 --max-duration 50
"""
import argparse
import logging
import re
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import whisper
from whisper.normalizers import BasicTextNormalizer
import k2
import torch
import torch.nn as nn
from asr_datamodule import WenetSpeechAsrDataModule
from model import load_model
import whisper
from icefall.checkpoint import load_checkpoint, average_checkpoints_with_averaged_model
from icefall.decode import (
get_lattice,
nbest_decoding,
nbest_oracle,
one_best_decoding,
rescore_with_attention_decoder,
)
from lhotse.cut import Cut
from asr_datamodule import WenetSpeechAsrDataModule
from tn.chinese.normalizer import Normalizer
from whisper.normalizers import BasicTextNormalizer
from whisper_encoder_forward_monkey_patch import replace_whisper_encoder_forward
from zhconv import convert
from icefall.checkpoint import average_checkpoints_with_averaged_model, load_checkpoint
from icefall.env import get_env_info
from icefall.lexicon import Lexicon
from icefall.utils import (
AttributeDict,
get_texts,
setup_logger,
store_transcripts,
str2bool,
write_error_stats,
)
from zhconv import convert
from tn.chinese.normalizer import Normalizer
import re
def average_checkpoints(
filenames: List[Path], device: torch.device = torch.device("cpu")
) -> dict:
"""Average a list of checkpoints.
The function is mainly used for deepspeed converted checkpoint averaging, which only include model state_dict.
Args:
filenames:
@ -71,9 +89,9 @@ def average_checkpoints(
n = len(filenames)
if "model" in torch.load(filenames[0], map_location=device):
avg = torch.load(filenames[0], map_location=device)["model"]
avg = torch.load(filenames[0], map_location=device)["model"]
else:
avg = torch.load(filenames[0], map_location=device)
avg = torch.load(filenames[0], map_location=device)
# Identify shared parameters. Two parameters are said to be shared
# if they have the same data_ptr
@ -89,9 +107,9 @@ def average_checkpoints(
for i in range(1, n):
if "model" in torch.load(filenames[i], map_location=device):
state_dict = torch.load(filenames[i], map_location=device)["model"]
state_dict = torch.load(filenames[i], map_location=device)["model"]
else:
state_dict = torch.load(filenames[i], map_location=device)
state_dict = torch.load(filenames[i], map_location=device)
for k in uniqued_names:
avg[k] += state_dict[k]
@ -103,33 +121,48 @@ def average_checkpoints(
return avg
def remove_punctuation(text: str or List[str]):
# https://github.com/yeyupiaoling/Whisper-Finetune/blob/master/utils/data_utils.py
punctuation = '!,.;:?、！，。；：？'
"""Modified from https://github.com/yeyupiaoling/Whisper-Finetune/blob/master/utils/data_utils.py
Args:
text: It can be a string or a list of strings.
Returns:
Return a string or a list of strings without any punctuation.
"""
punctuation = "!,.;:?、！，。；：？《》 "
if isinstance(text, str):
text = re.sub(r'[{}]+'.format(punctuation), '', text).strip()
text = re.sub(r"[{}]+".format(punctuation), "", text).strip()
return text
elif isinstance(text, list):
result_text = []
for t in text:
t = re.sub(r'[{}]+'.format(punctuation), '', t).strip()
t = re.sub(r"[{}]+".format(punctuation), "", t).strip()
result_text.append(t)
return result_text
else:
raise Exception(f'Not support type {type(text)}')
raise Exception(f"Not support type {type(text)}")
def to_simple(text: str or List[str]):
"""Convert traditional Chinese to simplified Chinese.
Args:
text: It can be a string or a list of strings.
Returns:
Return a string or a list of strings converted to simplified Chinese.
"""
if isinstance(text, str):
text = convert(text, 'zh-cn')
text = convert(text, "zh-cn")
return text
elif isinstance(text, list):
result_text = []
for t in text:
t = convert(t, 'zh-cn')
t = convert(t, "zh-cn")
result_text.append(t)
return result_text
else:
raise Exception(f'Not support type{type(text)}')
raise Exception(f"Not support type{type(text)}")
def get_parser():
parser = argparse.ArgumentParser(
@ -184,7 +217,14 @@ def get_parser():
help="""The model name to use.
""",
)
parser.add_argument(
"--remove-whisper-encoder-input-length-restriction",
type=str2bool,
default=True,
help="replace whisper encoder forward method to remove input length restriction",
)
return parser
@ -196,6 +236,7 @@ def get_params() -> AttributeDict:
)
return params
def decode_one_batch(
params: AttributeDict,
model: nn.Module,
@ -204,42 +245,17 @@ def decode_one_batch(
"""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 decoding method is 1best, the key is the string `no_rescore`.
If attention rescoring is used, the key is the string
`ngram_lm_scale_xxx_attention_scale_xxx`, where `xxx` is the
value of `lm_scale` and `attention_scale`. An example key is
`ngram_lm_scale_0.7_attention_scale_0.5`
- 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.
- key: "beam-search"
- value: A list of lists. Each sublist is a list of token IDs.
Args:
params:
It's the return value of :func:`get_params`.
- params.method is "1best", it uses 1best decoding without LM rescoring.
- params.method is "nbest", it uses nbest decoding without LM rescoring.
- params.method is "attention-decoder", it uses attention rescoring.
model:
The neural model.
HLG:
The decoding graph. Used when params.method is NOT ctc-decoding.
H:
The ctc topo. Used only when params.method is ctc-decoding.
batch:
It is the return value from iterating
`lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
for the format of the `batch`.
lexicon:
It contains the token symbol table and the word symbol table.
sos_id:
The token ID of the SOS.
eos_id:
The token ID of the EOS.
params:
It is returned by :func:`get_params`.
model:
The neural model.
batch:
It is returned by :meth:`torch.utils.data.DataLoader.__iter__`.
Returns:
Return the decoding result. See above description for the format of
the returned dict.
Return a dict, whose key may be "beam-search".
"""
dtype = torch.float16
device = torch.device("cuda")
@ -247,21 +263,30 @@ def decode_one_batch(
feature = batch["inputs"]
assert feature.ndim == 3
feature = feature.to(device, dtype=dtype).transpose(1, 2)
if not params.remove_whisper_encoder_input_length_restriction:
T = 3000
if feature.shape[2] < T:
feature = torch.cat(
[
feature,
torch.zeros(
feature.shape[0], feature.shape[1], T - feature.shape[2]
).to(device, dtype=dtype),
],
2,
)
supervisions = batch["supervisions"]
feature_len = supervisions["num_frames"]
feature_len = feature_len.to(device, dtype=dtype)
results = model.decode(feature, params.decoding_options)
hyps = [result.text for result in results]
hyps = remove_punctuation(hyps)
hyps = to_simple(hyps)
hyps = [params.normalizer.normalize(hyp) for hyp in hyps]
print(hyps)
key = "beam-search"
return {key: hyps}
return {"beam-search": hyps}
def decode_dataset(
@ -272,28 +297,14 @@ def decode_dataset(
"""Decode dataset.
Args:
dl:
PyTorch's dataloader containing the dataset to decode.
params:
It is returned by :func:`get_params`.
model:
The neural model.
HLG:
The decoding graph. Used when params.method is NOT ctc-decoding.
H:
The ctc topo. Used only when params.method is ctc-decoding.
lexicon:
It contains the token symbol table and the word symbol table.
sos_id:
The token ID for SOS.
eos_id:
The token ID for EOS.
dl:
The dataloader.
params:
It is returned by :func:`get_params`.
model:
The neural model.
Returns:
Return a dict, whose key may be "no-rescore" if the decoding method is
1best or it may be "ngram_lm_scale_0.7_attention_scale_0.5" if attention
rescoring 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.
Return a dict, whose key may be "beam-search".
"""
results = []
@ -342,7 +353,9 @@ def save_results(
enable_log = True
test_set_wers = dict()
for key, results in results_dict.items():
recog_path = params.exp_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
recog_path = (
params.exp_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
if enable_log:
@ -350,7 +363,9 @@ def save_results(
# The following prints out WERs, per-word error statistics and aligned
# ref/hyp pairs.
errs_filename = params.exp_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
errs_filename = (
params.exp_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
)
# we compute CER for aishell dataset.
results_char = []
for res in results:
@ -382,20 +397,27 @@ def save_results(
@torch.no_grad()
def main():
parser = get_parser()
WenetSpeechAsrDataModule.add_arguments(parser)
AishellAsrDataModule.add_arguments(parser)
args = parser.parse_args()
args.exp_dir = Path(args.exp_dir)
params = get_params()
params.update(vars(args))
params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
setup_logger(f"{params.exp_dir}/log-{params.method}-beam{params.beam_size}/log-decode-{params.suffix}")
setup_logger(
f"{params.exp_dir}/log-{params.method}-beam{params.beam_size}/log-decode-{params.suffix}"
)
options = whisper.DecodingOptions(task="transcribe", language="zh", without_timestamps=True, beam_size=params.beam_size)
options = whisper.DecodingOptions(
task="transcribe",
language="zh",
without_timestamps=True,
beam_size=params.beam_size,
)
params.decoding_options = options
params.cleaner = BasicTextNormalizer()
params.normalizer = Normalizer()
logging.info("Decoding started")
logging.info(params)
@ -405,39 +427,49 @@ def main():
logging.info(f"device: {device}")
model = load_model(params.model_name)
if params.remove_whisper_encoder_input_length_restriction:
replace_whisper_encoder_forward()
model = whisper.load_model(params.model_name, "cpu")
if params.epoch > 0:
if params.avg > 1:
start = params.epoch - params.avg
assert start >= 1, start
checkpoint = torch.load(f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location='cpu')
if 'model' not in checkpoint:
filenames = [f"{params.exp_dir}/epoch-{epoch}.pt" for epoch in range(start, params.epoch + 1)]
model.load_state_dict(average_checkpoints(filenames))
else:
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}"
if params.avg > 1:
start = params.epoch - params.avg
assert start >= 1, start
checkpoint = torch.load(
f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location="cpu"
)
model.to(device)
model.load_state_dict(
average_checkpoints_with_averaged_model(
filename_start=filename_start,
filename_end=filename_end,
device=device,
if "model" not in checkpoint:
# deepspeed converted checkpoint only contains model state_dict
filenames = [
f"{params.exp_dir}/epoch-{epoch}.pt"
for epoch in range(start, params.epoch + 1)
]
model.load_state_dict(average_checkpoints(filenames))
else:
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}"
)
)
# save checkpoints
filename = f"{params.exp_dir}/epoch-{params.epoch}-avg-{params.avg}.pt"
torch.save(model.state_dict(), filename)
else:
checkpoint = torch.load(f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location='cpu')
if 'model' not in checkpoint:
model.load_state_dict(checkpoint, strict=True)
model.to(device)
model.load_state_dict(
average_checkpoints_with_averaged_model(
filename_start=filename_start,
filename_end=filename_end,
device=device,
)
)
# save checkpoints
filename = f"{params.exp_dir}/epoch-{params.epoch}-avg-{params.avg}.pt"
torch.save(model.state_dict(), filename)
else:
load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
checkpoint = torch.load(
f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location="cpu"
)
if "model" not in checkpoint:
model.load_state_dict(checkpoint, strict=True)
else:
load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
model.to(device)
model.eval()
num_param = sum([p.numel() for p in model.parameters()])
@ -446,25 +478,13 @@ def main():
# we need cut ids to display recognition results.
args.return_cuts = True
wenetspeech = WenetSpeechAsrDataModule(args)
dev_cuts = wenetspeech.valid_cuts()
dev_dl = wenetspeech.valid_dataloaders(dev_cuts)
def remove_short_utt(c: Cut):
T = ((c.num_frames - 7) // 2 + 1) // 2
if T <= 0:
logging.warning(
f"Exclude cut with ID {c.id} from decoding, num_frames : {c.num_frames}."
)
return T > 0
# dev_cuts = wenetspeech.valid_cuts()
# dev_cuts = dev_cuts.filter(remove_short_utt)
# dev_dl = wenetspeech.valid_dataloaders(dev_cuts)
# test_net_cuts = wenetspeech.test_net_cuts()
# test_net_cuts = test_net_cuts.filter(remove_short_utt)
# test_net_dl = wenetspeech.test_dataloaders(test_net_cuts)
test_net_cuts = wenetspeech.test_net_cuts()
test_net_dl = wenetspeech.test_dataloaders(test_net_cuts)
test_meeting_cuts = wenetspeech.test_meeting_cuts()
test_meeting_cuts = test_meeting_cuts.filter(remove_short_utt)
test_meeting_dl = wenetspeech.test_dataloaders(test_meeting_cuts)
# test_sets = ["DEV", "TEST_NET", "TEST_MEETING"]

38
egs/wenetspeech/ASR/whisper/ds_config_zero1.json Normal file
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@ -0,0 +1,38 @@
{
"fp16": {
"enabled": true,
"loss_scale": 0,
"loss_scale_window": 100,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 0.01
},
"zero_optimization": {
"stage": 1,
"allgather_partitions": true,
"allgather_bucket_size": 2e8,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 2e8,
"contiguous_gradients": true
},
"optimizer": {
"type": "Adam",
"params": {
"lr": 1e-5
}
},
"scheduler": {
"type": "WarmupLR",
"params": {
"warmup_min_lr": 0,
"warmup_max_lr": 1e-5,
"warmup_num_steps": 100
}
},
"gradient_accumulation_steps": 1,
"gradient_clipping": 5,
"steps_per_print": 50,
"train_micro_batch_size_per_gpu": 1,
"wall_clock_breakdown": false
}

109
egs/wenetspeech/ASR/whisper/label_smoothing.py Normal file
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@ -0,0 +1,109 @@
# Copyright 2021 Xiaomi Corp. (authors: 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.
import torch
class LabelSmoothingLoss(torch.nn.Module):
"""
Implement the LabelSmoothingLoss proposed in the following paper
https://arxiv.org/pdf/1512.00567.pdf
(Rethinking the Inception Architecture for Computer Vision)
"""
def __init__(
self,
ignore_index: int = -1,
label_smoothing: float = 0.1,
reduction: str = "sum",
) -> None:
"""
Args:
ignore_index:
ignored class id
label_smoothing:
smoothing rate (0.0 means the conventional cross entropy loss)
reduction:
It has the same meaning as the reduction in
`torch.nn.CrossEntropyLoss`. It can be one of the following three
values: (1) "none": No reduction will be applied. (2) "mean": the
mean of the output is taken. (3) "sum": the output will be summed.
"""
super().__init__()
assert 0.0 <= label_smoothing < 1.0, f"{label_smoothing}"
assert reduction in ("none", "sum", "mean"), reduction
self.ignore_index = ignore_index
self.label_smoothing = label_smoothing
self.reduction = reduction
def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
"""
Compute loss between x and target.
Args:
x:
prediction of dimension
(batch_size, input_length, number_of_classes).
target:
target masked with self.ignore_index of
dimension (batch_size, input_length).
Returns:
A scalar tensor containing the loss without normalization.
"""
assert x.ndim == 3
assert target.ndim == 2
assert x.shape[:2] == target.shape
num_classes = x.size(-1)
x = x.reshape(-1, num_classes)
# Now x is of shape (N*T, C)
# We don't want to change target in-place below,
# so we make a copy of it here
target = target.clone().reshape(-1)
ignored = target == self.ignore_index
# See https://github.com/k2-fsa/icefall/issues/240
# and https://github.com/k2-fsa/icefall/issues/297
# for why we don't use target[ignored] = 0 here
target = torch.where(ignored, torch.zeros_like(target), target)
true_dist = torch.nn.functional.one_hot(target, num_classes=num_classes).to(x)
true_dist = (
true_dist * (1 - self.label_smoothing) + self.label_smoothing / num_classes
)
# Set the value of ignored indexes to 0
#
# See https://github.com/k2-fsa/icefall/issues/240
# and https://github.com/k2-fsa/icefall/issues/297
# for why we don't use true_dist[ignored] = 0 here
true_dist = torch.where(
ignored.unsqueeze(1).repeat(1, true_dist.shape[1]),
torch.zeros_like(true_dist),
true_dist,
)
loss = -1 * (torch.log_softmax(x, dim=1) * true_dist)
if self.reduction == "sum":
return loss.sum()
elif self.reduction == "mean":
return loss.sum() / (~ignored).sum()
else:
return loss.sum(dim=-1)

1248
egs/wenetspeech/ASR/whisper/optim.py Normal file
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924
egs/wenetspeech/ASR/whisper/train.py Normal file
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@ -0,0 +1,924 @@
#!/usr/bin/env python3
# Copyright 2023 Xiaomi Corp. (authors: Xiaoyu Yang)
# 2024 Yuekai Zhang
#
# 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:
#fine-tuning with deepspeed zero stage 1
torchrun --nproc-per-node 8 ./whisper/train.py \
--max-duration 200 \
--exp-dir whisper/exp_large_v2 \
--model-name large-v2 \
--deepspeed \
--deepspeed_config ./whisper/ds_config_zero1.json
# fine-tuning with ddp
torchrun --nproc-per-node 8 ./whisper/train.py \
--max-duration 200 \
--exp-dir whisper/exp_medium \
--base-lr 1e-5 \
--model-name medium
"""
import argparse
import copy
import logging
import random
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import deepspeed
import k2
import optim
import torch
import torch.multiprocessing as mp
import torch.nn as nn
import whisper
from asr_datamodule import WenetSpeechAsrDataModule
from deepspeed.utils.zero_to_fp32 import convert_zero_checkpoint_to_fp32_state_dict
from label_smoothing import LabelSmoothingLoss
from lhotse import CutSet, load_manifest
from lhotse.cut import Cut
from lhotse.dataset.sampling.base import CutSampler
from lhotse.utils import fix_random_seed
from optim import Eden, ScaledAdam
from torch import Tensor
from torch.cuda.amp import GradScaler
from torch.nn.functional import pad as pad_tensor
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.tensorboard import SummaryWriter
from whisper_encoder_forward_monkey_patch import replace_whisper_encoder_forward
from icefall import diagnostics
from icefall.checkpoint import load_checkpoint, remove_checkpoints
from icefall.checkpoint import save_checkpoint as save_checkpoint_impl
from icefall.checkpoint import update_averaged_model
from icefall.dist import cleanup_dist, get_rank, get_world_size, setup_dist
from icefall.env import get_env_info
from icefall.hooks import register_inf_check_hooks
from icefall.utils import (
AttributeDict,
MetricsTracker,
filter_uneven_sized_batch,
setup_logger,
str2bool,
)
LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
def set_batch_count(model: Union[nn.Module, DDP], batch_count: float) -> None:
if isinstance(model, DDP):
# get underlying nn.Module
model = model.module
for module in model.modules():
if hasattr(module, "batch_count"):
module.batch_count = batch_count
def get_parser():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument(
"--tensorboard",
type=str2bool,
default=True,
help="Should various information be logged in tensorboard.",
)
parser.add_argument(
"--num-epochs",
type=int,
default=10,
help="Number of epochs to train.",
)
parser.add_argument(
"--start-epoch",
type=int,
default=1,
help="""Resume training from this epoch. It should be positive.
If larger than 1, it will load checkpoint from
exp-dir/epoch-{start_epoch-1}.pt
""",
)
parser.add_argument(
"--start-batch",
type=int,
default=0,
help="""If positive, --start-epoch is ignored and
it loads the checkpoint from exp-dir/checkpoint-{start_batch}.pt
""",
)
parser.add_argument(
"--exp-dir",
type=str,
default="pruned_transducer_stateless7/exp",
help="""The experiment dir.
It specifies the directory where all training related
files, e.g., checkpoints, log, etc, are saved
""",
)
parser.add_argument(
"--model-name",
type=str,
default="large-v2",
choices=["large-v2", "large-v3", "medium", "small", "tiny"],
help="""The model name to use.
""",
)
parser.add_argument(
"--base-lr", type=float, default=1e-5, help="The base learning rate."
)
parser.add_argument(
"--lr-batches",
type=float,
default=5000,
help="""Number of steps that affects how rapidly the learning rate
decreases. We suggest not to change this.""",
)
parser.add_argument(
"--lr-epochs",
type=float,
default=6,
help="""Number of epochs that affects how rapidly the learning rate decreases.
""",
)
parser.add_argument(
"--seed",
type=int,
default=42,
help="The seed for random generators intended for reproducibility",
)
parser.add_argument(
"--print-diagnostics",
type=str2bool,
default=False,
help="Accumulate stats on activations, print them and exit.",
)
parser.add_argument(
"--inf-check",
type=str2bool,
default=False,
help="Add hooks to check for infinite module outputs and gradients.",
)
parser.add_argument(
"--keep-last-k",
type=int,
default=30,
help="""Only keep this number of checkpoints on disk.
For instance, if it is 3, there are only 3 checkpoints
in the exp-dir with filenames `checkpoint-xxx.pt`.
It does not affect checkpoints with name `epoch-xxx.pt`.
""",
)
parser.add_argument(
"--average-period",
type=int,
default=200,
help="""Update the averaged model, namely `model_avg`, after processing
this number of batches. `model_avg` is a separate version of model,
in which each floating-point parameter is the average of all the
parameters from the start of training. Each time we take the average,
we do: `model_avg = model * (average_period / batch_idx_train) +
model_avg * ((batch_idx_train - average_period) / batch_idx_train)`.
""",
)
parser.add_argument(
"--use-fp16",
type=str2bool,
default=True,
help="Whether to use half precision training.",
)
parser = deepspeed.add_config_arguments(parser)
return parser
def get_params() -> AttributeDict:
"""Return a dict containing training parameters.
All training related parameters that are not passed from the commandline
are saved in the variable `params`.
Commandline options are merged into `params` after they are parsed, so
you can also access them via `params`.
Explanation of options saved in `params`:
- frame_shift_ms: The frame shift in milliseconds.
- allowed_excess_duration_ratio: The allowed excess duration ratio.
- best_train_loss: The best training loss so far.
- best_valid_loss: The best validation loss so far.
- best_train_epoch: The epoch where the best training loss is achieved.
- best_valid_epoch: The epoch where the best validation loss is achieved.
- batch_idx_train: The batch index of the current batch.
- log_interval: Log training stats every `log_interval` batches.
- reset_interval: Reset the stats every `reset_interval` batches.
- valid_interval: Run validation every `valid_interval` batches.
- env_info: The environment information.
"""
params = AttributeDict(
{
"frame_shift_ms": 10.0,
"allowed_excess_duration_ratio": 0.1,
"best_train_loss": float("inf"),
"best_valid_loss": float("inf"),
"best_train_epoch": -1,
"best_valid_epoch": -1,
"batch_idx_train": 0,
"log_interval": 50,
"reset_interval": 200,
"valid_interval": 5000,
"env_info": get_env_info(),
}
)
return params
def load_checkpoint_if_available(
params: AttributeDict,
model: nn.Module,
model_avg: nn.Module = None,
optimizer: Optional[torch.optim.Optimizer] = None,
scheduler: Optional[LRSchedulerType] = None,
) -> Optional[Dict[str, Any]]:
"""Load checkpoint from file.
If params.start_batch is positive, it will load the checkpoint from
`params.exp_dir/checkpoint-{params.start_batch}.pt`. Otherwise, if
params.start_epoch is larger than 1, it will load the checkpoint from
`params.start_epoch - 1`.
Apart from loading state dict for `model` and `optimizer` it also updates
`best_train_epoch`, `best_train_loss`, `best_valid_epoch`,
and `best_valid_loss` in `params`.
Args:
params:
The return value of :func:`get_params`.
model:
The training model.
model_avg:
The stored model averaged from the start of training.
optimizer:
The optimizer that we are using.
scheduler:
The scheduler that we are using.
Returns:
Return a dict containing previously saved training info.
"""
if params.start_batch > 0:
filename = params.exp_dir / f"checkpoint-{params.start_batch}.pt"
elif params.start_epoch > 1:
filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt"
else:
return None
assert filename.is_file(), f"{filename} does not exist!"
saved_params = load_checkpoint(
filename,
model=model,
model_avg=model_avg,
optimizer=optimizer,
scheduler=scheduler,
)
keys = [
"best_train_epoch",
"best_valid_epoch",
"batch_idx_train",
"best_train_loss",
"best_valid_loss",
]
for k in keys:
params[k] = saved_params[k]
if params.start_batch > 0:
if "cur_epoch" in saved_params:
params["start_epoch"] = saved_params["cur_epoch"]
return saved_params
def save_checkpoint(
params: AttributeDict,
model: Union[nn.Module, DDP],
model_avg: Optional[nn.Module] = None,
optimizer: Optional[torch.optim.Optimizer] = None,
scheduler: Optional[LRSchedulerType] = None,
sampler: Optional[CutSampler] = None,
scaler: Optional[GradScaler] = None,
rank: int = 0,
) -> None:
"""Save model, optimizer, scheduler and training stats to file.
Args:
params:
It is returned by :func:`get_params`.
model:
The training model.
model_avg:
The stored model averaged from the start of training.
optimizer:
The optimizer used in the training.
sampler:
The sampler for the training dataset.
scaler:
The scaler used for mix precision training.
"""
if rank != 0:
return
filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt"
save_checkpoint_impl(
filename=filename,
model=model,
model_avg=model_avg,
params=params,
optimizer=optimizer,
scheduler=scheduler,
sampler=sampler,
scaler=scaler,
rank=rank,
)
if params.best_train_epoch == params.cur_epoch:
best_train_filename = params.exp_dir / "best-train-loss.pt"
copyfile(src=filename, dst=best_train_filename)
if params.best_valid_epoch == params.cur_epoch:
best_valid_filename = params.exp_dir / "best-valid-loss.pt"
copyfile(src=filename, dst=best_valid_filename)
def compute_loss(
params: AttributeDict,
tokenizer: whisper.tokenizer.Tokenizer,
model: Union[nn.Module, DDP],
batch: dict,
is_training: bool,
) -> Tuple[Tensor, MetricsTracker]:
"""
Compute the loss for the given batch.
Args:
params:
It is returned by :func:`get_params`.
tokenizer:
The tokenizer used to encode the text.
model:
The model for training.
batch:
A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()`
for the content in it.
is_training:
Whether it is training.
Returns:
Return a tuple of two elements. The first element is the loss tensor.
"""
# For the uneven-sized batch, the total duration after padding would possibly
# cause OOM. Hence, for each batch, which is sorted descendingly by length,
# we simply drop the last few shortest samples, so that the retained total frames
# (after padding) would not exceed `allowed_max_frames`:
# `allowed_max_frames = int(max_frames * (1.0 + allowed_excess_duration_ratio))`,
# where `max_frames = max_duration * 1000 // frame_shift_ms`.
# We set allowed_excess_duration_ratio=0.1.
if isinstance(model, DDP):
# get underlying nn.Module
model = model.module
def _batch_tensors(tensors: List[Tensor], pad_value: Any) -> Tensor:
padding_size = max(tensor.shape[0] for tensor in tensors)
dims = len(tensors[0].shape)
padded_tensors = []
for tensor in tensors:
padding = [0] * 2 * dims
padding[-1] = padding_size - tensor.shape[0]
padded_tensors.append(pad_tensor(tensor, padding, "constant", pad_value))
return torch.stack([tensor for tensor in padded_tensors], dim=0)
max_frames = params.max_duration * 1000 // params.frame_shift_ms
allowed_max_frames = int(max_frames * (1.0 + params.allowed_excess_duration_ratio))
batch = filter_uneven_sized_batch(batch, allowed_max_frames)
device = model.device if isinstance(model, DDP) else next(model.parameters()).device
feature = batch["inputs"]
assert feature.ndim == 3
feature = feature.to(device)
feature = feature.transpose(1, 2) # (N, C, T)
supervisions = batch["supervisions"]
feature_lens = supervisions["num_frames"].to(device)
batch_idx_train = params.batch_idx_train
texts = batch["supervisions"]["text"]
# remove spaces in texts
texts = [text.replace(" ", "") for text in texts]
text_tokens_list = [
list(tokenizer.sot_sequence_including_notimestamps)
+ tokenizer.encode(text)
+ [tokenizer.eot]
for text in texts
]
# convert it to torch tensor
text_tokens_list = [
torch.LongTensor(text_tokens) for text_tokens in text_tokens_list
]
# 50256 is the index of <pad> for all whisper models
prev_outputs_tokens = _batch_tensors(
[tokens[:-1] for tokens in text_tokens_list], pad_value=50256
)
target_tokens = _batch_tensors(
[tokens[1:] for tokens in text_tokens_list], pad_value=50256
)
target_lengths = torch.LongTensor(
[tokens.shape[0] - 1 for tokens in text_tokens_list]
)
decoder_criterion = LabelSmoothingLoss(
ignore_index=50256, label_smoothing=0.1, reduction="sum"
)
# ignore the first 3 tokens, which are always <|lang_id|>, <|transcibe|>, <|notimestampes|>
ignore_prefix_size = 3
with torch.set_grad_enabled(is_training):
encoder_out = model.encoder(feature)
text_logits = model.decoder(prev_outputs_tokens.to(device), encoder_out)
text_logits = text_logits[:, ignore_prefix_size:, :]
target_tokens = target_tokens[:, ignore_prefix_size:]
loss = decoder_criterion(text_logits, target_tokens.to(device))
assert loss.requires_grad == is_training
info = MetricsTracker()
with warnings.catch_warnings():
warnings.simplefilter("ignore")
info["frames"] = (feature_lens // params.subsampling_factor).sum().item()
# Note: We use reduction=sum while computing the loss.
info["loss"] = loss.detach().cpu().item()
return loss, info
def compute_validation_loss(
params: AttributeDict,
tokenizer: whisper.tokenizer.Tokenizer,
model: Union[nn.Module, DDP],
valid_dl: torch.utils.data.DataLoader,
world_size: int = 1,
) -> MetricsTracker:
"""Run the validation process."""
model.eval()
tot_loss = MetricsTracker()
for batch_idx, batch in enumerate(valid_dl):
with torch.cuda.amp.autocast(enabled=params.use_fp16):
loss, loss_info = compute_loss(
params=params,
tokenizer=tokenizer,
model=model,
batch=batch,
is_training=False,
)
assert loss.requires_grad is False
tot_loss = tot_loss + loss_info
if world_size > 1:
tot_loss.reduce(loss.device)
loss_value = tot_loss["loss"] / tot_loss["frames"]
if loss_value < params.best_valid_loss:
params.best_valid_epoch = params.cur_epoch
params.best_valid_loss = loss_value
return tot_loss
def train_one_epoch(
params: AttributeDict,
tokenizer: whisper.tokenizer.Tokenizer,
model: Union[nn.Module, DDP],
optimizer: torch.optim.Optimizer,
scheduler: LRSchedulerType,
train_dl: torch.utils.data.DataLoader,
valid_dl: torch.utils.data.DataLoader,
scaler: GradScaler,
model_avg: Optional[nn.Module] = None,
tb_writer: Optional[SummaryWriter] = None,
world_size: int = 1,
rank: int = 0,
) -> None:
"""Train the model for one epoch.
The training loss from the mean of all frames is saved in
`params.train_loss`. It runs the validation process every
`params.valid_interval` batches.
Args:
params:
It is returned by :func:`get_params`.
model:
The model for training.
optimizer:
The optimizer we are using.
scheduler:
The learning rate scheduler, we call step() every step.
train_dl:
Dataloader for the training dataset.
valid_dl:
Dataloader for the validation dataset.
scaler:
The scaler used for mix precision training.
model_avg:
The stored model averaged from the start of training.
tb_writer:
Writer to write log messages to tensorboard.
world_size:
Number of nodes in DDP training. If it is 1, DDP is disabled.
rank:
The rank of the node in DDP training. If no DDP is used, it should
be set to 0.
"""
model.train()
tot_loss = MetricsTracker()
for batch_idx, batch in enumerate(train_dl):
params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"])
if batch_idx % params.valid_interval == 0 and not params.print_diagnostics:
logging.info("Computing validation loss")
valid_info = compute_validation_loss(
params=params,
tokenizer=tokenizer,
model=model,
valid_dl=valid_dl,
world_size=world_size,
)
model.train()
logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}")
logging.info(
f"Maximum memory allocated so far is {torch.cuda.max_memory_allocated()//1000000}MB"
)
if tb_writer is not None:
valid_info.write_summary(
tb_writer, "train/valid_", params.batch_idx_train
)
try:
with torch.cuda.amp.autocast(enabled=params.use_fp16):
loss, loss_info = compute_loss(
params=params,
tokenizer=tokenizer,
model=model,
batch=batch,
is_training=True,
)
# summary stats
tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info
# NOTE: We use reduction==sum and loss is computed over utterances
# in the batch and there is no normalization to it so far.
if params.deepspeed:
# deepspeed's backward() is different from torch's backward()
# in that it does not accept a loss tensor as input.
# It computes the loss internally.
model.backward(loss)
model.step()
else:
scaler.scale(loss).backward()
set_batch_count(model, params.batch_idx_train)
scheduler.step_batch(params.batch_idx_train)
scaler.step(optimizer)
scaler.update()
optimizer.zero_grad()
except: # noqa
display_and_save_batch(batch, params=params)
raise
if params.print_diagnostics and batch_idx == 5:
return
if (
rank == 0
and params.batch_idx_train > 0
and params.batch_idx_train % params.average_period == 0
and not params.deepspeed
):
update_averaged_model(
params=params,
model_cur=model,
model_avg=model_avg,
)
if batch_idx % 100 == 0 and params.use_fp16 and not params.deepspeed:
# If the grad scale was less than 1, try increasing it. The _growth_interval
# of the grad scaler is configurable, but we can't configure it to have different
# behavior depending on the current grad scale.
cur_grad_scale = scaler._scale.item()
if cur_grad_scale < 1.0 or (cur_grad_scale < 8.0 and batch_idx % 400 == 0):
scaler.update(cur_grad_scale * 2.0)
if cur_grad_scale < 0.01:
logging.warning(f"Grad scale is small: {cur_grad_scale}")
if cur_grad_scale < 1.0e-05:
raise RuntimeError(
f"grad_scale is too small, exiting: {cur_grad_scale}"
)
if batch_idx % params.log_interval == 0:
try:
cur_lr = scheduler.get_last_lr()[0]
except: # noqa
cur_lr = 0.0
cur_grad_scale = (
scaler._scale.item()
if (params.use_fp16 and not params.deepspeed)
else 1.0
)
logging.info(
f"Epoch {params.cur_epoch}, "
f"batch {batch_idx}, loss[{loss_info}], "
f"tot_loss[{tot_loss}], batch size: {batch_size}, "
f"lr: {cur_lr:.2e}, "
+ (
f"grad_scale: {scaler._scale.item()}"
if (params.use_fp16 and not params.deepspeed)
else ""
)
)
if tb_writer is not None:
tb_writer.add_scalar(
"train/learning_rate", cur_lr, params.batch_idx_train
)
loss_info.write_summary(
tb_writer, "train/current_", params.batch_idx_train
)
tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
if params.use_fp16:
tb_writer.add_scalar(
"train/grad_scale",
cur_grad_scale,
params.batch_idx_train,
)
loss_value = tot_loss["loss"] / tot_loss["frames"]
params.train_loss = loss_value
if params.train_loss < params.best_train_loss:
params.best_train_epoch = params.cur_epoch
params.best_train_loss = params.train_loss
def run(rank, world_size, args):
"""
Args:
rank:
It is a value between 0 and `world_size-1`, which is
passed automatically by `mp.spawn()` in :func:`main`.
The node with rank 0 is responsible for saving checkpoint.
world_size:
Number of GPUs for DDP training.
args:
The return value of get_parser().parse_args()
"""
params = get_params()
params.update(vars(args))
fix_random_seed(params.seed)
setup_logger(f"{params.exp_dir}/log/log-train")
logging.info(params)
logging.info("About to create model")
replace_whisper_encoder_forward()
model = whisper.load_model(params.model_name, "cpu")
del model.alignment_heads
num_param = sum([p.numel() for p in model.parameters()])
logging.info(f"Number of model parameters: {num_param}")
tokenizer = whisper.tokenizer.get_tokenizer(
model.is_multilingual,
num_languages=model.num_languages,
language="zh",
task="transcribe",
)
model_avg: Optional[nn.Module] = None
if rank == 0:
# model_avg is only used with rank 0
model_avg = copy.deepcopy(model).to(torch.float64)
assert params.start_epoch > 0, params.start_epoch
checkpoints = load_checkpoint_if_available(
params=params, model=model, model_avg=model_avg
)
if torch.cuda.is_available():
device = torch.device("cuda", rank)
else:
device = torch.device("cpu")
logging.info(f"Device: {device}")
model.to(device)
optimizer = torch.optim.AdamW(model.parameters(), lr=params.base_lr)
scheduler = Eden(optimizer, params.lr_batches, params.lr_epochs)
if checkpoints and "optimizer" in checkpoints:
logging.info("Loading optimizer state dict")
optimizer.load_state_dict(checkpoints["optimizer"])
if (
checkpoints
and "scheduler" in checkpoints
and checkpoints["scheduler"] is not None
):
logging.info("Loading scheduler state dict")
scheduler.load_state_dict(checkpoints["scheduler"])
if world_size > 1:
if params.deepspeed:
logging.info("Using DeepSpeed")
model, optimizer, _, scheduler = deepspeed.initialize(
args=params, model=model, model_parameters=model.parameters()
)
else:
logging.info("Using DDP")
setup_dist(use_ddp_launch=True)
model = DDP(model, device_ids=[rank], find_unused_parameters=True)
if params.print_diagnostics:
opts = diagnostics.TensorDiagnosticOptions(
2**22
) # allow 4 megabytes per sub-module
diagnostic = diagnostics.attach_diagnostics(model, opts)
if params.inf_check:
register_inf_check_hooks(model)
wenetspeech = WenetSpeechAsrDataModule(args)
if params.start_batch > 0 and checkpoints and "sampler" in checkpoints:
# We only load the sampler's state dict when it loads a checkpoint
# saved in the middle of an epoch
sampler_state_dict = checkpoints["sampler"]
else:
sampler_state_dict = None
train_dl = wenetspeech.train_dataloaders(wenetspeech.train_cuts())
valid_dl = wenetspeech.valid_dataloaders(wenetspeech.valid_cuts())
scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0)
if checkpoints and "grad_scaler" in checkpoints:
logging.info("Loading grad scaler state dict")
scaler.load_state_dict(checkpoints["grad_scaler"])
if args.tensorboard and rank == 0:
tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard")
else:
tb_writer = None
logging.info(f"start training from epoch {params.start_epoch}")
for epoch in range(params.start_epoch, params.num_epochs + 1):
if not params.deepspeed:
scheduler.step_epoch(epoch - 1)
fix_random_seed(params.seed + epoch - 1)
train_dl.sampler.set_epoch(epoch - 1)
if tb_writer is not None:
tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train)
params.cur_epoch = epoch
train_one_epoch(
params=params,
tokenizer=tokenizer,
model=model,
model_avg=model_avg,
optimizer=optimizer,
scheduler=scheduler,
train_dl=train_dl,
valid_dl=valid_dl,
scaler=scaler,
tb_writer=tb_writer,
world_size=world_size,
rank=rank,
)
if params.print_diagnostics:
diagnostic.print_diagnostics()
break
if params.deepspeed:
model.save_checkpoint(
save_dir=params.exp_dir,
tag=f"epoch-{params.cur_epoch}",
client_state={},
)
if rank == 0:
convert_zero_checkpoint_to_fp32_state_dict(
params.exp_dir,
f"{params.exp_dir}/epoch-{params.cur_epoch}.pt",
tag=f"epoch-{params.cur_epoch}",
)
else:
save_checkpoint(
params=params,
model=model,
model_avg=model_avg,
optimizer=optimizer,
scheduler=scheduler,
sampler=train_dl.sampler,
scaler=scaler,
rank=rank,
)
logging.info("Done!")
if world_size > 1 and not params.deepspeed:
torch.distributed.barrier()
cleanup_dist()
def display_and_save_batch(
batch: dict,
params: AttributeDict,
) -> None:
"""Display the batch statistics and save the batch into disk.
Args:
batch:
A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()`
for the content in it.
params:
Parameters for training. See :func:`get_params`.
"""
from lhotse.utils import uuid4
filename = f"{params.exp_dir}/batch-{uuid4()}.pt"
logging.info(f"Saving batch to {filename}")
torch.save(batch, filename)
supervisions = batch["supervisions"]
features = batch["inputs"]
logging.info(f"features shape: {features.shape}")
def main():
parser = get_parser()
AishellAsrDataModule.add_arguments(parser)
args = parser.parse_args()
args.exp_dir = Path(args.exp_dir)
world_size = get_world_size()
rank = get_rank()
torch.set_num_threads(1)
torch.set_num_interop_threads(1)
run(rank=rank, world_size=world_size, args=args)
if __name__ == "__main__":
main()