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Whisper Fine-tuning Recipe on Aishell1 (#1466)

Browse Source 
* add decode seamlessm4t

* add requirements

* add decoding with avg model

* add token files

* add custom tokenizer

* support deepspeed to finetune large model

* support large-v3

* add model saving

* using monkey patch to replace models

* add manifest dir option
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Yuekai Zhang 2024-01-27 00:32:30 +08:00 committed by GitHub
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7
egs/aishell/ASR/README.md
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@ -24,3 +24,10 @@ The following table lists the differences among them.
The decoder in `transducer_stateless` is modified from the paper
[Rnn-Transducer with Stateless Prediction Network](https://ieeexplore.ieee.org/document/9054419/).
We place an additional Conv1d layer right after the input embedding layer.
# Whisper
Recipe to finetune large pretrained models
| | Encoder | Decoder | Comment |
|------------------------------------|-----------|--------------------|-----------------------------------------------------------------------------------|
| `whisper` | Transformer | Transformer | support fine-tuning using deepspeed

67
egs/aishell/ASR/RESULTS.md
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@ -1,5 +1,63 @@
## Results
### Aishell training results (Fine-tuning Pretrained Models)
#### Whisper
[./whisper](./whisper)
##### fine-tuning results on Aishell test set on whisper medium, large-v2, large-v3
| | test (before fine-tuning) | test (after fine-tuning) | comment |
|------------------------|------|------|-----------------------------------------|
| medium | 7.23 | 3.27 | --epoch 10 --avg 4, ddp |
| large-v2 | 6.56 | 2.47 | --epoch 10 --avg 6, deepspeed zero stage1 |
| large-v3 | 6.06 | 2.84 | --epoch 5 --avg 3, deepspeed zero stage1 |
Command for training is:
```bash
pip install -r whisper/requirements.txt
./prepare.sh --stage 30 --stop_stage 30
#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
```
Command for decoding using fine-tuned models:
```bash
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):
```bash
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
```
Fine-tuned models, training logs, decoding logs, tensorboard and decoding results
are available at
<https://huggingface.co/yuekai/icefall_asr_aishell_whisper>
### Aishell training result (Stateless Transducer)
#### Zipformer (Byte-level BPE)
@ -71,7 +129,7 @@ It's reworked Zipformer with Pruned RNNT loss.
Command for training is:
```bash
./prepare.sh
./prepare.sh
export CUDA_VISIBLE_DEVICES="0,1"
@ -136,7 +194,7 @@ export CUDA_VISIBLE_DEVICES="0,1"
--feedforward-dim 512,768,768,768,768,768 \
--encoder-dim 192,256,256,256,256,256 \
--encoder-unmasked-dim 192,192,192,192,192,192 \
--max-duration 1200
--max-duration 1200
```
Command for decoding is:
@ -186,7 +244,7 @@ export CUDA_VISIBLE_DEVICES="0,1"
--feedforward-dim 512,768,1536,2048,1536,768 \
--encoder-dim 192,256,512,768,512,256 \
--encoder-unmasked-dim 192,192,256,320,256,192 \
--max-duration 800
--max-duration 800
```
Command for decoding is:
@ -202,7 +260,7 @@ for m in greedy_search modified_beam_search fast_beam_search ; do
--num-encoder-layers 2,2,4,5,4,2 \
--feedforward-dim 512,768,1536,2048,1536,768 \
--encoder-dim 192,256,512,768,512,256 \
--encoder-unmasked-dim 192,192,256,320,256,192
--encoder-unmasked-dim 192,192,256,320,256,192
done
```
@ -755,7 +813,6 @@ python3 ./transducer_stateless/decode.py \
--max-sym-per-frame 3
```
### Aishell training results (Transducer-stateless)
#### 2022-02-18
(Pingfeng Luo) : The tensorboard log for training is available at <https://tensorboard.dev/experiment/k3QL6QMhRbCwCKYKM9po9w/>
And pretrained model is available at <https://huggingface.co/pfluo/icefall-aishell-transducer-stateless-char-2021-12-29>

45
egs/aishell/ASR/local/compute_fbank_aishell.py
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@ -29,7 +29,14 @@ import os
from pathlib import Path
import torch
from lhotse import CutSet, Fbank, FbankConfig, LilcomChunkyWriter
from lhotse import (
CutSet,
Fbank,
FbankConfig,
LilcomChunkyWriter,
WhisperFbank,
WhisperFbankConfig,
)
from lhotse.recipes.utils import read_manifests_if_cached
from icefall.utils import get_executor, str2bool
@ -42,9 +49,14 @@ torch.set_num_threads(1)
torch.set_num_interop_threads(1)
def compute_fbank_aishell(num_mel_bins: int = 80, perturb_speed: bool = False):
def compute_fbank_aishell(
num_mel_bins: int = 80,
perturb_speed: bool = False,
whisper_fbank: bool = False,
output_dir: str = "data/fbank",
):
src_dir = Path("data/manifests")
output_dir = Path("data/fbank")
output_dir = Path(output_dir)
num_jobs = min(15, os.cpu_count())
dataset_parts = (
@ -68,8 +80,12 @@ def compute_fbank_aishell(num_mel_bins: int = 80, perturb_speed: bool = False):
list(manifests.keys()),
dataset_parts,
)
extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
if whisper_fbank:
extractor = WhisperFbank(
WhisperFbankConfig(num_filters=num_mel_bins, device="cuda")
)
else:
extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
with get_executor() as ex: # Initialize the executor only once.
for partition, m in manifests.items():
@ -82,7 +98,7 @@ def compute_fbank_aishell(num_mel_bins: int = 80, perturb_speed: bool = False):
supervisions=m["supervisions"],
)
if "train" in partition and perturb_speed:
logging.info(f"Doing speed perturb")
logging.info("Doing speed perturb")
cut_set = (
cut_set + cut_set.perturb_speed(0.9) + cut_set.perturb_speed(1.1)
)
@ -111,6 +127,18 @@ def get_args():
default=False,
help="Enable 0.9 and 1.1 speed perturbation for data augmentation. Default: False.",
)
parser.add_argument(
"--whisper-fbank",
type=str2bool,
default=False,
help="Use WhisperFbank instead of Fbank. Default: False.",
)
parser.add_argument(
"--output-dir",
type=str,
default="data/fbank",
help="Output directory. Default: data/fbank.",
)
return parser.parse_args()
@ -121,5 +149,8 @@ if __name__ == "__main__":
args = get_args()
compute_fbank_aishell(
num_mel_bins=args.num_mel_bins, perturb_speed=args.perturb_speed
num_mel_bins=args.num_mel_bins,
perturb_speed=args.perturb_speed,
whisper_fbank=args.whisper_fbank,
output_dir=args.output_dir,
)

13
egs/aishell/ASR/prepare.sh
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@ -376,3 +376,16 @@ if [ $stage -le 12 ] && [ $stop_stage -ge 12 ]; then
--vocab-size 4336 \
--master-port 12345
fi
# whisper large-v3 using 128 mel bins, others using 80 mel bins
whisper_mel_bins=80
output_dir=data/fbank_whisper
if [ $stage -le 30 ] && [ $stop_stage -ge 30 ]; then
log "Stage 30: Compute ${whisper_mel_bins} dim fbank for whisper model fine-tuning"
if [ ! -f $output_dir/.aishell.whisper.done ]; then
mkdir -p $output_dir
./local/compute_fbank_aishell.py --perturb-speed ${perturb_speed} --num-mel-bins ${whisper_mel_bins} --whisper-fbank true --output-dir $output_dir
./local/compute_fbank_musan.py --num-mel-bins ${whisper_mel_bins} --whisper-fbank true --output-dir $output_dir
touch $output_dir/.aishell.whisper.done
fi
fi

1
egs/aishell/ASR/whisper/asr_datamodule.py Symbolic link
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@ -0,0 +1 @@
../tdnn_lstm_ctc/asr_datamodule.py

503
egs/aishell/ASR/whisper/decode.py Executable file
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@ -0,0 +1,503 @@
#!/usr/bin/env python3
# Copyright 2021 Xiaomi Corporation (Author: Liyong Guo,
# Fangjun Kuang,
# Wei Kang)
# 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:
# 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 \
--manifest-dir data/fbank_whisper \
--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 \
--manifest-dir data/fbank_whisper \
--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 k2
import torch
import torch.nn as nn
import whisper
from asr_datamodule import AishellAsrDataModule
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.utils import (
AttributeDict,
setup_logger,
store_transcripts,
str2bool,
write_error_stats,
)
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:
Filenames of the checkpoints to be averaged. We assume all
checkpoints are saved by :func:`save_checkpoint`.
device:
Move checkpoints to this device before averaging.
Returns:
Return a dict (i.e., state_dict) which is the average of all
model state dicts contained in the checkpoints.
"""
n = len(filenames)
if "model" in torch.load(filenames[0], map_location=device):
avg = torch.load(filenames[0], map_location=device)["model"]
else:
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
uniqued: Dict[int, str] = dict()
for k, v in avg.items():
v_data_ptr = v.data_ptr()
if v_data_ptr in uniqued:
continue
uniqued[v_data_ptr] = k
uniqued_names = list(uniqued.values())
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"]
else:
state_dict = torch.load(filenames[i], map_location=device)
for k in uniqued_names:
avg[k] += state_dict[k]
for k in uniqued_names:
if avg[k].is_floating_point():
avg[k] /= n
else:
avg[k] //= n
return avg
def remove_punctuation(text: str or List[str]):
"""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()
return text
elif isinstance(text, list):
result_text = []
for t in text:
t = re.sub(r"[{}]+".format(punctuation), "", t).strip()
result_text.append(t)
return result_text
else:
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")
return text
elif isinstance(text, list):
result_text = []
for t in text:
t = convert(t, "zh-cn")
result_text.append(t)
return result_text
else:
raise Exception(f"Not support type{type(text)}")
def get_parser():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument(
"--epoch",
type=int,
default=-1,
help="It specifies the checkpoint to use for decoding."
"Note: Epoch counts from 0.",
)
parser.add_argument(
"--avg",
type=int,
default=1,
help="Number of checkpoints to average. Automatically select "
"consecutive checkpoints before the checkpoint specified by "
"'--epoch'. ",
)
parser.add_argument(
"--method",
type=str,
default="beam-search",
help="""Decoding method.
Supported values are:
- beam-search
""",
)
parser.add_argument(
"--beam-size",
type=int,
default=1,
help="beam size for beam search decoding",
)
parser.add_argument(
"--exp-dir",
type=str,
default="whisper/exp",
help="The experiment dir",
)
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(
"--remove-whisper-encoder-input-length-restriction",
type=str2bool,
default=True,
help="replace whisper encoder forward method to remove input length restriction",
)
return parser
def get_params() -> AttributeDict:
params = AttributeDict(
{
"env_info": get_env_info(),
}
)
return params
def decode_one_batch(
params: AttributeDict,
model: nn.Module,
batch: dict,
) -> Dict[str, List[List[int]]]:
"""Decode one batch and return the result in a dict. The dict has the
following format:
- key: "beam-search"
- value: A list of lists. Each sublist is a list of token IDs.
Args:
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 a dict, whose key may be "beam-search".
"""
dtype = torch.float16
device = torch.device("cuda")
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]
return {"beam-search": hyps}
def decode_dataset(
dl: torch.utils.data.DataLoader,
params: AttributeDict,
model: nn.Module,
) -> Dict[str, List[Tuple[str, List[str], List[str]]]]:
"""Decode dataset.
Args:
dl:
The dataloader.
params:
It is returned by :func:`get_params`.
model:
The neural model.
Returns:
Return a dict, whose key may be "beam-search".
"""
results = []
num_cuts = 0
try:
num_batches = len(dl)
except TypeError:
num_batches = "?"
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
model=model,
batch=batch,
)
for lm_scale, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((cut_id, ref_words, hyp_words))
results[lm_scale].extend(this_batch)
num_cuts += len(batch["supervisions"]["text"])
if batch_idx % 100 == 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[str, List[str], List[str]]]],
):
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"
)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
if enable_log:
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.exp_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
)
# we compute CER for aishell dataset.
results_char = []
for res in results:
results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
with open(errs_filename, "w") as f:
wer = write_error_stats(
f, f"{test_set_name}-{key}", results_char, enable_log=enable_log
)
test_set_wers[key] = wer
if enable_log:
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.exp_dir / f"cer-summary-{test_set_name}-{params.suffix}.txt"
with open(errs_info, "w") as f:
print("settings\tCER", file=f)
for key, val in test_set_wers:
print("{}\t{}".format(key, val), file=f)
s = "\nFor {}, CER 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()
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}"
)
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)
device = torch.device("cpu")
if torch.cuda.is_available():
device = torch.device("cuda")
logging.info(f"device: {device}")
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:
# 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}"
)
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:
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()])
logging.info(f"Number of model parameters: {num_param}")
# we need cut ids to display recognition results.
args.return_cuts = True
aishell = AishellAsrDataModule(args)
valid_dl = aishell.valid_dataloaders(aishell.valid_cuts())
test_dl = aishell.test_dataloaders(aishell.test_cuts())
test_sets = ["valid", "test"]
test_dls = [valid_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,
)
save_results(params=params, test_set_name=test_set, results_dict=results_dict)
logging.info("Done!")
torch.set_num_threads(1)
torch.set_num_interop_threads(1)
if __name__ == "__main__":
main()

38
egs/aishell/ASR/whisper/ds_config_zero1.json Normal file
View File

@ -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
}

1
egs/aishell/ASR/whisper/label_smoothing.py Symbolic link
View File

@ -0,0 +1 @@
../../../librispeech/ASR/conformer_ctc/label_smoothing.py

1
egs/aishell/ASR/whisper/optim.py Symbolic link
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@ -0,0 +1 @@
../../../librispeech/ASR/zipformer/optim.py

10
egs/aishell/ASR/whisper/requirements.txt Executable file
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@ -0,0 +1,10 @@
k2
kaldialign
git+https://github.com/lhotse-speech/lhotse
sentencepiece
tensorboard
librosa
git+https://github.com/yuekaizhang/whisper.git
zhconv
WeTextProcessing
deepspeed

927
egs/aishell/ASR/whisper/train.py Executable file
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@ -0,0 +1,927 @@
#!/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 \
--manifest-dir data/fbank_whisper \
--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 \
--manifest-dir data/fbank_whisper \
--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 AishellAsrDataModule
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,
"subsampling_factor": 2,
"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)
aishell = AishellAsrDataModule(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 = aishell.train_dataloaders(aishell.train_cuts())
valid_dl = aishell.valid_dataloaders(aishell.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()

29
egs/aishell/ASR/whisper/whisper_encoder_forward_monkey_patch.py Normal file
View File

@ -0,0 +1,29 @@
import torch
import torch.nn.functional as F
import whisper
def forward(self, x: torch.Tensor):
"""
x : torch.Tensor, shape = (batch_size, n_mels, n_ctx)
the mel spectrogram of the audio
"""
x = F.gelu(self.conv1(x))
x = F.gelu(self.conv2(x))
x = x.permute(0, 2, 1)
x = (x + self.positional_embedding[: x.shape[1], :]).to(x.dtype)
for block in self.blocks:
x = block(x)
x = self.ln_post(x)
return x
def replace_whisper_encoder_forward():
"""
This function monkey patches the forward method of the whisper encoder.
To be called before the model is loaded, it changes whisper to process audio with any length < 30s.
"""
whisper.model.AudioEncoder.forward = forward

59
egs/librispeech/ASR/local/compute_fbank_musan.py
View File

@ -22,16 +22,25 @@ It looks for manifests in the directory data/manifests.
The generated fbank features are saved in data/fbank.
"""
import argparse
import logging
import os
from pathlib import Path
import torch
from lhotse import CutSet, Fbank, FbankConfig, LilcomChunkyWriter, MonoCut, combine
from lhotse import (
CutSet,
Fbank,
FbankConfig,
LilcomChunkyWriter,
MonoCut,
WhisperFbank,
WhisperFbankConfig,
combine,
)
from lhotse.recipes.utils import read_manifests_if_cached
from icefall.utils import get_executor
from icefall.utils import get_executor, str2bool
# Torch's multithreaded behavior needs to be disabled or
# it wastes a lot of CPU and slow things down.
@ -45,11 +54,12 @@ def is_cut_long(c: MonoCut) -> bool:
return c.duration > 5
def compute_fbank_musan():
def compute_fbank_musan(
num_mel_bins: int = 80, whisper_fbank: bool = False, output_dir: str = "data/fbank"
):
src_dir = Path("data/manifests")
output_dir = Path("data/fbank")
output_dir = Path(output_dir)
num_jobs = min(15, os.cpu_count())
num_mel_bins = 80
dataset_parts = (
"music",
@ -81,7 +91,12 @@ def compute_fbank_musan():
logging.info("Extracting features for Musan")
extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
if whisper_fbank:
extractor = WhisperFbank(
WhisperFbankConfig(num_filters=num_mel_bins, device="cuda")
)
else:
extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
with get_executor() as ex: # Initialize the executor only once.
# create chunks of Musan with duration 5 - 10 seconds
@ -102,8 +117,36 @@ def compute_fbank_musan():
musan_cuts.to_file(musan_cuts_path)
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--num-mel-bins",
type=int,
default=80,
help="""The number of mel bins for Fbank""",
)
parser.add_argument(
"--whisper-fbank",
type=str2bool,
default=False,
help="Use WhisperFbank instead of Fbank. Default: False.",
)
parser.add_argument(
"--output-dir",
type=str,
default="data/fbank",
help="Output directory. Default: data/fbank.",
)
return parser.parse_args()
if __name__ == "__main__":
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
logging.basicConfig(format=formatter, level=logging.INFO)
compute_fbank_musan()
args = get_args()
compute_fbank_musan(
num_mel_bins=args.num_mel_bins,
whisper_fbank=args.whisper_fbank,
output_dir=args.output_dir,
)

2
icefall/dist.py
View File

@ -22,7 +22,7 @@ from torch import distributed as dist
def setup_dist(
rank, world_size, master_port=None, use_ddp_launch=False, master_addr=None
rank=None, world_size=None, master_port=None, use_ddp_launch=False, master_addr=None
):
"""
rank and world_size are used only if use_ddp_launch is False.