From 6c2cd5b4c352f6336dfa5cccd22f5e8612eb169e Mon Sep 17 00:00:00 2001 From: Yuekai Zhang Date: Tue, 26 Sep 2023 10:46:35 +0800 Subject: [PATCH] support whisper ft --- egs/aishell/ASR/decode_whisper.sh | 8 + .../local/compute_whisper_fbank_aishell.py | 125 ++ .../ASR/local/compute_whisper_fbank_musan.py | 109 ++ egs/aishell/ASR/run_whisper.sh | 7 + egs/aishell/ASR/whisper/asr_datamodule.py | 1 + egs/aishell/ASR/whisper/decode.py | 428 ++++++ egs/aishell/ASR/whisper/label_smoothing.py | 1 + egs/aishell/ASR/whisper/requirements.txt | 8 + egs/aishell/ASR/whisper/train.py | 1207 +++++++++++++++++ 9 files changed, 1894 insertions(+) create mode 100644 egs/aishell/ASR/decode_whisper.sh create mode 100644 egs/aishell/ASR/local/compute_whisper_fbank_aishell.py create mode 100644 egs/aishell/ASR/local/compute_whisper_fbank_musan.py create mode 100644 egs/aishell/ASR/run_whisper.sh create mode 120000 egs/aishell/ASR/whisper/asr_datamodule.py create mode 100644 egs/aishell/ASR/whisper/decode.py create mode 120000 egs/aishell/ASR/whisper/label_smoothing.py create mode 100644 egs/aishell/ASR/whisper/requirements.txt create mode 100644 egs/aishell/ASR/whisper/train.py diff --git a/egs/aishell/ASR/decode_whisper.sh b/egs/aishell/ASR/decode_whisper.sh new file mode 100644 index 000000000..852359b69 --- /dev/null +++ b/egs/aishell/ASR/decode_whisper.sh @@ -0,0 +1,8 @@ + +#export CUDA_VISIBLE_DEVICES="1" +#pip install -r whisper/requirements.txt +#pip install k2==1.24.3.dev20230524+cuda11.8.torch2.0.1 -f https://k2-fsa.github.io/k2/cuda.html +export PYTHONPATH=$PYTHONPATH:/lustre/fsw/sa/yuekaiz/asr/icefall +#export PYTHONPATH=$PYTHONPATH:/mnt/samsung-t7/yuekai/asr/icefall/ + +python3 whisper/decode.py --exp-dir whisper/exp --max-duration 100 diff --git a/egs/aishell/ASR/local/compute_whisper_fbank_aishell.py b/egs/aishell/ASR/local/compute_whisper_fbank_aishell.py new file mode 100644 index 000000000..f1d8a7460 --- /dev/null +++ b/egs/aishell/ASR/local/compute_whisper_fbank_aishell.py @@ -0,0 +1,125 @@ +#!/usr/bin/env python3 +# 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. + + +""" +This file computes fbank features of the aishell dataset. +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, WhisperFbank, WhisperFbankConfig, LilcomChunkyWriter +from lhotse.recipes.utils import read_manifests_if_cached + +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. +# Do this outside of main() in case it needs to take effect +# even when we are not invoking the main (e.g. when spawning subprocesses). +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + + +def compute_fbank_aishell(num_mel_bins: int = 80, perturb_speed: bool = False): + src_dir = Path("data/manifests") + output_dir = Path("data/fbank") + num_jobs = min(15, os.cpu_count()) + + dataset_parts = ( + "train", + #"dev", + #"test", + ) + prefix = "aishell" + suffix = "jsonl.gz" + manifests = read_manifests_if_cached( + dataset_parts=dataset_parts, + output_dir=src_dir, + prefix=prefix, + suffix=suffix, + ) + assert manifests is not None + + assert len(manifests) == len(dataset_parts), ( + len(manifests), + len(dataset_parts), + list(manifests.keys()), + dataset_parts, + ) + + extractor = WhisperFbank(WhisperFbankConfig(device='cuda')) + + with get_executor() as ex: # Initialize the executor only once. + for partition, m in manifests.items(): + if (output_dir / f"{prefix}_cuts_{partition}.{suffix}").is_file(): + logging.info(f"{partition} already exists - skipping.") + continue + logging.info(f"Processing {partition}") + cut_set = CutSet.from_manifests( + recordings=m["recordings"], + supervisions=m["supervisions"], + ) + if "train" in partition and perturb_speed: + logging.info(f"Doing speed perturb") + cut_set = ( + cut_set + cut_set.perturb_speed(0.9) + cut_set.perturb_speed(1.1) + ) + cut_set = cut_set.compute_and_store_features( + extractor=extractor, + storage_path=f"{output_dir}/{prefix}_feats_{partition}", + # when an executor is specified, make more partitions + num_jobs=num_jobs if ex is None else 80, + executor=ex, + storage_type=LilcomChunkyWriter, + ) + cut_set.to_file(output_dir / f"{prefix}_cuts_{partition}.{suffix}") + + +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( + "--perturb-speed", + type=str2bool, + default=False, + help="Enable 0.9 and 1.1 speed perturbation for data augmentation. Default: False.", + ) + 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) + + args = get_args() + compute_fbank_aishell( + num_mel_bins=args.num_mel_bins, perturb_speed=args.perturb_speed + ) diff --git a/egs/aishell/ASR/local/compute_whisper_fbank_musan.py b/egs/aishell/ASR/local/compute_whisper_fbank_musan.py new file mode 100644 index 000000000..0378b359b --- /dev/null +++ b/egs/aishell/ASR/local/compute_whisper_fbank_musan.py @@ -0,0 +1,109 @@ +#!/usr/bin/env python3 +# 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. + + +""" +This file computes fbank features of the musan dataset. +It looks for manifests in the directory data/manifests. + +The generated fbank features are saved in data/fbank. +""" + +import logging +import os +from pathlib import Path + +import torch +from lhotse import CutSet, WhisperFbank, WhisperFbankConfig, LilcomChunkyWriter, MonoCut, combine +from lhotse.recipes.utils import read_manifests_if_cached + +from icefall.utils import get_executor + +# Torch's multithreaded behavior needs to be disabled or +# it wastes a lot of CPU and slow things down. +# Do this outside of main() in case it needs to take effect +# even when we are not invoking the main (e.g. when spawning subprocesses). +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + + +def is_cut_long(c: MonoCut) -> bool: + return c.duration > 5 + + +def compute_fbank_musan(): + src_dir = Path("data/manifests") + output_dir = Path("data/fbank") + num_jobs = min(15, os.cpu_count()) + num_mel_bins = 80 + + dataset_parts = ( + "music", + "speech", + "noise", + ) + prefix = "musan" + suffix = "jsonl.gz" + manifests = read_manifests_if_cached( + dataset_parts=dataset_parts, + output_dir=src_dir, + prefix=prefix, + suffix=suffix, + ) + assert manifests is not None + + assert len(manifests) == len(dataset_parts), ( + len(manifests), + len(dataset_parts), + list(manifests.keys()), + dataset_parts, + ) + + musan_cuts_path = output_dir / "musan_cuts.jsonl.gz" + + if musan_cuts_path.is_file(): + logging.info(f"{musan_cuts_path} already exists - skipping") + return + + logging.info("Extracting features for Musan") + + extractor = WhisperFbank(WhisperFbankConfig(device='cuda')) + + with get_executor() as ex: # Initialize the executor only once. + # create chunks of Musan with duration 5 - 10 seconds + musan_cuts = ( + CutSet.from_manifests( + recordings=combine(part["recordings"] for part in manifests.values()) + ) + .cut_into_windows(10.0) + .filter(is_cut_long) + .compute_and_store_features( + extractor=extractor, + storage_path=f"{output_dir}/musan_feats", + num_jobs=num_jobs if ex is None else 80, + executor=ex, + storage_type=LilcomChunkyWriter, + ) + ) + musan_cuts.to_file(musan_cuts_path) + + +if __name__ == "__main__": + formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + + logging.basicConfig(format=formatter, level=logging.INFO) + compute_fbank_musan() diff --git a/egs/aishell/ASR/run_whisper.sh b/egs/aishell/ASR/run_whisper.sh new file mode 100644 index 000000000..d96089db1 --- /dev/null +++ b/egs/aishell/ASR/run_whisper.sh @@ -0,0 +1,7 @@ + + +pip install -r whisper/requirements.txt +pip install k2==1.24.3.dev20230524+cuda11.8.torch2.0.1 -f https://k2-fsa.github.io/k2/cuda.html +export PYTHONPATH=$PYTHONPATH:/mnt/samsung-t7/yuekai/asr/icefall + +torchrun --nproc-per-node 8 whisper/train.py --use-fp16 1 --max-duration 20 --base-lr 1e-5 --exp-dir whisper/exp_medimum --start-epoch 1 diff --git a/egs/aishell/ASR/whisper/asr_datamodule.py b/egs/aishell/ASR/whisper/asr_datamodule.py new file mode 120000 index 000000000..fa1b8cca3 --- /dev/null +++ b/egs/aishell/ASR/whisper/asr_datamodule.py @@ -0,0 +1 @@ +../tdnn_lstm_ctc/asr_datamodule.py \ No newline at end of file diff --git a/egs/aishell/ASR/whisper/decode.py b/egs/aishell/ASR/whisper/decode.py new file mode 100644 index 000000000..44c6ea081 --- /dev/null +++ b/egs/aishell/ASR/whisper/decode.py @@ -0,0 +1,428 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corporation (Author: Liyong Guo, +# Fangjun Kuang, +# Wei Kang) +# +# 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 argparse +import logging +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 AishellAsrDataModule + +#from icefall.char_graph_compiler import CharCtcTrainingGraphCompiler +from icefall.checkpoint import average_checkpoints, 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 icefall.env import get_env_info +from icefall.lexicon import Lexicon +from icefall.utils import ( + AttributeDict, + get_texts, + setup_logger, + store_transcripts, + write_error_stats, +) +from zhconv import convert +from tn.chinese.normalizer import Normalizer +import re + +def remove_punctuation(text: str or List[str]): + # https://github.com/yeyupiaoling/Whisper-Finetune/blob/master/utils/data_utils.py + 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'不支持该类型{type(text)}') + + +# 将繁体中文总成简体中文 +def to_simple(text: str or List[str]): + 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'不支持该类型{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: + - (0) ctc-decoding. Use CTC decoding. It maps the tokens ids to + tokens using token symbol tabel directly. + - (1) 1best. Extract the best path from the decoding lattice as the + decoding result. + - (2) nbest. Extract n paths from the decoding lattice; the path + with the highest score is the decoding result. + - (3) attention-decoder. Extract n paths from the lattice, + the path with the highest score is the decoding result. + - (4) nbest-oracle. Its WER is the lower bound of any n-best + rescoring method can achieve. Useful for debugging n-best + rescoring method. + """, + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="whisper/exp", + help="The experiment dir", + ) + + return parser + + +def get_params() -> AttributeDict: + params = AttributeDict( + { + # parameters for conformer + "subsampling_factor": 4, + "feature_dim": 80, + "nhead": 4, + "attention_dim": 512, + "num_encoder_layers": 12, + "num_decoder_layers": 6, + "vgg_frontend": False, + "use_feat_batchnorm": True, + # parameters for decoder + "search_beam": 20, + "output_beam": 7, + "min_active_states": 30, + "max_active_states": 10000, + "use_double_scores": True, + "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: 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. + 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. + Returns: + Return the decoding result. See above description for the format of + the returned dict. + """ + dtype = torch.float16 + device = torch.device("cuda") + + feature = batch["inputs"] + assert feature.ndim == 3 + feature = feature.to(device, dtype=dtype).transpose(1, 2) + # pad feature to T = 3000 + 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) + print(feature.shape,23333) + # at entry, feature is (N, T, C) + + 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] + + key = "beam-search" + + return {key: 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: + 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. + 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. + """ + 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}/log-decode-{params.suffix}") + + #options = whisper.DecodingOptions(task="transcribe", language="zh", without_timestamps=True, beam_size=10) + options = whisper.DecodingOptions(task="transcribe", language="zh", without_timestamps=True, beam_size=None) + 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}") + + model = whisper.load_model("medium") + # if params.epoch > 0: + # if params.avg > 1: + # start = params.epoch - params.avg + # assert start >= 1, start + # filename_start = f"{params.exp_dir}/epoch-{start}.pt" + # filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt" + # logging.info( + # f"Calculating the averaged model over epoch range from " + # f"{start} (excluded) to {params.epoch}" + # ) + # model.to(device) + # model.load_state_dict( + # average_checkpoints_with_averaged_model( + # filename_start=filename_start, + # filename_end=filename_end, + # device=device, + # ) + # ) + # 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) + test_cuts = aishell.test_cuts() + test_dl = aishell.test_dataloaders(test_cuts) + + test_sets = ["test"] + test_dls = [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() diff --git a/egs/aishell/ASR/whisper/label_smoothing.py b/egs/aishell/ASR/whisper/label_smoothing.py new file mode 120000 index 000000000..e9d239fff --- /dev/null +++ b/egs/aishell/ASR/whisper/label_smoothing.py @@ -0,0 +1 @@ +../../../librispeech/ASR/conformer_ctc/label_smoothing.py \ No newline at end of file diff --git a/egs/aishell/ASR/whisper/requirements.txt b/egs/aishell/ASR/whisper/requirements.txt new file mode 100644 index 000000000..e0c221ded --- /dev/null +++ b/egs/aishell/ASR/whisper/requirements.txt @@ -0,0 +1,8 @@ +k2 +kaldialign +lhotse +sentencepiece +tensorboard +librosa +openai-whisper +zhconv diff --git a/egs/aishell/ASR/whisper/train.py b/egs/aishell/ASR/whisper/train.py new file mode 100644 index 000000000..b398ddb81 --- /dev/null +++ b/egs/aishell/ASR/whisper/train.py @@ -0,0 +1,1207 @@ +#!/usr/bin/env python3 +# Copyright 2023 Xiaomi Corp. (authors: Xiaoyu Yang) +# +# 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: + +./prepare.sh + +If you use --datatang-prob=0, then you don't need to run the above script. + +export CUDA_VISIBLE_DEVICES="0,1,2,3" + +./pruned_transducer_stateless7/train.py \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 1 \ + --use-fp16 1 \ + --exp-dir pruned_transducer_stateless7/exp \ + --full-libri 1 \ + --max-duration 550 +""" + + +import argparse +import copy +import logging +import random +import warnings +from pathlib import Path +from shutil import copyfile +from typing import Any, Dict, Optional, Tuple, Union + +import k2 +import optim +import torch +import torch.multiprocessing as mp +import torch.nn as nn +from typing import List +#from aishell import AIShell +#from asr_datamodule import AsrDataModule +from asr_datamodule import AishellAsrDataModule +#from decoder import Decoder +#from joiner import Joiner +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 model import Transducer +from optim import Eden, ScaledAdam +from torch import Tensor +from torch.cuda.amp import GradScaler +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.nn.functional import pad as pad_tensor +from torch.utils.tensorboard import SummaryWriter +#from zipformer import Zipformer + +from icefall import diagnostics +#from icefall.char_graph_compiler import CharCtcTrainingGraphCompiler +from icefall.checkpoint import load_checkpoint, remove_checkpoints +from icefall.checkpoint import save_checkpoint as save_checkpoint_impl +from icefall.checkpoint import ( + save_checkpoint_with_global_batch_idx, + update_averaged_model, +) +from icefall.dist import cleanup_dist, setup_dist, get_world_size, get_rank, get_local_rank +from icefall.env import get_env_info +from icefall.hooks import register_inf_check_hooks +from icefall.lexicon import Lexicon +from icefall.utils import ( + AttributeDict, + MetricsTracker, + filter_uneven_sized_batch, + setup_logger, + str2bool, +) + +import whisper + +from label_smoothing import LabelSmoothingLoss + +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 add_model_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--num-encoder-layers", + type=str, + default="2,4,3,2,4", + help="Number of zipformer encoder layers, comma separated.", + ) + + parser.add_argument( + "--feedforward-dims", + type=str, + default="1024,1024,2048,2048,1024", + help="Feedforward dimension of the zipformer encoder layers, comma separated.", + ) + + parser.add_argument( + "--nhead", + type=str, + default="8,8,8,8,8", + help="Number of attention heads in the zipformer encoder layers.", + ) + + parser.add_argument( + "--encoder-dims", + type=str, + default="384,384,384,384,384", + help="Embedding dimension in the 2 blocks of zipformer encoder layers, comma separated", + ) + + parser.add_argument( + "--attention-dims", + type=str, + default="192,192,192,192,192", + help="""Attention dimension in the 2 blocks of zipformer encoder layers, comma separated; + not the same as embedding dimension.""", + ) + + parser.add_argument( + "--encoder-unmasked-dims", + type=str, + default="256,256,256,256,256", + help="Unmasked dimensions in the encoders, relates to augmentation during training. " + "Must be <= each of encoder_dims. Empirically, less than 256 seems to make performance " + " worse.", + ) + + parser.add_argument( + "--zipformer-downsampling-factors", + type=str, + default="1,2,4,8,2", + help="Downsampling factor for each stack of encoder layers.", + ) + + parser.add_argument( + "--cnn-module-kernels", + type=str, + default="31,31,31,31,31", + help="Sizes of kernels in convolution modules", + ) + + parser.add_argument( + "--decoder-dim", + type=int, + default=512, + help="Embedding dimension in the decoder model.", + ) + + parser.add_argument( + "--joiner-dim", + type=int, + default=512, + help="""Dimension used in the joiner model. + Outputs from the encoder and decoder model are projected + to this dimension before adding. + """, + ) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--master-port", + type=int, + default=12354, + help="Master port to use for DDP training.", + ) + + parser.add_argument( + "--tensorboard", + type=str2bool, + default=True, + help="Should various information be logged in tensorboard.", + ) + + parser.add_argument( + "--num-epochs", + type=int, + default=30, + 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( + "--lang-dir", + type=str, + default="data/lang_char", + help="""The lang dir + It contains language related input files such as + "lexicon.txt" + """, + ) + + parser.add_argument( + "--base-lr", type=float, default=0.05, 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( + "--context-size", + type=int, + default=1, + help="The context size in the decoder. 1 means bigram; 2 means tri-gram", + ) + + parser.add_argument( + "--prune-range", + type=int, + default=5, + help="The prune range for rnnt loss, it means how many symbols(context)" + "we are using to compute the loss", + ) + + parser.add_argument( + "--lm-scale", + type=float, + default=0.25, + help="The scale to smooth the loss with lm " + "(output of prediction network) part.", + ) + + parser.add_argument( + "--am-scale", + type=float, + default=0.0, + help="The scale to smooth the loss with am (output of encoder network) part.", + ) + + parser.add_argument( + "--simple-loss-scale", + type=float, + default=0.5, + help="To get pruning ranges, we will calculate a simple version" + "loss(joiner is just addition), this simple loss also uses for" + "training (as a regularization item). We will scale the simple loss" + "with this parameter before adding to the final loss.", + ) + + 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( + "--save-every-n", + type=int, + default=4000, + help="""Save checkpoint after processing this number of batches" + periodically. We save checkpoint to exp-dir/ whenever + params.batch_idx_train % save_every_n == 0. The checkpoint filename + has the form: f'exp-dir/checkpoint-{params.batch_idx_train}.pt' + Note: It also saves checkpoint to `exp-dir/epoch-xxx.pt` at the + end of each epoch where `xxx` is the epoch number counting from 0. + """, + ) + + 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=False, + help="Whether to use half precision training.", + ) + + add_model_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`: + + - best_train_loss: Best training loss so far. It is used to select + the model that has the lowest training loss. It is + updated during the training. + + - best_valid_loss: Best validation loss so far. It is used to select + the model that has the lowest validation loss. It is + updated during the training. + + - best_train_epoch: It is the epoch that has the best training loss. + + - best_valid_epoch: It is the epoch that has the best validation loss. + + - batch_idx_train: Used to writing statistics to tensorboard. It + contains number of batches trained so far across + epochs. + + - log_interval: Print training loss if batch_idx % log_interval` is 0 + + - reset_interval: Reset statistics if batch_idx % reset_interval is 0 + + - valid_interval: Run validation if batch_idx % valid_interval is 0 + + - feature_dim: The model input dim. It has to match the one used + in computing features. + + - subsampling_factor: The subsampling factor for the model. + + - encoder_dim: Hidden dim for multi-head attention model. + + - num_decoder_layers: Number of decoder layer of transformer decoder. + + - warm_step: The warmup period that dictates the decay of the + scale on "simple" (un-pruned) loss. + """ + 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": 3000, # For the 100h subset, use 800 + # parameters for zipformer + "feature_dim": 80, + "subsampling_factor": 4, # not passed in, this is fixed. + "warm_step": 100, + "env_info": get_env_info(), + } + ) + + return params + + +# def get_transducer_model(params: AttributeDict) -> nn.Module: +# encoder = get_encoder_model(params) +# decoder = get_decoder_model(params) +# joiner = get_joiner_model(params) + +# model = Transducer( +# encoder=encoder, +# decoder=decoder, +# joiner=joiner, +# encoder_dim=int(params.encoder_dims.split(",")[-1]), +# decoder_dim=params.decoder_dim, +# joiner_dim=params.joiner_dim, +# vocab_size=params.vocab_size, +# ) +# return model + + +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, + model: Union[nn.Module, DDP], + batch: dict, + is_training: bool, +) -> Tuple[Tensor, MetricsTracker]: + """ + Compute RNN-T loss given the model and its inputs. + + Args: + params: + Parameters for training. See :func:`get_params`. + model: + The model for training. It is an instance of Zipformer in our case. + batch: + A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()` + for the content in it. + is_training: + True for training. False for validation. When it is True, this + function enables autograd during computation; when it is False, it + disables autograd. + warmup: a floating point value which increases throughout training; + values >= 1.0 are fully warmed up and have all modules present. + """ + # 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"] + # at entry, feature is (N, T, C) + assert feature.ndim == 3 + feature = feature.to(device) + feature = feature.transpose(1, 2) # (N, C, T) + # pad feature from B,80,T to B,80,3000 + feature = torch.nn.functional.pad(feature, (0, 3000 - feature.shape[-1])) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) + + batch_idx_train = params.batch_idx_train + warm_step = params.warm_step + + texts = batch["supervisions"]["text"] + + text_tokens_list = [list(params.tokenizer.sot_sequence_including_notimestamps) + params.tokenizer.encode(text) + [params.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] + + prev_outputs_tokens = _batch_tensors( + [tokens[:-1] for tokens in text_tokens_list], pad_value=params.tokenizer.eot + ) + target_tokens = _batch_tensors( + [tokens[1:] for tokens in text_tokens_list], pad_value=params.tokenizer.eot + ) + target_lengths = torch.LongTensor( + [tokens.shape[0] - 1 for tokens in text_tokens_list] + ) + decoder_criterion = LabelSmoothingLoss(ignore_index=params.tokenizer.eot, label_smoothing=0.1, reduction="sum") + + with torch.set_grad_enabled(is_training): + + encoder_out = model.encoder(feature) + text_logits = model.decoder(prev_outputs_tokens.to(device), encoder_out) + 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, + 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): + loss, loss_info = compute_loss( + params=params, + 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, + 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"]) + + try: + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, loss_info = compute_loss( + params=params, + 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. + 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 + ): + update_averaged_model( + params=params, + model_cur=model, + model_avg=model_avg, + ) + + if ( + params.batch_idx_train > 0 + and params.batch_idx_train % params.save_every_n == 0 + ): + save_checkpoint_with_global_batch_idx( + out_dir=params.exp_dir, + global_batch_idx=params.batch_idx_train, + model=model, + model_avg=model_avg, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=train_dl.sampler, + scaler=scaler, + rank=rank, + ) + remove_checkpoints( + out_dir=params.exp_dir, + topk=params.keep_last_k, + rank=rank, + ) + + if batch_idx % 100 == 0 and params.use_fp16: + # 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: + cur_lr = scheduler.get_last_lr()[0] + cur_grad_scale = scaler._scale.item() if params.use_fp16 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 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, + ) + + if batch_idx % params.valid_interval == 0 and not params.print_diagnostics: + logging.info("Computing validation loss") + valid_info = compute_validation_loss( + params=params, + 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 + ) + + 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) + # rank = get_rank() + # world_size = get_world_size() + # setup_dist(rank, world_size, use_ddp_launch=True) + setup_dist(use_ddp_launch=True) + + setup_logger(f"{params.exp_dir}/log/log-train") + logging.info("Training started") + + if args.tensorboard and rank == 0: + tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard") + else: + tb_writer = None + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", rank) + logging.info(f"Device: {device}") + + + + + logging.info("About to create model") + model = whisper.load_model("medium") + del model.alignment_heads + params.tokenizer = whisper.tokenizer.get_tokenizer( + model.is_multilingual, language="zh", task="transcribe" + ) + logging.info(params) + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + assert params.save_every_n >= params.average_period + 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 + ) + + model.to(device) + if world_size > 1: + logging.info("Using DDP") + model = DDP(model, device_ids=[rank], find_unused_parameters=True) + + #parameters_names = [] + #parameters_names.append( + # [name_param_pair[0] for name_param_pair in model.named_parameters()] + #) + # optimizer = ScaledAdam( + # model.parameters(), + # lr=params.base_lr, + # clipping_scale=2.0, + # parameters_names=parameters_names, + # ) + optimizer = ScaledAdam( + model.parameters(), + lr=params.base_lr, + clipping_scale=2.0, + ) + 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 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) + + def remove_short_and_long_utt(c: Cut): + # Keep only utterances with duration between 1 second and 20 seconds + # + # Caution: There is a reason to select 20.0 here. Please see + # ../local/display_manifest_statistics.py + # + # You should use ../local/display_manifest_statistics.py to get + # an utterance duration distribution for your dataset to select + # the threshold + if c.duration < 1.0 or c.duration > 12.0: + logging.warning( + f"Exclude cut with ID {c.id} from training. Duration: {c.duration}" + ) + return False + + # In pruned RNN-T, we require that T >= S + # where T is the number of feature frames after subsampling + # and S is the number of tokens in the utterance + + # In ./zipformer.py, the conv module uses the following expression + # for subsampling + # T = ((c.num_frames - 7) // 2 + 1) // 2 + # tokens = sp.encode(c.supervisions[0].text, out_type=str) + + # if T < len(tokens): + # logging.warning( + # f"Exclude cut with ID {c.id} from training. " + # f"Number of frames (before subsampling): {c.num_frames}. " + # f"Number of frames (after subsampling): {T}. " + # f"Text: {c.supervisions[0].text}. " + # f"Tokens: {tokens}. " + # f"Number of tokens: {len(tokens)}" + # ) + # return False + + return True + + #aishell = AIShell(manifest_dir=args.manifest_dir) + #train_cuts = aishell.train_cuts() + #asr_datamodule = AishellAsrDataModule(args) + + aishell = AishellAsrDataModule(args) + # train_cuts = asr_datamodule.train_cuts() + # train_cuts = train_cuts.filter(remove_short_and_long_utt) + + # if args.enable_musan: + # cuts_musan = load_manifest(Path(args.manifest_dir) / "musan_cuts.jsonl.gz") + # else: + # cuts_musan = None + + + + 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 = asr_datamodule.train_dataloaders( + # train_cuts, + # on_the_fly_feats=False, + # cuts_musan=cuts_musan, + # sampler_state_dict=sampler_state_dict, + # ) + + # valid_cuts = aishell.valid_cuts() + # valid_dl = asr_datamodule.valid_dataloaders(valid_cuts) + train_dl = aishell.train_dataloaders(aishell.train_cuts()) + valid_dl = aishell.valid_dataloaders(aishell.valid_cuts()) + # if not params.print_diagnostics: + # scan_pessimistic_batches_for_oom( + # model=model, + # train_dl=train_dl, + # optimizer=optimizer, + # graph_compiler=graph_compiler, + # params=params, + # ) + + 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"]) + + logging.info(f"start training from epoch {params.start_epoch}") + for epoch in range(params.start_epoch, params.num_epochs + 1): + 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, + 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 + + 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: + 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}") + + # y = graph_compiler.texts_to_ids(supervisions["text"]) + # num_tokens = sum(len(i) for i in y) + # logging.info(f"num tokens: {num_tokens}") + + +def scan_pessimistic_batches_for_oom( + model: Union[nn.Module, DDP], + train_dl: torch.utils.data.DataLoader, + optimizer: torch.optim.Optimizer, + params: AttributeDict, +): + from lhotse.dataset import find_pessimistic_batches + + logging.info( + "Sanity check -- see if any of the batches in epoch 1 would cause OOM." + ) + batches, crit_values = find_pessimistic_batches(train_dl.sampler) + for criterion, cuts in batches.items(): + batch = train_dl.dataset[cuts] + try: + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, _ = compute_loss( + params=params, + model=model, + batch=batch, + is_training=True, + ) + loss.backward() + optimizer.zero_grad() + except Exception as e: + if "CUDA out of memory" in str(e): + logging.error( + "Your GPU ran out of memory with the current " + "max_duration setting. We recommend decreasing " + "max_duration and trying again.\n" + f"Failing criterion: {criterion} " + f"(={crit_values[criterion]}) ..." + ) + display_and_save_batch(batch, params=params) + raise + logging.info( + f"Maximum memory allocated so far is {torch.cuda.max_memory_allocated()//1000000}MB" + ) + + +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() + assert world_size >= 1 + + run(rank=rank, world_size=world_size, args=args) + + +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + +if __name__ == "__main__": + main()