diff --git a/egs/speech_llm/SPEECH2SPEECH/local/compute_whisper_fbank.py b/egs/speech_llm/SPEECH2SPEECH/local/compute_whisper_fbank.py new file mode 100755 index 000000000..1c3a3d1e0 --- /dev/null +++ b/egs/speech_llm/SPEECH2SPEECH/local/compute_whisper_fbank.py @@ -0,0 +1,185 @@ +#!/usr/bin/env python3 +# Copyright 2021 Johns Hopkins University (Piotr Żelasko) +# Copyright 2021 Xiaomi Corp. (Fangjun Kuang) +# Copyright 2023 Xiaomi Corp. (Zengrui Jin) +# +# 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 pathlib import Path + +import torch +from datasets import load_dataset +from lhotse import ( + CutSet, + LilcomChunkyWriter, + WhisperFbank, + WhisperFbankConfig, +) + +from icefall.utils import 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 get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + 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=True, + help="Use WhisperFbank instead of Fbank. Default: False.", + ) + parser.add_argument( + "--resample-to-16kHz", + type=str2bool, + default=True, + help="Resample audio to 16kHz. Default: False.", + ) + parser.add_argument( + "--speed-perturb", + type=str2bool, + default=False, + help="Enable 0.9 and 1.1 speed perturbation for data augmentation. Default: False.", + ) + parser.add_argument( + "--out-dir", + type=str, + default="data/fbank", + help="Output directory for the computed features", + ) + parser.add_argument( + "--huggingface-dataset-path-or-name", + type=str, + default="/workspace/Belle_1.4M-SLAM-Omni", + help="The path or name of the Huggingface dataset", + ) + parser.add_argument( + "--audio-key", + type=str, + default="question_audio", + help="The key in the Huggingface dataset containing the audio data", + ) + parser.add_argument( + "--text-key", + type=str, + default="answer", + help="The key in the Huggingface dataset containing the text data", + ) + + return parser + + +def compute_fbank(args): + in_out_dir = Path(args.out_dir) + in_out_dir.mkdir(parents=True, exist_ok=True) + # number of workers in dataloader + num_workers = 4 + + # number of seconds in a batch + batch_duration = 10 + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + if args.whisper_fbank: + extractor = WhisperFbank( + WhisperFbankConfig(num_filters=args.num_mel_bins, device=device) + ) + else: + extractor = KaldifeatFbank(KaldifeatFbankConfig(device=device)) + + logging.info(f"device: {device}") + + start = 0 + stop = 1601 + num_digits = 5 + for i in range(start, stop): + idx = f"{i}".zfill(num_digits) + # dataset = load_dataset(args.huggingface_dataset_path_or_name, streaming=True, split=partition) + parquet_files = [ + f"data/train-{idx}-of-01601.parquet", + ] + parquet_files = [f"{args.huggingface_dataset_path_or_name}/{f}" for f in parquet_files] + file_name = parquet_files[0] + logging.info(f"Loading dataset from {file_name}") + dataset = load_dataset('parquet', data_files=parquet_files, streaming=True, split='train') + + cut_set = CutSet.from_huggingface_dataset(dataset, audio_key=args.audio_key, text_key=args.text_key) + + logging.info("Splitting cuts into smaller chunks") + cut_set = cut_set.trim_to_supervisions( + keep_overlapping=False, min_duration=None + ) + + if args.resample_to_16kHz: + cut_set = cut_set.resample(16000) + if args.speed_perturb: + cut_set = cut_set + cut_set.perturb_speed(0.9) + cut_set.perturb_speed(1.1) + + logging.info("Computing features") + cut_set = cut_set.compute_and_store_features_batch( + extractor=extractor, + storage_path=f"{in_out_dir}/feats_{idx}", + num_workers=num_workers, + batch_duration=batch_duration, + storage_type=LilcomChunkyWriter, + overwrite=True, + ) + cuts_path = f"{in_out_dir}/cuts_belle.{idx}.jsonl.gz" + logging.info(f"Saving to {cuts_path}") + # cut_set.to_file(cuts_path) + remove_recording_item(cut_set, cuts_path) + +def remove_recording_item( + cuts, + output_cuts, +): + """ + don't store recording item + """ + with CutSet.open_writer(output_cuts) as writer: + for cut in cuts: + cut.recording.sources = None + writer.write(cut) + +def main(): + formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + logging.basicConfig(format=formatter, level=logging.INFO) + + parser = get_parser() + args = parser.parse_args() + logging.info(vars(args)) + + compute_fbank(args) + + +if __name__ == "__main__": + main() diff --git a/egs/speech_llm/SPEECH2SPEECH/prepare.sh b/egs/speech_llm/SPEECH2SPEECH/prepare.sh new file mode 100644 index 000000000..87e7cd254 --- /dev/null +++ b/egs/speech_llm/SPEECH2SPEECH/prepare.sh @@ -0,0 +1,45 @@ +#!/usr/bin/env bash + +# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674 +export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python +export PYTHONPATH=$PYTHONPATH:/workspace/slam/icefall_omni +set -eou pipefail + +stage=2 +stop_stage=2 +# All files generated by this script are saved in "data". +# You can safely remove "data" and rerun this script to regenerate it. +mkdir -p data + +log() { + # This function is from espnet + local fname=${BASH_SOURCE[1]##*/} + echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*" +} + + +if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then + log "stage 0: " + + +fi + +if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then + log "stage 1: Download whisper-large-v2 multi-hans-zh fbank feature from huggingface" + + python3 local/compute_whisper_fbank.py +fi + +if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then + log "stage 2: " + python3 ./slam_omni/decode.py \ + --max-duration 80 \ + --exp-dir slam_omni/exp_test_whisper_qwen2_1.5B \ + --speech-encoder-path-or-name models/whisper/v1.1/whisper-large-v2-multi-hans-zh-epoch-3-avg-10.pt \ + --llm-path-or-name models/qwen \ + --epoch 999 --avg 1 \ + --manifest-dir data/fbank \ + --use-flash-attn True \ + --use-lora True + +fi diff --git a/egs/speech_llm/SPEECH2SPEECH/slam_omni/data_module.py b/egs/speech_llm/SPEECH2SPEECH/slam_omni/data_module.py new file mode 100644 index 000000000..35d1e3494 --- /dev/null +++ b/egs/speech_llm/SPEECH2SPEECH/slam_omni/data_module.py @@ -0,0 +1,437 @@ +# Copyright 2021 Piotr Żelasko +# Copyright 2022 Xiaomi Corporation (Author: Mingshuang Luo) +# +# 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 inspect +import logging +from functools import lru_cache +from pathlib import Path +from typing import Any, Dict, Optional + +import torch +from lhotse import CutSet, WhisperFbank, WhisperFbankConfig, load_manifest, load_manifest_lazy +from lhotse.dataset import ( # noqa F401 for PrecomputedFeatures + CutConcatenate, + CutMix, + DynamicBucketingSampler, + PrecomputedFeatures, + SimpleCutSampler, + SpecAugment, +) +from lhotse.dataset.input_strategies import ( # noqa F401 For AudioSamples + AudioSamples, + OnTheFlyFeatures, +) +from lhotse.utils import fix_random_seed +from torch.utils.data import DataLoader + +from icefall.utils import str2bool + +from speech_dataset import K2SpeechRecognitionDataset + +class _SeedWorkers: + def __init__(self, seed: int): + self.seed = seed + + def __call__(self, worker_id: int): + fix_random_seed(self.seed + worker_id) + + +class AsrDataModule: + """ + DataModule for k2 ASR experiments. + It assumes there is always one train and valid dataloader, + but there can be multiple test dataloaders (e.g. LibriSpeech test-clean + and test-other). + + It contains all the common data pipeline modules used in ASR + experiments, e.g.: + - dynamic batch size, + - bucketing samplers, + - cut concatenation, + - augmentation, + - on-the-fly feature extraction + + This class should be derived for specific corpora used in ASR tasks. + """ + + def __init__(self, args: argparse.Namespace): + self.args = args + + @classmethod + def add_arguments(cls, parser: argparse.ArgumentParser): + group = parser.add_argument_group( + title="ASR data related options", + description="These options are used for the preparation of " + "PyTorch DataLoaders from Lhotse CutSet's -- they control the " + "effective batch sizes, sampling strategies, applied data " + "augmentations, etc.", + ) + group.add_argument( + "--manifest-dir", + type=Path, + default=Path("data/fbank"), + help="Path to directory with train/valid/test cuts.", + ) + group.add_argument( + "--max-duration", + type=int, + default=300.0, + help="Maximum pooled recordings duration (seconds) in a " + "single batch. You can reduce it if it causes CUDA OOM.", + ) + group.add_argument( + "--bucketing-sampler", + type=str2bool, + default=True, + help="When enabled, the batches will come from buckets of " + "similar duration (saves padding frames).", + ) + group.add_argument( + "--num-buckets", + type=int, + default=30, + help="The number of buckets for the DynamicBucketingSampler" + "(you might want to increase it for larger datasets).", + ) + # group.add_argument( + # "--concatenate-cuts", + # type=str2bool, + # default=False, + # help="When enabled, utterances (cuts) will be concatenated " + # "to minimize the amount of padding.", + # ) + # group.add_argument( + # "--duration-factor", + # type=float, + # default=1.0, + # help="Determines the maximum duration of a concatenated cut " + # "relative to the duration of the longest cut in a batch.", + # ) + # group.add_argument( + # "--gap", + # type=float, + # default=1.0, + # help="The amount of padding (in seconds) inserted between " + # "concatenated cuts. This padding is filled with noise when " + # "noise augmentation is used.", + # ) + group.add_argument( + "--on-the-fly-feats", + type=str2bool, + default=False, + help="When enabled, use on-the-fly cut mixing and feature " + "extraction. Will drop existing precomputed feature manifests " + "if available.", + ) + group.add_argument( + "--shuffle", + type=str2bool, + default=True, + help="When enabled (=default), the examples will be " + "shuffled for each epoch.", + ) + group.add_argument( + "--drop-last", + type=str2bool, + default=True, + help="Whether to drop last batch. Used by sampler.", + ) + group.add_argument( + "--return-cuts", + type=str2bool, + default=True, + help="When enabled, each batch will have the " + "field: batch['supervisions']['cut'] with the cuts that " + "were used to construct it.", + ) + + group.add_argument( + "--num-workers", + type=int, + default=2, + help="The number of training dataloader workers that " + "collect the batches.", + ) + + group.add_argument( + "--enable-spec-aug", + type=str2bool, + default=True, + help="When enabled, use SpecAugment for training dataset.", + ) + + group.add_argument( + "--spec-aug-time-warp-factor", + type=int, + default=80, + help="Used only when --enable-spec-aug is True. " + "It specifies the factor for time warping in SpecAugment. " + "Larger values mean more warping. " + "A value less than 1 means to disable time warp.", + ) + + group.add_argument( + "--enable-musan", + type=str2bool, + default=True, + help="When enabled, select noise from MUSAN and mix it" + "with training dataset. ", + ) + + group.add_argument( + "--input-strategy", + type=str, + default="PrecomputedFeatures", + help="AudioSamples or PrecomputedFeatures", + ) + + group.add_argument( + "--huggingface-dataset-path-or-name", + type=str, + default="/workspace/Belle_1.4M-SLAM-Omni", + help="The path or name of the Huggingface dataset", + ) + group.add_argument( + "--audio-key", + type=str, + default="question_audio", + help="The key in the Huggingface dataset containing the audio data", + ) + group.add_argument( + "--text-key", + type=str, + default="answer", + help="The key in the Huggingface dataset containing the text data", + ) + group.add_argument( + "--resample-to-16kHz", + type=str2bool, + default=True, + help="Resample audio to 16kHz. Default: False.", + ) + + def train_dataloaders( + self, + cuts_train: CutSet, + sampler_state_dict: Optional[Dict[str, Any]] = None, + ) -> DataLoader: + """ + Args: + cuts_train: + CutSet for training. + sampler_state_dict: + The state dict for the training sampler. + """ + transforms = [] + if self.args.enable_musan: + logging.info("Enable MUSAN") + logging.info("About to get Musan cuts") + cuts_musan = load_manifest(self.args.manifest_dir / "musan_cuts.jsonl.gz") + transforms.append( + CutMix(cuts=cuts_musan, p=0.5, snr=(10, 20), preserve_id=True) + ) + else: + logging.info("Disable MUSAN") + + # if self.args.concatenate_cuts: + # logging.info( + # f"Using cut concatenation with duration factor " + # f"{self.args.duration_factor} and gap {self.args.gap}." + # ) + # # Cut concatenation should be the first transform in the list, + # # so that if we e.g. mix noise in, it will fill the gaps between + # # different utterances. + # transforms = [ + # CutConcatenate( + # duration_factor=self.args.duration_factor, gap=self.args.gap + # ) + # ] + transforms + + input_transforms = [] + if self.args.enable_spec_aug: + logging.info("Enable SpecAugment") + logging.info(f"Time warp factor: {self.args.spec_aug_time_warp_factor}") + # Set the value of num_frame_masks according to Lhotse's version. + # In different Lhotse's versions, the default of num_frame_masks is + # different. + num_frame_masks = 10 + num_frame_masks_parameter = inspect.signature( + SpecAugment.__init__ + ).parameters["num_frame_masks"] + if num_frame_masks_parameter.default == 1: + num_frame_masks = 2 + logging.info(f"Num frame mask: {num_frame_masks}") + input_transforms.append( + SpecAugment( + time_warp_factor=self.args.spec_aug_time_warp_factor, + num_frame_masks=num_frame_masks, + features_mask_size=27, + num_feature_masks=2, + frames_mask_size=100, + ) + ) + else: + logging.info("Disable SpecAugment") + + logging.info("About to create train dataset") + train = K2SpeechRecognitionDataset( + input_strategy=eval(self.args.input_strategy)(), + cut_transforms=transforms, + input_transforms=input_transforms, + return_cuts=self.args.return_cuts, + ) + + if self.args.on_the_fly_feats: + # NOTE: the PerturbSpeed transform should be added only if we + # remove it from data prep stage. + # Add on-the-fly speed perturbation; since originally it would + # have increased epoch size by 3, we will apply prob 2/3 and use + # 3x more epochs. + # Speed perturbation probably should come first before + # concatenation, but in principle the transforms order doesn't have + # to be strict (e.g. could be randomized) + # transforms = [PerturbSpeed(factors=[0.9, 1.1], p=2/3)] + transforms # noqa + # Drop feats to be on the safe side. + train = K2SpeechRecognitionDataset( + cut_transforms=transforms, + input_strategy=OnTheFlyFeatures(WhisperFbank(WhisperFbankConfig(num_filters=80, device='cuda'))), + input_transforms=input_transforms, + return_cuts=self.args.return_cuts, + ) + + if self.args.bucketing_sampler: + logging.info("Using DynamicBucketingSampler.") + train_sampler = DynamicBucketingSampler( + cuts_train, + max_duration=self.args.max_duration, + shuffle=self.args.shuffle, + num_buckets=self.args.num_buckets, + buffer_size=self.args.num_buckets * 2000, + shuffle_buffer_size=self.args.num_buckets * 5000, + drop_last=self.args.drop_last, + ) + else: + logging.info("Using SimpleCutSampler.") + train_sampler = SimpleCutSampler( + cuts_train, + max_duration=self.args.max_duration, + shuffle=self.args.shuffle, + ) + logging.info("About to create train dataloader") + + if sampler_state_dict is not None: + logging.info("Loading sampler state dict") + train_sampler.load_state_dict(sampler_state_dict) + + # 'seed' is derived from the current random state, which will have + # previously been set in the main process. + seed = torch.randint(0, 100000, ()).item() + worker_init_fn = _SeedWorkers(seed) + + train_dl = DataLoader( + train, + sampler=train_sampler, + batch_size=None, + num_workers=self.args.num_workers, + persistent_workers=True, + pin_memory=True, + worker_init_fn=worker_init_fn, + ) + + return train_dl + + def valid_dataloaders(self, cuts_valid: CutSet) -> DataLoader: + transforms = [] + # if self.args.concatenate_cuts: + # transforms = [ + # CutConcatenate( + # duration_factor=self.args.duration_factor, gap=self.args.gap + # ) + # ] + transforms + + logging.info("About to create dev dataset") + if self.args.on_the_fly_feats: + validate = K2SpeechRecognitionDataset( + cut_transforms=transforms, + input_strategy=OnTheFlyFeatures(WhisperFbank(WhisperFbankConfig(num_filters=80, device='cuda'))), + return_cuts=self.args.return_cuts, + ) + else: + validate = K2SpeechRecognitionDataset( + cut_transforms=transforms, + return_cuts=self.args.return_cuts, + ) + valid_sampler = DynamicBucketingSampler( + cuts_valid, + max_duration=self.args.max_duration, + shuffle=False, + ) + logging.info("About to create dev dataloader") + valid_dl = DataLoader( + validate, + sampler=valid_sampler, + batch_size=None, + num_workers=2, + persistent_workers=False, + ) + + return valid_dl + + def test_dataloaders(self, cuts: CutSet) -> DataLoader: + logging.debug("About to create test dataset") + test = K2SpeechRecognitionDataset( + input_strategy=OnTheFlyFeatures(WhisperFbank(WhisperFbankConfig(num_filters=80, device='cuda'))) + if self.args.on_the_fly_feats + else eval(self.args.input_strategy)(), + return_cuts=self.args.return_cuts, + ) + sampler = DynamicBucketingSampler( + cuts, + max_duration=self.args.max_duration, + shuffle=False, + ) + logging.debug("About to create test dataloader") + test_dl = DataLoader( + test, + batch_size=None, + sampler=sampler, + num_workers=self.args.num_workers, + ) + return test_dl + + @lru_cache() + def test_cuts(self) -> CutSet: + logging.info("About to get test cuts") + if self.args.on_the_fly_feats: + # dataset = load_dataset(args.huggingface_dataset_path_or_name, streaming=True, split=partition) + i, num_digits = 0, 5 + idx = f"{i}".zfill(num_digits) + parquet_files = [ + f"data/train-{idx}-of-01601.parquet", + ] + parquet_files = [f"{args.huggingface_dataset_path_or_name}/{f}" for f in parquet_files] + file_name = parquet_files[0] + logging.info(f"Loading dataset from {file_name}") + dataset = load_dataset('parquet', data_files=parquet_files, streaming=True, split='train') + cut_set = CutSet.from_huggingface_dataset(dataset, audio_key=args.audio_key, text_key=args.text_key) + if args.resample_to_16kHz: + cut_set = cut_set.resample(16000) + return cut_set + else: + return load_manifest_lazy(self.args.manifest_dir / "cuts_belle.00000.jsonl.gz") \ No newline at end of file diff --git a/egs/speech_llm/SPEECH2SPEECH/slam_omni/decode.py b/egs/speech_llm/SPEECH2SPEECH/slam_omni/decode.py new file mode 100755 index 000000000..5f5334142 --- /dev/null +++ b/egs/speech_llm/SPEECH2SPEECH/slam_omni/decode.py @@ -0,0 +1,653 @@ +#!/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: + +pip install huggingface_hub['cli'] +mkdir -p models/whisper models/qwen models/checkpoint +huggingface-cli download --local-dir models/checkpoint yuekai/icefall_asr_aishell_whisper_qwen2_1.5B + +# For aishell fine-tuned whisper model +huggingface-cli download --local-dir models/whisper yuekai/icefall_asr_aishell_whisper exp_large_v2/whisper-large-v2-aishell1-epoch-10-avg-6.pt +# For multi-hans fine-tuned whisper model +# huggingface-cli download --local-dir models/whisper yuekai/icefall_asr_multi-hans-zh_whisper v1.1/whisper-large-v2-multi-hans-zh-epoch-3-avg-10.pt + +huggingface-cli download --local-dir models/qwen Qwen/Qwen2-7B-Instruct + +mkdir -p whisper_llm_zh/exp_aishell_whisper_qwen2_1.5B +ln -s models/checkpoint/epoch-10-avg-5.pt whisper_llm_zh/exp_aishell_whisper_qwen2_1.5B/epoch-999.pt + +python3 ./whisper_llm_zh/decode.py \ + --max-duration 80 \ + --exp-dir whisper_llm_zh/exp_aishell_whisper_qwen2_1.5B \ + --speech-encoder-path-or-name models/whisper/exp_large_v2/whisper-large-v2-aishell1-epoch-10-avg-6.pt \ + --llm-path-or-name models/qwen \ + --epoch 999 --avg 1 \ + --manifest-dir data/fbank \ + --use-flash-attn True \ + --use-lora True --dataset aishell +""" + +import argparse +import logging +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 transformers +import whisper +from data_module import AsrDataModule +from lhotse.cut import Cut +from model import SPEECH_LLM, EncoderProjector +# from data_module import MultiDataset +from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training +from train import DEFAULT_SPEECH_TOKEN +from transformers import AutoModelForCausalLM, AutoTokenizer +from whisper_encoder_forward_monkey_patch import replace_whisper_encoder_forward + +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 add_model_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--llm-path-or-name", + type=str, + default="/workspace/asr/Qwen1.5-0.5B-Chat", + help="Path or name of the large language model.", + ) + + parser.add_argument( + "--speech-encoder-path-or-name", + type=str, + default="whisper-large-v2", + help="Path or name of the speech encoder.", + ) + + parser.add_argument( + "--encoder-projector-ds-rate", + type=int, + default=8, + help="Downsample rate for the encoder projector.", + ) + + parser.add_argument( + "--use-flash-attn", + type=str2bool, + default=True, + help="Whether to use flash attention.", + ) + + parser.add_argument( + "--use-lora", + type=str2bool, + default=True, + help="Whether to use lora fine-tuned llm checkpoint.", + ) + + +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( + "--remove-whisper-encoder-input-length-restriction", + type=str2bool, + default=True, + help="replace whisper encoder forward method to remove input length restriction", + ) + + # parser.add_argument( + # "--dataset", + # type=str, + # default="aishell", + # choices=["aishell", "speechio", "wenetspeech_test_meeting", "multi_hans_zh"], + # help="The dataset to decode", + # ) + + add_model_arguments(parser) + return parser + + +def get_params() -> AttributeDict: + params = AttributeDict( + { + "env_info": get_env_info(), + } + ) + return params + + +def decode_one_batch( + params: AttributeDict, + model: nn.Module, + tokenizer: AutoTokenizer, + 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". + """ + + def preprocess( + messages, + tokenizer: transformers.PreTrainedTokenizer, + max_len: int = 128, + ) -> Dict: + """Preprocesses the data for supervised fine-tuning.""" + texts = [] + TEMPLATE = "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content']}}{% if loop.last %}{{''}}{% else %}{{ '<|im_end|>\n' }}{% endif %}{% endfor %}" + for i, msg in enumerate(messages): + texts.append( + tokenizer.apply_chat_template( + msg, + tokenize=True, + add_generation_prompt=False, + chat_template=TEMPLATE, + padding="longest", + max_length=max_len, + truncation=True, + ) + ) + max_len_texts = max([len(text) for text in texts]) + if tokenizer.padding_side == "right": + texts = [ + text + [tokenizer.pad_token_id] * (max_len_texts - len(text)) + for text in texts + ] + else: + texts = [ + [tokenizer.pad_token_id] * (max_len_texts - len(text)) + text + for text in texts + ] + + input_ids = torch.tensor(texts, dtype=torch.int) + + attention_mask = input_ids.ne(tokenizer.pad_token_id) + + return input_ids, attention_mask + + dtype = torch.float32 + device = model.llm.device + + 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) + + messages = [ + [ + {"role": "user", "content": f"{DEFAULT_SPEECH_TOKEN}请转写音频为文字"}, + {"role": "assistant", "content": ""}, + ] + ] * len(feature) + + input_ids, attention_mask = preprocess(messages, tokenizer, max_len=128) + + generated_ids = model.decode( + feature, input_ids.to(device, dtype=torch.long), attention_mask.to(device) + ) + hyps = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) + + print(hyps) + print(supervisions) + + return {"beam-search": hyps} + + +def decode_dataset( + dl: torch.utils.data.DataLoader, + params: AttributeDict, + model: nn.Module, + tokenizer: AutoTokenizer, +) -> 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". + """ + + def normalize_text_alimeeting(text: str, normalize: str = "m2met") -> str: + """ + Text normalization similar to M2MeT challenge baseline. + See: https://github.com/yufan-aslp/AliMeeting/blob/main/asr/local/text_normalize.pl + """ + if normalize == "none": + return text + elif normalize == "m2met": + import re + + text = text.replace(" ", "") + text = text.replace("", "") + text = text.replace("<%>", "") + text = text.replace("<->", "") + text = text.replace("<$>", "") + text = text.replace("<#>", "") + text = text.replace("<_>", "") + text = text.replace("", "") + text = text.replace("`", "") + text = text.replace("&", "") + text = text.replace(",", "") + if re.search("[a-zA-Z]", text): + text = text.upper() + text = text.replace("Ａ", "A") + text = text.replace("ａ", "A") + text = text.replace("ｂ", "B") + text = text.replace("ｃ", "C") + text = text.replace("ｋ", "K") + text = text.replace("ｔ", "T") + text = text.replace("，", "") + text = text.replace("丶", "") + text = text.replace("。", "") + text = text.replace("、", "") + text = text.replace("？", "") + return text + + 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, + tokenizer=tokenizer, + ) + + 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_text = normalize_text_alimeeting(ref_text) + ref_words = ref_text.split() + print(f"ref: {ref_text}") + print(f"hyp: {''.join(hyp_words)}") + 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() + AsrDataModule.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}" + ) + + 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() + + whisper_model = whisper.load_model(params.speech_encoder_path_or_name, "cpu") + speech_encoder = whisper_model.encoder + speech_encoder_dim = whisper_model.dims.n_audio_state + tokenizer = AutoTokenizer.from_pretrained(params.llm_path_or_name) + + if params.use_flash_attn: + attn_implementation = "flash_attention_2" + # torch_dtype=torch.bfloat16 FIX ME + torch_dtype = torch.float16 + tokenizer.padding_side = "left" + + else: + attn_implementation = "eager" + torch_dtype = torch.float16 + tokenizer.padding_side = "right" + + llm = AutoModelForCausalLM.from_pretrained( + params.llm_path_or_name, + attn_implementation=attn_implementation, + torch_dtype=torch_dtype, + ) + if params.use_lora: + lora_config = LoraConfig( + r=64, + lora_alpha=16, + target_modules=[ + "q_proj", + "k_proj", + "v_proj", + "o_proj", + "up_proj", + "gate_proj", + "down_proj", + ], + task_type="CAUSAL_LM", + ) + llm = get_peft_model(llm, lora_config) + llm.print_trainable_parameters() + + special_tokens_dict = {"additional_special_tokens": [DEFAULT_SPEECH_TOKEN]} + tokenizer.add_special_tokens(special_tokens_dict) + llm.config.pad_token_id = tokenizer.convert_tokens_to_ids("<|endoftext|>") + llm.config.bos_token_id = tokenizer.convert_tokens_to_ids("<|im_start|>") + llm.config.eos_token_id = tokenizer.convert_tokens_to_ids("<|im_end|>") + + llm.config.default_speech_token_id = tokenizer.convert_tokens_to_ids( + DEFAULT_SPEECH_TOKEN + ) + + encoder_projector = EncoderProjector( + speech_encoder_dim, llm.config.hidden_size, params.encoder_projector_ds_rate + ) + + model = SPEECH_LLM( + speech_encoder, + llm, + encoder_projector, + ) + + if params.avg > 1: + start = params.epoch - params.avg + 1 + assert start >= 1, start + checkpoint = torch.load( + f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location="cpu" + ) + assert "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) + ] + avg_checkpoint = average_checkpoints(filenames) + model.load_state_dict(avg_checkpoint, strict=False) + + filename = f"{params.exp_dir}/epoch-{params.epoch}-avg-{params.avg}.pt" + torch.save(avg_checkpoint, filename) + else: + checkpoint = torch.load( + f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location="cpu" + ) + model.load_state_dict(checkpoint, strict=False) + + 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 + + data_module = AsrDataModule(args) + # data_module = MultiDataset(args.manifest_dir) + + def remove_long_utt(c: Cut): + # Keep only utterances with duration in 30 seconds + # + if c.duration > 30.0: + logging.warning( + f"Exclude cut with ID {c.id} from training. Duration: {c.duration}" + ) + return False + return True + + # if params.dataset == "aishell": + # test_sets_cuts = data_module.aishell_test_cuts() + # elif params.dataset == "speechio": + # test_sets_cuts = data_module.speechio_test_cuts() + # elif params.dataset == "wenetspeech_test_meeting": + # test_sets_cuts = data_module.wenetspeech_test_meeting_cuts() + # else: + test_sets_cuts = data_module.test_cuts() + + test_sets = test_sets_cuts.keys() + test_dls = [ + data_module.test_dataloaders(test_sets_cuts[cuts_name].filter(remove_long_utt)) + for cuts_name in test_sets + ] + + for test_set, test_dl in zip(test_sets, test_dls): + results_dict = decode_dataset( + dl=test_dl, + params=params, + model=model, + tokenizer=tokenizer, + ) + + 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/speech_llm/SPEECH2SPEECH/slam_omni/label_smoothing.py b/egs/speech_llm/SPEECH2SPEECH/slam_omni/label_smoothing.py new file mode 120000 index 000000000..e9d239fff --- /dev/null +++ b/egs/speech_llm/SPEECH2SPEECH/slam_omni/label_smoothing.py @@ -0,0 +1 @@ +../../../librispeech/ASR/conformer_ctc/label_smoothing.py \ No newline at end of file diff --git a/egs/speech_llm/SPEECH2SPEECH/slam_omni/model.py b/egs/speech_llm/SPEECH2SPEECH/slam_omni/model.py new file mode 100644 index 000000000..829ef4e2d --- /dev/null +++ b/egs/speech_llm/SPEECH2SPEECH/slam_omni/model.py @@ -0,0 +1,285 @@ +import torch +from torch import nn +from transformers.trainer_pt_utils import LabelSmoother + +IGNORE_TOKEN_ID = LabelSmoother.ignore_index + + +class EncoderProjector(nn.Module): + """ + The encoder projector module. It is used to project the encoder outputs to the same dimension as the language model. + Modified from https://github.com/X-LANCE/SLAM-LLM/blob/main/src/slam_llm/models/projector.py. + Args: + encoder_dim (:obj:`int`): The dimension of the encoder outputs. + llm_dim (:obj:`int`): The dimension of the language model. + downsample_rate (:obj:`int`, `optional`, defaults to 5): The downsample rate to use. + """ + + def __init__(self, encoder_dim, llm_dim, downsample_rate=5): + super().__init__() + self.downsample_rate = downsample_rate + self.linear1 = nn.Linear(encoder_dim * self.downsample_rate, llm_dim) + self.relu = nn.ReLU() + self.linear2 = nn.Linear(llm_dim, llm_dim) + + def forward(self, x): + + batch_size, seq_len, feat_dim = x.size() + num_frames_to_discard = seq_len % self.downsample_rate + if num_frames_to_discard > 0: + x = x[:, :-num_frames_to_discard, :] + seq_len = x.size(1) + + x = x.contiguous() + x = x.view( + batch_size, seq_len // self.downsample_rate, feat_dim * self.downsample_rate + ) + + x = self.linear1(x) + x = self.relu(x) + x = self.linear2(x) + return x + + +class SPEECH_LLM(nn.Module): + """ + The Speech-to-Text model. It consists of an encoder, a language model and an encoder projector. + The encoder is used to extract speech features from the input speech signal. + The encoder projector is used to project the encoder outputs to the same dimension as the language model. + The language model is used to generate the text from the speech features. + Args: + encoder (:obj:`nn.Module`): The encoder module. + llm (:obj:`nn.Module`): The language model module. + encoder_projector (:obj:`nn.Module`): The encoder projector module. + """ + + def __init__( + self, + encoder: nn.Module, + llm: nn.Module, + encoder_projector: nn.Module, + ): + super().__init__() + self.encoder = encoder + self.llm = llm + self.encoder_projector = encoder_projector + + def _merge_input_ids_with_speech_features( + self, speech_features, inputs_embeds, input_ids, attention_mask, labels=None + ): + """ + Merge the speech features with the input_ids and attention_mask. This is done by replacing the speech tokens + with the speech features and padding the input_ids to the maximum length of the speech features. + Modified from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava/modeling_llava.py#L277. + Args: + speech_features (:obj:`torch.Tensor`): The speech features to merge with the input_ids. + inputs_embeds (:obj:`torch.Tensor`): The embeddings of the input_ids. + input_ids (:obj:`torch.Tensor`): The input ids to merge. + attention_mask (:obj:`torch.Tensor`): The attention mask to merge. + labels (:obj:`torch.Tensor`, `optional`): The labels to merge. + Returns: + :obj:`Tuple(torch.Tensor)`: The merged embeddings, attention mask, labels and position ids. + """ + num_speechs, speech_len, embed_dim = speech_features.shape + batch_size, sequence_length = input_ids.shape + left_padding = not torch.sum( + input_ids[:, -1] == torch.tensor(self.llm.config.pad_token_id) + ) + # 1. Create a mask to know where special speech tokens are + special_speech_token_mask = input_ids == self.llm.config.default_speech_token_id + num_special_speech_tokens = torch.sum(special_speech_token_mask, dim=-1) + # Compute the maximum embed dimension + max_embed_dim = ( + num_special_speech_tokens.max() * (speech_len - 1) + ) + sequence_length + batch_indices, non_speech_indices = torch.where( + input_ids != self.llm.config.default_speech_token_id + ) + + # 2. Compute the positions where text should be written + # Calculate new positions for text tokens in merged speech-text sequence. + # `special_speech_token_mask` identifies speech tokens. Each speech token will be replaced by `nb_text_tokens_per_speechs - 1` text tokens. + # `torch.cumsum` computes how each speech token shifts subsequent text token positions. + # - 1 to adjust for zero-based indexing, as `cumsum` inherently increases indices by one. + new_token_positions = ( + torch.cumsum((special_speech_token_mask * (speech_len - 1) + 1), -1) - 1 + ) + nb_speech_pad = max_embed_dim - 1 - new_token_positions[:, -1] + if left_padding: + new_token_positions += nb_speech_pad[:, None] # offset for left padding + text_to_overwrite = new_token_positions[batch_indices, non_speech_indices] + + # 3. Create the full embedding, already padded to the maximum position + final_embedding = torch.zeros( + batch_size, + max_embed_dim, + embed_dim, + dtype=inputs_embeds.dtype, + device=inputs_embeds.device, + ) + final_attention_mask = torch.zeros( + batch_size, + max_embed_dim, + dtype=attention_mask.dtype, + device=inputs_embeds.device, + ) + if labels is not None: + final_labels = torch.full( + (batch_size, max_embed_dim), + IGNORE_TOKEN_ID, + dtype=input_ids.dtype, + device=input_ids.device, + ) + # In case the Vision model or the Language model has been offloaded to CPU, we need to manually + # set the corresponding tensors into their correct target device. + target_device = inputs_embeds.device + batch_indices, non_speech_indices, text_to_overwrite = ( + batch_indices.to(target_device), + non_speech_indices.to(target_device), + text_to_overwrite.to(target_device), + ) + attention_mask = attention_mask.to(target_device) + + # 4. Fill the embeddings based on the mask. If we have ["hey" "", "how", "are"] + # we need to index copy on [0, 577, 578, 579] for the text and [1:576] for the speech features + final_embedding[batch_indices, text_to_overwrite] = inputs_embeds[ + batch_indices, non_speech_indices + ] + final_attention_mask[batch_indices, text_to_overwrite] = attention_mask[ + batch_indices, non_speech_indices + ] + if labels is not None: + final_labels[batch_indices, text_to_overwrite] = labels[ + batch_indices, non_speech_indices + ] + + # 5. Fill the embeddings corresponding to the speechs. Anything that is not `text_positions` needs filling (#29835) + speech_to_overwrite = torch.full( + (batch_size, max_embed_dim), + True, + dtype=torch.bool, + device=inputs_embeds.device, + ) + speech_to_overwrite[batch_indices, text_to_overwrite] = False + speech_to_overwrite &= speech_to_overwrite.cumsum(-1) - 1 >= nb_speech_pad[ + :, None + ].to(target_device) + + if speech_to_overwrite.sum() != speech_features.shape[:-1].numel(): + raise ValueError( + f"The input provided to the model are wrong. The number of speech tokens is {torch.sum(special_speech_token_mask)} while" + f" the number of speech given to the model is {num_speechs}. This prevents correct indexing and breaks batch generation." + ) + + final_embedding[speech_to_overwrite] = ( + speech_features.contiguous().reshape(-1, embed_dim).to(target_device) + ) + final_attention_mask |= speech_to_overwrite + position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_( + (final_attention_mask == 0), 1 + ) + + # 6. Mask out the embedding at padding positions, as we later use the past_key_value value to determine the non-attended tokens. + batch_indices, pad_indices = torch.where( + input_ids == self.llm.config.pad_token_id + ) + indices_to_mask = new_token_positions[batch_indices, pad_indices] + + final_embedding[batch_indices, indices_to_mask] = 0 + + if labels is None: + final_labels = None + + return final_embedding, final_attention_mask, final_labels, position_ids + + def forward( + self, + fbank: torch.Tensor = None, + input_ids: torch.LongTensor = None, + attention_mask: torch.Tensor = None, + labels: torch.LongTensor = None, + ): + encoder_outs = self.encoder(fbank) + + speech_features = self.encoder_projector(encoder_outs) + + inputs_embeds = self.llm.get_input_embeddings()(input_ids) + + ( + inputs_embeds, + attention_mask, + labels, + _, + ) = self._merge_input_ids_with_speech_features( + speech_features, inputs_embeds, input_ids, attention_mask, labels + ) + + model_outputs = self.llm( + inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels + ) + + with torch.no_grad(): + preds = torch.argmax(model_outputs.logits, -1) + acc = compute_accuracy( + preds.detach()[:, :-1], + labels.detach()[:, 1:], + ignore_label=IGNORE_TOKEN_ID, + ) + return model_outputs, acc + + def decode( + self, + fbank: torch.Tensor = None, + input_ids: torch.LongTensor = None, + attention_mask: torch.Tensor = None, + **kwargs, + ): + + encoder_outs = self.encoder(fbank) + speech_features = self.encoder_projector(encoder_outs) + speech_features = speech_features.to(torch.float16) + inputs_embeds = self.llm.get_input_embeddings()(input_ids) + ( + inputs_embeds, + attention_mask, + _, + position_ids, + ) = self._merge_input_ids_with_speech_features( + speech_features, inputs_embeds, input_ids, attention_mask + ) + generated_ids = self.llm.generate( + inputs_embeds=inputs_embeds, + max_new_tokens=kwargs.get("max_new_tokens", 200), + num_beams=kwargs.get("num_beams", 1), + do_sample=kwargs.get("do_sample", False), + min_length=kwargs.get("min_length", 1), + top_p=kwargs.get("top_p", 1.0), + repetition_penalty=kwargs.get("repetition_penalty", 1.0), + length_penalty=kwargs.get("length_penalty", 1.0), + temperature=kwargs.get("temperature", 1.0), + bos_token_id=self.llm.config.bos_token_id, + eos_token_id=self.llm.config.eos_token_id, + pad_token_id=self.llm.config.pad_token_id, + ) + + return generated_ids + + +def compute_accuracy(pad_outputs, pad_targets, ignore_label): + """Calculate accuracy. + Copied from https://github.com/X-LANCE/SLAM-LLM/blob/main/src/slam_llm/utils/metric.py + Args: + pad_outputs (LongTensor): Prediction tensors (B, Lmax). + pad_targets (LongTensor): Target label tensors (B, Lmax). + ignore_label (int): Ignore label id. + + Returns: + float: Accuracy value (0.0 - 1.0). + + """ + mask = pad_targets != ignore_label + numerator = torch.sum( + pad_outputs.masked_select(mask) == pad_targets.masked_select(mask) + ) + denominator = torch.sum(mask) + return numerator.float() / denominator.float() diff --git a/egs/speech_llm/SPEECH2SPEECH/slam_omni/speech_dataset.py b/egs/speech_llm/SPEECH2SPEECH/slam_omni/speech_dataset.py new file mode 100644 index 000000000..d0a77fd0e --- /dev/null +++ b/egs/speech_llm/SPEECH2SPEECH/slam_omni/speech_dataset.py @@ -0,0 +1,176 @@ +from typing import Callable, Dict, List, Union + +import torch +from torch.utils.data.dataloader import DataLoader, default_collate + +from lhotse import validate +from lhotse.cut import CutSet +from lhotse.dataset.input_strategies import BatchIO, PrecomputedFeatures +from lhotse.utils import compute_num_frames, ifnone +from lhotse.workarounds import Hdf5MemoryIssueFix + + +class K2SpeechRecognitionDataset(torch.utils.data.Dataset): + """ + The PyTorch Dataset for the speech recognition task using k2 library. + + This dataset expects to be queried with lists of cut IDs, + for which it loads features and automatically collates/batches them. + + To use it with a PyTorch DataLoader, set ``batch_size=None`` + and provide a :class:`SimpleCutSampler` sampler. + + Each item in this dataset is a dict of: + + .. code-block:: + + { + 'inputs': float tensor with shape determined by :attr:`input_strategy`: + - single-channel: + - features: (B, T, F) + - audio: (B, T) + - multi-channel: currently not supported + 'supervisions': [ + { + 'sequence_idx': Tensor[int] of shape (S,) + 'text': List[str] of len S + + # For feature input strategies + 'start_frame': Tensor[int] of shape (S,) + 'num_frames': Tensor[int] of shape (S,) + + # For audio input strategies + 'start_sample': Tensor[int] of shape (S,) + 'num_samples': Tensor[int] of shape (S,) + + # Optionally, when return_cuts=True + 'cut': List[AnyCut] of len S + } + ] + } + + Dimension symbols legend: + * ``B`` - batch size (number of Cuts) + * ``S`` - number of supervision segments (greater or equal to B, as each Cut may have multiple supervisions) + * ``T`` - number of frames of the longest Cut + * ``F`` - number of features + + The 'sequence_idx' field is the index of the Cut used to create the example in the Dataset. + """ + + def __init__( + self, + return_cuts: bool = False, + cut_transforms: List[Callable[[CutSet], CutSet]] = None, + input_transforms: List[Callable[[torch.Tensor], torch.Tensor]] = None, + input_strategy: BatchIO = PrecomputedFeatures(), + ): + """ + k2 ASR IterableDataset constructor. + + :param return_cuts: When ``True``, will additionally return a "cut" field in each batch with the Cut + objects used to create that batch. + :param cut_transforms: A list of transforms to be applied on each sampled batch, + before converting cuts to an input representation (audio/features). + Examples: cut concatenation, noise cuts mixing, etc. + :param input_transforms: A list of transforms to be applied on each sampled batch, + after the cuts are converted to audio/features. + Examples: normalization, SpecAugment, etc. + :param input_strategy: Converts cuts into a collated batch of audio/features. + By default, reads pre-computed features from disk. + """ + super().__init__() + # Initialize the fields + self.return_cuts = return_cuts + self.cut_transforms = ifnone(cut_transforms, []) + self.input_transforms = ifnone(input_transforms, []) + self.input_strategy = input_strategy + + # This attribute is a workaround to constantly growing HDF5 memory + # throughout the epoch. It regularly closes open file handles to + # reset the internal HDF5 caches. + self.hdf5_fix = Hdf5MemoryIssueFix(reset_interval=100) + + def __getitem__(self, cuts: CutSet) -> Dict[str, Union[torch.Tensor, List[str]]]: + """ + Return a new batch, with the batch size automatically determined using the constraints + of max_duration and max_cuts. + """ + validate_for_asr(cuts) + + self.hdf5_fix.update() + + # Sort the cuts by duration so that the first one determines the batch time dimensions. + cuts = cuts.sort_by_duration(ascending=False) + + # Optional CutSet transforms - e.g. padding, or speed perturbation that adjusts + # the supervision boundaries. + for tnfm in self.cut_transforms: + cuts = tnfm(cuts) + + # Sort the cuts again after transforms + cuts = cuts.sort_by_duration(ascending=False) + + # Get a tensor with batched feature matrices, shape (B, T, F) + # Collation performs auto-padding, if necessary. + input_tpl = self.input_strategy(cuts) + if len(input_tpl) == 3: + # An input strategy with fault tolerant audio reading mode. + # "cuts" may be a subset of the original "cuts" variable, + # that only has cuts for which we succesfully read the audio. + inputs, _, cuts = input_tpl + else: + inputs, _ = input_tpl + + # Get a dict of tensors that encode the positional information about supervisions + # in the batch of feature matrices. The tensors are named "sequence_idx", + # "start_frame/sample" and "num_frames/samples". + supervision_intervals = self.input_strategy.supervision_intervals(cuts) + + # Apply all available transforms on the inputs, i.e. either audio or features. + # This could be feature extraction, global MVN, SpecAugment, etc. + segments = torch.stack(list(supervision_intervals.values()), dim=1) + for tnfm in self.input_transforms: + inputs = tnfm(inputs, supervision_segments=segments) + + batch = { + "inputs": inputs, + "supervisions": default_collate( + [ + { + "text": supervision.text, + } + for sequence_idx, cut in enumerate(cuts) + for supervision in cut.supervisions + ] + ), + } + # Update the 'supervisions' field with sequence_idx and start/num frames/samples + batch["supervisions"].update(supervision_intervals) + if self.return_cuts: + batch["supervisions"]["cut"] = [ + cut for cut in cuts for sup in cut.supervisions + ] + + return batch + + +def validate_for_asr(cuts: CutSet) -> None: + validate(cuts) + tol = 2e-3 # 1ms + for cut in cuts: + for supervision in cut.supervisions: + assert supervision.start >= -tol, ( + f"Supervisions starting before the cut are not supported for ASR" + f" (sup id: {supervision.id}, cut id: {cut.id})" + ) + + # Supervision start time is relative to Cut ... + # https://lhotse.readthedocs.io/en/v0.10_e/cuts.html + # + # 'supervision.end' is end of supervision inside the Cut + assert supervision.end <= cut.duration + tol, ( + f"Supervisions ending after the cut " + f"are not supported for ASR" + f" (sup id: {supervision.id}, cut id: {cut.id})" + ) diff --git a/egs/speech_llm/SPEECH2SPEECH/slam_omni/train.py b/egs/speech_llm/SPEECH2SPEECH/slam_omni/train.py new file mode 100755 index 000000000..d9489b1ae --- /dev/null +++ b/egs/speech_llm/SPEECH2SPEECH/slam_omni/train.py @@ -0,0 +1,872 @@ +#!/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 whisper and Qwen2 +pip install huggingface_hub['cli'] +mkdir -p models/whisper models/qwen + +# For aishell fine-tuned whisper model +huggingface-cli download --local-dir models/whisper yuekai/icefall_asr_aishell_whisper exp_large_v2/whisper-large-v2-aishell1-epoch-10-avg-6.pt +# For multi-hans fine-tuned whisper model +# huggingface-cli download --local-dir models/whisper yuekai/icefall_asr_multi-hans-zh_whisper v1.1/whisper-large-v2-multi-hans-zh-epoch-3-avg-10.pt + +# huggingface-clie download --local-dir models/qwen Qwen/Qwen2-7B-Instruct +huggingface-clie download --local-dir models/qwen Qwen/Qwen2-1.5B-Instruct + +torchrun --nproc_per_node 8 ./whisper_llm_zh/train.py \ + --max-duration 200 \ + --exp-dir ./whisper_llm_zh/exp_test \ + --speech-encoder-path-or-name models/whisper/exp_large_v2/whisper-large-v2-aishell1-epoch-10-avg-6.pt \ + --llm-path-or-name Qwen/Qwen2-1.5B-Instruct \ + --manifest-dir data/fbank \ + --deepspeed \ + --deepspeed_config ./whisper_llm_zh/ds_config_zero1.json \ + --use-flash-attn True \ + --use-lora True --unfreeze-llm True +""" + +import argparse +import copy +import logging +import os +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 torch +import torch.multiprocessing as mp +import torch.nn as nn +import transformers +import whisper +from data_module import AsrDataModule +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 model import IGNORE_TOKEN_ID, SPEECH_LLM, EncoderProjector +from multi_dataset import MultiDataset +from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training +from torch import Tensor +from torch.utils.tensorboard import SummaryWriter +from transformers import AutoModelForCausalLM, AutoTokenizer +from whisper_encoder_forward_monkey_patch import replace_whisper_encoder_forward + +from icefall import diagnostics +from icefall.dist import get_rank, get_world_size +from icefall.env import get_env_info +from icefall.utils import ( + AttributeDict, + MetricsTracker, + filter_uneven_sized_batch, + setup_logger, + str2bool, +) + +DEFAULT_SPEECH_TOKEN = "" + + +def set_batch_count(model: nn.Module, batch_count: float) -> None: + 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( + "--llm-path-or-name", + type=str, + default="/workspace/asr/Qwen1.5-0.5B-Chat", + help="Path or name of the large language model.", + ) + + parser.add_argument( + "--speech-encoder-path-or-name", + type=str, + default="whisper-large-v2", + help="Path or name of the speech encoder.", + ) + + parser.add_argument( + "--encoder-projector-ds-rate", + type=int, + default=8, + help="Downsample rate for the encoder projector.", + ) + parser.add_argument( + "--use-flash-attn", + type=str2bool, + default=True, + help="Whether to use flash attention.", + ) + + parser.add_argument( + "--use-lora", + type=str2bool, + default=False, + help="Whether to use lora to fine-tune llm.", + ) + + parser.add_argument( + "--unfreeze-llm", + type=str2bool, + default=False, + help="Whether to unfreeze llm during training.", + ) + + +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( + "--exp-dir", + type=str, + default="whisper_qwen/exp", + help="""The experiment dir. + It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--pretrained-model-path", + type=str, + default=None, + help="""The path to the pretrained model if it is not None. Training will + start from this model. e.g. ./wenetspeech/ASR/whisper/exp_large_v2/epoch-4-avg-3.pt + """, + ) + + parser.add_argument( + "--sampler-state-dict-path", + type=str, + default=None, + help="""The path to the sampler state dict if it is not None. Training will start from this sampler state dict. + """, + ) + + parser.add_argument( + "--seed", + type=int, + default=42, + help="The seed for random generators intended for reproducibility", + ) + + parser.add_argument( + "--use-fp16", + type=str2bool, + default=True, + help="Whether to use half precision training.", + ) + + parser.add_argument( + "--use-aishell", + type=str2bool, + default=True, + help="Whether to only use aishell1 dataset for training.", + ) + + parser = deepspeed.add_config_arguments(parser) + 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`: + + - 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( + { + "allowed_excess_duration_ratio": 0.1, + "subsampling_factor": 2, + "frame_shift_ms": 10, + "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 compute_loss( + params: AttributeDict, + tokenizer: AutoTokenizer, + model: nn.Module, + 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. + + def preprocess( + messages, + tokenizer: transformers.PreTrainedTokenizer, + max_len: int, + ) -> Dict: + """Preprocesses the data for supervised fine-tuning.""" + texts = [] + TEMPLATE = "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content']}}{% if loop.last %}{{ '<|im_end|>'}}{% else %}{{ '<|im_end|>\n' }}{% endif %}{% endfor %}" + for i, msg in enumerate(messages): + texts.append( + tokenizer.apply_chat_template( + msg, + tokenize=True, + chat_template=TEMPLATE, + add_generation_prompt=False, + padding="longest", # FIX me change padding to longest + max_length=max_len, + truncation=True, + ) + ) + # padding texts to the same length, texts is a list of list, padding with tokenzier.pad_token_id + max_len_texts = max([len(text) for text in texts]) + if tokenizer.padding_side == "right": + texts = [ + text + [tokenizer.pad_token_id] * (max_len_texts - len(text)) + for text in texts + ] + else: + texts = [ + [tokenizer.pad_token_id] * (max_len_texts - len(text)) + text + for text in texts + ] + input_ids = torch.tensor(texts, dtype=torch.int) + # response = tokenizer.batch_decode(input_ids, skip_special_tokens=True)[0] + target_ids = input_ids.clone() + target_ids[target_ids == tokenizer.pad_token_id] = IGNORE_TOKEN_ID + # mask all tokens before token_id 151646 with IGNORE_TOKEN_ID + # first get the indices of the tokens + mask_prompt = True + if mask_prompt: + mask_indices = torch.where( + input_ids == tokenizer.convert_tokens_to_ids("assistant") + ) + for i in range(mask_indices[0].size(0)): + row = mask_indices[0][i] + col = mask_indices[1][i] + # + 2 to skip: 'assistant', '\n' + target_ids[row, : col + 2] = IGNORE_TOKEN_ID + + attention_mask = input_ids.ne(tokenizer.pad_token_id) + + return input_ids, attention_mask, target_ids + + def normalize_text_alimeeting(text: str, normalize: str = "m2met") -> str: + """ + Text normalization similar to M2MeT challenge baseline. + See: https://github.com/yufan-aslp/AliMeeting/blob/main/asr/local/text_normalize.pl + """ + if normalize == "none": + return text + elif normalize == "m2met": + import re + + text = text.replace(" ", "") + text = text.replace("", "") + text = text.replace("<%>", "") + text = text.replace("<->", "") + text = text.replace("<$>", "") + text = text.replace("<#>", "") + text = text.replace("<_>", "") + text = text.replace("", "") + text = text.replace("`", "") + text = text.replace("&", "") + text = text.replace(",", "") + if re.search("[a-zA-Z]", text): + text = text.upper() + text = text.replace("Ａ", "A") + text = text.replace("ａ", "A") + text = text.replace("ｂ", "B") + text = text.replace("ｃ", "C") + text = text.replace("ｋ", "K") + text = text.replace("ｔ", "T") + text = text.replace("，", "") + text = text.replace("丶", "") + text = text.replace("。", "") + text = text.replace("、", "") + text = text.replace("？", "") + return text + + 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 = next(model.parameters()).device + feature = batch["inputs"] + + assert feature.ndim == 3 + feature = feature.to(device) + feature = feature.transpose(1, 2) # (N, C, T) + + batch_idx_train = params.batch_idx_train + supervisions = batch["supervisions"] + texts = batch["supervisions"]["text"] + # remove spaces in texts + texts = [normalize_text_alimeeting(text) for text in texts] + + messages = [] + for i, text in enumerate(texts): + message = [ + {"role": "user", "content": f"{DEFAULT_SPEECH_TOKEN}请转写音频为文字"}, + {"role": "assistant", "content": text}, + ] + messages.append(message) + + input_ids, attention_mask, target_ids = preprocess(messages, tokenizer, max_len=128) + + target_ids = target_ids.type(torch.LongTensor) + input_ids = input_ids.type(torch.LongTensor) + + with torch.set_grad_enabled(is_training): + model_outputs, acc = model( + fbank=feature, + input_ids=input_ids.to(device), + attention_mask=attention_mask.to(device), + labels=target_ids.to(device), + ) + loss = model_outputs.loss + assert loss.requires_grad == is_training + + info = MetricsTracker() + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + feature_lens = supervisions["num_frames"] + info["frames"] = (feature_lens // params.subsampling_factor).sum().item() + + # Note: We use reduction=sum while computing the loss. + info["loss"] = loss.detach().cpu().item() + info["acc"] = ( + acc * info["frames"] + ) # WAR: to avoid normalization by the number of frames + + return loss, info + + +def compute_validation_loss( + params: AttributeDict, + tokenizer: whisper.tokenizer.Tokenizer, + model: nn.Module, + 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: AutoTokenizer, + model: nn.Module, + optimizer: torch.optim.Optimizer, + scheduler: torch.optim.lr_scheduler, + train_dl: torch.utils.data.DataLoader, + valid_dl: torch.utils.data.DataLoader, + 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.encoder_projector.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 + ) + if batch_idx != 0: + model.save_checkpoint( + save_dir=params.exp_dir, + tag=f"epoch-{params.cur_epoch}-checkpoint-{batch_idx}", + client_state={}, + exclude_frozen_parameters=True, + ) + + if rank == 0: + convert_zero_checkpoint_to_fp32_state_dict( + params.exp_dir, + f"{params.exp_dir}/epoch-{params.cur_epoch}-checkpoint-{batch_idx}.pt", + tag=f"epoch-{params.cur_epoch}-checkpoint-{batch_idx}", + exclude_frozen_parameters=True, + ) + # save sampler state dict into checkpoint + sampler_state_dict = train_dl.sampler.state_dict() + torch.save( + sampler_state_dict, + f"{params.exp_dir}/epoch-{params.cur_epoch}-checkpoint-{batch_idx}-sampler.pt", + ) + os.system( + f"rm -rf {params.exp_dir}/epoch-{params.cur_epoch}-checkpoint-{batch_idx}" + ) + 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. + + # 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() + + except: # noqa + display_and_save_batch(batch, params=params) + raise + + if batch_idx % params.log_interval == 0: + try: + cur_lr = scheduler.get_last_lr()[0] + except: # noqa + cur_lr = 0.0 + + 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}, " + ) + + 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) + + 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() + whisper_model = whisper.load_model(params.speech_encoder_path_or_name, "cpu") + speech_encoder = whisper_model.encoder + speech_encoder_dim = whisper_model.dims.n_audio_state + for name, param in speech_encoder.named_parameters(): + param.requires_grad = False + speech_encoder.eval() + + tokenizer = AutoTokenizer.from_pretrained(params.llm_path_or_name) + if params.use_flash_attn: + attn_implementation = "flash_attention_2" + # torch_dtype=torch.bfloat16 FIX ME + torch_dtype = torch.float16 + tokenizer.padding_side = "left" + + else: + attn_implementation = "eager" + torch_dtype = torch.float16 + tokenizer.padding_side = "right" + + llm = AutoModelForCausalLM.from_pretrained( + params.llm_path_or_name, + attn_implementation=attn_implementation, + torch_dtype=torch_dtype, + ) + + if not params.unfreeze_llm: + for name, param in llm.named_parameters(): + param.requires_grad = False + llm.eval() + else: + if params.use_lora: + lora_config = LoraConfig( + r=64, + lora_alpha=16, + target_modules=[ + "q_proj", + "k_proj", + "v_proj", + "o_proj", + "up_proj", + "gate_proj", + "down_proj", + ], + lora_dropout=0.05, + task_type="CAUSAL_LM", + ) + llm = get_peft_model(llm, lora_config) + llm.print_trainable_parameters() + + special_tokens_dict = {"additional_special_tokens": [DEFAULT_SPEECH_TOKEN]} + tokenizer.add_special_tokens(special_tokens_dict) + llm.config.pad_token_id = tokenizer.pad_token_id + llm.config.default_speech_token_id = tokenizer.convert_tokens_to_ids( + DEFAULT_SPEECH_TOKEN + ) + + encoder_projector = EncoderProjector( + speech_encoder_dim, llm.config.hidden_size, params.encoder_projector_ds_rate + ) + + model = SPEECH_LLM( + speech_encoder, + llm, + encoder_projector, + ) + + if params.pretrained_model_path: + checkpoint = torch.load(params.pretrained_model_path, map_location="cpu") + missing_keys, unexpected_keys = model.load_state_dict(checkpoint, strict=False) + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + logging.info("Trainable parameters (excluding model.eval modules):") + for name, param in model.named_parameters(): + if param.requires_grad: + logging.info(f"{name}: {param.shape}") + + if torch.cuda.is_available(): + device = torch.device("cuda", rank) + else: + device = torch.device("cpu") + logging.info(f"Device: {device}") + model.to(device) + + assert params.deepspeed and world_size > 1 + logging.info("Using DeepSpeed") + model, optimizer, _, scheduler = deepspeed.initialize( + args=params, model=model, model_parameters=model.parameters() + ) + + data_module = AsrDataModule(args) + multi_dataset = MultiDataset(args.manifest_dir) + + 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 > 20.0: + # logging.warning( + # f"Exclude cut with ID {c.id} from training. Duration: {c.duration}" + # ) + return False + return True + + if params.use_aishell: + train_cuts = multi_dataset.aishell_train_cuts() + else: + train_cuts = multi_dataset.train_cuts() + + train_cuts = train_cuts.filter(remove_short_and_long_utt) + + sampler_state_dict = None + if params.sampler_state_dict_path: + sampler_state_dict = torch.load(params.sampler_state_dict_path) + sampler_state_dict["max_duration"] = params.max_duration + # TODO: load sampler state dict + train_dl = data_module.train_dataloaders( + train_cuts, sampler_state_dict=sampler_state_dict + ) + + if params.use_aishell: + valid_cuts = multi_dataset.aishell_dev_cuts() + else: + valid_cuts = multi_dataset.dev_cuts() + valid_dl = data_module.valid_dataloaders(valid_cuts) + + 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): + + 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, + optimizer=optimizer, + scheduler=scheduler, + train_dl=train_dl, + valid_dl=valid_dl, + tb_writer=tb_writer, + world_size=world_size, + rank=rank, + ) + + model.save_checkpoint( + save_dir=params.exp_dir, + tag=f"epoch-{params.cur_epoch}", + client_state={}, + exclude_frozen_parameters=True, + ) + 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}", + exclude_frozen_parameters=True, + ) + # save sampler state dict into checkpoint + sampler_state_dict = train_dl.sampler.state_dict() + torch.save( + sampler_state_dict, + f"{params.exp_dir}/epoch-{params.cur_epoch}-sampler.pt", + ) + + os.system(f"rm -rf {params.exp_dir}/epoch-{params.cur_epoch}") + + logging.info("Done!") + + +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() + AsrDataModule.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() diff --git a/egs/speech_llm/SPEECH2SPEECH/slam_omni/whisper_encoder_forward_monkey_patch.py b/egs/speech_llm/SPEECH2SPEECH/slam_omni/whisper_encoder_forward_monkey_patch.py new file mode 120000 index 000000000..2a7808921 --- /dev/null +++ b/egs/speech_llm/SPEECH2SPEECH/slam_omni/whisper_encoder_forward_monkey_patch.py @@ -0,0 +1 @@ +../../../aishell/ASR/whisper/whisper_encoder_forward_monkey_patch.py \ No newline at end of file