From 76d3df55a09dff83e1a9911331539979d58f4c67 Mon Sep 17 00:00:00 2001 From: RedSheep Date: Sat, 8 Mar 2025 13:32:21 +0800 Subject: [PATCH 1/5] intial commit --- egs/librispeech/ASR/chech_version.py | 11 + .../ASR/local/compute_fbank_librispeech.py | 13 +- egs/librispeech/ASR/prepare.sh | 8 +- egs/librispeech/k2SSL/conf/train_config.py | 42 + .../k2SSL/conf/zipformer_config.py | 17 + egs/librispeech/k2SSL/hubert_ce.py | 601 +++++++ egs/librispeech/k2SSL/pretrain.py | 1380 +++++++++++++++++ 7 files changed, 2066 insertions(+), 6 deletions(-) create mode 100755 egs/librispeech/ASR/chech_version.py create mode 100755 egs/librispeech/k2SSL/conf/train_config.py create mode 100755 egs/librispeech/k2SSL/conf/zipformer_config.py create mode 100644 egs/librispeech/k2SSL/hubert_ce.py create mode 100644 egs/librispeech/k2SSL/pretrain.py diff --git a/egs/librispeech/ASR/chech_version.py b/egs/librispeech/ASR/chech_version.py new file mode 100755 index 000000000..ba9383d9d --- /dev/null +++ b/egs/librispeech/ASR/chech_version.py @@ -0,0 +1,11 @@ +import torch +import torchaudio + +print("PyTorch version:", torch.__version__) +# print("Torchvision version:", torchvision.__version__) +print("Torchaudio version:", torchaudio.__version__) +print("CUDA available:", torch.cuda.is_available()) +if torch.cuda.is_available(): + print("CUDA version:", torch.version.cuda) + print("cuDNN version:", torch.backends.cudnn.version()) + print("GPU name:", torch.cuda.get_device_name(0)) diff --git a/egs/librispeech/ASR/local/compute_fbank_librispeech.py b/egs/librispeech/ASR/local/compute_fbank_librispeech.py index 25d6050bb..13c417066 100755 --- a/egs/librispeech/ASR/local/compute_fbank_librispeech.py +++ b/egs/librispeech/ASR/local/compute_fbank_librispeech.py @@ -68,6 +68,13 @@ def get_args(): help="""Perturb speed with factor 0.9 and 1.1 on train subset.""", ) + parser.add_argument( + "--num-workers", + type=int, + default=15, + help="Number of worker processes for feature extraction.", + ) + return parser.parse_args() @@ -75,10 +82,11 @@ def compute_fbank_librispeech( bpe_model: Optional[str] = None, dataset: Optional[str] = None, perturb_speed: Optional[bool] = True, + num_workers: int = 15, ): src_dir = Path("data/manifests") output_dir = Path("data/fbank") - num_jobs = min(15, os.cpu_count()) + num_jobs = min(num_workers, os.cpu_count()) num_mel_bins = 80 if bpe_model: @@ -144,7 +152,7 @@ def compute_fbank_librispeech( 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, + num_jobs=num_jobs if ex is None else min(num_jobs * 2, 20), executor=ex, storage_type=LilcomChunkyWriter, ) @@ -161,4 +169,5 @@ if __name__ == "__main__": bpe_model=args.bpe_model, dataset=args.dataset, perturb_speed=args.perturb_speed, + num_workers=args.num_workers, ) diff --git a/egs/librispeech/ASR/prepare.sh b/egs/librispeech/ASR/prepare.sh index 40dc3260d..45d5a26c2 100755 --- a/egs/librispeech/ASR/prepare.sh +++ b/egs/librispeech/ASR/prepare.sh @@ -99,9 +99,9 @@ if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then # # ln -sfv /path/to/LibriSpeech $dl_dir/LibriSpeech # - if [ ! -d $dl_dir/LibriSpeech/train-other-500 ]; then - lhotse download librispeech --full $dl_dir - fi + #if [ ! -d $dl_dir/LibriSpeech/train-other-500 ]; then + # lhotse download librispeech --full $dl_dir + #fi # If you have pre-downloaded it to /path/to/musan, # you can create a symlink @@ -139,7 +139,7 @@ if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then log "Stage 3: Compute fbank for librispeech" mkdir -p data/fbank if [ ! -e data/fbank/.librispeech.done ]; then - ./local/compute_fbank_librispeech.py + ./local/compute_fbank_librispeech.py --num-workers 4 touch data/fbank/.librispeech.done fi diff --git a/egs/librispeech/k2SSL/conf/train_config.py b/egs/librispeech/k2SSL/conf/train_config.py new file mode 100755 index 000000000..9cc6b6114 --- /dev/null +++ b/egs/librispeech/k2SSL/conf/train_config.py @@ -0,0 +1,42 @@ +# 預訓練參數設置 +train_params = { + # 模型參數 + "label_rate": 50, + "sample_rate": 16000, + "extractor_mode": "default", + "conv_feature_layers": "[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2", + "conv_bias": False, + "feature_grad_mult": 1.0, + + # 掩碼參數 + "mask_length": 10, + "mask_prob": 0.65, + "mask_selection": "static", + "mask_other": 0, + "no_mask_overlap": False, + "mask_min_space": 1, + + # 通道掩碼參數 + "mask_channel_length": 10, + "mask_channel_prob": 0.0, + "mask_channel_selection": "static", + "mask_channel_other": 0, + "no_mask_channel_overlap": False, + "mask_channel_min_space": 1, + + # 損失計算參數 + "skip_masked": False, + "skip_nomask": False, + "pred_masked_weight": 1, + "pred_nomask_weight": 0, + "loss_weights": [10], + "checkpoint_activations": False, + + # 其他參數 + "dropout_input": 0.0, + "dropout_features": 0.0, + "num_classes": [504], + "untie_final_proj": False, + "required_seq_len_multiple": 2, + "logit_temp": 0.1, +} diff --git a/egs/librispeech/k2SSL/conf/zipformer_config.py b/egs/librispeech/k2SSL/conf/zipformer_config.py new file mode 100755 index 000000000..29aaabf7d --- /dev/null +++ b/egs/librispeech/k2SSL/conf/zipformer_config.py @@ -0,0 +1,17 @@ +def get_zipformer_base_config(): + return { + "output_downsampling_factor": 1, + "downsampling_factor": (1, 2, 4, 8, 4, 2), + "encoder_dim": (192, 256, 384, 512, 384, 256), + "num_encoder_layers": (2, 2, 3, 4, 3, 2), + "encoder_unmasked_dim": (192, 192, 256, 256, 256, 192), + "query_head_dim": 32, + "pos_head_dim": 4, + "value_head_dim": 12, + "pos_dim": 48, + "num_heads": (4, 4, 4, 8, 4, 4), + "feedforward_dim": (512, 768, 1024, 1536, 1024, 768), + "cnn_module_kernel": (31, 31, 15, 15, 15, 31), + "dropout": 0.1, + "warmup_batches": 4000.0, + } diff --git a/egs/librispeech/k2SSL/hubert_ce.py b/egs/librispeech/k2SSL/hubert_ce.py new file mode 100644 index 000000000..1ac368a1d --- /dev/null +++ b/egs/librispeech/k2SSL/hubert_ce.py @@ -0,0 +1,601 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import argparse +import logging +from typing import Dict, List, Optional, Tuple + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from scaling import ScheduledFloat +from utils import GradMultiply, LayerNorm +from wav2vec2_module import ConvFeatureExtractionModel +from zipformer import Zipformer2 + + +def compute_mask_indices( + shape: Tuple[int, int], + padding_mask: Optional[torch.Tensor], + mask_prob: float, + mask_length: int, + mask_type: str = "static", + mask_other: float = 0.0, + min_masks: int = 0, + no_overlap: bool = False, + min_space: int = 0, + require_same_masks: bool = True, + mask_dropout: float = 0.0, + add_masks: bool = False, + seed: Optional[int] = None, + epoch: Optional[int] = None, + indices: Optional[torch.Tensor] = None, + idc_select_ver: int = 1, # 2 to reproduce mask_tokens_dataset + num_mask_ver: int = 2, # 2 to reproduce mask_tokens_dataset +) -> np.ndarray: + """ + Computes random mask spans for a given shape + + Args: + shape: the the shape for which to compute masks. + should be of size 2 where first element is batch size and 2nd is timesteps + padding_mask: optional padding mask of the same size as shape, which will prevent masking padded elements + mask_prob: probability for each token to be chosen as start of the span to be masked. this will be multiplied by + number of timesteps divided by length of mask span to mask approximately this percentage of all elements. + however due to overlaps, the actual number will be smaller (unless no_overlap is True) + mask_type: how to compute mask lengths + static = fixed size + uniform = sample from uniform distribution [mask_other, mask_length*2] + normal = sample from normal distribution with mean mask_length and stdev mask_other. mask is min 1 element + poisson = sample from possion distribution with lambda = mask length + min_masks: minimum number of masked spans + no_overlap: if false, will switch to an alternative recursive algorithm that prevents spans from overlapping + min_space: only used if no_overlap is True, this is how many elements to keep unmasked between spans + require_same_masks: if true, will randomly drop out masks until same amount of masks remains in each sample + mask_dropout: randomly dropout this percentage of masks in each example + """ + + bsz, all_sz = shape + mask = np.full((bsz, all_sz), False) + + if num_mask_ver == 1: + all_num_mask = int( + # add a random number for probabilistic rounding + mask_prob * all_sz / float(mask_length) + + np.random.rand() + ) + all_num_mask = max(min_masks, all_num_mask) + + mask_idcs = [] + for i in range(bsz): + if seed is not None and epoch is not None and indices is not None: + seed_i = int(hash((seed, epoch, indices[i].item())) % 1e6) + else: + seed_i = None + + rng = np.random.default_rng(seed_i) + + if padding_mask is not None: + sz = all_sz - padding_mask[i].long().sum().item() + assert sz >= 0, sz + else: + sz = all_sz + + if num_mask_ver == 1: + if padding_mask is not None: + num_mask = int( + # add a random number for probabilistic rounding + mask_prob * sz / float(mask_length) + + np.random.rand() + ) + num_mask = max(min_masks, num_mask) + else: + num_mask = all_num_mask + elif num_mask_ver == 2: + num_mask = int( + # add a random number for probabilistic rounding + mask_prob * sz / float(mask_length) + + rng.random() + ) + num_mask = max(min_masks, num_mask) + else: + raise ValueError() + + if mask_type == "static": + lengths = np.full(num_mask, mask_length) + elif mask_type == "uniform": + lengths = rng.randint(mask_other, mask_length * 2 + 1, size=num_mask) + elif mask_type == "normal": + lengths = rng.normal(mask_length, mask_other, size=num_mask) + lengths = [max(1, int(round(x))) for x in lengths] + elif mask_type == "poisson": + lengths = rng.poisson(mask_length, size=num_mask) + lengths = [int(round(x)) for x in lengths] + else: + raise Exception("unknown mask selection " + mask_type) + + if sum(lengths) == 0: + if mask_type == "static": + raise ValueError(f"this should never happens") + else: + lengths = [min(mask_length, sz - 1)] + + if no_overlap: + mask_idc = [] + + def arrange(s, e, length, keep_length): + span_start = rng.randint(s, e - length) + mask_idc.extend(span_start + i for i in range(length)) + + new_parts = [] + if span_start - s - min_space >= keep_length: + new_parts.append((s, span_start - min_space + 1)) + if e - span_start - length - min_space > keep_length: + new_parts.append((span_start + length + min_space, e)) + return new_parts + + parts = [(0, sz)] + min_length = min(lengths) + for length in sorted(lengths, reverse=True): + lens = np.fromiter( + (e - s if e - s >= length + min_space else 0 for s, e in parts), + np.int, + ) + l_sum = np.sum(lens) + if l_sum == 0: + break + probs = lens / np.sum(lens) + c = rng.choice(len(parts), p=probs) + s, e = parts.pop(c) + parts.extend(arrange(s, e, length, min_length)) + mask_idc = np.asarray(mask_idc) + else: + if idc_select_ver == 1: + min_len = min(lengths) + if sz - min_len <= num_mask: + min_len = sz - num_mask - 1 + mask_idc = rng.choice(sz - min_len, num_mask, replace=False) + elif idc_select_ver == 2: + mask_idc = rng.choice(sz, num_mask, replace=False) + else: + raise ValueError() + + mask_idc = np.asarray( + [ + mask_idc[j] + offset + for j in range(len(mask_idc)) + for offset in range(lengths[j]) + ] + ) + + mask_idc = np.unique(mask_idc[mask_idc < sz]) + if len(mask_idc) >= sz: + raise ValueError( + ( + f"the entire sequence is masked. " + f"sz={sz}; mask_idc[mask_idc]; " + f"index={indices[i] if indices is not None else None}" + ) + ) + mask_idcs.append(mask_idc) + + target_len = None + if require_same_masks: + if add_masks: + target_len = max([len(m) for m in mask_idcs]) + else: + target_len = min([len(m) for m in mask_idcs]) + + for i, mask_idc in enumerate(mask_idcs): + if target_len is not None and len(mask_idc) > target_len: + mask_idc = rng.choice(mask_idc, target_len, replace=False) + + mask[i, mask_idc] = True + + if target_len is not None and len(mask_idc) < target_len: + unmasked = np.flatnonzero(~mask[i]) + to_mask = rng.choice(unmasked, target_len - len(mask_idc), replace=False) + mask[i, to_mask] = True + + if mask_dropout > 0: + masked = np.flatnonzero(mask[i]) + num_holes = np.rint(len(masked) * mask_dropout).astype(int) + to_drop = rng.choice(masked, num_holes, replace=False) + mask[i, to_drop] = False + + return mask + + +def _to_int_tuple(s: str): + return tuple(map(int, s.split(","))) + + +class HubertModel(nn.Module): + def __init__( + self, + cfg, + ) -> None: + super().__init__() + feature_enc_layers = eval(cfg.conv_feature_layers) # noqa + self.embed = feature_enc_layers[-1][0] + + self.feature_extractor = ConvFeatureExtractionModel( + conv_layers=feature_enc_layers, + dropout=0.0, + mode=cfg.extractor_mode, + conv_bias=cfg.conv_bias, + ) + feature_ds_rate = np.prod([s for _, _, s in feature_enc_layers]) + self.feat2tar_ratio = cfg.label_rate * feature_ds_rate / cfg.sample_rate + encoder_input_dim = _to_int_tuple(cfg.encoder_dim)[0] + encoder_output_dim = max(_to_int_tuple(cfg.encoder_dim)) + self.post_extract_proj = ( + nn.Linear(self.embed, encoder_input_dim) + if self.embed != encoder_input_dim + else None + ) + + self.mask_prob = cfg.mask_prob + self.mask_selection = cfg.mask_selection + self.mask_other = cfg.mask_other + self.mask_length = cfg.mask_length + self.no_mask_overlap = cfg.no_mask_overlap + self.mask_min_space = cfg.mask_min_space + + self.mask_channel_prob = cfg.mask_channel_prob + self.mask_channel_selection = cfg.mask_channel_selection + self.mask_channel_other = cfg.mask_channel_other + self.mask_channel_length = cfg.mask_channel_length + self.no_mask_channel_overlap = cfg.no_mask_channel_overlap + self.mask_channel_min_space = cfg.mask_channel_min_space + + self.dropout_input = nn.Dropout(cfg.dropout_input) + self.dropout_features = nn.Dropout(cfg.dropout_features) + + self.feature_grad_mult = cfg.feature_grad_mult + self.logit_temp = cfg.logit_temp + self.skip_masked = cfg.skip_masked + self.skip_nomask = cfg.skip_nomask + + self.mask_emb = nn.Parameter(torch.FloatTensor(encoder_input_dim).uniform_()) + + self.encoder = Zipformer2( + output_downsampling_factor=1, + downsampling_factor=_to_int_tuple(cfg.downsampling_factor), + num_encoder_layers=_to_int_tuple(cfg.num_encoder_layers), + encoder_dim=_to_int_tuple(cfg.encoder_dim), + encoder_unmasked_dim=_to_int_tuple(cfg.encoder_unmasked_dim), + query_head_dim=_to_int_tuple(cfg.query_head_dim), + pos_head_dim=_to_int_tuple(cfg.pos_head_dim), + value_head_dim=_to_int_tuple(cfg.value_head_dim), + pos_dim=cfg.pos_dim, + num_heads=_to_int_tuple(cfg.num_heads), + feedforward_dim=_to_int_tuple(cfg.feedforward_dim), + cnn_module_kernel=_to_int_tuple(cfg.cnn_module_kernel), + dropout=ScheduledFloat((0.0, 0.3), (20000.0, 0.1)), + warmup_batches=4000.0, + ) + + self.layer_norm = LayerNorm(self.embed) + + self.untie_final_proj = cfg.untie_final_proj + self.final_proj = nn.Linear(encoder_output_dim, sum(cfg.num_classes)) + + # modules below are not needed during fine-tuning + self.num_classes = cfg.num_classes + self.pred_masked_weight = cfg.pred_masked_weight + self.pred_nomask_weight = cfg.pred_nomask_weight + self.loss_weights = cfg.loss_weights + + def upgrade_state_dict_named(self, state_dict, name): + """Upgrade a (possibly old) state dict for new versions of fairseq.""" + + super().upgrade_state_dict_named(state_dict, name) + return state_dict + + def apply_mask(self, x, padding_mask, target_list): + B, T, C = x.shape + if self.mask_prob > 0: + mask_indices = compute_mask_indices( + (B, T), + padding_mask, + self.mask_prob, + self.mask_length, + self.mask_selection, + self.mask_other, + min_masks=2, + no_overlap=self.no_mask_overlap, + min_space=self.mask_min_space, + ) + mask_indices = torch.from_numpy(mask_indices).to(x.device) + x[mask_indices] = self.mask_emb.to(x.dtype) + else: + mask_indices = None + + if self.mask_channel_prob > 0: + mask_channel_indices = compute_mask_indices( + (B, C), + None, + self.mask_channel_prob, + self.mask_channel_length, + self.mask_channel_selection, + self.mask_channel_other, + no_overlap=self.no_mask_channel_overlap, + min_space=self.mask_channel_min_space, + ) + mask_channel_indices = ( + torch.from_numpy(mask_channel_indices) + .to(x.device) + .unsqueeze(1) + .expand(-1, T, -1) + ) + x[mask_channel_indices] = 0 + + return x, mask_indices + + def forward_features(self, source: torch.Tensor) -> torch.Tensor: + if self.feature_grad_mult > 0: + features = self.feature_extractor(source) + if self.feature_grad_mult != 1.0: + features = GradMultiply.apply(features, self.feature_grad_mult) + else: + with torch.no_grad(): + features = self.feature_extractor(source) + return features + + def forward_targets( + self, + features: torch.Tensor, + target_list: List[torch.Tensor], + ) -> Tuple[torch.Tensor, torch.Tensor]: + # Trim features to ensure labels exist and then get aligned labels + feat_tsz = features.size(2) + targ_tsz = min([t.size(1) for t in target_list]) + if self.feat2tar_ratio * feat_tsz > targ_tsz: + feat_tsz = int(targ_tsz / self.feat2tar_ratio) + features = features[..., :feat_tsz] + target_inds = torch.arange(feat_tsz).float() * self.feat2tar_ratio + target_list = [t[:, target_inds.long()] for t in target_list] + return features, target_list + + def forward_padding_mask( + self, + features: torch.Tensor, + padding_mask: torch.Tensor, + ) -> torch.Tensor: + extra = padding_mask.size(1) % features.size(1) + if extra > 0: + padding_mask = padding_mask[:, :-extra] + padding_mask = padding_mask.view(padding_mask.size(0), features.size(1), -1) + padding_mask = padding_mask.all(-1) + return padding_mask + + def forward( + self, + source: torch.Tensor, + target_list: Optional[List[torch.Tensor]] = None, + padding_mask: Optional[torch.Tensor] = None, + mask: bool = True, + features_only: bool = False, + output_layer: Optional[int] = None, + ): + """output layer is 1-based""" + features = self.forward_features(source) + if target_list is not None: + features, target_list = self.forward_targets(features, target_list) + + features_pen = features.float().pow(2).mean() + + features = features.transpose(1, 2) + features = self.layer_norm(features) + unmasked_features = features.clone() + + if padding_mask is not None: + padding_mask = self.forward_padding_mask(features, padding_mask) + + if self.post_extract_proj is not None: + features = self.post_extract_proj(features) + + features = self.dropout_input(features) + unmasked_features = self.dropout_features(unmasked_features) + + if mask: + x, mask_indices = self.apply_mask(features, padding_mask, target_list) + else: + x = features + mask_indices = None + + # feature: (B, T, D), float + # target: (B, T), long + # x: (B, T, D), float -> (T, B, D), float + # padding_mask: (B, T), bool + # mask_indices: (B, T), bool + x = x.transpose(0, 1) + x, x_lens = self.encoder(x, (~padding_mask).sum(dim=-1)) + x = x.transpose(0, 1) + + if features_only: + return {"x": x, "padding_mask": padding_mask, "features": features} + + if not self.skip_masked: + masked_indices = torch.logical_and(~padding_mask, mask_indices) + proj_x_m = self.final_proj(x[masked_indices]) + proj_x_m /= self.logit_temp + logit_m_list = [proj_x_m for _ in range(len(target_list))] + else: + logit_m_list = [None for _ in target_list] + + if not self.skip_nomask: + nomask_indices = torch.logical_and(~padding_mask, ~mask_indices) + proj_x_u = self.final_proj(x[nomask_indices]) + proj_x_u /= self.logit_temp + logit_u_list = [proj_x_u for _ in range(len(target_list))] + else: + logit_u_list = [None for _ in target_list] + + # result = { + # "logit_m_list": logit_m_list, + # "logit_u_list": logit_u_list, + # "padding_mask": padding_mask, + # "features_pen": features_pen, + # } + targ_m_list = target_list[0][masked_indices] + targ_m_list = targ_m_list.long() + targ_m_list = [targ_m_list for _ in range(len(target_list))] + + targ_u_list = target_list[0][nomask_indices] + targ_u_list = targ_u_list.long() + targ_u_list = [targ_u_list for _ in range(len(target_list))] + return self.compute_loss( + logit_m_list, logit_u_list, targ_m_list, targ_u_list, features_pen + ) + + def extract_features( + self, + source: torch.Tensor, + padding_mask: Optional[torch.Tensor] = None, + mask: bool = False, + ret_conv: bool = False, + output_layer: Optional[int] = None, + ) -> Tuple[torch.Tensor, torch.Tensor]: + res = self.forward( + source, + padding_mask=padding_mask, + mask=mask, + features_only=True, + output_layer=output_layer, + ) + feature = res["features"] if ret_conv else res["x"] + return feature, res["padding_mask"] + + def get_logits(self, net_output, is_masked=True): + if is_masked: + logits_list = net_output["logit_m_list"] + else: + logits_list = net_output["logit_u_list"] + logits_list = [x.float() for x in logits_list if x is not None] + return logits_list + + def get_targets(self, net_output, is_masked=True): + logits_list = self.get_logits(net_output, is_masked) + targets_list = [x.new_zeros(x.size(0), dtype=torch.long) for x in logits_list] + return targets_list + + def get_extra_losses(self, net_output): + extra_losses = [] + names = [] + + if "features_pen" in net_output: + extra_losses.append(net_output["features_pen"]) + names.append("features_pen") + + return extra_losses, names + + def remove_pretraining_modules(self): + self.final_proj = None + + def compute_loss( + self, logit_m_list, logit_u_list, targ_m_list, targ_u_list, features_pen + ): + loss = 0.0 + sample_size = 0 + logging_output = {} + reduce = True + reduction = "sum" if reduce else "none" + + loss_m_list = [] + logp_m_list = [x.float() for x in logit_m_list if x is not None] + logp_m_list = torch.cat(logp_m_list) + targ_m_list = torch.cat(targ_m_list) + + loss_m = F.cross_entropy(logp_m_list, targ_m_list, reduction=reduction) + loss_m_list.append(loss_m) + logging_output[f"loss_m_0"] = loss_m.detach().item() + + assert self.pred_masked_weight == 0 or len(logp_m_list) > 0 + if self.pred_masked_weight > 0: + loss += self.pred_masked_weight * sum(loss_m_list) + sample_size += len(targ_m_list) + + loss_u_list = [] + logp_u_list = [x.float() for x in logit_u_list if x is not None] + logp_u_list = torch.cat(logp_u_list) + targ_u_list = torch.cat(targ_u_list) + + loss_u = F.cross_entropy(logp_u_list, targ_u_list, reduction=reduction) + loss_u_list.append(loss_u) + logging_output[f"loss_u_0"] = loss_u.detach().item() + + assert self.pred_nomask_weight == 0 or len(logp_u_list) > 0 + if self.pred_nomask_weight > 0: + loss += self.pred_nomask_weight * sum(loss_u_list) + sample_size += len(targ_u_list) + + if self.loss_weights is not None: + extra_losses = [] + names = [] + extra_losses.append(features_pen) + names.append("features_pen") + if torch.is_tensor(extra_losses): + extra_losses = [extra_losses] + names = [names] + if len(self.loss_weights) == 1 and len(extra_losses) != 1: + self.loss_weights = [self.loss_weights[0]] * len(extra_losses) + assert len(extra_losses) == len( + self.loss_weights + ), f"{len(extra_losses)}, {len(self.loss_weights)}" + for p, n, coef in zip(extra_losses, names, self.loss_weights): + if coef != 0 and p is not None: + p = coef * p.float() * sample_size + loss += p + logging_output[f"loss_{n}"] = p.item() + + logging_output = { + "loss": loss.item() if reduce else loss, + **logging_output, + } + + # for lk in self.log_keys: + # if lk in net_output: + # logging_output[lk] = float((net_output[lk])) + + def compute_correct(logits, target): + if logits.numel() == 0: + return 0, 0 + else: + assert logits.dim() > 1, logits.shape + max = logits.argmax(-1) == target + min = logits.argmin(-1) == target + both = max & min + corr = max.long().sum().item() - both.long().sum().item() + count = max.numel() + return corr, count + + with torch.no_grad(): + corr_m, count_m = compute_correct(logp_m_list, targ_m_list) + logging_output[f"correct_m_0"] = corr_m + logging_output[f"count_m_0"] = count_m + + corr_u, count_u = compute_correct(logp_u_list, targ_u_list) + logging_output[f"correct_u_0"] = corr_u + logging_output[f"count_u_0"] = count_u + + return loss, sample_size, logging_output diff --git a/egs/librispeech/k2SSL/pretrain.py b/egs/librispeech/k2SSL/pretrain.py new file mode 100644 index 000000000..937fb382e --- /dev/null +++ b/egs/librispeech/k2SSL/pretrain.py @@ -0,0 +1,1380 @@ +#!/usr/bin/env python3 +# Copyright 2021-2024 Xiaomi Corp. (authors: Fangjun Kuang, +# Wei Kang, +# Mingshuang Luo, +# Zengwei Yao, +# Yifan Yang, +# Daniel Povey) +# Copyright 2024 Shanghai Jiao Tong University (authors: Jianheng Zhuo) +# +# 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: + +export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" + +# For hubert model pretraining: +./zipformer/pretrain.py \ + --world-size 8 \ + --num-epochs 400 \ + --start-epoch 1 \ + --use-fp16 1 \ + --exp-dir hubert/exp \ + --full-libri 1 \ + --max-duration 87.5 \ + --accum-grad 4 +""" + + +import argparse +import copy +import logging +import sys +import warnings +from pathlib import Path +from shutil import copyfile +from typing import Any, Dict, Optional, Tuple, Union + +import optim +import torch +import torch.multiprocessing as mp +import torch.nn as nn +from hubert_ce import HubertModel +from lhotse.cut import Cut +from lhotse.dataset.sampling.base import CutSampler +from lhotse.utils import fix_random_seed +from optim import Eden, ScaledAdam +from ssl_datamodule import LibriSpeechDataModule +from torch import Tensor +from torch.cuda.amp import GradScaler +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.tensorboard import SummaryWriter + +from icefall import diagnostics +from icefall.checkpoint import load_checkpoint, remove_checkpoints +from icefall.checkpoint import save_checkpoint as save_checkpoint_impl +from icefall.checkpoint import ( + save_checkpoint_with_global_batch_idx, + update_averaged_model, +) +from icefall.dist import cleanup_dist, setup_dist +from icefall.env import get_env_info +from icefall.hooks import register_inf_check_hooks +from icefall.utils import ( + AttributeDict, + MetricsTracker, + get_parameter_groups_with_lrs, + setup_logger, + str2bool, +) + +LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler] + + +def get_adjusted_batch_count(params: AttributeDict) -> float: + # returns the number of batches we would have used so far if we had used the reference + # duration. This is for purposes of set_batch_count(). + return ( + params.batch_idx_train + * params.accum_grad + * (params.max_duration * params.world_size) + / params.ref_duration + ) + + +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 name, module in model.named_modules(): + if hasattr(module, "batch_count"): + module.batch_count = batch_count + if hasattr(module, "name"): + module.name = name + + +def add_model_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--num-encoder-layers", + type=str, + default="2,2,3,4,3,2", + help="Number of zipformer encoder layers per stack, comma separated.", + ) + + parser.add_argument( + "--downsampling-factor", + type=str, + default="1,2,4,8,4,2", + help="Downsampling factor for each stack of encoder layers.", + ) + + parser.add_argument( + "--feedforward-dim", + type=str, + default="512,768,1024,1536,1024,768", + help="Feedforward dimension of the zipformer encoder layers, per stack, comma separated.", + ) + + parser.add_argument( + "--num-heads", + type=str, + default="4,4,4,8,4,4", + help="Number of attention heads in the zipformer encoder layers: a single int or comma-separated list.", + ) + + parser.add_argument( + "--encoder-dim", + type=str, + default="192,256,384,512,384,256", + help="Embedding dimension in encoder stacks: a single int or comma-separated list.", + ) + + parser.add_argument( + "--query-head-dim", + type=str, + default="32", + help="Query/key dimension per head in encoder stacks: a single int or comma-separated list.", + ) + + parser.add_argument( + "--value-head-dim", + type=str, + default="12", + help="Value dimension per head in encoder stacks: a single int or comma-separated list.", + ) + + parser.add_argument( + "--pos-head-dim", + type=str, + default="4", + help="Positional-encoding dimension per head in encoder stacks: a single int or comma-separated list.", + ) + + parser.add_argument( + "--pos-dim", + type=int, + default="48", + help="Positional-encoding embedding dimension", + ) + + parser.add_argument( + "--encoder-unmasked-dim", + type=str, + default="192,192,256,256,256,192", + help="Unmasked dimensions in the encoders, relates to augmentation during training. " + "A single int or comma-separated list. Must be <= each corresponding encoder_dim.", + ) + + parser.add_argument( + "--cnn-module-kernel", + type=str, + default="31,31,15,15,15,31", + help="Sizes of convolutional kernels in convolution modules in each encoder stack: " + "a single int or comma-separated list.", + ) + + # hubert parameters + parser.add_argument( + "--label-rate", + type=float, + default=50, + ) + + parser.add_argument( + "--sample-rate", + type=float, + default=16000, + ) + + parser.add_argument( + "--extractor-mode", + type=str, + default="default", + help="""mode for feature extractor, should in EXTRACTOR_MODE_CHOICES. default has a single group + norm with d groups in the first conv block, whereas layer_norm + has layer norms in every block (meant to use with normalize=True)""", + ) + + parser.add_argument( + "--conv-feature-layers", + type=str, + default="[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2", + help="string describing convolutional feature extraction layers in form of a python list that contains [(dim, kernel_size, stride), ...]", + ) + + parser.add_argument( + "--conv-bias", type=bool, default=False, help="include bias in conv encoder" + ) + + parser.add_argument( + "--feature-grad-mult", + type=float, + default=1.0, + help="multiply feature extractor var grads by this", + ) + + # masking + parser.add_argument("--mask-length", type=int, default=10, help="mask_length") + + parser.add_argument( + "--mask-prob", + type=float, + default=0.65, + help="probability of replacing a token with mask", + ) + + parser.add_argument( + "--mask-selection", + type=str, + choices=["static", "uniform", "normal", "poisson"], + default="static", + help="how to choose mask length", + ) + + parser.add_argument( + "--mask-other", + type=float, + default=0, + help="secondary mask argument (used for more complex distributions),see help in compute_mask_indicesh", + ) + + parser.add_argument( + "--no-mask-overlap", + type=bool, + default=False, + help="whether to allow masks to overlap", + ) + + parser.add_argument( + "--mask-min-space", + type=int, + default=1, + help="min space between spans (if no overlap is enabled)", + ) + + # channel masking + parser.add_argument( + "--mask-channel-length", + type=int, + default=10, + help="length of the mask for features (channels)", + ) + + parser.add_argument( + "--mask-channel-prob", + type=float, + default=0.0, + help="probability of replacing a feature with 0", + ) + + parser.add_argument( + "--mask-channel-selection", + type=str, + choices=["static", "uniform", "normal", "poisson"], + default="static", + help="how to choose mask length for channel masking", + ) + + parser.add_argument( + "--mask-channel-other", + type=float, + default=0, + help="secondary mask argument (used for more complex distributions), see help in compute_mask_indicesh", + ) + + parser.add_argument( + "--no-mask-channel-overlap", + type=bool, + default=False, + help="whether to allow channel masks to overlap", + ) + + parser.add_argument( + "--mask-channel-min-space", + type=int, + default=1, + help="min space between spans (if no overlap is enabled)", + ) + + # loss computation + parser.add_argument( + "--skip-masked", + type=bool, + default=False, + help="skip computing losses over masked frames", + ) + + parser.add_argument( + "--skip-nomask", + type=bool, + default=False, + help="skip computing losses over unmasked frames", + ) + + parser.add_argument( + "--checkpoint-activations", + type=bool, + default=False, + help="recompute activations and save memory for extra compute", + ) + + parser.add_argument( + "--pred-masked-weight", + type=float, + default=1, + help="weight for masked part in ssl loss", + ) + + parser.add_argument( + "--pred-nomask-weight", + type=float, + default=0, + help="weight for masked part in ssl loss", + ) + + parser.add_argument( + "--loss-weights", + type=float, + nargs="*", + default=[10], + help="weight for masked part in ssl loss", + ) + + # FP16 optimization + parser.add_argument( + "--required-seq-len-multiple", + type=int, + default=2, + help="pad the input to encoder such that the sequence length is divisible by multiple", + ) + + parser.add_argument( + "--attn-type", type=str, default="", help="if espnet use ESPNET MHA" + ) + + parser.add_argument( + "--pos-enc-type", + type=str, + default="abs", + help="Positional encoding type to use in conformer", + ) + + parser.add_argument( + "--logit-temp", type=float, default=0.1, help="temperature to divide logits by" + ) + + parser.add_argument( + "--dropout-input", + type=float, + default=0.0, + help="dropout to apply to the input (after feat extr)", + ) + + parser.add_argument( + "--dropout-features", + type=float, + default=0.0, + help="dropout to apply to the features (after feat extr)", + ) + + parser.add_argument( + "--num-classes", + type=int, + nargs="*", + default=[504], + help="""num class, a little larger than the number of cluster, + the largest is for padding, + and the value should be the multiple of 4, for faster computation""", + ) + + parser.add_argument( + "--untie-final-proj", + type=bool, + default=False, + help="use separate projection for each target", + ) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--world-size", + type=int, + default=1, + help="Number of GPUs for DDP training.", + ) + + 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=400, + 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="zipformer/exp", + help="""The experiment dir. + It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--base-lr", type=float, default=0.045, help="The base learning rate." + ) + + parser.add_argument( + "--lr-batches", + type=float, + default=7500, + 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=10.5, + help="""Number of epochs that affects how rapidly the learning rate decreases. + """, + ) + + parser.add_argument( + "--warmup-batches", + type=float, + default=5000, + help="Eden warmup steps", + ) + + parser.add_argument( + "--warmup-start", + type=float, + default=0, + help="Eden warmup start learning rate", + ) + + parser.add_argument( + "--ref-duration", + type=float, + default=600, + help="Reference batch duration for purposes of adjusting batch counts for setting various " + "schedules inside the model", + ) + + 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( + "--sanity-check", + type=str2bool, + default=False, + help="Check if any of the batches in epoch 1 would cause OOM.", + ) + + 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=100000, + 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 1. + """, + ) + + 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( + "--accum-grad", + type=int, + default=4, + help="""update gradient when batch_idx_train % accum_grad == 0. + """, + ) + + parser.add_argument( + "--use-fp16", + type=str2bool, + default=False, + help="Whether to use half precision training.", + ) + + parser.add_argument( + "--max-keep-size", + type=int, + default=sys.maxsize, + help="exclude sample longer than this.", + ) + + parser.add_argument( + "--min-keep-size", + type=float, + default=32000, + help="exclude sample longer less than this.", + ) + + parser.add_argument( + "--max-sample-size", + type=float, + default=250000, + help="max sample size to crop to for batching.", + ) + + 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 updates happen to the model so far across + epochs. + + - sub_batch_idx_train: It contains number of batch 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 + """ + params = AttributeDict( + { + "best_train_loss": float("inf"), + "best_valid_loss": float("inf"), + "best_train_epoch": -1, + "best_valid_epoch": -1, + "batch_idx_train": 0, + "sub_batch_idx_train": 0, + "log_interval": 50, + "reset_interval": 200, + "valid_interval": 3000, # For the 100h subset, use 800 + "env_info": get_env_info(), + } + ) + + return params + + +def _to_int_tuple(s: str): + return tuple(map(int, s.split(","))) + + +def get_model(params: AttributeDict) -> nn.Module: + model = HubertModel(params) + 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 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 `dataset.HubertDataset()` + 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. + """ + device = model.device if isinstance(model, DDP) else next(model.parameters()).device + audio = batch["audio"].to(device) + padding_mask = batch["padding_mask"].to(device) + kmeans = batch["kmeans"].to(device) + + with torch.set_grad_enabled(is_training): + loss, num_masked_tokens, logging_output = model( + source=audio, target_list=[kmeans], padding_mask=padding_mask + ) + + assert loss.requires_grad == is_training + + info = MetricsTracker() + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + info["frames"] = num_masked_tokens + for item in logging_output: + info[item] = logging_output[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() + + saved_bad_model = False + + def save_bad_model(suffix: str = ""): + save_checkpoint_impl( + filename=params.exp_dir / f"bad-model{suffix}-{rank}.pt", + model=model, + model_avg=model_avg, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=train_dl.sampler, + scaler=scaler, + rank=0, + ) + + for sub_batch_idx, batch in enumerate(train_dl): + params.sub_batch_idx_train += 1 + batch_idx = sub_batch_idx // params.accum_grad + + if batch_idx % 10 == 0: + set_batch_count(model, get_adjusted_batch_count(params)) + + batch_size = batch["kmeans"].shape[0] + + 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 / params.accum_grad).backward() + + if sub_batch_idx % params.accum_grad == params.accum_grad - 1: + params.batch_idx_train += 1 + scheduler.step_batch(params.batch_idx_train) + + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad() + else: + continue + + except: # noqa + save_bad_model() + 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 < 8.0 or (cur_grad_scale < 32.0 and batch_idx % 400 == 0): + scaler.update(cur_grad_scale * 2.0) + if cur_grad_scale < 0.01: + if not saved_bad_model: + save_bad_model(suffix="-first-warning") + saved_bad_model = True + logging.warning(f"Grad scale is small: {cur_grad_scale}") + if cur_grad_scale < 1.0e-05: + save_bad_model() + raise RuntimeError( + f"grad_scale is too small, exiting: {cur_grad_scale}" + ) + + if batch_idx % params.log_interval == 0: + cur_lr = max(scheduler.get_last_lr()) + 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 + ) + + if sub_batch_idx % params.accum_grad != params.accum_grad - 1: + optimizer.zero_grad() + 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) + if world_size > 1: + setup_dist(rank, world_size, params.master_port) + + 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(params) + + logging.info("About to create model") + model = get_model(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) + + optimizer = ScaledAdam( + get_parameter_groups_with_lrs(model, lr=params.base_lr, include_names=True), + lr=params.base_lr, # should have no effect + clipping_scale=2.0, + ) + + scheduler = Eden( + optimizer, + params.lr_batches, + params.lr_epochs, + params.warmup_batches, + params.warmup_start, + ) + + 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( + 512 + ) # allow 4 megabytes per sub-module + diagnostic = diagnostics.attach_diagnostics(model, opts) + + if params.inf_check: + register_inf_check_hooks(model) + + librispeech = LibriSpeechDataModule(args) + + train_cuts = ( + librispeech.train_all_shuf_cuts() + if params.full_libri + else librispeech.train_clean_100_cuts() + ) + + 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 < params.min_keep_size / params.sample_rate + or c.duration > params.max_keep_size / params.sample_rate + ): + logging.warning( + f"Exclude cut with ID {c.id} from training. Duration: {c.duration}" + ) + return False + + return True + + train_cuts = train_cuts.filter(remove_short_and_long_utt) + + 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 = librispeech.train_dataloaders( + train_cuts, + max_sample_size=params.max_sample_size, + sample_rate=params.sample_rate, + label_rate=params.label_rate, + random_crop=params.random_crop, + pad_audio=False, + num_classes=params.num_classes, + do_normalize=params.do_normalize, + sampler_state_dict=sampler_state_dict, + ) + + valid_cuts = librispeech.dev_clean_cuts() + # valid_cuts += librispeech.dev_other_cuts() + valid_cuts = valid_cuts.filter(remove_short_and_long_utt) + + valid_dl = librispeech.valid_dataloaders( + valid_cuts, + max_sample_size=params.max_sample_size, + sample_rate=params.sample_rate, + label_rate=params.label_rate, + random_crop=params.random_crop, + pad_audio=False, + num_classes=params.num_classes, + do_normalize=params.do_normalize, + ) + + if params.sanity_check and not params.print_diagnostics: + scan_pessimistic_batches_for_oom( + model=model, + train_dl=train_dl, + optimizer=optimizer, + 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"]) + + 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 `dataset.HubertDataset()` + for the content in it. + params: + Parameters for training. See :func:`get_params`. + sp: + The BPE model. + """ + from lhotse.utils import uuid4 + + filename = f"{params.exp_dir}/batch-{uuid4()}.pt" + logging.info(f"Saving batch to {filename}") + torch.save(batch, filename) + + audio = batch["audio"] + logging.info(f"audio shape: {audio.shape}") + + +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() + LibriSpeechDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + world_size = args.world_size + assert world_size >= 1 + if world_size > 1: + mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True) + else: + run(rank=0, world_size=1, args=args) + + +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + +if __name__ == "__main__": + main() From b793c5e958b9af61a7eb0e093c6890efa4997b05 Mon Sep 17 00:00:00 2001 From: RedSheep Date: Wed, 12 Mar 2025 12:00:48 +0800 Subject: [PATCH 2/5] modified compute_fbank_librispeech.py to minimize resource utilization --- .../ASR/local/compute_fbank_librispeech.py | 53 ++++++++++++++----- 1 file changed, 39 insertions(+), 14 deletions(-) diff --git a/egs/librispeech/ASR/local/compute_fbank_librispeech.py b/egs/librispeech/ASR/local/compute_fbank_librispeech.py index 13c417066..b41ef8771 100755 --- a/egs/librispeech/ASR/local/compute_fbank_librispeech.py +++ b/egs/librispeech/ASR/local/compute_fbank_librispeech.py @@ -69,10 +69,10 @@ def get_args(): ) parser.add_argument( - "--num-workers", - type=int, - default=15, - help="Number of worker processes for feature extraction.", + "--num-workers", + type=int, + default=15, + help="Number of worker processes for feature extraction.", ) return parser.parse_args() @@ -133,6 +133,7 @@ def compute_fbank_librispeech( logging.info(f"{partition} already exists - skipping.") continue logging.info(f"Processing {partition}") + cut_set = CutSet.from_manifests( recordings=m["recordings"], supervisions=m["supervisions"], @@ -142,20 +143,44 @@ def compute_fbank_librispeech( if bpe_model: cut_set = filter_cuts(cut_set, sp) if perturb_speed: - logging.info(f"Doing speed perturb") + logging.info("Doing speed perturb") cut_set = ( cut_set + cut_set.perturb_speed(0.9) + cut_set.perturb_speed(1.1) ) - 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 min(num_jobs * 2, 20), - executor=ex, - storage_type=LilcomChunkyWriter, - ) + # 修改這部分代碼 + if ex is None: + # 為 None 的情況（本地執行）創建自定義進程池上下文 + import multiprocessing as mp + from concurrent.futures import ProcessPoolExecutor + + # 計算工作數 + actual_jobs = ( + min(num_jobs * 2, 20) if "train" in partition else num_jobs + ) + + # 使用 forkserver 方法 + ctx = mp.get_context("forkserver") + with ProcessPoolExecutor( + max_workers=actual_jobs, mp_context=ctx + ) as local_executor: + cut_set = cut_set.compute_and_store_features( + extractor=extractor, + storage_path=f"{output_dir}/{prefix}_feats_{partition}", + executor=local_executor, + storage_type=LilcomChunkyWriter, + ) + else: + # 分佈式環境，使用提供的執行器 + cut_set = cut_set.compute_and_store_features( + extractor=extractor, + storage_path=f"{output_dir}/{prefix}_feats_{partition}", + num_jobs=min(num_jobs * 2, 20), + executor=ex, + storage_type=LilcomChunkyWriter, + ) + cut_set.to_file(output_dir / cuts_filename) @@ -169,5 +194,5 @@ if __name__ == "__main__": bpe_model=args.bpe_model, dataset=args.dataset, perturb_speed=args.perturb_speed, - num_workers=args.num_workers, + num_workers=args.num_workers, ) From 04a81f53f0baec797e5cf93f5c095ea8cfff1cab Mon Sep 17 00:00:00 2001 From: RedSheep Date: Wed, 12 Mar 2025 12:08:03 +0800 Subject: [PATCH 3/5] deleted k2SSL files --- egs/librispeech/k2SSL/conf/train_config.py | 42 - .../k2SSL/conf/zipformer_config.py | 17 - egs/librispeech/k2SSL/hubert_ce.py | 601 ------- egs/librispeech/k2SSL/pretrain.py | 1380 ----------------- 4 files changed, 2040 deletions(-) delete mode 100755 egs/librispeech/k2SSL/conf/train_config.py delete mode 100755 egs/librispeech/k2SSL/conf/zipformer_config.py delete mode 100644 egs/librispeech/k2SSL/hubert_ce.py delete mode 100644 egs/librispeech/k2SSL/pretrain.py diff --git a/egs/librispeech/k2SSL/conf/train_config.py b/egs/librispeech/k2SSL/conf/train_config.py deleted file mode 100755 index 9cc6b6114..000000000 --- a/egs/librispeech/k2SSL/conf/train_config.py +++ /dev/null @@ -1,42 +0,0 @@ -# 預訓練參數設置 -train_params = { - # 模型參數 - "label_rate": 50, - "sample_rate": 16000, - "extractor_mode": "default", - "conv_feature_layers": "[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2", - "conv_bias": False, - "feature_grad_mult": 1.0, - - # 掩碼參數 - "mask_length": 10, - "mask_prob": 0.65, - "mask_selection": "static", - "mask_other": 0, - "no_mask_overlap": False, - "mask_min_space": 1, - - # 通道掩碼參數 - "mask_channel_length": 10, - "mask_channel_prob": 0.0, - "mask_channel_selection": "static", - "mask_channel_other": 0, - "no_mask_channel_overlap": False, - "mask_channel_min_space": 1, - - # 損失計算參數 - "skip_masked": False, - "skip_nomask": False, - "pred_masked_weight": 1, - "pred_nomask_weight": 0, - "loss_weights": [10], - "checkpoint_activations": False, - - # 其他參數 - "dropout_input": 0.0, - "dropout_features": 0.0, - "num_classes": [504], - "untie_final_proj": False, - "required_seq_len_multiple": 2, - "logit_temp": 0.1, -} diff --git a/egs/librispeech/k2SSL/conf/zipformer_config.py b/egs/librispeech/k2SSL/conf/zipformer_config.py deleted file mode 100755 index 29aaabf7d..000000000 --- a/egs/librispeech/k2SSL/conf/zipformer_config.py +++ /dev/null @@ -1,17 +0,0 @@ -def get_zipformer_base_config(): - return { - "output_downsampling_factor": 1, - "downsampling_factor": (1, 2, 4, 8, 4, 2), - "encoder_dim": (192, 256, 384, 512, 384, 256), - "num_encoder_layers": (2, 2, 3, 4, 3, 2), - "encoder_unmasked_dim": (192, 192, 256, 256, 256, 192), - "query_head_dim": 32, - "pos_head_dim": 4, - "value_head_dim": 12, - "pos_dim": 48, - "num_heads": (4, 4, 4, 8, 4, 4), - "feedforward_dim": (512, 768, 1024, 1536, 1024, 768), - "cnn_module_kernel": (31, 31, 15, 15, 15, 31), - "dropout": 0.1, - "warmup_batches": 4000.0, - } diff --git a/egs/librispeech/k2SSL/hubert_ce.py b/egs/librispeech/k2SSL/hubert_ce.py deleted file mode 100644 index 1ac368a1d..000000000 --- a/egs/librispeech/k2SSL/hubert_ce.py +++ /dev/null @@ -1,601 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import argparse -import logging -from typing import Dict, List, Optional, Tuple - -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -from scaling import ScheduledFloat -from utils import GradMultiply, LayerNorm -from wav2vec2_module import ConvFeatureExtractionModel -from zipformer import Zipformer2 - - -def compute_mask_indices( - shape: Tuple[int, int], - padding_mask: Optional[torch.Tensor], - mask_prob: float, - mask_length: int, - mask_type: str = "static", - mask_other: float = 0.0, - min_masks: int = 0, - no_overlap: bool = False, - min_space: int = 0, - require_same_masks: bool = True, - mask_dropout: float = 0.0, - add_masks: bool = False, - seed: Optional[int] = None, - epoch: Optional[int] = None, - indices: Optional[torch.Tensor] = None, - idc_select_ver: int = 1, # 2 to reproduce mask_tokens_dataset - num_mask_ver: int = 2, # 2 to reproduce mask_tokens_dataset -) -> np.ndarray: - """ - Computes random mask spans for a given shape - - Args: - shape: the the shape for which to compute masks. - should be of size 2 where first element is batch size and 2nd is timesteps - padding_mask: optional padding mask of the same size as shape, which will prevent masking padded elements - mask_prob: probability for each token to be chosen as start of the span to be masked. this will be multiplied by - number of timesteps divided by length of mask span to mask approximately this percentage of all elements. - however due to overlaps, the actual number will be smaller (unless no_overlap is True) - mask_type: how to compute mask lengths - static = fixed size - uniform = sample from uniform distribution [mask_other, mask_length*2] - normal = sample from normal distribution with mean mask_length and stdev mask_other. mask is min 1 element - poisson = sample from possion distribution with lambda = mask length - min_masks: minimum number of masked spans - no_overlap: if false, will switch to an alternative recursive algorithm that prevents spans from overlapping - min_space: only used if no_overlap is True, this is how many elements to keep unmasked between spans - require_same_masks: if true, will randomly drop out masks until same amount of masks remains in each sample - mask_dropout: randomly dropout this percentage of masks in each example - """ - - bsz, all_sz = shape - mask = np.full((bsz, all_sz), False) - - if num_mask_ver == 1: - all_num_mask = int( - # add a random number for probabilistic rounding - mask_prob * all_sz / float(mask_length) - + np.random.rand() - ) - all_num_mask = max(min_masks, all_num_mask) - - mask_idcs = [] - for i in range(bsz): - if seed is not None and epoch is not None and indices is not None: - seed_i = int(hash((seed, epoch, indices[i].item())) % 1e6) - else: - seed_i = None - - rng = np.random.default_rng(seed_i) - - if padding_mask is not None: - sz = all_sz - padding_mask[i].long().sum().item() - assert sz >= 0, sz - else: - sz = all_sz - - if num_mask_ver == 1: - if padding_mask is not None: - num_mask = int( - # add a random number for probabilistic rounding - mask_prob * sz / float(mask_length) - + np.random.rand() - ) - num_mask = max(min_masks, num_mask) - else: - num_mask = all_num_mask - elif num_mask_ver == 2: - num_mask = int( - # add a random number for probabilistic rounding - mask_prob * sz / float(mask_length) - + rng.random() - ) - num_mask = max(min_masks, num_mask) - else: - raise ValueError() - - if mask_type == "static": - lengths = np.full(num_mask, mask_length) - elif mask_type == "uniform": - lengths = rng.randint(mask_other, mask_length * 2 + 1, size=num_mask) - elif mask_type == "normal": - lengths = rng.normal(mask_length, mask_other, size=num_mask) - lengths = [max(1, int(round(x))) for x in lengths] - elif mask_type == "poisson": - lengths = rng.poisson(mask_length, size=num_mask) - lengths = [int(round(x)) for x in lengths] - else: - raise Exception("unknown mask selection " + mask_type) - - if sum(lengths) == 0: - if mask_type == "static": - raise ValueError(f"this should never happens") - else: - lengths = [min(mask_length, sz - 1)] - - if no_overlap: - mask_idc = [] - - def arrange(s, e, length, keep_length): - span_start = rng.randint(s, e - length) - mask_idc.extend(span_start + i for i in range(length)) - - new_parts = [] - if span_start - s - min_space >= keep_length: - new_parts.append((s, span_start - min_space + 1)) - if e - span_start - length - min_space > keep_length: - new_parts.append((span_start + length + min_space, e)) - return new_parts - - parts = [(0, sz)] - min_length = min(lengths) - for length in sorted(lengths, reverse=True): - lens = np.fromiter( - (e - s if e - s >= length + min_space else 0 for s, e in parts), - np.int, - ) - l_sum = np.sum(lens) - if l_sum == 0: - break - probs = lens / np.sum(lens) - c = rng.choice(len(parts), p=probs) - s, e = parts.pop(c) - parts.extend(arrange(s, e, length, min_length)) - mask_idc = np.asarray(mask_idc) - else: - if idc_select_ver == 1: - min_len = min(lengths) - if sz - min_len <= num_mask: - min_len = sz - num_mask - 1 - mask_idc = rng.choice(sz - min_len, num_mask, replace=False) - elif idc_select_ver == 2: - mask_idc = rng.choice(sz, num_mask, replace=False) - else: - raise ValueError() - - mask_idc = np.asarray( - [ - mask_idc[j] + offset - for j in range(len(mask_idc)) - for offset in range(lengths[j]) - ] - ) - - mask_idc = np.unique(mask_idc[mask_idc < sz]) - if len(mask_idc) >= sz: - raise ValueError( - ( - f"the entire sequence is masked. " - f"sz={sz}; mask_idc[mask_idc]; " - f"index={indices[i] if indices is not None else None}" - ) - ) - mask_idcs.append(mask_idc) - - target_len = None - if require_same_masks: - if add_masks: - target_len = max([len(m) for m in mask_idcs]) - else: - target_len = min([len(m) for m in mask_idcs]) - - for i, mask_idc in enumerate(mask_idcs): - if target_len is not None and len(mask_idc) > target_len: - mask_idc = rng.choice(mask_idc, target_len, replace=False) - - mask[i, mask_idc] = True - - if target_len is not None and len(mask_idc) < target_len: - unmasked = np.flatnonzero(~mask[i]) - to_mask = rng.choice(unmasked, target_len - len(mask_idc), replace=False) - mask[i, to_mask] = True - - if mask_dropout > 0: - masked = np.flatnonzero(mask[i]) - num_holes = np.rint(len(masked) * mask_dropout).astype(int) - to_drop = rng.choice(masked, num_holes, replace=False) - mask[i, to_drop] = False - - return mask - - -def _to_int_tuple(s: str): - return tuple(map(int, s.split(","))) - - -class HubertModel(nn.Module): - def __init__( - self, - cfg, - ) -> None: - super().__init__() - feature_enc_layers = eval(cfg.conv_feature_layers) # noqa - self.embed = feature_enc_layers[-1][0] - - self.feature_extractor = ConvFeatureExtractionModel( - conv_layers=feature_enc_layers, - dropout=0.0, - mode=cfg.extractor_mode, - conv_bias=cfg.conv_bias, - ) - feature_ds_rate = np.prod([s for _, _, s in feature_enc_layers]) - self.feat2tar_ratio = cfg.label_rate * feature_ds_rate / cfg.sample_rate - encoder_input_dim = _to_int_tuple(cfg.encoder_dim)[0] - encoder_output_dim = max(_to_int_tuple(cfg.encoder_dim)) - self.post_extract_proj = ( - nn.Linear(self.embed, encoder_input_dim) - if self.embed != encoder_input_dim - else None - ) - - self.mask_prob = cfg.mask_prob - self.mask_selection = cfg.mask_selection - self.mask_other = cfg.mask_other - self.mask_length = cfg.mask_length - self.no_mask_overlap = cfg.no_mask_overlap - self.mask_min_space = cfg.mask_min_space - - self.mask_channel_prob = cfg.mask_channel_prob - self.mask_channel_selection = cfg.mask_channel_selection - self.mask_channel_other = cfg.mask_channel_other - self.mask_channel_length = cfg.mask_channel_length - self.no_mask_channel_overlap = cfg.no_mask_channel_overlap - self.mask_channel_min_space = cfg.mask_channel_min_space - - self.dropout_input = nn.Dropout(cfg.dropout_input) - self.dropout_features = nn.Dropout(cfg.dropout_features) - - self.feature_grad_mult = cfg.feature_grad_mult - self.logit_temp = cfg.logit_temp - self.skip_masked = cfg.skip_masked - self.skip_nomask = cfg.skip_nomask - - self.mask_emb = nn.Parameter(torch.FloatTensor(encoder_input_dim).uniform_()) - - self.encoder = Zipformer2( - output_downsampling_factor=1, - downsampling_factor=_to_int_tuple(cfg.downsampling_factor), - num_encoder_layers=_to_int_tuple(cfg.num_encoder_layers), - encoder_dim=_to_int_tuple(cfg.encoder_dim), - encoder_unmasked_dim=_to_int_tuple(cfg.encoder_unmasked_dim), - query_head_dim=_to_int_tuple(cfg.query_head_dim), - pos_head_dim=_to_int_tuple(cfg.pos_head_dim), - value_head_dim=_to_int_tuple(cfg.value_head_dim), - pos_dim=cfg.pos_dim, - num_heads=_to_int_tuple(cfg.num_heads), - feedforward_dim=_to_int_tuple(cfg.feedforward_dim), - cnn_module_kernel=_to_int_tuple(cfg.cnn_module_kernel), - dropout=ScheduledFloat((0.0, 0.3), (20000.0, 0.1)), - warmup_batches=4000.0, - ) - - self.layer_norm = LayerNorm(self.embed) - - self.untie_final_proj = cfg.untie_final_proj - self.final_proj = nn.Linear(encoder_output_dim, sum(cfg.num_classes)) - - # modules below are not needed during fine-tuning - self.num_classes = cfg.num_classes - self.pred_masked_weight = cfg.pred_masked_weight - self.pred_nomask_weight = cfg.pred_nomask_weight - self.loss_weights = cfg.loss_weights - - def upgrade_state_dict_named(self, state_dict, name): - """Upgrade a (possibly old) state dict for new versions of fairseq.""" - - super().upgrade_state_dict_named(state_dict, name) - return state_dict - - def apply_mask(self, x, padding_mask, target_list): - B, T, C = x.shape - if self.mask_prob > 0: - mask_indices = compute_mask_indices( - (B, T), - padding_mask, - self.mask_prob, - self.mask_length, - self.mask_selection, - self.mask_other, - min_masks=2, - no_overlap=self.no_mask_overlap, - min_space=self.mask_min_space, - ) - mask_indices = torch.from_numpy(mask_indices).to(x.device) - x[mask_indices] = self.mask_emb.to(x.dtype) - else: - mask_indices = None - - if self.mask_channel_prob > 0: - mask_channel_indices = compute_mask_indices( - (B, C), - None, - self.mask_channel_prob, - self.mask_channel_length, - self.mask_channel_selection, - self.mask_channel_other, - no_overlap=self.no_mask_channel_overlap, - min_space=self.mask_channel_min_space, - ) - mask_channel_indices = ( - torch.from_numpy(mask_channel_indices) - .to(x.device) - .unsqueeze(1) - .expand(-1, T, -1) - ) - x[mask_channel_indices] = 0 - - return x, mask_indices - - def forward_features(self, source: torch.Tensor) -> torch.Tensor: - if self.feature_grad_mult > 0: - features = self.feature_extractor(source) - if self.feature_grad_mult != 1.0: - features = GradMultiply.apply(features, self.feature_grad_mult) - else: - with torch.no_grad(): - features = self.feature_extractor(source) - return features - - def forward_targets( - self, - features: torch.Tensor, - target_list: List[torch.Tensor], - ) -> Tuple[torch.Tensor, torch.Tensor]: - # Trim features to ensure labels exist and then get aligned labels - feat_tsz = features.size(2) - targ_tsz = min([t.size(1) for t in target_list]) - if self.feat2tar_ratio * feat_tsz > targ_tsz: - feat_tsz = int(targ_tsz / self.feat2tar_ratio) - features = features[..., :feat_tsz] - target_inds = torch.arange(feat_tsz).float() * self.feat2tar_ratio - target_list = [t[:, target_inds.long()] for t in target_list] - return features, target_list - - def forward_padding_mask( - self, - features: torch.Tensor, - padding_mask: torch.Tensor, - ) -> torch.Tensor: - extra = padding_mask.size(1) % features.size(1) - if extra > 0: - padding_mask = padding_mask[:, :-extra] - padding_mask = padding_mask.view(padding_mask.size(0), features.size(1), -1) - padding_mask = padding_mask.all(-1) - return padding_mask - - def forward( - self, - source: torch.Tensor, - target_list: Optional[List[torch.Tensor]] = None, - padding_mask: Optional[torch.Tensor] = None, - mask: bool = True, - features_only: bool = False, - output_layer: Optional[int] = None, - ): - """output layer is 1-based""" - features = self.forward_features(source) - if target_list is not None: - features, target_list = self.forward_targets(features, target_list) - - features_pen = features.float().pow(2).mean() - - features = features.transpose(1, 2) - features = self.layer_norm(features) - unmasked_features = features.clone() - - if padding_mask is not None: - padding_mask = self.forward_padding_mask(features, padding_mask) - - if self.post_extract_proj is not None: - features = self.post_extract_proj(features) - - features = self.dropout_input(features) - unmasked_features = self.dropout_features(unmasked_features) - - if mask: - x, mask_indices = self.apply_mask(features, padding_mask, target_list) - else: - x = features - mask_indices = None - - # feature: (B, T, D), float - # target: (B, T), long - # x: (B, T, D), float -> (T, B, D), float - # padding_mask: (B, T), bool - # mask_indices: (B, T), bool - x = x.transpose(0, 1) - x, x_lens = self.encoder(x, (~padding_mask).sum(dim=-1)) - x = x.transpose(0, 1) - - if features_only: - return {"x": x, "padding_mask": padding_mask, "features": features} - - if not self.skip_masked: - masked_indices = torch.logical_and(~padding_mask, mask_indices) - proj_x_m = self.final_proj(x[masked_indices]) - proj_x_m /= self.logit_temp - logit_m_list = [proj_x_m for _ in range(len(target_list))] - else: - logit_m_list = [None for _ in target_list] - - if not self.skip_nomask: - nomask_indices = torch.logical_and(~padding_mask, ~mask_indices) - proj_x_u = self.final_proj(x[nomask_indices]) - proj_x_u /= self.logit_temp - logit_u_list = [proj_x_u for _ in range(len(target_list))] - else: - logit_u_list = [None for _ in target_list] - - # result = { - # "logit_m_list": logit_m_list, - # "logit_u_list": logit_u_list, - # "padding_mask": padding_mask, - # "features_pen": features_pen, - # } - targ_m_list = target_list[0][masked_indices] - targ_m_list = targ_m_list.long() - targ_m_list = [targ_m_list for _ in range(len(target_list))] - - targ_u_list = target_list[0][nomask_indices] - targ_u_list = targ_u_list.long() - targ_u_list = [targ_u_list for _ in range(len(target_list))] - return self.compute_loss( - logit_m_list, logit_u_list, targ_m_list, targ_u_list, features_pen - ) - - def extract_features( - self, - source: torch.Tensor, - padding_mask: Optional[torch.Tensor] = None, - mask: bool = False, - ret_conv: bool = False, - output_layer: Optional[int] = None, - ) -> Tuple[torch.Tensor, torch.Tensor]: - res = self.forward( - source, - padding_mask=padding_mask, - mask=mask, - features_only=True, - output_layer=output_layer, - ) - feature = res["features"] if ret_conv else res["x"] - return feature, res["padding_mask"] - - def get_logits(self, net_output, is_masked=True): - if is_masked: - logits_list = net_output["logit_m_list"] - else: - logits_list = net_output["logit_u_list"] - logits_list = [x.float() for x in logits_list if x is not None] - return logits_list - - def get_targets(self, net_output, is_masked=True): - logits_list = self.get_logits(net_output, is_masked) - targets_list = [x.new_zeros(x.size(0), dtype=torch.long) for x in logits_list] - return targets_list - - def get_extra_losses(self, net_output): - extra_losses = [] - names = [] - - if "features_pen" in net_output: - extra_losses.append(net_output["features_pen"]) - names.append("features_pen") - - return extra_losses, names - - def remove_pretraining_modules(self): - self.final_proj = None - - def compute_loss( - self, logit_m_list, logit_u_list, targ_m_list, targ_u_list, features_pen - ): - loss = 0.0 - sample_size = 0 - logging_output = {} - reduce = True - reduction = "sum" if reduce else "none" - - loss_m_list = [] - logp_m_list = [x.float() for x in logit_m_list if x is not None] - logp_m_list = torch.cat(logp_m_list) - targ_m_list = torch.cat(targ_m_list) - - loss_m = F.cross_entropy(logp_m_list, targ_m_list, reduction=reduction) - loss_m_list.append(loss_m) - logging_output[f"loss_m_0"] = loss_m.detach().item() - - assert self.pred_masked_weight == 0 or len(logp_m_list) > 0 - if self.pred_masked_weight > 0: - loss += self.pred_masked_weight * sum(loss_m_list) - sample_size += len(targ_m_list) - - loss_u_list = [] - logp_u_list = [x.float() for x in logit_u_list if x is not None] - logp_u_list = torch.cat(logp_u_list) - targ_u_list = torch.cat(targ_u_list) - - loss_u = F.cross_entropy(logp_u_list, targ_u_list, reduction=reduction) - loss_u_list.append(loss_u) - logging_output[f"loss_u_0"] = loss_u.detach().item() - - assert self.pred_nomask_weight == 0 or len(logp_u_list) > 0 - if self.pred_nomask_weight > 0: - loss += self.pred_nomask_weight * sum(loss_u_list) - sample_size += len(targ_u_list) - - if self.loss_weights is not None: - extra_losses = [] - names = [] - extra_losses.append(features_pen) - names.append("features_pen") - if torch.is_tensor(extra_losses): - extra_losses = [extra_losses] - names = [names] - if len(self.loss_weights) == 1 and len(extra_losses) != 1: - self.loss_weights = [self.loss_weights[0]] * len(extra_losses) - assert len(extra_losses) == len( - self.loss_weights - ), f"{len(extra_losses)}, {len(self.loss_weights)}" - for p, n, coef in zip(extra_losses, names, self.loss_weights): - if coef != 0 and p is not None: - p = coef * p.float() * sample_size - loss += p - logging_output[f"loss_{n}"] = p.item() - - logging_output = { - "loss": loss.item() if reduce else loss, - **logging_output, - } - - # for lk in self.log_keys: - # if lk in net_output: - # logging_output[lk] = float((net_output[lk])) - - def compute_correct(logits, target): - if logits.numel() == 0: - return 0, 0 - else: - assert logits.dim() > 1, logits.shape - max = logits.argmax(-1) == target - min = logits.argmin(-1) == target - both = max & min - corr = max.long().sum().item() - both.long().sum().item() - count = max.numel() - return corr, count - - with torch.no_grad(): - corr_m, count_m = compute_correct(logp_m_list, targ_m_list) - logging_output[f"correct_m_0"] = corr_m - logging_output[f"count_m_0"] = count_m - - corr_u, count_u = compute_correct(logp_u_list, targ_u_list) - logging_output[f"correct_u_0"] = corr_u - logging_output[f"count_u_0"] = count_u - - return loss, sample_size, logging_output diff --git a/egs/librispeech/k2SSL/pretrain.py b/egs/librispeech/k2SSL/pretrain.py deleted file mode 100644 index 937fb382e..000000000 --- a/egs/librispeech/k2SSL/pretrain.py +++ /dev/null @@ -1,1380 +0,0 @@ -#!/usr/bin/env python3 -# Copyright 2021-2024 Xiaomi Corp. (authors: Fangjun Kuang, -# Wei Kang, -# Mingshuang Luo, -# Zengwei Yao, -# Yifan Yang, -# Daniel Povey) -# Copyright 2024 Shanghai Jiao Tong University (authors: Jianheng Zhuo) -# -# 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: - -export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" - -# For hubert model pretraining: -./zipformer/pretrain.py \ - --world-size 8 \ - --num-epochs 400 \ - --start-epoch 1 \ - --use-fp16 1 \ - --exp-dir hubert/exp \ - --full-libri 1 \ - --max-duration 87.5 \ - --accum-grad 4 -""" - - -import argparse -import copy -import logging -import sys -import warnings -from pathlib import Path -from shutil import copyfile -from typing import Any, Dict, Optional, Tuple, Union - -import optim -import torch -import torch.multiprocessing as mp -import torch.nn as nn -from hubert_ce import HubertModel -from lhotse.cut import Cut -from lhotse.dataset.sampling.base import CutSampler -from lhotse.utils import fix_random_seed -from optim import Eden, ScaledAdam -from ssl_datamodule import LibriSpeechDataModule -from torch import Tensor -from torch.cuda.amp import GradScaler -from torch.nn.parallel import DistributedDataParallel as DDP -from torch.utils.tensorboard import SummaryWriter - -from icefall import diagnostics -from icefall.checkpoint import load_checkpoint, remove_checkpoints -from icefall.checkpoint import save_checkpoint as save_checkpoint_impl -from icefall.checkpoint import ( - save_checkpoint_with_global_batch_idx, - update_averaged_model, -) -from icefall.dist import cleanup_dist, setup_dist -from icefall.env import get_env_info -from icefall.hooks import register_inf_check_hooks -from icefall.utils import ( - AttributeDict, - MetricsTracker, - get_parameter_groups_with_lrs, - setup_logger, - str2bool, -) - -LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler] - - -def get_adjusted_batch_count(params: AttributeDict) -> float: - # returns the number of batches we would have used so far if we had used the reference - # duration. This is for purposes of set_batch_count(). - return ( - params.batch_idx_train - * params.accum_grad - * (params.max_duration * params.world_size) - / params.ref_duration - ) - - -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 name, module in model.named_modules(): - if hasattr(module, "batch_count"): - module.batch_count = batch_count - if hasattr(module, "name"): - module.name = name - - -def add_model_arguments(parser: argparse.ArgumentParser): - parser.add_argument( - "--num-encoder-layers", - type=str, - default="2,2,3,4,3,2", - help="Number of zipformer encoder layers per stack, comma separated.", - ) - - parser.add_argument( - "--downsampling-factor", - type=str, - default="1,2,4,8,4,2", - help="Downsampling factor for each stack of encoder layers.", - ) - - parser.add_argument( - "--feedforward-dim", - type=str, - default="512,768,1024,1536,1024,768", - help="Feedforward dimension of the zipformer encoder layers, per stack, comma separated.", - ) - - parser.add_argument( - "--num-heads", - type=str, - default="4,4,4,8,4,4", - help="Number of attention heads in the zipformer encoder layers: a single int or comma-separated list.", - ) - - parser.add_argument( - "--encoder-dim", - type=str, - default="192,256,384,512,384,256", - help="Embedding dimension in encoder stacks: a single int or comma-separated list.", - ) - - parser.add_argument( - "--query-head-dim", - type=str, - default="32", - help="Query/key dimension per head in encoder stacks: a single int or comma-separated list.", - ) - - parser.add_argument( - "--value-head-dim", - type=str, - default="12", - help="Value dimension per head in encoder stacks: a single int or comma-separated list.", - ) - - parser.add_argument( - "--pos-head-dim", - type=str, - default="4", - help="Positional-encoding dimension per head in encoder stacks: a single int or comma-separated list.", - ) - - parser.add_argument( - "--pos-dim", - type=int, - default="48", - help="Positional-encoding embedding dimension", - ) - - parser.add_argument( - "--encoder-unmasked-dim", - type=str, - default="192,192,256,256,256,192", - help="Unmasked dimensions in the encoders, relates to augmentation during training. " - "A single int or comma-separated list. Must be <= each corresponding encoder_dim.", - ) - - parser.add_argument( - "--cnn-module-kernel", - type=str, - default="31,31,15,15,15,31", - help="Sizes of convolutional kernels in convolution modules in each encoder stack: " - "a single int or comma-separated list.", - ) - - # hubert parameters - parser.add_argument( - "--label-rate", - type=float, - default=50, - ) - - parser.add_argument( - "--sample-rate", - type=float, - default=16000, - ) - - parser.add_argument( - "--extractor-mode", - type=str, - default="default", - help="""mode for feature extractor, should in EXTRACTOR_MODE_CHOICES. default has a single group - norm with d groups in the first conv block, whereas layer_norm - has layer norms in every block (meant to use with normalize=True)""", - ) - - parser.add_argument( - "--conv-feature-layers", - type=str, - default="[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2", - help="string describing convolutional feature extraction layers in form of a python list that contains [(dim, kernel_size, stride), ...]", - ) - - parser.add_argument( - "--conv-bias", type=bool, default=False, help="include bias in conv encoder" - ) - - parser.add_argument( - "--feature-grad-mult", - type=float, - default=1.0, - help="multiply feature extractor var grads by this", - ) - - # masking - parser.add_argument("--mask-length", type=int, default=10, help="mask_length") - - parser.add_argument( - "--mask-prob", - type=float, - default=0.65, - help="probability of replacing a token with mask", - ) - - parser.add_argument( - "--mask-selection", - type=str, - choices=["static", "uniform", "normal", "poisson"], - default="static", - help="how to choose mask length", - ) - - parser.add_argument( - "--mask-other", - type=float, - default=0, - help="secondary mask argument (used for more complex distributions),see help in compute_mask_indicesh", - ) - - parser.add_argument( - "--no-mask-overlap", - type=bool, - default=False, - help="whether to allow masks to overlap", - ) - - parser.add_argument( - "--mask-min-space", - type=int, - default=1, - help="min space between spans (if no overlap is enabled)", - ) - - # channel masking - parser.add_argument( - "--mask-channel-length", - type=int, - default=10, - help="length of the mask for features (channels)", - ) - - parser.add_argument( - "--mask-channel-prob", - type=float, - default=0.0, - help="probability of replacing a feature with 0", - ) - - parser.add_argument( - "--mask-channel-selection", - type=str, - choices=["static", "uniform", "normal", "poisson"], - default="static", - help="how to choose mask length for channel masking", - ) - - parser.add_argument( - "--mask-channel-other", - type=float, - default=0, - help="secondary mask argument (used for more complex distributions), see help in compute_mask_indicesh", - ) - - parser.add_argument( - "--no-mask-channel-overlap", - type=bool, - default=False, - help="whether to allow channel masks to overlap", - ) - - parser.add_argument( - "--mask-channel-min-space", - type=int, - default=1, - help="min space between spans (if no overlap is enabled)", - ) - - # loss computation - parser.add_argument( - "--skip-masked", - type=bool, - default=False, - help="skip computing losses over masked frames", - ) - - parser.add_argument( - "--skip-nomask", - type=bool, - default=False, - help="skip computing losses over unmasked frames", - ) - - parser.add_argument( - "--checkpoint-activations", - type=bool, - default=False, - help="recompute activations and save memory for extra compute", - ) - - parser.add_argument( - "--pred-masked-weight", - type=float, - default=1, - help="weight for masked part in ssl loss", - ) - - parser.add_argument( - "--pred-nomask-weight", - type=float, - default=0, - help="weight for masked part in ssl loss", - ) - - parser.add_argument( - "--loss-weights", - type=float, - nargs="*", - default=[10], - help="weight for masked part in ssl loss", - ) - - # FP16 optimization - parser.add_argument( - "--required-seq-len-multiple", - type=int, - default=2, - help="pad the input to encoder such that the sequence length is divisible by multiple", - ) - - parser.add_argument( - "--attn-type", type=str, default="", help="if espnet use ESPNET MHA" - ) - - parser.add_argument( - "--pos-enc-type", - type=str, - default="abs", - help="Positional encoding type to use in conformer", - ) - - parser.add_argument( - "--logit-temp", type=float, default=0.1, help="temperature to divide logits by" - ) - - parser.add_argument( - "--dropout-input", - type=float, - default=0.0, - help="dropout to apply to the input (after feat extr)", - ) - - parser.add_argument( - "--dropout-features", - type=float, - default=0.0, - help="dropout to apply to the features (after feat extr)", - ) - - parser.add_argument( - "--num-classes", - type=int, - nargs="*", - default=[504], - help="""num class, a little larger than the number of cluster, - the largest is for padding, - and the value should be the multiple of 4, for faster computation""", - ) - - parser.add_argument( - "--untie-final-proj", - type=bool, - default=False, - help="use separate projection for each target", - ) - - -def get_parser(): - parser = argparse.ArgumentParser( - formatter_class=argparse.ArgumentDefaultsHelpFormatter - ) - - parser.add_argument( - "--world-size", - type=int, - default=1, - help="Number of GPUs for DDP training.", - ) - - 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=400, - 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="zipformer/exp", - help="""The experiment dir. - It specifies the directory where all training related - files, e.g., checkpoints, log, etc, are saved - """, - ) - - parser.add_argument( - "--base-lr", type=float, default=0.045, help="The base learning rate." - ) - - parser.add_argument( - "--lr-batches", - type=float, - default=7500, - 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=10.5, - help="""Number of epochs that affects how rapidly the learning rate decreases. - """, - ) - - parser.add_argument( - "--warmup-batches", - type=float, - default=5000, - help="Eden warmup steps", - ) - - parser.add_argument( - "--warmup-start", - type=float, - default=0, - help="Eden warmup start learning rate", - ) - - parser.add_argument( - "--ref-duration", - type=float, - default=600, - help="Reference batch duration for purposes of adjusting batch counts for setting various " - "schedules inside the model", - ) - - 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( - "--sanity-check", - type=str2bool, - default=False, - help="Check if any of the batches in epoch 1 would cause OOM.", - ) - - 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=100000, - 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 1. - """, - ) - - 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( - "--accum-grad", - type=int, - default=4, - help="""update gradient when batch_idx_train % accum_grad == 0. - """, - ) - - parser.add_argument( - "--use-fp16", - type=str2bool, - default=False, - help="Whether to use half precision training.", - ) - - parser.add_argument( - "--max-keep-size", - type=int, - default=sys.maxsize, - help="exclude sample longer than this.", - ) - - parser.add_argument( - "--min-keep-size", - type=float, - default=32000, - help="exclude sample longer less than this.", - ) - - parser.add_argument( - "--max-sample-size", - type=float, - default=250000, - help="max sample size to crop to for batching.", - ) - - 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 updates happen to the model so far across - epochs. - - - sub_batch_idx_train: It contains number of batch 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 - """ - params = AttributeDict( - { - "best_train_loss": float("inf"), - "best_valid_loss": float("inf"), - "best_train_epoch": -1, - "best_valid_epoch": -1, - "batch_idx_train": 0, - "sub_batch_idx_train": 0, - "log_interval": 50, - "reset_interval": 200, - "valid_interval": 3000, # For the 100h subset, use 800 - "env_info": get_env_info(), - } - ) - - return params - - -def _to_int_tuple(s: str): - return tuple(map(int, s.split(","))) - - -def get_model(params: AttributeDict) -> nn.Module: - model = HubertModel(params) - 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 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 `dataset.HubertDataset()` - 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. - """ - device = model.device if isinstance(model, DDP) else next(model.parameters()).device - audio = batch["audio"].to(device) - padding_mask = batch["padding_mask"].to(device) - kmeans = batch["kmeans"].to(device) - - with torch.set_grad_enabled(is_training): - loss, num_masked_tokens, logging_output = model( - source=audio, target_list=[kmeans], padding_mask=padding_mask - ) - - assert loss.requires_grad == is_training - - info = MetricsTracker() - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - info["frames"] = num_masked_tokens - for item in logging_output: - info[item] = logging_output[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() - - saved_bad_model = False - - def save_bad_model(suffix: str = ""): - save_checkpoint_impl( - filename=params.exp_dir / f"bad-model{suffix}-{rank}.pt", - model=model, - model_avg=model_avg, - params=params, - optimizer=optimizer, - scheduler=scheduler, - sampler=train_dl.sampler, - scaler=scaler, - rank=0, - ) - - for sub_batch_idx, batch in enumerate(train_dl): - params.sub_batch_idx_train += 1 - batch_idx = sub_batch_idx // params.accum_grad - - if batch_idx % 10 == 0: - set_batch_count(model, get_adjusted_batch_count(params)) - - batch_size = batch["kmeans"].shape[0] - - 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 / params.accum_grad).backward() - - if sub_batch_idx % params.accum_grad == params.accum_grad - 1: - params.batch_idx_train += 1 - scheduler.step_batch(params.batch_idx_train) - - scaler.step(optimizer) - scaler.update() - optimizer.zero_grad() - else: - continue - - except: # noqa - save_bad_model() - 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 < 8.0 or (cur_grad_scale < 32.0 and batch_idx % 400 == 0): - scaler.update(cur_grad_scale * 2.0) - if cur_grad_scale < 0.01: - if not saved_bad_model: - save_bad_model(suffix="-first-warning") - saved_bad_model = True - logging.warning(f"Grad scale is small: {cur_grad_scale}") - if cur_grad_scale < 1.0e-05: - save_bad_model() - raise RuntimeError( - f"grad_scale is too small, exiting: {cur_grad_scale}" - ) - - if batch_idx % params.log_interval == 0: - cur_lr = max(scheduler.get_last_lr()) - 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 - ) - - if sub_batch_idx % params.accum_grad != params.accum_grad - 1: - optimizer.zero_grad() - 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) - if world_size > 1: - setup_dist(rank, world_size, params.master_port) - - 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(params) - - logging.info("About to create model") - model = get_model(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) - - optimizer = ScaledAdam( - get_parameter_groups_with_lrs(model, lr=params.base_lr, include_names=True), - lr=params.base_lr, # should have no effect - clipping_scale=2.0, - ) - - scheduler = Eden( - optimizer, - params.lr_batches, - params.lr_epochs, - params.warmup_batches, - params.warmup_start, - ) - - 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( - 512 - ) # allow 4 megabytes per sub-module - diagnostic = diagnostics.attach_diagnostics(model, opts) - - if params.inf_check: - register_inf_check_hooks(model) - - librispeech = LibriSpeechDataModule(args) - - train_cuts = ( - librispeech.train_all_shuf_cuts() - if params.full_libri - else librispeech.train_clean_100_cuts() - ) - - 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 < params.min_keep_size / params.sample_rate - or c.duration > params.max_keep_size / params.sample_rate - ): - logging.warning( - f"Exclude cut with ID {c.id} from training. Duration: {c.duration}" - ) - return False - - return True - - train_cuts = train_cuts.filter(remove_short_and_long_utt) - - 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 = librispeech.train_dataloaders( - train_cuts, - max_sample_size=params.max_sample_size, - sample_rate=params.sample_rate, - label_rate=params.label_rate, - random_crop=params.random_crop, - pad_audio=False, - num_classes=params.num_classes, - do_normalize=params.do_normalize, - sampler_state_dict=sampler_state_dict, - ) - - valid_cuts = librispeech.dev_clean_cuts() - # valid_cuts += librispeech.dev_other_cuts() - valid_cuts = valid_cuts.filter(remove_short_and_long_utt) - - valid_dl = librispeech.valid_dataloaders( - valid_cuts, - max_sample_size=params.max_sample_size, - sample_rate=params.sample_rate, - label_rate=params.label_rate, - random_crop=params.random_crop, - pad_audio=False, - num_classes=params.num_classes, - do_normalize=params.do_normalize, - ) - - if params.sanity_check and not params.print_diagnostics: - scan_pessimistic_batches_for_oom( - model=model, - train_dl=train_dl, - optimizer=optimizer, - 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"]) - - 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 `dataset.HubertDataset()` - for the content in it. - params: - Parameters for training. See :func:`get_params`. - sp: - The BPE model. - """ - from lhotse.utils import uuid4 - - filename = f"{params.exp_dir}/batch-{uuid4()}.pt" - logging.info(f"Saving batch to {filename}") - torch.save(batch, filename) - - audio = batch["audio"] - logging.info(f"audio shape: {audio.shape}") - - -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() - LibriSpeechDataModule.add_arguments(parser) - args = parser.parse_args() - args.exp_dir = Path(args.exp_dir) - - world_size = args.world_size - assert world_size >= 1 - if world_size > 1: - mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True) - else: - run(rank=0, world_size=1, args=args) - - -torch.set_num_threads(1) -torch.set_num_interop_threads(1) - -if __name__ == "__main__": - main() From 7a8bc65fe2068152a6eb2b94b0fea187bc388587 Mon Sep 17 00:00:00 2001 From: RedSheep Date: Wed, 12 Mar 2025 14:25:09 +0800 Subject: [PATCH 4/5] updated Implementation --- egs/librispeech/ASR/chech_version.py | 11 ----------- .../ASR/local/compute_fbank_librispeech.py | 10 +++++----- egs/librispeech/ASR/prepare.sh | 8 ++++---- 3 files changed, 9 insertions(+), 20 deletions(-) delete mode 100755 egs/librispeech/ASR/chech_version.py diff --git a/egs/librispeech/ASR/chech_version.py b/egs/librispeech/ASR/chech_version.py deleted file mode 100755 index ba9383d9d..000000000 --- a/egs/librispeech/ASR/chech_version.py +++ /dev/null @@ -1,11 +0,0 @@ -import torch -import torchaudio - -print("PyTorch version:", torch.__version__) -# print("Torchvision version:", torchvision.__version__) -print("Torchaudio version:", torchaudio.__version__) -print("CUDA available:", torch.cuda.is_available()) -if torch.cuda.is_available(): - print("CUDA version:", torch.version.cuda) - print("cuDNN version:", torch.backends.cudnn.version()) - print("GPU name:", torch.cuda.get_device_name(0)) diff --git a/egs/librispeech/ASR/local/compute_fbank_librispeech.py b/egs/librispeech/ASR/local/compute_fbank_librispeech.py index b41ef8771..ce29ddfdd 100755 --- a/egs/librispeech/ASR/local/compute_fbank_librispeech.py +++ b/egs/librispeech/ASR/local/compute_fbank_librispeech.py @@ -149,18 +149,18 @@ def compute_fbank_librispeech( + cut_set.perturb_speed(0.9) + cut_set.perturb_speed(1.1) ) - # 修改這部分代碼 + if ex is None: - # 為 None 的情況（本地執行）創建自定義進程池上下文 + # Create a custom process pool context for None (local execution) import multiprocessing as mp from concurrent.futures import ProcessPoolExecutor - # 計算工作數 + # Calculate the number of jobs actual_jobs = ( min(num_jobs * 2, 20) if "train" in partition else num_jobs ) - # 使用 forkserver 方法 + # Use the forkserver method ctx = mp.get_context("forkserver") with ProcessPoolExecutor( max_workers=actual_jobs, mp_context=ctx @@ -172,7 +172,7 @@ def compute_fbank_librispeech( storage_type=LilcomChunkyWriter, ) else: - # 分佈式環境，使用提供的執行器 + # Distributed environment, use the provided executor cut_set = cut_set.compute_and_store_features( extractor=extractor, storage_path=f"{output_dir}/{prefix}_feats_{partition}", diff --git a/egs/librispeech/ASR/prepare.sh b/egs/librispeech/ASR/prepare.sh index 45d5a26c2..81f2eac1d 100755 --- a/egs/librispeech/ASR/prepare.sh +++ b/egs/librispeech/ASR/prepare.sh @@ -99,9 +99,9 @@ if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then # # ln -sfv /path/to/LibriSpeech $dl_dir/LibriSpeech # - #if [ ! -d $dl_dir/LibriSpeech/train-other-500 ]; then - # lhotse download librispeech --full $dl_dir - #fi + if [ ! -d $dl_dir/LibriSpeech/train-other-500 ]; then + lhotse download librispeech --full $dl_dir + fi # If you have pre-downloaded it to /path/to/musan, # you can create a symlink @@ -139,7 +139,7 @@ if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then log "Stage 3: Compute fbank for librispeech" mkdir -p data/fbank if [ ! -e data/fbank/.librispeech.done ]; then - ./local/compute_fbank_librispeech.py --num-workers 4 + ./local/compute_fbank_librispeech.py --num-workers $nj touch data/fbank/.librispeech.done fi From 1775f36a612b4bbc351077d51c4e40540678d61f Mon Sep 17 00:00:00 2001 From: RedSheep Date: Tue, 18 Mar 2025 14:10:47 +0800 Subject: [PATCH 5/5] feat(logging): Add .log extension to log filenames --- icefall/utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/icefall/utils.py b/icefall/utils.py index aab479e56..6702508f2 100644 --- a/icefall/utils.py +++ b/icefall/utils.py @@ -136,10 +136,10 @@ def setup_logger( world_size = dist.get_world_size() rank = dist.get_rank() formatter = f"%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] ({rank}/{world_size}) %(message)s" # noqa - log_filename = f"{log_filename}-{date_time}-{rank}" + log_filename = f"{log_filename}-{date_time}-{rank}.log" else: formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" - log_filename = f"{log_filename}-{date_time}" + log_filename = f"{log_filename}-{date_time}.log" os.makedirs(os.path.dirname(log_filename), exist_ok=True)