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https://github.com/k2-fsa/icefall.git
synced 2025-08-09 10:02:22 +00:00
remove bilingual tag from train.py
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Bailey Hirota
parent
b2df5bbb83
commit
7ef1811063
@ -25,7 +25,6 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
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# For non-streaming model training:
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./zipformer/train.py \
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--bilingual 1 \
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--world-size 4 \
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--num-epochs 30 \
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--start-epoch 1 \
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@ -35,7 +34,6 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
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# For streaming model training:
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./zipformer/train.py \
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--bilingual 1 \
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--world-size 4 \
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--num-epochs 30 \
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--start-epoch 1 \
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@ -50,6 +48,7 @@ It supports training with:
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- transducer loss & ctc loss, with `--use-transducer True --use-ctc True`
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"""
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import argparse
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import copy
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import logging
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@ -77,7 +76,6 @@ from multi_dataset import MultiDataset
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from optim import Eden, ScaledAdam
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from scaling import ScheduledFloat
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from subsampling import Conv2dSubsampling
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from tokenizer import Tokenizer
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from torch import Tensor
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from torch.cuda.amp import GradScaler
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from torch.nn.parallel import DistributedDataParallel as DDP
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@ -269,13 +267,6 @@ def get_parser():
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formatter_class=argparse.ArgumentDefaultsHelpFormatter
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)
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parser.add_argument(
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"--bilingual",
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type=str2bool,
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default=False,
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help="Whether the model is bilingual or not. 1 = bilingual.",
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)
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parser.add_argument(
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"--world-size",
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type=int,
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@ -333,7 +324,6 @@ def get_parser():
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""",
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)
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# changed - not used in monolingual streaming
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parser.add_argument(
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"--bpe-model",
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type=str,
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@ -763,11 +753,9 @@ def save_checkpoint(
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copyfile(src=filename, dst=best_valid_filename)
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# fix implementation for sentencepiece_processor: spm.SentencePieceProcessor, stuff
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def compute_loss(
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params: AttributeDict,
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model: Union[nn.Module, DDP],
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tokenizer: Tokenizer,
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sentencepiece_processor: spm.SentencePieceProcessor,
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batch: dict,
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is_training: bool,
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@ -803,9 +791,6 @@ def compute_loss(
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warm_step = params.warm_step
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texts = batch["supervisions"]["text"]
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if not params.bilingual:
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y = tokenizer.encode(texts, out_type=int)
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else:
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y = sentencepiece_processor.encode(texts, out_type=int)
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y = k2.RaggedTensor(y)
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@ -862,7 +847,6 @@ def compute_loss(
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def compute_validation_loss(
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params: AttributeDict,
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model: Union[nn.Module, DDP],
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tokenizer: Tokenizer,
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sentencepiece_processor: spm.SentencePieceProcessor,
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valid_dl: torch.utils.data.DataLoader,
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world_size: int = 1,
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@ -876,7 +860,6 @@ def compute_validation_loss(
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loss, loss_info = compute_loss(
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params=params,
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model=model,
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tokenizer=tokenizer,
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sentencepiece_processor=sentencepiece_processor,
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batch=batch,
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is_training=False,
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@ -900,7 +883,6 @@ def train_one_epoch(
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model: Union[nn.Module, DDP],
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optimizer: torch.optim.Optimizer,
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scheduler: LRSchedulerType,
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tokenizer: Tokenizer,
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sentencepiece_processor: spm.SentencePieceProcessor,
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train_dl: torch.utils.data.DataLoader,
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valid_dl: torch.utils.data.DataLoader,
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@ -972,7 +954,6 @@ def train_one_epoch(
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loss, loss_info = compute_loss(
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params=params,
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model=model,
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tokenizer=tokenizer,
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sentencepiece_processor=sentencepiece_processor,
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batch=batch,
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is_training=True,
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@ -993,7 +974,6 @@ def train_one_epoch(
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display_and_save_batch(
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batch,
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params=params,
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tokenizer=tokenizer,
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sentencepiece_processor=sentencepiece_processor,
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)
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raise
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@ -1082,7 +1062,6 @@ def train_one_epoch(
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valid_info = compute_validation_loss(
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params=params,
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model=model,
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tokenizer=tokenizer,
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sentencepiece_processor=sentencepiece_processor,
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valid_dl=valid_dl,
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world_size=world_size,
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@ -1136,25 +1115,12 @@ def run(rank, world_size, args):
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device = torch.device("cuda", rank)
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logging.info(f"Device: {device}")
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# Use lang_dir for further operations
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# tokenizer = Tokenizer.load(args.lang, args.lang_type)
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# sentencepiece_processor = spm.SentencePieceProcessor()
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# sentencepiece_processor.load(params.bpe_model)
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tokenizer = None
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sentencepiece_processor = None
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sentencepiece_processor = spm.SentencePieceProcessor()
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sentencepiece_processor.load(params.bpe_model)
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# <blk> is defined in local/prepare_lang_char.py
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if not params.bilingual:
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tokenizer = Tokenizer.load(args.lang, args.lang_type)
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params.blank_id = tokenizer.piece_to_id("<blk>")
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params.vocab_size = tokenizer.get_piece_size()
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else:
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sentencepiece_processor = spm.SentencePieceProcessor()
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sentencepiece_processor.load(params.bpe_model)
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params.blank_id = sentencepiece_processor.piece_to_id("<blk>")
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params.vocab_size = sentencepiece_processor.get_piece_size()
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params.blank_id = sentencepiece_processor.piece_to_id("<blk>")
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arams.vocab_size = sentencepiece_processor.get_piece_size()
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if not params.use_transducer:
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params.ctc_loss_scale = 1.0
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@ -1213,26 +1179,25 @@ def run(rank, world_size, args):
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register_inf_check_hooks(model)
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reazonspeech_corpus = ReazonSpeechAsrDataModule(args)
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if params.bilingual:
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multi_dataset = MultiDataset(args)
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train_cuts = multi_dataset.train_cuts()
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else:
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train_cuts = reazonspeech_corpus.train_cuts()
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multi_dataset = MultiDataset(args)
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train_cuts = multi_dataset.train_cuts()
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def remove_short_and_long_utt(c: Cut):
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# Keep only utterances with duration between 1 second and 20 seconds
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# Keep only utterances with duration between 1 second and 30 seconds
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#
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# Caution: There is a reason to select 20.0 here. Please see
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# Caution: There is a reason to select 30.0 here. Please see
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# ../local/display_manifest_statistics.py
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#
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# You should use ../local/display_manifest_statistics.py to get
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# an utterance duration distribution for your dataset to select
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# the threshold
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# if c.duration < 1.0 or c.duration > 30.0:
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# logging.warning(
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# f"Exclude cut with ID {c.id} from training. Duration: {c.duration}"
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# )
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# return False
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if c.duration < 1.0 or c.duration > 30.0:
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logging.warning(
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f"Exclude cut with ID {c.id} from training. Duration: {c.duration}"
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)
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return False
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# In pruned RNN-T, we require that T >= S
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# where T is the number of feature frames after subsampling
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@ -1240,18 +1205,13 @@ def run(rank, world_size, args):
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# In ./zipformer.py, the conv module uses the following expression
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# for subsampling
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T = ((c.num_samples - 7) // 2 + 1) // 2
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if not params.bilingual:
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tokens = tokenizer.encode(c.supervisions[0].text, out_type=str)
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else:
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tokens = sentencepiece_processor.encode(
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c.supervisions[0].text, out_type=str
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)
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T = ((c.num_frames - 7) // 2 + 1) // 2
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tokens = sentencepiece_processor.encode(c.supervisions[0].text, out_type=str)
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if T < len(tokens):
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logging.warning(
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f"Exclude cut with ID {c.id} from training. "
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f"Number of frames (before subsampling): {c.num_samples}. "
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f"Number of frames (before subsampling): {c.num_frames}. "
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f"Number of frames (after subsampling): {T}. "
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f"Text: {c.supervisions[0].text}. "
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f"Tokens: {tokens}. "
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@ -1270,8 +1230,7 @@ def run(rank, world_size, args):
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train_cuts = train_cuts.filter(remove_short_and_long_utt)
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if params.bilingual:
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train_cuts = train_cuts.map(tokenize_and_encode_text)
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train_cuts = train_cuts.map(tokenize_and_encode_text)
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if params.start_batch > 0 and checkpoints and "sampler" in checkpoints:
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# We only load the sampler's state dict when it loads a checkpoint
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@ -1284,10 +1243,7 @@ def run(rank, world_size, args):
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train_cuts, sampler_state_dict=sampler_state_dict
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)
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if params.bilingual:
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valid_cuts = reazonspeech_corpus.valid_cuts()
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else:
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valid_cuts = multi_dataset.dev_cuts()
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valid_cuts = multi_dataset.dev_cuts()
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valid_dl = reazonspeech_corpus.valid_dataloaders(valid_cuts)
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if not params.print_diagnostics:
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@ -1295,7 +1251,6 @@ def run(rank, world_size, args):
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model=model,
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train_dl=train_dl,
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optimizer=optimizer,
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tokenizer=tokenizer,
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sentencepiece_processor=sentencepiece_processor,
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params=params,
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)
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@ -1321,7 +1276,6 @@ def run(rank, world_size, args):
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model_avg=model_avg,
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optimizer=optimizer,
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scheduler=scheduler,
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tokenizer=tokenizer,
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sentencepiece_processor=sentencepiece_processor,
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train_dl=train_dl,
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valid_dl=valid_dl,
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@ -1356,7 +1310,6 @@ def run(rank, world_size, args):
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def display_and_save_batch(
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batch: dict,
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params: AttributeDict,
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tokenizer: Tokenizer,
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sentencepiece_processor: spm.SentencePieceProcessor,
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) -> None:
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"""Display the batch statistics and save the batch into disk.
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@ -1367,10 +1320,8 @@ def display_and_save_batch(
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for the content in it.
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params:
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Parameters for training. See :func:`get_params`.
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tokenizer:
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The BPE Tokenizer model.
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sentencepiece_processor:
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The BPE SentencePieceProcessor model.
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The BPE model.
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"""
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from lhotse.utils import uuid4
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@ -1382,11 +1333,7 @@ def display_and_save_batch(
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features = batch["inputs"]
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logging.info(f"features shape: {features.shape}")
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if params.bilingual:
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y = sentencepiece_processor.encode(supervisions["text"], out_type=int)
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else:
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y = tokenizer.encode(supervisions["text"], out_type=int)
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y = sentencepiece_processor.encode(supervisions["text"], out_type=int)
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num_tokens = sum(len(i) for i in y)
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logging.info(f"num tokens: {num_tokens}")
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@ -1395,7 +1342,6 @@ def scan_pessimistic_batches_for_oom(
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model: Union[nn.Module, DDP],
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train_dl: torch.utils.data.DataLoader,
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optimizer: torch.optim.Optimizer,
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tokenizer: Tokenizer,
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sentencepiece_processor: spm.SentencePieceProcessor,
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params: AttributeDict,
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):
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@ -1412,7 +1358,6 @@ def scan_pessimistic_batches_for_oom(
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loss, _ = compute_loss(
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params=params,
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model=model,
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tokenizer=tokenizer,
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sentencepiece_processor=sentencepiece_processor,
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batch=batch,
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is_training=True,
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@ -1431,7 +1376,6 @@ def scan_pessimistic_batches_for_oom(
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display_and_save_batch(
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batch,
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params=params,
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tokenizer=tokenizer,
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sentencepiece_processor=sentencepiece_processor,
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)
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raise
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@ -1443,7 +1387,6 @@ def scan_pessimistic_batches_for_oom(
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def main():
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parser = get_parser()
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ReazonSpeechAsrDataModule.add_arguments(parser)
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Tokenizer.add_arguments(parser)
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args = parser.parse_args()
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args.exp_dir = Path(args.exp_dir)
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