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add whisper fbank for wenetspeech
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@ -20,7 +20,7 @@ import logging
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from pathlib import Path
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import torch
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from lhotse import CutSet, KaldifeatFbank, KaldifeatFbankConfig, LilcomChunkyWriter
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from lhotse import CutSet, WhisperFbank, WhisperFbankConfig, KaldifeatFbank, KaldifeatFbankConfig, LilcomChunkyWriter
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# Torch's multithreaded behavior needs to be disabled or
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# it wastes a lot of CPU and slow things down.
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@ -30,8 +30,27 @@ torch.set_num_threads(1)
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torch.set_num_interop_threads(1)
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torch.multiprocessing.set_sharing_strategy("file_system")
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def get_parser():
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter
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)
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def compute_fbank_wenetspeech_dev_test():
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parser.add_argument(
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"--num-mel-bins",
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type=int,
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default=80,
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help="""The number of mel bins for Fbank""",
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)
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parser.add_argument(
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"--whisper-fbank",
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type=str2bool,
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default=False,
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help="Use WhisperFbank instead of Fbank. Default: False.",
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)
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return parser
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def compute_fbank_wenetspeech_dev_test(args):
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in_out_dir = Path("data/fbank")
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# number of workers in dataloader
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num_workers = 42
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@ -44,6 +63,9 @@ def compute_fbank_wenetspeech_dev_test():
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device = torch.device("cpu")
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if torch.cuda.is_available():
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device = torch.device("cuda", 0)
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if args.whisper_fbank:
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extractor = WhisperFbank(WhisperFbankConfig(num_filters=args.num_mel_bins, device='cuda'))
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else:
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extractor = KaldifeatFbank(KaldifeatFbankConfig(device=device))
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logging.info(f"device: {device}")
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@ -82,7 +104,11 @@ def main():
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formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
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logging.basicConfig(format=formatter, level=logging.INFO)
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compute_fbank_wenetspeech_dev_test()
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parser = get_parser()
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args = parser.parse_args()
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logging.info(vars(args))
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compute_fbank_wenetspeech_dev_test(args)
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if __name__ == "__main__":
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@ -24,6 +24,8 @@ from pathlib import Path
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import torch
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from lhotse import (
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CutSet,
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WhisperFbank,
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WhisperFbankConfig,
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KaldifeatFbank,
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KaldifeatFbankConfig,
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LilcomChunkyWriter,
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@ -87,6 +89,20 @@ def get_parser():
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default=-1,
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help="Stop processing pieces until this number (excluded).",
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)
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parser.add_argument(
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"--num-mel-bins",
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type=int,
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default=80,
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help="""The number of mel bins for Fbank""",
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)
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parser.add_argument(
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"--whisper-fbank",
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type=str2bool,
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default=False,
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help="Use WhisperFbank instead of Fbank. Default: False.",
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)
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return parser
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@ -110,6 +126,11 @@ def compute_fbank_wenetspeech_splits(args):
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device = torch.device("cpu")
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if torch.cuda.is_available():
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device = torch.device("cuda", 0)
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if args.whisper_fbank:
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extractor = WhisperFbank(
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WhisperFbankConfig(num_filters=args.num_mel_bins, device=device)
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)
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else:
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extractor = KaldifeatFbank(KaldifeatFbankConfig(device=device))
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logging.info(f"device: {device}")
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@ -182,6 +182,34 @@ if [ $stage -le 13 ] && [ $stop_stage -ge 13 ]; then
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fi
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fi
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whisper_mel_bins=80
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if [ $stage -le 129 ] && [ $stop_stage -ge 129 ]; then
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log "Stage 129: compute whisper fbank for dev and test sets"
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python3 ./local/compute_fbank_wenetspeech_dev_test.py --num-mel-bins ${whisper_mel_bins} --whisper-fbank true
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fi
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if [ $stage -le 130 ] && [ $stop_stage -ge 130 ]; then
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log "Stage 130: Comute features for whisper training set"
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split_dir=data/fbank/L_split_${num_splits}
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if [ ! -f $split_dir/.split_completed ]; then
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lhotse split $num_splits ./data/fbank/cuts_L_raw.jsonl.gz $split_dir
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touch $split_dir/.split_completed
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fi
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python3 ./local/compute_fbank_wenetspeech_splits.py \
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--training-subset L \
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--num-workers 20 \
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--batch-duration 600 \
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--start 0 \
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--num-mel-bins ${whisper_mel_bins} --whisper-fbank true \
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--num-splits $num_splits
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if [ ! -f data/fbank/cuts_L.jsonl.gz ]; then
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pieces=$(find data/fbank/L_split_1000 -name "cuts_L.*.jsonl.gz")
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lhotse combine $pieces data/fbank/cuts_L.jsonl.gz
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fi
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fi
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if [ $stage -le 14 ] && [ $stop_stage -ge 14 ]; then
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log "Stage 14: Compute fbank for musan"
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mkdir -p data/fbank
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