Zipformer CTC Blank Skip ======================== .. hint:: Please scroll down to the bottom of this page to find download links for pretrained models if you don't want to train a model from scratch. This tutorial shows you how to train a Zipformer model based on the guidance from a co-trained CTC model using `blank skip method `_ with the `LibriSpeech `_ dataset. .. note:: We use both CTC and RNN-T loss to train. During the forward pass, the encoder output is first used to calculate the CTC posterior probability; then for each output frame, if its blank posterior is bigger than some threshold, it will be simply discarded from the encoder output. To prevent information loss, we also put a convolution module similar to the one used in conformer (referred to as “LConv”) before the frame reduction. Data preparation ---------------- .. code-block:: bash $ cd egs/librispeech/ASR $ ./prepare.sh The script ``./prepare.sh`` handles the data preparation for you, **automagically**. All you need to do is to run it. .. note:: We encourage you to read ``./prepare.sh``. The data preparation contains several stages. You can use the following two options: - ``--stage`` - ``--stop-stage`` to control which stage(s) should be run. By default, all stages are executed. For example, .. code-block:: bash $ cd egs/librispeech/ASR $ ./prepare.sh --stage 0 --stop-stage 0 means to run only stage 0. To run stage 2 to stage 5, use: .. code-block:: bash $ ./prepare.sh --stage 2 --stop-stage 5 .. hint:: If you have pre-downloaded the `LibriSpeech `_ dataset and the `musan `_ dataset, say, they are saved in ``/tmp/LibriSpeech`` and ``/tmp/musan``, you can modify the ``dl_dir`` variable in ``./prepare.sh`` to point to ``/tmp`` so that ``./prepare.sh`` won't re-download them. .. note:: All generated files by ``./prepare.sh``, e.g., features, lexicon, etc, are saved in ``./data`` directory. We provide the following YouTube video showing how to run ``./prepare.sh``. .. note:: To get the latest news of `next-gen Kaldi `_, please subscribe the following YouTube channel by `Nadira Povey `_: ``_ .. youtube:: ofEIoJL-mGM Training -------- For stability, it doesn`t use blank skip method until model warm-up. Configurable options ~~~~~~~~~~~~~~~~~~~~ .. code-block:: bash $ cd egs/librispeech/ASR $ ./pruned_transducer_stateless7_ctc_bs/train.py --help shows you the training options that can be passed from the commandline. The following options are used quite often: - ``--full-libri`` If it's True, the training part uses all the training data, i.e., 960 hours. Otherwise, the training part uses only the subset ``train-clean-100``, which has 100 hours of training data. .. CAUTION:: The training set is perturbed by speed with two factors: 0.9 and 1.1. If ``--full-libri`` is True, each epoch actually processes ``3x960 == 2880`` hours of data. - ``--num-epochs`` It is the number of epochs to train. For instance, ``./pruned_transducer_stateless7_ctc_bs/train.py --num-epochs 30`` trains for 30 epochs and generates ``epoch-1.pt``, ``epoch-2.pt``, ..., ``epoch-30.pt`` in the folder ``./pruned_transducer_stateless7_ctc_bs/exp``. - ``--start-epoch`` It's used to resume training. ``./pruned_transducer_stateless7_ctc_bs/train.py --start-epoch 10`` loads the checkpoint ``./pruned_transducer_stateless7_ctc_bs/exp/epoch-9.pt`` and starts training from epoch 10, based on the state from epoch 9. - ``--world-size`` It is used for multi-GPU single-machine DDP training. - (a) If it is 1, then no DDP training is used. - (b) If it is 2, then GPU 0 and GPU 1 are used for DDP training. The following shows some use cases with it. **Use case 1**: You have 4 GPUs, but you only want to use GPU 0 and GPU 2 for training. You can do the following: .. code-block:: bash $ cd egs/librispeech/ASR $ export CUDA_VISIBLE_DEVICES="0,2" $ ./pruned_transducer_stateless7_ctc_bs/train.py --world-size 2 **Use case 2**: You have 4 GPUs and you want to use all of them for training. You can do the following: .. code-block:: bash $ cd egs/librispeech/ASR $ ./pruned_transducer_stateless7_ctc_bs/train.py --world-size 4 **Use case 3**: You have 4 GPUs but you only want to use GPU 3 for training. You can do the following: .. code-block:: bash $ cd egs/librispeech/ASR $ export CUDA_VISIBLE_DEVICES="3" $ ./pruned_transducer_stateless7_ctc_bs/train.py --world-size 1 .. caution:: Only multi-GPU single-machine DDP training is implemented at present. Multi-GPU multi-machine DDP training will be added later. - ``--max-duration`` It specifies the number of seconds over all utterances in a batch, before **padding**. If you encounter CUDA OOM, please reduce it. .. HINT:: Due to padding, the number of seconds of all utterances in a batch will usually be larger than ``--max-duration``. A larger value for ``--max-duration`` may cause OOM during training, while a smaller value may increase the training time. You have to tune it. Pre-configured options ~~~~~~~~~~~~~~~~~~~~~~ There are some training options, e.g., weight decay, number of warmup steps, results dir, etc, that are not passed from the commandline. They are pre-configured by the function ``get_params()`` in `pruned_transducer_stateless7_ctc_bs/train.py `_ You don't need to change these pre-configured parameters. If you really need to change them, please modify ``./pruned_transducer_stateless7_ctc_bs/train.py`` directly. Training logs ~~~~~~~~~~~~~ Training logs and checkpoints are saved in ``pruned_transducer_stateless7_ctc_bs/exp``. You will find the following files in that directory: - ``epoch-1.pt``, ``epoch-2.pt``, ... These are checkpoint files saved at the end of each epoch, containing model ``state_dict`` and optimizer ``state_dict``. To resume training from some checkpoint, say ``epoch-10.pt``, you can use: .. code-block:: bash $ ./pruned_transducer_stateless7_ctc_bs/train.py --start-epoch 11 - ``checkpoint-436000.pt``, ``checkpoint-438000.pt``, ... These are checkpoint files saved every ``--save-every-n`` batches, containing model ``state_dict`` and optimizer ``state_dict``. To resume training from some checkpoint, say ``checkpoint-436000``, you can use: .. code-block:: bash $ ./pruned_transducer_stateless7_ctc_bs/train.py --start-batch 436000 - ``tensorboard/`` This folder contains tensorBoard logs. Training loss, validation loss, learning rate, etc, are recorded in these logs. You can visualize them by: .. code-block:: bash $ cd pruned_transducer_stateless7_ctc_bs/exp/tensorboard $ tensorboard dev upload --logdir . --description "Zipformer-CTC co-training using blank skip for LibriSpeech with icefall" It will print something like below: .. code-block:: TensorFlow installation not found - running with reduced feature set. Upload started and will continue reading any new data as it's added to the logdir. To stop uploading, press Ctrl-C. New experiment created. View your TensorBoard at: https://tensorboard.dev/experiment/xyOZUKpEQm62HBIlUD4uPA/ Note there is a URL in the above output. Click it and you will see tensorboard. .. hint:: If you don't have access to google, you can use the following command to view the tensorboard log locally: .. code-block:: bash cd pruned_transducer_stateless7_ctc_bs/exp/tensorboard tensorboard --logdir . --port 6008 It will print the following message: .. code-block:: Serving TensorBoard on localhost; to expose to the network, use a proxy or pass --bind_all TensorBoard 2.8.0 at http://localhost:6008/ (Press CTRL+C to quit) Now start your browser and go to ``_ to view the tensorboard logs. - ``log/log-train-xxxx`` It is the detailed training log in text format, same as the one you saw printed to the console during training. Usage example ~~~~~~~~~~~~~ You can use the following command to start the training using 4 GPUs: .. code-block:: bash export CUDA_VISIBLE_DEVICES="0,1,2,3" ./pruned_transducer_stateless7_ctc_bs/train.py \ --world-size 4 \ --num-epochs 30 \ --start-epoch 1 \ --full-libri 1 \ --exp-dir pruned_transducer_stateless7_ctc_bs/exp \ --max-duration 600 \ --use-fp16 1 Decoding -------- The decoding part uses checkpoints saved by the training part, so you have to run the training part first. .. hint:: There are two kinds of checkpoints: - (1) ``epoch-1.pt``, ``epoch-2.pt``, ..., which are saved at the end of each epoch. You can pass ``--epoch`` to ``pruned_transducer_stateless7_ctc_bs/ctc_guide_decode_bs.py`` to use them. - (2) ``checkpoints-436000.pt``, ``epoch-438000.pt``, ..., which are saved every ``--save-every-n`` batches. You can pass ``--iter`` to ``pruned_transducer_stateless7_ctc_bs/ctc_guide_decode_bs.py`` to use them. We suggest that you try both types of checkpoints and choose the one that produces the lowest WERs. .. code-block:: bash $ cd egs/librispeech/ASR $ ./pruned_transducer_stateless7_ctc_bs/ctc_guide_decode_bs.py --help shows the options for decoding. The following shows the example using ``epoch-*.pt``: .. code-block:: bash for m in greedy_search fast_beam_search modified_beam_search; do ./pruned_transducer_stateless7_ctc_bs/ctc_guide_decode_bs.py \ --epoch 30 \ --avg 13 \ --exp-dir pruned_transducer_stateless7_ctc_bs/exp \ --max-duration 600 \ --decoding-method $m done To test CTC branch, you can use the following command: .. code-block:: bash for m in ctc-decoding 1best; do ./pruned_transducer_stateless7_ctc_bs/ctc_guide_decode_bs.py \ --epoch 30 \ --avg 13 \ --exp-dir pruned_transducer_stateless7_ctc_bs/exp \ --max-duration 600 \ --decoding-method $m done Export models ------------- `pruned_transducer_stateless7_ctc_bs/export.py `_ supports exporting checkpoints from ``pruned_transducer_stateless7_ctc_bs/exp`` in the following ways. Export ``model.state_dict()`` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Checkpoints saved by ``pruned_transducer_stateless7_ctc_bs/train.py`` also include ``optimizer.state_dict()``. It is useful for resuming training. But after training, we are interested only in ``model.state_dict()``. You can use the following command to extract ``model.state_dict()``. .. code-block:: bash ./pruned_transducer_stateless7_ctc_bs/export.py \ --exp-dir ./pruned_transducer_stateless7_ctc_bs/exp \ --bpe-model data/lang_bpe_500/bpe.model \ --epoch 30 \ --avg 13 \ --jit 0 It will generate a file ``./pruned_transducer_stateless7_ctc_bs/exp/pretrained.pt``. .. hint:: To use the generated ``pretrained.pt`` for ``pruned_transducer_stateless7_ctc_bs/ctc_guide_decode_bs.py``, you can run: .. code-block:: bash cd pruned_transducer_stateless7_ctc_bs/exp ln -s pretrained epoch-9999.pt And then pass ``--epoch 9999 --avg 1 --use-averaged-model 0`` to ``./pruned_transducer_stateless7_ctc_bs/ctc_guide_decode_bs.py``. To use the exported model with ``./pruned_transducer_stateless7_ctc_bs/pretrained.py``, you can run: .. code-block:: bash ./pruned_transducer_stateless7_ctc_bs/pretrained.py \ --checkpoint ./pruned_transducer_stateless7_ctc_bs/exp/pretrained.pt \ --bpe-model ./data/lang_bpe_500/bpe.model \ --method greedy_search \ /path/to/foo.wav \ /path/to/bar.wav To test CTC branch using the exported model with ``./pruned_transducer_stateless7_ctc_bs/pretrained_ctc.py``: .. code-block:: bash ./pruned_transducer_stateless7_ctc_bs/jit_pretrained_ctc.py \ --checkpoint ./pruned_transducer_stateless7_ctc_bs/exp/pretrained.pt \ --bpe-model data/lang_bpe_500/bpe.model \ --method ctc-decoding \ --sample-rate 16000 \ /path/to/foo.wav \ /path/to/bar.wav Export model using ``torch.jit.script()`` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. code-block:: bash ./pruned_transducer_stateless7_ctc_bs/export.py \ --exp-dir ./pruned_transducer_stateless7_ctc_bs/exp \ --bpe-model data/lang_bpe_500/bpe.model \ --epoch 30 \ --avg 13 \ --jit 1 It will generate a file ``cpu_jit.pt`` in the given ``exp_dir``. You can later load it by ``torch.jit.load("cpu_jit.pt")``. Note ``cpu`` in the name ``cpu_jit.pt`` means the parameters when loaded into Python are on CPU. You can use ``to("cuda")`` to move them to a CUDA device. To use the generated files with ``./pruned_transducer_stateless7_ctc_bs/jit_pretrained.py``: .. code-block:: bash ./pruned_transducer_stateless7_ctc_bs/jit_pretrained.py \ --nn-model-filename ./pruned_transducer_stateless7_ctc_bs/exp/cpu_jit.pt \ /path/to/foo.wav \ /path/to/bar.wav To test CTC branch using the generated files with ``./pruned_transducer_stateless7_ctc_bs/jit_pretrained_ctc.py``: .. code-block:: bash ./pruned_transducer_stateless7_ctc_bs/jit_pretrained_ctc.py \ --model-filename ./pruned_transducer_stateless7_ctc_bs/exp/cpu_jit.pt \ --bpe-model data/lang_bpe_500/bpe.model \ --method ctc-decoding \ --sample-rate 16000 \ /path/to/foo.wav \ /path/to/bar.wav Download pretrained models -------------------------- If you don't want to train from scratch, you can download the pretrained models by visiting the following links: - trained on LibriSpeech 100h: ``_ - trained on LibriSpeech 960h: ``_ See ``_ for the details of the above pretrained models