+
+ +
+

Zipformer MMI

+
+

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 an Zipformer MMI model +with the LibriSpeech dataset.

+

We use LF-MMI to compute the loss.

+
+

Note

+

You can find the document about LF-MMI training at the following address:

+

https://github.com/k2-fsa/next-gen-kaldi-wechat/blob/master/pdf/LF-MMI-training-and-decoding-in-k2-Part-I.pdf

+
+
+

Data preparation

+
$ 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,

+
$ 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:

+
$ ./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:

+
+
+
+
+
+
+

Training

+

For stability, it uses CTC loss for model warm-up and then switches to MMI loss.

+
+

Configurable options

+
$ cd egs/librispeech/ASR
+$ ./zipformer_mmi/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, +./zipformer_mmi/train.py --num-epochs 30 trains for 30 epochs +and generates epoch-1.pt, epoch-2.pt, …, epoch-30.pt +in the folder ./zipformer_mmi/exp.

    +
    • +

  • --start-epoch

    +

    It’s used to resume training. +./zipformer_mmi/train.py --start-epoch 10 loads the +checkpoint ./zipformer_mmi/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.

    +
    +
      +
      • +

      1. If it is 1, then no DDP training is used.

        2. +
      +
      • +
      • +

      1. If it is 2, then GPU 0 and GPU 1 are used for DDP training.

        2. +
      +
      • +
    +
    +

    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:

    +
    +
    $ cd egs/librispeech/ASR
+$ export CUDA_VISIBLE_DEVICES="0,2"
+$ ./zipformer_mmi/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:

    +
    +
    $ cd egs/librispeech/ASR
+$ ./zipformer_mmi/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:

    +
    +
    $ cd egs/librispeech/ASR
+$ export CUDA_VISIBLE_DEVICES="3"
+$ ./zipformer_mmi/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 +zipformer_mmi/train.py

+

You don’t need to change these pre-configured parameters. If you really need to change +them, please modify ./zipformer_mmi/train.py directly.

+
+
+

Training logs

+

Training logs and checkpoints are saved in zipformer_mmi/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:

    +
    +
    $ ./zipformer_mmi/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:

    +
    +
    $ ./zipformer_mmi/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:

    +
    +
    $ cd zipformer_mmi/exp/tensorboard
+$ tensorboard dev upload --logdir . --description "Zipformer MMI training for LibriSpeech with icefall"
+
    +
    +
    +

    It will print something like below:

    +
    +
    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:

+
+
cd zipformer_mmi/exp/tensorboard
+tensorboard --logdir . --port 6008
+
+
+
+

It will print the following message:

+
+
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 http://localhost:6008 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 8 GPUs:

+
export CUDA_VISIBLE_DEVICES="0,1,2,3"
+./zipformer_mmi/train.py \
+  --world-size 4 \
+  --num-epochs 30 \
+  --start-epoch 1 \
+  --full-libri 1 \
+  --exp-dir zipformer_mmi/exp \
+  --max-duration 500 \
+  --use-fp16 1 \
+  --num-workers 2
+
+
+
+
+
+

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 +zipformer_mmi/decode.py to use them.

    • +

  • (2) checkpoints-436000.pt, epoch-438000.pt, …, which are saved +every --save-every-n batches. You can pass --iter to +zipformer_mmi/decode.py to use them.

    • +
+

We suggest that you try both types of checkpoints and choose the one +that produces the lowest WERs.

+
+
+
$ cd egs/librispeech/ASR
+$ ./zipformer_mmi/decode.py --help
+
+
+

shows the options for decoding.

+

The following shows the example using epoch-*.pt:

+
for m in nbest nbest-rescoring-LG nbest-rescoring-3-gram nbest-rescoring-4-gram; do
+  ./zipformer_mmi/decode.py \
+    --epoch 30 \
+    --avg 10 \
+    --exp-dir ./zipformer_mmi/exp/ \
+    --max-duration 100 \
+    --lang-dir data/lang_bpe_500 \
+    --nbest-scale 1.2 \
+    --hp-scale 1.0 \
+    --decoding-method $m
+done
+
+
+
+
+

Export models

+

zipformer_mmi/export.py supports exporting checkpoints from zipformer_mmi/exp in the following ways.

+
+

Export model.state_dict()

+

Checkpoints saved by zipformer_mmi/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().

+
./zipformer_mmi/export.py \
+  --exp-dir ./zipformer_mmi/exp \
+  --bpe-model data/lang_bpe_500/bpe.model \
+  --epoch 30 \
+  --avg 9 \
+  --jit 0
+
+
+

It will generate a file ./zipformer_mmi/exp/pretrained.pt.

+
+

Hint

+

To use the generated pretrained.pt for zipformer_mmi/decode.py, +you can run:

+
cd zipformer_mmi/exp
+ln -s pretrained epoch-9999.pt
+
+
+

And then pass --epoch 9999 --avg 1 --use-averaged-model 0 to +./zipformer_mmi/decode.py.

+
+

To use the exported model with ./zipformer_mmi/pretrained.py, you +can run:

+
./zipformer_mmi/pretrained.py \
+  --checkpoint ./zipformer_mmi/exp/pretrained.pt \
+  --bpe-model ./data/lang_bpe_500/bpe.model \
+  --method 1best \
+  /path/to/foo.wav \
+  /path/to/bar.wav
+
+
+
+
+

Export model using torch.jit.script()

+
./zipformer_mmi/export.py \
+  --exp-dir ./zipformer_mmi/exp \
+  --bpe-model data/lang_bpe_500/bpe.model \
+  --epoch 30 \
+  --avg 9 \
+  --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 ./zipformer_mmi/jit_pretrained.py:

+
./zipformer_mmi/jit_pretrained.py \
+  --nn-model-filename ./zipformer_mmi/exp/cpu_jit.pt \
+  --bpe-model ./data/lang_bpe_500/bpe.model \
+  --method 1best \
+  /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:

+
+
+
+
+ + +
+