This project implements pruned rnnt for tensorflow with small limitations.

1. supports GPU only.
2. boundary information is mandatory.

It's based on the original fast RNN-T loss implementation in k2 project and following is based from https://github.com/k2-fsa/fast_rnnt

---

This project implements a method for faster and more memory-efficient RNN-T loss computation, called pruned rnnt.

Note: The original fast RNN-T loss implementation is in k2 project and this project is based on https://github.com/k2-fsa/fast_rnnt. We make tf_fast_rnnt a stand-alone project for tensorflow users.

How does the pruned-rnnt work ?

We first obtain pruning bounds for the RNN-T recursion using a simple joiner network that is just an addition of the encoder and decoder, then we use those pruning bounds to evaluate the full, non-linear joiner network.

The picture below display the gradients (obtained by rnnt_loss_simple with calc_gradients=true) of lattice nodes, at each time frame, only a small set of nodes have a non-zero gradient, which justifies the pruned RNN-T loss, i.e., putting a limit on the number of symbols per frame.

This picture is taken from here

Installation

You can install it via pip:

pip install tf_fast_rnnt

You can also install from source:

cd tf_fast_rnnt
pip install .

To check that tf_fast_rnnt was installed successfully, please run

python3 -c "import tf_fast_rnnt; print(tf_fast_rnnt.__version__)"

which should print the version of the installed tf_fast_rnnt, e.g., 1.2.

How to display installation log ?

Use

pip install --verbose tf_fast_rnnt

How to reduce installation time ?

Use

export FT_MAKE_ARGS="-j"
pip install --verbose tf_fast_rnnt

It will pass -j to make.

Which version of tensorflow is supported ?

It has been tested on tensorflow >= 2.9.3.

Note: The cuda version of the tensorflow should be the same as the cuda version in your environment, or it will cause a compilation error.

Usage

For rnnt_loss_simple

This is a simple case of the RNN-T loss, where the joiner network is just addition.

Note: termination_symbol plays the role of blank in other RNN-T loss implementations, we call it termination_symbol as it terminates symbols of current frame.

am = np.random.randn(B, T, C).astype('f')
lm = np.random.randn(B, S + 1, C).astype('f')
symbols = np.random.randint(0, C - 1, (B, S)).astype(np.int32)
terminal_symbol = C - 1

boundary = np.zeros((B, 4))
boundary[:, 2] = seq_length
boundary[:, 3] = frames

simple_loss, (px_grad, py_grad) = tf_fast_rnnt.rnnt_loss_simple(
    lm=lm,
    am=am,
    symbols=symbols,
    termination_symbol=terminal_symbol,
    boundary=boundary,
    rnnt_type=rnnt_type,
    calc_gradients=True,
    reduction="none",
    delay_penalty=0.2,
)

For rnnt_loss_pruned

rnnt_loss_pruned can not be used alone, it needs the gradients returned by rnnt_loss_simple/rnnt_loss_smoothed to get pruning bounds.

am = np.random.randn(B, T, C).astype('f')
lm = np.random.randn(B, S + 1, C).astype('f')
symbols = np.random.randint(0, C - 1, (B, S)).astype(np.int32)
terminal_symbol = C - 1

boundary = np.zeros((B, 4))
boundary[:, 2] = seq_length
boundary[:, 3] = frames

simple_loss, (px_grad, py_grad) = tf_fast_rnnt.rnnt_loss_simple(
    lm=lm,
    am=am,
    symbols=symbols,
    termination_symbol=terminal_symbol,
    boundary=boundary,
    rnnt_type=rnnt_type,
    calc_gradients=True,
    reduction="none",
    delay_penalty=0.2,
)
s_range = 5  # can be other values
ranges = tf_fast_rnnt.get_rnnt_prune_ranges(
    px_grad=px_grad,
    py_grad=py_grad,
    boundary=boundary,
    s_range=s_range,
)

am_pruned, lm_pruned = tf_fast_rnnt.do_rnnt_pruning(am=am, lm=lm, ranges=ranges)

logits = model.joiner(am_pruned, lm_pruned)
pruned_loss = tf_fast_rnnt.rnnt_loss_pruned(
    logits=logits,
    symbols=symbols,
    ranges=ranges,
    termination_symbol=termination_symbol,
    boundary=boundary,
    reduction="sum",
)

You can also find recipes here that uses rnnt_loss_pruned to train a model.

Benchmarking

The repo compares the speed and memory usage of several transducer losses, the summary in the following table is taken from there, you can check the repository for more details.

Note: As we declared above, fast_rnnt is also implemented in k2 project, so k2 and fast_rnnt are equivalent in the benchmarking.

Name	Average step time (us)	Peak memory usage (MB)
torchaudio	601447	12959.2
fast_rnnt(unpruned)	274407	15106.5
fast_rnnt(pruned)	38112	2647.8
optimized_transducer	567684	10903.1
warprnnt_numba	229340	13061.8
warp-transducer	210772	13061.8