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Simplify ONNX export (#881)

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* Simplify ONNX export

* Fix ONNX CI tests
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Fangjun Kuang 2023-02-07 15:01:59 +08:00 committed by GitHub
parent 52f3a747be
commit 8d3810e289
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11 changed files with 874 additions and 637 deletions
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30
.github/scripts/run-librispeech-pruned-transducer-stateless3-2022-05-13.sh vendored
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@ -27,14 +27,6 @@ ln -s pretrained-iter-1224000-avg-14.pt pretrained.pt
ln -s pretrained-iter-1224000-avg-14.pt epoch-99.pt
popd
log "Test exporting to ONNX format"
./pruned_transducer_stateless3/export.py \
--exp-dir $repo/exp \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--epoch 99 \
--avg 1 \
--onnx 1
log "Export to torchscript model"
./pruned_transducer_stateless3/export.py \
@ -51,30 +43,8 @@ log "Export to torchscript model"
--avg 1 \
--jit-trace 1
ls -lh $repo/exp/*.onnx
ls -lh $repo/exp/*.pt
log "Decode with ONNX models"
./pruned_transducer_stateless3/onnx_check.py \
--jit-filename $repo/exp/cpu_jit.pt \
--onnx-encoder-filename $repo/exp/encoder.onnx \
--onnx-decoder-filename $repo/exp/decoder.onnx \
--onnx-joiner-filename $repo/exp/joiner.onnx \
--onnx-joiner-encoder-proj-filename $repo/exp/joiner_encoder_proj.onnx \
--onnx-joiner-decoder-proj-filename $repo/exp/joiner_decoder_proj.onnx
./pruned_transducer_stateless3/onnx_pretrained.py \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--encoder-model-filename $repo/exp/encoder.onnx \
--decoder-model-filename $repo/exp/decoder.onnx \
--joiner-model-filename $repo/exp/joiner.onnx \
--joiner-encoder-proj-model-filename $repo/exp/joiner_encoder_proj.onnx \
--joiner-decoder-proj-model-filename $repo/exp/joiner_decoder_proj.onnx \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav
log "Decode with models exported by torch.jit.trace()"
./pruned_transducer_stateless3/jit_pretrained.py \

56
.github/scripts/test-onnx-export.sh vendored
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@ -10,9 +10,8 @@ log() {
cd egs/librispeech/ASR
repo_url=https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29
log "=========================================================================="
repo_url=https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29
log "Downloading pre-trained model from $repo_url"
git lfs install
GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
@ -68,3 +67,56 @@ log "Run onnx_pretrained.py"
rm -rf $repo
log "--------------------------------------------------------------------------"
log "=========================================================================="
repo_url=https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13
log "Downloading pre-trained model from $repo_url"
git lfs install
GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
repo=$(basename $repo_url)
pushd $repo
git lfs pull --include "data/lang_bpe_500/bpe.model"
git lfs pull --include "exp/pretrained-iter-1224000-avg-14.pt"
cd exp
ln -s pretrained-iter-1224000-avg-14.pt epoch-9999.pt
popd
log "Export via torch.jit.script()"
./pruned_transducer_stateless3/export.py \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--epoch 9999 \
--avg 1 \
--exp-dir $repo/exp/ \
--jit 1
log "Test exporting to ONNX format"
./pruned_transducer_stateless3/export-onnx.py \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--epoch 9999 \
--avg 1 \
--exp-dir $repo/exp/
ls -lh $repo/exp
log "Run onnx_check.py"
./pruned_transducer_stateless3/onnx_check.py \
--jit-filename $repo/exp/cpu_jit.pt \
--onnx-encoder-filename $repo/exp/encoder-epoch-9999-avg-1.onnx \
--onnx-decoder-filename $repo/exp/decoder-epoch-9999-avg-1.onnx \
--onnx-joiner-filename $repo/exp/joiner-epoch-9999-avg-1.onnx
log "Run onnx_pretrained.py"
./pruned_transducer_stateless3/onnx_pretrained.py \
--encoder-model-filename $repo/exp/encoder-epoch-9999-avg-1.onnx \
--decoder-model-filename $repo/exp/decoder-epoch-9999-avg-1.onnx \
--joiner-model-filename $repo/exp/joiner-epoch-9999-avg-1.onnx \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav

2
.github/workflows/run-librispeech-pruned-transducer-stateless3-2022-05-13.yml vendored
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@ -39,7 +39,7 @@ concurrency:
jobs:
run_librispeech_pruned_transducer_stateless3_2022_05_13:
if: github.event.label.name == 'onnx' || github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule'
if: github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule'
runs-on: ${{ matrix.os }}
strategy:
matrix:

2
egs/librispeech/ASR/pruned_transducer_stateless2/joiner.py
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@ -56,8 +56,6 @@ class Joiner(nn.Module):
"""
if not is_jit_tracing():
assert encoder_out.ndim == decoder_out.ndim
assert encoder_out.ndim in (2, 4)
assert encoder_out.shape == decoder_out.shape
if project_input:
logit = self.encoder_proj(encoder_out) + self.decoder_proj(decoder_out)

497
egs/librispeech/ASR/pruned_transducer_stateless3/export-onnx.py Executable file
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@ -0,0 +1,497 @@
#!/usr/bin/env python3
#
# Copyright 2023 Xiaomi Corporation (Author: Fangjun Kuang)
"""
This script exports a transducer model from PyTorch to ONNX.
We use the pre-trained model from
https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29
as an example to show how to use this file.
1. Download the pre-trained model
cd egs/librispeech/ASR
repo_url=https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13
GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
repo=$(basename $repo_url)
pushd $repo
git lfs pull --include "data/lang_bpe_500/bpe.model"
git lfs pull --include "exp/pretrained-iter-1224000-avg-14.pt"
cd exp
ln -s pretrained-iter-1224000-avg-14.pt epoch-9999.pt
popd
2. Export the model to ONNX
./pruned_transducer_stateless3/export-onnx.py \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--epoch 9999 \
--avg 1 \
--exp-dir $repo/exp/
It will generate the following 3 files inside $repo/exp:
- encoder-epoch-9999-avg-1.onnx
- decoder-epoch-9999-avg-1.onnx
- joiner-epoch-9999-avg-1.onnx
See ./onnx_pretrained.py and ./onnx_check.py for how to
use the exported ONNX models.
"""
import argparse
import logging
from pathlib import Path
from typing import Dict, Tuple
import onnx
import sentencepiece as spm
import torch
import torch.nn as nn
from conformer import Conformer
from decoder import Decoder
from scaling_converter import convert_scaled_to_non_scaled
from train import add_model_arguments, get_params, get_transducer_model
from icefall.checkpoint import average_checkpoints, find_checkpoints, load_checkpoint
from icefall.utils import setup_logger
def get_parser():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument(
"--epoch",
type=int,
default=28,
help="""It specifies the checkpoint to use for averaging.
Note: Epoch counts from 0.
You can specify --avg to use more checkpoints for model averaging.""",
)
parser.add_argument(
"--iter",
type=int,
default=0,
help="""If positive, --epoch is ignored and it
will use the checkpoint exp_dir/checkpoint-iter.pt.
You can specify --avg to use more checkpoints for model averaging.
""",
)
parser.add_argument(
"--avg",
type=int,
default=15,
help="Number of checkpoints to average. Automatically select "
"consecutive checkpoints before the checkpoint specified by "
"'--epoch' and '--iter'",
)
parser.add_argument(
"--exp-dir",
type=str,
default="pruned_transducer_stateless3/exp",
help="""It specifies the directory where all training related
files, e.g., checkpoints, log, etc, are saved
""",
)
parser.add_argument(
"--bpe-model",
type=str,
default="data/lang_bpe_500/bpe.model",
help="Path to the BPE model",
)
parser.add_argument(
"--context-size",
type=int,
default=2,
help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
)
add_model_arguments(parser)
return parser
def add_meta_data(filename: str, meta_data: Dict[str, str]):
"""Add meta data to an ONNX model. It is changed in-place.
Args:
filename:
Filename of the ONNX model to be changed.
meta_data:
Key-value pairs.
"""
model = onnx.load(filename)
for key, value in meta_data.items():
meta = model.metadata_props.add()
meta.key = key
meta.value = value
onnx.save(model, filename)
class OnnxEncoder(nn.Module):
"""A wrapper for Conformer and the encoder_proj from the joiner"""
def __init__(self, encoder: Conformer, encoder_proj: nn.Linear):
"""
Args:
encoder:
A Conformer encoder.
encoder_proj:
The projection layer for encoder from the joiner.
"""
super().__init__()
self.encoder = encoder
self.encoder_proj = encoder_proj
def forward(
self,
x: torch.Tensor,
x_lens: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Please see the help information of Conformer.forward
Args:
x:
A 3-D tensor of shape (N, T, C)
x_lens:
A 1-D tensor of shape (N,). Its dtype is torch.int64
Returns:
Return a tuple containing:
- encoder_out, A 3-D tensor of shape (N, T', joiner_dim)
- encoder_out_lens, A 1-D tensor of shape (N,)
"""
encoder_out, encoder_out_lens = self.encoder(x, x_lens)
encoder_out = self.encoder_proj(encoder_out)
# Now encoder_out is of shape (N, T, joiner_dim)
return encoder_out, encoder_out_lens
class OnnxDecoder(nn.Module):
"""A wrapper for Decoder and the decoder_proj from the joiner"""
def __init__(self, decoder: Decoder, decoder_proj: nn.Linear):
super().__init__()
self.decoder = decoder
self.decoder_proj = decoder_proj
def forward(self, y: torch.Tensor) -> torch.Tensor:
"""
Args:
y:
A 2-D tensor of shape (N, context_size).
Returns
Return a 2-D tensor of shape (N, joiner_dim)
"""
need_pad = False
decoder_output = self.decoder(y, need_pad=need_pad)
decoder_output = decoder_output.squeeze(1)
output = self.decoder_proj(decoder_output)
return output
class OnnxJoiner(nn.Module):
"""A wrapper for the joiner"""
def __init__(self, output_linear: nn.Linear):
super().__init__()
self.output_linear = output_linear
def forward(
self,
encoder_out: torch.Tensor,
decoder_out: torch.Tensor,
) -> torch.Tensor:
"""
Args:
encoder_out:
A 2-D tensor of shape (N, joiner_dim)
decoder_out:
A 2-D tensor of shape (N, joiner_dim)
Returns:
Return a 2-D tensor of shape (N, vocab_size)
"""
logit = encoder_out + decoder_out
logit = self.output_linear(torch.tanh(logit))
return logit
def export_encoder_model_onnx(
encoder_model: OnnxEncoder,
encoder_filename: str,
opset_version: int = 11,
) -> None:
"""Export the given encoder model to ONNX format.
The exported model has two inputs:
- x, a tensor of shape (N, T, C); dtype is torch.float32
- x_lens, a tensor of shape (N,); dtype is torch.int64
and it has two outputs:
- encoder_out, a tensor of shape (N, T', joiner_dim)
- encoder_out_lens, a tensor of shape (N,)
Args:
encoder_model:
The input encoder model
encoder_filename:
The filename to save the exported ONNX model.
opset_version:
The opset version to use.
"""
x = torch.zeros(1, 100, 80, dtype=torch.float32)
x_lens = torch.tensor([100], dtype=torch.int64)
torch.onnx.export(
encoder_model,
(x, x_lens),
encoder_filename,
verbose=False,
opset_version=opset_version,
input_names=["x", "x_lens"],
output_names=["encoder_out", "encoder_out_lens"],
dynamic_axes={
"x": {0: "N", 1: "T"},
"x_lens": {0: "N"},
"encoder_out": {0: "N", 1: "T"},
"encoder_out_lens": {0: "N"},
},
)
def export_decoder_model_onnx(
decoder_model: OnnxDecoder,
decoder_filename: str,
opset_version: int = 11,
) -> None:
"""Export the decoder model to ONNX format.
The exported model has one input:
- y: a torch.int64 tensor of shape (N, decoder_model.context_size)
and has one output:
- decoder_out: a torch.float32 tensor of shape (N, joiner_dim)
Args:
decoder_model:
The decoder model to be exported.
decoder_filename:
Filename to save the exported ONNX model.
opset_version:
The opset version to use.
"""
context_size = decoder_model.decoder.context_size
vocab_size = decoder_model.decoder.vocab_size
y = torch.zeros(10, context_size, dtype=torch.int64)
torch.onnx.export(
decoder_model,
y,
decoder_filename,
verbose=False,
opset_version=opset_version,
input_names=["y"],
output_names=["decoder_out"],
dynamic_axes={
"y": {0: "N"},
"decoder_out": {0: "N"},
},
)
meta_data = {
"context_size": str(context_size),
"vocab_size": str(vocab_size),
}
add_meta_data(filename=decoder_filename, meta_data=meta_data)
def export_joiner_model_onnx(
joiner_model: nn.Module,
joiner_filename: str,
opset_version: int = 11,
) -> None:
"""Export the joiner model to ONNX format.
The exported joiner model has two inputs:
- encoder_out: a tensor of shape (N, joiner_dim)
- decoder_out: a tensor of shape (N, joiner_dim)
and produces one output:
- logit: a tensor of shape (N, vocab_size)
"""
joiner_dim = joiner_model.output_linear.weight.shape[1]
logging.info(f"joiner dim: {joiner_dim}")
projected_encoder_out = torch.rand(11, joiner_dim, dtype=torch.float32)
projected_decoder_out = torch.rand(11, joiner_dim, dtype=torch.float32)
torch.onnx.export(
joiner_model,
(projected_encoder_out, projected_decoder_out),
joiner_filename,
verbose=False,
opset_version=opset_version,
input_names=[
"encoder_out",
"decoder_out",
],
output_names=["logit"],
dynamic_axes={
"encoder_out": {0: "N"},
"decoder_out": {0: "N"},
"logit": {0: "N"},
},
)
meta_data = {
"joiner_dim": str(joiner_dim),
}
add_meta_data(filename=joiner_filename, meta_data=meta_data)
@torch.no_grad()
def main():
args = get_parser().parse_args()
args.exp_dir = Path(args.exp_dir)
params = get_params()
params.update(vars(args))
device = torch.device("cpu")
if torch.cuda.is_available():
device = torch.device("cuda", 0)
setup_logger(f"{params.exp_dir}/log-export/log-export-onnx")
logging.info(f"device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()
logging.info(params)
logging.info("About to create model")
model = get_transducer_model(params, enable_giga=False)
model.to(device)
if params.iter > 0:
filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
: params.avg
]
if len(filenames) == 0:
raise ValueError(
f"No checkpoints found for --iter {params.iter}, --avg {params.avg}"
)
elif len(filenames) < params.avg:
raise ValueError(
f"Not enough checkpoints ({len(filenames)}) found for"
f" --iter {params.iter}, --avg {params.avg}"
)
logging.info(f"averaging {filenames}")
model.to(device)
model.load_state_dict(
average_checkpoints(filenames, device=device), strict=False
)
elif params.avg == 1:
load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
else:
start = params.epoch - params.avg + 1
filenames = []
for i in range(start, params.epoch + 1):
if start >= 0:
filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
logging.info(f"averaging {filenames}")
model.to(device)
model.load_state_dict(
average_checkpoints(filenames, device=device), strict=False
)
model.to("cpu")
model.eval()
convert_scaled_to_non_scaled(model, inplace=True)
encoder = OnnxEncoder(
encoder=model.encoder,
encoder_proj=model.joiner.encoder_proj,
)
decoder = OnnxDecoder(
decoder=model.decoder,
decoder_proj=model.joiner.decoder_proj,
)
joiner = OnnxJoiner(output_linear=model.joiner.output_linear)
encoder_num_param = sum([p.numel() for p in encoder.parameters()])
decoder_num_param = sum([p.numel() for p in decoder.parameters()])
joiner_num_param = sum([p.numel() for p in joiner.parameters()])
total_num_param = encoder_num_param + decoder_num_param + joiner_num_param
logging.info(f"encoder parameters: {encoder_num_param}")
logging.info(f"decoder parameters: {decoder_num_param}")
logging.info(f"joiner parameters: {joiner_num_param}")
logging.info(f"total parameters: {total_num_param}")
if params.iter > 0:
suffix = f"iter-{params.iter}"
else:
suffix = f"epoch-{params.epoch}"
suffix += f"-avg-{params.avg}"
opset_version = 13
logging.info("Exporting encoder")
encoder_filename = params.exp_dir / f"encoder-{suffix}.onnx"
export_encoder_model_onnx(
encoder,
encoder_filename,
opset_version=opset_version,
)
logging.info(f"Exported encoder to {encoder_filename}")
logging.info("Exporting decoder")
decoder_filename = params.exp_dir / f"decoder-{suffix}.onnx"
export_decoder_model_onnx(
decoder,
decoder_filename,
opset_version=opset_version,
)
logging.info(f"Exported decoder to {decoder_filename}")
logging.info("Exporting joiner")
joiner_filename = params.exp_dir / f"joiner-{suffix}.onnx"
export_joiner_model_onnx(
joiner,
joiner_filename,
opset_version=opset_version,
)
logging.info(f"Exported joiner to {joiner_filename}")
if __name__ == "__main__":
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
main()

270
egs/librispeech/ASR/pruned_transducer_stateless3/export.py
View File

@ -52,32 +52,7 @@ It will also generate 3 other files: `encoder_jit_script.pt`,
It will generates 3 files: `encoder_jit_trace.pt`,
`decoder_jit_trace.pt`, and `joiner_jit_trace.pt`.
(3) Export to ONNX format
./pruned_transducer_stateless3/export.py \
--exp-dir ./pruned_transducer_stateless3/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--epoch 20 \
--avg 10 \
--onnx 1
It will generate the following files in the given `exp_dir`.
Check `onnx_check.py` for how to use them.
- encoder.onnx
- decoder.onnx
- joiner.onnx
- joiner_encoder_proj.onnx
- joiner_decoder_proj.onnx
Please see ./onnx_pretrained.py for usage of the generated files
Check
https://github.com/k2-fsa/sherpa-onnx
for how to use the exported models outside of icefall.
(4) Export `model.state_dict()`
(3) Export `model.state_dict()`
./pruned_transducer_stateless3/export.py \
--exp-dir ./pruned_transducer_stateless3/exp \
@ -210,23 +185,6 @@ def get_parser():
""",
)
parser.add_argument(
"--onnx",
type=str2bool,
default=False,
help="""If True, --jit is ignored and it exports the model
to onnx format. It will generate the following files:
- encoder.onnx
- decoder.onnx
- joiner.onnx
- joiner_encoder_proj.onnx
- joiner_decoder_proj.onnx
Refer to ./onnx_check.py and ./onnx_pretrained.py for how to use them.
""",
)
parser.add_argument(
"--context-size",
type=int,
@ -370,206 +328,6 @@ def export_joiner_model_jit_trace(
logging.info(f"Saved to {joiner_filename}")
def export_encoder_model_onnx(
encoder_model: nn.Module,
encoder_filename: str,
opset_version: int = 11,
) -> None:
"""Export the given encoder model to ONNX format.
The exported model has two inputs:
- x, a tensor of shape (N, T, C); dtype is torch.float32
- x_lens, a tensor of shape (N,); dtype is torch.int64
and it has two outputs:
- encoder_out, a tensor of shape (N, T, C)
- encoder_out_lens, a tensor of shape (N,)
Note: The warmup argument is fixed to 1.
Args:
encoder_model:
The input encoder model
encoder_filename:
The filename to save the exported ONNX model.
opset_version:
The opset version to use.
"""
x = torch.zeros(1, 100, 80, dtype=torch.float32)
x_lens = torch.tensor([100], dtype=torch.int64)
# encoder_model = torch.jit.script(encoder_model)
# It throws the following error for the above statement
#
# RuntimeError: Exporting the operator __is_ to ONNX opset version
# 11 is not supported. Please feel free to request support or
# submit a pull request on PyTorch GitHub.
#
# I cannot find which statement causes the above error.
# torch.onnx.export() will use torch.jit.trace() internally, which
# works well for the current reworked model
warmup = 1.0
torch.onnx.export(
encoder_model,
(x, x_lens, warmup),
encoder_filename,
verbose=False,
opset_version=opset_version,
input_names=["x", "x_lens", "warmup"],
output_names=["encoder_out", "encoder_out_lens"],
dynamic_axes={
"x": {0: "N", 1: "T"},
"x_lens": {0: "N"},
"encoder_out": {0: "N", 1: "T"},
"encoder_out_lens": {0: "N"},
},
)
logging.info(f"Saved to {encoder_filename}")
def export_decoder_model_onnx(
decoder_model: nn.Module,
decoder_filename: str,
opset_version: int = 11,
) -> None:
"""Export the decoder model to ONNX format.
The exported model has one input:
- y: a torch.int64 tensor of shape (N, decoder_model.context_size)
and has one output:
- decoder_out: a torch.float32 tensor of shape (N, 1, C)
Note: The argument need_pad is fixed to False.
Args:
decoder_model:
The decoder model to be exported.
decoder_filename:
Filename to save the exported ONNX model.
opset_version:
The opset version to use.
"""
y = torch.zeros(10, decoder_model.context_size, dtype=torch.int64)
need_pad = False # Always False, so we can use torch.jit.trace() here
# Note(fangjun): torch.jit.trace() is more efficient than torch.jit.script()
# in this case
torch.onnx.export(
decoder_model,
(y, need_pad),
decoder_filename,
verbose=False,
opset_version=opset_version,
input_names=["y", "need_pad"],
output_names=["decoder_out"],
dynamic_axes={
"y": {0: "N"},
"decoder_out": {0: "N"},
},
)
logging.info(f"Saved to {decoder_filename}")
def export_joiner_model_onnx(
joiner_model: nn.Module,
joiner_filename: str,
opset_version: int = 11,
) -> None:
"""Export the joiner model to ONNX format.
The exported joiner model has two inputs:
- projected_encoder_out: a tensor of shape (N, joiner_dim)
- projected_decoder_out: a tensor of shape (N, joiner_dim)
and produces one output:
- logit: a tensor of shape (N, vocab_size)
The exported encoder_proj model has one input:
- encoder_out: a tensor of shape (N, encoder_out_dim)
and produces one output:
- projected_encoder_out: a tensor of shape (N, joiner_dim)
The exported decoder_proj model has one input:
- decoder_out: a tensor of shape (N, decoder_out_dim)
and produces one output:
- projected_decoder_out: a tensor of shape (N, joiner_dim)
"""
encoder_proj_filename = str(joiner_filename).replace(".onnx", "_encoder_proj.onnx")
decoder_proj_filename = str(joiner_filename).replace(".onnx", "_decoder_proj.onnx")
encoder_out_dim = joiner_model.encoder_proj.weight.shape[1]
decoder_out_dim = joiner_model.decoder_proj.weight.shape[1]
joiner_dim = joiner_model.decoder_proj.weight.shape[0]
projected_encoder_out = torch.rand(1, joiner_dim, dtype=torch.float32)
projected_decoder_out = torch.rand(1, joiner_dim, dtype=torch.float32)
project_input = False
# Note: It uses torch.jit.trace() internally
torch.onnx.export(
joiner_model,
(projected_encoder_out, projected_decoder_out, project_input),
joiner_filename,
verbose=False,
opset_version=opset_version,
input_names=[
"projected_encoder_out",
"projected_decoder_out",
"project_input",
],
output_names=["logit"],
dynamic_axes={
"projected_encoder_out": {0: "N"},
"projected_decoder_out": {0: "N"},
"logit": {0: "N"},
},
)
logging.info(f"Saved to {joiner_filename}")
encoder_out = torch.rand(1, encoder_out_dim, dtype=torch.float32)
torch.onnx.export(
joiner_model.encoder_proj,
encoder_out,
encoder_proj_filename,
verbose=False,
opset_version=opset_version,
input_names=["encoder_out"],
output_names=["projected_encoder_out"],
dynamic_axes={
"encoder_out": {0: "N"},
"projected_encoder_out": {0: "N"},
},
)
logging.info(f"Saved to {encoder_proj_filename}")
decoder_out = torch.rand(1, decoder_out_dim, dtype=torch.float32)
torch.onnx.export(
joiner_model.decoder_proj,
decoder_out,
decoder_proj_filename,
verbose=False,
opset_version=opset_version,
input_names=["decoder_out"],
output_names=["projected_decoder_out"],
dynamic_axes={
"decoder_out": {0: "N"},
"projected_decoder_out": {0: "N"},
},
)
logging.info(f"Saved to {decoder_proj_filename}")
@torch.no_grad()
def main():
args = get_parser().parse_args()
@ -636,31 +394,7 @@ def main():
model.to("cpu")
model.eval()
if params.onnx is True:
convert_scaled_to_non_scaled(model, inplace=True)
opset_version = 11
logging.info("Exporting to onnx format")
encoder_filename = params.exp_dir / "encoder.onnx"
export_encoder_model_onnx(
model.encoder,
encoder_filename,
opset_version=opset_version,
)
decoder_filename = params.exp_dir / "decoder.onnx"
export_decoder_model_onnx(
model.decoder,
decoder_filename,
opset_version=opset_version,
)
joiner_filename = params.exp_dir / "joiner.onnx"
export_joiner_model_onnx(
model.joiner,
joiner_filename,
opset_version=opset_version,
)
elif params.jit is True:
if params.jit is True:
convert_scaled_to_non_scaled(model, inplace=True)
logging.info("Using torch.jit.script()")
# We won't use the forward() method of the model in C++, so just ignore

301
egs/librispeech/ASR/pruned_transducer_stateless3/onnx_check.py
View File

@ -19,21 +19,70 @@
"""
This script checks that exported onnx models produce the same output
with the given torchscript model for the same input.
We use the pre-trained model from
https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13
as an example to show how to use this file.
1. Download the pre-trained model
cd egs/librispeech/ASR
repo_url=https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13
GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
repo=$(basename $repo_url)
pushd $repo
git lfs pull --include "data/lang_bpe_500/bpe.model"
git lfs pull --include "exp/pretrained-iter-1224000-avg-14.pt"
cd exp
ln -s pretrained-iter-1224000-avg-14.pt epoch-9999.pt
popd
2. Export the model via torchscript (torch.jit.script())
./pruned_transducer_stateless3/export.py \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--epoch 9999 \
--avg 1 \
--exp-dir $repo/exp/ \
--jit 1
It will generate the following file in $repo/exp:
- cpu_jit.pt
3. Export the model to ONNX
./pruned_transducer_stateless3/export-onnx.py \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--epoch 9999 \
--avg 1 \
--exp-dir $repo/exp/
It will generate the following 3 files inside $repo/exp:
- encoder-epoch-9999-avg-1.onnx
- decoder-epoch-9999-avg-1.onnx
- joiner-epoch-9999-avg-1.onnx
4. Run this file
./pruned_transducer_stateless3/onnx_check.py \
--jit-filename $repo/exp/cpu_jit.pt \
--onnx-encoder-filename $repo/exp/encoder-epoch-9999-avg-1.onnx \
--onnx-decoder-filename $repo/exp/decoder-epoch-9999-avg-1.onnx \
--onnx-joiner-filename $repo/exp/joiner-epoch-9999-avg-1.onnx
"""
import argparse
import logging
from icefall import is_module_available
from onnx_pretrained import OnnxModel
if not is_module_available("onnxruntime"):
raise ValueError("Please 'pip install onnxruntime' first.")
import onnxruntime as ort
import torch
ort.set_default_logger_severity(3)
def get_parser():
parser = argparse.ArgumentParser(
@ -68,163 +117,81 @@ def get_parser():
help="Path to the onnx joiner model",
)
parser.add_argument(
"--onnx-joiner-encoder-proj-filename",
required=True,
type=str,
help="Path to the onnx joiner encoder projection model",
)
parser.add_argument(
"--onnx-joiner-decoder-proj-filename",
required=True,
type=str,
help="Path to the onnx joiner decoder projection model",
)
return parser
def test_encoder(
model: torch.jit.ScriptModule,
encoder_session: ort.InferenceSession,
torch_model: torch.jit.ScriptModule,
onnx_model: OnnxModel,
):
inputs = encoder_session.get_inputs()
outputs = encoder_session.get_outputs()
input_names = [n.name for n in inputs]
output_names = [n.name for n in outputs]
C = 80
for i in range(10):
N = torch.randint(low=1, high=20, size=(1,)).item()
T = torch.randint(low=50, high=100, size=(1,)).item()
logging.info(f"test_encoder: iter {i}, N={N}, T={T}")
assert inputs[0].shape == ["N", "T", 80]
assert inputs[1].shape == ["N"]
x = torch.rand(N, T, C)
x_lens = torch.randint(low=10, high=T + 1, size=(N,))
x_lens[0] = T
for N in [1, 5]:
for T in [12, 25]:
print("N, T", N, T)
x = torch.rand(N, T, 80, dtype=torch.float32)
x_lens = torch.randint(low=10, high=T + 1, size=(N,))
x_lens[0] = T
torch_encoder_out, torch_encoder_out_lens = torch_model.encoder(x, x_lens)
torch_encoder_out = torch_model.joiner.encoder_proj(torch_encoder_out)
encoder_inputs = {
input_names[0]: x.numpy(),
input_names[1]: x_lens.numpy(),
}
encoder_out, encoder_out_lens = encoder_session.run(
output_names,
encoder_inputs,
)
onnx_encoder_out, onnx_encoder_out_lens = onnx_model.run_encoder(x, x_lens)
torch_encoder_out, torch_encoder_out_lens = model.encoder(x, x_lens)
encoder_out = torch.from_numpy(encoder_out)
assert torch.allclose(encoder_out, torch_encoder_out, atol=1e-05), (
(encoder_out - torch_encoder_out).abs().max(),
encoder_out.shape,
torch_encoder_out.shape,
)
assert torch.allclose(torch_encoder_out, onnx_encoder_out, atol=1e-05), (
(torch_encoder_out - onnx_encoder_out).abs().max()
)
def test_decoder(
model: torch.jit.ScriptModule,
decoder_session: ort.InferenceSession,
torch_model: torch.jit.ScriptModule,
onnx_model: OnnxModel,
):
inputs = decoder_session.get_inputs()
outputs = decoder_session.get_outputs()
input_names = [n.name for n in inputs]
output_names = [n.name for n in outputs]
context_size = onnx_model.context_size
vocab_size = onnx_model.vocab_size
for i in range(10):
N = torch.randint(1, 100, size=(1,)).item()
logging.info(f"test_decoder: iter {i}, N={N}")
x = torch.randint(
low=1,
high=vocab_size,
size=(N, context_size),
dtype=torch.int64,
)
torch_decoder_out = torch_model.decoder(x, need_pad=torch.tensor([False]))
torch_decoder_out = torch_model.joiner.decoder_proj(torch_decoder_out)
torch_decoder_out = torch_decoder_out.squeeze(1)
assert inputs[0].shape == ["N", 2]
for N in [1, 5, 10]:
y = torch.randint(low=1, high=500, size=(10, 2))
decoder_inputs = {input_names[0]: y.numpy()}
decoder_out = decoder_session.run(
output_names,
decoder_inputs,
)[0]
decoder_out = torch.from_numpy(decoder_out)
torch_decoder_out = model.decoder(y, need_pad=False)
assert torch.allclose(decoder_out, torch_decoder_out, atol=1e-5), (
(decoder_out - torch_decoder_out).abs().max()
onnx_decoder_out = onnx_model.run_decoder(x)
assert torch.allclose(torch_decoder_out, onnx_decoder_out, atol=1e-4), (
(torch_decoder_out - onnx_decoder_out).abs().max()
)
def test_joiner(
model: torch.jit.ScriptModule,
joiner_session: ort.InferenceSession,
joiner_encoder_proj_session: ort.InferenceSession,
joiner_decoder_proj_session: ort.InferenceSession,
torch_model: torch.jit.ScriptModule,
onnx_model: OnnxModel,
):
joiner_inputs = joiner_session.get_inputs()
joiner_outputs = joiner_session.get_outputs()
joiner_input_names = [n.name for n in joiner_inputs]
joiner_output_names = [n.name for n in joiner_outputs]
encoder_dim = torch_model.joiner.encoder_proj.weight.shape[1]
decoder_dim = torch_model.joiner.decoder_proj.weight.shape[1]
for i in range(10):
N = torch.randint(1, 100, size=(1,)).item()
logging.info(f"test_joiner: iter {i}, N={N}")
encoder_out = torch.rand(N, encoder_dim)
decoder_out = torch.rand(N, decoder_dim)
assert joiner_inputs[0].shape == ["N", 512]
assert joiner_inputs[1].shape == ["N", 512]
projected_encoder_out = torch_model.joiner.encoder_proj(encoder_out)
projected_decoder_out = torch_model.joiner.decoder_proj(decoder_out)
joiner_encoder_proj_inputs = joiner_encoder_proj_session.get_inputs()
encoder_proj_input_name = joiner_encoder_proj_inputs[0].name
assert joiner_encoder_proj_inputs[0].shape == ["N", 512]
joiner_encoder_proj_outputs = joiner_encoder_proj_session.get_outputs()
encoder_proj_output_name = joiner_encoder_proj_outputs[0].name
joiner_decoder_proj_inputs = joiner_decoder_proj_session.get_inputs()
decoder_proj_input_name = joiner_decoder_proj_inputs[0].name
assert joiner_decoder_proj_inputs[0].shape == ["N", 512]
joiner_decoder_proj_outputs = joiner_decoder_proj_session.get_outputs()
decoder_proj_output_name = joiner_decoder_proj_outputs[0].name
for N in [1, 5, 10]:
encoder_out = torch.rand(N, 512)
decoder_out = torch.rand(N, 512)
projected_encoder_out = torch.rand(N, 512)
projected_decoder_out = torch.rand(N, 512)
joiner_inputs = {
joiner_input_names[0]: projected_encoder_out.numpy(),
joiner_input_names[1]: projected_decoder_out.numpy(),
}
joiner_out = joiner_session.run(joiner_output_names, joiner_inputs)[0]
joiner_out = torch.from_numpy(joiner_out)
torch_joiner_out = model.joiner(
projected_encoder_out,
projected_decoder_out,
project_input=False,
)
assert torch.allclose(joiner_out, torch_joiner_out, atol=1e-5), (
(joiner_out - torch_joiner_out).abs().max()
torch_joiner_out = torch_model.joiner(encoder_out, decoder_out)
onnx_joiner_out = onnx_model.run_joiner(
projected_encoder_out, projected_decoder_out
)
# Now test encoder_proj
joiner_encoder_proj_inputs = {encoder_proj_input_name: encoder_out.numpy()}
joiner_encoder_proj_out = joiner_encoder_proj_session.run(
[encoder_proj_output_name], joiner_encoder_proj_inputs
)[0]
joiner_encoder_proj_out = torch.from_numpy(joiner_encoder_proj_out)
torch_joiner_encoder_proj_out = model.joiner.encoder_proj(encoder_out)
assert torch.allclose(
joiner_encoder_proj_out, torch_joiner_encoder_proj_out, atol=1e-5
), ((joiner_encoder_proj_out - torch_joiner_encoder_proj_out).abs().max())
# Now test decoder_proj
joiner_decoder_proj_inputs = {decoder_proj_input_name: decoder_out.numpy()}
joiner_decoder_proj_out = joiner_decoder_proj_session.run(
[decoder_proj_output_name], joiner_decoder_proj_inputs
)[0]
joiner_decoder_proj_out = torch.from_numpy(joiner_decoder_proj_out)
torch_joiner_decoder_proj_out = model.joiner.decoder_proj(decoder_out)
assert torch.allclose(
joiner_decoder_proj_out, torch_joiner_decoder_proj_out, atol=1e-5
), ((joiner_decoder_proj_out - torch_joiner_decoder_proj_out).abs().max())
assert torch.allclose(torch_joiner_out, onnx_joiner_out, atol=1e-4), (
(torch_joiner_out - onnx_joiner_out).abs().max()
)
@torch.no_grad()
@ -232,48 +199,38 @@ def main():
args = get_parser().parse_args()
logging.info(vars(args))
model = torch.jit.load(args.jit_filename)
torch_model = torch.jit.load(args.jit_filename)
options = ort.SessionOptions()
options.inter_op_num_threads = 1
options.intra_op_num_threads = 1
onnx_model = OnnxModel(
encoder_model_filename=args.onnx_encoder_filename,
decoder_model_filename=args.onnx_decoder_filename,
joiner_model_filename=args.onnx_joiner_filename,
)
logging.info("Test encoder")
encoder_session = ort.InferenceSession(
args.onnx_encoder_filename,
sess_options=options,
)
test_encoder(model, encoder_session)
test_encoder(torch_model, onnx_model)
logging.info("Test decoder")
decoder_session = ort.InferenceSession(
args.onnx_decoder_filename,
sess_options=options,
)
test_decoder(model, decoder_session)
test_decoder(torch_model, onnx_model)
logging.info("Test joiner")
joiner_session = ort.InferenceSession(
args.onnx_joiner_filename,
sess_options=options,
)
joiner_encoder_proj_session = ort.InferenceSession(
args.onnx_joiner_encoder_proj_filename,
sess_options=options,
)
joiner_decoder_proj_session = ort.InferenceSession(
args.onnx_joiner_decoder_proj_filename,
sess_options=options,
)
test_joiner(
model,
joiner_session,
joiner_encoder_proj_session,
joiner_decoder_proj_session,
)
test_joiner(torch_model, onnx_model)
logging.info("Finished checking ONNX models")
torch.set_num_threads(1)
torch.set_num_interop_threads(1)
# See https://github.com/pytorch/pytorch/issues/38342
# and https://github.com/pytorch/pytorch/issues/33354
#
# If we don't do this, the delay increases whenever there is
# a new request that changes the actual batch size.
# If you use `py-spy dump --pid <server-pid> --native`, you will
# see a lot of time is spent in re-compiling the torch script model.
torch._C._jit_set_profiling_executor(False)
torch._C._jit_set_profiling_mode(False)
torch._C._set_graph_executor_optimize(False)
if __name__ == "__main__":
torch.manual_seed(20220727)
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"

344
egs/librispeech/ASR/pruned_transducer_stateless3/onnx_pretrained.py
View File

@ -18,35 +18,61 @@
This script loads ONNX models and uses them to decode waves.
You can use the following command to get the exported models:
./pruned_transducer_stateless3/export.py \
--exp-dir ./pruned_transducer_stateless3/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--epoch 20 \
--avg 10 \
--onnx 1
We use the pre-trained model from
https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13
as an example to show how to use this file.
Usage of this script:
1. Download the pre-trained model
cd egs/librispeech/ASR
repo_url=https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13
GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
repo=$(basename $repo_url)
pushd $repo
git lfs pull --include "data/lang_bpe_500/bpe.model"
git lfs pull --include "exp/pretrained-iter-1224000-avg-14.pt"
cd exp
ln -s pretrained-iter-1224000-avg-14.pt epoch-9999.pt
popd
2. Export the model to ONNX
./pruned_transducer_stateless3/export-onnx.py \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--epoch 9999 \
--avg 1 \
--exp-dir $repo/exp/
It will generate the following 3 files inside $repo/exp:
- encoder-epoch-9999-avg-1.onnx
- decoder-epoch-9999-avg-1.onnx
- joiner-epoch-9999-avg-1.onnx
3. Run this file
./pruned_transducer_stateless3/onnx_pretrained.py \
--encoder-model-filename ./pruned_transducer_stateless3/exp/encoder.onnx \
--decoder-model-filename ./pruned_transducer_stateless3/exp/decoder.onnx \
--joiner-model-filename ./pruned_transducer_stateless3/exp/joiner.onnx \
--joiner-encoder-proj-model-filename ./pruned_transducer_stateless3/exp/joiner_encoder_proj.onnx \
--joiner-decoder-proj-model-filename ./pruned_transducer_stateless3/exp/joiner_decoder_proj.onnx \
--bpe-model ./data/lang_bpe_500/bpe.model \
/path/to/foo.wav \
/path/to/bar.wav
--encoder-model-filename $repo/exp/encoder-epoch-9999-avg-1.onnx \
--decoder-model-filename $repo/exp/decoder-epoch-9999-avg-1.onnx \
--joiner-model-filename $repo/exp/joiner-epoch-9999-avg-1.onnx \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav
"""
import argparse
import logging
import math
from typing import List
from typing import List, Tuple
import k2
import kaldifeat
import numpy as np
import onnxruntime as ort
import sentencepiece as spm
import torch
import torchaudio
from torch.nn.utils.rnn import pad_sequence
@ -79,23 +105,9 @@ def get_parser():
)
parser.add_argument(
"--joiner-encoder-proj-model-filename",
"--tokens",
type=str,
required=True,
help="Path to the joiner encoder_proj onnx model. ",
)
parser.add_argument(
"--joiner-decoder-proj-model-filename",
type=str,
required=True,
help="Path to the joiner decoder_proj onnx model. ",
)
parser.add_argument(
"--bpe-model",
type=str,
help="""Path to bpe.model.""",
help="""Path to tokens.txt.""",
)
parser.add_argument(
@ -115,16 +127,122 @@ def get_parser():
help="The sample rate of the input sound file",
)
parser.add_argument(
"--context-size",
type=int,
default=2,
help="Context size of the decoder model",
)
return parser
class OnnxModel:
def __init__(
self,
encoder_model_filename: str,
decoder_model_filename: str,
joiner_model_filename: str,
):
session_opts = ort.SessionOptions()
session_opts.inter_op_num_threads = 1
session_opts.intra_op_num_threads = 1
self.session_opts = session_opts
self.init_encoder(encoder_model_filename)
self.init_decoder(decoder_model_filename)
self.init_joiner(joiner_model_filename)
def init_encoder(self, encoder_model_filename: str):
self.encoder = ort.InferenceSession(
encoder_model_filename,
sess_options=self.session_opts,
)
def init_decoder(self, decoder_model_filename: str):
self.decoder = ort.InferenceSession(
decoder_model_filename,
sess_options=self.session_opts,
)
decoder_meta = self.decoder.get_modelmeta().custom_metadata_map
self.context_size = int(decoder_meta["context_size"])
self.vocab_size = int(decoder_meta["vocab_size"])
logging.info(f"context_size: {self.context_size}")
logging.info(f"vocab_size: {self.vocab_size}")
def init_joiner(self, joiner_model_filename: str):
self.joiner = ort.InferenceSession(
joiner_model_filename,
sess_options=self.session_opts,
)
joiner_meta = self.joiner.get_modelmeta().custom_metadata_map
self.joiner_dim = int(joiner_meta["joiner_dim"])
logging.info(f"joiner_dim: {self.joiner_dim}")
def run_encoder(
self,
x: torch.Tensor,
x_lens: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Args:
x:
A 3-D tensor of shape (N, T, C)
x_lens:
A 2-D tensor of shape (N,). Its dtype is torch.int64
Returns:
Return a tuple containing:
- encoder_out, its shape is (N, T', joiner_dim)
- encoder_out_lens, its shape is (N,)
"""
out = self.encoder.run(
[
self.encoder.get_outputs()[0].name,
self.encoder.get_outputs()[1].name,
],
{
self.encoder.get_inputs()[0].name: x.numpy(),
self.encoder.get_inputs()[1].name: x_lens.numpy(),
},
)
return torch.from_numpy(out[0]), torch.from_numpy(out[1])
def run_decoder(self, decoder_input: torch.Tensor) -> torch.Tensor:
"""
Args:
decoder_input:
A 2-D tensor of shape (N, context_size)
Returns:
Return a 2-D tensor of shape (N, joiner_dim)
"""
out = self.decoder.run(
[self.decoder.get_outputs()[0].name],
{self.decoder.get_inputs()[0].name: decoder_input.numpy()},
)[0]
return torch.from_numpy(out)
def run_joiner(
self, encoder_out: torch.Tensor, decoder_out: torch.Tensor
) -> torch.Tensor:
"""
Args:
encoder_out:
A 2-D tensor of shape (N, joiner_dim)
decoder_out:
A 2-D tensor of shape (N, joiner_dim)
Returns:
Return a 2-D tensor of shape (N, vocab_size)
"""
out = self.joiner.run(
[self.joiner.get_outputs()[0].name],
{
self.joiner.get_inputs()[0].name: encoder_out.numpy(),
self.joiner.get_inputs()[1].name: decoder_out.numpy(),
},
)[0]
return torch.from_numpy(out)
def read_sound_files(
filenames: List[str], expected_sample_rate: float
) -> List[torch.Tensor]:
@ -149,36 +267,22 @@ def read_sound_files(
def greedy_search(
decoder: ort.InferenceSession,
joiner: ort.InferenceSession,
joiner_encoder_proj: ort.InferenceSession,
joiner_decoder_proj: ort.InferenceSession,
encoder_out: np.ndarray,
encoder_out_lens: np.ndarray,
context_size: int,
model: OnnxModel,
encoder_out: torch.Tensor,
encoder_out_lens: torch.Tensor,
) -> List[List[int]]:
"""Greedy search in batch mode. It hardcodes --max-sym-per-frame=1.
Args:
decoder:
The decoder model.
joiner:
The joiner model.
joiner_encoder_proj:
The joiner encoder projection model.
joiner_decoder_proj:
The joiner decoder projection model.
model:
The transducer model.
encoder_out:
A 3-D tensor of shape (N, T, C)
A 3-D tensor of shape (N, T, joiner_dim)
encoder_out_lens:
A 1-D tensor of shape (N,).
context_size:
The context size of the decoder model.
Returns:
Return the decoded results for each utterance.
"""
encoder_out = torch.from_numpy(encoder_out)
encoder_out_lens = torch.from_numpy(encoder_out_lens)
assert encoder_out.ndim == 3
assert encoder_out.ndim == 3, encoder_out.shape
assert encoder_out.size(0) >= 1, encoder_out.size(0)
packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence(
@ -188,11 +292,6 @@ def greedy_search(
enforce_sorted=False,
)
projected_encoder_out = joiner_encoder_proj.run(
[joiner_encoder_proj.get_outputs()[0].name],
{joiner_encoder_proj.get_inputs()[0].name: packed_encoder_out.data.numpy()},
)[0]
blank_id = 0 # hard-code to 0
batch_size_list = packed_encoder_out.batch_sizes.tolist()
@ -201,50 +300,27 @@ def greedy_search(
assert torch.all(encoder_out_lens > 0), encoder_out_lens
assert N == batch_size_list[0], (N, batch_size_list)
context_size = model.context_size
hyps = [[blank_id] * context_size for _ in range(N)]
decoder_input_nodes = decoder.get_inputs()
decoder_output_nodes = decoder.get_outputs()
joiner_input_nodes = joiner.get_inputs()
joiner_output_nodes = joiner.get_outputs()
decoder_input = torch.tensor(
hyps,
dtype=torch.int64,
) # (N, context_size)
decoder_out = decoder.run(
[decoder_output_nodes[0].name],
{
decoder_input_nodes[0].name: decoder_input.numpy(),
},
)[0].squeeze(1)
projected_decoder_out = joiner_decoder_proj.run(
[joiner_decoder_proj.get_outputs()[0].name],
{joiner_decoder_proj.get_inputs()[0].name: decoder_out},
)[0]
projected_decoder_out = torch.from_numpy(projected_decoder_out)
decoder_out = model.run_decoder(decoder_input)
offset = 0
for batch_size in batch_size_list:
start = offset
end = offset + batch_size
current_encoder_out = projected_encoder_out[start:end]
# current_encoder_out's shape: (batch_size, encoder_out_dim)
current_encoder_out = packed_encoder_out.data[start:end]
# current_encoder_out's shape: (batch_size, joiner_dim)
offset = end
projected_decoder_out = projected_decoder_out[:batch_size]
decoder_out = decoder_out[:batch_size]
logits = model.run_joiner(current_encoder_out, decoder_out)
logits = joiner.run(
[joiner_output_nodes[0].name],
{
joiner_input_nodes[0].name: current_encoder_out,
joiner_input_nodes[1].name: projected_decoder_out.numpy(),
},
)[0]
logits = torch.from_numpy(logits).squeeze(1).squeeze(1)
# logits'shape (batch_size, vocab_size)
assert logits.ndim == 2, logits.shape
@ -261,17 +337,7 @@ def greedy_search(
decoder_input,
dtype=torch.int64,
)
decoder_out = decoder.run(
[decoder_output_nodes[0].name],
{
decoder_input_nodes[0].name: decoder_input.numpy(),
},
)[0].squeeze(1)
projected_decoder_out = joiner_decoder_proj.run(
[joiner_decoder_proj.get_outputs()[0].name],
{joiner_decoder_proj.get_inputs()[0].name: decoder_out},
)[0]
projected_decoder_out = torch.from_numpy(projected_decoder_out)
decoder_out = model.run_decoder(decoder_input)
sorted_ans = [h[context_size:] for h in hyps]
ans = []
@ -287,39 +353,12 @@ def main():
parser = get_parser()
args = parser.parse_args()
logging.info(vars(args))
session_opts = ort.SessionOptions()
session_opts.inter_op_num_threads = 1
session_opts.intra_op_num_threads = 1
encoder = ort.InferenceSession(
args.encoder_model_filename,
sess_options=session_opts,
model = OnnxModel(
encoder_model_filename=args.encoder_model_filename,
decoder_model_filename=args.decoder_model_filename,
joiner_model_filename=args.joiner_model_filename,
)
decoder = ort.InferenceSession(
args.decoder_model_filename,
sess_options=session_opts,
)
joiner = ort.InferenceSession(
args.joiner_model_filename,
sess_options=session_opts,
)
joiner_encoder_proj = ort.InferenceSession(
args.joiner_encoder_proj_model_filename,
sess_options=session_opts,
)
joiner_decoder_proj = ort.InferenceSession(
args.joiner_decoder_proj_model_filename,
sess_options=session_opts,
)
sp = spm.SentencePieceProcessor()
sp.load(args.bpe_model)
logging.info("Constructing Fbank computer")
opts = kaldifeat.FbankOptions()
opts.device = "cpu"
@ -347,30 +386,27 @@ def main():
)
feature_lengths = torch.tensor(feature_lengths, dtype=torch.int64)
encoder_input_nodes = encoder.get_inputs()
encoder_out_nodes = encoder.get_outputs()
encoder_out, encoder_out_lens = encoder.run(
[encoder_out_nodes[0].name, encoder_out_nodes[1].name],
{
encoder_input_nodes[0].name: features.numpy(),
encoder_input_nodes[1].name: feature_lengths.numpy(),
},
)
encoder_out, encoder_out_lens = model.run_encoder(features, feature_lengths)
hyps = greedy_search(
decoder=decoder,
joiner=joiner,
joiner_encoder_proj=joiner_encoder_proj,
joiner_decoder_proj=joiner_decoder_proj,
model=model,
encoder_out=encoder_out,
encoder_out_lens=encoder_out_lens,
context_size=args.context_size,
)
s = "\n"
symbol_table = k2.SymbolTable.from_file(args.tokens)
def token_ids_to_words(token_ids: List[int]) -> str:
text = ""
for i in token_ids:
text += symbol_table[i]
return text.replace("▁", " ").strip()
context_size = model.context_size
for filename, hyp in zip(args.sound_files, hyps):
words = sp.decode(hyp)
s += f"{filename}:\n{words}\n\n"
words = token_ids_to_words(hyp[context_size:])
s += f"{filename}:\n{words}\n"
logging.info(s)
logging.info("Decoding Done")

2
egs/librispeech/ASR/pruned_transducer_stateless7_streaming/export-onnx.py
View File

@ -146,7 +146,7 @@ class OnnxEncoder(nn.Module):
"""
Args:
encoder:
A zipformer encoder.
A Zipformer encoder.
encoder_proj:
The projection layer for encoder from the joiner.
"""

6
egs/librispeech/ASR/pruned_transducer_stateless7_streaming/onnx_check.py
View File

@ -76,14 +76,8 @@ from zipformer import stack_states
from icefall import is_module_available
if not is_module_available("onnxruntime"):
raise ValueError("Please 'pip install onnxruntime' first.")
import onnxruntime as ort
import torch
ort.set_default_logger_severity(3)
def get_parser():
parser = argparse.ArgumentParser(

1
egs/librispeech/ASR/pruned_transducer_stateless7_streaming/onnx_pretrained.py
View File

@ -333,7 +333,6 @@ class OnnxModel:
self.joiner.get_inputs()[1].name: decoder_out.numpy(),
},
)[0]
return torch.from_numpy(out)
return torch.from_numpy(out)