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add onnx export for stateless2 (#1086)
This commit is contained in:
Fangjun Kuang
GitHub
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ea8b15309f
commit
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egs/wenetspeech/ASR/pruned_transducer_stateless2/export-onnx.py
Executable file
517
egs/wenetspeech/ASR/pruned_transducer_stateless2/export-onnx.py
Executable file
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#!/usr/bin/env python3
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#
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# Copyright 2023 Xiaomi Corporation (Author: Fangjun Kuang)
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"""
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This script exports a transducer model from PyTorch to ONNX.
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We use the pre-trained model from
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https://huggingface.co/luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless2
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as an example to show how to use this file.
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1. Download the pre-trained model
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cd egs/wenetspeech/ASR
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repo_url=icefall_asr_wenetspeech_pruned_transducer_stateless2
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GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
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repo=$(basename $repo_url)
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pushd $repo
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git lfs pull --include "data/lang_char/Linv.pt"
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git lfs pull --include "exp/pretrained_epoch_10_avg_2.pt"
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cd exp
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ln -s pretrained_epoch_10_avg_2.pt epoch-99.pt
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popd
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2. Export the model to ONNX
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./pruned_transducer_stateless2/export-onnx.py \
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--lang-dir $repo/data/lang_char \
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--epoch 99 \
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--avg 1 \
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--exp-dir $repo/exp
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It will generate the following 3 files inside $repo/exp:
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- encoder-epoch-99-avg-1.onnx
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- decoder-epoch-99-avg-1.onnx
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- joiner-epoch-99-avg-1.onnx
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See ./onnx_pretrained.py for how to
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use the exported ONNX models.
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"""
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import argparse
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import logging
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from pathlib import Path
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from typing import Dict, Tuple
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import onnx
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import torch
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import torch.nn as nn
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from conformer import Conformer
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from decoder import Decoder
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from onnxruntime.quantization import QuantType, quantize_dynamic
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from scaling_converter import convert_scaled_to_non_scaled
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from train import get_params, get_transducer_model
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from icefall.checkpoint import (
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average_checkpoints,
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average_checkpoints_with_averaged_model,
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find_checkpoints,
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load_checkpoint,
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)
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from icefall.lexicon import Lexicon
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from icefall.utils import setup_logger, str2bool
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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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parser.add_argument(
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"--epoch",
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type=int,
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default=28,
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help="""It specifies the checkpoint to use for averaging.
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Note: Epoch counts from 0.
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You can specify --avg to use more checkpoints for model averaging.""",
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)
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parser.add_argument(
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"--iter",
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type=int,
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default=0,
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help="""If positive, --epoch is ignored and it
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will use the checkpoint exp_dir/checkpoint-iter.pt.
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You can specify --avg to use more checkpoints for model averaging.
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""",
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)
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parser.add_argument(
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"--avg",
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type=int,
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default=15,
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help="Number of checkpoints to average. Automatically select "
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"consecutive checkpoints before the checkpoint specified by "
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"'--epoch' and '--iter'",
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)
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parser.add_argument(
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"--exp-dir",
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type=str,
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default="pruned_transducer_stateless5/exp",
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help="""It specifies the directory where all training related
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files, e.g., checkpoints, log, etc, are saved
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""",
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)
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parser.add_argument(
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"--lang-dir",
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type=str,
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default="data/lang_char",
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help="The lang dir",
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)
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parser.add_argument(
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"--context-size",
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type=int,
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default=2,
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help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
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)
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return parser
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def add_meta_data(filename: str, meta_data: Dict[str, str]):
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"""Add meta data to an ONNX model. It is changed in-place.
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Args:
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filename:
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Filename of the ONNX model to be changed.
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meta_data:
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Key-value pairs.
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"""
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model = onnx.load(filename)
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for key, value in meta_data.items():
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meta = model.metadata_props.add()
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meta.key = key
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meta.value = value
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onnx.save(model, filename)
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class OnnxEncoder(nn.Module):
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"""A wrapper for Conformer and the encoder_proj from the joiner"""
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def __init__(self, encoder: Conformer, encoder_proj: nn.Linear):
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"""
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Args:
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encoder:
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A Conformer encoder.
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encoder_proj:
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The projection layer for encoder from the joiner.
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"""
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super().__init__()
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self.encoder = encoder
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self.encoder_proj = encoder_proj
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def forward(
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self,
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x: torch.Tensor,
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x_lens: torch.Tensor,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Please see the help information of Conformer.forward
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Args:
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x:
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A 3-D tensor of shape (N, T, C)
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x_lens:
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A 1-D tensor of shape (N,). Its dtype is torch.int64
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Returns:
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Return a tuple containing:
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- encoder_out, A 3-D tensor of shape (N, T', joiner_dim)
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- encoder_out_lens, A 1-D tensor of shape (N,)
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"""
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encoder_out, encoder_out_lens = self.encoder(x, x_lens)
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encoder_out = self.encoder_proj(encoder_out)
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# Now encoder_out is of shape (N, T, joiner_dim)
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return encoder_out, encoder_out_lens
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class OnnxDecoder(nn.Module):
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"""A wrapper for Decoder and the decoder_proj from the joiner"""
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def __init__(self, decoder: Decoder, decoder_proj: nn.Linear):
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super().__init__()
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self.decoder = decoder
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self.decoder_proj = decoder_proj
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def forward(self, y: torch.Tensor) -> torch.Tensor:
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"""
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Args:
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y:
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A 2-D tensor of shape (N, context_size).
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Returns
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Return a 2-D tensor of shape (N, joiner_dim)
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"""
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need_pad = False
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decoder_output = self.decoder(y, need_pad=need_pad)
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decoder_output = decoder_output.squeeze(1)
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output = self.decoder_proj(decoder_output)
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return output
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class OnnxJoiner(nn.Module):
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"""A wrapper for the joiner"""
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def __init__(self, output_linear: nn.Linear):
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super().__init__()
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self.output_linear = output_linear
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def forward(
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self,
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encoder_out: torch.Tensor,
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decoder_out: torch.Tensor,
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) -> torch.Tensor:
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"""
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Args:
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encoder_out:
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A 2-D tensor of shape (N, joiner_dim)
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decoder_out:
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A 2-D tensor of shape (N, joiner_dim)
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Returns:
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Return a 2-D tensor of shape (N, vocab_size)
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"""
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logit = encoder_out + decoder_out
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logit = self.output_linear(torch.tanh(logit))
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return logit
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def export_encoder_model_onnx(
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encoder_model: OnnxEncoder,
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encoder_filename: str,
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opset_version: int = 11,
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) -> None:
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"""Export the given encoder model to ONNX format.
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The exported model has two inputs:
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- x, a tensor of shape (N, T, C); dtype is torch.float32
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- x_lens, a tensor of shape (N,); dtype is torch.int64
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and it has two outputs:
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- encoder_out, a tensor of shape (N, T', joiner_dim)
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- encoder_out_lens, a tensor of shape (N,)
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Args:
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encoder_model:
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The input encoder model
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encoder_filename:
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The filename to save the exported ONNX model.
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opset_version:
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The opset version to use.
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"""
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x = torch.zeros(1, 100, 80, dtype=torch.float32)
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x_lens = torch.tensor([100], dtype=torch.int64)
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torch.onnx.export(
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encoder_model,
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(x, x_lens),
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encoder_filename,
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verbose=False,
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opset_version=opset_version,
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input_names=["x", "x_lens"],
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output_names=["encoder_out", "encoder_out_lens"],
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dynamic_axes={
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"x": {0: "N", 1: "T"},
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"x_lens": {0: "N"},
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"encoder_out": {0: "N", 1: "T"},
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"encoder_out_lens": {0: "N"},
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},
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)
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meta_data = {
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"model_type": "conformer",
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"version": "1",
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"model_author": "k2-fsa",
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"comment": "stateless5",
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}
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logging.info(f"meta_data: {meta_data}")
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add_meta_data(filename=encoder_filename, meta_data=meta_data)
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def export_decoder_model_onnx(
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decoder_model: OnnxDecoder,
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decoder_filename: str,
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opset_version: int = 11,
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) -> None:
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"""Export the decoder model to ONNX format.
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The exported model has one input:
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- y: a torch.int64 tensor of shape (N, decoder_model.context_size)
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and has one output:
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- decoder_out: a torch.float32 tensor of shape (N, joiner_dim)
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Args:
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decoder_model:
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The decoder model to be exported.
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decoder_filename:
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Filename to save the exported ONNX model.
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opset_version:
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The opset version to use.
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"""
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context_size = decoder_model.decoder.context_size
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vocab_size = decoder_model.decoder.vocab_size
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y = torch.zeros(10, context_size, dtype=torch.int64)
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torch.onnx.export(
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decoder_model,
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y,
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decoder_filename,
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verbose=False,
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opset_version=opset_version,
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input_names=["y"],
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output_names=["decoder_out"],
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dynamic_axes={
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"y": {0: "N"},
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"decoder_out": {0: "N"},
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},
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)
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meta_data = {
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"context_size": str(context_size),
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"vocab_size": str(vocab_size),
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}
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add_meta_data(filename=decoder_filename, meta_data=meta_data)
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def export_joiner_model_onnx(
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joiner_model: nn.Module,
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joiner_filename: str,
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opset_version: int = 11,
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) -> None:
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"""Export the joiner model to ONNX format.
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The exported joiner model has two inputs:
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- encoder_out: a tensor of shape (N, joiner_dim)
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- decoder_out: a tensor of shape (N, joiner_dim)
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and produces one output:
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- logit: a tensor of shape (N, vocab_size)
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"""
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joiner_dim = joiner_model.output_linear.weight.shape[1]
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logging.info(f"joiner dim: {joiner_dim}")
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projected_encoder_out = torch.rand(11, joiner_dim, dtype=torch.float32)
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projected_decoder_out = torch.rand(11, joiner_dim, dtype=torch.float32)
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torch.onnx.export(
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joiner_model,
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(projected_encoder_out, projected_decoder_out),
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joiner_filename,
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verbose=False,
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opset_version=opset_version,
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input_names=[
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"encoder_out",
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"decoder_out",
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],
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output_names=["logit"],
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dynamic_axes={
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"encoder_out": {0: "N"},
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"decoder_out": {0: "N"},
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"logit": {0: "N"},
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},
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)
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meta_data = {
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"joiner_dim": str(joiner_dim),
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}
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add_meta_data(filename=joiner_filename, meta_data=meta_data)
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@torch.no_grad()
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def main():
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args = get_parser().parse_args()
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args.exp_dir = Path(args.exp_dir)
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params = get_params()
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params.update(vars(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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setup_logger(f"{params.exp_dir}/log-export/log-export-onnx")
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logging.info(f"device: {device}")
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lexicon = Lexicon(params.lang_dir)
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params.blank_id = 0
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params.vocab_size = max(lexicon.tokens) + 1
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logging.info(params)
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logging.info("About to create model")
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model = get_transducer_model(params)
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model.to(device)
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if params.avg == 1:
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load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
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else:
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start = params.epoch - params.avg + 1
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filenames = []
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for i in range(start, params.epoch + 1):
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if start >= 0:
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filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
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logging.info(f"averaging {filenames}")
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model.to(device)
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model.load_state_dict(average_checkpoints(filenames, device=device))
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model.to("cpu")
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model.eval()
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convert_scaled_to_non_scaled(model, inplace=True)
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encoder = OnnxEncoder(
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encoder=model.encoder,
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encoder_proj=model.joiner.encoder_proj,
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)
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decoder = OnnxDecoder(
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decoder=model.decoder,
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decoder_proj=model.joiner.decoder_proj,
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)
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joiner = OnnxJoiner(output_linear=model.joiner.output_linear)
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encoder_num_param = sum([p.numel() for p in encoder.parameters()])
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decoder_num_param = sum([p.numel() for p in decoder.parameters()])
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joiner_num_param = sum([p.numel() for p in joiner.parameters()])
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total_num_param = encoder_num_param + decoder_num_param + joiner_num_param
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logging.info(f"encoder parameters: {encoder_num_param}")
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logging.info(f"decoder parameters: {decoder_num_param}")
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logging.info(f"joiner parameters: {joiner_num_param}")
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logging.info(f"total parameters: {total_num_param}")
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if params.iter > 0:
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suffix = f"iter-{params.iter}"
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else:
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suffix = f"epoch-{params.epoch}"
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suffix += f"-avg-{params.avg}"
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opset_version = 13
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logging.info("Exporting encoder")
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encoder_filename = params.exp_dir / f"encoder-{suffix}.onnx"
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export_encoder_model_onnx(
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encoder,
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encoder_filename,
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opset_version=opset_version,
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)
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logging.info(f"Exported encoder to {encoder_filename}")
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logging.info("Exporting decoder")
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decoder_filename = params.exp_dir / f"decoder-{suffix}.onnx"
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export_decoder_model_onnx(
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decoder,
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decoder_filename,
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opset_version=opset_version,
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)
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logging.info(f"Exported decoder to {decoder_filename}")
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logging.info("Exporting joiner")
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joiner_filename = params.exp_dir / f"joiner-{suffix}.onnx"
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export_joiner_model_onnx(
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joiner,
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joiner_filename,
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opset_version=opset_version,
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)
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logging.info(f"Exported joiner to {joiner_filename}")
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# Generate int8 quantization models
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# See https://onnxruntime.ai/docs/performance/model-optimizations/quantization.html#data-type-selection
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logging.info("Generate int8 quantization models")
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encoder_filename_int8 = params.exp_dir / f"encoder-{suffix}.int8.onnx"
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quantize_dynamic(
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model_input=encoder_filename,
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model_output=encoder_filename_int8,
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op_types_to_quantize=["MatMul"],
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weight_type=QuantType.QInt8,
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)
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decoder_filename_int8 = params.exp_dir / f"decoder-{suffix}.int8.onnx"
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quantize_dynamic(
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model_input=decoder_filename,
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model_output=decoder_filename_int8,
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op_types_to_quantize=["MatMul"],
|
||||
weight_type=QuantType.QInt8,
|
||||
)
|
||||
|
||||
joiner_filename_int8 = params.exp_dir / f"joiner-{suffix}.int8.onnx"
|
||||
quantize_dynamic(
|
||||
model_input=joiner_filename,
|
||||
model_output=joiner_filename_int8,
|
||||
op_types_to_quantize=["MatMul"],
|
||||
weight_type=QuantType.QInt8,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
|
||||
|
||||
main()
|
||||
@ -59,23 +59,7 @@ It will generate the following files:
|
||||
|
||||
Check ./jit_pretrained.py for usage.
|
||||
|
||||
(3) Export to ONNX format
|
||||
|
||||
./pruned_transducer_stateless2/export.py \
|
||||
--exp-dir ./pruned_transducer_stateless2/exp \
|
||||
--lang-dir data/lang_char \
|
||||
--epoch 10 \
|
||||
--avg 2 \
|
||||
--onnx 1
|
||||
|
||||
Refer to ./onnx_check.py and ./onnx_pretrained.py
|
||||
for usage.
|
||||
|
||||
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_stateless2/export.py \
|
||||
--exp-dir ./pruned_transducer_stateless2/exp \
|
||||
@ -184,23 +168,6 @@ 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,
|
||||
@ -333,206 +300,6 @@ 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}")
|
||||
|
||||
|
||||
def main():
|
||||
args = get_parser().parse_args()
|
||||
args.exp_dir = Path(args.exp_dir)
|
||||
@ -573,31 +340,7 @@ 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:
|
||||
if params.jit:
|
||||
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
|
||||
|
||||
@ -1,390 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
# Copyright 2022 Xiaomi Corp. (authors: Fangjun Kuang)
|
||||
#
|
||||
# See ../../../../LICENSE for clarification regarding multiple authors
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""
|
||||
This script loads ONNX models and uses them to decode waves.
|
||||
You can use the following command to get the exported models:
|
||||
|
||||
./pruned_transducer_stateless2/export.py \
|
||||
--exp-dir ./pruned_transducer_stateless3/exp \
|
||||
--lang-dir data/lang_char \
|
||||
--epoch 20 \
|
||||
--avg 10 \
|
||||
--onnx 1
|
||||
|
||||
Usage of this script:
|
||||
|
||||
./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 \
|
||||
--tokens data/lang_char/tokens.txt \
|
||||
/path/to/foo.wav \
|
||||
/path/to/bar.wav
|
||||
|
||||
We provide pretrained models at:
|
||||
https://huggingface.co/luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless2/tree/main/exp
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
import math
|
||||
from typing import List
|
||||
|
||||
import k2
|
||||
import kaldifeat
|
||||
import numpy as np
|
||||
|
||||
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
|
||||
import torchaudio
|
||||
from torch.nn.utils.rnn import pad_sequence
|
||||
|
||||
|
||||
def get_parser():
|
||||
parser = argparse.ArgumentParser(
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--encoder-model-filename",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Path to the encoder onnx model. ",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--decoder-model-filename",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Path to the decoder onnx model. ",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--joiner-model-filename",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Path to the joiner onnx model. ",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--joiner-encoder-proj-model-filename",
|
||||
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(
|
||||
"--tokens",
|
||||
type=str,
|
||||
help="""Path to tokens.txt""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"sound_files",
|
||||
type=str,
|
||||
nargs="+",
|
||||
help="The input sound file(s) to transcribe. "
|
||||
"Supported formats are those supported by torchaudio.load(). "
|
||||
"For example, wav and flac are supported. "
|
||||
"The sample rate has to be 16kHz.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--sample-rate",
|
||||
type=int,
|
||||
default=16000,
|
||||
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
|
||||
|
||||
|
||||
def read_sound_files(
|
||||
filenames: List[str], expected_sample_rate: float
|
||||
) -> List[torch.Tensor]:
|
||||
"""Read a list of sound files into a list 1-D float32 torch tensors.
|
||||
Args:
|
||||
filenames:
|
||||
A list of sound filenames.
|
||||
expected_sample_rate:
|
||||
The expected sample rate of the sound files.
|
||||
Returns:
|
||||
Return a list of 1-D float32 torch tensors.
|
||||
"""
|
||||
ans = []
|
||||
for f in filenames:
|
||||
wave, sample_rate = torchaudio.load(f)
|
||||
assert (
|
||||
sample_rate == expected_sample_rate
|
||||
), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
|
||||
# We use only the first channel
|
||||
ans.append(wave[0])
|
||||
return ans
|
||||
|
||||
|
||||
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,
|
||||
) -> 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.
|
||||
encoder_out:
|
||||
A 3-D tensor of shape (N, T, C)
|
||||
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.size(0) >= 1, encoder_out.size(0)
|
||||
|
||||
packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence(
|
||||
input=encoder_out,
|
||||
lengths=encoder_out_lens.cpu(),
|
||||
batch_first=True,
|
||||
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()
|
||||
N = encoder_out.size(0)
|
||||
|
||||
assert torch.all(encoder_out_lens > 0), encoder_out_lens
|
||||
assert N == batch_size_list[0], (N, batch_size_list)
|
||||
|
||||
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)
|
||||
|
||||
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)
|
||||
offset = end
|
||||
|
||||
projected_decoder_out = projected_decoder_out[:batch_size]
|
||||
|
||||
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
|
||||
y = logits.argmax(dim=1).tolist()
|
||||
emitted = False
|
||||
for i, v in enumerate(y):
|
||||
if v != blank_id:
|
||||
hyps[i].append(v)
|
||||
emitted = True
|
||||
if emitted:
|
||||
# update decoder output
|
||||
decoder_input = [h[-context_size:] for h in hyps[:batch_size]]
|
||||
decoder_input = torch.tensor(
|
||||
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)
|
||||
|
||||
sorted_ans = [h[context_size:] for h in hyps]
|
||||
ans = []
|
||||
unsorted_indices = packed_encoder_out.unsorted_indices.tolist()
|
||||
for i in range(N):
|
||||
ans.append(sorted_ans[unsorted_indices[i]])
|
||||
|
||||
return ans
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
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,
|
||||
)
|
||||
|
||||
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,
|
||||
)
|
||||
|
||||
logging.info("Constructing Fbank computer")
|
||||
opts = kaldifeat.FbankOptions()
|
||||
opts.device = "cpu"
|
||||
opts.frame_opts.dither = 0
|
||||
opts.frame_opts.snip_edges = False
|
||||
opts.frame_opts.samp_freq = args.sample_rate
|
||||
opts.mel_opts.num_bins = 80
|
||||
|
||||
fbank = kaldifeat.Fbank(opts)
|
||||
|
||||
logging.info(f"Reading sound files: {args.sound_files}")
|
||||
waves = read_sound_files(
|
||||
filenames=args.sound_files,
|
||||
expected_sample_rate=args.sample_rate,
|
||||
)
|
||||
|
||||
logging.info("Decoding started")
|
||||
features = fbank(waves)
|
||||
feature_lengths = [f.size(0) for f in features]
|
||||
|
||||
features = pad_sequence(
|
||||
features,
|
||||
batch_first=True,
|
||||
padding_value=math.log(1e-10),
|
||||
)
|
||||
|
||||
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(),
|
||||
},
|
||||
)
|
||||
|
||||
hyps = greedy_search(
|
||||
decoder=decoder,
|
||||
joiner=joiner,
|
||||
joiner_encoder_proj=joiner_encoder_proj,
|
||||
joiner_decoder_proj=joiner_decoder_proj,
|
||||
encoder_out=encoder_out,
|
||||
encoder_out_lens=encoder_out_lens,
|
||||
context_size=args.context_size,
|
||||
)
|
||||
symbol_table = k2.SymbolTable.from_file(args.tokens)
|
||||
s = "\n"
|
||||
for filename, hyp in zip(args.sound_files, hyps):
|
||||
words = "".join([symbol_table[i] for i in hyp])
|
||||
s += f"{filename}:\n{words}\n\n"
|
||||
logging.info(s)
|
||||
|
||||
logging.info("Decoding Done")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
|
||||
|
||||
logging.basicConfig(format=formatter, level=logging.INFO)
|
||||
main()
|
||||
@ -0,0 +1 @@
|
||||
../pruned_transducer_stateless5/onnx_pretrained.py
|
||||
Loading…
x
Reference in New Issue
Block a user