#!/usr/bin/env python3 # # Copyright 2021-2022 Xiaomi Corporation (Author: Fangjun Kuang, Zengwei Yao) # # 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 converts several saved checkpoints # to a single one using model averaging. """ Usage: (1) Export to torchscript model using torch.jit.trace() ./lstm_transducer_stateless3/export.py \ --exp-dir ./lstm_transducer_stateless3/exp \ --tokens data/lang_bpe_500/tokens.txt \ --epoch 40 \ --avg 20 \ --jit-trace 1 It will generate 3 files: `encoder_jit_trace.pt`, `decoder_jit_trace.pt`, and `joiner_jit_trace.pt`. (2) Export `model.state_dict()` ./lstm_transducer_stateless3/export.py \ --exp-dir ./lstm_transducer_stateless3/exp \ --tokens data/lang_bpe_500/tokens.txt \ --epoch 40 \ --avg 20 It will generate a file `pretrained.pt` in the given `exp_dir`. You can later load it by `icefall.checkpoint.load_checkpoint()`. To use the generated file with `lstm_transducer_stateless3/decode.py`, you can do: cd /path/to/exp_dir ln -s pretrained.pt epoch-9999.pt cd /path/to/egs/librispeech/ASR ./lstm_transducer_stateless3/decode.py \ --exp-dir ./lstm_transducer_stateless3/exp \ --epoch 9999 \ --avg 1 \ --max-duration 600 \ --decoding-method greedy_search \ --bpe-model data/lang_bpe_500/bpe.model Check ./pretrained.py for its usage. Note: If you don't want to train a model from scratch, we have provided one for you. You can get it at https://huggingface.co/Zengwei/icefall-asr-librispeech-lstm-transducer-stateless-2022-08-18 with the following commands: sudo apt-get install git-lfs git lfs install git clone https://huggingface.co/Zengwei/icefall-asr-librispeech-lstm-transducer-stateless-2022-08-18 # You will find the pre-trained model in icefall-asr-librispeech-lstm-transducer-stateless-2022-08-18/exp """ import argparse import logging from pathlib import Path import k2 import torch import torch.nn as nn 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, average_checkpoints_with_averaged_model, find_checkpoints, load_checkpoint, ) from icefall.utils import num_tokens, str2bool 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( "--use-averaged-model", type=str2bool, default=True, help="Whether to load averaged model. Currently it only supports " "using --epoch. If True, it would decode with the averaged model " "over the epoch range from `epoch-avg` (excluded) to `epoch`." "Actually only the models with epoch number of `epoch-avg` and " "`epoch` are loaded for averaging. ", ) 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( "--tokens", type=str, default="data/lang_bpe_500/tokens.txt", help="Path to tokens.txt.", ) parser.add_argument( "--jit-trace", type=str2bool, default=False, help="""True to save a model after applying torch.jit.trace. It will generate 3 files: - encoder_jit_trace.pt - decoder_jit_trace.pt - joiner_jit_trace.pt Check ./jit_pretrained.py for how to use them. """, ) 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 export_encoder_model_jit_trace( encoder_model: nn.Module, encoder_filename: str, ) -> None: """Export the given encoder model with torch.jit.trace() Note: The warmup argument is fixed to 1. Args: encoder_model: The input encoder model encoder_filename: The filename to save the exported model. """ x = torch.zeros(1, 100, 80, dtype=torch.float32) x_lens = torch.tensor([100], dtype=torch.int64) states = encoder_model.get_init_states() traced_model = torch.jit.trace(encoder_model, (x, x_lens, states)) traced_model.save(encoder_filename) logging.info(f"Saved to {encoder_filename}") def export_decoder_model_jit_trace( decoder_model: nn.Module, decoder_filename: str, ) -> None: """Export the given decoder model with torch.jit.trace() Note: The argument need_pad is fixed to False. Args: decoder_model: The input decoder model decoder_filename: The filename to save the exported model. """ y = torch.zeros(10, decoder_model.context_size, dtype=torch.int64) need_pad = torch.tensor([False]) traced_model = torch.jit.trace(decoder_model, (y, need_pad)) traced_model.save(decoder_filename) logging.info(f"Saved to {decoder_filename}") def export_joiner_model_jit_trace( joiner_model: nn.Module, joiner_filename: str, ) -> None: """Export the given joiner model with torch.jit.trace() Note: The argument project_input is fixed to True. A user should not project the encoder_out/decoder_out by himself/herself. The exported joiner will do that for the user. Args: joiner_model: The input joiner model joiner_filename: The filename to save the exported model. """ encoder_out_dim = joiner_model.encoder_proj.weight.shape[1] decoder_out_dim = joiner_model.decoder_proj.weight.shape[1] encoder_out = torch.rand(1, encoder_out_dim, dtype=torch.float32) decoder_out = torch.rand(1, decoder_out_dim, dtype=torch.float32) traced_model = torch.jit.trace(joiner_model, (encoder_out, decoder_out)) traced_model.save(joiner_filename) logging.info(f"Saved to {joiner_filename}") @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) logging.info(f"device: {device}") # Load tokens.txt here token_table = k2.SymbolTable.from_file(params.tokens) # Load id of the token and the vocab size, is # defined in local/train_bpe_model.py params.blank_id = token_table[""] params.vocab_size = num_tokens(token_table) + 1 # +1 for logging.info(params) logging.info("About to create model") model = get_transducer_model(params) if not params.use_averaged_model: 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" f" --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)) 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 i >= 1: 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)) else: if params.iter > 0: filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[ : params.avg + 1 ] if len(filenames) == 0: raise ValueError( f"No checkpoints found for" f" --iter {params.iter}, --avg {params.avg}" ) elif len(filenames) < params.avg + 1: raise ValueError( f"Not enough checkpoints ({len(filenames)}) found for" f" --iter {params.iter}, --avg {params.avg}" ) filename_start = filenames[-1] filename_end = filenames[0] logging.info( "Calculating the averaged model over iteration checkpoints" f" from {filename_start} (excluded) to {filename_end}" ) model.to(device) model.load_state_dict( average_checkpoints_with_averaged_model( filename_start=filename_start, filename_end=filename_end, device=device, ) ) else: assert params.avg > 0, params.avg start = params.epoch - params.avg assert start >= 1, start filename_start = f"{params.exp_dir}/epoch-{start}.pt" filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt" logging.info( f"Calculating the averaged model over epoch range from " f"{start} (excluded) to {params.epoch}" ) model.to(device) model.load_state_dict( average_checkpoints_with_averaged_model( filename_start=filename_start, filename_end=filename_end, device=device, ) ) model.to("cpu") model.eval() if params.jit_trace is True: convert_scaled_to_non_scaled(model, inplace=True) logging.info("Using torch.jit.trace()") encoder_filename = params.exp_dir / "encoder_jit_trace.pt" export_encoder_model_jit_trace(model.encoder, encoder_filename) decoder_filename = params.exp_dir / "decoder_jit_trace.pt" export_decoder_model_jit_trace(model.decoder, decoder_filename) joiner_filename = params.exp_dir / "joiner_jit_trace.pt" export_joiner_model_jit_trace(model.joiner, joiner_filename) else: logging.info("Not using torchscript") # Save it using a format so that it can be loaded # by :func:`load_checkpoint` filename = params.exp_dir / "pretrained.pt" torch.save({"model": model.state_dict()}, str(filename)) logging.info(f"Saved to {filename}") if __name__ == "__main__": formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" logging.basicConfig(format=formatter, level=logging.INFO) main()