From 724e387c6fa836fe73cfea6ed0e39b9cc2463fdb Mon Sep 17 00:00:00 2001 From: pkufool Date: Fri, 2 Feb 2024 12:23:14 +0800 Subject: [PATCH] symbol link export.py --- egs/wenetspeech/KWS/zipformer/export.py | 527 +----------------------- 1 file changed, 1 insertion(+), 526 deletions(-) mode change 100755 => 120000 egs/wenetspeech/KWS/zipformer/export.py diff --git a/egs/wenetspeech/KWS/zipformer/export.py b/egs/wenetspeech/KWS/zipformer/export.py deleted file mode 100755 index 2b8d1aaf36..0000000000 --- a/egs/wenetspeech/KWS/zipformer/export.py +++ /dev/null @@ -1,526 +0,0 @@ -#!/usr/bin/env python3 -# -# Copyright 2021-2023 Xiaomi Corporation (Author: Fangjun Kuang, -# Zengwei Yao, -# Wei Kang) -# -# 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: - -Note: This is a example for librispeech dataset, if you are using different -dataset, you should change the argument values according to your dataset. - -(1) Export to torchscript model using torch.jit.script() - -- For non-streaming model: - -./zipformer/export.py \ - --exp-dir ./zipformer/exp \ - --tokens data/lang_bpe_500/tokens.txt \ - --epoch 30 \ - --avg 9 \ - --jit 1 - -It will generate a file `jit_script.pt` in the given `exp_dir`. You can later -load it by `torch.jit.load("jit_script.pt")`. - -Check ./jit_pretrained.py for its usage. - -Check https://github.com/k2-fsa/sherpa -for how to use the exported models outside of icefall. - -- For streaming model: - -./zipformer/export.py \ - --exp-dir ./zipformer/exp \ - --causal 1 \ - --chunk-size 16 \ - --left-context-frames 128 \ - --tokens data/lang_bpe_500/tokens.txt \ - --epoch 30 \ - --avg 9 \ - --jit 1 - -It will generate a file `jit_script_chunk_16_left_128.pt` in the given `exp_dir`. -You can later load it by `torch.jit.load("jit_script_chunk_16_left_128.pt")`. - -Check ./jit_pretrained_streaming.py for its usage. - -Check https://github.com/k2-fsa/sherpa -for how to use the exported models outside of icefall. - -(2) Export `model.state_dict()` - -- For non-streaming model: - -./zipformer/export.py \ - --exp-dir ./zipformer/exp \ - --tokens data/lang_bpe_500/tokens.txt \ - --epoch 30 \ - --avg 9 - -- For streaming model: - -./zipformer/export.py \ - --exp-dir ./zipformer/exp \ - --causal 1 \ - --tokens data/lang_bpe_500/tokens.txt \ - --epoch 30 \ - --avg 9 - -It will generate a file `pretrained.pt` in the given `exp_dir`. You can later -load it by `icefall.checkpoint.load_checkpoint()`. - -- For non-streaming model: - -To use the generated file with `zipformer/decode.py`, -you can do: - - cd /path/to/exp_dir - ln -s pretrained.pt epoch-9999.pt - - cd /path/to/egs/librispeech/ASR - ./zipformer/decode.py \ - --exp-dir ./zipformer/exp \ - --epoch 9999 \ - --avg 1 \ - --max-duration 600 \ - --decoding-method greedy_search \ - --bpe-model data/lang_bpe_500/bpe.model - -- For streaming model: - -To use the generated file with `zipformer/decode.py` and `zipformer/streaming_decode.py`, you can do: - - cd /path/to/exp_dir - ln -s pretrained.pt epoch-9999.pt - - cd /path/to/egs/librispeech/ASR - - # simulated streaming decoding - ./zipformer/decode.py \ - --exp-dir ./zipformer/exp \ - --epoch 9999 \ - --avg 1 \ - --max-duration 600 \ - --causal 1 \ - --chunk-size 16 \ - --left-context-frames 128 \ - --decoding-method greedy_search \ - --bpe-model data/lang_bpe_500/bpe.model - - # chunk-wise streaming decoding - ./zipformer/streaming_decode.py \ - --exp-dir ./zipformer/exp \ - --epoch 9999 \ - --avg 1 \ - --max-duration 600 \ - --causal 1 \ - --chunk-size 16 \ - --left-context-frames 128 \ - --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 - -- non-streaming model: -https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-2023-05-15 - -- streaming model: -https://huggingface.co/Zengwei/icefall-asr-librispeech-streaming-zipformer-2023-05-17 - -with the following commands: - - sudo apt-get install git-lfs - git lfs install - git clone https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-2023-05-15 - git clone https://huggingface.co/Zengwei/icefall-asr-librispeech-streaming-zipformer-2023-05-17 - # You will find the pre-trained models in exp dir -""" - -import argparse -import logging -from pathlib import Path -from typing import List, Tuple - -import k2 -import torch -from scaling_converter import convert_scaled_to_non_scaled -from torch import Tensor, nn -from train import add_model_arguments, get_model, get_params - -from icefall.checkpoint import ( - average_checkpoints, - average_checkpoints_with_averaged_model, - find_checkpoints, - load_checkpoint, -) -from icefall.utils import make_pad_mask, num_tokens, str2bool - - -def get_parser(): - parser = argparse.ArgumentParser( - formatter_class=argparse.ArgumentDefaultsHelpFormatter - ) - - parser.add_argument( - "--epoch", - type=int, - default=30, - help="""It specifies the checkpoint to use for decoding. - Note: Epoch counts from 1. - 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=9, - 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="zipformer/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 the tokens.txt", - ) - - parser.add_argument( - "--jit", - type=str2bool, - default=False, - help="""True to save a model after applying torch.jit.script. - It will generate a file named jit_script.pt. - Check ./jit_pretrained.py for how to use it. - """, - ) - - 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 - - -class EncoderModel(nn.Module): - """A wrapper for encoder and encoder_embed""" - - def __init__(self, encoder: nn.Module, encoder_embed: nn.Module) -> None: - super().__init__() - self.encoder = encoder - self.encoder_embed = encoder_embed - - def forward( - self, features: Tensor, feature_lengths: Tensor - ) -> Tuple[Tensor, Tensor]: - """ - Args: - features: (N, T, C) - feature_lengths: (N,) - """ - x, x_lens = self.encoder_embed(features, feature_lengths) - - src_key_padding_mask = make_pad_mask(x_lens) - x = x.permute(1, 0, 2) # (N, T, C) -> (T, N, C) - - encoder_out, encoder_out_lens = self.encoder(x, x_lens, src_key_padding_mask) - encoder_out = encoder_out.permute(1, 0, 2) # (T, N, C) ->(N, T, C) - - return encoder_out, encoder_out_lens - - -class StreamingEncoderModel(nn.Module): - """A wrapper for encoder and encoder_embed""" - - def __init__(self, encoder: nn.Module, encoder_embed: nn.Module) -> None: - super().__init__() - assert len(encoder.chunk_size) == 1, encoder.chunk_size - assert len(encoder.left_context_frames) == 1, encoder.left_context_frames - self.chunk_size = encoder.chunk_size[0] - self.left_context_len = encoder.left_context_frames[0] - - # The encoder_embed subsample features (T - 7) // 2 - # The ConvNeXt module needs (7 - 1) // 2 = 3 frames of right padding after subsampling - self.pad_length = 7 + 2 * 3 - - self.encoder = encoder - self.encoder_embed = encoder_embed - - def forward( - self, features: Tensor, feature_lengths: Tensor, states: List[Tensor] - ) -> Tuple[Tensor, Tensor, List[Tensor]]: - """Streaming forward for encoder_embed and encoder. - - Args: - features: (N, T, C) - feature_lengths: (N,) - states: a list of Tensors - - Returns encoder outputs, output lengths, and updated states. - """ - chunk_size = self.chunk_size - left_context_len = self.left_context_len - - cached_embed_left_pad = states[-2] - x, x_lens, new_cached_embed_left_pad = self.encoder_embed.streaming_forward( - x=features, - x_lens=feature_lengths, - cached_left_pad=cached_embed_left_pad, - ) - assert x.size(1) == chunk_size, (x.size(1), chunk_size) - - src_key_padding_mask = make_pad_mask(x_lens) - - # processed_mask is used to mask out initial states - processed_mask = torch.arange(left_context_len, device=x.device).expand( - x.size(0), left_context_len - ) - processed_lens = states[-1] # (batch,) - # (batch, left_context_size) - processed_mask = (processed_lens.unsqueeze(1) <= processed_mask).flip(1) - # Update processed lengths - new_processed_lens = processed_lens + x_lens - - # (batch, left_context_size + chunk_size) - src_key_padding_mask = torch.cat([processed_mask, src_key_padding_mask], dim=1) - - x = x.permute(1, 0, 2) # (N, T, C) -> (T, N, C) - encoder_states = states[:-2] - - ( - encoder_out, - encoder_out_lens, - new_encoder_states, - ) = self.encoder.streaming_forward( - x=x, - x_lens=x_lens, - states=encoder_states, - src_key_padding_mask=src_key_padding_mask, - ) - encoder_out = encoder_out.permute(1, 0, 2) # (T, N, C) ->(N, T, C) - - new_states = new_encoder_states + [ - new_cached_embed_left_pad, - new_processed_lens, - ] - return encoder_out, encoder_out_lens, new_states - - @torch.jit.export - def get_init_states( - self, - batch_size: int = 1, - device: torch.device = torch.device("cpu"), - ) -> List[torch.Tensor]: - """ - Returns a list of cached tensors of all encoder layers. For layer-i, states[i*6:(i+1)*6] - is (cached_key, cached_nonlin_attn, cached_val1, cached_val2, cached_conv1, cached_conv2). - states[-2] is the cached left padding for ConvNeXt module, - of shape (batch_size, num_channels, left_pad, num_freqs) - states[-1] is processed_lens of shape (batch,), which records the number - of processed frames (at 50hz frame rate, after encoder_embed) for each sample in batch. - """ - states = self.encoder.get_init_states(batch_size, device) - - embed_states = self.encoder_embed.get_init_states(batch_size, device) - states.append(embed_states) - - processed_lens = torch.zeros(batch_size, dtype=torch.int32, device=device) - states.append(processed_lens) - - return states - - -@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}") - - token_table = k2.SymbolTable.from_file(params.tokens) - params.blank_id = token_table[""] - params.vocab_size = num_tokens(token_table) + 1 - - logging.info(params) - - logging.info("About to create model") - model = get_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.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.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.load_state_dict( - average_checkpoints_with_averaged_model( - filename_start=filename_start, - filename_end=filename_end, - device=device, - ) - ) - elif params.avg == 1: - load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) - 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.load_state_dict( - average_checkpoints_with_averaged_model( - filename_start=filename_start, - filename_end=filename_end, - device=device, - ) - ) - - model.eval() - - if params.jit is True: - convert_scaled_to_non_scaled(model, inplace=True) - # We won't use the forward() method of the model in C++, so just ignore - # it here. - # Otherwise, one of its arguments is a ragged tensor and is not - # torch scriptabe. - model.__class__.forward = torch.jit.ignore(model.__class__.forward) - - # Wrap encoder and encoder_embed as a module - if params.causal: - model.encoder = StreamingEncoderModel(model.encoder, model.encoder_embed) - chunk_size = model.encoder.chunk_size - left_context_len = model.encoder.left_context_len - filename = f"jit_script_chunk_{chunk_size}_left_{left_context_len}.pt" - else: - model.encoder = EncoderModel(model.encoder, model.encoder_embed) - filename = "jit_script.pt" - - logging.info("Using torch.jit.script") - model = torch.jit.script(model) - model.save(str(params.exp_dir / filename)) - logging.info(f"Saved to {filename}") - else: - logging.info("Not using torchscript. Export model.state_dict()") - # 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() diff --git a/egs/wenetspeech/KWS/zipformer/export.py b/egs/wenetspeech/KWS/zipformer/export.py new file mode 120000 index 0000000000..dfc1bec080 --- /dev/null +++ b/egs/wenetspeech/KWS/zipformer/export.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/export.py \ No newline at end of file