diff --git a/egs/tal_csasr/ASR/lstm_transducer_stateless3/asr_datamodule.py b/egs/tal_csasr/ASR/lstm_transducer_stateless3/asr_datamodule.py new file mode 120000 index 000000000..c473a600a --- /dev/null +++ b/egs/tal_csasr/ASR/lstm_transducer_stateless3/asr_datamodule.py @@ -0,0 +1 @@ +../pruned_transducer_stateless5/asr_datamodule.py \ No newline at end of file diff --git a/egs/tal_csasr/ASR/lstm_transducer_stateless3/beam_search.py b/egs/tal_csasr/ASR/lstm_transducer_stateless3/beam_search.py new file mode 120000 index 000000000..4eef3d295 --- /dev/null +++ b/egs/tal_csasr/ASR/lstm_transducer_stateless3/beam_search.py @@ -0,0 +1 @@ +../pruned_transducer_stateless5/beam_search.py \ No newline at end of file diff --git a/egs/tal_csasr/ASR/lstm_transducer_stateless3/decode.py b/egs/tal_csasr/ASR/lstm_transducer_stateless3/decode.py new file mode 100755 index 000000000..f40d22cd8 --- /dev/null +++ b/egs/tal_csasr/ASR/lstm_transducer_stateless3/decode.py @@ -0,0 +1,748 @@ +#!/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. +""" +Usage: +(1) greedy search +./lstm_transducer_stateless3/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./lstm_transducer_stateless3/exp \ + --max-duration 600 \ + --decoding-method greedy_search + +(2) beam search (not recommended) +./lstm_transducer_stateless3/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./lstm_transducer_stateless3/exp \ + --max-duration 600 \ + --decoding-method beam_search \ + --beam-size 4 + +(3) modified beam search +./lstm_transducer_stateless3/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./lstm_transducer_stateless3/exp \ + --max-duration 600 \ + --decoding-method modified_beam_search \ + --beam-size 4 + +(4) fast beam search +./lstm_transducer_stateless3/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./lstm_transducer_stateless3/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search \ + --beam 4 \ + --max-contexts 4 \ + --max-states 8 +""" + + +import argparse +import logging +import re +from collections import defaultdict +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +import k2 +import sentencepiece as spm +import torch +import torch.nn as nn +from asr_datamodule import TAL_CSASRAsrDataModule +from beam_search import ( + beam_search, + fast_beam_search_one_best, + greedy_search, + greedy_search_batch, + modified_beam_search, +) +from lhotse.cut import Cut +from local.text_normalize import text_normalize +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.lexicon import Lexicon +from icefall.utils import ( + AttributeDict, + setup_logger, + store_transcripts, + str2bool, + write_error_stats, +) + + +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=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=False, + 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="lstm_transducer_stateless3/exp", + help="The experiment dir", + ) + + parser.add_argument( + "--lang-dir", + type=str, + default="data/lang_char", + help="""The lang dir + It contains language related input files such as + "lexicon.txt" + """, + ) + + parser.add_argument( + "--decoding-method", + type=str, + default="greedy_search", + help="""Possible values are: + - greedy_search + - beam_search + - modified_beam_search + - fast_beam_search + """, + ) + + parser.add_argument( + "--beam-size", + type=int, + default=4, + help="""An integer indicating how many candidates we will keep for each + frame. Used only when --decoding-method is beam_search or + modified_beam_search.""", + ) + + parser.add_argument( + "--beam", + type=float, + default=4, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --decoding-method is fast_beam_search""", + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=4, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=8, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; 2 means tri-gram", + ) + parser.add_argument( + "--max-sym-per-frame", + type=int, + default=1, + help="""Maximum number of symbols per frame. + Used only when --decoding_method is greedy_search""", + ) + + add_model_arguments(parser) + + return parser + + +def decode_one_batch( + params: AttributeDict, + model: nn.Module, + lexicon: Lexicon, + batch: dict, + decoding_graph: Optional[k2.Fsa] = None, + sp: spm.SentencePieceProcessor = None, +) -> Dict[str, List[List[str]]]: + """Decode one batch and return the result in a dict. The dict has the + following format: + + - key: It indicates the setting used for decoding. For example, + if greedy_search is used, it would be "greedy_search" + If beam search with a beam size of 7 is used, it would be + "beam_7" + - value: It contains the decoding result. `len(value)` equals to + batch size. `value[i]` is the decoding result for the i-th + utterance in the given batch. + Args: + params: + It's the return value of :func:`get_params`. + model: + The neural model. + batch: + It is the return value from iterating + `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation + for the format of the `batch`. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. + Returns: + Return the decoding result. See above description for the format of + the returned dict. + """ + device = next(model.parameters()).device + feature = batch["inputs"] + assert feature.ndim == 3 + + feature = feature.to(device) + # at entry, feature is (N, T, C) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) + + encoder_out, encoder_out_lens, _ = model.encoder(x=feature, x_lens=feature_lens) + hyps = [] + zh_hyps = [] + en_hyps = [] + pattern = re.compile(r"([\u4e00-\u9fff])") + en_letter = "[\u0041-\u005a|\u0061-\u007a]+" # English letters + zh_char = "[\u4e00-\u9fa5]+" # Chinese chars + if params.decoding_method == "fast_beam_search": + hyp_tokens = fast_beam_search_one_best( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + for i in range(encoder_out.size(0)): + hyp = sp.decode([lexicon.token_table[idx] for idx in hyp_tokens[i]]) + chars = pattern.split(hyp.upper()) + chars_new = [] + zh_text = [] + en_text = [] + for char in chars: + if char != "": + tokens = char.strip().split(" ") + chars_new.extend(tokens) + for token in tokens: + zh_text.extend(re.findall(zh_char, token)) + en_text.extend(re.findall(en_letter, token)) + hyps.append(chars_new) + zh_hyps.append(zh_text) + en_hyps.append(en_text) + elif params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1: + hyp_tokens = greedy_search_batch( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + ) + for i in range(encoder_out.size(0)): + hyp = sp.decode([lexicon.token_table[idx] for idx in hyp_tokens[i]]) + chars = pattern.split(hyp.upper()) + chars_new = [] + zh_text = [] + en_text = [] + for char in chars: + if char != "": + tokens = char.strip().split(" ") + chars_new.extend(tokens) + for token in tokens: + zh_text.extend(re.findall(zh_char, token)) + en_text.extend(re.findall(en_letter, token)) + hyps.append(chars_new) + zh_hyps.append(zh_text) + en_hyps.append(en_text) + elif params.decoding_method == "modified_beam_search": + hyp_tokens = modified_beam_search( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam_size, + ) + for i in range(encoder_out.size(0)): + hyp = sp.decode([lexicon.token_table[idx] for idx in hyp_tokens[i]]) + chars = pattern.split(hyp.upper()) + chars_new = [] + zh_text = [] + en_text = [] + for char in chars: + if char != "": + tokens = char.strip().split(" ") + chars_new.extend(tokens) + for token in tokens: + zh_text.extend(re.findall(zh_char, token)) + en_text.extend(re.findall(en_letter, token)) + hyps.append(chars_new) + zh_hyps.append(zh_text) + en_hyps.append(en_text) + else: + batch_size = encoder_out.size(0) + + for i in range(batch_size): + # fmt: off + encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]] + # fmt: on + if params.decoding_method == "greedy_search": + hyp = greedy_search( + model=model, + encoder_out=encoder_out_i, + max_sym_per_frame=params.max_sym_per_frame, + ) + elif params.decoding_method == "beam_search": + hyp = beam_search( + model=model, + encoder_out=encoder_out_i, + beam=params.beam_size, + ) + else: + raise ValueError( + f"Unsupported decoding method: {params.decoding_method}" + ) + for i in range(encoder_out.size(0)): + hyp = sp.decode([lexicon.token_table[idx] for idx in hyp_tokens[i]]) + chars = pattern.split(hyp.upper()) + chars_new = [] + zh_text = [] + en_text = [] + for char in chars: + if char != "": + tokens = char.strip().split(" ") + chars_new.extend(tokens) + for token in tokens: + zh_text.extend(re.findall(zh_char, token)) + en_text.extend(re.findall(en_letter, token)) + hyps.append(chars_new) + zh_hyps.append(zh_text) + en_hyps.append(en_text) + if params.decoding_method == "greedy_search": + return {"greedy_search": (hyps, zh_hyps, en_hyps)} + elif params.decoding_method == "fast_beam_search": + return { + ( + f"beam_{params.beam}_" + f"max_contexts_{params.max_contexts}_" + f"max_states_{params.max_states}" + ): (hyps, zh_hyps, en_hyps) + } + else: + return {f"beam_size_{params.beam_size}": (hyps, zh_hyps, en_hyps)} + + +def decode_dataset( + dl: torch.utils.data.DataLoader, + params: AttributeDict, + model: nn.Module, + lexicon: Lexicon, + decoding_graph: Optional[k2.Fsa] = None, + sp: spm.SentencePieceProcessor = None, +) -> Dict[str, List[Tuple[str, List[str], List[str]]]]: + """Decode dataset. + + Args: + dl: + PyTorch's dataloader containing the dataset to decode. + params: + It is returned by :func:`get_params`. + model: + The neural model. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. + Returns: + Return a dict, whose key may be "greedy_search" if greedy search + is used, or it may be "beam_7" if beam size of 7 is used. + Its value is a list of tuples. Each tuple contains two elements: + The first is the reference transcript, and the second is the + predicted result. + """ + num_cuts = 0 + + try: + num_batches = len(dl) + except TypeError: + num_batches = "?" + + if params.decoding_method == "greedy_search": + log_interval = 50 + else: + log_interval = 20 + + results = defaultdict(list) + zh_results = defaultdict(list) + en_results = defaultdict(list) + pattern = re.compile(r"([\u4e00-\u9fff])") + en_letter = "[\u0041-\u005a|\u0061-\u007a]+" # English letters + zh_char = "[\u4e00-\u9fa5]+" # Chinese chars + for batch_idx, batch in enumerate(dl): + texts = batch["supervisions"]["text"] + cut_ids = [cut.id for cut in batch["supervisions"]["cut"]] + zh_texts = [] + en_texts = [] + for i in range(len(texts)): + text = texts[i] + chars = pattern.split(text.upper()) + chars_new = [] + zh_text = [] + en_text = [] + for char in chars: + if char != "": + tokens = char.strip().split(" ") + chars_new.extend(tokens) + for token in tokens: + zh_text.extend(re.findall(zh_char, token)) + en_text.extend(re.findall(en_letter, token)) + zh_texts.append(zh_text) + en_texts.append(en_text) + texts[i] = chars_new + hyps_dict = decode_one_batch( + params=params, + model=model, + lexicon=lexicon, + decoding_graph=decoding_graph, + batch=batch, + sp=sp, + ) + + for name, hyps_texts in hyps_dict.items(): + this_batch = [] + this_batch_zh = [] + this_batch_en = [] + # print(hyps_texts) + hyps, zh_hyps, en_hyps = hyps_texts + assert len(hyps) == len(texts) + for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts): + this_batch.append((cut_id, ref_text, hyp_words)) + + for cut_id, hyp_words, ref_text in zip(cut_ids, zh_hyps, zh_texts): + this_batch_zh.append((cut_id, ref_text, hyp_words)) + + for cut_id, hyp_words, ref_text in zip(cut_ids, en_hyps, en_texts): + this_batch_en.append((cut_id, ref_text, hyp_words)) + + results[name].extend(this_batch) + zh_results[name + "_zh"].extend(this_batch_zh) + en_results[name + "_en"].extend(this_batch_en) + + num_cuts += len(texts) + + if batch_idx % log_interval == 0: + batch_str = f"{batch_idx}/{num_batches}" + + logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}") + return results, zh_results, en_results + + +def save_results( + params: AttributeDict, + test_set_name: str, + results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]], +): + test_set_wers = dict() + for key, results in results_dict.items(): + recog_path = ( + params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt" + ) + results = sorted(results) + store_transcripts(filename=recog_path, texts=results) + logging.info(f"The transcripts are stored in {recog_path}") + + # The following prints out WERs, per-word error statistics and aligned + # ref/hyp pairs. + errs_filename = ( + params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_filename, "w") as f: + wer = write_error_stats( + f, f"{test_set_name}-{key}", results, enable_log=True + ) + test_set_wers[key] = wer + + logging.info("Wrote detailed error stats to {}".format(errs_filename)) + + test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1]) + errs_info = ( + params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_info, "w") as f: + print("settings\tWER", file=f) + for key, val in test_set_wers: + print("{}\t{}".format(key, val), file=f) + + s = "\nFor {}, WER of different settings are:\n".format(test_set_name) + note = "\tbest for {}".format(test_set_name) + for key, val in test_set_wers: + s += "{}\t{}{}\n".format(key, val, note) + note = "" + logging.info(s) + + +@torch.no_grad() +def main(): + parser = get_parser() + TAL_CSASRAsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + params = get_params() + params.update(vars(args)) + + assert params.decoding_method in ( + "greedy_search", + "beam_search", + "fast_beam_search", + "modified_beam_search", + ) + params.res_dir = params.exp_dir / params.decoding_method + + if params.iter > 0: + params.suffix = f"iter-{params.iter}-avg-{params.avg}" + else: + params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" + + if "fast_beam_search" in params.decoding_method: + params.suffix += f"-beam-{params.beam}" + params.suffix += f"-max-contexts-{params.max_contexts}" + params.suffix += f"-max-states-{params.max_states}" + elif "beam_search" in params.decoding_method: + params.suffix += f"-{params.decoding_method}-beam-size-{params.beam_size}" + else: + params.suffix += f"-context-{params.context_size}" + params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}" + + if params.use_averaged_model: + params.suffix += "-use-averaged-model" + + setup_logger(f"{params.res_dir}/log-decode-{params.suffix}") + logging.info("Decoding started") + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"Device: {device}") + + bpe_model = params.lang_dir + "/bpe.model" + sp = spm.SentencePieceProcessor() + sp.load(bpe_model) + + lexicon = Lexicon(params.lang_dir) + params.blank_id = lexicon.token_table[""] + params.vocab_size = max(lexicon.tokens) + 1 + + 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(device) + model.eval() + + if params.decoding_method == "fast_beam_search": + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + else: + decoding_graph = None + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + def text_normalize_for_cut(c: Cut): + # Text normalize for each sample + text = c.supervisions[0].text + text = text.strip("\n").strip("\t") + c.supervisions[0].text = text_normalize(text) + return c + + # we need cut ids to display recognition results. + args.return_cuts = True + tal_csasr = TAL_CSASRAsrDataModule(args) + + dev_cuts = tal_csasr.valid_cuts() + dev_cuts = dev_cuts.subset(first=300) + dev_cuts = dev_cuts.map(text_normalize_for_cut) + dev_dl = tal_csasr.valid_dataloaders(dev_cuts) + + test_cuts = tal_csasr.test_cuts() + test_cuts = test_cuts.subset(first=300) + test_cuts = test_cuts.map(text_normalize_for_cut) + test_dl = tal_csasr.test_dataloaders(test_cuts) + + test_sets = ["dev", "test"] + test_dl = [dev_dl, test_dl] + + for test_set, test_dl in zip(test_sets, test_dl): + results_dict, zh_results_dict, en_results_dict = decode_dataset( + dl=test_dl, + params=params, + model=model, + lexicon=lexicon, + decoding_graph=decoding_graph, + sp=sp, + ) + save_results( + params=params, + test_set_name=test_set, + results_dict=results_dict, + ) + save_results( + params=params, + test_set_name=test_set, + results_dict=zh_results_dict, + ) + save_results( + params=params, + test_set_name=test_set, + results_dict=en_results_dict, + ) + + logging.info("Done!") + + +if __name__ == "__main__": + main() diff --git a/egs/tal_csasr/ASR/lstm_transducer_stateless3/decoder.py b/egs/tal_csasr/ASR/lstm_transducer_stateless3/decoder.py new file mode 120000 index 000000000..a04a903f6 --- /dev/null +++ b/egs/tal_csasr/ASR/lstm_transducer_stateless3/decoder.py @@ -0,0 +1 @@ +../pruned_transducer_stateless5/decoder.py \ No newline at end of file diff --git a/egs/tal_csasr/ASR/lstm_transducer_stateless3/encoder_interface.py b/egs/tal_csasr/ASR/lstm_transducer_stateless3/encoder_interface.py new file mode 120000 index 000000000..083f693ef --- /dev/null +++ b/egs/tal_csasr/ASR/lstm_transducer_stateless3/encoder_interface.py @@ -0,0 +1 @@ +../pruned_transducer_stateless5/encoder_interface.py \ No newline at end of file diff --git a/egs/tal_csasr/ASR/lstm_transducer_stateless3/export-for-ncnn.py b/egs/tal_csasr/ASR/lstm_transducer_stateless3/export-for-ncnn.py new file mode 100755 index 000000000..83e1b8936 --- /dev/null +++ b/egs/tal_csasr/ASR/lstm_transducer_stateless3/export-for-ncnn.py @@ -0,0 +1,336 @@ +#!/usr/bin/env python3 + +""" +Please see +https://k2-fsa.github.io/icefall/model-export/export-ncnn.html +for more details about how to use this file. + +We use the pre-trained model from +https://huggingface.co/csukuangfj/icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03 +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-lstm-transducer-stateless2-2022-09-03 +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-468000-avg-16.pt" + +cd exp +ln -s pretrained-iter-468000-avg-16.pt epoch-99.pt +popd + +2. Export via torch.jit.trace() + +./lstm_transducer_stateless3/export-for-ncnn.py \ + --exp-dir $repo/exp \ + --lang-dir $repo/data/lang_char \ + --epoch 99 \ + --avg 1 \ + --use-averaged-model 0 \ + +cd ./lstm_transducer_stateless3/exp +pnnx encoder_jit_trace-pnnx.pt +pnnx decoder_jit_trace-pnnx.pt +pnnx joiner_jit_trace-pnnx.pt + +See ./streaming-ncnn-decode.py +and +https://github.com/k2-fsa/sherpa-ncnn +for usage. +""" + +import argparse +import logging +from pathlib import Path + +import sentencepiece as spm +import torch +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.lexicon import Lexicon +from icefall.utils import setup_logger, 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( + "--exp-dir", + type=str, + default="pruned_transducer_stateless2/exp", + help="""It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--lang-dir", + type=str, + default="data/lang_char", + help="Path to the lang", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; 2 means tri-gram", + ) + + 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. ", + ) + + add_model_arguments(parser) + + return parser + + +def export_encoder_model_jit_trace( + encoder_model: torch.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: torch.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: torch.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") + + setup_logger(f"{params.exp_dir}/log-export/log-export-ncnn") + + logging.info(f"device: {device}") + + lexicon = Lexicon(params.lang_dir) + + params.blank_id = lexicon.token_table[""] + params.vocab_size = max(lexicon.tokens) + 1 + + logging.info(params) + + params.is_pnnx = True + + 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() + + convert_scaled_to_non_scaled(model, inplace=True) + logging.info("Using torch.jit.trace()") + + logging.info("Exporting encoder") + encoder_filename = params.exp_dir / "encoder_jit_trace-pnnx.pt" + export_encoder_model_jit_trace(model.encoder, encoder_filename) + + logging.info("Exporting decoder") + decoder_filename = params.exp_dir / "decoder_jit_trace-pnnx.pt" + export_decoder_model_jit_trace(model.decoder, decoder_filename) + + logging.info("Exporting joiner") + joiner_filename = params.exp_dir / "joiner_jit_trace-pnnx.pt" + export_joiner_model_jit_trace(model.joiner, joiner_filename) + + +if __name__ == "__main__": + formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + + main() diff --git a/egs/tal_csasr/ASR/lstm_transducer_stateless3/export-onnx.py b/egs/tal_csasr/ASR/lstm_transducer_stateless3/export-onnx.py new file mode 120000 index 000000000..4b84732ad --- /dev/null +++ b/egs/tal_csasr/ASR/lstm_transducer_stateless3/export-onnx.py @@ -0,0 +1 @@ +../../../librispeech/ASR/lstm_transducer_stateless3/export-onnx.py \ No newline at end of file diff --git a/egs/tal_csasr/ASR/lstm_transducer_stateless3/export.py b/egs/tal_csasr/ASR/lstm_transducer_stateless3/export.py new file mode 100644 index 000000000..5b81866dd --- /dev/null +++ b/egs/tal_csasr/ASR/lstm_transducer_stateless3/export.py @@ -0,0 +1,382 @@ +#!/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 \ + --lang-dir data/lang_char \ + --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 \ + --lang-dir data/lang_char \ + --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 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.lexicon import Lexicon +from icefall.utils import 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( + "--lang-dir", + type=str, + default="data/lang_char", + help="Path to the dir containing 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}") + + lexicon = Lexicon(params.lang_dir) + params.blank_id = lexicon.token_table[""] + params.vocab_size = max(lexicon.tokens) + 1 + + 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() diff --git a/egs/tal_csasr/ASR/lstm_transducer_stateless3/jit_pretrained.py b/egs/tal_csasr/ASR/lstm_transducer_stateless3/jit_pretrained.py new file mode 100644 index 000000000..2bb6e4df5 --- /dev/null +++ b/egs/tal_csasr/ASR/lstm_transducer_stateless3/jit_pretrained.py @@ -0,0 +1,328 @@ +#!/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 torchscript models, either exported by `torch.jit.trace()` +or by `torch.jit.script()`, and uses them to decode waves. +You can use the following command to get the exported models: + +./lstm_transducer_stateless3/export.py \ + --exp-dir ./lstm_transducer_stateless3/exp \ + --bpe-model data/lang_bpe_500/bpe.model \ + --epoch 40 \ + --avg 15 \ + --jit-trace 1 + +Usage of this script: + +./lstm_transducer_stateless3/jit_pretrained.py \ + --encoder-model-filename ./lstm_transducer_stateless3/exp/encoder_jit_trace.pt \ + --decoder-model-filename ./lstm_transducer_stateless3/exp/decoder_jit_trace.pt \ + --joiner-model-filename ./lstm_transducer_stateless3/exp/joiner_jit_trace.pt \ + --bpe-model ./data/lang_bpe_500/bpe.model \ + /path/to/foo.wav \ + /path/to/bar.wav +""" + +import argparse +import logging +import math +from typing import List + +import kaldifeat +import sentencepiece as spm +import torch +import torchaudio +from torch.nn.utils.rnn import pad_sequence + +from icefall.lexicon import Lexicon + + +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 torchscript model. ", + ) + + parser.add_argument( + "--decoder-model-filename", + type=str, + required=True, + help="Path to the decoder torchscript model. ", + ) + + parser.add_argument( + "--joiner-model-filename", + type=str, + required=True, + help="Path to the joiner torchscript model. ", + ) + + parser.add_argument( + "--bpe-model", + type=str, + help="""Path to bpe.model.""", + ) + + parser.add_argument( + "--lang-dir", + type=str, + default="data/lang_char", + help="Path to the dir containing 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: torch.jit.ScriptModule, + joiner: torch.jit.ScriptModule, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + 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. + 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. + """ + 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, + ) + + device = encoder_out.device + 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 = torch.tensor( + hyps, + device=device, + dtype=torch.int64, + ) # (N, context_size) + + decoder_out = decoder( + decoder_input, + need_pad=torch.tensor([False]), + ).squeeze(1) + + offset = 0 + for batch_size in batch_size_list: + start = offset + end = offset + batch_size + current_encoder_out = packed_encoder_out.data[start:end] + current_encoder_out = current_encoder_out + # current_encoder_out's shape: (batch_size, encoder_out_dim) + offset = end + + decoder_out = decoder_out[:batch_size] + + logits = joiner( + current_encoder_out, + decoder_out, + ) + # 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, + device=device, + dtype=torch.int64, + ) + decoder_out = decoder( + decoder_input, + need_pad=torch.tensor([False]), + ) + decoder_out = decoder_out.squeeze(1) + + 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)) + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"device: {device}") + + encoder = torch.jit.load(args.encoder_model_filename) + decoder = torch.jit.load(args.decoder_model_filename) + joiner = torch.jit.load(args.joiner_model_filename) + + encoder.eval() + decoder.eval() + joiner.eval() + + encoder.to(device) + decoder.to(device) + joiner.to(device) + + lexicon = Lexicon(args.lang_dir) + + logging.info("Constructing Fbank computer") + opts = kaldifeat.FbankOptions() + opts.device = device + 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, + ) + waves = [w.to(device) for w in waves] + + 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, device=device) + + states = encoder.get_init_states(batch_size=features.size(0), device=device) + + encoder_out, encoder_out_lens, _ = encoder( + x=features, + x_lens=feature_lengths, + states=states, + ) + + hyps = greedy_search( + decoder=decoder, + joiner=joiner, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + context_size=args.context_size, + ) + s = "\n" + for filename, hyp in zip(args.sound_files, hyps): + words = [lexicon.token_table[idx].replace("▁", " ") for idx in hyp] + words = "".join(words) + 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() diff --git a/egs/tal_csasr/ASR/lstm_transducer_stateless3/joiner.py b/egs/tal_csasr/ASR/lstm_transducer_stateless3/joiner.py new file mode 120000 index 000000000..6319a7f24 --- /dev/null +++ b/egs/tal_csasr/ASR/lstm_transducer_stateless3/joiner.py @@ -0,0 +1 @@ +../pruned_transducer_stateless5/joiner.py \ No newline at end of file diff --git a/egs/tal_csasr/ASR/lstm_transducer_stateless3/lstm.py b/egs/tal_csasr/ASR/lstm_transducer_stateless3/lstm.py new file mode 120000 index 000000000..fb2df81b2 --- /dev/null +++ b/egs/tal_csasr/ASR/lstm_transducer_stateless3/lstm.py @@ -0,0 +1 @@ +../../../librispeech/ASR/lstm_transducer_stateless3/lstm.py \ No newline at end of file diff --git a/egs/tal_csasr/ASR/lstm_transducer_stateless3/lstmp.py b/egs/tal_csasr/ASR/lstm_transducer_stateless3/lstmp.py new file mode 120000 index 000000000..b82e115fc --- /dev/null +++ b/egs/tal_csasr/ASR/lstm_transducer_stateless3/lstmp.py @@ -0,0 +1 @@ +../../../librispeech/ASR/lstm_transducer_stateless2/lstmp.py \ No newline at end of file diff --git a/egs/tal_csasr/ASR/lstm_transducer_stateless3/model.py b/egs/tal_csasr/ASR/lstm_transducer_stateless3/model.py new file mode 120000 index 000000000..ce6f089fd --- /dev/null +++ b/egs/tal_csasr/ASR/lstm_transducer_stateless3/model.py @@ -0,0 +1 @@ +../../../librispeech/ASR/lstm_transducer_stateless3/model.py \ No newline at end of file diff --git a/egs/tal_csasr/ASR/lstm_transducer_stateless3/onnx_check.py b/egs/tal_csasr/ASR/lstm_transducer_stateless3/onnx_check.py new file mode 120000 index 000000000..bb4dfb468 --- /dev/null +++ b/egs/tal_csasr/ASR/lstm_transducer_stateless3/onnx_check.py @@ -0,0 +1 @@ +../../../librispeech/ASR/lstm_transducer_stateless3/onnx_check.py \ No newline at end of file diff --git a/egs/tal_csasr/ASR/lstm_transducer_stateless3/onnx_pretrained.py b/egs/tal_csasr/ASR/lstm_transducer_stateless3/onnx_pretrained.py new file mode 120000 index 000000000..d3f9cbdfb --- /dev/null +++ b/egs/tal_csasr/ASR/lstm_transducer_stateless3/onnx_pretrained.py @@ -0,0 +1 @@ +../../../librispeech/ASR/lstm_transducer_stateless3/onnx_pretrained.py \ No newline at end of file diff --git a/egs/tal_csasr/ASR/lstm_transducer_stateless3/optim.py b/egs/tal_csasr/ASR/lstm_transducer_stateless3/optim.py new file mode 120000 index 000000000..b6d7f207a --- /dev/null +++ b/egs/tal_csasr/ASR/lstm_transducer_stateless3/optim.py @@ -0,0 +1 @@ +../../../librispeech/ASR/lstm_transducer_stateless3/optim.py \ No newline at end of file diff --git a/egs/tal_csasr/ASR/lstm_transducer_stateless3/pretrained.py b/egs/tal_csasr/ASR/lstm_transducer_stateless3/pretrained.py new file mode 120000 index 000000000..9e0d38fe7 --- /dev/null +++ b/egs/tal_csasr/ASR/lstm_transducer_stateless3/pretrained.py @@ -0,0 +1 @@ +../../../librispeech/ASR/lstm_transducer_stateless3/pretrained.py \ No newline at end of file diff --git a/egs/tal_csasr/ASR/lstm_transducer_stateless3/scaling.py b/egs/tal_csasr/ASR/lstm_transducer_stateless3/scaling.py new file mode 120000 index 000000000..9e6d51be0 --- /dev/null +++ b/egs/tal_csasr/ASR/lstm_transducer_stateless3/scaling.py @@ -0,0 +1 @@ +../pruned_transducer_stateless5/scaling.py \ No newline at end of file diff --git a/egs/tal_csasr/ASR/lstm_transducer_stateless3/scaling_converter.py b/egs/tal_csasr/ASR/lstm_transducer_stateless3/scaling_converter.py new file mode 120000 index 000000000..db93d155b --- /dev/null +++ b/egs/tal_csasr/ASR/lstm_transducer_stateless3/scaling_converter.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless3/scaling_converter.py \ No newline at end of file diff --git a/egs/tal_csasr/ASR/lstm_transducer_stateless3/stream.py b/egs/tal_csasr/ASR/lstm_transducer_stateless3/stream.py new file mode 120000 index 000000000..a78cd70ff --- /dev/null +++ b/egs/tal_csasr/ASR/lstm_transducer_stateless3/stream.py @@ -0,0 +1 @@ +../../../librispeech/ASR/lstm_transducer_stateless/stream.py \ No newline at end of file diff --git a/egs/tal_csasr/ASR/lstm_transducer_stateless3/streaming-ncnn-decode.py b/egs/tal_csasr/ASR/lstm_transducer_stateless3/streaming-ncnn-decode.py new file mode 100755 index 000000000..910a799bd --- /dev/null +++ b/egs/tal_csasr/ASR/lstm_transducer_stateless3/streaming-ncnn-decode.py @@ -0,0 +1,372 @@ +#!/usr/bin/env python3 +# flake8: noqa +# +# Copyright 2022 Xiaomi Corp. (authors: 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. +""" +Please see +https://k2-fsa.github.io/icefall/model-export/export-ncnn.html +for usage +""" + +import argparse +import logging +from typing import List, Optional + +import k2 +import ncnn +import torch +import torchaudio +from kaldifeat import FbankOptions, OnlineFbank, OnlineFeature + + +def get_args(): + parser = argparse.ArgumentParser() + + parser.add_argument( + "--tokens", + type=str, + help="Path to tokens.txt", + ) + + parser.add_argument( + "--encoder-param-filename", + type=str, + help="Path to encoder.ncnn.param", + ) + + parser.add_argument( + "--encoder-bin-filename", + type=str, + help="Path to encoder.ncnn.bin", + ) + + parser.add_argument( + "--decoder-param-filename", + type=str, + help="Path to decoder.ncnn.param", + ) + + parser.add_argument( + "--decoder-bin-filename", + type=str, + help="Path to decoder.ncnn.bin", + ) + + parser.add_argument( + "--joiner-param-filename", + type=str, + help="Path to joiner.ncnn.param", + ) + + parser.add_argument( + "--joiner-bin-filename", + type=str, + help="Path to joiner.ncnn.bin", + ) + + parser.add_argument( + "--num-encoder-layers", + type=int, + default=12, + help="Number of RNN encoder layers..", + ) + + parser.add_argument( + "--encoder-dim", + type=int, + default=512, + help="Encoder output dimesion.", + ) + + parser.add_argument( + "--rnn-hidden-size", + type=int, + default=2048, + help="Dimension of feed forward.", + ) + + parser.add_argument( + "sound_filename", + type=str, + help="Path to foo.wav", + ) + + return parser.parse_args() + + +class Model: + def __init__(self, args): + self.init_encoder(args) + self.init_decoder(args) + self.init_joiner(args) + + def init_encoder(self, args): + encoder_net = ncnn.Net() + encoder_net.opt.use_packing_layout = False + encoder_net.opt.use_fp16_storage = False + encoder_net.opt.num_threads = 4 + + encoder_param = args.encoder_param_filename + encoder_model = args.encoder_bin_filename + + encoder_net.load_param(encoder_param) + encoder_net.load_model(encoder_model) + + self.encoder_net = encoder_net + + def init_decoder(self, args): + decoder_param = args.decoder_param_filename + decoder_model = args.decoder_bin_filename + + decoder_net = ncnn.Net() + decoder_net.opt.use_packing_layout = False + decoder_net.opt.num_threads = 4 + + decoder_net.load_param(decoder_param) + decoder_net.load_model(decoder_model) + + self.decoder_net = decoder_net + + def init_joiner(self, args): + joiner_param = args.joiner_param_filename + joiner_model = args.joiner_bin_filename + joiner_net = ncnn.Net() + joiner_net.opt.use_packing_layout = False + joiner_net.opt.num_threads = 4 + + joiner_net.load_param(joiner_param) + joiner_net.load_model(joiner_model) + + self.joiner_net = joiner_net + + def run_encoder(self, x, states): + with self.encoder_net.create_extractor() as ex: + ex.input("in0", ncnn.Mat(x.numpy()).clone()) + x_lens = torch.tensor([x.size(0)], dtype=torch.float32) + ex.input("in1", ncnn.Mat(x_lens.numpy()).clone()) + ex.input("in2", ncnn.Mat(states[0].numpy()).clone()) + ex.input("in3", ncnn.Mat(states[1].numpy()).clone()) + + ret, ncnn_out0 = ex.extract("out0") + assert ret == 0, ret + + ret, ncnn_out1 = ex.extract("out1") + assert ret == 0, ret + + ret, ncnn_out2 = ex.extract("out2") + assert ret == 0, ret + + ret, ncnn_out3 = ex.extract("out3") + assert ret == 0, ret + + encoder_out = torch.from_numpy(ncnn_out0.numpy()).clone() + encoder_out_lens = torch.from_numpy(ncnn_out1.numpy()).to(torch.int32) + hx = torch.from_numpy(ncnn_out2.numpy()).clone() + cx = torch.from_numpy(ncnn_out3.numpy()).clone() + return encoder_out, encoder_out_lens, hx, cx + + def run_decoder(self, decoder_input): + assert decoder_input.dtype == torch.int32 + + with self.decoder_net.create_extractor() as ex: + ex.input("in0", ncnn.Mat(decoder_input.numpy()).clone()) + ret, ncnn_out0 = ex.extract("out0") + assert ret == 0, ret + decoder_out = torch.from_numpy(ncnn_out0.numpy()).clone() + return decoder_out + + def run_joiner(self, encoder_out, decoder_out): + with self.joiner_net.create_extractor() as ex: + ex.input("in0", ncnn.Mat(encoder_out.numpy()).clone()) + ex.input("in1", ncnn.Mat(decoder_out.numpy()).clone()) + ret, ncnn_out0 = ex.extract("out0") + assert ret == 0, ret + joiner_out = torch.from_numpy(ncnn_out0.numpy()).clone() + return joiner_out + + +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 create_streaming_feature_extractor() -> OnlineFeature: + """Create a CPU streaming feature extractor. + + At present, we assume it returns a fbank feature extractor with + fixed options. In the future, we will support passing in the options + from outside. + + Returns: + Return a CPU streaming feature extractor. + """ + opts = FbankOptions() + opts.device = "cpu" + opts.frame_opts.dither = 0 + opts.frame_opts.snip_edges = False + opts.frame_opts.samp_freq = 16000 + opts.mel_opts.num_bins = 80 + return OnlineFbank(opts) + + +def greedy_search( + model: Model, + encoder_out: torch.Tensor, + decoder_out: Optional[torch.Tensor] = None, + hyp: Optional[List[int]] = None, +): + assert encoder_out.ndim == 1 + context_size = 2 + blank_id = 0 + + if decoder_out is None: + assert hyp is None, hyp + hyp = [blank_id] * context_size + decoder_input = torch.tensor(hyp, dtype=torch.int32) # (1, context_size) + decoder_out = model.run_decoder(decoder_input).squeeze(0) + else: + assert decoder_out.ndim == 1 + assert hyp is not None, hyp + + joiner_out = model.run_joiner(encoder_out, decoder_out) + y = joiner_out.argmax(dim=0).item() + if y != blank_id: + hyp.append(y) + decoder_input = hyp[-context_size:] + decoder_input = torch.tensor(decoder_input, dtype=torch.int32) + decoder_out = model.run_decoder(decoder_input).squeeze(0) + + return hyp, decoder_out + + +def main(): + args = get_args() + logging.info(vars(args)) + + model = Model(args) + + sound_file = args.sound_filename + + sample_rate = 16000 + + logging.info("Constructing Fbank computer") + online_fbank = create_streaming_feature_extractor() + + logging.info(f"Reading sound files: {sound_file}") + wave_samples = read_sound_files( + filenames=[sound_file], + expected_sample_rate=sample_rate, + )[0] + logging.info(wave_samples.shape) + + num_encoder_layers = args.num_encoder_layers + batch_size = 1 + d_model = args.encoder_dim + rnn_hidden_size = args.rnn_hidden_size + + states = ( + torch.zeros(num_encoder_layers, batch_size, d_model), + torch.zeros( + num_encoder_layers, + batch_size, + rnn_hidden_size, + ), + ) + + hyp = None + decoder_out = None + + num_processed_frames = 0 + segment = 9 + offset = 4 + + chunk = 3200 # 0.2 second + + start = 0 + while start < wave_samples.numel(): + end = min(start + chunk, wave_samples.numel()) + samples = wave_samples[start:end] + start += chunk + + online_fbank.accept_waveform( + sampling_rate=sample_rate, + waveform=samples, + ) + while online_fbank.num_frames_ready - num_processed_frames >= segment: + frames = [] + for i in range(segment): + frames.append(online_fbank.get_frame(num_processed_frames + i)) + num_processed_frames += offset + frames = torch.cat(frames, dim=0) + encoder_out, encoder_out_lens, hx, cx = model.run_encoder(frames, states) + states = (hx, cx) + hyp, decoder_out = greedy_search( + model, encoder_out.squeeze(0), decoder_out, hyp + ) + online_fbank.accept_waveform( + sampling_rate=sample_rate, waveform=torch.zeros(8000, dtype=torch.int32) + ) + + online_fbank.input_finished() + while online_fbank.num_frames_ready - num_processed_frames >= segment: + frames = [] + for i in range(segment): + frames.append(online_fbank.get_frame(num_processed_frames + i)) + num_processed_frames += offset + frames = torch.cat(frames, dim=0) + encoder_out, encoder_out_lens, hx, cx = model.run_encoder(frames, states) + states = (hx, cx) + hyp, decoder_out = greedy_search( + model, encoder_out.squeeze(0), decoder_out, hyp + ) + + symbol_table = k2.SymbolTable.from_file(args.tokens) + + context_size = 2 + text = "" + for i in hyp[context_size:]: + text += symbol_table[i] + text = text.replace("▁", " ").strip() + + logging.info(sound_file) + logging.info(text) + + +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/tal_csasr/ASR/lstm_transducer_stateless3/streaming_decode.py b/egs/tal_csasr/ASR/lstm_transducer_stateless3/streaming_decode.py new file mode 100644 index 000000000..f49de2983 --- /dev/null +++ b/egs/tal_csasr/ASR/lstm_transducer_stateless3/streaming_decode.py @@ -0,0 +1,992 @@ +#!/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. +""" +Usage: +(1) greedy search +./lstm_transducer_stateless3/streaming_decode.py \ + --epoch 40 \ + --avg 20 \ + --exp-dir lstm_transducer_stateless3/exp \ + --num-decode-streams 2000 \ + --num-encoder-layers 12 \ + --rnn-hidden-size 1024 \ + --decoding-method greedy_search \ + --use-averaged-model True + +(2) modified beam search +./lstm_transducer_stateless3/streaming_decode.py \ + --epoch 40 \ + --avg 20 \ + --exp-dir lstm_transducer_stateless3/exp \ + --num-decode-streams 2000 \ + --num-encoder-layers 12 \ + --rnn-hidden-size 1024 \ + --decoding-method modified_beam_search \ + --use-averaged-model True \ + --beam-size 4 + +(3) fast beam search +./lstm_transducer_stateless3/streaming_decode.py \ + --epoch 40 \ + --avg 20 \ + --exp-dir lstm_transducer_stateless3/exp \ + --num-decode-streams 2000 \ + --num-encoder-layers 12 \ + --rnn-hidden-size 1024 \ + --decoding-method fast_beam_search \ + --use-averaged-model True \ + --beam 4 \ + --max-contexts 4 \ + --max-states 8 +""" +import argparse +import logging +import re +import warnings +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +import k2 +import numpy as np +import sentencepiece as spm +import torch +import torch.nn as nn +from asr_datamodule import TAL_CSASRAsrDataModule +from beam_search import Hypothesis, HypothesisList, get_hyps_shape +from kaldifeat import Fbank, FbankOptions +from lhotse import CutSet +from lhotse.cut import Cut +from local.text_normalize import text_normalize +from lstm import LOG_EPSILON, stack_states, unstack_states +from stream import Stream +from torch.nn.utils.rnn import pad_sequence +from train import add_model_arguments, get_params, get_transducer_model + +from icefall.char_graph_compiler import CharCtcTrainingGraphCompiler +from icefall.checkpoint import ( + average_checkpoints, + average_checkpoints_with_averaged_model, + find_checkpoints, + load_checkpoint, +) +from icefall.decode import one_best_decoding +from icefall.lexicon import Lexicon +from icefall.utils import ( + AttributeDict, + get_texts, + setup_logger, + store_transcripts, + str2bool, + write_error_stats, +) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=40, + help="It specifies the checkpoint to use for decoding." + "Note: Epoch counts from 0.", + ) + + 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=20, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch'. ", + ) + + parser.add_argument( + "--use-averaged-model", + type=str2bool, + default=False, + 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="lstm_transducer_stateless3/exp", + help="The experiment dir", + ) + + parser.add_argument( + "--lang-dir", + type=str, + default="data/lang_char", + help="Path to the dir containing bpe.model and tokens.txt", + ) + + parser.add_argument( + "--decoding-method", + type=str, + default="greedy_search", + help="""Possible values are: + - greedy_search + - modified_beam_search + - fast_beam_search + """, + ) + + parser.add_argument( + "--beam-size", + type=int, + default=4, + help="""An interger indicating how many candidates we will keep for each + frame. Used only when --decoding-method is beam_search or + modified_beam_search.""", + ) + + parser.add_argument( + "--beam", + type=float, + default=20.0, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --decoding-method is fast_beam_search""", + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=8, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=64, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; 2 means tri-gram", + ) + parser.add_argument( + "--max-sym-per-frame", + type=int, + default=1, + help="""Maximum number of symbols per frame. + Used only when --decoding_method is greedy_search""", + ) + + parser.add_argument( + "--sampling-rate", + type=float, + default=16000, + help="Sample rate of the audio", + ) + + parser.add_argument( + "--num-decode-streams", + type=int, + default=2000, + help="The number of streams that can be decoded in parallel", + ) + + add_model_arguments(parser) + + return parser + + +def greedy_search( + model: nn.Module, + encoder_out: torch.Tensor, + streams: List[Stream], +) -> None: + """Greedy search in batch mode. It hardcodes --max-sym-per-frame=1. + + Args: + model: + The transducer model. + encoder_out: + Output from the encoder. Its shape is (N, T, C), where N >= 1. + streams: + A list of Stream objects. + """ + assert len(streams) == encoder_out.size(0) + assert encoder_out.ndim == 3 + + blank_id = model.decoder.blank_id + context_size = model.decoder.context_size + device = next(model.parameters()).device + T = encoder_out.size(1) + + encoder_out = model.joiner.encoder_proj(encoder_out) + + decoder_input = torch.tensor( + [stream.hyp[-context_size:] for stream in streams], + device=device, + dtype=torch.int64, + ) + # decoder_out is of shape (batch_size, 1, decoder_out_dim) + decoder_out = model.decoder(decoder_input, need_pad=False) + decoder_out = model.joiner.decoder_proj(decoder_out) + + for t in range(T): + # current_encoder_out's shape: (batch_size, 1, encoder_out_dim) + current_encoder_out = encoder_out[:, t : t + 1, :] # noqa + + logits = model.joiner( + current_encoder_out.unsqueeze(2), + decoder_out.unsqueeze(1), + project_input=False, + ) + # logits'shape (batch_size, vocab_size) + logits = logits.squeeze(1).squeeze(1) + + assert logits.ndim == 2, logits.shape + y = logits.argmax(dim=1).tolist() + emitted = False + for i, v in enumerate(y): + if v != blank_id: + streams[i].hyp.append(v) + emitted = True + if emitted: + # update decoder output + decoder_input = torch.tensor( + [stream.hyp[-context_size:] for stream in streams], + device=device, + dtype=torch.int64, + ) + decoder_out = model.decoder( + decoder_input, + need_pad=False, + ) + decoder_out = model.joiner.decoder_proj(decoder_out) + + +def modified_beam_search( + model: nn.Module, + encoder_out: torch.Tensor, + streams: List[Stream], + beam: int = 4, +): + """Beam search in batch mode with --max-sym-per-frame=1 being hardcoded. + + Args: + model: + The RNN-T model. + encoder_out: + A 3-D tensor of shape (N, T, encoder_out_dim) containing the output of + the encoder model. + streams: + A list of stream objects. + beam: + Number of active paths during the beam search. + """ + assert encoder_out.ndim == 3, encoder_out.shape + assert len(streams) == encoder_out.size(0) + + blank_id = model.decoder.blank_id + context_size = model.decoder.context_size + device = next(model.parameters()).device + batch_size = len(streams) + T = encoder_out.size(1) + + B = [stream.hyps for stream in streams] + + encoder_out = model.joiner.encoder_proj(encoder_out) + + for t in range(T): + current_encoder_out = encoder_out[:, t].unsqueeze(1).unsqueeze(1) + # current_encoder_out's shape: (batch_size, 1, 1, encoder_out_dim) + + hyps_shape = get_hyps_shape(B).to(device) + + A = [list(b) for b in B] + B = [HypothesisList() for _ in range(batch_size)] + + ys_log_probs = torch.stack( + [hyp.log_prob.reshape(1) for hyps in A for hyp in hyps], dim=0 + ) # (num_hyps, 1) + + decoder_input = torch.tensor( + [hyp.ys[-context_size:] for hyps in A for hyp in hyps], + device=device, + dtype=torch.int64, + ) # (num_hyps, context_size) + + decoder_out = model.decoder(decoder_input, need_pad=False).unsqueeze(1) + decoder_out = model.joiner.decoder_proj(decoder_out) + # decoder_out is of shape (num_hyps, 1, 1, decoder_output_dim) + + # Note: For torch 1.7.1 and below, it requires a torch.int64 tensor + # as index, so we use `to(torch.int64)` below. + current_encoder_out = torch.index_select( + current_encoder_out, + dim=0, + index=hyps_shape.row_ids(1).to(torch.int64), + ) # (num_hyps, encoder_out_dim) + + logits = model.joiner(current_encoder_out, decoder_out, project_input=False) + # logits is of shape (num_hyps, 1, 1, vocab_size) + + logits = logits.squeeze(1).squeeze(1) + + log_probs = logits.log_softmax(dim=-1) # (num_hyps, vocab_size) + + log_probs.add_(ys_log_probs) + + vocab_size = log_probs.size(-1) + + log_probs = log_probs.reshape(-1) + + row_splits = hyps_shape.row_splits(1) * vocab_size + log_probs_shape = k2.ragged.create_ragged_shape2( + row_splits=row_splits, cached_tot_size=log_probs.numel() + ) + ragged_log_probs = k2.RaggedTensor(shape=log_probs_shape, value=log_probs) + + for i in range(batch_size): + topk_log_probs, topk_indexes = ragged_log_probs[i].topk(beam) + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + topk_hyp_indexes = (topk_indexes // vocab_size).tolist() + topk_token_indexes = (topk_indexes % vocab_size).tolist() + + for k in range(len(topk_hyp_indexes)): + hyp_idx = topk_hyp_indexes[k] + hyp = A[i][hyp_idx] + + new_ys = hyp.ys[:] + new_token = topk_token_indexes[k] + if new_token != blank_id: + new_ys.append(new_token) + + new_log_prob = topk_log_probs[k] + new_hyp = Hypothesis(ys=new_ys, log_prob=new_log_prob) + B[i].add(new_hyp) + + for i in range(batch_size): + streams[i].hyps = B[i] + + +def fast_beam_search_one_best( + model: nn.Module, + streams: List[Stream], + encoder_out: torch.Tensor, + processed_lens: torch.Tensor, + beam: float, + max_states: int, + max_contexts: int, +) -> None: + """It limits the maximum number of symbols per frame to 1. + + A lattice is first obtained using modified beam search, and then + the shortest path within the lattice is used as the final output. + + Args: + model: + An instance of `Transducer`. + streams: + A list of stream objects. + encoder_out: + A tensor of shape (N, T, C) from the encoder. + processed_lens: + A tensor of shape (N,) containing the number of processed frames + in `encoder_out` before padding. + beam: + Beam value, similar to the beam used in Kaldi.. + max_states: + Max states per stream per frame. + max_contexts: + Max contexts pre stream per frame. + """ + assert encoder_out.ndim == 3 + + context_size = model.decoder.context_size + vocab_size = model.decoder.vocab_size + + B, T, C = encoder_out.shape + assert B == len(streams) + + config = k2.RnntDecodingConfig( + vocab_size=vocab_size, + decoder_history_len=context_size, + beam=beam, + max_contexts=max_contexts, + max_states=max_states, + ) + individual_streams = [] + for i in range(B): + individual_streams.append(streams[i].rnnt_decoding_stream) + decoding_streams = k2.RnntDecodingStreams(individual_streams, config) + + encoder_out = model.joiner.encoder_proj(encoder_out) + + for t in range(T): + # shape is a RaggedShape of shape (B, context) + # contexts is a Tensor of shape (shape.NumElements(), context_size) + shape, contexts = decoding_streams.get_contexts() + # `nn.Embedding()` in torch below v1.7.1 supports only torch.int64 + contexts = contexts.to(torch.int64) + # decoder_out is of shape (shape.NumElements(), 1, decoder_out_dim) + decoder_out = model.decoder(contexts, need_pad=False) + decoder_out = model.joiner.decoder_proj(decoder_out) + # current_encoder_out is of shape + # (shape.NumElements(), 1, joiner_dim) + # fmt: off + current_encoder_out = torch.index_select( + encoder_out[:, t:t + 1, :], 0, shape.row_ids(1).to(torch.int64) + ) + # fmt: on + logits = model.joiner( + current_encoder_out.unsqueeze(2), + decoder_out.unsqueeze(1), + project_input=False, + ) + logits = logits.squeeze(1).squeeze(1) + log_probs = logits.log_softmax(dim=-1) + decoding_streams.advance(log_probs) + + decoding_streams.terminate_and_flush_to_streams() + + lattice = decoding_streams.format_output(processed_lens.tolist()) + + best_path = one_best_decoding(lattice) + hyps = get_texts(best_path) + + for i in range(B): + streams[i].hyp = hyps[i] + + +def decode_one_chunk( + model: nn.Module, + streams: List[Stream], + params: AttributeDict, + decoding_graph: Optional[k2.Fsa] = None, +) -> List[int]: + """ + Args: + model: + The Transducer model. + streams: + A list of Stream objects. + params: + It is returned by :func:`get_params`. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or LG, Used + only when --decoding_method is fast_beam_search. + + Returns: + A list of indexes indicating the finished streams. + """ + device = next(model.parameters()).device + + feature_list = [] + feature_len_list = [] + state_list = [] + num_processed_frames_list = [] + + for stream in streams: + # We should first get `stream.num_processed_frames` + # before calling `stream.get_feature_chunk()` + # since `stream.num_processed_frames` would be updated + num_processed_frames_list.append(stream.num_processed_frames) + feature = stream.get_feature_chunk() + feature_len = feature.size(0) + feature_list.append(feature) + feature_len_list.append(feature_len) + state_list.append(stream.states) + + features = pad_sequence( + feature_list, batch_first=True, padding_value=LOG_EPSILON + ).to(device) + feature_lens = torch.tensor(feature_len_list, device=device) + num_processed_frames = torch.tensor(num_processed_frames_list, device=device) + + # Make sure it has at least 1 frame after subsampling + tail_length = params.subsampling_factor + 5 + if features.size(1) < tail_length: + pad_length = tail_length - features.size(1) + feature_lens += pad_length + features = torch.nn.functional.pad( + features, + (0, 0, 0, pad_length), + mode="constant", + value=LOG_EPSILON, + ) + + # Stack states of all streams + states = stack_states(state_list) + + encoder_out, encoder_out_lens, states = model.encoder( + x=features, + x_lens=feature_lens, + states=states, + ) + + if params.decoding_method == "greedy_search": + greedy_search( + model=model, + streams=streams, + encoder_out=encoder_out, + ) + elif params.decoding_method == "modified_beam_search": + modified_beam_search( + model=model, + streams=streams, + encoder_out=encoder_out, + beam=params.beam_size, + ) + elif params.decoding_method == "fast_beam_search": + # feature_len is needed to get partial results. + # The rnnt_decoding_stream for fast_beam_search. + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + processed_lens = ( + num_processed_frames // params.subsampling_factor + encoder_out_lens + ) + fast_beam_search_one_best( + model=model, + streams=streams, + encoder_out=encoder_out, + processed_lens=processed_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + else: + raise ValueError(f"Unsupported decoding method: {params.decoding_method}") + + # Update cached states of each stream + state_list = unstack_states(states) + for i, s in enumerate(state_list): + streams[i].states = s + + finished_streams = [i for i, stream in enumerate(streams) if stream.done] + return finished_streams + + +def create_streaming_feature_extractor() -> Fbank: + """Create a CPU streaming feature extractor. + + At present, we assume it returns a fbank feature extractor with + fixed options. In the future, we will support passing in the options + from outside. + + Returns: + Return a CPU streaming feature extractor. + """ + opts = FbankOptions() + opts.device = "cpu" + opts.frame_opts.dither = 0 + opts.frame_opts.snip_edges = False + opts.frame_opts.samp_freq = 16000 + opts.mel_opts.num_bins = 80 + return Fbank(opts) + + +def filter_zh_en(text: str): + pattern = re.compile(r"([\u4e00-\u9fff])") + + chars = pattern.split(text.upper()) + chars_new = [] + for char in chars: + if char != "": + tokens = char.strip().split(" ") + chars_new.extend(tokens) + return chars_new + + +def decode_dataset( + cuts: CutSet, + model: nn.Module, + params: AttributeDict, + sp: spm.SentencePieceProcessor, + lexicon: Lexicon, + graph_compiler: CharCtcTrainingGraphCompiler, + decoding_graph: Optional[k2.Fsa] = None, +): + """Decode dataset. + + Args: + cuts: + Lhotse Cutset containing the dataset to decode. + params: + It is returned by :func:`get_params`. + model: + The Transducer model. + sp: + The BPE model. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or LG, Used + only when --decoding_method is fast_beam_search. + + Returns: + Return a dict, whose key may be "greedy_search" if greedy search + is used, or it may be "beam_7" if beam size of 7 is used. + Its value is a list of tuples. Each tuple contains two elements: + The first is the reference transcript, and the second is the + predicted result. + """ + device = next(model.parameters()).device + + log_interval = 300 + + fbank = create_streaming_feature_extractor() + + decode_results = [] + streams = [] + for num, cut in enumerate(cuts): + # Each utterance has a Stream. + stream = Stream( + params=params, + cut_id=cut.id, + decoding_graph=decoding_graph, + device=device, + LOG_EPS=LOG_EPSILON, + ) + + stream.states = model.encoder.get_init_states(device=device) + + audio: np.ndarray = cut.load_audio() + # audio.shape: (1, num_samples) + assert len(audio.shape) == 2 + assert audio.shape[0] == 1, "Should be single channel" + assert audio.dtype == np.float32, audio.dtype + # The trained model is using normalized samples + assert audio.max() <= 1, "Should be normalized to [-1, 1])" + + samples = torch.from_numpy(audio).squeeze(0) + feature = fbank(samples) + stream.set_feature(feature) + stream.ground_truth = cut.supervisions[0].text + + streams.append(stream) + + while len(streams) >= params.num_decode_streams: + finished_streams = decode_one_chunk( + model=model, + streams=streams, + params=params, + decoding_graph=decoding_graph, + ) + + for i in sorted(finished_streams, reverse=True): + hyp = streams[i].decoding_result() + decode_results.append( + ( + streams[i].id, + filter_zh_en(streams[i].ground_truth), + sp.decode([lexicon.token_table[idx] for idx in hyp]), + ) + ) + del streams[i] + + if num % log_interval == 0: + logging.info(f"Cuts processed until now is {num}.") + + while len(streams) > 0: + finished_streams = decode_one_chunk( + model=model, + streams=streams, + params=params, + decoding_graph=decoding_graph, + ) + + for i in sorted(finished_streams, reverse=True): + hyp = streams[i].decoding_result() + decode_results.append( + ( + streams[i].id, + filter_zh_en(streams[i].ground_truth), + [sp.decode(lexicon.token_table[idx]) for idx in hyp], + ) + ) + del streams[i] + + if params.decoding_method == "greedy_search": + key = "greedy_search" + elif params.decoding_method == "fast_beam_search": + key = ( + f"beam_{params.beam}_" + f"max_contexts_{params.max_contexts}_" + f"max_states_{params.max_states}" + ) + else: + key = f"beam_size_{params.beam_size}" + + return {key: decode_results} + + +def save_results( + params: AttributeDict, + test_set_name: str, + results_dict: Dict[str, List[Tuple[List[str], List[str]]]], +): + test_set_wers = dict() + for key, results in results_dict.items(): + recog_path = ( + params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt" + ) + store_transcripts(filename=recog_path, texts=sorted(results)) + logging.info(f"The transcripts are stored in {recog_path}") + + # The following prints out WERs, per-word error statistics and aligned + # ref/hyp pairs. + errs_filename = ( + params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_filename, "w") as f: + wer = write_error_stats( + f, f"{test_set_name}-{key}", results, enable_log=True + ) + test_set_wers[key] = wer + + logging.info("Wrote detailed error stats to {}".format(errs_filename)) + + test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1]) + errs_info = ( + params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_info, "w") as f: + print("settings\tWER", file=f) + for key, val in test_set_wers: + print("{}\t{}".format(key, val), file=f) + + s = "\nFor {}, WER of different settings are:\n".format(test_set_name) + note = "\tbest for {}".format(test_set_name) + for key, val in test_set_wers: + s += "{}\t{}{}\n".format(key, val, note) + note = "" + logging.info(s) + + +@torch.no_grad() +def main(): + parser = get_parser() + TAL_CSASRAsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + params = get_params() + params.update(vars(args)) + + assert params.decoding_method in ( + "greedy_search", + "fast_beam_search", + "modified_beam_search", + ) + params.res_dir = params.exp_dir / "streaming" / params.decoding_method + + if params.iter > 0: + params.suffix = f"iter-{params.iter}-avg-{params.avg}" + else: + params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" + + if "fast_beam_search" in params.decoding_method: + params.suffix += f"-beam-{params.beam}" + params.suffix += f"-max-contexts-{params.max_contexts}" + params.suffix += f"-max-states-{params.max_states}" + elif "beam_search" in params.decoding_method: + params.suffix += f"-{params.decoding_method}-beam-size-{params.beam_size}" + else: + params.suffix += f"-context-{params.context_size}" + params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}" + + if params.use_averaged_model: + params.suffix += "-use-averaged-model" + + setup_logger(f"{params.res_dir}/log-streaming-decode") + logging.info("Decoding started") + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"Device: {device}") + + bpe_model = params.lang_dir + "/bpe.model" + sp = spm.SentencePieceProcessor() + sp.load(bpe_model) + + lexicon = Lexicon(params.lang_dir) + graph_compiler = CharCtcTrainingGraphCompiler( + lexicon=lexicon, + device=device, + ) + params.blank_id = lexicon.token_table[""] + params.vocab_size = max(lexicon.tokens) + 1 + + params.device = device + + 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.eval() + + if params.decoding_method == "fast_beam_search": + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + else: + decoding_graph = None + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + def text_normalize_for_cut(c: Cut): + # Text normalize for each sample + text = c.supervisions[0].text + text = text.strip("\n").strip("\t") + c.supervisions[0].text = text_normalize(text) + return c + + tal_csasr = TAL_CSASRAsrDataModule(args) + + dev_cuts = tal_csasr.valid_cuts() + dev_cuts = dev_cuts.map(text_normalize_for_cut) + + test_cuts = tal_csasr.test_cuts() + test_cuts = test_cuts.map(text_normalize_for_cut) + + test_sets = ["dev", "test"] + test_cuts = [dev_cuts, test_cuts] + + for test_set, test_cut in zip(test_sets, test_cuts): + results_dict = decode_dataset( + cuts=test_cut, + model=model, + params=params, + sp=sp, + lexicon=lexicon, + graph_compiler=graph_compiler, + decoding_graph=decoding_graph, + ) + + save_results( + params=params, + test_set_name=test_set, + results_dict=results_dict, + ) + + logging.info("Done!") + + +if __name__ == "__main__": + torch.manual_seed(20220810) + main() diff --git a/egs/tal_csasr/ASR/lstm_transducer_stateless3/train.py b/egs/tal_csasr/ASR/lstm_transducer_stateless3/train.py new file mode 100755 index 000000000..bc1b9290e --- /dev/null +++ b/egs/tal_csasr/ASR/lstm_transducer_stateless3/train.py @@ -0,0 +1,1183 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang, +# Wei Kang, +# Mingshuang Luo +# Zengwei Yao, +# Xiaoyu Yang) +# +# 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. +""" +Usage: + +export CUDA_VISIBLE_DEVICES="0,1,2,3" + +./lstm_transducer_stateless3/train.py \ + --world-size 4 \ + --num-epochs 40 \ + --start-epoch 1 \ + --exp-dir lstm_transducer_stateless3/exp \ + --full-libri 1 \ + --max-duration 500 + +# For mix precision training: + +./lstm_transducer_stateless3/train.py \ + --world-size 4 \ + --num-epochs 40 \ + --start-epoch 1 \ + --use-fp16 1 \ + --exp-dir lstm_transducer_stateless3/exp \ + --full-libri 1 \ + --max-duration 550 +""" + +import argparse +import copy +import logging +import warnings +from pathlib import Path +from shutil import copyfile +from typing import Any, Dict, Optional, Tuple, Union + +import k2 +import optim +import torch +import torch.multiprocessing as mp +import torch.nn as nn +from asr_datamodule import TAL_CSASRAsrDataModule +from decoder import Decoder +from joiner import Joiner +from lhotse.cut import Cut +from lhotse.dataset.sampling.base import CutSampler +from lhotse.utils import fix_random_seed +from local.text_normalize import text_normalize +from local.tokenize_with_bpe_model import tokenize_by_bpe_model +from lstm import RNN +from model import Transducer +from optim import Eden, Eve +from torch import Tensor +from torch.cuda.amp import GradScaler +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.tensorboard import SummaryWriter + +from icefall import diagnostics +from icefall.char_graph_compiler import CharCtcTrainingGraphCompiler +from icefall.checkpoint import load_checkpoint, remove_checkpoints +from icefall.checkpoint import save_checkpoint as save_checkpoint_impl +from icefall.checkpoint import ( + save_checkpoint_with_global_batch_idx, + update_averaged_model, +) +from icefall.dist import cleanup_dist, setup_dist +from icefall.env import get_env_info +from icefall.lexicon import Lexicon +from icefall.utils import ( + AttributeDict, + MetricsTracker, + display_and_save_batch, + setup_logger, + str2bool, +) + +LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler] + + +def add_model_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--num-encoder-layers", + type=int, + default=12, + help="Number of RNN encoder layers..", + ) + + parser.add_argument( + "--encoder-dim", + type=int, + default=512, + help="Encoder output dimesion.", + ) + + parser.add_argument( + "--decoder-dim", + type=int, + default=512, + help="Decoder output dimension.", + ) + + parser.add_argument( + "--joiner-dim", + type=int, + default=512, + help="Joiner output dimension.", + ) + + parser.add_argument( + "--dim-feedforward", + type=int, + default=2048, + help="Dimension of feed forward.", + ) + + parser.add_argument( + "--rnn-hidden-size", + type=int, + default=1024, + help="Hidden dim for LSTM layers.", + ) + + parser.add_argument( + "--aux-layer-period", + type=int, + default=0, + help="""Peroid of auxiliary layers used for randomly combined during training. + If set to 0, will not use the random combiner (Default). + You can set a positive integer to use the random combiner, e.g., 3. + """, + ) + + parser.add_argument( + "--grad-norm-threshold", + type=float, + default=25.0, + help="""For each sequence element in batch, its gradient will be + filtered out if the gradient norm is larger than + `grad_norm_threshold * median`, where `median` is the median + value of gradient norms of all elememts in batch.""", + ) + + parser.add_argument( + "--is-pnnx", + type=str2bool, + default=False, + help="Only used when exporting model with pnnx.", + ) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--world-size", + type=int, + default=1, + help="Number of GPUs for DDP training.", + ) + + parser.add_argument( + "--master-port", + type=int, + default=12354, + help="Master port to use for DDP training.", + ) + + parser.add_argument( + "--tensorboard", + type=str2bool, + default=True, + help="Should various information be logged in tensorboard.", + ) + + parser.add_argument( + "--num-epochs", + type=int, + default=40, + help="Number of epochs to train.", + ) + + parser.add_argument( + "--start-epoch", + type=int, + default=1, + help="""Resume training from this epoch. It should be positive. + If larger than 1, it will load checkpoint from + exp-dir/epoch-{start_epoch-1}.pt + """, + ) + + parser.add_argument( + "--start-batch", + type=int, + default=0, + help="""If positive, --start-epoch is ignored and + it loads the checkpoint from exp-dir/checkpoint-{start_batch}.pt + """, + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="lstm_transducer_stateless3/exp", + help="""The experiment dir. + It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--lang-dir", + type=str, + default="data/lang_char", + help="""The lang dir + It contains language related input files such as + "lexicon.txt" + """, + ) + + parser.add_argument( + "--initial-lr", + type=float, + default=0.003, + help="""The initial learning rate. This value should not need to be + changed.""", + ) + + parser.add_argument( + "--lr-batches", + type=float, + default=5000, + help="""Number of steps that affects how rapidly the learning rate decreases. + We suggest not to change this.""", + ) + + parser.add_argument( + "--lr-epochs", + type=float, + default=10, + help="""Number of epochs that affects how rapidly the learning rate decreases. + """, + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; 2 means tri-gram", + ) + + parser.add_argument( + "--prune-range", + type=int, + default=5, + help="The prune range for rnnt loss, it means how many symbols(context)" + "we are using to compute the loss", + ) + + parser.add_argument( + "--lm-scale", + type=float, + default=0.25, + help="The scale to smooth the loss with lm " + "(output of prediction network) part.", + ) + + parser.add_argument( + "--am-scale", + type=float, + default=0.0, + help="The scale to smooth the loss with am (output of encoder network) part.", + ) + + parser.add_argument( + "--simple-loss-scale", + type=float, + default=0.5, + help="To get pruning ranges, we will calculate a simple version" + "loss(joiner is just addition), this simple loss also uses for" + "training (as a regularization item). We will scale the simple loss" + "with this parameter before adding to the final loss.", + ) + + parser.add_argument( + "--seed", + type=int, + default=42, + help="The seed for random generators intended for reproducibility", + ) + + parser.add_argument( + "--print-diagnostics", + type=str2bool, + default=False, + help="Accumulate stats on activations, print them and exit.", + ) + + parser.add_argument( + "--save-every-n", + type=int, + default=4000, + help="""Save checkpoint after processing this number of batches" + periodically. We save checkpoint to exp-dir/ whenever + params.batch_idx_train % save_every_n == 0. The checkpoint filename + has the form: f'exp-dir/checkpoint-{params.batch_idx_train}.pt' + Note: It also saves checkpoint to `exp-dir/epoch-xxx.pt` at the + end of each epoch where `xxx` is the epoch number counting from 0. + """, + ) + + parser.add_argument( + "--keep-last-k", + type=int, + default=20, + help="""Only keep this number of checkpoints on disk. + For instance, if it is 3, there are only 3 checkpoints + in the exp-dir with filenames `checkpoint-xxx.pt`. + It does not affect checkpoints with name `epoch-xxx.pt`. + """, + ) + + parser.add_argument( + "--average-period", + type=int, + default=100, + help="""Update the averaged model, namely `model_avg`, after processing + this number of batches. `model_avg` is a separate version of model, + in which each floating-point parameter is the average of all the + parameters from the start of training. Each time we take the average, + we do: `model_avg = model * (average_period / batch_idx_train) + + model_avg * ((batch_idx_train - average_period) / batch_idx_train)`. + """, + ) + + parser.add_argument( + "--use-fp16", + type=str2bool, + default=False, + help="Whether to use half precision training.", + ) + + parser.add_argument( + "--delay-penalty", + type=float, + default=0.0, + help="""A constant value used to penalize symbol delay, + to encourage streaming models to emit symbols earlier. + See https://github.com/k2-fsa/k2/issues/955 and + https://arxiv.org/pdf/2211.00490.pdf for more details.""", + ) + + add_model_arguments(parser) + + return parser + + +def get_params() -> AttributeDict: + """Return a dict containing training parameters. + + All training related parameters that are not passed from the commandline + are saved in the variable `params`. + + Commandline options are merged into `params` after they are parsed, so + you can also access them via `params`. + + Explanation of options saved in `params`: + + - best_train_loss: Best training loss so far. It is used to select + the model that has the lowest training loss. It is + updated during the training. + + - best_valid_loss: Best validation loss so far. It is used to select + the model that has the lowest validation loss. It is + updated during the training. + + - best_train_epoch: It is the epoch that has the best training loss. + + - best_valid_epoch: It is the epoch that has the best validation loss. + + - batch_idx_train: Used to writing statistics to tensorboard. It + contains number of batches trained so far across + epochs. + + - log_interval: Print training loss if batch_idx % log_interval` is 0 + + - reset_interval: Reset statistics if batch_idx % reset_interval is 0 + + - valid_interval: Run validation if batch_idx % valid_interval is 0 + + - feature_dim: The model input dim. It has to match the one used + in computing features. + + - subsampling_factor: The subsampling factor for the model. + + - num_decoder_layers: Number of decoder layer of transformer decoder. + + - warm_step: The warm_step for Noam optimizer. + """ + params = AttributeDict( + { + "frame_shift_ms": 10.0, + "best_train_loss": float("inf"), + "best_valid_loss": float("inf"), + "best_train_epoch": -1, + "best_valid_epoch": -1, + "batch_idx_train": 0, + "log_interval": 50, + "reset_interval": 200, + "valid_interval": 3000, # For the 100h subset, use 800 + # parameters for conformer + "feature_dim": 80, + "subsampling_factor": 4, + # parameters for Noam + "model_warm_step": 3000, # arg given to model, not for lrate + "env_info": get_env_info(), + } + ) + + return params + + +def get_encoder_model(params: AttributeDict) -> nn.Module: + encoder = RNN( + num_features=params.feature_dim, + subsampling_factor=params.subsampling_factor, + d_model=params.encoder_dim, + rnn_hidden_size=params.rnn_hidden_size, + grad_norm_threshold=params.grad_norm_threshold, + dim_feedforward=params.dim_feedforward, + num_encoder_layers=params.num_encoder_layers, + aux_layer_period=params.aux_layer_period, + is_pnnx=params.is_pnnx, + ) + return encoder + + +def get_decoder_model(params: AttributeDict) -> nn.Module: + decoder = Decoder( + vocab_size=params.vocab_size, + decoder_dim=params.decoder_dim, + blank_id=params.blank_id, + context_size=params.context_size, + ) + return decoder + + +def get_joiner_model(params: AttributeDict) -> nn.Module: + joiner = Joiner( + encoder_dim=params.encoder_dim, + decoder_dim=params.decoder_dim, + joiner_dim=params.joiner_dim, + vocab_size=params.vocab_size, + ) + return joiner + + +def get_transducer_model(params: AttributeDict) -> nn.Module: + encoder = get_encoder_model(params) + decoder = get_decoder_model(params) + joiner = get_joiner_model(params) + + model = Transducer( + encoder=encoder, + decoder=decoder, + joiner=joiner, + encoder_dim=params.encoder_dim, + decoder_dim=params.decoder_dim, + joiner_dim=params.joiner_dim, + vocab_size=params.vocab_size, + ) + return model + + +def load_checkpoint_if_available( + params: AttributeDict, + model: nn.Module, + model_avg: nn.Module = None, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[LRSchedulerType] = None, +) -> Optional[Dict[str, Any]]: + """Load checkpoint from file. + + If params.start_batch is positive, it will load the checkpoint from + `params.exp_dir/checkpoint-{params.start_batch}.pt`. Otherwise, if + params.start_epoch is larger than 1, it will load the checkpoint from + `params.start_epoch - 1`. + + Apart from loading state dict for `model` and `optimizer` it also updates + `best_train_epoch`, `best_train_loss`, `best_valid_epoch`, + and `best_valid_loss` in `params`. + + Args: + params: + The return value of :func:`get_params`. + model: + The training model. + model_avg: + The stored model averaged from the start of training. + optimizer: + The optimizer that we are using. + scheduler: + The scheduler that we are using. + Returns: + Return a dict containing previously saved training info. + """ + if params.start_batch > 0: + filename = params.exp_dir / f"checkpoint-{params.start_batch}.pt" + elif params.start_epoch > 1: + filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt" + else: + return None + + assert filename.is_file(), f"{filename} does not exist!" + + saved_params = load_checkpoint( + filename, + model=model, + model_avg=model_avg, + optimizer=optimizer, + scheduler=scheduler, + ) + + keys = [ + "best_train_epoch", + "best_valid_epoch", + "batch_idx_train", + "best_train_loss", + "best_valid_loss", + ] + for k in keys: + params[k] = saved_params[k] + + if params.start_batch > 0: + if "cur_epoch" in saved_params: + params["start_epoch"] = saved_params["cur_epoch"] + + return saved_params + + +def save_checkpoint( + params: AttributeDict, + model: Union[nn.Module, DDP], + model_avg: Optional[nn.Module] = None, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[LRSchedulerType] = None, + sampler: Optional[CutSampler] = None, + scaler: Optional[GradScaler] = None, + rank: int = 0, +) -> None: + """Save model, optimizer, scheduler and training stats to file. + + Args: + params: + It is returned by :func:`get_params`. + model: + The training model. + model_avg: + The stored model averaged from the start of training. + optimizer: + The optimizer used in the training. + sampler: + The sampler for the training dataset. + scaler: + The scaler used for mix precision training. + """ + if rank != 0: + return + filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt" + save_checkpoint_impl( + filename=filename, + model=model, + model_avg=model_avg, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=sampler, + scaler=scaler, + rank=rank, + ) + + if params.best_train_epoch == params.cur_epoch: + best_train_filename = params.exp_dir / "best-train-loss.pt" + copyfile(src=filename, dst=best_train_filename) + + if params.best_valid_epoch == params.cur_epoch: + best_valid_filename = params.exp_dir / "best-valid-loss.pt" + copyfile(src=filename, dst=best_valid_filename) + + +def compute_loss( + params: AttributeDict, + model: Union[nn.Module, DDP], + graph_compiler: CharCtcTrainingGraphCompiler, + batch: dict, + is_training: bool, + warmup: float = 1.0, +) -> Tuple[Tensor, MetricsTracker]: + """ + Compute RNN-T loss given the model and its inputs. + + Args: + params: + Parameters for training. See :func:`get_params`. + model: + The model for training. It is an instance of Conformer in our case. + batch: + A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()` + for the content in it. + is_training: + True for training. False for validation. When it is True, this + function enables autograd during computation; when it is False, it + disables autograd. + warmup: a floating point value which increases throughout training; + values >= 1.0 are fully warmed up and have all modules present. + """ + device = model.device if isinstance(model, DDP) else next(model.parameters()).device + feature = batch["inputs"] + # at entry, feature is (N, T, C) + assert feature.ndim == 3 + feature = feature.to(device) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) + + texts = batch["supervisions"]["text"] + # import pdb; pdb.set_trace() + y = graph_compiler.texts_to_ids_with_bpe(texts) + if type(y) == list: + y = k2.RaggedTensor(y).to(device) + else: + y = y.to(device) + with torch.set_grad_enabled(is_training): + simple_loss, pruned_loss = model( + x=feature, + x_lens=feature_lens, + y=y, + prune_range=params.prune_range, + am_scale=params.am_scale, + lm_scale=params.lm_scale, + warmup=warmup, + reduction="none", + delay_penalty=params.delay_penalty if warmup >= 2.0 else 0, + ) + simple_loss_is_finite = torch.isfinite(simple_loss) + pruned_loss_is_finite = torch.isfinite(pruned_loss) + is_finite = simple_loss_is_finite & pruned_loss_is_finite + if not torch.all(is_finite): + logging.info( + "Not all losses are finite!\n" + f"simple_loss: {simple_loss}\n" + f"pruned_loss: {pruned_loss}" + ) + display_and_save_batch(batch, params=params) + simple_loss = simple_loss[simple_loss_is_finite] + pruned_loss = pruned_loss[pruned_loss_is_finite] + + # If either all simple_loss or pruned_loss is inf or nan, + # we stop the training process by raising an exception + if torch.all(~simple_loss_is_finite) or torch.all(~pruned_loss_is_finite): + raise ValueError( + "There are too many utterances in this batch " + "leading to inf or nan losses." + ) + + simple_loss = simple_loss.sum() + pruned_loss = pruned_loss.sum() + # after the main warmup step, we keep pruned_loss_scale small + # for the same amount of time (model_warm_step), to avoid + # overwhelming the simple_loss and causing it to diverge, + # in case it had not fully learned the alignment yet. + pruned_loss_scale = ( + 0.0 if warmup < 1.0 else (0.1 if warmup > 1.0 and warmup < 2.0 else 1.0) + ) + loss = params.simple_loss_scale * simple_loss + pruned_loss_scale * pruned_loss + + assert loss.requires_grad == is_training + + info = MetricsTracker() + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + # info["frames"] is an approximate number for two reasons: + # (1) The acutal subsampling factor is ((lens - 1) // 2 - 1) // 2 + # (2) If some utterances in the batch lead to inf/nan loss, they + # are filtered out. + info["frames"] = (feature_lens // params.subsampling_factor).sum().item() + + # `utt_duration` and `utt_pad_proportion` would be normalized by `utterances` # noqa + info["utterances"] = feature.size(0) + # averaged input duration in frames over utterances + info["utt_duration"] = feature_lens.sum().item() + # averaged padding proportion over utterances + info["utt_pad_proportion"] = ( + ((feature.size(1) - feature_lens) / feature.size(1)).sum().item() + ) + + # Note: We use reduction=sum while computing the loss. + info["loss"] = loss.detach().cpu().item() + info["simple_loss"] = simple_loss.detach().cpu().item() + info["pruned_loss"] = pruned_loss.detach().cpu().item() + + return loss, info + + +def compute_validation_loss( + params: AttributeDict, + model: Union[nn.Module, DDP], + graph_compiler: CharCtcTrainingGraphCompiler, + valid_dl: torch.utils.data.DataLoader, + world_size: int = 1, +) -> MetricsTracker: + """Run the validation process.""" + model.eval() + + tot_loss = MetricsTracker() + + for batch_idx, batch in enumerate(valid_dl): + loss, loss_info = compute_loss( + params=params, + model=model, + graph_compiler=graph_compiler, + batch=batch, + is_training=False, + ) + assert loss.requires_grad is False + tot_loss = tot_loss + loss_info + + if world_size > 1: + tot_loss.reduce(loss.device) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + if loss_value < params.best_valid_loss: + params.best_valid_epoch = params.cur_epoch + params.best_valid_loss = loss_value + + return tot_loss + + +def train_one_epoch( + params: AttributeDict, + model: Union[nn.Module, DDP], + optimizer: torch.optim.Optimizer, + scheduler: LRSchedulerType, + graph_compiler: CharCtcTrainingGraphCompiler, + train_dl: torch.utils.data.DataLoader, + valid_dl: torch.utils.data.DataLoader, + scaler: GradScaler, + model_avg: Optional[nn.Module] = None, + tb_writer: Optional[SummaryWriter] = None, + world_size: int = 1, + rank: int = 0, +) -> None: + """Train the model for one epoch. + + The training loss from the mean of all frames is saved in + `params.train_loss`. It runs the validation process every + `params.valid_interval` batches. + + Args: + params: + It is returned by :func:`get_params`. + model: + The model for training. + optimizer: + The optimizer we are using. + scheduler: + The learning rate scheduler, we call step() every step. + train_dl: + Dataloader for the training dataset. + valid_dl: + Dataloader for the validation dataset. + scaler: + The scaler used for mix precision training. + model_avg: + The stored model averaged from the start of training. + tb_writer: + Writer to write log messages to tensorboard. + world_size: + Number of nodes in DDP training. If it is 1, DDP is disabled. + rank: + The rank of the node in DDP training. If no DDP is used, it should + be set to 0. + """ + model.train() + + tot_loss = MetricsTracker() + + cur_batch_idx = params.get("cur_batch_idx", 0) + + for batch_idx, batch in enumerate(train_dl): + if batch_idx < cur_batch_idx: + continue + cur_batch_idx = batch_idx + params.batch_idx_train += 1 + batch_size = len(batch["supervisions"]["text"]) + + try: + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, loss_info = compute_loss( + params=params, + model=model, + graph_compiler=graph_compiler, + batch=batch, + is_training=True, + warmup=(params.batch_idx_train / params.model_warm_step), + ) + # summary stats + tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info + + # NOTE: We use reduction==sum and loss is computed over utterances + # in the batch and there is no normalization to it so far. + scaler.scale(loss).backward() + scheduler.step_batch(params.batch_idx_train) + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad() + except: # noqa + display_and_save_batch(batch, params=params) + raise + + if params.print_diagnostics and batch_idx == 30: + return + + if ( + rank == 0 + and params.batch_idx_train > 0 + and params.batch_idx_train % params.average_period == 0 + ): + update_averaged_model( + params=params, + model_cur=model, + model_avg=model_avg, + ) + + if ( + params.batch_idx_train > 0 + and params.batch_idx_train % params.save_every_n == 0 + ): + params.cur_batch_idx = batch_idx + save_checkpoint_with_global_batch_idx( + out_dir=params.exp_dir, + global_batch_idx=params.batch_idx_train, + model=model, + model_avg=model_avg, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=train_dl.sampler, + scaler=scaler, + rank=rank, + ) + del params.cur_batch_idx + remove_checkpoints( + out_dir=params.exp_dir, + topk=params.keep_last_k, + rank=rank, + ) + + if batch_idx % params.log_interval == 0 and not params.print_diagnostics: + cur_lr = scheduler.get_last_lr()[0] + logging.info( + f"Epoch {params.cur_epoch}, " + f"batch {batch_idx}, loss[{loss_info}], " + f"tot_loss[{tot_loss}], batch size: {batch_size}, " + f"lr: {cur_lr:.2e}" + ) + + if tb_writer is not None: + tb_writer.add_scalar( + "train/learning_rate", cur_lr, params.batch_idx_train + ) + + loss_info.write_summary( + tb_writer, "train/current_", params.batch_idx_train + ) + tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train) + + if ( + batch_idx > 0 + and batch_idx % params.valid_interval == 0 + and not params.print_diagnostics + ): + logging.info("Computing validation loss") + valid_info = compute_validation_loss( + params=params, + model=model, + graph_compiler=graph_compiler, + valid_dl=valid_dl, + world_size=world_size, + ) + model.train() + logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}") + if tb_writer is not None: + valid_info.write_summary( + tb_writer, "train/valid_", params.batch_idx_train + ) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + params.train_loss = loss_value + if params.train_loss < params.best_train_loss: + params.best_train_epoch = params.cur_epoch + params.best_train_loss = params.train_loss + + +def run(rank, world_size, args): + """ + Args: + rank: + It is a value between 0 and `world_size-1`, which is + passed automatically by `mp.spawn()` in :func:`main`. + The node with rank 0 is responsible for saving checkpoint. + world_size: + Number of GPUs for DDP training. + args: + The return value of get_parser().parse_args() + """ + params = get_params() + params.update(vars(args)) + + fix_random_seed(params.seed) + if world_size > 1: + setup_dist(rank, world_size, params.master_port) + + setup_logger(f"{params.exp_dir}/log/log-train") + logging.info("Training started") + + if args.tensorboard and rank == 0: + tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard") + else: + tb_writer = None + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", rank) + logging.info(f"Device: {device}") + + bpe_model = params.lang_dir + "/bpe.model" + import sentencepiece as spm + + sp = spm.SentencePieceProcessor() + sp.load(bpe_model) + + lexicon = Lexicon(params.lang_dir) + graph_compiler = CharCtcTrainingGraphCompiler( + lexicon=lexicon, + device=device, + ) + + params.blank_id = lexicon.token_table[""] + params.vocab_size = max(lexicon.tokens) + 1 + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + assert params.save_every_n >= params.average_period + model_avg: Optional[nn.Module] = None + if rank == 0: + # model_avg is only used with rank 0 + model_avg = copy.deepcopy(model) + + assert params.start_epoch > 0, params.start_epoch + checkpoints = load_checkpoint_if_available( + params=params, model=model, model_avg=model_avg + ) + + model.to(device) + if world_size > 1: + logging.info("Using DDP") + model = DDP(model, device_ids=[rank]) + + optimizer = Eve(model.parameters(), lr=params.initial_lr) + + scheduler = Eden(optimizer, params.lr_batches, params.lr_epochs) + + if checkpoints and "optimizer" in checkpoints: + logging.info("Loading optimizer state dict") + optimizer.load_state_dict(checkpoints["optimizer"]) + + if ( + checkpoints + and "scheduler" in checkpoints + and checkpoints["scheduler"] is not None + ): + logging.info("Loading scheduler state dict") + scheduler.load_state_dict(checkpoints["scheduler"]) + + # # overwrite it + # scheduler.base_lrs = [params.initial_lr for _ in scheduler.base_lrs] + # print(scheduler.base_lrs) + + if params.print_diagnostics: + opts = diagnostics.TensorDiagnosticOptions( + 2**22 + ) # allow 4 megabytes per sub-module + diagnostic = diagnostics.attach_diagnostics(model, opts) + + tal_csasr = TAL_CSASRAsrDataModule(args) + train_cuts = tal_csasr.train_cuts() + + def remove_short_and_long_utt(c: Cut): + # Keep only utterances with duration between 1 second and 20 seconds + # + # Caution: There is a reason to select 20.0 here. Please see + # ../local/display_manifest_statistics.py + # + # You should use ../local/display_manifest_statistics.py to get + # an utterance duration distribution for your dataset to select + # the threshold + return 1.0 <= c.duration <= 20.0 + + def text_normalize_for_cut(c: Cut): + # Text normalize for each sample + text = c.supervisions[0].text + text = text.strip("\n").strip("\t") + text = text_normalize(text) + text = tokenize_by_bpe_model(sp, text) + c.supervisions[0].text = text + return c + + train_cuts = train_cuts.filter(remove_short_and_long_utt) + train_cuts = train_cuts.map(text_normalize_for_cut) + + if params.start_batch > 0 and checkpoints and "sampler" in checkpoints: + # We only load the sampler's state dict when it loads a checkpoint + # saved in the middle of an epoch + sampler_state_dict = checkpoints["sampler"] + else: + sampler_state_dict = None + + train_dl = tal_csasr.train_dataloaders( + train_cuts, sampler_state_dict=sampler_state_dict + ) + + valid_cuts = tal_csasr.valid_cuts() + valid_cuts = valid_cuts.map(text_normalize_for_cut) + valid_dl = tal_csasr.valid_dataloaders(valid_cuts) + + # if not params.print_diagnostics: + # scan_pessimistic_batches_for_oom( + # model=model, + # train_dl=train_dl, + # optimizer=optimizer, + # graph_compiler=graph_compiler, + # params=params, + # warmup=0.0 if params.start_epoch == 1 else 1.0, + # ) + + scaler = GradScaler(enabled=params.use_fp16) + if checkpoints and "grad_scaler" in checkpoints: + logging.info("Loading grad scaler state dict") + scaler.load_state_dict(checkpoints["grad_scaler"]) + + for epoch in range(params.start_epoch, params.num_epochs + 1): + scheduler.step_epoch(epoch - 1) + fix_random_seed(params.seed + epoch - 1) + train_dl.sampler.set_epoch(epoch - 1) + + if tb_writer is not None: + tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train) + + params.cur_epoch = epoch + + train_one_epoch( + params=params, + model=model, + model_avg=model_avg, + optimizer=optimizer, + scheduler=scheduler, + graph_compiler=graph_compiler, + train_dl=train_dl, + valid_dl=valid_dl, + scaler=scaler, + tb_writer=tb_writer, + world_size=world_size, + rank=rank, + ) + + if params.print_diagnostics: + diagnostic.print_diagnostics() + break + + save_checkpoint( + params=params, + model=model, + model_avg=model_avg, + optimizer=optimizer, + scheduler=scheduler, + sampler=train_dl.sampler, + scaler=scaler, + rank=rank, + ) + + logging.info("Done!") + + if world_size > 1: + torch.distributed.barrier() + cleanup_dist() + + +def scan_pessimistic_batches_for_oom( + model: Union[nn.Module, DDP], + train_dl: torch.utils.data.DataLoader, + optimizer: torch.optim.Optimizer, + graph_compiler: CharCtcTrainingGraphCompiler, + params: AttributeDict, + warmup: float, +): + from lhotse.dataset import find_pessimistic_batches + + logging.info( + "Sanity check -- see if any of the batches in epoch 1 would cause OOM." + ) + batches, crit_values = find_pessimistic_batches(train_dl.sampler) + for criterion, cuts in batches.items(): + batch = train_dl.dataset[cuts] + try: + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, _ = compute_loss( + params=params, + model=model, + graph_compiler=graph_compiler, + batch=batch, + is_training=True, + warmup=warmup, + ) + loss.backward() + optimizer.step() + optimizer.zero_grad() + except RuntimeError as e: + if "CUDA out of memory" in str(e): + logging.error( + "Your GPU ran out of memory with the current " + "max_duration setting. We recommend decreasing " + "max_duration and trying again.\n" + f"Failing criterion: {criterion} " + f"(={crit_values[criterion]}) ..." + ) + raise + + +def main(): + parser = get_parser() + TAL_CSASRAsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + world_size = args.world_size + assert world_size >= 1 + if world_size > 1: + mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True) + else: + run(rank=0, world_size=1, args=args) + + +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + +if __name__ == "__main__": + main()