Commit more scripts for wenetspeech kws recipe

egs/gigaspeech/KWS/zipformer/finetune.py

@@ -158,6 +158,13 @@ def get_parser():
         formatter_class=argparse.ArgumentDefaultsHelpFormatter
     )
 
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        default="data/lang_bpe_500/bpe.model",
+        help="Path to the BPE model",
+    )
+
     add_training_arguments(parser)
     add_model_arguments(parser)
     add_finetune_arguments(parser)

egs/wenetspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py

@@ -407,10 +407,3 @@ class WenetSpeechAsrDataModule:
     def test_meeting_cuts(self) -> List[CutSet]:
         logging.info("About to get TEST_MEETING cuts")
         return load_manifest_lazy(self.args.manifest_dir / "cuts_TEST_MEETING.jsonl.gz")
-
-    @lru_cache()
-    def test_open_commands_cuts(self) -> CutSet:
-        logging.info("About to get open commands cuts")
-        return load_manifest_lazy(
-            self.args.manifest_dir / "open-commands-cn_cuts_test.jsonl.gz"
-        )

egs/wenetspeech/KWS/shared

@@ -0,0 +1 @@
+../../../icefall/shared

egs/wenetspeech/KWS/zipformer/asr_datamodule.py

@@ -1,4 +1,5 @@
 # Copyright      2021  Piotr Żelasko
+# Copyright      2024  Xiaomi Corporation     (Author: Wei Kang)
 #
 # See ../../../../LICENSE for clarification regarding multiple authors
 #
@@ -409,8 +410,50 @@ class WenetSpeechAsrDataModule:
         return load_manifest_lazy(self.args.manifest_dir / "cuts_TEST_MEETING.jsonl.gz")
 
     @lru_cache()
-    def test_open_commands_cuts(self) -> CutSet:
+    def cn_speech_commands_small_cuts(self) -> CutSet:
-        logging.info("About to get open commands cuts")
+        logging.info("About to get cn speech commands small cuts")
         return load_manifest_lazy(
-            self.args.manifest_dir / "open-commands-cn_cuts_test.jsonl.gz"
+            self.args.manifest_dir / "cn_speech_commands_cuts_small.jsonl.gz"
+        )
+
+    @lru_cache()
+    def cn_speech_commands_large_cuts(self) -> CutSet:
+        logging.info("About to get cn speech commands large cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "cn_speech_commands_cuts_large.jsonl.gz"
+        )
+
+    @lru_cache()
+    def nihaowenwen_dev_cuts(self) -> CutSet:
+        logging.info("About to get nihaowenwen dev cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "nihaowenwen_cuts_dev.jsonl.gz"
+        )
+
+    @lru_cache()
+    def nihaowenwen_test_cuts(self) -> CutSet:
+        logging.info("About to get nihaowenwen test cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "nihaowenwen_cuts_test.jsonl.gz"
+        )
+
+    @lru_cache()
+    def nihaowenwen_train_cuts(self) -> CutSet:
+        logging.info("About to get nihaowenwen train cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "nihaowenwen_cuts_train.jsonl.gz"
+        )
+
+    @lru_cache()
+    def xiaoyun_clean_cuts(self) -> CutSet:
+        logging.info("About to get xiaoyun clean cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "xiaoyun_cuts_clean.jsonl.gz"
+        )
+
+    @lru_cache()
+    def xiaoyun_noisy_cuts(self) -> CutSet:
+        logging.info("About to get xiaoyun noisy cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "xiaoyun_cuts_noisy.jsonl.gz"
         )

egs/wenetspeech/KWS/zipformer/decode.py

@@ -178,16 +178,47 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--keyword-file",
+        "--keywords-file",
         type=str,
         help="File contains keywords.",
     )
 
     parser.add_argument(
-        "--keyword-score",
+        "--test-set",
+        type=str,
+        default="small",
+        help="small or large",
+    )
+
+    parser.add_argument(
+        "--keywords-score",
         type=float,
-        default=0.75,
+        default=1.5,
-        help="The threshold (probability) to boost the keyword.",
+        help="""
+        The default boosting score (token level) for keywords. it will boost the
+        paths that match keywords to make them survive beam search.
+        """,
+    )
+
+    parser.add_argument(
+        "--keywords-threshold",
+        type=float,
+        default=0.35,
+        help="The default threshold (probability) to trigger the keyword.",
+    )
+
+    parser.add_argument(
+        "--keywords-version",
+        type=str,
+        default="",
+        help="The keywords configuration version, just to save results to different files.",
+    )
+
+    parser.add_argument(
+        "--num-tailing-blanks",
+        type=int,
+        default=1,
+        help="The number of tailing blanks should have after hitting one keyword.",
     )
 
     add_model_arguments(parser)
@@ -261,7 +292,7 @@ def decode_one_batch(
         model=model,
         encoder_out=encoder_out,
         encoder_out_lens=encoder_out_lens,
-        context_graph=kws_graph,
+        keywords_graph=kws_graph,
         beam=params.beam_size,
         num_tailing_blanks=8,
     )
@@ -288,6 +319,7 @@ def decode_dataset(
     lexicon: Lexicon,
     kws_graph: ContextGraph,
     keywords: Set[str],
+    test_only_keywords: bool,
 ) -> Dict[str, List[Tuple[List[str], List[str]]]]:
     """Decode dataset.
 
@@ -342,32 +374,62 @@ def decode_dataset(
             hyp_words = [x[0] for x in hyp_words]
             this_batch.append((cut_id, ref_words, list("".join(hyp_words))))
             hyp_set = set(hyp_words)
-            hyp_str = " | ".join(hyp_words)
+            if len(hyp_words) > 1:
+                logging.warning(
+                    f"Cut {cut_id} triggers more than one keywords : {hyp_words},"
+                    f"please check the transcript to see if it really has more "
+                    f"than one keywords, if so consider splitting this audio and"
+                    f"keep only one keyword for each audio."
+                )
+            hyp_str = " | ".join(
+                hyp_words
+            )  # The triggered keywords for this utterance.
+            TP = False
+            FP = False
             for x in hyp_set:
-                assert x in keywords, x
+                assert x in keywords, x  # can only trigger keywords
-                if x in ref_text and x in keywords:
+                if (test_only_keywords and x == ref_text) or (
-                    metric["all"].TP += 1
+                    not test_only_keywords and x in ref_text
+                ):
+                    TP = True
                     metric[x].TP += 1
                     metric[x].TP_list.append(f"({ref_text} -> {x})")
-                if x not in ref_text and x in keywords:
+                if (test_only_keywords and x != ref_text) or (
-                    metric["all"].FP += 1
+                    not test_only_keywords and x not in ref_text
+                ):
+                    FP = True
                     metric[x].FP += 1
-                    metric[x].FP_list.append(f"({ref_text} -> {x}/{cut_id})")
+                    metric[x].FP_list.append(f"({ref_text} -> {x})")
+            if TP:
+                metric["all"].TP += 1
+            if FP:
+                metric["all"].FP += 1
+            TN = True  # all keywords are true negative then the summery is true negative.
+            FN = False
             for x in keywords:
                 if x not in ref_text and x not in hyp_set:
-                    metric["all"].TN += 1
                     metric[x].TN += 1
+                    continue
 
-                if x in ref_text:
+                TN = False
+                if (test_only_keywords and x == ref_text) or (
+                    not test_only_keywords and x in ref_text
+                ):
                     fn = True
                     for y in hyp_set:
-                        if y in ref_text:
+                        if (test_only_keywords and y == ref_text) or (
+                            not test_only_keywords and y in ref_text
+                        ):
                             fn = False
                             break
-                    if fn and ref_text.endswith(x):
+                    if fn:
-                        metric["all"].FN += 1
+                        FN = True
                         metric[x].FN += 1
-                        metric[x].FN_list.append(f"({ref_text} -> {hyp_str}/{cut_id})")
+                        metric[x].FN_list.append(f"({ref_text} -> {hyp_str})")
+            if TN:
+                metric["all"].TN += 1
+            if FN:
+                metric["all"].FN += 1
 
         results.extend(this_batch)
 
@@ -399,16 +461,17 @@ def save_results(
 
     metric_filename = params.res_dir / f"metric-{test_set_name}-{params.suffix}.txt"
 
-    print_s = ""
     with open(metric_filename, "w") as of:
         width = 10
         for key, item in sorted(
             metric.items(), key=lambda x: (x[1].FP, x[1].FN), reverse=True
         ):
             acc = (item.TP + item.TN) / (item.TP + item.TN + item.FP + item.FN)
-            precision = (item.TP + 1) / (item.TP + item.FP + 1)
+            precision = (
-            recall = (item.TP + 1) / (item.TP + item.FN + 1)
+                0.0 if (item.TP + item.FP) == 0 else item.TP / (item.TP + item.FP)
-            fpr = (item.FP + 1) / (item.FP + item.TN + 1)
+            )
+            recall = 0.0 if (item.TP + item.FN) == 0 else item.TP / (item.TP + item.FN)
+            fpr = 0.0 if (item.FP + item.TN) == 0 else item.FP / (item.FP + item.TN)
             s = f"{key}:\n"
             s += f"\t{'TP':{width}}{'FP':{width}}{'FN':{width}}{'TN':{width}}\n"
             s += f"\t{str(item.TP):{width}}{str(item.FP):{width}}{str(item.FN):{width}}{str(item.TN):{width}}\n"
@@ -417,12 +480,14 @@ def save_results(
             s += f"\tRecall(PPR): {recall:.3f}\n"
             s += f"\tFPR: {fpr:.3f}\n"
             s += f"\tF1: {2 * precision * recall / (precision + recall):.3f}\n"
-            s += f"\tTP list: {' # '.join(item.TP_list)}\n"
+            if key != "all":
-            s += f"\tFP list: {' # '.join(item.FP_list)}\n"
+                s += f"\tTP list: {' # '.join(item.TP_list)}\n"
-            s += f"\tFN list: {' # '.join(item.FN_list)}\n"
+                s += f"\tFP list: {' # '.join(item.FP_list)}\n"
+                s += f"\tFN list: {' # '.join(item.FN_list)}\n"
             of.write(s + "\n")
             if key == "all":
                 logging.info(s)
+        of.write(f"\n\n{params.keywords_config}")
 
     logging.info("Wrote metric stats to {}".format(metric_filename))
 
@@ -439,6 +504,7 @@ def main():
 
     params.res_dir = params.exp_dir / "kws"
 
+    params.suffix = params.test_set
     if params.iter > 0:
         params.suffix = f"iter-{params.iter}-avg-{params.avg}"
     else:
@@ -454,9 +520,12 @@ def main():
         params.suffix += f"-chunk-{params.chunk_size}"
         params.suffix += f"-left-context-{params.left_context_frames}"
 
-    if params.use_averaged_model:
+    params.suffix += f"-score-{params.keywords_score}"
-        params.suffix += "-use-averaged-model"
+    params.suffix += f"-threshold-{params.keywords_threshold}"
-    params.suffix += f"-keyword-score-{params.keyword_score}"
+    params.suffix += f"-tailing-blanks-{params.num_tailing_blanks}"
+    if params.blank_penalty != 0:
+        params.suffix += f"-blank-penalty-{params.blank_penalty}"
+    params.suffix += f"-version-{params.keywords_version}"
 
     setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")
     logging.info("Decoding started")
@@ -473,18 +542,30 @@ def main():
 
     logging.info(params)
 
-    keywords = []
+    phrases = []
-    keywords_id = []
+    token_ids = []
-    with open(params.keyword_file, "r") as f:
+    keywords_scores = []
+    keywords_thresholds = []
+    keywords_config = []
+    with open(params.keywords_file, "r") as f:
         for line in f.readlines():
+            keywords_config.append(line)
             score = 0
-            kws = line.strip().upper().split()
+            threshold = 0
-            if kws[-1][0] == ":":
+            keyword = []
-                score = float(kws[-1][1:])
+            words = line.strip().upper().split()
-                kws = kws[0:-1]
+            for word in words:
+                word = word.strip()
+                if word[0] == ":":
+                    score = float(word[1:])
+                    continue
+                if word[0] == "#":
+                    threshold = float(word[1:])
+                    continue
+                keyword.append(word)
+            keyword = "".join(keyword)
             tmp_ids = []
-            kws = "".join(kws)
+            kws_py = text_to_pinyin(keyword, mode=params.pinyin_type)
-            kws_py = text_to_pinyin(kws, mode=params.pinyin_type)
             for k in kws_py:
                 if k in lexicon.token_table:
                     tmp_ids.append(lexicon.token_table[k])
@@ -493,11 +574,23 @@ def main():
                     tmp_ids = []
                     break
             if tmp_ids:
-                logging.info(f"Adding keyword : {kws}")
+                logging.info(f"Adding keyword : {keyword}")
-                keywords.append(kws)
+                phrases.append(keyword)
-                keywords_id.append((tmp_ids, score, kws))
+                token_ids.append(tmp_ids)
-    kws_graph = ContextGraph(context_score=params.keyword_score)
+                keywords_scores.append(score)
-    kws_graph.build(keywords_id)
+                keywords_thresholds.append(threshold)
+    params.keywords_config = "".join(keywords_config)
+
+    kws_graph = ContextGraph(
+        context_score=params.keywords_score, ac_threshold=params.keywords_threshold
+    )
+    kws_graph.build(
+        token_ids=token_ids,
+        phrases=phrases,
+        scores=keywords_scores,
+        ac_thresholds=keywords_thresholds,
+    )
+    keywords = set(phrases)
 
     logging.info("About to create model")
     model = get_model(params)
@@ -597,21 +690,51 @@ def main():
             )
         return T > 0
 
-    def select_keywords(c: Cut):
-        text = c.supervisions[0].text.strip()
-        return text in keywords
-
-    commands_cuts = wenetspeech.test_open_commands_cuts()
-    commands_cuts = commands_cuts.filter(select_keywords)
-    commands_cuts = commands_cuts.filter(remove_short_utt)
-    commands_dl = wenetspeech.test_dataloaders(commands_cuts)
-
     test_net_cuts = wenetspeech.test_net_cuts()
     test_net_cuts = test_net_cuts.filter(remove_short_utt)
     test_net_dl = wenetspeech.test_dataloaders(test_net_cuts)
 
-    test_sets = ["COMMANDS"]  # , "TEST_NET"]
+    cn_commands_small_cuts = wenetspeech.cn_speech_commands_small_cuts()
-    test_dls = [commands_dl]  # , test_net_dl]
+    cn_commands_small_cuts = cn_commands_small_cuts.filter(remove_short_utt)
+    cn_commands_small_dl = wenetspeech.test_dataloaders(cn_commands_small_cuts)
+
+    cn_commands_large_cuts = wenetspeech.cn_speech_commands_large_cuts()
+    cn_commands_large_cuts = cn_commands_large_cuts.filter(remove_short_utt)
+    cn_commands_large_dl = wenetspeech.test_dataloaders(cn_commands_large_cuts)
+
+    nihaowenwen_test_cuts = wenetspeech.nihaowenwen_test_cuts()
+    nihaowenwen_test_cuts = nihaowenwen_test_cuts.filter(remove_short_utt)
+    nihaowenwen_test_dl = wenetspeech.test_dataloaders(nihaowenwen_test_cuts)
+
+    xiaoyun_clean_cuts = wenetspeech.xiaoyun_clean_cuts()
+    xiaoyun_clean_cuts = xiaoyun_clean_cuts.filter(remove_short_utt)
+    xiaoyun_clean_dl = wenetspeech.test_dataloaders(xiaoyun_clean_cuts)
+
+    xiaoyun_noisy_cuts = wenetspeech.xiaoyun_noisy_cuts()
+    xiaoyun_noisy_cuts = xiaoyun_noisy_cuts.filter(remove_short_utt)
+    xiaoyun_noisy_dl = wenetspeech.test_dataloaders(xiaoyun_noisy_cuts)
+
+    test_sets = []
+    test_dls = []
+    if params.test_set == "large":
+        test_sets.append("cn_commands_large")
+        test_dls.append(cn_commands_large_dl)
+    else:
+        assert params.test_set == "small", params.test_set
+        test_sets += [
+            "cn_commands_small",
+            "nihaowenwen",
+            "xiaoyun_clean",
+            "xiaoyun_noisy",
+            "test_net",
+        ]
+        test_dls += [
+            cn_commands_small_dl,
+            nihaowenwen_test_dl,
+            xiaoyun_clean_dl,
+            xiaoyun_noisy_dl,
+            test_net_dl,
+        ]
 
     for test_set, test_dl in zip(test_sets, test_dls):
         results, metric = decode_dataset(
@@ -620,7 +743,8 @@ def main():
             model=model,
             lexicon=lexicon,
             kws_graph=kws_graph,
-            keywords=set(keywords),
+            keywords=keywords,
+            test_only_keywords="test_net" not in test_set,
         )
 
         save_results(

egs/wenetspeech/KWS/zipformer/export.py

@@ -0,0 +1,526 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021-2023 Xiaomi Corporation (Author: Fangjun Kuang,
+#                                                 Zengwei Yao,
+#                                                 Wei Kang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script converts several saved checkpoints
+# to a single one using model averaging.
+"""
+
+Usage:
+
+Note: This is a example for librispeech dataset, if you are using different
+dataset, you should change the argument values according to your dataset.
+
+(1) Export to torchscript model using torch.jit.script()
+
+- For non-streaming model:
+
+./zipformer/export.py \
+  --exp-dir ./zipformer/exp \
+  --tokens data/lang_bpe_500/tokens.txt \
+  --epoch 30 \
+  --avg 9 \
+  --jit 1
+
+It will generate a file `jit_script.pt` in the given `exp_dir`. You can later
+load it by `torch.jit.load("jit_script.pt")`.
+
+Check ./jit_pretrained.py for its usage.
+
+Check https://github.com/k2-fsa/sherpa
+for how to use the exported models outside of icefall.
+
+- For streaming model:
+
+./zipformer/export.py \
+  --exp-dir ./zipformer/exp \
+  --causal 1 \
+  --chunk-size 16 \
+  --left-context-frames 128 \
+  --tokens data/lang_bpe_500/tokens.txt \
+  --epoch 30 \
+  --avg 9 \
+  --jit 1
+
+It will generate a file `jit_script_chunk_16_left_128.pt` in the given `exp_dir`.
+You can later load it by `torch.jit.load("jit_script_chunk_16_left_128.pt")`.
+
+Check ./jit_pretrained_streaming.py for its usage.
+
+Check https://github.com/k2-fsa/sherpa
+for how to use the exported models outside of icefall.
+
+(2) Export `model.state_dict()`
+
+- For non-streaming model:
+
+./zipformer/export.py \
+  --exp-dir ./zipformer/exp \
+  --tokens data/lang_bpe_500/tokens.txt \
+  --epoch 30 \
+  --avg 9
+
+- For streaming model:
+
+./zipformer/export.py \
+  --exp-dir ./zipformer/exp \
+  --causal 1 \
+  --tokens data/lang_bpe_500/tokens.txt \
+  --epoch 30 \
+  --avg 9
+
+It will generate a file `pretrained.pt` in the given `exp_dir`. You can later
+load it by `icefall.checkpoint.load_checkpoint()`.
+
+- For non-streaming model:
+
+To use the generated file with `zipformer/decode.py`,
+you can do:
+
+    cd /path/to/exp_dir
+    ln -s pretrained.pt epoch-9999.pt
+
+    cd /path/to/egs/librispeech/ASR
+    ./zipformer/decode.py \
+        --exp-dir ./zipformer/exp \
+        --epoch 9999 \
+        --avg 1 \
+        --max-duration 600 \
+        --decoding-method greedy_search \
+        --bpe-model data/lang_bpe_500/bpe.model
+
+- For streaming model:
+
+To use the generated file with `zipformer/decode.py` and `zipformer/streaming_decode.py`, you can do:
+
+    cd /path/to/exp_dir
+    ln -s pretrained.pt epoch-9999.pt
+
+    cd /path/to/egs/librispeech/ASR
+
+    # simulated streaming decoding
+    ./zipformer/decode.py \
+        --exp-dir ./zipformer/exp \
+        --epoch 9999 \
+        --avg 1 \
+        --max-duration 600 \
+        --causal 1 \
+        --chunk-size 16 \
+        --left-context-frames 128 \
+        --decoding-method greedy_search \
+        --bpe-model data/lang_bpe_500/bpe.model
+
+    # chunk-wise streaming decoding
+    ./zipformer/streaming_decode.py \
+        --exp-dir ./zipformer/exp \
+        --epoch 9999 \
+        --avg 1 \
+        --max-duration 600 \
+        --causal 1 \
+        --chunk-size 16 \
+        --left-context-frames 128 \
+        --decoding-method greedy_search \
+        --bpe-model data/lang_bpe_500/bpe.model
+
+Check ./pretrained.py for its usage.
+
+Note: If you don't want to train a model from scratch, we have
+provided one for you. You can get it at
+
+- non-streaming model:
+https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-2023-05-15
+
+- streaming model:
+https://huggingface.co/Zengwei/icefall-asr-librispeech-streaming-zipformer-2023-05-17
+
+with the following commands:
+
+    sudo apt-get install git-lfs
+    git lfs install
+    git clone https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-2023-05-15
+    git clone https://huggingface.co/Zengwei/icefall-asr-librispeech-streaming-zipformer-2023-05-17
+    # You will find the pre-trained models in exp dir
+"""
+
+import argparse
+import logging
+from pathlib import Path
+from typing import List, Tuple
+
+import k2
+import torch
+from scaling_converter import convert_scaled_to_non_scaled
+from torch import Tensor, nn
+from train import add_model_arguments, get_model, get_params
+
+from icefall.checkpoint import (
+    average_checkpoints,
+    average_checkpoints_with_averaged_model,
+    find_checkpoints,
+    load_checkpoint,
+)
+from icefall.utils import make_pad_mask, num_tokens, str2bool
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=30,
+        help="""It specifies the checkpoint to use for decoding.
+        Note: Epoch counts from 1.
+        You can specify --avg to use more checkpoints for model averaging.""",
+    )
+
+    parser.add_argument(
+        "--iter",
+        type=int,
+        default=0,
+        help="""If positive, --epoch is ignored and it
+        will use the checkpoint exp_dir/checkpoint-iter.pt.
+        You can specify --avg to use more checkpoints for model averaging.
+        """,
+    )
+
+    parser.add_argument(
+        "--avg",
+        type=int,
+        default=9,
+        help="Number of checkpoints to average. Automatically select "
+        "consecutive checkpoints before the checkpoint specified by "
+        "'--epoch' and '--iter'",
+    )
+
+    parser.add_argument(
+        "--use-averaged-model",
+        type=str2bool,
+        default=True,
+        help="Whether to load averaged model. Currently it only supports "
+        "using --epoch. If True, it would decode with the averaged model "
+        "over the epoch range from `epoch-avg` (excluded) to `epoch`."
+        "Actually only the models with epoch number of `epoch-avg` and "
+        "`epoch` are loaded for averaging. ",
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="zipformer/exp",
+        help="""It specifies the directory where all training related
+        files, e.g., checkpoints, log, etc, are saved
+        """,
+    )
+
+    parser.add_argument(
+        "--tokens",
+        type=str,
+        default="data/lang_bpe_500/tokens.txt",
+        help="Path to the tokens.txt",
+    )
+
+    parser.add_argument(
+        "--jit",
+        type=str2bool,
+        default=False,
+        help="""True to save a model after applying torch.jit.script.
+        It will generate a file named jit_script.pt.
+        Check ./jit_pretrained.py for how to use it.
+        """,
+    )
+
+    parser.add_argument(
+        "--context-size",
+        type=int,
+        default=2,
+        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+class EncoderModel(nn.Module):
+    """A wrapper for encoder and encoder_embed"""
+
+    def __init__(self, encoder: nn.Module, encoder_embed: nn.Module) -> None:
+        super().__init__()
+        self.encoder = encoder
+        self.encoder_embed = encoder_embed
+
+    def forward(
+        self, features: Tensor, feature_lengths: Tensor
+    ) -> Tuple[Tensor, Tensor]:
+        """
+        Args:
+            features: (N, T, C)
+            feature_lengths: (N,)
+        """
+        x, x_lens = self.encoder_embed(features, feature_lengths)
+
+        src_key_padding_mask = make_pad_mask(x_lens)
+        x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)
+
+        encoder_out, encoder_out_lens = self.encoder(x, x_lens, src_key_padding_mask)
+        encoder_out = encoder_out.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)
+
+        return encoder_out, encoder_out_lens
+
+
+class StreamingEncoderModel(nn.Module):
+    """A wrapper for encoder and encoder_embed"""
+
+    def __init__(self, encoder: nn.Module, encoder_embed: nn.Module) -> None:
+        super().__init__()
+        assert len(encoder.chunk_size) == 1, encoder.chunk_size
+        assert len(encoder.left_context_frames) == 1, encoder.left_context_frames
+        self.chunk_size = encoder.chunk_size[0]
+        self.left_context_len = encoder.left_context_frames[0]
+
+        # The encoder_embed subsample features (T - 7) // 2
+        # The ConvNeXt module needs (7 - 1) // 2 = 3 frames of right padding after subsampling
+        self.pad_length = 7 + 2 * 3
+
+        self.encoder = encoder
+        self.encoder_embed = encoder_embed
+
+    def forward(
+        self, features: Tensor, feature_lengths: Tensor, states: List[Tensor]
+    ) -> Tuple[Tensor, Tensor, List[Tensor]]:
+        """Streaming forward for encoder_embed and encoder.
+
+        Args:
+            features: (N, T, C)
+            feature_lengths: (N,)
+            states: a list of Tensors
+
+        Returns encoder outputs, output lengths, and updated states.
+        """
+        chunk_size = self.chunk_size
+        left_context_len = self.left_context_len
+
+        cached_embed_left_pad = states[-2]
+        x, x_lens, new_cached_embed_left_pad = self.encoder_embed.streaming_forward(
+            x=features,
+            x_lens=feature_lengths,
+            cached_left_pad=cached_embed_left_pad,
+        )
+        assert x.size(1) == chunk_size, (x.size(1), chunk_size)
+
+        src_key_padding_mask = make_pad_mask(x_lens)
+
+        # processed_mask is used to mask out initial states
+        processed_mask = torch.arange(left_context_len, device=x.device).expand(
+            x.size(0), left_context_len
+        )
+        processed_lens = states[-1]  # (batch,)
+        # (batch, left_context_size)
+        processed_mask = (processed_lens.unsqueeze(1) <= processed_mask).flip(1)
+        # Update processed lengths
+        new_processed_lens = processed_lens + x_lens
+
+        # (batch, left_context_size + chunk_size)
+        src_key_padding_mask = torch.cat([processed_mask, src_key_padding_mask], dim=1)
+
+        x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)
+        encoder_states = states[:-2]
+
+        (
+            encoder_out,
+            encoder_out_lens,
+            new_encoder_states,
+        ) = self.encoder.streaming_forward(
+            x=x,
+            x_lens=x_lens,
+            states=encoder_states,
+            src_key_padding_mask=src_key_padding_mask,
+        )
+        encoder_out = encoder_out.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)
+
+        new_states = new_encoder_states + [
+            new_cached_embed_left_pad,
+            new_processed_lens,
+        ]
+        return encoder_out, encoder_out_lens, new_states
+
+    @torch.jit.export
+    def get_init_states(
+        self,
+        batch_size: int = 1,
+        device: torch.device = torch.device("cpu"),
+    ) -> List[torch.Tensor]:
+        """
+        Returns a list of cached tensors of all encoder layers. For layer-i, states[i*6:(i+1)*6]
+        is (cached_key, cached_nonlin_attn, cached_val1, cached_val2, cached_conv1, cached_conv2).
+        states[-2] is the cached left padding for ConvNeXt module,
+        of shape (batch_size, num_channels, left_pad, num_freqs)
+        states[-1] is processed_lens of shape (batch,), which records the number
+        of processed frames (at 50hz frame rate, after encoder_embed) for each sample in batch.
+        """
+        states = self.encoder.get_init_states(batch_size, device)
+
+        embed_states = self.encoder_embed.get_init_states(batch_size, device)
+        states.append(embed_states)
+
+        processed_lens = torch.zeros(batch_size, dtype=torch.int32, device=device)
+        states.append(processed_lens)
+
+        return states
+
+
+@torch.no_grad()
+def main():
+    args = get_parser().parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    device = torch.device("cpu")
+    # if torch.cuda.is_available():
+    #     device = torch.device("cuda", 0)
+
+    logging.info(f"device: {device}")
+
+    token_table = k2.SymbolTable.from_file(params.tokens)
+    params.blank_id = token_table["<blk>"]
+    params.vocab_size = num_tokens(token_table) + 1
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_model(params)
+
+    if not params.use_averaged_model:
+        if params.iter > 0:
+            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
+                : params.avg
+            ]
+            if len(filenames) == 0:
+                raise ValueError(
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            elif len(filenames) < params.avg:
+                raise ValueError(
+                    f"Not enough checkpoints ({len(filenames)}) found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            logging.info(f"averaging {filenames}")
+            model.load_state_dict(average_checkpoints(filenames, device=device))
+        elif params.avg == 1:
+            load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
+        else:
+            start = params.epoch - params.avg + 1
+            filenames = []
+            for i in range(start, params.epoch + 1):
+                if i >= 1:
+                    filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
+            logging.info(f"averaging {filenames}")
+            model.load_state_dict(average_checkpoints(filenames, device=device))
+    else:
+        if params.iter > 0:
+            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
+                : params.avg + 1
+            ]
+            if len(filenames) == 0:
+                raise ValueError(
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            elif len(filenames) < params.avg + 1:
+                raise ValueError(
+                    f"Not enough checkpoints ({len(filenames)}) found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            filename_start = filenames[-1]
+            filename_end = filenames[0]
+            logging.info(
+                "Calculating the averaged model over iteration checkpoints"
+                f" from {filename_start} (excluded) to {filename_end}"
+            )
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+        elif params.avg == 1:
+            load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
+        else:
+            assert params.avg > 0, params.avg
+            start = params.epoch - params.avg
+            assert start >= 1, start
+            filename_start = f"{params.exp_dir}/epoch-{start}.pt"
+            filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
+            logging.info(
+                f"Calculating the averaged model over epoch range from "
+                f"{start} (excluded) to {params.epoch}"
+            )
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+
+    model.eval()
+
+    if params.jit is True:
+        convert_scaled_to_non_scaled(model, inplace=True)
+        # We won't use the forward() method of the model in C++, so just ignore
+        # it here.
+        # Otherwise, one of its arguments is a ragged tensor and is not
+        # torch scriptabe.
+        model.__class__.forward = torch.jit.ignore(model.__class__.forward)
+
+        # Wrap encoder and encoder_embed as a module
+        if params.causal:
+            model.encoder = StreamingEncoderModel(model.encoder, model.encoder_embed)
+            chunk_size = model.encoder.chunk_size
+            left_context_len = model.encoder.left_context_len
+            filename = f"jit_script_chunk_{chunk_size}_left_{left_context_len}.pt"
+        else:
+            model.encoder = EncoderModel(model.encoder, model.encoder_embed)
+            filename = "jit_script.pt"
+
+        logging.info("Using torch.jit.script")
+        model = torch.jit.script(model)
+        model.save(str(params.exp_dir / filename))
+        logging.info(f"Saved to {filename}")
+    else:
+        logging.info("Not using torchscript. Export model.state_dict()")
+        # Save it using a format so that it can be loaded
+        # by :func:`load_checkpoint`
+        filename = params.exp_dir / "pretrained.pt"
+        torch.save({"model": model.state_dict()}, str(filename))
+        logging.info(f"Saved to {filename}")
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()

egs/wenetspeech/KWS/zipformer/finetune.py

@@ -3,6 +3,7 @@
 #                                                       Wei Kang,
 #                                                       Mingshuang Luo,
 #                                                       Zengwei Yao,
+#                                                       Yifan Yang,
 #                                                       Daniel Povey)
 #
 # See ../../../../LICENSE for clarification regarding multiple authors
@@ -23,29 +24,44 @@ Usage:
 
 export CUDA_VISIBLE_DEVICES="0,1,2,3"
 
-# For non-streaming model training:
+# For non-streaming model finetuning:
-./zipformer/train.py \
+./zipformer/finetune.py \
   --world-size 4 \
-  --num-epochs 30 \
+  --num-epochs 10 \
   --start-epoch 1 \
   --use-fp16 1 \
   --exp-dir zipformer/exp \
   --max-duration 1000
 
-# For streaming model training:
+# For non-streaming model finetuning with mux (original dataset):
-./zipformer/train.py \
+./zipformer/finetune.py \
   --world-size 4 \
-  --num-epochs 30 \
+  --num-epochs 10 \
+  --start-epoch 1 \
+  --use-mux 1 \
+  --use-fp16 1 \
+  --exp-dir zipformer/exp \
+  --max-duration 1000
+
+# For streaming model finetuning:
+./zipformer/fintune.py \
+  --world-size 4 \
+  --num-epochs 10 \
   --start-epoch 1 \
   --use-fp16 1 \
   --exp-dir zipformer/exp \
   --causal 1 \
   --max-duration 1000
 
-It supports training with:
+# For streaming model finetuning with mux (original dataset):
-  - transducer loss (default), with `--use-transducer True --use-ctc False`
+./zipformer/fintune.py \
-  - ctc loss (not recommended), with `--use-transducer False --use-ctc True`
+  --world-size 4 \
-  - transducer loss & ctc loss, with `--use-transducer True --use-ctc True`
+  --num-epochs 10 \
+  --start-epoch 1 \
+  --use-fp16 1 \
+  --exp-dir zipformer/exp \
+  --causal 1 \
+  --max-duration 1000
 """
 
 
@@ -55,7 +71,7 @@ import logging
 import warnings
 from pathlib import Path
 from shutil import copyfile
-from typing import Any, Dict, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import k2
 import optim
@@ -63,12 +79,10 @@ import torch
 import torch.multiprocessing as mp
 import torch.nn as nn
 from asr_datamodule import WenetSpeechAsrDataModule
-from lhotse.cut import Cut
+from lhotse.cut import Cut, CutSet
 from lhotse.dataset.sampling.base import CutSampler
 from lhotse.utils import fix_random_seed
-from model import AsrModel
 from optim import Eden, ScaledAdam
-from scaling import ScheduledFloat
 from torch import Tensor
 from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
@@ -76,7 +90,7 @@ from torch.utils.tensorboard import SummaryWriter
 
 from icefall import diagnostics
 from icefall.char_graph_compiler import CharCtcTrainingGraphCompiler
-from icefall.checkpoint import remove_checkpoints
+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,
@@ -109,9 +123,50 @@ from train import (
     set_batch_count,
 )
 
+
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
 
 
+def add_finetune_arguments(parser: argparse.ArgumentParser):
+    parser.add_argument(
+        "--use-mux",
+        type=str2bool,
+        default=False,
+        help="""
+        Whether to adapt. If true, we will mix 5% of the new data
+        with 95% of the original data to fine-tune.
+        """,
+    )
+
+    parser.add_argument(
+        "--init-modules",
+        type=str,
+        default=None,
+        help="""
+        Modules to be initialized. It matches all parameters starting with
+        a specific key. The keys are given with Comma seperated. If None,
+        all modules will be initialised. For example, if you only want to
+        initialise all parameters staring with "encoder", use "encoder";
+        if you want to initialise parameters starting with encoder or decoder,
+        use "encoder,joiner".
+        """,
+    )
+
+    parser.add_argument(
+        "--finetune-ckpt",
+        type=str,
+        default=None,
+        help="Fine-tuning from which checkpoint (a path to a .pt file)",
+    )
+
+    parser.add_argument(
+        "--continue-finetune",
+        type=str2bool,
+        default=False,
+        help="Continue finetuning or finetune from pre-trained model",
+    )
+
+
 def get_parser():
     parser = argparse.ArgumentParser(
         formatter_class=argparse.ArgumentDefaultsHelpFormatter
@@ -148,10 +203,58 @@ def get_parser():
 
     add_training_arguments(parser)
     add_model_arguments(parser)
+    add_finetune_arguments(parser)
 
     return parser
 
 
+def load_model_params(
+    ckpt: str, model: nn.Module, init_modules: List[str] = None, strict: bool = True
+):
+    """Load model params from checkpoint
+
+    Args:
+        ckpt (str): Path to the checkpoint
+        model (nn.Module): model to be loaded
+
+    """
+    logging.info(f"Loading checkpoint from {ckpt}")
+    checkpoint = torch.load(ckpt, map_location="cpu")
+
+    # if module list is empty, load the whole model from ckpt
+    if not init_modules:
+        if next(iter(checkpoint["model"])).startswith("module."):
+            logging.info("Loading checkpoint saved by DDP")
+
+            dst_state_dict = model.state_dict()
+            src_state_dict = checkpoint["model"]
+            for key in dst_state_dict.keys():
+                src_key = "{}.{}".format("module", key)
+                dst_state_dict[key] = src_state_dict.pop(src_key)
+            assert len(src_state_dict) == 0
+            model.load_state_dict(dst_state_dict, strict=strict)
+        else:
+            model.load_state_dict(checkpoint["model"], strict=strict)
+    else:
+        src_state_dict = checkpoint["model"]
+        dst_state_dict = model.state_dict()
+        for module in init_modules:
+            logging.info(f"Loading parameters starting with prefix {module}")
+            src_keys = [
+                k for k in src_state_dict.keys() if k.startswith(module.strip() + ".")
+            ]
+            dst_keys = [
+                k for k in dst_state_dict.keys() if k.startswith(module.strip() + ".")
+            ]
+            assert set(src_keys) == set(dst_keys)  # two sets should match exactly
+            for key in src_keys:
+                dst_state_dict[key] = src_state_dict.pop(key)
+
+        model.load_state_dict(dst_state_dict, strict=strict)
+
+    return None
+
+
 def compute_loss(
     params: AttributeDict,
     model: Union[nn.Module, DDP],
@@ -160,7 +263,7 @@ def compute_loss(
     is_training: bool,
 ) -> Tuple[Tensor, MetricsTracker]:
     """
-    Compute CTC loss given the model and its inputs.
+    Compute loss given the model and its inputs.
 
     Args:
       params:
@@ -191,10 +294,10 @@ def compute_loss(
 
     texts = batch["supervisions"]["text"]
     y = graph_compiler.texts_to_ids(texts, sep="/")
-    y = k2.RaggedTensor(y).to(device)
+    y = k2.RaggedTensor(y)
 
     with torch.set_grad_enabled(is_training):
-        simple_loss, pruned_loss, _ = model(
+        simple_loss, pruned_loss, ctc_loss = model(
             x=feature,
             x_lens=feature_lens,
             y=y,
@@ -203,21 +306,26 @@ def compute_loss(
             lm_scale=params.lm_scale,
         )
 
-        s = params.simple_loss_scale
+        loss = 0.0
-        # take down the scale on the simple loss from 1.0 at the start
-        # to params.simple_loss scale by warm_step.
-        simple_loss_scale = (
-            s
-            if batch_idx_train >= warm_step
-            else 1.0 - (batch_idx_train / warm_step) * (1.0 - s)
-        )
-        pruned_loss_scale = (
-            1.0
-            if batch_idx_train >= warm_step
-            else 0.1 + 0.9 * (batch_idx_train / warm_step)
-        )
 
-        loss = simple_loss_scale * simple_loss + pruned_loss_scale * pruned_loss
+        if params.use_transducer:
+            s = params.simple_loss_scale
+            # take down the scale on the simple loss from 1.0 at the start
+            # to params.simple_loss scale by warm_step.
+            simple_loss_scale = (
+                s
+                if batch_idx_train >= warm_step
+                else 1.0 - (batch_idx_train / warm_step) * (1.0 - s)
+            )
+            pruned_loss_scale = (
+                1.0
+                if batch_idx_train >= warm_step
+                else 0.1 + 0.9 * (batch_idx_train / warm_step)
+            )
+            loss += simple_loss_scale * simple_loss + pruned_loss_scale * pruned_loss
+
+        if params.use_ctc:
+            loss += params.ctc_loss_scale * ctc_loss
 
     assert loss.requires_grad == is_training
 
@@ -228,8 +336,11 @@ def compute_loss(
 
     # Note: We use reduction=sum while computing the loss.
     info["loss"] = loss.detach().cpu().item()
-    info["simple_loss"] = simple_loss.detach().cpu().item()
+    if params.use_transducer:
-    info["pruned_loss"] = pruned_loss.detach().cpu().item()
+        info["simple_loss"] = simple_loss.detach().cpu().item()
+        info["pruned_loss"] = pruned_loss.detach().cpu().item()
+    if params.use_ctc:
+        info["ctc_loss"] = ctc_loss.detach().cpu().item()
 
     return loss, info
 
@@ -317,8 +428,6 @@ def train_one_epoch(
 
     tot_loss = MetricsTracker()
 
-    cur_batch_idx = params.get("cur_batch_idx", 0)
-
     saved_bad_model = False
 
     def save_bad_model(suffix: str = ""):
@@ -336,10 +445,7 @@ def train_one_epoch(
 
     for batch_idx, batch in enumerate(train_dl):
         if batch_idx % 10 == 0:
-            set_batch_count(model, get_adjusted_batch_count(params))
+            set_batch_count(model, get_adjusted_batch_count(params) + 100000)
-        if batch_idx < cur_batch_idx:
-            continue
-        cur_batch_idx = batch_idx
 
         params.batch_idx_train += 1
         batch_size = len(batch["supervisions"]["text"])
@@ -359,6 +465,7 @@ def train_one_epoch(
             # 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)
@@ -387,7 +494,6 @@ def train_one_epoch(
             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,
@@ -400,7 +506,6 @@ def train_one_epoch(
                 scaler=scaler,
                 rank=rank,
             )
-            del params.cur_batch_idx
             remove_checkpoints(
                 out_dir=params.exp_dir,
                 topk=params.keep_last_k,
@@ -532,14 +637,20 @@ def run(rank, world_size, args):
 
     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).to(torch.float64)
 
-    assert params.start_epoch > 0, params.start_epoch
+    if params.continue_finetune:
-    checkpoints = load_checkpoint_if_available(
+        assert params.start_epoch > 0, params.start_epoch
-        params=params, model=model, model_avg=model_avg
+        checkpoints = load_checkpoint_if_available(
-    )
+            params=params, model=model, model_avg=model_avg
+        )
+    else:
+        modules = params.init_modules.split(",") if params.init_modules else None
+        checkpoints = load_model_params(
+            ckpt=params.finetune_ckpt, model=model, init_modules=modules
+        )
+        if rank == 0:
+            # model_avg is only used with rank 0
+            model_avg = copy.deepcopy(model).to(torch.float64)
 
     model.to(device)
     if world_size > 1:
@@ -552,7 +663,7 @@ def run(rank, world_size, args):
         clipping_scale=2.0,
     )
 
-    scheduler = Eden(optimizer, params.lr_batches, params.lr_epochs)
+    scheduler = Eden(optimizer, params.lr_batches, params.lr_epochs, warmup_start=1.0)
 
     if checkpoints and "optimizer" in checkpoints:
         logging.info("Loading optimizer state dict")
@@ -568,33 +679,31 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 
     if params.inf_check:
         register_inf_check_hooks(model)
 
+    def remove_short_utt(c: Cut):
+        if c.duration > 15:
+            return False
+        # In ./zipformer.py, the conv module uses the following expression
+        # for subsampling
+        T = ((c.num_frames - 7) // 2 + 1) // 2
+        return T > 0
+
     wenetspeech = WenetSpeechAsrDataModule(args)
 
-    train_cuts = wenetspeech.train_cuts()
+    if params.use_mux:
-
+        train_cuts = CutSet.mux(
-    def remove_short_and_long_utt(c: Cut):
+            wenetspeech.train_cuts(),
-        # Keep only utterances with duration between 1 second and 20 seconds
+            wenetspeech.nihaowenwen_train_cuts(),
-        #
+            weights=[0.9, 0.1],
-        # Caution: There is a reason to select 20.0 here. Please see
+        )
-        # ../local/display_manifest_statistics.py
+    else:
-        #
+        train_cuts = wenetspeech.nihaowenwen_train_cuts()
-        # You should use ../local/display_manifest_statistics.py to get
-        # an utterance duration distribution for your dataset to select
-        # the threshold
-        if c.duration < 1.0 or c.duration > 15.0:
-            # logging.warning(
-            #     f"Exclude cut with ID {c.id} from training. Duration: {c.duration}"
-            # )
-            return False
-
-        return True
 
     def encode_text(c: Cut):
         # Text normalize for each sample
@@ -605,7 +714,7 @@ def run(rank, world_size, args):
         c.supervisions[0].text = text
         return c
 
-    train_cuts = train_cuts.filter(remove_short_and_long_utt)
+    train_cuts = train_cuts.filter(remove_short_utt)
     train_cuts = train_cuts.map(encode_text)
 
     if params.start_batch > 0 and checkpoints and "sampler" in checkpoints:
@@ -619,19 +728,19 @@ def run(rank, world_size, args):
         train_cuts, sampler_state_dict=sampler_state_dict
     )
 
-    valid_cuts = wenetspeech.valid_cuts()
+    valid_cuts = wenetspeech.nihaowenwen_dev_cuts()
+    valid_cuts = valid_cuts.filter(remove_short_utt)
     valid_cuts = valid_cuts.map(encode_text)
     valid_dl = wenetspeech.valid_dataloaders(valid_cuts)
 
-    if not params.print_diagnostics:
+    if not params.print_diagnostics and params.scan_for_oom_batches:
-        # scan_pessimistic_batches_for_oom(
+        scan_pessimistic_batches_for_oom(
-        #    model=model,
+            model=model,
-        #    train_dl=train_dl,
+            train_dl=train_dl,
-        #    optimizer=optimizer,
+            optimizer=optimizer,
-        #    graph_compiler=graph_compiler,
+            graph_compiler=graph_compiler,
-        #    params=params,
+            params=params,
-        # )
+        )
-        pass
 
     scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0)
     if checkpoints and "grad_scaler" in checkpoints:
@@ -689,7 +798,6 @@ def main():
     parser = get_parser()
     WenetSpeechAsrDataModule.add_arguments(parser)
     args = parser.parse_args()
-    args.lang_dir = Path(args.lang_dir)
     args.exp_dir = Path(args.exp_dir)
 
     world_size = args.world_size
@@ -701,4 +809,6 @@ def main():
 
 
 if __name__ == "__main__":
+    torch.set_num_threads(1)
+    torch.set_num_interop_threads(1)
     main()

egs/wenetspeech/KWS/zipformer/scaling_converter.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/scaling_converter.py

icefall/utils.py

@@ -1609,9 +1609,9 @@ def text_to_pinyin(
         The input Chinese text.
       mode:
         The style of the output pinyin, should be:
-          full_with_tone : zhong1 guo2
+          full_with_tone : zhōng guó
           full_no_tone : zhong guo
-          partial_with_tone : zh ong1 g uo2
+          partial_with_tone : zh ōng g uó
           partial_no_tone : zh ong g uo
       errors:
         How to handle the characters (latin) that has no pinyin.