Merge branch 'k2-fsa:master' into master

egs/aidatatang_200zh/ASR/local/compute_fbank_aidatatang_200zh.py

@@ -43,7 +43,7 @@ torch.set_num_interop_threads(1)
 
 
 def compute_fbank_aidatatang_200zh(num_mel_bins: int = 80):
-    src_dir = Path("data/manifests")
+    src_dir = Path("data/manifests/aidatatang_200zh")
     output_dir = Path("data/fbank")
     num_jobs = min(15, os.cpu_count())
 
@@ -62,6 +62,13 @@ def compute_fbank_aidatatang_200zh(num_mel_bins: int = 80):
     )
     assert manifests is not None
 
+    assert len(manifests) == len(dataset_parts), (
+        len(manifests),
+        len(dataset_parts),
+        list(manifests.keys()),
+        dataset_parts,
+    )
+
     extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
 
     with get_executor() as ex:  # Initialize the executor only once.

egs/aidatatang_200zh/ASR/prepare.sh

@@ -50,28 +50,19 @@ if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
 fi
 
 if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
-  log "Stage 2: Process aidatatang_200zh"
+  log "Stage 2: Prepare musan manifest"
-  if [ ! -f data/fbank/aidatatang_200zh/.fbank.done ]; then
+  # We assume that you have downloaded the musan corpus
-    mkdir -p data/fbank/aidatatang_200zh
+  # to data/musan
-    lhotse prepare aidatatang-200zh $dl_dir data/manifests/aidatatang_200zh
+  if [ ! -f data/manifests/.manifests.done ]; then
-    touch data/fbank/aidatatang_200zh/.fbank.done
+    log "It may take 6 minutes"
+    mkdir -p data/manifests/
+    lhotse prepare musan $dl_dir/musan data/manifests/
+    touch data/manifests/.manifests.done
   fi
 fi
 
 if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
-  log "Stage 3: Prepare musan manifest"
+  log "Stage 3: Compute fbank for musan"
-  # We assume that you have downloaded the musan corpus
-  # to data/musan
-  if [ ! -f data/manifests/.musan_manifests.done ]; then
-    log "It may take 6 minutes"
-    mkdir -p data/manifests
-    lhotse prepare musan $dl_dir/musan data/manifests
-    touch data/manifests/.musan_manifests.done
-  fi
-fi
-
-if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
-  log "Stage 4: Compute fbank for musan"
   if [ ! -f data/fbank/.msuan.done ]; then
     mkdir -p data/fbank
     ./local/compute_fbank_musan.py
@@ -79,8 +70,8 @@ if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
   fi
 fi
 
-if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
+if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
-  log "Stage 5: Compute fbank for aidatatang_200zh"
+  log "Stage 4: Compute fbank for aidatatang_200zh"
   if [ ! -f data/fbank/.aidatatang_200zh.done ]; then
     mkdir -p data/fbank
     ./local/compute_fbank_aidatatang_200zh.py
@@ -88,31 +79,38 @@ if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
   fi
 fi
 
-if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
+if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
-  log "Stage 6: Prepare char based lang"
+  log "Stage 5: Prepare char based lang"
   lang_char_dir=data/lang_char
   mkdir -p $lang_char_dir
-
   # Prepare text.
-  grep "\"text\":" data/manifests/aidatatang_200zh/supervisions_train.json \
+  # Note: in Linux, you can install jq with the following command:
-    | sed -e 's/["text:\t ]*//g' | sed 's/,//g' \
+  # 1. wget -O jq https://github.com/stedolan/jq/releases/download/jq-1.6/jq-linux64
-    | ./local/text2token.py -t "char" > $lang_char_dir/text
+  # 2. chmod +x ./jq
-
+  # 3. cp jq /usr/bin
+  if [ ! -f $lang_char_dir/text ]; then
+    gunzip -c data/manifests/aidatatang_200zh/aidatatang_supervisions_train.jsonl.gz \
+      |jq '.text' |sed -e 's/["text:\t ]*//g' | sed 's/"//g' \
+      | ./local/text2token.py -t "char" > $lang_char_dir/text
+  fi
   # Prepare words.txt
-  grep "\"text\":" data/manifests/aidatatang_200zh/supervisions_train.json \
+  if [ ! -f $lang_char_dir/text_words ]; then
-    | sed -e 's/["text:\t]*//g' | sed 's/,//g' \
+    gunzip -c data/manifests/aidatatang_200zh/aidatatang_supervisions_train.jsonl.gz \
-    | ./local/text2token.py -t "char" > $lang_char_dir/text_words
+      | jq '.text' | sed -e 's/["text:\t]*//g' | sed 's/"//g' \
+      | ./local/text2token.py -t "char" > $lang_char_dir/text_words
+  fi
 
   cat $lang_char_dir/text_words | sed 's/ /\n/g' | sort -u | sed '/^$/d' \
     | uniq > $lang_char_dir/words_no_ids.txt
 
   if [ ! -f $lang_char_dir/words.txt ]; then
     ./local/prepare_words.py \
-      --input-file $lang_char_dir/words_no_ids.txt
+      --input-file $lang_char_dir/words_no_ids.txt \
-      --output-file $lang_char_dir/words.txt
+      --output-file $lang_char_dir/words.txt 
   fi
 
   if [ ! -f $lang_char_dir/L_disambig.pt ]; then
     ./local/prepare_char.py
   fi
 fi
+

egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/decode.py

@@ -522,63 +522,14 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    # Note: Please use "pip install webdataset==0.1.103"
-    # for installing the webdataset.
-    import glob
-    import os
-
-    from lhotse import CutSet
-    from lhotse.dataset.webdataset import export_to_webdataset
-
     # we need cut ids to display recognition results.
     args.return_cuts = True
     aidatatang_200zh = Aidatatang_200zhAsrDataModule(args)
 
-    dev = "dev"
+    dev_cuts = aidatatang_200zh.valid_cuts()
-    test = "test"
+    test_cuts = aidatatang_200zh.test_cuts()
-
+    dev_dl = aidatatang_200zh.valid_dataloaders(dev_cuts)
-    if not os.path.exists(f"{dev}/shared-0.tar"):
+    test_dl = aidatatang_200zh.test_dataloaders(test_cuts)
-        os.makedirs(dev)
-        dev_cuts = aidatatang_200zh.valid_cuts()
-        export_to_webdataset(
-            dev_cuts,
-            output_path=f"{dev}/shared-%d.tar",
-            shard_size=300,
-        )
-
-    if not os.path.exists(f"{test}/shared-0.tar"):
-        os.makedirs(test)
-        test_cuts = aidatatang_200zh.test_cuts()
-        export_to_webdataset(
-            test_cuts,
-            output_path=f"{test}/shared-%d.tar",
-            shard_size=300,
-        )
-
-    dev_shards = [
-        str(path)
-        for path in sorted(glob.glob(os.path.join(dev, "shared-*.tar")))
-    ]
-    cuts_dev_webdataset = CutSet.from_webdataset(
-        dev_shards,
-        split_by_worker=True,
-        split_by_node=True,
-        shuffle_shards=True,
-    )
-
-    test_shards = [
-        str(path)
-        for path in sorted(glob.glob(os.path.join(test, "shared-*.tar")))
-    ]
-    cuts_test_webdataset = CutSet.from_webdataset(
-        test_shards,
-        split_by_worker=True,
-        split_by_node=True,
-        shuffle_shards=True,
-    )
-
-    dev_dl = aidatatang_200zh.valid_dataloaders(cuts_dev_webdataset)
-    test_dl = aidatatang_200zh.test_dataloaders(cuts_test_webdataset)
 
     test_sets = ["dev", "test"]
     test_dl = [dev_dl, test_dl]

egs/aishell/ASR/local/compute_fbank_aidatatang_200zh.py

@@ -62,6 +62,13 @@ def compute_fbank_aidatatang_200zh(num_mel_bins: int = 80):
     )
     assert manifests is not None
 
+    assert len(manifests) == len(dataset_parts), (
+        len(manifests),
+        len(dataset_parts),
+        list(manifests.keys()),
+        dataset_parts,
+    )
+
     extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
 
     with get_executor() as ex:  # Initialize the executor only once.

egs/aishell/ASR/local/compute_fbank_aishell.py

@@ -62,6 +62,13 @@ def compute_fbank_aishell(num_mel_bins: int = 80):
     )
     assert manifests is not None
 
+    assert len(manifests) == len(dataset_parts), (
+        len(manifests),
+        len(dataset_parts),
+        list(manifests.keys()),
+        dataset_parts,
+    )
+
     extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
 
     with get_executor() as ex:  # Initialize the executor only once.

egs/aishell2/ASR/local/compute_fbank_aishell2.py

@@ -62,6 +62,13 @@ def compute_fbank_aishell2(num_mel_bins: int = 80):
     )
     assert manifests is not None
 
+    assert len(manifests) == len(dataset_parts), (
+        len(manifests),
+        len(dataset_parts),
+        list(manifests.keys()),
+        dataset_parts,
+    )
+
     extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
 
     with get_executor() as ex:  # Initialize the executor only once.

egs/aishell4/ASR/local/compute_fbank_aishell4.py

@@ -63,6 +63,13 @@ def compute_fbank_aishell4(num_mel_bins: int = 80):
     )
     assert manifests is not None
 
+    assert len(manifests) == len(dataset_parts), (
+        len(manifests),
+        len(dataset_parts),
+        list(manifests.keys()),
+        dataset_parts,
+    )
+
     extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
 
     with get_executor() as ex:  # Initialize the executor only once.

egs/alimeeting/ASR/local/compute_fbank_alimeeting.py

@@ -43,7 +43,7 @@ torch.set_num_interop_threads(1)
 
 
 def compute_fbank_alimeeting(num_mel_bins: int = 80):
-    src_dir = Path("data/manifests")
+    src_dir = Path("data/manifests/alimeeting")
     output_dir = Path("data/fbank")
     num_jobs = min(15, os.cpu_count())
 
@@ -63,6 +63,13 @@ def compute_fbank_alimeeting(num_mel_bins: int = 80):
     )
     assert manifests is not None
 
+    assert len(manifests) == len(dataset_parts), (
+        len(manifests),
+        len(dataset_parts),
+        list(manifests.keys()),
+        dataset_parts,
+    )
+
     extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
 
     with get_executor() as ex:  # Initialize the executor only once.

egs/alimeeting/ASR/local/text2segments.py

@@ -30,9 +30,11 @@ with word segmenting:
 
 import argparse
 
+import paddle
 import jieba
 from tqdm import tqdm
 
+paddle.enable_static()
 jieba.enable_paddle()
 
 

egs/alimeeting/ASR/prepare.sh

@@ -107,7 +107,7 @@ if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
   # Prepare text.
   # Note: in Linux, you can install jq with the  following command:
   # wget -O jq https://github.com/stedolan/jq/releases/download/jq-1.6/jq-linux64
-  gunzip -c data/manifests/alimeeting/supervisions_train.jsonl.gz \
+  gunzip -c data/manifests/alimeeting/alimeeting_supervisions_train.jsonl.gz \
     | jq ".text" | sed 's/"//g' \
     | ./local/text2token.py -t "char" > $lang_char_dir/text
 

egs/gigaspeech/ASR/local/preprocess_gigaspeech.py

@@ -62,6 +62,13 @@ def preprocess_giga_speech():
     )
     assert manifests is not None
 
+    assert len(manifests) == len(dataset_parts), (
+        len(manifests),
+        len(dataset_parts),
+        list(manifests.keys()),
+        dataset_parts,
+    )
+
     for partition, m in manifests.items():
         logging.info(f"Processing {partition}")
         raw_cuts_path = output_dir / f"cuts_{partition}_raw.jsonl.gz"

egs/librispeech/ASR/distillation_with_hubert.sh

@@ -81,9 +81,9 @@ if [ $stage -le 0 ] && [ $stop_stage -ge 0 ] && [ ! "$use_extracted_codebook" ==
   # or
   # pip install multi_quantization
 
-  has_quantization=$(python3 -c "import importlib; print(importlib.util.find_spec('quantization') is not None)")
+  has_quantization=$(python3 -c "import importlib; print(importlib.util.find_spec('multi_quantization') is not None)")
   if [ $has_quantization == 'False' ]; then
-    log "Please install quantization before running following stages"
+    log "Please install multi_quantization before running following stages"
     exit 1
   fi
 

egs/librispeech/ASR/local/compute_fbank_librispeech.py

@@ -66,6 +66,13 @@ def compute_fbank_librispeech():
     )
     assert manifests is not None
 
+    assert len(manifests) == len(dataset_parts), (
+        len(manifests),
+        len(dataset_parts),
+        list(manifests.keys()),
+        dataset_parts,
+    )
+
     extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
 
     with get_executor() as ex:  # Initialize the executor only once.

egs/librispeech/ASR/local/compute_fbank_musan.py

@@ -65,6 +65,8 @@ def compute_fbank_musan():
     assert len(manifests) == len(dataset_parts), (
         len(manifests),
         len(dataset_parts),
+        list(manifests.keys()),
+        dataset_parts,
     )
 
     musan_cuts_path = output_dir / "musan_cuts.jsonl.gz"

egs/librispeech/ASR/local/preprocess_gigaspeech.py

@@ -68,6 +68,13 @@ def preprocess_giga_speech():
     )
     assert manifests is not None
 
+    assert len(manifests) == len(dataset_parts), (
+        len(manifests),
+        len(dataset_parts),
+        list(manifests.keys()),
+        dataset_parts,
+    )
+
     for partition, m in manifests.items():
         logging.info(f"Processing {partition}")
         raw_cuts_path = output_dir / f"{prefix}_cuts_{partition}_raw.{suffix}"

egs/librispeech/ASR/pruned_transducer_stateless2/optim.py

@@ -164,6 +164,10 @@ class Eve(Optimizer):
                     p.mul_(1 - (weight_decay * is_above_target_rms))
                 p.addcdiv_(exp_avg, denom, value=-step_size)
 
+                # Constrain the range of scalar weights
+                if p.numel() == 1:
+                    p.clamp_(min=-10, max=2)
+
         return loss
 
 

egs/librispeech/ASR/pruned_transducer_stateless3/export.py

@@ -652,13 +652,13 @@ def main():
 
         # Also export encoder/decoder/joiner separately
         encoder_filename = params.exp_dir / "encoder_jit_script.pt"
-        export_encoder_model_jit_trace(model.encoder, encoder_filename)
+        export_encoder_model_jit_script(model.encoder, encoder_filename)
 
         decoder_filename = params.exp_dir / "decoder_jit_script.pt"
-        export_decoder_model_jit_trace(model.decoder, decoder_filename)
+        export_decoder_model_jit_script(model.decoder, decoder_filename)
 
         joiner_filename = params.exp_dir / "joiner_jit_script.pt"
-        export_joiner_model_jit_trace(model.joiner, joiner_filename)
+        export_joiner_model_jit_script(model.joiner, joiner_filename)
 
     elif params.jit_trace is True:
         convert_scaled_to_non_scaled(model, inplace=True)

egs/librispeech/ASR/pruned_transducer_stateless3/test_scaling_converter.py

@@ -181,7 +181,7 @@ def test_convert_scaled_to_non_scaled():
         y = torch.randint(low=1, high=vocab_size - 1, size=(N, U))
 
         d1 = model.decoder(y)
-        d2 = model.decoder(y)
+        d2 = converted_model.decoder(y)
 
         assert torch.allclose(d1, d2)
 

egs/librispeech/ASR/pruned_transducer_stateless6/hubert_decode.py

@@ -81,18 +81,17 @@ def decode_dataset(
 
     results = defaultdict(list)
     for batch_idx, batch in enumerate(dl):
-
+        # hyps is a list, every element is decode result of a sentence.
         hyps = hubert_model.ctc_greedy_search(batch)
 
         texts = batch["supervisions"]["text"]
-        assert len(hyps) == len(texts)
+        cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
         this_batch = []
-
+        assert len(hyps) == len(texts)
-        for hyp_text, ref_text in zip(hyps, texts):
+        for cut_id, hyp_text, ref_text in zip(cut_ids, hyps, texts):
             ref_words = ref_text.split()
             hyp_words = hyp_text.split()
-            this_batch.append((ref_words, hyp_words))
+            this_batch.append((cut_id, ref_words, hyp_words))
-
         results["ctc_greedy_search"].extend(this_batch)
 
         num_cuts += len(texts)

egs/librispeech/ASR/pruned_transducer_stateless6/vq_utils.py

@@ -28,7 +28,7 @@ from typing import List, Tuple
 import numpy as np
 import torch
 import torch.multiprocessing as mp
-import quantization
+import multi_quantization as quantization
 
 from asr_datamodule import LibriSpeechAsrDataModule
 from hubert_xlarge import HubertXlargeFineTuned

egs/spgispeech/ASR/local/compute_fbank_musan.py

@@ -69,6 +69,13 @@ def compute_fbank_musan():
     )
     assert manifests is not None
 
+    assert len(manifests) == len(dataset_parts), (
+        len(manifests),
+        len(dataset_parts),
+        list(manifests.keys()),
+        dataset_parts,
+    )
+
     musan_cuts_path = src_dir / "cuts_musan.jsonl.gz"
 
     if musan_cuts_path.is_file():

egs/tal_csasr/ASR/local/compute_fbank_tal_csasr.py

@@ -62,6 +62,13 @@ def compute_fbank_tal_csasr(num_mel_bins: int = 80):
     )
     assert manifests is not None
 
+    assert len(manifests) == len(dataset_parts), (
+        len(manifests),
+        len(dataset_parts),
+        list(manifests.keys()),
+        dataset_parts,
+    )
+
     extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
 
     with get_executor() as ex:  # Initialize the executor only once.

egs/tedlium3/ASR/local/compute_fbank_tedlium.py

@@ -62,6 +62,13 @@ def compute_fbank_tedlium():
     )
     assert manifests is not None
 
+    assert len(manifests) == len(dataset_parts), (
+        len(manifests),
+        len(dataset_parts),
+        list(manifests.keys()),
+        dataset_parts,
+    )
+
     extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
 
     with get_executor() as ex:  # Initialize the executor only once.

egs/timit/ASR/local/compute_fbank_timit.py

@@ -63,6 +63,13 @@ def compute_fbank_timit():
     )
     assert manifests is not None
 
+    assert len(manifests) == len(dataset_parts), (
+        len(manifests),
+        len(dataset_parts),
+        list(manifests.keys()),
+        dataset_parts,
+    )
+
     extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
 
     with get_executor() as ex:  # Initialize the executor only once.

egs/wenetspeech/ASR/local/preprocess_wenetspeech.py

@@ -23,6 +23,8 @@ from pathlib import Path
 from lhotse import CutSet, SupervisionSegment
 from lhotse.recipes.utils import read_manifests_if_cached
 
+from icefall import setup_logger
+
 # Similar text filtering and normalization procedure as in:
 # https://github.com/SpeechColab/WenetSpeech/blob/main/toolkits/kaldi/wenetspeech_data_prep.sh
 
@@ -48,13 +50,17 @@ def preprocess_wenet_speech():
     output_dir = Path("data/fbank")
     output_dir.mkdir(exist_ok=True)
 
+    # Note: By default, we preprocess all sub-parts.
+    # You can delete those that you don't need.
+    # For instance, if you don't want to use the L subpart, just remove
+    # the line below containing "L"
     dataset_parts = (
-        "L",
-        "M",
-        "S",
         "DEV",
         "TEST_NET",
         "TEST_MEETING",
+        "S",
+        "M",
+        "L",
     )
 
     logging.info("Loading manifest (may take 10 minutes)")
@@ -66,6 +72,13 @@ def preprocess_wenet_speech():
     )
     assert manifests is not None
 
+    assert len(manifests) == len(dataset_parts), (
+        len(manifests),
+        len(dataset_parts),
+        list(manifests.keys()),
+        dataset_parts,
+    )
+
     for partition, m in manifests.items():
         logging.info(f"Processing {partition}")
         raw_cuts_path = output_dir / f"cuts_{partition}_raw.jsonl.gz"
@@ -81,10 +94,13 @@ def preprocess_wenet_speech():
         logging.info(f"Normalizing text in {partition}")
         for sup in m["supervisions"]:
             text = str(sup.text)
-            logging.info(f"Original text: {text}")
+            orig_text = text
             sup.text = normalize_text(sup.text)
             text = str(sup.text)
-            logging.info(f"Normalize text: {text}")
+            if len(orig_text) != len(text):
+                logging.info(
+                    f"\nOriginal text vs normalized text:\n{orig_text}\n{text}"
+                )
 
         # Create long-recording cut manifests.
         logging.info(f"Processing {partition}")
@@ -109,12 +125,10 @@ def preprocess_wenet_speech():
 
 
 def main():
-    formatter = (
+    setup_logger(log_filename="./log-preprocess-wenetspeech")
-        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
-    )
-    logging.basicConfig(format=formatter, level=logging.INFO)
 
     preprocess_wenet_speech()
+    logging.info("Done")
 
 
 if __name__ == "__main__":

egs/wenetspeech/ASR/pruned_transducer_stateless2/train.py

@@ -81,7 +81,6 @@ For training with the S subset:
 
 import argparse
 import logging
-import os
 import warnings
 from pathlib import Path
 from shutil import copyfile
@@ -120,8 +119,6 @@ LRSchedulerType = Union[
     torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler
 ]
 
-os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
-
 
 def get_parser():
     parser = argparse.ArgumentParser(
@@ -162,7 +159,7 @@ def get_parser():
         default=0,
         help="""Resume training from from this epoch.
         If it is positive, it will load checkpoint from
-        transducer_stateless2/exp/epoch-{start_epoch-1}.pt
+        pruned_transducer_stateless2/exp/epoch-{start_epoch-1}.pt
         """,
     )
 
@@ -361,8 +358,8 @@ def get_params() -> AttributeDict:
             "best_valid_loss": float("inf"),
             "best_train_epoch": -1,
             "best_valid_epoch": -1,
-            "batch_idx_train": 10,
+            "batch_idx_train": 0,
-            "log_interval": 1,
+            "log_interval": 50,
             "reset_interval": 200,
             # parameters for conformer
             "feature_dim": 80,
@@ -545,7 +542,7 @@ def compute_loss(
     warmup: float = 1.0,
 ) -> Tuple[Tensor, MetricsTracker]:
     """
-    Compute CTC loss given the model and its inputs.
+    Compute RNN-T loss given the model and its inputs.
     Args:
       params:
         Parameters for training. See :func:`get_params`.
@@ -573,7 +570,7 @@ def compute_loss(
     texts = batch["supervisions"]["text"]
 
     y = graph_compiler.texts_to_ids(texts)
-    if type(y) == list:
+    if isinstance(y, list):
         y = k2.RaggedTensor(y).to(device)
     else:
         y = y.to(device)
@@ -697,7 +694,6 @@ def train_one_epoch(
     tot_loss = MetricsTracker()
 
     for batch_idx, batch in enumerate(train_dl):
-
         params.batch_idx_train += 1
         batch_size = len(batch["supervisions"]["text"])
 

egs/wenetspeech/ASR/pruned_transducer_stateless5/train.py

@@ -61,7 +61,6 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
 import argparse
 import copy
 import logging
-import os
 import warnings
 from pathlib import Path
 from shutil import copyfile
@@ -103,8 +102,6 @@ LRSchedulerType = Union[
     torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler
 ]
 
-os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
-
 
 def add_model_arguments(parser: argparse.ArgumentParser):
     parser.add_argument(
@@ -684,7 +681,7 @@ def compute_loss(
     texts = batch["supervisions"]["text"]
 
     y = graph_compiler.texts_to_ids(texts)
-    if type(y) == list:
+    if isinstance(y, list):
         y = k2.RaggedTensor(y).to(device)
     else:
         y = y.to(device)

egs/yesno/ASR/local/compute_fbank_yesno.py

@@ -47,6 +47,13 @@ def compute_fbank_yesno():
     )
     assert manifests is not None
 
+    assert len(manifests) == len(dataset_parts), (
+        len(manifests),
+        len(dataset_parts),
+        list(manifests.keys()),
+        dataset_parts,
+    )
+
     extractor = Fbank(
         FbankConfig(sampling_rate=8000, num_mel_bins=num_mel_bins)
     )

icefall/diagnostics.py

@@ -130,6 +130,8 @@ class TensorDiagnostic(object):
             x = x[0]
         if not isinstance(x, Tensor):
             return
+        if x.numel() == 0:  # for empty tensor
+            return
         x = x.detach().clone()
         if x.ndim == 0:
             x = x.unsqueeze(0)