remove streaming/greedy_search results folder

egs/librispeech/ASR/zipformer/decode.py

@@ -103,9 +103,10 @@ 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 ReazonSpeechAsrDataModule
+from asr_datamodule import LibriSpeechAsrDataModule
 from beam_search import (
     beam_search,
     fast_beam_search_nbest,
@@ -134,7 +135,6 @@ from icefall.checkpoint import (
 from icefall.lexicon import Lexicon
 from icefall.utils import (
     AttributeDict,
-    make_pad_mask,
     setup_logger,
     store_transcripts,
     str2bool,
@@ -205,7 +205,7 @@ def get_parser():
     parser.add_argument(
         "--lang-dir",
         type=Path,
-        default="data/lang_char",
+        default="data/lang_bpe_500",
         help="The lang dir containing word table and LG graph",
     )
 
@@ -371,6 +371,7 @@ def get_parser():
         modified_beam_search_LODR.
         """,
     )
+<<<<<<< HEAD
 
     parser.add_argument(
         "--skip-scoring",
@@ -398,7 +399,7 @@ def get_parser():
 def decode_one_batch(
     params: AttributeDict,
     model: nn.Module,
-    sp: Tokenizer,
+    sp: spm.SentencePieceProcessor,
     batch: dict,
     word_table: Optional[k2.SymbolTable] = None,
     decoding_graph: Optional[k2.Fsa] = None,
@@ -477,10 +478,9 @@ def decode_one_batch(
             beam=params.beam,
             max_contexts=params.max_contexts,
             max_states=params.max_states,
-            blank_penalty=params.blank_penalty,
         )
         for hyp in sp.decode(hyp_tokens):
-            hyps.append(sp.text2word(hyp))
+            hyps.append(hyp.split())
     elif params.decoding_method == "fast_beam_search_nbest_LG":
         hyp_tokens = fast_beam_search_nbest_LG(
             model=model,
@@ -492,7 +492,6 @@ def decode_one_batch(
             max_states=params.max_states,
             num_paths=params.num_paths,
             nbest_scale=params.nbest_scale,
-            blank_penalty=params.blank_penalty,
         )
         for hyp in hyp_tokens:
             hyps.append([word_table[i] for i in hyp])
@@ -507,10 +506,9 @@ def decode_one_batch(
             max_states=params.max_states,
             num_paths=params.num_paths,
             nbest_scale=params.nbest_scale,
-            blank_penalty=params.blank_penalty,
         )
         for hyp in sp.decode(hyp_tokens):
-            hyps.append(sp.text2word(hyp))
+            hyps.append(hyp.split())
     elif params.decoding_method == "fast_beam_search_nbest_oracle":
         hyp_tokens = fast_beam_search_nbest_oracle(
             model=model,
@@ -523,19 +521,17 @@ def decode_one_batch(
             num_paths=params.num_paths,
             ref_texts=sp.encode(supervisions["text"]),
             nbest_scale=params.nbest_scale,
-            blank_penalty=params.blank_penalty,
         )
         for hyp in sp.decode(hyp_tokens):
-            hyps.append(sp.text2word(hyp))
+            hyps.append(hyp.split())
     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,
-            blank_penalty=params.blank_penalty,
         )
         for hyp in sp.decode(hyp_tokens):
-            hyps.append(sp.text2word(hyp))
+            hyps.append(hyp.split())
     elif params.decoding_method == "modified_beam_search":
         hyp_tokens = modified_beam_search(
             model=model,
@@ -543,10 +539,9 @@ def decode_one_batch(
             encoder_out_lens=encoder_out_lens,
             beam=params.beam_size,
             context_graph=context_graph,
-            blank_penalty=params.blank_penalty,
         )
         for hyp in sp.decode(hyp_tokens):
-            hyps.append(sp.text2word(hyp))
+            hyps.append(hyp.split())
     elif params.decoding_method == "modified_beam_search_lm_shallow_fusion":
         hyp_tokens = modified_beam_search_lm_shallow_fusion(
             model=model,
@@ -556,7 +551,7 @@ def decode_one_batch(
             LM=LM,
         )
         for hyp in sp.decode(hyp_tokens):
-            hyps.append(sp.text2word(hyp))
+            hyps.append(hyp.split())
     elif params.decoding_method == "modified_beam_search_LODR":
         hyp_tokens = modified_beam_search_LODR(
             model=model,
@@ -569,7 +564,7 @@ def decode_one_batch(
             context_graph=context_graph,
         )
         for hyp in sp.decode(hyp_tokens):
-            hyps.append(sp.text2word(hyp))
+            hyps.append(hyp.split())
     elif params.decoding_method == "modified_beam_search_lm_rescore":
         lm_scale_list = [0.01 * i for i in range(10, 50)]
         ans_dict = modified_beam_search_lm_rescore(
@@ -615,7 +610,7 @@ def decode_one_batch(
                 raise ValueError(
                     f"Unsupported decoding method: {params.decoding_method}"
                 )
-            hyps.append(sp.text2word(sp.decode(hyp)))
+            hyps.append(sp.decode(hyp).split())
 
     # prefix = ( "greedy_search" | "fast_beam_search_nbest" | "modified_beam_search" )
     prefix = f"{params.decoding_method}"
@@ -636,9 +631,9 @@ def decode_one_batch(
     elif "modified_beam_search" in params.decoding_method:
         prefix += f"_beam-size-{params.beam_size}"
         if params.decoding_method in (
-            "modified_beam_search_lm_rescore",
-            "modified_beam_search_lm_rescore_LODR",
-        ):
+                "modified_beam_search_lm_rescore",
+                "modified_beam_search_lm_rescore_LODR",
+                ):
             ans = dict()
             assert ans_dict is not None
             for key, hyps in ans_dict.items():
@@ -655,17 +650,17 @@ def decode_one_batch(
 
 
 def decode_dataset(
-    dl: torch.utils.data.DataLoader,
-    params: AttributeDict,
-    model: nn.Module,
-    sp: Tokenizer,
-    word_table: Optional[k2.SymbolTable] = None,
-    decoding_graph: Optional[k2.Fsa] = None,
-    context_graph: Optional[ContextGraph] = None,
-    LM: Optional[LmScorer] = None,
-    ngram_lm=None,
-    ngram_lm_scale: float = 0.0,
-) -> Dict[str, List[Tuple[str, List[str], List[str]]]]:
+        dl: torch.utils.data.DataLoader,
+        params: AttributeDict,
+        model: nn.Module,
+        sp: spm.SentencePieceProcessor,
+        word_table: Optional[k2.SymbolTable] = None,
+        decoding_graph: Optional[k2.Fsa] = None,
+        context_graph: Optional[ContextGraph] = None,
+        LM: Optional[LmScorer] = None,
+        ngram_lm=None,
+        ngram_lm_scale: float = 0.0,
+        ) -> Dict[str, List[Tuple[str, List[str], List[str]]]]:
     """Decode dataset.
 
     Args:
@@ -708,23 +703,23 @@ def decode_dataset(
         cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
 
         hyps_dict = decode_one_batch(
-            params=params,
-            model=model,
-            sp=sp,
-            decoding_graph=decoding_graph,
-            context_graph=context_graph,
-            word_table=word_table,
-            batch=batch,
-            LM=LM,
-            ngram_lm=ngram_lm,
-            ngram_lm_scale=ngram_lm_scale,
-        )
+                params=params,
+                model=model,
+                sp=sp,
+                decoding_graph=decoding_graph,
+                context_graph=context_graph,
+                word_table=word_table,
+                batch=batch,
+                LM=LM,
+                ngram_lm=ngram_lm,
+                ngram_lm_scale=ngram_lm_scale,
+                )
 
         for name, hyps in hyps_dict.items():
             this_batch = []
             assert len(hyps) == len(texts)
             for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
-                ref_words = sp.text2word(ref_text)
+                ref_words = ref_text.split()
                 this_batch.append((cut_id, ref_words, hyp_words))
 
             results[name].extend(this_batch)
@@ -739,10 +734,10 @@ def decode_dataset(
 
 
 def save_asr_output(
-    params: AttributeDict,
-    test_set_name: str,
-    results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]],
-):
+        params: AttributeDict,
+        test_set_name: str,
+        results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]],
+        ):
     """
     Save text produced by ASR.
     """
@@ -757,10 +752,10 @@ def save_asr_output(
 
 
 def save_wer_results(
-    params: AttributeDict,
-    test_set_name: str,
-    results_dict: Dict[str, List[Tuple[str, List[str], List[str], Tuple]]],
-):
+        params: AttributeDict,
+        test_set_name: str,
+        results_dict: Dict[str, List[Tuple[str, List[str], List[str], Tuple]]],
+        ):
     """
     Save WER and per-utterance word alignments.
     """
@@ -771,8 +766,8 @@ def save_wer_results(
         errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
         with open(errs_filename, "w", encoding="utf8") as fd:
             wer = write_error_stats(
-                fd, f"{test_set_name}-{key}", results, enable_log=True
-            )
+                    fd, f"{test_set_name}-{key}", results, enable_log=True
+                    )
             test_set_wers[key] = wer
 
         logging.info(f"Wrote detailed error stats to {errs_filename}")
@@ -797,8 +792,8 @@ def save_wer_results(
 @torch.no_grad()
 def main():
     parser = get_parser()
-    ReazonSpeechAsrDataModule.add_arguments(parser)
-    Tokenizer.add_arguments(parser)
+    LibriSpeechAsrDataModule.add_arguments(parser)
+    LmScorer.add_arguments(parser)
     args = parser.parse_args()
     args.exp_dir = Path(args.exp_dir)
 
@@ -809,18 +804,18 @@ def main():
     set_caching_enabled(True)  # lhotse
 
     assert params.decoding_method in (
-        "greedy_search",
-        "beam_search",
-        "fast_beam_search",
-        "fast_beam_search_nbest",
-        "fast_beam_search_nbest_LG",
-        "fast_beam_search_nbest_oracle",
-        "modified_beam_search",
-        "modified_beam_search_LODR",
-        "modified_beam_search_lm_shallow_fusion",
-        "modified_beam_search_lm_rescore",
-        "modified_beam_search_lm_rescore_LODR",
-    )
+            "greedy_search",
+            "beam_search",
+            "fast_beam_search",
+            "fast_beam_search_nbest",
+            "fast_beam_search_nbest_LG",
+            "fast_beam_search_nbest_oracle",
+            "modified_beam_search",
+            "modified_beam_search_LODR",
+            "modified_beam_search_lm_shallow_fusion",
+            "modified_beam_search_lm_rescore",
+            "modified_beam_search_lm_rescore_LODR",
+            )
     params.res_dir = params.exp_dir / params.decoding_method
 
     if os.path.exists(params.context_file):
@@ -835,11 +830,11 @@ def main():
 
     if params.causal:
         assert (
-            "," not in params.chunk_size
-        ), "chunk_size should be one value in decoding."
+                "," not in params.chunk_size
+                ), "chunk_size should be one value in decoding."
         assert (
-            "," not in params.left_context_frames
-        ), "left_context_frames should be one value in decoding."
+                "," not in params.left_context_frames
+                ), "left_context_frames should be one value in decoding."
         params.suffix += f"_chunk-{params.chunk_size}"
         params.suffix += f"_left-context-{params.left_context_frames}"
 
@@ -855,9 +850,9 @@ def main():
     elif "beam_search" in params.decoding_method:
         params.suffix += f"__{params.decoding_method}__beam-size-{params.beam_size}"
         if params.decoding_method in (
-            "modified_beam_search",
-            "modified_beam_search_LODR",
-        ):
+                "modified_beam_search",
+                "modified_beam_search_LODR",
+                ):
             if params.has_contexts:
                 params.suffix += f"-context-score-{params.context_score}"
     else:
@@ -869,10 +864,8 @@ def main():
 
         if "LODR" in params.decoding_method:
             params.suffix += (
-                f"_LODR-{params.tokens_ngram}gram-scale-{params.ngram_lm_scale}"
-            )
-
-    params.suffix += f"-blank-penalty-{params.blank_penalty}"
+                    f"_LODR-{params.tokens_ngram}gram-scale-{params.ngram_lm_scale}"
+                    )
 
     if params.use_averaged_model:
         params.suffix += "_use-averaged-model"
@@ -886,9 +879,10 @@ def main():
 
     logging.info(f"Device: {device}")
 
-    sp = Tokenizer.load(params.lang, params.lang_type)
+    sp = spm.SentencePieceProcessor()
+    sp.load(params.bpe_model)
 
-    # <blk> and <unk> are defined in local/prepare_lang_char.py
+    # <blk> and <unk> are defined in local/train_bpe_model.py
     params.blank_id = sp.piece_to_id("<blk>")
     params.unk_id = sp.piece_to_id("<unk>")
     params.vocab_size = sp.get_piece_size()
@@ -901,18 +895,18 @@ def main():
     if not params.use_averaged_model:
         if params.iter > 0:
             filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
-                : params.avg
-            ]
+                    : params.avg
+                    ]
             if len(filenames) == 0:
                 raise ValueError(
-                    f"No checkpoints found for"
-                    f" --iter {params.iter}, --avg {params.avg}"
-                )
+                        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}"
-                )
+                        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))
@@ -930,32 +924,32 @@ def main():
     else:
         if params.iter > 0:
             filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
-                : params.avg + 1
-            ]
+                    : params.avg + 1
+                    ]
             if len(filenames) == 0:
                 raise ValueError(
-                    f"No checkpoints found for"
-                    f" --iter {params.iter}, --avg {params.avg}"
-                )
+                        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}"
-                )
+                        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}"
-            )
+                    "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,
-                )
-            )
+                    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
@@ -963,34 +957,34 @@ def main():
             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}"
-            )
+                    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,
-                )
-            )
+                    average_checkpoints_with_averaged_model(
+                        filename_start=filename_start,
+                        filename_end=filename_end,
+                        device=device,
+                        )
+                    )
 
     model.to(device)
     model.eval()
 
     # only load the neural network LM if required
     if params.use_shallow_fusion or params.decoding_method in (
-        "modified_beam_search_lm_rescore",
-        "modified_beam_search_lm_rescore_LODR",
-        "modified_beam_search_lm_shallow_fusion",
-        "modified_beam_search_LODR",
-    ):
+            "modified_beam_search_lm_rescore",
+            "modified_beam_search_lm_rescore_LODR",
+            "modified_beam_search_lm_shallow_fusion",
+            "modified_beam_search_LODR",
+            ):
         LM = LmScorer(
-            lm_type=params.lm_type,
-            params=params,
-            device=device,
-            lm_scale=params.lm_scale,
-        )
+                lm_type=params.lm_type,
+                params=params,
+                device=device,
+                lm_scale=params.lm_scale,
+                )
         LM.to(device)
         LM.eval()
     else:
@@ -1016,10 +1010,10 @@ def main():
         lm_filename = f"{params.tokens_ngram}gram.fst.txt"
         logging.info(f"Loading token level lm: {lm_filename}")
         ngram_lm = NgramLm(
-            str(params.lang_dir / lm_filename),
-            backoff_id=params.backoff_id,
-            is_binary=False,
-        )
+                str(params.lang_dir / lm_filename),
+                backoff_id=params.backoff_id,
+                is_binary=False,
+                )
         logging.info(f"num states: {ngram_lm.lm.num_states}")
         ngram_lm_scale = params.ngram_lm_scale
     else:
@@ -1033,8 +1027,8 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
-            )
+                    torch.load(lg_filename, map_location=device)
+                    )
             decoding_graph.scores *= params.ngram_lm_scale
         else:
             word_table = None
@@ -1060,40 +1054,36 @@ def main():
 
     # we need cut ids to display recognition results.
     args.return_cuts = True
-    reazonspeech_corpus = ReazonSpeechAsrDataModule(args)
+    librispeech = LibriSpeechAsrDataModule(args)
 
-    for subdir in ["valid"]:
+    test_clean_cuts = librispeech.test_clean_cuts()
+    test_other_cuts = librispeech.test_other_cuts()
+
+    test_clean_dl = librispeech.test_dataloaders(test_clean_cuts)
+    test_other_dl = librispeech.test_dataloaders(test_other_cuts)
+
+    test_sets = ["test-clean", "test-other"]
+    test_dl = [test_clean_dl, test_other_dl]
+
+    for test_set, test_dl in zip(test_sets, test_dl):
         results_dict = decode_dataset(
-            dl=reazonspeech_corpus.test_dataloaders(
-                getattr(reazonspeech_corpus, f"{subdir}_cuts")()
-            ),
-            params=params,
-            model=model,
-            sp=sp,
-            word_table=word_table,
-            decoding_graph=decoding_graph,
-            context_graph=context_graph,
-            LM=LM,
-            ngram_lm=ngram_lm,
-            ngram_lm_scale=ngram_lm_scale,
-        )
+                dl=test_dl,
+                params=params,
+                model=model,
+                sp=sp,
+                word_table=word_table,
+                decoding_graph=decoding_graph,
+                context_graph=context_graph,
+                LM=LM,
+                ngram_lm=ngram_lm,
+                ngram_lm_scale=ngram_lm_scale,
+                )
 
         save_asr_output(
             params=params,
-            test_set_name=subdir,
+            test_set_name=test_set,
             results_dict=results_dict,
         )
-        # with (
-        #     params.res_dir
-        #     / (
-        #         f"{subdir}-{params.decode_chunk_len}_{params.beam_size}"
-        #         f"_{params.avg}_{params.epoch}.cer"
-        #     )
-        # ).open("w") as fout:
-        #     if len(tot_err) == 1:
-        #         fout.write(f"{tot_err[0][1]}")
-        #     else:
-        #         fout.write("\n".join(f"{k}\t{v}") for k, v in tot_err)
 
         if not params.skip_scoring:
             save_wer_results(

egs/reazonspeech/ASR/zipformer/streaming/greedy_search/log-decode-epoch-28-avg-15-chunk-32-left-context-256-use-averaged-model-2024-07-29-17-52-11

@@ -1,2 +0,0 @@
-2024-07-29 17:52:11,668 INFO [streaming_decode.py:736] Decoding started
-2024-07-29 17:52:11,669 INFO [streaming_decode.py:742] Device: cuda:0

egs/reazonspeech/ASR/zipformer/streaming/greedy_search/log-decode-epoch-28-avg-15-chunk-32-left-context-256-use-averaged-model-2024-07-29-17-54-22

@@ -1,2 +0,0 @@
-2024-07-29 17:54:22,556 INFO [streaming_decode.py:736] Decoding started
-2024-07-29 17:54:22,556 INFO [streaming_decode.py:742] Device: cuda:0

egs/reazonspeech/ASR/zipformer/streaming/greedy_search/log-decode-epoch-28-avg-15-chunk-32-left-context-256-use-averaged-model-2024-07-29-17-55-15

@@ -1,2 +0,0 @@
-2024-07-29 17:55:15,276 INFO [streaming_decode.py:736] Decoding started
-2024-07-29 17:55:15,277 INFO [streaming_decode.py:742] Device: cuda:0

egs/reazonspeech/ASR/zipformer/streaming/greedy_search/log-decode-epoch-28-avg-15-chunk-32-left-context-256-use-averaged-model-2024-07-29-17-59-02

@@ -1,2 +0,0 @@
-2024-07-29 17:59:02,028 INFO [streaming_decode.py:736] Decoding started
-2024-07-29 17:59:02,029 INFO [streaming_decode.py:742] Device: cuda:0

egs/reazonspeech/ASR/zipformer/streaming/greedy_search/log-decode-epoch-28-avg-15-chunk-32-left-context-256-use-averaged-model-2024-07-29-18-01-06

@@ -1,5 +0,0 @@
-2024-07-29 18:01:06,736 INFO [streaming_decode.py:736] Decoding started
-2024-07-29 18:01:06,736 INFO [streaming_decode.py:742] Device: cuda:0
-2024-07-29 18:01:06,740 INFO [streaming_decode.py:753] {'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, 'best_valid_epoch': -1, 'batch_idx_train': 0, 'log_interval': 50, 'reset_interval': 200, 'valid_interval': 3000, 'feature_dim': 80, 'subsampling_factor': 4, 'warm_step': 2000, 'env_info': {'k2-version': '1.24.4', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': '8f976a1e1407e330e2a233d68f81b1eb5269fdaa', 'k2-git-date': 'Thu Jun 6 02:13:08 2024', 'lhotse-version': '1.26.0.dev+git.bd12d5d.clean', 'torch-version': '2.3.1+cu121', 'torch-cuda-available': True, 'torch-cuda-version': '12.1', 'python-version': '3.10', 'icefall-git-branch': 'jp-streaming', 'icefall-git-sha1': '4af81af-dirty', 'icefall-git-date': 'Thu Jul 18 22:05:59 2024', 'icefall-path': '/root/tmp/icefall', 'k2-path': '/root/miniconda3/envs/myenv/lib/python3.10/site-packages/k2/__init__.py', 'lhotse-path': '/root/miniconda3/envs/myenv/lib/python3.10/site-packages/lhotse/__init__.py', 'hostname': 'KDA00', 'IP address': '192.168.0.1'}, 'epoch': 28, 'iter': 0, 'avg': 15, 'use_averaged_model': True, 'exp_dir': PosixPath('zipformer'), 'bpe_model': 'data/lang_bpe_500/bpe.model', 'lang_dir': PosixPath('data/lang_char'), 'decoding_method': 'greedy_search', 'num_active_paths': 4, 'beam': 4, 'max_contexts': 4, 'max_states': 32, 'context_size': 2, 'num_decode_streams': 2000, 'num_encoder_layers': '2,2,3,4,3,2', 'downsampling_factor': '1,2,4,8,4,2', 'feedforward_dim': '512,768,1024,1536,1024,768', 'num_heads': '4,4,4,8,4,4', 'encoder_dim': '192,256,384,512,384,256', 'query_head_dim': '32', 'value_head_dim': '12', 'pos_head_dim': '4', 'pos_dim': 48, 'encoder_unmasked_dim': '192,192,256,256,256,192', 'cnn_module_kernel': '31,31,15,15,15,31', 'decoder_dim': 512, 'joiner_dim': 512, 'causal': True, 'chunk_size': '32', 'left_context_frames': '256', 'use_transducer': True, 'use_ctc': False, 'manifest_dir': PosixPath('data/manifests'), 'max_duration': 200.0, 'bucketing_sampler': True, 'num_buckets': 30, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': True, 'drop_last': True, 'return_cuts': False, 'num_workers': 2, 'enable_spec_aug': True, 'spec_aug_time_warp_factor': 80, 'enable_musan': False, 'lang': PosixPath('data/lang_char'), 'lang_type': None, 'res_dir': PosixPath('zipformer/streaming/greedy_search'), 'suffix': 'epoch-28-avg-15-chunk-32-left-context-256-use-averaged-model', 'blank_id': 0, 'unk_id': 2990, 'vocab_size': 2992}
-2024-07-29 18:01:06,740 INFO [streaming_decode.py:755] About to create model
-2024-07-29 18:01:07,118 INFO [streaming_decode.py:822] Calculating the averaged model over epoch range from 13 (excluded) to 28