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remove streaming/greedy_search results folder
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@ -103,9 +103,10 @@ from pathlib import Path
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from typing import Dict, List, Optional, Tuple
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import k2
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import sentencepiece as spm
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import torch
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import torch.nn as nn
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from asr_datamodule import ReazonSpeechAsrDataModule
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from asr_datamodule import LibriSpeechAsrDataModule
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from beam_search import (
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beam_search,
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fast_beam_search_nbest,
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@ -134,7 +135,6 @@ from icefall.checkpoint import (
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from icefall.lexicon import Lexicon
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from icefall.utils import (
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AttributeDict,
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make_pad_mask,
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setup_logger,
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store_transcripts,
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str2bool,
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@ -205,7 +205,7 @@ def get_parser():
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parser.add_argument(
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"--lang-dir",
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type=Path,
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default="data/lang_char",
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default="data/lang_bpe_500",
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help="The lang dir containing word table and LG graph",
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)
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@ -371,6 +371,7 @@ def get_parser():
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modified_beam_search_LODR.
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""",
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)
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<<<<<<< HEAD
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parser.add_argument(
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"--skip-scoring",
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@ -398,7 +399,7 @@ def get_parser():
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def decode_one_batch(
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params: AttributeDict,
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model: nn.Module,
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sp: Tokenizer,
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sp: spm.SentencePieceProcessor,
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batch: dict,
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word_table: Optional[k2.SymbolTable] = None,
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decoding_graph: Optional[k2.Fsa] = None,
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@ -477,10 +478,9 @@ def decode_one_batch(
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beam=params.beam,
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max_contexts=params.max_contexts,
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max_states=params.max_states,
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blank_penalty=params.blank_penalty,
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)
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for hyp in sp.decode(hyp_tokens):
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hyps.append(sp.text2word(hyp))
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hyps.append(hyp.split())
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elif params.decoding_method == "fast_beam_search_nbest_LG":
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hyp_tokens = fast_beam_search_nbest_LG(
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model=model,
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@ -492,7 +492,6 @@ def decode_one_batch(
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max_states=params.max_states,
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num_paths=params.num_paths,
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nbest_scale=params.nbest_scale,
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blank_penalty=params.blank_penalty,
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)
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for hyp in hyp_tokens:
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hyps.append([word_table[i] for i in hyp])
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@ -507,10 +506,9 @@ def decode_one_batch(
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max_states=params.max_states,
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num_paths=params.num_paths,
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nbest_scale=params.nbest_scale,
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blank_penalty=params.blank_penalty,
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)
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for hyp in sp.decode(hyp_tokens):
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hyps.append(sp.text2word(hyp))
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hyps.append(hyp.split())
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elif params.decoding_method == "fast_beam_search_nbest_oracle":
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hyp_tokens = fast_beam_search_nbest_oracle(
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model=model,
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@ -523,19 +521,17 @@ def decode_one_batch(
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num_paths=params.num_paths,
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ref_texts=sp.encode(supervisions["text"]),
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nbest_scale=params.nbest_scale,
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blank_penalty=params.blank_penalty,
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)
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for hyp in sp.decode(hyp_tokens):
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hyps.append(sp.text2word(hyp))
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hyps.append(hyp.split())
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elif params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1:
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hyp_tokens = greedy_search_batch(
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model=model,
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encoder_out=encoder_out,
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encoder_out_lens=encoder_out_lens,
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blank_penalty=params.blank_penalty,
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)
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for hyp in sp.decode(hyp_tokens):
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hyps.append(sp.text2word(hyp))
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hyps.append(hyp.split())
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elif params.decoding_method == "modified_beam_search":
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hyp_tokens = modified_beam_search(
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model=model,
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@ -543,10 +539,9 @@ def decode_one_batch(
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encoder_out_lens=encoder_out_lens,
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beam=params.beam_size,
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context_graph=context_graph,
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blank_penalty=params.blank_penalty,
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)
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for hyp in sp.decode(hyp_tokens):
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hyps.append(sp.text2word(hyp))
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hyps.append(hyp.split())
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elif params.decoding_method == "modified_beam_search_lm_shallow_fusion":
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hyp_tokens = modified_beam_search_lm_shallow_fusion(
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model=model,
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@ -556,7 +551,7 @@ def decode_one_batch(
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LM=LM,
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)
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for hyp in sp.decode(hyp_tokens):
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hyps.append(sp.text2word(hyp))
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hyps.append(hyp.split())
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elif params.decoding_method == "modified_beam_search_LODR":
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hyp_tokens = modified_beam_search_LODR(
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model=model,
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@ -569,7 +564,7 @@ def decode_one_batch(
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context_graph=context_graph,
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)
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for hyp in sp.decode(hyp_tokens):
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hyps.append(sp.text2word(hyp))
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hyps.append(hyp.split())
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elif params.decoding_method == "modified_beam_search_lm_rescore":
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lm_scale_list = [0.01 * i for i in range(10, 50)]
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ans_dict = modified_beam_search_lm_rescore(
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@ -615,7 +610,7 @@ def decode_one_batch(
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raise ValueError(
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f"Unsupported decoding method: {params.decoding_method}"
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)
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hyps.append(sp.text2word(sp.decode(hyp)))
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hyps.append(sp.decode(hyp).split())
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# prefix = ( "greedy_search" | "fast_beam_search_nbest" | "modified_beam_search" )
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prefix = f"{params.decoding_method}"
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@ -658,14 +653,14 @@ def decode_dataset(
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dl: torch.utils.data.DataLoader,
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params: AttributeDict,
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model: nn.Module,
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sp: Tokenizer,
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sp: spm.SentencePieceProcessor,
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word_table: Optional[k2.SymbolTable] = None,
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decoding_graph: Optional[k2.Fsa] = None,
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context_graph: Optional[ContextGraph] = None,
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LM: Optional[LmScorer] = None,
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ngram_lm=None,
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ngram_lm_scale: float = 0.0,
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) -> Dict[str, List[Tuple[str, List[str], List[str]]]]:
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) -> Dict[str, List[Tuple[str, List[str], List[str]]]]:
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"""Decode dataset.
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Args:
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@ -724,7 +719,7 @@ def decode_dataset(
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this_batch = []
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assert len(hyps) == len(texts)
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for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
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ref_words = sp.text2word(ref_text)
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ref_words = ref_text.split()
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this_batch.append((cut_id, ref_words, hyp_words))
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results[name].extend(this_batch)
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@ -742,7 +737,7 @@ def save_asr_output(
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params: AttributeDict,
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test_set_name: str,
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results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]],
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):
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):
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"""
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Save text produced by ASR.
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"""
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@ -760,7 +755,7 @@ def save_wer_results(
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params: AttributeDict,
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test_set_name: str,
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results_dict: Dict[str, List[Tuple[str, List[str], List[str], Tuple]]],
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):
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):
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"""
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Save WER and per-utterance word alignments.
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"""
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@ -797,8 +792,8 @@ def save_wer_results(
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@torch.no_grad()
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def main():
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parser = get_parser()
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ReazonSpeechAsrDataModule.add_arguments(parser)
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Tokenizer.add_arguments(parser)
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LibriSpeechAsrDataModule.add_arguments(parser)
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LmScorer.add_arguments(parser)
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args = parser.parse_args()
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args.exp_dir = Path(args.exp_dir)
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@ -872,8 +867,6 @@ def main():
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f"_LODR-{params.tokens_ngram}gram-scale-{params.ngram_lm_scale}"
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)
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params.suffix += f"-blank-penalty-{params.blank_penalty}"
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if params.use_averaged_model:
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params.suffix += "_use-averaged-model"
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@ -886,9 +879,10 @@ def main():
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logging.info(f"Device: {device}")
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sp = Tokenizer.load(params.lang, params.lang_type)
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sp = spm.SentencePieceProcessor()
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sp.load(params.bpe_model)
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# <blk> and <unk> are defined in local/prepare_lang_char.py
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# <blk> and <unk> are defined in local/train_bpe_model.py
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params.blank_id = sp.piece_to_id("<blk>")
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params.unk_id = sp.piece_to_id("<unk>")
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params.vocab_size = sp.get_piece_size()
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@ -1060,13 +1054,20 @@ def main():
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# we need cut ids to display recognition results.
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args.return_cuts = True
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reazonspeech_corpus = ReazonSpeechAsrDataModule(args)
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librispeech = LibriSpeechAsrDataModule(args)
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for subdir in ["valid"]:
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test_clean_cuts = librispeech.test_clean_cuts()
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test_other_cuts = librispeech.test_other_cuts()
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test_clean_dl = librispeech.test_dataloaders(test_clean_cuts)
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test_other_dl = librispeech.test_dataloaders(test_other_cuts)
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test_sets = ["test-clean", "test-other"]
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test_dl = [test_clean_dl, test_other_dl]
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for test_set, test_dl in zip(test_sets, test_dl):
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results_dict = decode_dataset(
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dl=reazonspeech_corpus.test_dataloaders(
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getattr(reazonspeech_corpus, f"{subdir}_cuts")()
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),
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dl=test_dl,
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params=params,
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model=model,
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sp=sp,
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@ -1080,20 +1081,9 @@ def main():
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save_asr_output(
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params=params,
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test_set_name=subdir,
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test_set_name=test_set,
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results_dict=results_dict,
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)
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# with (
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# params.res_dir
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# / (
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# f"{subdir}-{params.decode_chunk_len}_{params.beam_size}"
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# f"_{params.avg}_{params.epoch}.cer"
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# )
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# ).open("w") as fout:
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# if len(tot_err) == 1:
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# fout.write(f"{tot_err[0][1]}")
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# else:
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# fout.write("\n".join(f"{k}\t{v}") for k, v in tot_err)
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if not params.skip_scoring:
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save_wer_results(
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@ -1,2 +0,0 @@
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2024-07-29 17:52:11,668 INFO [streaming_decode.py:736] Decoding started
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2024-07-29 17:52:11,669 INFO [streaming_decode.py:742] Device: cuda:0
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@ -1,2 +0,0 @@
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2024-07-29 17:54:22,556 INFO [streaming_decode.py:736] Decoding started
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2024-07-29 17:54:22,556 INFO [streaming_decode.py:742] Device: cuda:0
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@ -1,2 +0,0 @@
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2024-07-29 17:55:15,276 INFO [streaming_decode.py:736] Decoding started
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2024-07-29 17:55:15,277 INFO [streaming_decode.py:742] Device: cuda:0
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@ -1,2 +0,0 @@
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2024-07-29 17:59:02,028 INFO [streaming_decode.py:736] Decoding started
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2024-07-29 17:59:02,029 INFO [streaming_decode.py:742] Device: cuda:0
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@ -1,5 +0,0 @@
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2024-07-29 18:01:06,736 INFO [streaming_decode.py:736] Decoding started
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2024-07-29 18:01:06,736 INFO [streaming_decode.py:742] Device: cuda:0
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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}
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2024-07-29 18:01:06,740 INFO [streaming_decode.py:755] About to create model
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2024-07-29 18:01:07,118 INFO [streaming_decode.py:822] Calculating the averaged model over epoch range from 13 (excluded) to 28
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