Source code for espnet2.bin.s2t_inference

#!/usr/bin/env python3
import argparse
import logging
import sys
from itertools import groupby
from pathlib import Path
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union

import numpy as np
import torch
import torch.nn.functional as F
import torch.quantization
from typeguard import typechecked

from espnet2.asr.decoder.s4_decoder import S4Decoder
from espnet2.asr.partially_AR_model import PartiallyARInference
from espnet2.fileio.datadir_writer import DatadirWriter
from espnet2.tasks.lm import LMTask
from espnet2.tasks.s2t import S2TTask
from espnet2.text.build_tokenizer import build_tokenizer
from espnet2.text.token_id_converter import TokenIDConverter
from espnet2.text.whisper_token_id_converter import OpenAIWhisperTokenIDConverter
from espnet2.torch_utils.device_funcs import to_device
from espnet2.torch_utils.set_all_random_seed import set_all_random_seed
from espnet2.utils import config_argparse
from espnet2.utils.types import str2bool, str2triple_str, str_or_none
from espnet.nets.batch_beam_search import BatchBeamSearch
from espnet.nets.beam_search import BeamSearch, Hypothesis
from espnet.nets.pytorch_backend.transformer.subsampling import TooShortUttError
from espnet.nets.scorer_interface import BatchScorerInterface
from espnet.nets.scorers.ctc import CTCPrefixScorer
from espnet.nets.scorers.length_bonus import LengthBonus
from espnet.utils.cli_utils import get_commandline_args

# Alias for typing
ListOfHypothesis = List[

[docs]class ScoreFilter(BatchScorerInterface, torch.nn.Module): """Filter scores based on pre-defined rules. See comments in the score method. """ def __init__( self, notimestamps: int, first_time: int, last_time: int, sos: int, eos: int, vocab_size: int, ): super().__init__() self.notimestamps = notimestamps self.first_time = first_time self.last_time = last_time self.sos = sos self.eos = eos self.vocab_size = vocab_size # dummy param used to obtain the current dtype and device self.param = torch.nn.Parameter(torch.tensor(0.0, dtype=torch.float32))
[docs] def score( self, y: torch.Tensor, state: Any, x: torch.Tensor ) -> Tuple[torch.Tensor, Any]: """Score new token (required). Args: y (torch.Tensor): 1D torch.int64 prefix tokens. state: Scorer state for prefix tokens x (torch.Tensor): The encoder feature that generates ys. Returns: tuple[torch.Tensor, Any]: Tuple of scores for next token that has a shape of `(n_vocab)` and next state for ys """ score = torch.zeros( self.vocab_size, dtype=self.param.dtype, device=self.param.device ) if self.notimestamps in y: # Suppress timestamp tokens if we don't predict time score[self.first_time : self.last_time + 1] = -np.inf elif y[-3] == self.sos: # The first token must be a timestamp if we predict time score[: self.first_time] = -np.inf score[self.last_time + 1 :] = -np.inf else: prev_times = y[torch.logical_and(y >= self.first_time, y <= self.last_time)] if len(prev_times) % 2 == 1: # there are an odd number of timestamps, so the sentence is incomplete score[self.eos] = -np.inf # timestamps are monotonic score[self.first_time : prev_times[-1] + 1] = -np.inf else: # there are an even number of timestamps (all are paired) if y[-1] >= self.first_time and y[-1] <= self.last_time: # the next tokon should be a timestamp or eos score[: y[-1]] = -np.inf score[self.last_time + 1 :] = -np.inf score[self.eos] = 0.0 else: # this is an illegal hyp score[:] = -np.inf return score, None
[docs] def batch_score( self, ys: torch.Tensor, states: List[Any], xs: torch.Tensor ) -> Tuple[torch.Tensor, List[Any]]: """Score new token batch (required). Args: ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen). states (List[Any]): Scorer states for prefix tokens. xs (torch.Tensor): The encoder feature that generates ys (n_batch, xlen, n_feat). Returns: tuple[torch.Tensor, List[Any]]: Tuple of batchfied scores for next token with shape of `(n_batch, n_vocab)` and next state list for ys. """ scores = list() outstates = list() for i, (y, state, x) in enumerate(zip(ys, states, xs)): score, outstate = self.score(y, state, x) outstates.append(outstate) scores.append(score) scores =, 0).view(ys.shape[0], -1) return scores, outstates
[docs]class Speech2Text: """Speech2Text class Examples: >>> import soundfile >>> speech2text = Speech2Text("s2t_config.yml", "s2t.pth") >>> audio, rate ="speech.wav") >>> speech2text(audio) [(text, token, token_int, text_nospecial, hypothesis object), ...] """ @typechecked def __init__( self, s2t_train_config: Union[Path, str, None] = None, s2t_model_file: Union[Path, str, None] = None, lm_train_config: Union[Path, str, None] = None, lm_file: Union[Path, str, None] = None, ngram_scorer: str = "full", ngram_file: Union[Path, str, None] = None, token_type: Optional[str] = None, bpemodel: Optional[str] = None, device: str = "cpu", maxlenratio: float = 0.0, minlenratio: float = 0.0, batch_size: int = 1, dtype: str = "float32", beam_size: int = 5, ctc_weight: float = 0.0, lm_weight: float = 0.0, ngram_weight: float = 0.0, penalty: float = 0.0, nbest: int = 1, normalize_length: bool = False, quantize_s2t_model: bool = False, quantize_lm: bool = False, quantize_modules: List[str] = ["Linear"], quantize_dtype: str = "qint8", partial_ar: bool = False, threshold_probability: float = 0.99, max_seq_len: int = 5, max_mask_parallel: int = -1, # default values that can be overwritten in __call__ lang_sym: str = "<eng>", task_sym: str = "<asr>", predict_time: bool = False, ): if ctc_weight > 0.0 and predict_time: raise ValueError("CTC cannot predict timestamps") qconfig_spec = set([getattr(torch.nn, q) for q in quantize_modules]) quantize_dtype: torch.dtype = getattr(torch, quantize_dtype) # 1. Build S2T model s2t_model, s2t_train_args = S2TTask.build_model_from_file( s2t_train_config, s2t_model_file, device ), dtype)).eval() if quantize_s2t_model:"Use quantized s2t model for decoding.") s2t_model = torch.quantization.quantize_dynamic( s2t_model, qconfig_spec=qconfig_spec, dtype=quantize_dtype ) decoder = s2t_model.decoder ctc = CTCPrefixScorer(ctc=s2t_model.ctc, eos=s2t_model.eos) token_list = s2t_model.token_list scorers = dict( decoder=decoder, ctc=ctc, length_bonus=LengthBonus(len(token_list)), scorefilter=ScoreFilter( notimestamps=token_list.index( s2t_train_args.preprocessor_conf["notime_symbol"] ), first_time=token_list.index( s2t_train_args.preprocessor_conf["first_time_symbol"] ), last_time=token_list.index( s2t_train_args.preprocessor_conf["last_time_symbol"] ), sos=s2t_model.sos, eos=s2t_model.eos, vocab_size=len(token_list), ), ) # 2. Build language model if lm_train_config is not None: lm, lm_train_args = LMTask.build_model_from_file( lm_train_config, lm_file, device ) if quantize_lm:"Use quantized lm for decoding.") lm = torch.quantization.quantize_dynamic( lm, qconfig_spec=qconfig_spec, dtype=quantize_dtype ) scorers["lm"] = lm.lm # 3. Build ngram model if ngram_file is not None: if ngram_scorer == "full": from espnet.nets.scorers.ngram import NgramFullScorer ngram = NgramFullScorer(ngram_file, token_list) else: from espnet.nets.scorers.ngram import NgramPartScorer ngram = NgramPartScorer(ngram_file, token_list) scorers["ngram"] = ngram # 4. Build BeamSearch object weights = dict( decoder=1.0 - ctc_weight, ctc=ctc_weight, lm=lm_weight, ngram=ngram_weight, length_bonus=penalty, scorefilter=1.0, ) if partial_ar: beam_search = PartiallyARInference( s2t_model.ctc, s2t_model.decoder, threshold_probability=threshold_probability, sos=s2t_model.sos, eos=s2t_model.eos, mask_token=len(token_list), token_list=token_list, scorers={"decoder": s2t_model.decoder}, weights=weights, beam_size=beam_size, max_seq_len=max_seq_len, max_mask_parallel=max_mask_parallel, ) else: beam_search = BeamSearch( beam_size=beam_size, weights=weights, scorers=scorers, sos=s2t_model.sos, eos=s2t_model.eos, vocab_size=len(token_list), token_list=token_list, pre_beam_score_key=None if ctc_weight == 1.0 else "full", normalize_length=normalize_length, ) # TODO(karita): make all scorers batchfied if batch_size == 1: non_batch = [ k for k, v in beam_search.full_scorers.items() if not isinstance(v, BatchScorerInterface) ] if len(non_batch) == 0: beam_search.__class__ = BatchBeamSearch"BatchBeamSearch implementation is selected.") else: logging.warning( f"As non-batch scorers {non_batch} are found, " f"fall back to non-batch implementation." ), dtype=getattr(torch, dtype)).eval() for scorer in scorers.values(): if isinstance(scorer, torch.nn.Module):, dtype=getattr(torch, dtype)).eval()"Beam_search: {beam_search}")"Decoding device={device}, dtype={dtype}") # 5. [Optional] Build Text converter: e.g. bpe-sym -> Text if token_type is None: token_type = s2t_train_args.token_type if bpemodel is None: bpemodel = s2t_train_args.bpemodel if token_type is None: tokenizer = None elif ( token_type == "bpe" or token_type == "hugging_face" or "whisper" in token_type ): if bpemodel is not None: tokenizer = build_tokenizer(token_type=token_type, bpemodel=bpemodel) else: tokenizer = None else: tokenizer = build_tokenizer(token_type=token_type) if bpemodel not in ["whisper_en", "whisper_multilingual"]: converter = TokenIDConverter(token_list=token_list) else: converter = OpenAIWhisperTokenIDConverter(model_type=bpemodel) beam_search.set_hyp_primer( list(converter.tokenizer.sot_sequence_including_notimestamps) )"Text tokenizer: {tokenizer}") self.s2t_model = s2t_model self.s2t_train_args = s2t_train_args self.preprocessor_conf = s2t_train_args.preprocessor_conf self.converter = converter self.tokenizer = tokenizer self.beam_search = beam_search self.maxlenratio = maxlenratio self.minlenratio = minlenratio self.device = device self.dtype = dtype self.nbest = nbest self.lang_sym = lang_sym self.task_sym = task_sym self.predict_time = predict_time self.partial_ar = partial_ar @torch.no_grad() @typechecked def __call__( self, speech: Union[torch.Tensor, np.ndarray], text_prev: Optional[Union[torch.Tensor, np.ndarray, str, List]] = None, lang_sym: Optional[str] = None, task_sym: Optional[str] = None, predict_time: Optional[bool] = None, ) -> Union[ ListOfHypothesis, Tuple[ ListOfHypothesis, Optional[Dict[int, List[str]]], ], ]: """Inference for a single utterance. The input speech will be padded or trimmed to the fixed length, which is consistent with training. Args: speech: input speech of shape (nsamples,) or (nsamples, nchannels=1) text_prev: previous text used as condition (optional) Returns: n-best list of (text, token, token_int, text_nospecial, hyp) """ lang_sym = lang_sym if lang_sym is not None else self.lang_sym task_sym = task_sym if task_sym is not None else self.task_sym predict_time = predict_time if predict_time is not None else self.predict_time lang_id = self.converter.token2id[lang_sym] task_id = self.converter.token2id[task_sym] notime_id = self.converter.token2id[self.preprocessor_conf["notime_symbol"]] # Prepare hyp_primer hyp_primer = [self.s2t_model.sos, lang_id, task_id] if not predict_time: hyp_primer.append(notime_id) if text_prev is not None: if isinstance(text_prev, str): text_prev = self.converter.tokens2ids( self.tokenizer.text2tokens(text_prev) ) else: text_prev = text_prev.tolist() # Check if text_prev is valid if in text_prev: text_prev = None if text_prev is not None: hyp_primer = [self.s2t_model.sop] + text_prev + hyp_primer self.beam_search.set_hyp_primer(hyp_primer) # Preapre speech if isinstance(speech, np.ndarray): speech = torch.tensor(speech) # Only support single-channel speech if speech.dim() > 1: assert ( speech.dim() == 2 and speech.size(1) == 1 ), f"speech of size {speech.size()} is not supported" speech = speech.squeeze(1) # (nsamples, 1) --> (nsamples,) speech_length = int( self.preprocessor_conf["fs"] * self.preprocessor_conf["speech_length"] ) # Pad or trim speech to the fixed length if speech.size(-1) >= speech_length: speech = speech[:speech_length] else: speech = F.pad(speech, (0, speech_length - speech.size(-1))) # Batchify input # speech: (nsamples,) -> (1, nsamples) speech = speech.unsqueeze(0).to(getattr(torch, self.dtype)) # lengths: (1,) lengths = speech.new_full([1], dtype=torch.long, fill_value=speech.size(1)) batch = {"speech": speech, "speech_lengths": lengths}"speech length: " + str(speech.size(1))) # a. To device batch = to_device(batch, device=self.device) # b. Forward Encoder enc, enc_olens = self.s2t_model.encode(**batch) intermediate_outs = None if isinstance(enc, tuple): enc, intermediate_outs = enc assert len(enc) == 1, len(enc) # c. Pass the encoder result to the beam search results = self._decode_single_sample(enc[0]) # Encoder intermediate CTC predictions if intermediate_outs is not None: encoder_interctc_res = self._decode_interctc(intermediate_outs) results = (results, encoder_interctc_res) return results def _decode_single_sample(self, enc: torch.Tensor): if hasattr(self.beam_search.nn_dict, "decoder"): if isinstance(self.beam_search.nn_dict.decoder, S4Decoder): # Setup: required for S4 autoregressive generation for module in self.beam_search.nn_dict.decoder.modules(): if hasattr(module, "setup_step"): module.setup_step() nbest_hyps = self.beam_search( x=enc, maxlenratio=self.maxlenratio, minlenratio=self.minlenratio ) nbest_hyps = nbest_hyps[: self.nbest] results = [] for hyp in nbest_hyps: assert isinstance(hyp, Hypothesis), type(hyp) # remove sos/eos and get results last_pos = -1 start_pos = 1 if self.partial_ar else 0 if isinstance(hyp.yseq, list): token_int = hyp.yseq[start_pos:last_pos] else: token_int = hyp.yseq[start_pos:last_pos].tolist() if not self.partial_ar: token_int = token_int[token_int.index(self.s2t_model.sos) + 1 :] # remove blank symbol id token_int = list(filter(lambda x: x != self.s2t_model.blank_id, token_int)) # Change integer-ids to tokens token = self.converter.ids2tokens(token_int) # remove special tokens (task, timestamp, etc.) token_nospecial = [x for x in token if not (x[0] == "<" and x[-1] == ">")] text, text_nospecial = None, None if self.tokenizer is not None: text = self.tokenizer.tokens2text(token) text_nospecial = self.tokenizer.tokens2text(token_nospecial) results.append((text, token, token_int, text_nospecial, hyp)) return results @typechecked def _decode_interctc( self, intermediate_outs: List[Tuple[int, torch.Tensor]] ) -> Dict[int, List[str]]: exclude_ids = [self.s2t_model.blank_id, self.s2t_model.sos, self.s2t_model.eos] res = {} token_list = self.beam_search.token_list for layer_idx, encoder_out in intermediate_outs: y = self.s2t_model.ctc.argmax(encoder_out)[0] # batch_size = 1 y = [x[0] for x in groupby(y) if x[0] not in exclude_ids] y = [token_list[x] for x in y] res[layer_idx] = y return res
[docs] @torch.no_grad() @typechecked def decode_long( self, speech: Union[torch.Tensor, np.ndarray], condition_on_prev_text: bool = False, init_text: Optional[str] = None, end_time_threshold: str = "<29.00>", lang_sym: Optional[str] = None, task_sym: Optional[str] = None, skip_last_chunk_threshold: float = 0.2, ): """Decode unsegmented long-form speech. Args: speech: 1D long-form input speech condition_on_prev_text (bool): whether to condition on previous text init_text: text used as condition for the first segment end_time_threshold: the last utterance is considered as incomplete if its end timestamp exceeds this threshold Returns: utterances: list of tuples of (start_time, end_time, text) """ lang_sym = lang_sym if lang_sym is not None else self.lang_sym task_sym = task_sym if task_sym is not None else self.task_sym segment_len = int( self.preprocessor_conf["speech_length"] * self.preprocessor_conf["fs"] ) end_time_id_threshold = self.converter.token2id[end_time_threshold] first_time_id = self.converter.token2id[ self.preprocessor_conf["first_time_symbol"] ] last_time_id = self.converter.token2id[ self.preprocessor_conf["last_time_symbol"] ] resolution = self.preprocessor_conf["speech_resolution"] fs = self.preprocessor_conf["fs"] if isinstance(speech, np.ndarray): speech = torch.tensor(speech) if speech.dim() > 1: assert ( speech.dim() == 2 and speech.size(1) == 1 ), f"speech of size {speech.size()} is not supported" speech = speech.squeeze(1) # (nsamples, 1) --> (nsamples,) utterances = [] offset = 0 text_prev = init_text while offset < len(speech):"Current start time in seconds: {offset / fs:.2f}") segment = speech[offset : offset + segment_len] if len(segment) / fs < skip_last_chunk_threshold: logging.warning( f"Skip the last chunk as it's too short: {len(segment) / fs:.2f}s" ) offset += segment_len continue # segment will be padded in __call__ result = self.__call__( speech=segment, text_prev=text_prev if condition_on_prev_text else None, lang_sym=lang_sym, task_sym=task_sym, predict_time=True, ) if isinstance(result, tuple): result = result[0] # NOTE(yifan): sos and eos have been removed text, token, token_int, text_nospecial, hyp = result[0] # best hyp token_int = token_int[2:] # remove lang and task # Find all timestamp positions time_pos = [ idx for idx, tok in enumerate(token_int) if tok >= first_time_id and tok <= last_time_id ] # NOTE(yifan): this is an edge case with only a start time if len(time_pos) == 1: token_int.append(last_time_id) time_pos.append(len(token_int) - 1) if len(time_pos) % 2 == 0: # Timestamps are all paired if ( len(time_pos) > 2 and token_int[time_pos[-1]] > end_time_id_threshold ): # The last utterance is incomplete new_start_time_id = token_int[time_pos[-2]] time_pos = time_pos[:-2] else: new_start_time_id = token_int[time_pos[-1]] else: # The last utterance only has start time new_start_time_id = token_int[time_pos[-1]] time_pos = time_pos[:-1] # Get utterances in this segment text_prev = "" for i in range(0, len(time_pos), 2): utt = ( round( (token_int[time_pos[i]] - first_time_id) * resolution + offset / fs, 2, ), round( (token_int[time_pos[i + 1]] - first_time_id) * resolution + offset / fs, 2, ), self.tokenizer.tokens2text( self.converter.ids2tokens( token_int[time_pos[i] + 1 : time_pos[i + 1]] ) ), ) text_prev = text_prev + utt[-1] utterances.append(utt) offset += round((new_start_time_id - first_time_id) * resolution * fs) return utterances
[docs] @staticmethod def from_pretrained( model_tag: Optional[str] = None, **kwargs: Optional[Any], ): """Build Speech2Text instance from the pretrained model. Args: model_tag (Optional[str]): Model tag of the pretrained models. Currently, the tags of espnet_model_zoo are supported. Returns: Speech2Text: Speech2Text instance. """ if model_tag is not None: try: from espnet_model_zoo.downloader import ModelDownloader except ImportError: logging.error( "`espnet_model_zoo` is not installed. " "Please install via `pip install -U espnet_model_zoo`." ) raise d = ModelDownloader() kwargs.update(**d.download_and_unpack(model_tag)) return Speech2Text(**kwargs)
[docs]@typechecked def inference( output_dir: str, maxlenratio: float, minlenratio: float, batch_size: int, dtype: str, beam_size: int, ngpu: int, seed: int, ctc_weight: float, lm_weight: float, ngram_weight: float, penalty: float, nbest: int, normalize_length: bool, num_workers: int, log_level: Union[int, str], data_path_and_name_and_type: Sequence[Tuple[str, str, str]], key_file: Optional[str], s2t_train_config: Optional[str], s2t_model_file: Optional[str], lm_train_config: Optional[str], lm_file: Optional[str], word_lm_train_config: Optional[str], word_lm_file: Optional[str], ngram_file: Optional[str], model_tag: Optional[str], token_type: Optional[str], bpemodel: Optional[str], allow_variable_data_keys: bool, quantize_s2t_model: bool, quantize_lm: bool, quantize_modules: List[str], quantize_dtype: str, lang_sym: str, task_sym: str, predict_time: bool, partial_ar: bool, threshold_probability: float, max_seq_len: int, max_mask_parallel: int, ): if batch_size > 1: raise NotImplementedError("batch decoding is not implemented") if word_lm_train_config is not None: raise NotImplementedError("Word LM is not implemented") if ngpu > 1: raise NotImplementedError("only single GPU decoding is supported") logging.basicConfig( level=log_level, format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s", ) if ngpu >= 1: device = "cuda" else: device = "cpu" # NOTE(yifan): < and > cannot be passed in command line lang_sym = f"<{lang_sym.lstrip('<').rstrip('>')}>" task_sym = f"<{task_sym.lstrip('<').rstrip('>')}>" # 1. Set random-seed set_all_random_seed(seed) # 2. Build speech2text speech2text_kwargs = dict( s2t_train_config=s2t_train_config, s2t_model_file=s2t_model_file, lm_train_config=lm_train_config, lm_file=lm_file, ngram_file=ngram_file, token_type=token_type, bpemodel=bpemodel, device=device, maxlenratio=maxlenratio, minlenratio=minlenratio, dtype=dtype, beam_size=beam_size, ctc_weight=ctc_weight, lm_weight=lm_weight, ngram_weight=ngram_weight, penalty=penalty, nbest=nbest, normalize_length=normalize_length, quantize_s2t_model=quantize_s2t_model, quantize_lm=quantize_lm, quantize_modules=quantize_modules, quantize_dtype=quantize_dtype, lang_sym=lang_sym, task_sym=task_sym, predict_time=predict_time, partial_ar=partial_ar, threshold_probability=threshold_probability, max_seq_len=max_seq_len, max_mask_parallel=max_mask_parallel, ) speech2text = Speech2Text.from_pretrained( model_tag=model_tag, **speech2text_kwargs, ) # 3. Build data-iterator loader = S2TTask.build_streaming_iterator( data_path_and_name_and_type, dtype=dtype, batch_size=batch_size, key_file=key_file, num_workers=num_workers, preprocess_fn=S2TTask.build_preprocess_fn(speech2text.s2t_train_args, False), collate_fn=S2TTask.build_collate_fn(speech2text.s2t_train_args, False), allow_variable_data_keys=allow_variable_data_keys, inference=True, ) # 7 .Start for-loop # FIXME(kamo): The output format should be discussed about with DatadirWriter(output_dir) as writer: for keys, batch in loader: assert isinstance(batch, dict), type(batch) assert all(isinstance(s, str) for s in keys), keys _bs = len(next(iter(batch.values()))) assert len(keys) == _bs, f"{len(keys)} != {_bs}" batch = {k: v[0] for k, v in batch.items() if not k.endswith("_lengths")} # N-best list of (text, token, token_int, text_nospecial, hyp_object) try: results = speech2text(**batch) except TooShortUttError as e: logging.warning(f"Utterance {keys} {e}") hyp = Hypothesis(score=0.0, scores={}, states={}, yseq=[]) results = [[" ", ["<space>"], [2], " ", hyp]] * nbest # Only supporting batch_size==1 key = keys[0] encoder_interctc_res = None if isinstance(results, tuple): results, encoder_interctc_res = results for n, (text, token, token_int, text_nospecial, hyp) in zip( range(1, nbest + 1), results ): # Create a directory: outdir/{n}best_recog ibest_writer = writer[f"{n}best_recog"] # Write the result to each file ibest_writer["token"][key] = " ".join(token) ibest_writer["token_int"][key] = " ".join(map(str, token_int)) ibest_writer["score"][key] = str(hyp.score) if text is not None: ibest_writer["text"][key] = text if text_nospecial is not None: ibest_writer["text_nospecial"][key] = text_nospecial # Write intermediate predictions to # encoder_interctc_layer<layer_idx>.txt ibest_writer = writer["1best_recog"] if encoder_interctc_res is not None: for idx, text in encoder_interctc_res.items(): ibest_writer[f"encoder_interctc_layer{idx}.txt"][key] = " ".join( text )
[docs]def get_parser(): parser = config_argparse.ArgumentParser( description="S2T Decoding", formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) # Note(kamo): Use '_' instead of '-' as separator. # '-' is confusing if written in yaml. parser.add_argument( "--log_level", type=lambda x: x.upper(), default="INFO", choices=("CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"), help="The verbose level of logging", ) parser.add_argument("--output_dir", type=str, required=True) parser.add_argument( "--ngpu", type=int, default=0, help="The number of gpus. 0 indicates CPU mode", ) parser.add_argument("--seed", type=int, default=0, help="Random seed") parser.add_argument( "--dtype", default="float32", choices=["float16", "float32", "float64"], help="Data type", ) parser.add_argument( "--num_workers", type=int, default=1, help="The number of workers used for DataLoader", ) group = parser.add_argument_group("Input data related") group.add_argument( "--data_path_and_name_and_type", type=str2triple_str, required=True, action="append", ) group.add_argument("--key_file", type=str_or_none) group.add_argument("--allow_variable_data_keys", type=str2bool, default=False) group = parser.add_argument_group("Model configuration related") group.add_argument( "--s2t_train_config", type=str, help="S2T training configuration", ) group.add_argument( "--s2t_model_file", type=str, help="S2T model parameter file", ) group.add_argument( "--lm_train_config", type=str, help="LM training configuration", ) group.add_argument( "--lm_file", type=str, help="LM parameter file", ) group.add_argument( "--word_lm_train_config", type=str, help="Word LM training configuration", ) group.add_argument( "--word_lm_file", type=str, help="Word LM parameter file", ) group.add_argument( "--ngram_file", type=str, help="N-gram parameter file", ) group.add_argument( "--model_tag", type=str, help="Pretrained model tag. If specify this option, *_train_config and " "*_file will be overwritten", ) group.add_argument("--lang_sym", type=str, default="<eng>", help="Language symbol.") group.add_argument("--task_sym", type=str, default="<asr>", help="Task symbol.") group.add_argument( "--predict_time", type=str2bool, default=False, help="Predict timestamps.", ) group = parser.add_argument_group("Quantization related") group.add_argument( "--quantize_s2t_model", type=str2bool, default=False, help="Apply dynamic quantization to S2T model.", ) group.add_argument( "--quantize_lm", type=str2bool, default=False, help="Apply dynamic quantization to LM.", ) group.add_argument( "--quantize_modules", type=str, nargs="*", default=["Linear"], help="""List of modules to be dynamically quantized. E.g.: --quantize_modules=[Linear,LSTM,GRU]. Each specified module should be an attribute of 'torch.nn', e.g.: torch.nn.Linear, torch.nn.LSTM, torch.nn.GRU, ...""", ) group.add_argument( "--quantize_dtype", type=str, default="qint8", choices=["float16", "qint8"], help="Dtype for dynamic quantization.", ) group = parser.add_argument_group("Beam-search related") group.add_argument( "--batch_size", type=int, default=1, help="The batch size for inference", ) group.add_argument("--nbest", type=int, default=1, help="Output N-best hypotheses") group.add_argument("--beam_size", type=int, default=20, help="Beam size") group.add_argument("--penalty", type=float, default=0.0, help="Insertion penalty") group.add_argument( "--maxlenratio", type=float, default=0.0, help="Input length ratio to obtain max output length. " "If maxlenratio=0.0 (default), it uses a end-detect " "function " "to automatically find maximum hypothesis lengths." "If maxlenratio<0.0, its absolute value is interpreted" "as a constant max output length", ) group.add_argument( "--minlenratio", type=float, default=0.0, help="Input length ratio to obtain min output length", ) group.add_argument( "--ctc_weight", type=float, default=0.0, help="CTC weight in joint decoding", ) group.add_argument("--lm_weight", type=float, default=0.0, help="RNNLM weight") group.add_argument("--ngram_weight", type=float, default=0.0, help="ngram weight") group.add_argument( "--normalize_length", type=str2bool, default=False, help="If true, best hypothesis is selected by length-normalized scores", ) group = parser.add_argument_group("Text converter related") group.add_argument( "--token_type", type=str_or_none, default=None, choices=["char", "bpe", "word", None], help="The token type for S2T model. " "If not given, refers from the training args", ) group.add_argument( "--bpemodel", type=str_or_none, default=None, help="The model path of sentencepiece. " "If not given, refers from the training args", ) group = parser.add_argument_group("Partially AR related") group.add_argument( "--partial_ar", type=str2bool, default=False, help="Flag to use the partially AR decoding", ) group.add_argument( "--threshold_probability", type=float, default=0.99, help="Threshold for probability of the token to be masked", ) group.add_argument( "--max_seq_len", type=int, default=5, help="Maximum sequence length for each hypothesis." + "Will stop beam_search after max_seq_len iteration in partially AR decoding.", ) group.add_argument( "--max_mask_parallel", type=int, default=-1, help="Maximum number of masks to predict in parallel." + "If you got OOM error, try to decrease this value." + "Default to -1, which means always predict all masks simultaneously.", ) return parser
[docs]def main(cmd=None): print(get_commandline_args(), file=sys.stderr) parser = get_parser() args = parser.parse_args(cmd) kwargs = vars(args) kwargs.pop("config", None) inference(**kwargs)
if __name__ == "__main__": main()