Source code for espnet2.bin.asr_transducer_inference

#!/usr/bin/env python3

""" Inference class definition for Transducer models."""

from __future__ import annotations

import argparse
import logging
import sys
from pathlib import Path
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union

import numpy as np
import torch
from packaging.version import parse as V
from typeguard import typechecked

from espnet2.asr_transducer.beam_search_transducer import (
    BeamSearchTransducer,
    Hypothesis,
)
from espnet2.asr_transducer.frontend.online_audio_processor import OnlineAudioProcessor
from espnet2.asr_transducer.utils import TooShortUttError
from espnet2.fileio.datadir_writer import DatadirWriter
from espnet2.tasks.asr_transducer import ASRTransducerTask
from espnet2.tasks.lm import LMTask
from espnet2.text.build_tokenizer import build_tokenizer
from espnet2.text.token_id_converter import TokenIDConverter
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.utils.cli_utils import get_commandline_args


[docs]class Speech2Text: """Speech2Text class for Transducer models. Args: asr_train_config: ASR model training config path. asr_model_file: ASR model path. beam_search_config: Beam search config path. lm_train_config: Language Model training config path. lm_file: Language Model config path. token_type: Type of token units. bpemodel: BPE model path. device: Device to use for inference. beam_size: Size of beam during search. dtype: Data type. lm_weight: Language model weight. quantize_asr_model: Whether to apply dynamic quantization to ASR model. quantize_modules: List of module names to apply dynamic quantization on. quantize_dtype: Dynamic quantization data type. nbest: Number of final hypothesis. streaming: Whether to perform chunk-by-chunk inference. decoding_window: Size of the decoding window (in milliseconds). left_context: Number of previous frames the attention module can see in current chunk (used by Conformer and Branchformer block). """ @typechecked def __init__( self, asr_train_config: Union[Path, str, None] = None, asr_model_file: Union[Path, str, None] = None, beam_search_config: Optional[Dict[str, Any]] = None, lm_train_config: Union[Path, str, None] = None, lm_file: Union[Path, str, None] = None, token_type: Optional[str] = None, bpemodel: Optional[str] = None, device: str = "cpu", beam_size: int = 5, dtype: str = "float32", lm_weight: float = 1.0, quantize_asr_model: bool = False, quantize_modules: Optional[List[str]] = None, quantize_dtype: str = "qint8", nbest: int = 1, streaming: bool = False, decoding_window: int = 640, left_context: int = 32, ) -> None: """Construct a Speech2Text object.""" super().__init__() asr_model, asr_train_args = ASRTransducerTask.build_model_from_file( asr_train_config, asr_model_file, device ) if quantize_asr_model: if quantize_modules is not None: if not all([q in ["LSTM", "Linear"] for q in quantize_modules]): raise ValueError( "Only 'Linear' and 'LSTM' modules are currently supported" " by PyTorch and in --quantize_modules" ) q_config = set([getattr(torch.nn, q) for q in quantize_modules]) else: q_config = {torch.nn.Linear} if quantize_dtype == "float16" and (V(torch.__version__) < V("1.5.0")): raise ValueError( "float16 dtype for dynamic quantization is not supported with torch" " version < 1.5.0. Switching to qint8 dtype instead." ) q_dtype = getattr(torch, quantize_dtype) asr_model = torch.quantization.quantize_dynamic( asr_model, q_config, dtype=q_dtype ).eval() else: asr_model.to(dtype=getattr(torch, dtype)).eval() if hasattr(asr_model.decoder, "rescale_every") and ( asr_model.decoder.rescale_every > 0 ): rescale_every = asr_model.decoder.rescale_every with torch.no_grad(): for block_id, block in enumerate(asr_model.decoder.rwkv_blocks): block.att.proj_output.weight.div_( 2 ** int(block_id // rescale_every) ) block.ffn.proj_value.weight.div_( 2 ** int(block_id // rescale_every) ) asr_model.decoder.rescaled_layers = True if lm_train_config is not None: lm, lm_train_args = LMTask.build_model_from_file( lm_train_config, lm_file, device ) lm_scorer = lm.lm else: lm_scorer = None # 4. Build BeamSearch object if beam_search_config is None: beam_search_config = {} beam_search = BeamSearchTransducer( asr_model.decoder, asr_model.joint_network, beam_size, lm=lm_scorer, lm_weight=lm_weight, nbest=nbest, **beam_search_config, ) token_list = asr_model.token_list if token_type is None: token_type = asr_train_args.token_type if bpemodel is None: bpemodel = asr_train_args.bpemodel if token_type == "bpe": 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) converter = TokenIDConverter(token_list=token_list) self.asr_model = asr_model self.asr_train_args = asr_train_args self.device = device self.dtype = dtype self.nbest = nbest self.converter = converter self.tokenizer = tokenizer self.beam_search = beam_search self.streaming = streaming and decoding_window >= 0 self.asr_model.encoder.dynamic_chunk_training = False self.left_context = max(left_context, 0) if streaming: self.audio_processor = OnlineAudioProcessor( asr_model._extract_feats, asr_model.normalize, decoding_window, asr_model.encoder.embed.subsampling_factor, asr_train_args.frontend_conf, device, ) self.reset_streaming_cache()
[docs] def reset_streaming_cache(self) -> None: """Reset Speech2Text parameters.""" self.asr_model.encoder.reset_cache(self.left_context, device=self.device) self.beam_search.reset_cache() self.audio_processor.reset_cache() self.num_processed_frames = torch.tensor([[0]], device=self.device)
[docs] @torch.no_grad() def streaming_decode( self, speech: Union[torch.Tensor, np.ndarray], is_final: bool = False, ) -> List[Hypothesis]: """Speech2Text streaming call. Args: speech: Chunk of speech data. (S) is_final: Whether speech corresponds to the final chunk of data. Returns: nbest_hypothesis: N-best hypothesis. """ nbest_hyps = [] if isinstance(speech, np.ndarray): speech = torch.tensor(speech) speech = speech.to(device=self.device) feats, feats_length = self.audio_processor.compute_features( speech.to(getattr(torch, self.dtype)), is_final ) enc_out = self.asr_model.encoder.chunk_forward( feats, feats_length, self.num_processed_frames, left_context=self.left_context, ) self.num_processed_frames += enc_out.size(1) nbest_hyps = self.beam_search(enc_out[0], is_final=is_final) if is_final: self.reset_streaming_cache() return nbest_hyps
@torch.no_grad() @typechecked def __call__(self, speech: Union[torch.Tensor, np.ndarray]) -> List[Hypothesis]: """Speech2Text call. Args: speech: Speech data. (S) Returns: nbest_hypothesis: N-best hypothesis. """ if isinstance(speech, np.ndarray): speech = torch.tensor(speech) speech = speech.unsqueeze(0).to( dtype=getattr(torch, self.dtype), device=self.device ) lengths = speech.new_full( [1], dtype=torch.long, fill_value=speech.size(1), device=self.device ) feats, feats_length = self.asr_model._extract_feats(speech, lengths) if self.asr_model.normalize is not None: feats, feats_length = self.asr_model.normalize(feats, feats_length) enc_out, _ = self.asr_model.encoder(feats, feats_length) nbest_hyps = self.beam_search(enc_out[0]) return nbest_hyps
[docs] def hypotheses_to_results(self, nbest_hyps: List[Hypothesis]) -> List[Any]: """Build partial or final results from the hypotheses. Args: nbest_hyps: N-best hypothesis. Returns: results: Results containing different representation for the hypothesis. """ results = [] for hyp in nbest_hyps: token_int = list(filter(lambda x: x != 0, hyp.yseq)) token = self.converter.ids2tokens(token_int) if self.tokenizer is not None: text = self.tokenizer.tokens2text(token) else: text = None results.append((text, token, token_int, hyp)) return results
[docs] @staticmethod def from_pretrained( model_tag: Optional[str] = None, **kwargs: Optional[Any], ) -> Speech2Text: """Build Speech2Text instance from the pretrained model. Args: model_tag: Model tag of the pretrained models. Return: : 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, batch_size: int, dtype: str, beam_size: int, ngpu: int, seed: int, lm_weight: float, nbest: int, num_workers: int, log_level: Union[int, str], data_path_and_name_and_type: Sequence[Tuple[str, str, str]], asr_train_config: Optional[str], asr_model_file: Optional[str], beam_search_config: Optional[dict], lm_train_config: Optional[str], lm_file: Optional[str], model_tag: Optional[str], token_type: Optional[str], bpemodel: Optional[str], key_file: Optional[str], allow_variable_data_keys: bool, quantize_asr_model: Optional[bool], quantize_modules: Optional[List[str]], quantize_dtype: Optional[str], streaming: bool, decoding_window: int, left_context: int, display_hypotheses: bool, ) -> None: """Transducer model inference. Args: output_dir: Output directory path. batch_size: Batch decoding size. dtype: Data type. beam_size: Beam size. ngpu: Number of GPUs. seed: Random number generator seed. lm_weight: Weight of language model. nbest: Number of final hypothesis. num_workers: Number of workers. log_level: Level of verbose for logs. data_path_and_name_and_type: asr_train_config: ASR model training config path. asr_model_file: ASR model path. beam_search_config: Beam search config path. lm_train_config: Language Model training config path. lm_file: Language Model path. model_tag: Model tag. token_type: Type of token units. bpemodel: BPE model path. key_file: File key. allow_variable_data_keys: Whether to allow variable data keys. quantize_asr_model: Whether to apply dynamic quantization to ASR model. quantize_modules: List of module names to apply dynamic quantization on. quantize_dtype: Dynamic quantization data type. streaming: Whether to perform chunk-by-chunk inference. decoding_window: Audio length (in milliseconds) to process during decoding. left_context: Number of previous frames the attention module can see in current chunk (used by Conformer and Branchformer block). display_hypotheses: Whether to display (partial and full) hypotheses. """ if batch_size > 1: raise NotImplementedError("batch decoding 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" # 1. Set random-seed set_all_random_seed(seed) # 2. Build speech2text speech2text_kwargs = dict( asr_train_config=asr_train_config, asr_model_file=asr_model_file, beam_search_config=beam_search_config, lm_train_config=lm_train_config, lm_file=lm_file, token_type=token_type, bpemodel=bpemodel, device=device, dtype=dtype, beam_size=beam_size, lm_weight=lm_weight, nbest=nbest, quantize_asr_model=quantize_asr_model, quantize_modules=quantize_modules, quantize_dtype=quantize_dtype, streaming=streaming, decoding_window=decoding_window, left_context=left_context, ) speech2text = Speech2Text.from_pretrained( model_tag=model_tag, **speech2text_kwargs, ) if speech2text.streaming: decoding_samples = speech2text.audio_processor.decoding_samples # 3. Build data-iterator loader = ASRTransducerTask.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=ASRTransducerTask.build_preprocess_fn( speech2text.asr_train_args, False ), collate_fn=ASRTransducerTask.build_collate_fn( speech2text.asr_train_args, False ), allow_variable_data_keys=allow_variable_data_keys, inference=True, ) # 4 .Start for-loop 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")} assert len(batch.keys()) == 1 try: if speech2text.streaming: speech = batch["speech"] decoding_steps = len(speech) // decoding_samples for i in range(0, decoding_steps + 1, 1): _start = i * decoding_samples if i == decoding_steps: final_hyps = speech2text.streaming_decode( speech[i * decoding_samples : len(speech)], is_final=True, ) else: part_hyps = speech2text.streaming_decode( speech[ (i * decoding_samples) : _start + decoding_samples ], is_final=False, ) if display_hypotheses: _result = speech2text.hypotheses_to_results(part_hyps) _length = (i + 1) * decoding_window logging.info( f"Current best hypothesis (0-{_length}ms): " f"{keys}: {_result[0][0]}" ) else: final_hyps = speech2text(**batch) results = speech2text.hypotheses_to_results(final_hyps) if display_hypotheses: logging.info(f"Final best hypothesis: {keys}: {results[0][0]}") except TooShortUttError as e: logging.warning(f"Utterance {keys} {e}") hyp = Hypothesis(score=0.0, yseq=[], dec_state=None) results = [[" ", ["<space>"], [2], hyp]] * nbest key = keys[0] for n, (text, token, token_int, hyp) in zip(range(1, nbest + 1), results): ibest_writer = writer[f"{n}best_recog"] 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
[docs]def get_parser(): """Get Transducer model inference parser.""" parser = config_argparse.ArgumentParser( description="ASR Transducer Decoding", formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) 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("The model configuration related") group.add_argument( "--asr_train_config", type=str, help="ASR training configuration", ) group.add_argument( "--asr_model_file", type=str, help="ASR 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( "--model_tag", type=str, help="Pretrained model tag. If specify this option, *_train_config and " "*_file will be overwritten", ) 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=5, help="Beam size") group.add_argument("--lm_weight", type=float, default=1.0, help="RNNLM weight") group.add_argument( "--beam_search_config", default={}, help="The keyword arguments for transducer beam search.", ) group = parser.add_argument_group("Text converter related") group.add_argument( "--token_type", type=str_or_none, default=None, choices=["char", "bpe", None], help="The token type for ASR 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("Dynamic quantization related") parser.add_argument( "--quantize_asr_model", type=bool, default=False, help="Apply dynamic quantization to ASR model.", ) parser.add_argument( "--quantize_modules", nargs="*", default=None, help="""Module names to apply dynamic quantization on. The module names are provided as a list, where each name is separated by a comma (e.g.: --quantize-config=[Linear,LSTM,GRU]). Each specified name should be an attribute of 'torch.nn', e.g.: torch.nn.Linear, torch.nn.LSTM, torch.nn.GRU, ...""", ) parser.add_argument( "--quantize_dtype", type=str, default="qint8", choices=["float16", "qint8"], help="Dtype for dynamic quantization.", ) group = parser.add_argument_group("Streaming related") parser.add_argument( "--streaming", type=bool, default=False, help="Whether to perform chunk-by-chunk inference.", ) parser.add_argument( "--decoding_window", type=int, default=640, help="Audio length (in milliseconds) to process during decoding.", ) parser.add_argument( "--left_context", type=int, default=32, help="""Number of previous frames (AFTER subsamplingà the attention module can see in current chunk (used by Conformer and Branchformer block).""", ) parser.add_argument( "--display_hypotheses", type=bool, default=False, help="""Whether to display hypotheses during inference. If streaming=True, partial hypotheses will also be shown.""", ) 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()