From 7fe37e0352ca6f8b5937bcda7263a26529723715 Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 10 五月 2023 19:17:04 +0800
Subject: [PATCH] Merge pull request #491 from alibaba-damo-academy/main
---
funasr/bin/asr_inference_paraformer_vad_punc.py | 1231 +++++++++++++++++++++++-----------------------------------
1 files changed, 488 insertions(+), 743 deletions(-)
diff --git a/funasr/bin/asr_inference_paraformer_vad_punc.py b/funasr/bin/asr_inference_paraformer_vad_punc.py
index 265e054..09b6a0a 100644
--- a/funasr/bin/asr_inference_paraformer_vad_punc.py
+++ b/funasr/bin/asr_inference_paraformer_vad_punc.py
@@ -1,8 +1,14 @@
#!/usr/bin/env python3
+
+import json
import argparse
import logging
import sys
import time
+import os
+import codecs
+import tempfile
+import requests
from pathlib import Path
from typing import Optional
from typing import Sequence
@@ -12,6 +18,7 @@
from typing import Any
from typing import List
import math
+import copy
import numpy as np
import torch
from typeguard import check_argument_types
@@ -36,568 +43,366 @@
from funasr.utils import asr_utils, wav_utils, postprocess_utils
from funasr.models.frontend.wav_frontend import WavFrontend
from funasr.tasks.vad import VADTask
-from funasr.utils.timestamp_tools import time_stamp_lfr6
-from funasr.tasks.punctuation import PunctuationTask
-from funasr.torch_utils.forward_adaptor import ForwardAdaptor
-from funasr.datasets.preprocessor import CommonPreprocessor
-from funasr.punctuation.text_preprocessor import split_words, split_to_mini_sentence
-
-header_colors = '\033[95m'
-end_colors = '\033[0m'
-
-global_asr_language: str = 'zh-cn'
-global_sample_rate: Union[int, Dict[Any, int]] = {
- 'audio_fs': 16000,
- 'model_fs': 16000
-}
-
-class Speech2Text:
- """Speech2Text class
-
- Examples:
- >>> import soundfile
- >>> speech2text = Speech2Text("asr_config.yml", "asr.pth")
- >>> audio, rate = soundfile.read("speech.wav")
- >>> speech2text(audio)
- [(text, token, token_int, hypothesis object), ...]
-
- """
-
- def __init__(
- self,
- asr_train_config: Union[Path, str] = None,
- asr_model_file: Union[Path, str] = None,
- cmvn_file: Union[Path, str] = None,
- lm_train_config: Union[Path, str] = None,
- lm_file: Union[Path, str] = None,
- token_type: str = None,
- bpemodel: str = None,
- device: str = "cpu",
- maxlenratio: float = 0.0,
- minlenratio: float = 0.0,
- dtype: str = "float32",
- beam_size: int = 20,
- ctc_weight: float = 0.5,
- lm_weight: float = 1.0,
- ngram_weight: float = 0.9,
- penalty: float = 0.0,
- nbest: int = 1,
- frontend_conf: dict = None,
- **kwargs,
- ):
- assert check_argument_types()
-
- # 1. Build ASR model
- scorers = {}
- asr_model, asr_train_args = ASRTask.build_model_from_file(
- asr_train_config, asr_model_file, cmvn_file=cmvn_file, device=device
- )
- frontend = None
- if asr_model.frontend is not None and asr_train_args.frontend_conf is not None:
- frontend = WavFrontend(cmvn_file=cmvn_file, **asr_train_args.frontend_conf)
-
- # logging.info("asr_model: {}".format(asr_model))
- # logging.info("asr_train_args: {}".format(asr_train_args))
- asr_model.to(dtype=getattr(torch, dtype)).eval()
-
- ctc = CTCPrefixScorer(ctc=asr_model.ctc, eos=asr_model.eos)
- token_list = asr_model.token_list
- scorers.update(
- ctc=ctc,
- length_bonus=LengthBonus(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
- )
- scorers["lm"] = lm.lm
-
- # 3. Build ngram model
- # ngram is not supported now
- ngram = None
- scorers["ngram"] = ngram
-
- # 4. Build BeamSearch object
- # transducer is not supported now
- beam_search_transducer = None
-
- weights = dict(
- decoder=1.0 - ctc_weight,
- ctc=ctc_weight,
- lm=lm_weight,
- ngram=ngram_weight,
- length_bonus=penalty,
- )
- beam_search = BeamSearch(
- beam_size=beam_size,
- weights=weights,
- scorers=scorers,
- sos=asr_model.sos,
- eos=asr_model.eos,
- vocab_size=len(token_list),
- token_list=token_list,
- pre_beam_score_key=None if ctc_weight == 1.0 else "full",
- )
-
- beam_search.to(device=device, dtype=getattr(torch, dtype)).eval()
- for scorer in scorers.values():
- if isinstance(scorer, torch.nn.Module):
- scorer.to(device=device, dtype=getattr(torch, dtype)).eval()
-
- logging.info(f"Decoding device={device}, dtype={dtype}")
-
- # 5. [Optional] Build Text converter: e.g. bpe-sym -> Text
- if token_type is None:
- token_type = asr_train_args.token_type
- if bpemodel is None:
- bpemodel = asr_train_args.bpemodel
-
- if token_type is None:
- tokenizer = None
- elif 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)
- logging.info(f"Text tokenizer: {tokenizer}")
-
- self.asr_model = asr_model
- self.asr_train_args = asr_train_args
- self.converter = converter
- self.tokenizer = tokenizer
- is_use_lm = lm_weight != 0.0 and lm_file is not None
- if ctc_weight == 0.0 and not is_use_lm:
- beam_search = None
- self.beam_search = beam_search
- logging.info(f"Beam_search: {self.beam_search}")
- self.beam_search_transducer = beam_search_transducer
- self.maxlenratio = maxlenratio
- self.minlenratio = minlenratio
- self.device = device
- self.dtype = dtype
- self.nbest = nbest
- self.frontend = frontend
- self.encoder_downsampling_factor = 1
- if asr_train_args.encoder_conf["input_layer"] == "conv2d":
- self.encoder_downsampling_factor = 4
-
-
-
- @torch.no_grad()
- def __call__(
- self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None, begin_time: int = 0, end_time: int = None,
- ):
- """Inference
-
- Args:
- speech: Input speech data
- Returns:
- text, token, token_int, hyp
-
- """
- assert check_argument_types()
-
- # Input as audio signal
- if isinstance(speech, np.ndarray):
- speech = torch.tensor(speech)
-
- if self.frontend is not None:
- # feats, feats_len = self.frontend.forward(speech, speech_lengths)
- # fbanks, fbanks_len = self.frontend.forward_fbank(speech, speech_lengths)
- feats, feats_len = self.frontend.forward_lfr_cmvn(speech, speech_lengths)
- feats = to_device(feats, device=self.device)
- feats_len = feats_len.int()
- self.asr_model.frontend = None
- else:
- feats = speech
- feats_len = speech_lengths
- lfr_factor = max(1, (feats.size()[-1]//80)-1)
- batch = {"speech": feats, "speech_lengths": feats_len}
-
- # a. To device
- batch = to_device(batch, device=self.device)
-
- # b. Forward Encoder
- enc, enc_len = self.asr_model.encode(**batch)
- if isinstance(enc, tuple):
- enc = enc[0]
- # assert len(enc) == 1, len(enc)
- enc_len_batch_total = torch.sum(enc_len).item() * self.encoder_downsampling_factor
-
- predictor_outs = self.asr_model.calc_predictor(enc, enc_len)
- pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = predictor_outs[0], predictor_outs[1], predictor_outs[2], predictor_outs[3]
- pre_token_length = pre_token_length.round().long()
- decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, enc_len, pre_acoustic_embeds, pre_token_length)
- decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
-
- results = []
- b, n, d = decoder_out.size()
- for i in range(b):
- x = enc[i, :enc_len[i], :]
- am_scores = decoder_out[i, :pre_token_length[i], :]
- if self.beam_search is not None:
- nbest_hyps = self.beam_search(
- x=x, am_scores=am_scores, maxlenratio=self.maxlenratio, minlenratio=self.minlenratio
- )
-
- nbest_hyps = nbest_hyps[: self.nbest]
- else:
- yseq = am_scores.argmax(dim=-1)
- score = am_scores.max(dim=-1)[0]
- score = torch.sum(score, dim=-1)
- # pad with mask tokens to ensure compatibility with sos/eos tokens
- yseq = torch.tensor(
- [self.asr_model.sos] + yseq.tolist() + [self.asr_model.eos], device=yseq.device
- )
- nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
-
- for hyp in nbest_hyps:
- assert isinstance(hyp, (Hypothesis)), type(hyp)
-
- # remove sos/eos and get results
- last_pos = -1
- if isinstance(hyp.yseq, list):
- token_int = hyp.yseq[1:last_pos]
- else:
- token_int = hyp.yseq[1:last_pos].tolist()
-
- # remove blank symbol id, which is assumed to be 0
- token_int = list(filter(lambda x: x != 0 and x != 2, token_int))
-
- # Change integer-ids to tokens
- token = self.converter.ids2tokens(token_int)
-
- if self.tokenizer is not None:
- text = self.tokenizer.tokens2text(token)
- else:
- text = None
-
- time_stamp = time_stamp_lfr6(alphas[i:i+1,], enc_len[i:i+1,], token, begin_time, end_time)
-
- results.append((text, token, token_int, time_stamp, enc_len_batch_total, lfr_factor))
-
- # assert check_return_type(results)
- return results
-
-class Speech2VadSegment:
- """Speech2VadSegment class
-
- Examples:
- >>> import soundfile
- >>> speech2segment = Speech2VadSegment("vad_config.yml", "vad.pt")
- >>> audio, rate = soundfile.read("speech.wav")
- >>> speech2segment(audio)
- [[10, 230], [245, 450], ...]
-
- """
-
- def __init__(
- self,
- vad_infer_config: Union[Path, str] = None,
- vad_model_file: Union[Path, str] = None,
- vad_cmvn_file: Union[Path, str] = None,
- device: str = "cpu",
- batch_size: int = 1,
- dtype: str = "float32",
- **kwargs,
- ):
- assert check_argument_types()
-
- # 1. Build vad model
- vad_model, vad_infer_args = VADTask.build_model_from_file(
- vad_infer_config, vad_model_file, device
- )
- frontend = None
- if vad_infer_args.frontend is not None:
- frontend = WavFrontend(cmvn_file=vad_cmvn_file, **vad_infer_args.frontend_conf)
-
- # logging.info("vad_model: {}".format(vad_model))
- # logging.info("vad_infer_args: {}".format(vad_infer_args))
- vad_model.to(dtype=getattr(torch, dtype)).eval()
-
- self.vad_model = vad_model
- self.vad_infer_args = vad_infer_args
- self.device = device
- self.dtype = dtype
- self.frontend = frontend
-
- @torch.no_grad()
- def __call__(
- self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None
- ) -> List[List[int]]:
- """Inference
-
- Args:
- speech: Input speech data
- Returns:
- text, token, token_int, hyp
-
- """
- assert check_argument_types()
-
- # Input as audio signal
- if isinstance(speech, np.ndarray):
- speech = torch.tensor(speech)
-
- if self.frontend is not None:
- self.frontend.filter_length_max = math.inf
- fbanks, fbanks_len = self.frontend.forward_fbank(speech, speech_lengths)
- feats, feats_len = self.frontend.forward_lfr_cmvn(fbanks, fbanks_len)
- fbanks = to_device(fbanks, device=self.device)
- feats = to_device(feats, device=self.device)
- feats_len = feats_len.int()
- else:
- raise Exception("Need to extract feats first, please configure frontend configuration")
- batch = {"feats": feats, "feats_lengths": feats_len, "waveform": speech}
-
- # a. To device
- batch = to_device(batch, device=self.device)
-
- # b. Forward Encoder
- segments = self.vad_model(**batch)
-
- return fbanks, segments
-
-
-# def inference(
-# maxlenratio: float,
-# minlenratio: float,
-# batch_size: int,
-# beam_size: int,
-# ngpu: int,
-# ctc_weight: float,
-# lm_weight: float,
-# penalty: float,
-# log_level: Union[int, str],
-# data_path_and_name_and_type,
-# asr_train_config: Optional[str],
-# asr_model_file: Optional[str],
-# cmvn_file: Optional[str] = None,
-# raw_inputs: Union[np.ndarray, torch.Tensor] = None,
-# lm_train_config: Optional[str] = None,
-# lm_file: Optional[str] = None,
-# token_type: Optional[str] = None,
-# key_file: Optional[str] = None,
-# word_lm_train_config: Optional[str] = None,
-# bpemodel: Optional[str] = None,
-# allow_variable_data_keys: bool = False,
-# streaming: bool = False,
-# output_dir: Optional[str] = None,
-# dtype: str = "float32",
-# seed: int = 0,
-# ngram_weight: float = 0.9,
-# nbest: int = 1,
-# num_workers: int = 1,
-# vad_infer_config: Optional[str] = None,
-# vad_model_file: Optional[str] = None,
-# vad_cmvn_file: Optional[str] = None,
-# time_stamp_writer: bool = False,
-# punc_infer_config: Optional[str] = None,
-# punc_model_file: Optional[str] = None,
-# **kwargs,
-# ):
-# assert check_argument_types()
+from funasr.bin.vad_inference import Speech2VadSegment
+from funasr.utils.timestamp_tools import time_stamp_sentence, ts_prediction_lfr6_standard
+from funasr.bin.punctuation_infer import Text2Punc
+from funasr.models.e2e_asr_paraformer import BiCifParaformer, ContextualParaformer
+from funasr.utils.vad_utils import slice_padding_fbank
+from funasr.bin.asr_inference_paraformer import Speech2Text
+# class Speech2Text:
+# """Speech2Text class
#
-# 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")
+# Examples:
+# >>> import soundfile
+# >>> speech2text = Speech2Text("asr_config.yml", "asr.pb")
+# >>> audio, rate = soundfile.read("speech.wav")
+# >>> speech2text(audio)
+# [(text, token, token_int, hypothesis object), ...]
#
-# logging.basicConfig(
-# level=log_level,
-# format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
-# )
+# """
#
-# if ngpu >= 1 and torch.cuda.is_available():
-# device = "cuda"
-# else:
-# device = "cpu"
+# def __init__(
+# self,
+# asr_train_config: Union[Path, str] = None,
+# asr_model_file: Union[Path, str] = None,
+# cmvn_file: Union[Path, str] = None,
+# lm_train_config: Union[Path, str] = None,
+# lm_file: Union[Path, str] = None,
+# token_type: str = None,
+# bpemodel: str = None,
+# device: str = "cpu",
+# maxlenratio: float = 0.0,
+# minlenratio: float = 0.0,
+# dtype: str = "float32",
+# beam_size: int = 20,
+# ctc_weight: float = 0.5,
+# lm_weight: float = 1.0,
+# ngram_weight: float = 0.9,
+# penalty: float = 0.0,
+# nbest: int = 1,
+# frontend_conf: dict = None,
+# hotword_list_or_file: str = None,
+# **kwargs,
+# ):
+# assert check_argument_types()
#
-# # 1. Set random-seed
-# set_all_random_seed(seed)
+# # 1. Build ASR model
+# scorers = {}
+# asr_model, asr_train_args = ASRTask.build_model_from_file(
+# asr_train_config, asr_model_file, cmvn_file=cmvn_file, device=device
+# )
+# frontend = None
+# if asr_model.frontend is not None and asr_train_args.frontend_conf is not None:
+# frontend = WavFrontend(cmvn_file=cmvn_file, **asr_train_args.frontend_conf)
#
-# # 2. Build speech2vadsegment
-# speech2vadsegment_kwargs = dict(
-# vad_infer_config=vad_infer_config,
-# vad_model_file=vad_model_file,
-# vad_cmvn_file=vad_cmvn_file,
-# device=device,
-# dtype=dtype,
-# )
-# # logging.info("speech2vadsegment_kwargs: {}".format(speech2vadsegment_kwargs))
-# speech2vadsegment = Speech2VadSegment(**speech2vadsegment_kwargs)
+# # logging.info("asr_model: {}".format(asr_model))
+# # logging.info("asr_train_args: {}".format(asr_train_args))
+# asr_model.to(dtype=getattr(torch, dtype)).eval()
#
-# # 3. Build speech2text
-# speech2text_kwargs = dict(
-# asr_train_config=asr_train_config,
-# asr_model_file=asr_model_file,
-# cmvn_file=cmvn_file,
-# lm_train_config=lm_train_config,
-# lm_file=lm_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,
-# frontend_conf=frontend_conf,
-# )
-# speech2text = Speech2Text(**speech2text_kwargs)
+# if asr_model.ctc != None:
+# ctc = CTCPrefixScorer(ctc=asr_model.ctc, eos=asr_model.eos)
+# scorers.update(
+# ctc=ctc
+# )
+# token_list = asr_model.token_list
+# scorers.update(
+# length_bonus=LengthBonus(len(token_list)),
+# )
#
-# text2punc = Text2Punc(punc_infer_config, punc_model_file, device=device, dtype=dtype)
+# # 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
+# )
+# scorers["lm"] = lm.lm
#
-# # 3. Build data-iterator
-# loader = ASRTask.build_streaming_iterator(
-# data_path_and_name_and_type,
-# dtype=dtype,
-# batch_size=1,
-# key_file=key_file,
-# num_workers=num_workers,
-# preprocess_fn=VADTask.build_preprocess_fn(speech2vadsegment.vad_infer_args, False),
-# collate_fn=VADTask.build_collate_fn(speech2vadsegment.vad_infer_args, False),
-# allow_variable_data_keys=allow_variable_data_keys,
-# inference=True,
-# )
+# # 3. Build ngram model
+# # ngram is not supported now
+# ngram = None
+# scorers["ngram"] = ngram
#
-# forward_time_total = 0.0
-# length_total = 0.0
-# finish_count = 0
-# file_count = 1
-# # 7 .Start for-loop
-# asr_result_list = []
-# if output_dir is not None:
-# writer = DatadirWriter(output_dir)
-# else:
-# writer = None
+# # 4. Build BeamSearch object
+# # transducer is not supported now
+# beam_search_transducer = None
#
-# 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 for k, v in batch.items() if not k.endswith("_lengths")}
+# weights = dict(
+# decoder=1.0 - ctc_weight,
+# ctc=ctc_weight,
+# lm=lm_weight,
+# ngram=ngram_weight,
+# length_bonus=penalty,
+# )
+# beam_search = BeamSearch(
+# beam_size=beam_size,
+# weights=weights,
+# scorers=scorers,
+# sos=asr_model.sos,
+# eos=asr_model.eos,
+# vocab_size=len(token_list),
+# token_list=token_list,
+# pre_beam_score_key=None if ctc_weight == 1.0 else "full",
+# )
#
-# logging.info("decoding, utt_id: {}".format(keys))
-# # N-best list of (text, token, token_int, hyp_object)
-# time_beg = time.time()
-# vad_results = speech2vadsegment(**batch)
-# time_end = time.time()
-# fbanks, vadsegments = vad_results[0], vad_results[1]
-# for i, segments in enumerate(vadsegments):
-# result_segments = [["", [], [], ]]
-# for j, segment_idx in enumerate(segments):
-# bed_idx, end_idx = int(segment_idx[0]/10), int(segment_idx[1]/10)
-# segment = fbanks[:, bed_idx:end_idx, :].to(device)
-# speech_lengths = torch.Tensor([end_idx-bed_idx]).int().to(device)
-# batch = {"speech": segment, "speech_lengths": speech_lengths, "begin_time": vadsegments[i][j][0], "end_time": vadsegments[i][j][1]}
-# results = speech2text(**batch)
-# if len(results) < 1:
-# hyp = Hypothesis(score=0.0, scores={}, states={}, yseq=[])
-# results = [[" ", ["<space>"], [2], 10, 6]] * nbest
-# time_end = time.time()
-# forward_time = time_end - time_beg
-# lfr_factor = results[0][-1]
-# length = results[0][-2]
-# forward_time_total += forward_time
-# length_total += length
-# logging.info(
-# "decoding, feature length: {}, forward_time: {:.4f}, rtf: {:.4f}".
-# format(length, forward_time, 100 * forward_time / (length*lfr_factor)))
-# result_cur = [results[0][:-2]]
-# if j == 0:
-# result_segments = result_cur
+# beam_search.to(device=device, dtype=getattr(torch, dtype)).eval()
+# for scorer in scorers.values():
+# if isinstance(scorer, torch.nn.Module):
+# scorer.to(device=device, dtype=getattr(torch, dtype)).eval()
+#
+# logging.info(f"Decoding device={device}, dtype={dtype}")
+#
+# # 5. [Optional] Build Text converter: e.g. bpe-sym -> Text
+# if token_type is None:
+# token_type = asr_train_args.token_type
+# if bpemodel is None:
+# bpemodel = asr_train_args.bpemodel
+#
+# if token_type is None:
+# tokenizer = None
+# elif 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)
+# logging.info(f"Text tokenizer: {tokenizer}")
+#
+# self.asr_model = asr_model
+# self.asr_train_args = asr_train_args
+# self.converter = converter
+# self.tokenizer = tokenizer
+#
+# # 6. [Optional] Build hotword list from str, local file or url
+# self.hotword_list = None
+# self.hotword_list = self.generate_hotwords_list(hotword_list_or_file)
+#
+# is_use_lm = lm_weight != 0.0 and lm_file is not None
+# if (ctc_weight == 0.0 or asr_model.ctc == None) and not is_use_lm:
+# beam_search = None
+# self.beam_search = beam_search
+# logging.info(f"Beam_search: {self.beam_search}")
+# self.beam_search_transducer = beam_search_transducer
+# self.maxlenratio = maxlenratio
+# self.minlenratio = minlenratio
+# self.device = device
+# self.dtype = dtype
+# self.nbest = nbest
+# self.frontend = frontend
+# self.encoder_downsampling_factor = 1
+# if asr_train_args.encoder_conf["input_layer"] == "conv2d":
+# self.encoder_downsampling_factor = 4
+#
+# @torch.no_grad()
+# def __call__(
+# self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None,
+# begin_time: int = 0, end_time: int = None,
+# ):
+# """Inference
+#
+# Args:
+# speech: Input speech data
+# Returns:
+# text, token, token_int, hyp
+#
+# """
+# assert check_argument_types()
+#
+# # Input as audio signal
+# if isinstance(speech, np.ndarray):
+# speech = torch.tensor(speech)
+#
+# if self.frontend is not None:
+# feats, feats_len = self.frontend.forward(speech, speech_lengths)
+# # fbanks, fbanks_len = self.frontend.forward_fbank(speech, speech_lengths)
+# # feats, feats_len = self.frontend.forward_lfr_cmvn(speech, speech_lengths)
+# feats = to_device(feats, device=self.device)
+# feats_len = feats_len.int()
+# self.asr_model.frontend = None
+# else:
+# feats = speech
+# feats_len = speech_lengths
+# lfr_factor = max(1, (feats.size()[-1] // 80) - 1)
+# batch = {"speech": feats, "speech_lengths": feats_len}
+#
+# # a. To device
+# batch = to_device(batch, device=self.device)
+#
+# # b. Forward Encoder
+# enc, enc_len = self.asr_model.encode(**batch)
+# if isinstance(enc, tuple):
+# enc = enc[0]
+# # assert len(enc) == 1, len(enc)
+# enc_len_batch_total = torch.sum(enc_len).item() * self.encoder_downsampling_factor
+#
+# predictor_outs = self.asr_model.calc_predictor(enc, enc_len)
+# pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = predictor_outs[0], predictor_outs[1], \
+# predictor_outs[2], predictor_outs[3]
+# pre_token_length = pre_token_length.round().long()
+# if torch.max(pre_token_length) < 1:
+# return []
+#
+# if not isinstance(self.asr_model, ContextualParaformer):
+# if self.hotword_list:
+# logging.warning("Hotword is given but asr model is not a ContextualParaformer.")
+# decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, enc_len, pre_acoustic_embeds, pre_token_length)
+# decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
+# else:
+# decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, enc_len, pre_acoustic_embeds, pre_token_length, hw_list=self.hotword_list)
+# decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
+#
+# if isinstance(self.asr_model, BiCifParaformer):
+# _, _, us_alphas, us_peaks = self.asr_model.calc_predictor_timestamp(enc, enc_len,
+# pre_token_length) # test no bias cif2
+#
+# results = []
+# b, n, d = decoder_out.size()
+# for i in range(b):
+# x = enc[i, :enc_len[i], :]
+# am_scores = decoder_out[i, :pre_token_length[i], :]
+# if self.beam_search is not None:
+# nbest_hyps = self.beam_search(
+# x=x, am_scores=am_scores, maxlenratio=self.maxlenratio, minlenratio=self.minlenratio
+# )
+#
+# nbest_hyps = nbest_hyps[: self.nbest]
+# else:
+# yseq = am_scores.argmax(dim=-1)
+# score = am_scores.max(dim=-1)[0]
+# score = torch.sum(score, dim=-1)
+# # pad with mask tokens to ensure compatibility with sos/eos tokens
+# yseq = torch.tensor(
+# [self.asr_model.sos] + yseq.tolist() + [self.asr_model.eos], device=yseq.device
+# )
+# nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
+#
+# for hyp in nbest_hyps:
+# assert isinstance(hyp, (Hypothesis)), type(hyp)
+#
+# # remove sos/eos and get results
+# last_pos = -1
+# if isinstance(hyp.yseq, list):
+# token_int = hyp.yseq[1:last_pos]
# else:
-# result_segments = [[result_segments[0][i] + result_cur[0][i] for i in range(len(result_cur[0]))]]
+# token_int = hyp.yseq[1:last_pos].tolist()
#
-# key = keys[0]
-# result = result_segments[0]
-# text, token, token_int, time_stamp = result
+# # remove blank symbol id, which is assumed to be 0
+# token_int = list(filter(lambda x: x != 0 and x != 2, token_int))
+# if len(token_int) == 0:
+# continue
#
-# # Create a directory: outdir/{n}best_recog
-# if writer is not None:
-# ibest_writer = writer[f"1best_recog"]
+# # Change integer-ids to tokens
+# token = self.converter.ids2tokens(token_int)
#
-# # Write the result to each file
-# ibest_writer["token"][key] = " ".join(token)
-# ibest_writer["token_int"][key] = " ".join(map(str, token_int))
-#
-# if text is not None:
-# postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp)
-# if len(postprocessed_result) == 3:
-# text_postprocessed, time_stamp_postprocessed, word_lists = postprocessed_result[0], postprocessed_result[1], postprocessed_result[2]
-# text_postprocessed_punc, punc_id_list = text2punc(word_lists, 20)
-# text_postprocessed_punc_time_stamp = "predictions: {} time_stamp: {}".format(text_postprocessed_punc, time_stamp_postprocessed)
+# if self.tokenizer is not None:
+# text = self.tokenizer.tokens2text(token)
# else:
-# text_postprocessed = postprocessed_result
-# time_stamp_postprocessed = None
-# word_lists = None
-# text_postprocessed_punc_time_stamp = None
-# punc_id_list = None
+# text = None
#
-# item = {'key': key, 'value': text_postprocessed_punc_time_stamp, 'text': text_postprocessed, 'time_stamp': time_stamp_postprocessed, 'punc': punc_id_list}
-# asr_result_list.append(item)
-# finish_count += 1
-# # asr_utils.print_progress(finish_count / file_count)
-# if writer is not None:
-# ibest_writer["text"][key] = text_postprocessed
-# if time_stamp_writer and time_stamp_postprocessed is not None:
-# ibest_writer["time_stamp"][key] = " ".join(["-".join(map(str, ts)) for ts in time_stamp_postprocessed])
+# if isinstance(self.asr_model, BiCifParaformer):
+# _, timestamp = ts_prediction_lfr6_standard(us_alphas[i],
+# us_peaks[i],
+# copy.copy(token),
+# vad_offset=begin_time)
+# results.append((text, token, token_int, timestamp, enc_len_batch_total, lfr_factor))
+# else:
+# results.append((text, token, token_int, enc_len_batch_total, lfr_factor))
#
-# logging.info("decoding, utt: {}, predictions: {}, time_stamp: {}".format(key, text_postprocessed_punc, time_stamp_postprocessed))
+# # assert check_return_type(results)
+# return results
#
-# logging.info("decoding, feature length total: {}, forward_time total: {:.4f}, rtf avg: {:.4f}".
-# format(length_total, forward_time_total, 100 * forward_time_total / (length_total*lfr_factor)))
-# return asr_result_list
+# def generate_hotwords_list(self, hotword_list_or_file):
+# # for None
+# if hotword_list_or_file is None:
+# hotword_list = None
+# # for local txt inputs
+# elif os.path.exists(hotword_list_or_file) and hotword_list_or_file.endswith('.txt'):
+# logging.info("Attempting to parse hotwords from local txt...")
+# hotword_list = []
+# hotword_str_list = []
+# with codecs.open(hotword_list_or_file, 'r') as fin:
+# for line in fin.readlines():
+# hw = line.strip()
+# hotword_str_list.append(hw)
+# hotword_list.append(self.converter.tokens2ids([i for i in hw]))
+# hotword_list.append([self.asr_model.sos])
+# hotword_str_list.append('<s>')
+# logging.info("Initialized hotword list from file: {}, hotword list: {}."
+# .format(hotword_list_or_file, hotword_str_list))
+# # for url, download and generate txt
+# elif hotword_list_or_file.startswith('http'):
+# logging.info("Attempting to parse hotwords from url...")
+# work_dir = tempfile.TemporaryDirectory().name
+# if not os.path.exists(work_dir):
+# os.makedirs(work_dir)
+# text_file_path = os.path.join(work_dir, os.path.basename(hotword_list_or_file))
+# local_file = requests.get(hotword_list_or_file)
+# open(text_file_path, "wb").write(local_file.content)
+# hotword_list_or_file = text_file_path
+# hotword_list = []
+# hotword_str_list = []
+# with codecs.open(hotword_list_or_file, 'r') as fin:
+# for line in fin.readlines():
+# hw = line.strip()
+# hotword_str_list.append(hw)
+# hotword_list.append(self.converter.tokens2ids([i for i in hw]))
+# hotword_list.append([self.asr_model.sos])
+# hotword_str_list.append('<s>')
+# logging.info("Initialized hotword list from file: {}, hotword list: {}."
+# .format(hotword_list_or_file, hotword_str_list))
+# # for text str input
+# elif not hotword_list_or_file.endswith('.txt'):
+# logging.info("Attempting to parse hotwords as str...")
+# hotword_list = []
+# hotword_str_list = []
+# for hw in hotword_list_or_file.strip().split():
+# hotword_str_list.append(hw)
+# hotword_list.append(self.converter.tokens2ids([i for i in hw]))
+# hotword_list.append([self.asr_model.sos])
+# hotword_str_list.append('<s>')
+# logging.info("Hotword list: {}.".format(hotword_str_list))
+# else:
+# hotword_list = None
+# return hotword_list
+
def inference(
- maxlenratio: float,
- minlenratio: float,
- batch_size: int,
- beam_size: int,
- ngpu: int,
- ctc_weight: float,
- lm_weight: float,
- penalty: float,
- log_level: Union[int, str],
- data_path_and_name_and_type,
- asr_train_config: Optional[str],
- asr_model_file: Optional[str],
- cmvn_file: Optional[str] = None,
- raw_inputs: Union[np.ndarray, torch.Tensor] = None,
- lm_train_config: Optional[str] = None,
- lm_file: Optional[str] = None,
- token_type: Optional[str] = None,
- key_file: Optional[str] = None,
- word_lm_train_config: Optional[str] = None,
- bpemodel: Optional[str] = None,
- allow_variable_data_keys: bool = False,
- streaming: bool = False,
- output_dir: Optional[str] = None,
- dtype: str = "float32",
- seed: int = 0,
- ngram_weight: float = 0.9,
- nbest: int = 1,
- num_workers: int = 1,
- vad_infer_config: Optional[str] = None,
- vad_model_file: Optional[str] = None,
- vad_cmvn_file: Optional[str] = None,
- time_stamp_writer: bool = False,
- punc_infer_config: Optional[str] = None,
- punc_model_file: Optional[str] = None,
- **kwargs,
+ maxlenratio: float,
+ minlenratio: float,
+ batch_size: int,
+ beam_size: int,
+ ngpu: int,
+ ctc_weight: float,
+ lm_weight: float,
+ penalty: float,
+ log_level: Union[int, str],
+ data_path_and_name_and_type,
+ asr_train_config: Optional[str],
+ asr_model_file: Optional[str],
+ cmvn_file: Optional[str] = None,
+ raw_inputs: Union[np.ndarray, torch.Tensor] = None,
+ lm_train_config: Optional[str] = None,
+ lm_file: Optional[str] = None,
+ token_type: Optional[str] = None,
+ key_file: Optional[str] = None,
+ word_lm_train_config: Optional[str] = None,
+ bpemodel: Optional[str] = None,
+ allow_variable_data_keys: bool = False,
+ streaming: bool = False,
+ output_dir: Optional[str] = None,
+ dtype: str = "float32",
+ seed: int = 0,
+ ngram_weight: float = 0.9,
+ nbest: int = 1,
+ num_workers: int = 1,
+ vad_infer_config: Optional[str] = None,
+ vad_model_file: Optional[str] = None,
+ vad_cmvn_file: Optional[str] = None,
+ time_stamp_writer: bool = False,
+ punc_infer_config: Optional[str] = None,
+ punc_model_file: Optional[str] = None,
+ **kwargs,
):
-
inference_pipeline = inference_modelscope(
maxlenratio=maxlenratio,
minlenratio=minlenratio,
@@ -636,61 +441,71 @@
)
return inference_pipeline(data_path_and_name_and_type, raw_inputs)
+
def inference_modelscope(
- maxlenratio: float,
- minlenratio: float,
- batch_size: int,
- beam_size: int,
- ngpu: int,
- ctc_weight: float,
- lm_weight: float,
- penalty: float,
- log_level: Union[int, str],
- # data_path_and_name_and_type,
- asr_train_config: Optional[str],
- asr_model_file: Optional[str],
- cmvn_file: Optional[str] = None,
- lm_train_config: Optional[str] = None,
- lm_file: Optional[str] = None,
- token_type: Optional[str] = None,
- key_file: Optional[str] = None,
- word_lm_train_config: Optional[str] = None,
- bpemodel: Optional[str] = None,
- allow_variable_data_keys: bool = False,
- output_dir: Optional[str] = None,
- dtype: str = "float32",
- seed: int = 0,
- ngram_weight: float = 0.9,
- nbest: int = 1,
- num_workers: int = 1,
- vad_infer_config: Optional[str] = None,
- vad_model_file: Optional[str] = None,
- vad_cmvn_file: Optional[str] = None,
- time_stamp_writer: bool = False,
- punc_infer_config: Optional[str] = None,
- punc_model_file: Optional[str] = None,
- **kwargs,
+ maxlenratio: float,
+ minlenratio: float,
+ batch_size: int,
+ beam_size: int,
+ ngpu: int,
+ ctc_weight: float,
+ lm_weight: float,
+ penalty: float,
+ log_level: Union[int, str],
+ # data_path_and_name_and_type,
+ asr_train_config: Optional[str],
+ asr_model_file: Optional[str],
+ cmvn_file: Optional[str] = None,
+ lm_train_config: Optional[str] = None,
+ lm_file: Optional[str] = None,
+ token_type: Optional[str] = None,
+ key_file: Optional[str] = None,
+ word_lm_train_config: Optional[str] = None,
+ bpemodel: Optional[str] = None,
+ allow_variable_data_keys: bool = False,
+ output_dir: Optional[str] = None,
+ dtype: str = "float32",
+ seed: int = 0,
+ ngram_weight: float = 0.9,
+ nbest: int = 1,
+ num_workers: int = 1,
+ vad_infer_config: Optional[str] = None,
+ vad_model_file: Optional[str] = None,
+ vad_cmvn_file: Optional[str] = None,
+ time_stamp_writer: bool = True,
+ punc_infer_config: Optional[str] = None,
+ punc_model_file: Optional[str] = None,
+ outputs_dict: Optional[bool] = True,
+ param_dict: dict = None,
+ **kwargs,
):
assert check_argument_types()
-
+ ncpu = kwargs.get("ncpu", 1)
+ torch.set_num_threads(ncpu)
+
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 param_dict is not None:
+ hotword_list_or_file = param_dict.get('hotword')
+ else:
+ hotword_list_or_file = None
+
if ngpu >= 1 and torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
-
+
# 1. Set random-seed
set_all_random_seed(seed)
-
+
# 2. Build speech2vadsegment
speech2vadsegment_kwargs = dict(
vad_infer_config=vad_infer_config,
@@ -701,7 +516,7 @@
)
# logging.info("speech2vadsegment_kwargs: {}".format(speech2vadsegment_kwargs))
speech2vadsegment = Speech2VadSegment(**speech2vadsegment_kwargs)
-
+
# 3. Build speech2text
speech2text_kwargs = dict(
asr_train_config=asr_train_config,
@@ -721,15 +536,36 @@
ngram_weight=ngram_weight,
penalty=penalty,
nbest=nbest,
+ hotword_list_or_file=hotword_list_or_file,
)
speech2text = Speech2Text(**speech2text_kwargs)
-
- text2punc = Text2Punc(punc_infer_config, punc_model_file, device=device, dtype=dtype)
-
+ text2punc = None
+ if punc_model_file is not None:
+ text2punc = Text2Punc(punc_infer_config, punc_model_file, device=device, dtype=dtype)
+
+ if output_dir is not None:
+ writer = DatadirWriter(output_dir)
+ ibest_writer = writer[f"1best_recog"]
+ ibest_writer["token_list"][""] = " ".join(speech2text.asr_train_args.token_list)
+
def _forward(data_path_and_name_and_type,
raw_inputs: Union[np.ndarray, torch.Tensor] = None,
output_dir_v2: Optional[str] = None,
+ fs: dict = None,
+ param_dict: dict = None,
+ **kwargs,
):
+
+ hotword_list_or_file = None
+ if param_dict is not None:
+ hotword_list_or_file = param_dict.get('hotword')
+
+ if 'hotword' in kwargs:
+ hotword_list_or_file = kwargs['hotword']
+
+ if speech2text.hotword_list is None:
+ speech2text.hotword_list = speech2text.generate_hotwords_list(hotword_list_or_file)
+
# 3. Build data-iterator
if data_path_and_name_and_type is None and raw_inputs is not None:
if isinstance(raw_inputs, torch.Tensor):
@@ -738,6 +574,7 @@
loader = ASRTask.build_streaming_iterator(
data_path_and_name_and_type,
dtype=dtype,
+ fs=fs,
batch_size=1,
key_file=key_file,
num_workers=num_workers,
@@ -746,195 +583,103 @@
allow_variable_data_keys=allow_variable_data_keys,
inference=True,
)
-
- forward_time_total = 0.0
- length_total = 0.0
+
+ if param_dict is not None:
+ use_timestamp = param_dict.get('use_timestamp', True)
+ else:
+ use_timestamp = True
+
finish_count = 0
file_count = 1
+ lfr_factor = 6
# 7 .Start for-loop
asr_result_list = []
output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
+ writer = None
if output_path is not None:
writer = DatadirWriter(output_path)
- else:
- writer = None
-
+ ibest_writer = writer[f"1best_recog"]
+
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 for k, v in batch.items() if not k.endswith("_lengths")}
-
- logging.info("decoding, utt_id: {}".format(keys))
- # N-best list of (text, token, token_int, hyp_object)
- time_beg = time.time()
+
vad_results = speech2vadsegment(**batch)
- time_end = time.time()
- fbanks, vadsegments = vad_results[0], vad_results[1]
+ _, vadsegments = vad_results[0], vad_results[1]
+ speech, speech_lengths = batch["speech"], batch["speech_lengths"]
for i, segments in enumerate(vadsegments):
- result_segments = [["", [], [], ]]
- for j, segment_idx in enumerate(segments):
- bed_idx, end_idx = int(segment_idx[0] / 10), int(segment_idx[1] / 10)
- segment = fbanks[:, bed_idx:end_idx, :].to(device)
- speech_lengths = torch.Tensor([end_idx - bed_idx]).int().to(device)
- batch = {"speech": segment, "speech_lengths": speech_lengths, "begin_time": vadsegments[i][j][0],
- "end_time": vadsegments[i][j][1]}
+ result_segments = [["", [], [], []]]
+ # for j, segment_idx in enumerate(segments):
+ for j, beg_idx in enumerate(range(0, len(segments), batch_size)):
+ end_idx = min(len(segments), beg_idx + batch_size)
+ speech_j, speech_lengths_j = slice_padding_fbank(speech, speech_lengths, segments[beg_idx:end_idx])
+
+ batch = {"speech": speech_j, "speech_lengths": speech_lengths_j}
+ batch = to_device(batch, device=device)
results = speech2text(**batch)
if len(results) < 1:
- hyp = Hypothesis(score=0.0, scores={}, states={}, yseq=[])
- results = [[" ", ["<space>"], [2], 10, 6]] * nbest
- time_end = time.time()
- forward_time = time_end - time_beg
- lfr_factor = results[0][-1]
- length = results[0][-2]
- forward_time_total += forward_time
- length_total += length
- logging.info(
- "decoding, feature length: {}, forward_time: {:.4f}, rtf: {:.4f}".
- format(length, forward_time, 100 * forward_time / (length * lfr_factor)))
+ continue
+
result_cur = [results[0][:-2]]
if j == 0:
result_segments = result_cur
else:
- result_segments = [[result_segments[0][i] + result_cur[0][i] for i in range(len(result_cur[0]))]]
-
+ result_segments = [
+ [result_segments[0][i] + result_cur[0][i] for i in range(len(result_cur[0]))]]
+
key = keys[0]
result = result_segments[0]
- text, token, token_int, time_stamp = result
-
- # Create a directory: outdir/{n}best_recog
+ text, token, token_int, hyp = result[0], result[1], result[2], result[3]
+ time_stamp = None if len(result) < 5 else result[4]
+
+
+ if use_timestamp and time_stamp is not None:
+ postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp)
+ else:
+ postprocessed_result = postprocess_utils.sentence_postprocess(token)
+ text_postprocessed = ""
+ time_stamp_postprocessed = ""
+ text_postprocessed_punc = postprocessed_result
+ if len(postprocessed_result) == 3:
+ text_postprocessed, time_stamp_postprocessed, word_lists = postprocessed_result[0], \
+ postprocessed_result[1], \
+ postprocessed_result[2]
+ else:
+ text_postprocessed, word_lists = postprocessed_result[0], postprocessed_result[1]
+
+ text_postprocessed_punc = text_postprocessed
+ punc_id_list = []
+ if len(word_lists) > 0 and text2punc is not None:
+ text_postprocessed_punc, punc_id_list = text2punc(word_lists, 20)
+
+ item = {'key': key, 'value': text_postprocessed_punc}
+ if text_postprocessed != "":
+ item['text_postprocessed'] = text_postprocessed
+ if time_stamp_postprocessed != "":
+ item['time_stamp'] = time_stamp_postprocessed
+
+ item['sentences'] = time_stamp_sentence(punc_id_list, time_stamp_postprocessed, text_postprocessed)
+
+ asr_result_list.append(item)
+ finish_count += 1
+ # asr_utils.print_progress(finish_count / file_count)
if writer is not None:
- ibest_writer = writer[f"1best_recog"]
-
# Write the result to each file
ibest_writer["token"][key] = " ".join(token)
- # ibest_writer["token_int"][key] = " ".join(map(str, token_int))
-
- if text is not None:
- postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp)
- if len(postprocessed_result) == 3:
- text_postprocessed, time_stamp_postprocessed, word_lists = postprocessed_result[0], \
- postprocessed_result[1], \
- postprocessed_result[2]
- text_postprocessed_punc, punc_id_list = text2punc(word_lists, 20)
- text_postprocessed_punc_time_stamp = "predictions: {} time_stamp: {}".format(
- text_postprocessed_punc, time_stamp_postprocessed)
- else:
- text_postprocessed = postprocessed_result
- time_stamp_postprocessed = None
- word_lists = None
- text_postprocessed_punc_time_stamp = None
- punc_id_list = None
-
- item = {'key': key, 'value': text_postprocessed_punc_time_stamp, 'text': text_postprocessed,
- 'time_stamp': time_stamp_postprocessed, 'punc': punc_id_list}
- asr_result_list.append(item)
- finish_count += 1
- # asr_utils.print_progress(finish_count / file_count)
- if writer is not None:
- ibest_writer["text"][key] = text_postprocessed
- if time_stamp_writer and time_stamp_postprocessed is not None:
- ibest_writer["time_stamp"][key] = " ".join(
- ["-".join(map(str, ts)) for ts in time_stamp_postprocessed])
-
- logging.info("decoding, utt: {}, predictions: {}, time_stamp: {}".format(key, text_postprocessed_punc,
- time_stamp_postprocessed))
-
- logging.info("decoding, feature length total: {}, forward_time total: {:.4f}, rtf avg: {:.4f}".
- format(length_total, forward_time_total, 100 * forward_time_total / (length_total * lfr_factor)))
+ ibest_writer["token_int"][key] = " ".join(map(str, token_int))
+ ibest_writer["vad"][key] = "{}".format(vadsegments)
+ ibest_writer["text"][key] = " ".join(word_lists)
+ ibest_writer["text_with_punc"][key] = text_postprocessed_punc
+ if time_stamp_postprocessed is not None:
+ ibest_writer["time_stamp"][key] = "{}".format(time_stamp_postprocessed)
+
+ logging.info("decoding, utt: {}, predictions: {}".format(key, text_postprocessed_punc))
return asr_result_list
+
return _forward
-def Text2Punc(
- train_config: Optional[str],
- model_file: Optional[str],
- device: str = "cpu",
- dtype: str = "float32",
-):
-
- # 2. Build Model
- model, train_args = PunctuationTask.build_model_from_file(
- train_config, model_file, device)
- # Wrape model to make model.nll() data-parallel
- wrapped_model = ForwardAdaptor(model, "inference")
- wrapped_model.to(dtype=getattr(torch, dtype)).to(device=device).eval()
- # logging.info(f"Model:\n{model}")
- punc_list = train_args.punc_list
- period = 0
- for i in range(len(punc_list)):
- if punc_list[i] == ",":
- punc_list[i] = "锛�"
- elif punc_list[i] == "?":
- punc_list[i] = "锛�"
- elif punc_list[i] == "銆�":
- period = i
-
- preprocessor = CommonPreprocessor(
- train=False,
- token_type="word",
- token_list=train_args.token_list,
- bpemodel=train_args.bpemodel,
- text_cleaner=train_args.cleaner,
- g2p_type=train_args.g2p,
- text_name="text",
- non_linguistic_symbols=train_args.non_linguistic_symbols,
- )
-
- print("start decoding!!!")
-
- def _forward(words, split_size = 20):
- cache_sent = []
- mini_sentences = split_to_mini_sentence(words, split_size)
- new_mini_sentence = ""
- new_mini_sentence_punc = ""
- for mini_sentence_i in range(len(mini_sentences)):
- mini_sentence = mini_sentences[mini_sentence_i]
- mini_sentence = cache_sent + mini_sentence
- data = {"text": " ".join(mini_sentence)}
- batch = preprocessor(data=data, uid="12938712838719")
- batch["text_lengths"] = torch.from_numpy(np.array([len(batch["text"])], dtype='int32'))
- batch["text"] = torch.from_numpy(batch["text"])
- # Extend one dimension to fake a batch dim.
- batch["text"] = torch.unsqueeze(batch["text"], 0)
- batch = to_device(batch, device)
- y, _ = wrapped_model(**batch)
- _, indices = y.view(-1, y.shape[-1]).topk(1, dim=1)
- punctuations = indices
- if indices.size()[0] != 1:
- punctuations = torch.squeeze(indices)
- assert punctuations.size()[0] == len(mini_sentence)
-
- # Search for the last Period/QuestionMark as cache
- if mini_sentence_i < len(mini_sentences) - 1:
- sentenceEnd = -1
- for i in range(len(punctuations) - 2, 1, -1):
- if punc_list[punctuations[i]] == "銆�" or punc_list[punctuations[i]] == "锛�":
- sentenceEnd = i
- break
-
- cache_sent = mini_sentence[sentenceEnd + 1:]
- mini_sentence = mini_sentence[0:sentenceEnd + 1]
- punctuations = punctuations[0:sentenceEnd + 1]
-
- # if len(punctuations) == 0:
- # continue
-
- punctuations_np = punctuations.cpu().numpy()
- new_mini_sentence_punc += "".join([str(x) for x in punctuations_np])
- words_with_punc = []
- for i in range(len(mini_sentence)):
- if i > 0:
- if len(mini_sentence[i][0].encode()) == 1 and len(mini_sentence[i - 1][0].encode()) == 1:
- mini_sentence[i] = " " + mini_sentence[i]
- words_with_punc.append(mini_sentence[i])
- if punc_list[punctuations[i]] != "_":
- words_with_punc.append(punc_list[punctuations[i]])
- new_mini_sentence += "".join(words_with_punc)
-
- return new_mini_sentence, new_mini_sentence_punc
-
- return _forward
def get_parser():
parser = config_argparse.ArgumentParser(
--
Gitblit v1.9.1