From 8c732716d62a2c2bdf2f302a7904f1811aa4301d Mon Sep 17 00:00:00 2001
From: shixian.shi <shixian.shi@alibaba-inc.com>
Date: 星期四, 11 五月 2023 16:55:50 +0800
Subject: [PATCH] update timestamp for batch inference
---
funasr/bin/asr_inference_paraformer_vad_punc.py | 864 ++++++++++++++++++++++++++-------------------------------
1 files changed, 393 insertions(+), 471 deletions(-)
diff --git a/funasr/bin/asr_inference_paraformer_vad_punc.py b/funasr/bin/asr_inference_paraformer_vad_punc.py
index 96f70ef..edaad37 100644
--- a/funasr/bin/asr_inference_paraformer_vad_punc.py
+++ b/funasr/bin/asr_inference_paraformer_vad_punc.py
@@ -3,6 +3,7 @@
import json
import argparse
import logging
+from re import L
import sys
import time
import os
@@ -43,421 +44,327 @@
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_pl
+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.timestamp_tools import time_stamp_sentence
-
-header_colors = '\033[95m'
-end_colors = '\033[0m'
-
-
-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,
- hotword_list_or_file: str = 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()
-
- 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)),
- )
-
- # 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
-
- # 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_cif_peak = 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:
- 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
-
- if isinstance(self.asr_model, BiCifParaformer):
- timestamp = time_stamp_lfr6_pl(us_alphas[i], us_cif_peak[i], copy.copy(token), begin_time, end_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))
-
- # assert check_return_type(results)
- return results
-
- 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
-
-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
- self.batch_size = batch_size
-
- @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")
-
- # b. Forward Encoder streaming
- t_offset = 0
- step = min(feats_len, 6000)
- segments = [[]] * self.batch_size
- for t_offset in range(0, feats_len, min(step, feats_len - t_offset)):
- if t_offset + step >= feats_len - 1:
- step = feats_len - t_offset
- is_final_send = True
- else:
- is_final_send = False
- batch = {
- "feats": feats[:, t_offset:t_offset + step, :],
- "waveform": speech[:, t_offset * 160:min(speech.shape[-1], (t_offset + step - 1) * 160 + 400)],
- "is_final_send": is_final_send
- }
- # a. To device
- batch = to_device(batch, device=self.device)
- segments_part = self.vad_model(**batch)
- if segments_part:
- for batch_num in range(0, self.batch_size):
- segments[batch_num] += segments_part[batch_num]
-
- return fbanks, segments
+from funasr.utils.vad_utils import slice_padding_fbank
+from funasr.bin.asr_inference_paraformer import Speech2Text
+# class Speech2Text:
+# """Speech2Text class
+#
+# Examples:
+# >>> import soundfile
+# >>> speech2text = Speech2Text("asr_config.yml", "asr.pb")
+# >>> 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,
+# hotword_list_or_file: str = 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()
+#
+# 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)),
+# )
+#
+# # 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
+#
+# # 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:
+# 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))
+# if len(token_int) == 0:
+# continue
+#
+# # 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
+#
+# 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))
+#
+# # assert check_return_type(results)
+# return results
+#
+# 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(
@@ -574,6 +481,8 @@
**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")
@@ -699,73 +608,86 @@
assert len(keys) == _bs, f"{len(keys)} != {_bs}"
vad_results = speech2vadsegment(**batch)
- 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:
- continue
+ _, vadsegments = vad_results[0], vad_results[1][0]
+
+ speech, speech_lengths = batch["speech"], batch["speech_lengths"]
- 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]))]]
+ n = len(vadsegments)
+ data_with_index = [(vadsegments[i], i) for i in range(n)]
+ sorted_data = sorted(data_with_index, key=lambda x: x[0][1] - x[0][0])
+ results_sorted = []
+ for j, beg_idx in enumerate(range(0, n, batch_size)):
+ end_idx = min(n, beg_idx + batch_size)
+ speech_j, speech_lengths_j = slice_padding_fbank(speech, speech_lengths, sorted_data[beg_idx:end_idx])
- key = keys[0]
- result = result_segments[0]
- text, token, token_int = result[0], result[1], result[2]
- time_stamp = None if len(result) < 4 else result[3]
+ batch = {"speech": speech_j, "speech_lengths": speech_lengths_j}
+ batch = to_device(batch, device=device)
+ results = speech2text(**batch)
+
+ if len(results) < 1:
+ results = [["", [], [], [], [], [], []]]
+ results_sorted.extend(results)
+ restored_data = [0] * n
+ for j in range(n):
+ index = sorted_data[j][1]
+ restored_data[index] = results_sorted[j]
+ result = ["", [], [], [], [], [], []]
+ for j in range(n):
+ result[0] += restored_data[j][0]
+ result[1] += restored_data[j][1]
+ result[2] += restored_data[j][2]
+ for t in restored_data[j][4]:
+ t[0] += vadsegments[j][0]
+ t[1] += vadsegments[j][0]
+ result[4] += restored_data[j][4]
+ # result = [result[k]+restored_data[j][k] for k in range(len(result[:-2]))]
+ key = keys[0]
+ # result = result_segments[0]
+ text, token, token_int = result[0], result[1], result[2]
+ 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]
+ 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)
+ 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 = {'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)
+ 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:
- # Write the result to each file
- ibest_writer["token"][key] = " ".join(token)
- ibest_writer["token_int"][key] = " ".join(map(str, token_int))
- ibest_writer["vad"][key] = "{}".format(vadsegments)
- ibest_writer["text"][key] = text_postprocessed
- 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)
+ asr_result_list.append(item)
+ finish_count += 1
+ # asr_utils.print_progress(finish_count / file_count)
+ if writer is not None:
+ # Write the result to each file
+ ibest_writer["token"][key] = " ".join(token)
+ 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))
+ logging.info("decoding, utt: {}, predictions: {}".format(key, text_postprocessed_punc))
return asr_result_list
return _forward
--
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