From 0271fbe4fd11114c259ec970b1b04938010a16e3 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期二, 16 五月 2023 18:53:33 +0800
Subject: [PATCH] inference
---
funasr/bin/asr_infer.py | 251 ++++++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 250 insertions(+), 1 deletions(-)
diff --git a/funasr/bin/asr_infer.py b/funasr/bin/asr_infer.py
index bcf5718..f8736e9 100644
--- a/funasr/bin/asr_infer.py
+++ b/funasr/bin/asr_infer.py
@@ -32,6 +32,7 @@
from funasr.modules.beam_search.beam_search import Hypothesis
from funasr.modules.beam_search.beam_search_transducer import BeamSearchTransducer
from funasr.modules.beam_search.beam_search_transducer import Hypothesis as HypothesisTransducer
+from funasr.modules.beam_search.beam_search_sa_asr import Hypothesis as HypothesisSAASR
from funasr.modules.scorers.ctc import CTCPrefixScorer
from funasr.modules.scorers.length_bonus import LengthBonus
from funasr.modules.subsampling import TooShortUttError
@@ -58,7 +59,7 @@
from funasr.utils.vad_utils import slice_padding_fbank
from funasr.tasks.vad import VADTask
from funasr.utils.timestamp_tools import time_stamp_sentence, ts_prediction_lfr6_standard
-
+from funasr.tasks.asr import frontend_choices
class Speech2Text:
"""Speech2Text class
@@ -1599,3 +1600,251 @@
return Speech2Text(**kwargs)
+
+class Speech2TextSAASR:
+ """Speech2Text class
+
+ Examples:
+ >>> import soundfile
+ >>> speech2text = Speech2TextSAASR("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,
+ batch_size: int = 1,
+ 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,
+ streaming: bool = False,
+ frontend_conf: dict = None,
+ **kwargs,
+ ):
+ assert check_argument_types()
+
+ # 1. Build ASR model
+ from funasr.modules.beam_search.beam_search_sa_asr import BeamSearch
+ scorers = {}
+ asr_model, asr_train_args = ASRTask.build_model_from_file(
+ asr_train_config, asr_model_file, cmvn_file, device
+ )
+ frontend = None
+ if asr_train_args.frontend is not None and asr_train_args.frontend_conf is not None:
+ if asr_train_args.frontend == 'wav_frontend':
+ frontend = WavFrontend(cmvn_file=cmvn_file, **asr_train_args.frontend_conf)
+ else:
+ frontend_class = frontend_choices.get_class(asr_train_args.frontend)
+ frontend = frontend_class(**asr_train_args.frontend_conf).eval()
+
+ logging.info("asr_model: {}".format(asr_model))
+ logging.info("asr_train_args: {}".format(asr_train_args))
+ asr_model.to(dtype=getattr(torch, dtype)).eval()
+
+ decoder = asr_model.decoder
+
+ ctc = CTCPrefixScorer(ctc=asr_model.ctc, eos=asr_model.eos)
+ token_list = asr_model.token_list
+ scorers.update(
+ decoder=decoder,
+ 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, None, 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",
+ )
+
+ # 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
+ self.beam_search = 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
+
+ @torch.no_grad()
+ def __call__(
+ self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray],
+ profile: Union[torch.Tensor, np.ndarray], profile_lengths: Union[torch.Tensor, np.ndarray]
+ ) -> List[
+ Tuple[
+ Optional[str],
+ Optional[str],
+ List[str],
+ List[int],
+ Union[HypothesisSAASR],
+ ]
+ ]:
+ """Inference
+
+ Args:
+ speech: Input speech data
+ Returns:
+ text, text_id, token, token_int, hyp
+
+ """
+ assert check_argument_types()
+
+ # Input as audio signal
+ if isinstance(speech, np.ndarray):
+ speech = torch.tensor(speech)
+
+ if isinstance(profile, np.ndarray):
+ profile = torch.tensor(profile)
+
+ if self.frontend is not None:
+ feats, feats_len = self.frontend.forward(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
+ asr_enc, _, spk_enc = self.asr_model.encode(**batch)
+ if isinstance(asr_enc, tuple):
+ asr_enc = asr_enc[0]
+ if isinstance(spk_enc, tuple):
+ spk_enc = spk_enc[0]
+ assert len(asr_enc) == 1, len(asr_enc)
+ assert len(spk_enc) == 1, len(spk_enc)
+
+ # c. Passed the encoder result and the beam search
+ nbest_hyps = self.beam_search(
+ asr_enc[0], spk_enc[0], profile[0], maxlenratio=self.maxlenratio, minlenratio=self.minlenratio
+ )
+
+ nbest_hyps = nbest_hyps[: self.nbest]
+
+ results = []
+ for hyp in nbest_hyps:
+ assert isinstance(hyp, (HypothesisSAASR)), 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()
+
+ spk_weigths = torch.stack(hyp.spk_weigths, dim=0)
+
+ token_ori = self.converter.ids2tokens(token_int)
+ text_ori = self.tokenizer.tokens2text(token_ori)
+
+ text_ori_spklist = text_ori.split('$')
+ cur_index = 0
+ spk_choose = []
+ for i in range(len(text_ori_spklist)):
+ text_ori_split = text_ori_spklist[i]
+ n = len(text_ori_split)
+ spk_weights_local = spk_weigths[cur_index: cur_index + n]
+ cur_index = cur_index + n + 1
+ spk_weights_local = spk_weights_local.mean(dim=0)
+ spk_choose_local = spk_weights_local.argmax(-1)
+ spk_choose.append(spk_choose_local.item() + 1)
+
+ # remove blank symbol id, which is assumed to be 0
+ token_int = list(filter(lambda x: x != 0, token_int))
+
+ # Change integer-ids to tokens
+ token = self.converter.ids2tokens(token_int)
+
+ if self.tokenizer is not None:
+ text = self.tokenizer.tokens2text(token)
+ else:
+ text = None
+
+ text_spklist = text.split('$')
+ assert len(spk_choose) == len(text_spklist)
+
+ spk_list = []
+ for i in range(len(text_spklist)):
+ text_split = text_spklist[i]
+ n = len(text_split)
+ spk_list.append(str(spk_choose[i]) * n)
+
+ text_id = '$'.join(spk_list)
+
+ assert len(text) == len(text_id)
+
+ results.append((text, text_id, token, token_int, hyp))
+
+ assert check_return_type(results)
+ return results
--
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