# -*- encoding: utf-8 -*- #!/usr/bin/env python3 # Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. # MIT License (https://opensource.org/licenses/MIT) import argparse import logging import sys import time import copy import os import codecs import tempfile import requests from pathlib import Path from typing import Optional from typing import Sequence from typing import Tuple from typing import Union from typing import Dict from typing import Any from typing import List import numpy as np import torch from packaging.version import parse as V from typeguard import check_argument_types from typeguard import check_return_type from funasr.fileio.datadir_writer import DatadirWriter from funasr.modules.beam_search.beam_search import BeamSearch # from funasr.modules.beam_search.beam_search import BeamSearchPara as BeamSearch 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 from funasr.tasks.asr import ASRTask from funasr.tasks.lm import LMTask from funasr.text.build_tokenizer import build_tokenizer from funasr.text.token_id_converter import TokenIDConverter from funasr.torch_utils.device_funcs import to_device from funasr.torch_utils.set_all_random_seed import set_all_random_seed from funasr.utils import config_argparse from funasr.utils.cli_utils import get_commandline_args from funasr.utils.types import str2bool from funasr.utils.types import str2triple_str from funasr.utils.types import str_or_none from funasr.utils import asr_utils, wav_utils, postprocess_utils from funasr.models.frontend.wav_frontend import WavFrontend, WavFrontendOnline from funasr.models.e2e_asr_paraformer import BiCifParaformer, ContextualParaformer from funasr.models.e2e_asr_contextual_paraformer import NeatContextualParaformer from funasr.export.models.e2e_asr_paraformer import Paraformer as Paraformer_export from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard from funasr.bin.tp_infer import Speech2Timestamp from funasr.bin.vad_infer import Speech2VadSegment from funasr.bin.punc_infer import Text2Punc 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 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, 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 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: from funasr.tasks.asr import frontend_choices 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 from funasr.modules.beam_search.beam_search import BeamSearch 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] = None ) -> List[ Tuple[ Optional[str], List[str], List[int], Union[Hypothesis], ] ]: """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) 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, _ = self.asr_model.encode(**batch) if isinstance(enc, tuple): enc = enc[0] assert len(enc) == 1, len(enc) # c. Passed the encoder result and the beam search nbest_hyps = self.beam_search( x=enc[0], maxlenratio=self.maxlenratio, minlenratio=self.minlenratio ) nbest_hyps = nbest_hyps[: self.nbest] results = [] for hyp in nbest_hyps: assert isinstance(hyp, (Hypothesis)), type(hyp) # remove sos/eos and get results last_pos = -1 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, 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 results.append((text, token, token_int, hyp)) assert check_return_type(results) return results class Speech2TextParaformer: """Speech2Text class Examples: >>> import soundfile >>> speech2text = Speech2TextParaformer("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 = {} from funasr.tasks.asr import ASRTaskParaformer as ASRTask 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: 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 from funasr.modules.beam_search.beam_search import BeamSearchPara as BeamSearch 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 == "data2vec_encoder" or 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) 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) and not isinstance(self.asr_model, NeatContextualParaformer): 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)) # 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 timestamp = [] if isinstance(self.asr_model, BiCifParaformer): _, timestamp = ts_prediction_lfr6_standard(us_alphas[i][:enc_len[i]*3], us_peaks[i][:enc_len[i]*3], copy.copy(token), vad_offset=begin_time) results.append((text, token, token_int, hyp, timestamp, 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('') 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('') 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('') logging.info("Hotword list: {}.".format(hotword_str_list)) else: hotword_list = None return hotword_list class Speech2TextParaformerOnline: """Speech2Text class Examples: >>> import soundfile >>> speech2text = Speech2TextParaformerOnline("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 = {} from funasr.tasks.asr import ASRTaskParaformer as ASRTask 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: frontend = WavFrontendOnline(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 from funasr.modules.beam_search.beam_search import BeamSearchPara as BeamSearch 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 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 == "data2vec_encoder" or asr_train_args.encoder_conf["input_layer"] == "conv2d": self.encoder_downsampling_factor = 4 @torch.no_grad() def __call__( self, cache: dict, speech: Union[torch.Tensor], speech_lengths: Union[torch.Tensor] = None ): """Inference Args: speech: Input speech data Returns: text, token, token_int, hyp """ assert check_argument_types() results = [] cache_en = cache["encoder"] if speech.shape[1] < 16 * 60 and cache_en["is_final"]: if cache_en["start_idx"] == 0: return [] cache_en["tail_chunk"] = True feats = cache_en["feats"] feats_len = torch.tensor([feats.shape[1]]) self.asr_model.frontend = None self.frontend.cache_reset() results = self.infer(feats, feats_len, cache) return results else: if self.frontend is not None: if cache_en["start_idx"] == 0: self.frontend.cache_reset() feats, feats_len = self.frontend.forward(speech, speech_lengths, cache_en["is_final"]) feats = to_device(feats, device=self.device) feats_len = feats_len.int() self.asr_model.frontend = None else: feats = speech feats_len = speech_lengths if feats.shape[1] != 0: results = self.infer(feats, feats_len, cache) return results @torch.no_grad() def infer(self, feats: Union[torch.Tensor], feats_len: Union[torch.Tensor], cache: List = None): batch = {"speech": feats, "speech_lengths": feats_len} batch = to_device(batch, device=self.device) # b. Forward Encoder enc, enc_len = self.asr_model.encode_chunk(feats, feats_len, cache=cache) 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_chunk(enc, cache) pre_acoustic_embeds, pre_token_length= predictor_outs[0], predictor_outs[1] if torch.max(pre_token_length) < 1: return [] decoder_outs = self.asr_model.cal_decoder_with_predictor_chunk(enc, pre_acoustic_embeds, cache) decoder_out = decoder_outs 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) token = " ".join(token) results.append(token) # assert check_return_type(results) return results class Speech2TextUniASR: """Speech2Text class Examples: >>> import soundfile >>> speech2text = Speech2TextUniASR("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, token_num_relax: int = 1, decoding_ind: int = 0, decoding_mode: str = "model1", frontend_conf: dict = None, **kwargs, ): assert check_argument_types() # 1. Build ASR model scorers = {} from funasr.tasks.asr import ASRTaskUniASR as ASRTask 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: frontend = WavFrontend(cmvn_file=cmvn_file, **asr_train_args.frontend_conf) logging.info("asr_train_args: {}".format(asr_train_args)) asr_model.to(dtype=getattr(torch, dtype)).eval() if decoding_mode == "model1": decoder = asr_model.decoder else: decoder = asr_model.decoder2 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( decoder=decoder, 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 from funasr.modules.beam_search.beam_search import BeamSearchScama as BeamSearch 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"Beam_search: {beam_search}") 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 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.token_num_relax = token_num_relax self.decoding_ind = decoding_ind self.decoding_mode = decoding_mode self.frontend = frontend @torch.no_grad() def __call__( self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None ) -> List[ Tuple[ Optional[str], List[str], List[int], Union[Hypothesis], ] ]: """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) 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) feats_raw = feats.clone().to(self.device) 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, ind=self.decoding_ind) if isinstance(enc, tuple): enc = enc[0] assert len(enc) == 1, len(enc) if self.decoding_mode == "model1": predictor_outs = self.asr_model.calc_predictor_mask(enc, enc_len) else: enc, enc_len = self.asr_model.encode2(enc, enc_len, feats_raw, feats_len, ind=self.decoding_ind) predictor_outs = self.asr_model.calc_predictor_mask2(enc, enc_len) scama_mask = predictor_outs[4] pre_token_length = predictor_outs[1] pre_acoustic_embeds = predictor_outs[0] maxlen = pre_token_length.sum().item() + self.token_num_relax minlen = max(0, pre_token_length.sum().item() - self.token_num_relax) # c. Passed the encoder result and the beam search nbest_hyps = self.beam_search( x=enc[0], scama_mask=scama_mask, pre_acoustic_embeds=pre_acoustic_embeds, maxlenratio=self.maxlenratio, minlenratio=self.minlenratio, maxlen=int(maxlen), minlen=int(minlen), ) nbest_hyps = nbest_hyps[: self.nbest] results = [] for hyp in nbest_hyps: assert isinstance(hyp, (Hypothesis)), type(hyp) # remove sos/eos and get results last_pos = -1 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, token_int)) # Change integer-ids to tokens token = self.converter.ids2tokens(token_int) token = list(filter(lambda x: x != "", token)) if self.tokenizer is not None: text = self.tokenizer.tokens2text(token) else: text = None results.append((text, token, token_int, hyp)) assert check_return_type(results) return results class Speech2TextMFCCA: """Speech2Text class Examples: >>> import soundfile >>> speech2text = Speech2TextMFCCA("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, **kwargs, ): assert check_argument_types() # 1. Build ASR model from funasr.tasks.asr import ASRTaskMFCCA as ASRTask scorers = {} asr_model, asr_train_args = ASRTask.build_model_from_file( asr_train_config, asr_model_file, cmvn_file, device ) 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, device ) lm.to(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.__class__ = BatchBeamSearch # 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 @torch.no_grad() def __call__( self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None ) -> List[ Tuple[ Optional[str], List[str], List[int], Union[Hypothesis], ] ]: """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 (speech.dim() == 3): speech = torch.squeeze(speech, 2) # speech = speech.unsqueeze(0).to(getattr(torch, self.dtype)) speech = speech.to(getattr(torch, self.dtype)) # lenghts: (1,) lengths = speech.new_full([1], dtype=torch.long, fill_value=speech.size(1)) batch = {"speech": speech, "speech_lengths": lengths} # a. To device batch = to_device(batch, device=self.device) # b. Forward Encoder enc, _ = self.asr_model.encode(**batch) assert len(enc) == 1, len(enc) # c. Passed the encoder result and the beam search nbest_hyps = self.beam_search( x=enc[0], maxlenratio=self.maxlenratio, minlenratio=self.minlenratio ) nbest_hyps = nbest_hyps[: self.nbest] results = [] for hyp in nbest_hyps: assert isinstance(hyp, (Hypothesis)), type(hyp) # remove sos/eos and get results last_pos = -1 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, 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 results.append((text, token, token_int, hyp)) assert check_return_type(results) return results class Speech2TextTransducer: """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. chunk_size: Number of frames in chunk AFTER subsampling. left_context: Number of frames in left context AFTER subsampling. right_context: Number of frames in right context AFTER subsampling. display_partial_hypotheses: Whether to display partial hypotheses. """ def __init__( self, asr_train_config: Union[Path, str] = None, asr_model_file: Union[Path, str] = None, cmvn_file: Union[Path, str] = None, beam_search_config: Dict[str, Any] = None, lm_train_config: Union[Path, str] = None, lm_file: Union[Path, str] = None, token_type: str = None, bpemodel: str = None, device: str = "cpu", beam_size: int = 5, dtype: str = "float32", lm_weight: float = 1.0, quantize_asr_model: bool = False, quantize_modules: List[str] = None, quantize_dtype: str = "qint8", nbest: int = 1, streaming: bool = False, simu_streaming: bool = False, chunk_size: int = 16, left_context: int = 32, right_context: int = 0, display_partial_hypotheses: bool = False, ) -> None: """Construct a Speech2Text object.""" super().__init__() assert check_argument_types() from funasr.tasks.asr import ASRTransducerTask asr_model, asr_train_args = ASRTransducerTask.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: frontend = WavFrontend(cmvn_file=cmvn_file, **asr_train_args.frontend_conf) 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 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 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.device = device self.dtype = dtype self.nbest = nbest self.converter = converter self.tokenizer = tokenizer self.beam_search = beam_search self.streaming = streaming self.simu_streaming = simu_streaming self.chunk_size = max(chunk_size, 0) self.left_context = left_context self.right_context = max(right_context, 0) if not streaming or chunk_size == 0: self.streaming = False self.asr_model.encoder.dynamic_chunk_training = False if not simu_streaming or chunk_size == 0: self.simu_streaming = False self.asr_model.encoder.dynamic_chunk_training = False self.frontend = frontend self.window_size = self.chunk_size + self.right_context if self.streaming: self._ctx = self.asr_model.encoder.get_encoder_input_size( self.window_size ) self.last_chunk_length = ( self.asr_model.encoder.embed.min_frame_length + self.right_context + 1 ) self.reset_inference_cache() def reset_inference_cache(self) -> None: """Reset Speech2Text parameters.""" self.frontend_cache = None self.asr_model.encoder.reset_streaming_cache( self.left_context, device=self.device ) self.beam_search.reset_inference_cache() self.num_processed_frames = torch.tensor([[0]], device=self.device) @torch.no_grad() def streaming_decode( self, speech: Union[torch.Tensor, np.ndarray], is_final: bool = True, ) -> List[HypothesisTransducer]: """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. """ if isinstance(speech, np.ndarray): speech = torch.tensor(speech) if is_final: if self.streaming and speech.size(0) < self.last_chunk_length: pad = torch.zeros( self.last_chunk_length - speech.size(0), speech.size(1), dtype=speech.dtype ) speech = torch.cat([speech, pad], dim=0) # feats, feats_length = self.apply_frontend(speech, is_final=is_final) feats = speech.unsqueeze(0).to(getattr(torch, self.dtype)) feats_lengths = feats.new_full([1], dtype=torch.long, fill_value=feats.size(1)) if self.asr_model.normalize is not None: feats, feats_lengths = self.asr_model.normalize(feats, feats_lengths) feats = to_device(feats, device=self.device) feats_lengths = to_device(feats_lengths, device=self.device) enc_out = self.asr_model.encoder.chunk_forward( feats, feats_lengths, self.num_processed_frames, chunk_size=self.chunk_size, left_context=self.left_context, right_context=self.right_context, ) nbest_hyps = self.beam_search(enc_out[0], is_final=is_final) self.num_processed_frames += self.chunk_size if is_final: self.reset_inference_cache() return nbest_hyps @torch.no_grad() def simu_streaming_decode(self, speech: Union[torch.Tensor, np.ndarray]) -> List[HypothesisTransducer]: """Speech2Text call. Args: speech: Speech data. (S) Returns: nbest_hypothesis: N-best hypothesis. """ assert check_argument_types() if isinstance(speech, np.ndarray): speech = torch.tensor(speech) feats = speech.unsqueeze(0).to(getattr(torch, self.dtype)) feats_lengths = feats.new_full([1], dtype=torch.long, fill_value=feats.size(1)) if self.asr_model.normalize is not None: feats, feats_lengths = self.asr_model.normalize(feats, feats_lengths) feats = to_device(feats, device=self.device) feats_lengths = to_device(feats_lengths, device=self.device) enc_out = self.asr_model.encoder.simu_chunk_forward(feats, feats_lengths, self.chunk_size, self.left_context, self.right_context) nbest_hyps = self.beam_search(enc_out[0]) return nbest_hyps @torch.no_grad() def __call__(self, speech: Union[torch.Tensor, np.ndarray]) -> List[HypothesisTransducer]: """Speech2Text call. Args: speech: Speech data. (S) Returns: nbest_hypothesis: N-best hypothesis. """ assert check_argument_types() if isinstance(speech, np.ndarray): speech = torch.tensor(speech) feats = speech.unsqueeze(0).to(getattr(torch, self.dtype)) feats_lengths = feats.new_full([1], dtype=torch.long, fill_value=feats.size(1)) feats = to_device(feats, device=self.device) feats_lengths = to_device(feats_lengths, device=self.device) enc_out, _ = self.asr_model.encoder(feats, feats_lengths) nbest_hyps = self.beam_search(enc_out[0]) return nbest_hyps def hypotheses_to_results(self, nbest_hyps: List[HypothesisTransducer]) -> 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)) assert check_return_type(results) return results @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 Speech2TextTransducer(**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.tasks.sa_asr import ASRTask 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 from funasr.modules.beam_search.beam_search_sa_asr import BeamSearch 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