#!/usr/bin/env python3 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 typeguard import check_argument_types from funasr.fileio.datadir_writer import DatadirWriter from funasr.modules.beam_search.beam_search import BeamSearchPara as BeamSearch from funasr.modules.beam_search.beam_search import Hypothesis 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 ASRTaskParaformer as 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 from funasr.models.e2e_asr_paraformer import BiCifParaformer, ContextualParaformer 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_inference import SpeechText2Timestamp 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, 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 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): 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 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, hyp, timestamp, enc_len_batch_total, lfr_factor)) else: results.append((text, token, token_int, hyp, 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 Speech2TextExport: """Speech2TextExport class """ 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, ): # 1. Build ASR model 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() token_list = asr_model.token_list 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.device = device self.dtype = dtype self.nbest = nbest self.frontend = frontend model = Paraformer_export(asr_model, onnx=False) self.asr_model = model @torch.no_grad() def __call__( self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = 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 enc_len_batch_total = feats_len.sum() 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) decoder_outs = self.asr_model(**batch) decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1] results = [] b, n, d = decoder_out.size() for i in range(b): am_scores = decoder_out[i, :ys_pad_lens[i], :] 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( yseq.tolist(), 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 results.append((text, token, token_int, hyp, enc_len_batch_total, lfr_factor)) return results 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, timestamp_infer_config: Union[Path, str] = None, timestamp_model_file: Union[Path, str] = None, **kwargs, ): inference_pipeline = inference_modelscope( maxlenratio=maxlenratio, minlenratio=minlenratio, batch_size=batch_size, beam_size=beam_size, ngpu=ngpu, ctc_weight=ctc_weight, lm_weight=lm_weight, penalty=penalty, log_level=log_level, asr_train_config=asr_train_config, asr_model_file=asr_model_file, cmvn_file=cmvn_file, raw_inputs=raw_inputs, lm_train_config=lm_train_config, lm_file=lm_file, token_type=token_type, key_file=key_file, word_lm_train_config=word_lm_train_config, bpemodel=bpemodel, allow_variable_data_keys=allow_variable_data_keys, streaming=streaming, output_dir=output_dir, dtype=dtype, seed=seed, ngram_weight=ngram_weight, nbest=nbest, num_workers=num_workers, **kwargs, ) 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, dtype: str = "float32", seed: int = 0, ngram_weight: float = 0.9, nbest: int = 1, num_workers: int = 1, output_dir: Optional[str] = None, timestamp_infer_config: Union[Path, str] = None, timestamp_model_file: Union[Path, str] = None, param_dict: dict = None, **kwargs, ): assert check_argument_types() 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", ) export_mode = False if param_dict is not None: hotword_list_or_file = param_dict.get('hotword') export_mode = param_dict.get("export_mode", False) else: hotword_list_or_file = None if ngpu >= 1 and torch.cuda.is_available(): device = "cuda" else: device = "cpu" batch_size = 1 # 1. Set random-seed set_all_random_seed(seed) # 2. Build speech2text speech2text_kwargs = dict( asr_train_config=asr_train_config, asr_model_file=asr_model_file, 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, hotword_list_or_file=hotword_list_or_file, ) if export_mode: speech2text = Speech2TextExport(**speech2text_kwargs) else: speech2text = Speech2Text(**speech2text_kwargs) if timestamp_model_file is not None: speechtext2timestamp = SpeechText2Timestamp( timestamp_cmvn_file=cmvn_file, timestamp_model_file=timestamp_model_file, timestamp_infer_config=timestamp_infer_config, ) else: speechtext2timestamp = None 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 hotword_list_or_file is not None or 'hotword' in kwargs: 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): raw_inputs = raw_inputs.numpy() data_path_and_name_and_type = [raw_inputs, "speech", "waveform"] loader = ASRTask.build_streaming_iterator( data_path_and_name_and_type, dtype=dtype, fs=fs, batch_size=batch_size, key_file=key_file, num_workers=num_workers, preprocess_fn=ASRTask.build_preprocess_fn(speech2text.asr_train_args, False), collate_fn=ASRTask.build_collate_fn(speech2text.asr_train_args, False), allow_variable_data_keys=allow_variable_data_keys, inference=True, ) if param_dict is not None: use_timestamp = param_dict.get('use_timestamp', True) else: use_timestamp = True forward_time_total = 0.0 length_total = 0.0 finish_count = 0 file_count = 1 # 7 .Start for-loop # FIXME(kamo): The output format should be discussed about asr_result_list = [] output_path = output_dir_v2 if output_dir_v2 is not None else output_dir if output_path is not None: writer = DatadirWriter(output_path) else: writer = 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")} logging.info("decoding, utt_id: {}".format(keys)) # N-best list of (text, token, token_int, hyp_object) time_beg = time.time() results = speech2text(**batch) if len(results) < 1: hyp = Hypothesis(score=0.0, scores={}, states={}, yseq=[]) results = [[" ", ["sil"], [2], hyp, 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 rtf_cur = "decoding, feature length: {}, forward_time: {:.4f}, rtf: {:.4f}".format(length, forward_time, 100 * forward_time / (length * lfr_factor)) logging.info(rtf_cur) for batch_id in range(_bs): result = [results[batch_id][:-2]] key = keys[batch_id] for n, result in zip(range(1, nbest + 1), result): text, token, token_int, hyp = result[0], result[1], result[2], result[3] timestamp = None if len(result) < 5 else result[4] # conduct timestamp prediction here # timestamp inference requires token length # thus following inference cannot be conducted in batch if timestamp is None and speechtext2timestamp: ts_batch = {} ts_batch['speech'] = batch['speech'][batch_id].unsqueeze(0) ts_batch['speech_lengths'] = torch.tensor([batch['speech_lengths'][batch_id]]) ts_batch['text_lengths'] = torch.tensor([len(token)]) us_alphas, us_peaks = speechtext2timestamp(**ts_batch) ts_str, timestamp = ts_prediction_lfr6_standard(us_alphas[0], us_peaks[0], token, force_time_shift=-3.0) # Create a directory: outdir/{n}best_recog if writer is not None: ibest_writer = writer[f"{n}best_recog"] # Write the result to each file ibest_writer["token"][key] = " ".join(token) # ibest_writer["token_int"][key] = " ".join(map(str, token_int)) ibest_writer["score"][key] = str(hyp.score) ibest_writer["rtf"][key] = rtf_cur if text is not None: if use_timestamp and timestamp is not None: postprocessed_result = postprocess_utils.sentence_postprocess(token, timestamp) else: postprocessed_result = postprocess_utils.sentence_postprocess(token) timestamp_postprocessed = "" if len(postprocessed_result) == 3: text_postprocessed, timestamp_postprocessed, word_lists = postprocessed_result[0], \ postprocessed_result[1], \ postprocessed_result[2] else: text_postprocessed, word_lists = postprocessed_result[0], postprocessed_result[1] item = {'key': key, 'value': text_postprocessed} if timestamp_postprocessed != "": item['timestamp'] = timestamp_postprocessed 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] = " ".join(word_lists) logging.info("decoding, utt: {}, predictions: {}".format(key, text)) rtf_avg = "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)) logging.info(rtf_avg) if writer is not None: ibest_writer["rtf"]["rtf_avf"] = rtf_avg return asr_result_list return _forward def get_parser(): parser = config_argparse.ArgumentParser( description="ASR Decoding", formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) # Note(kamo): Use '_' instead of '-' as separator. # '-' is confusing if written in yaml. parser.add_argument( "--log_level", type=lambda x: x.upper(), default="INFO", choices=("CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"), help="The verbose level of logging", ) parser.add_argument("--output_dir", type=str, required=True) parser.add_argument( "--ngpu", type=int, default=0, help="The number of gpus. 0 indicates CPU mode", ) parser.add_argument("--seed", type=int, default=0, help="Random seed") parser.add_argument( "--dtype", default="float32", choices=["float16", "float32", "float64"], help="Data type", ) parser.add_argument( "--num_workers", type=int, default=1, help="The number of workers used for DataLoader", ) parser.add_argument( "--hotword", type=str_or_none, default=None, help="hotword file path or hotwords seperated by space" ) group = parser.add_argument_group("Input data related") group.add_argument( "--data_path_and_name_and_type", type=str2triple_str, required=False, action="append", ) group.add_argument("--key_file", type=str_or_none) group.add_argument("--allow_variable_data_keys", type=str2bool, default=False) group = parser.add_argument_group("The model configuration related") group.add_argument( "--asr_train_config", type=str, help="ASR training configuration", ) group.add_argument( "--asr_model_file", type=str, help="ASR model parameter file", ) group.add_argument( "--cmvn_file", type=str, help="Global cmvn file", ) group.add_argument( "--lm_train_config", type=str, help="LM training configuration", ) group.add_argument( "--lm_file", type=str, help="LM parameter file", ) group.add_argument( "--word_lm_train_config", type=str, help="Word LM training configuration", ) group.add_argument( "--word_lm_file", type=str, help="Word LM parameter file", ) group.add_argument( "--ngram_file", type=str, help="N-gram parameter file", ) group.add_argument( "--model_tag", type=str, help="Pretrained model tag. If specify this option, *_train_config and " "*_file will be overwritten", ) group = parser.add_argument_group("Beam-search related") group.add_argument( "--batch_size", type=int, default=1, help="The batch size for inference", ) group.add_argument("--nbest", type=int, default=1, help="Output N-best hypotheses") group.add_argument("--beam_size", type=int, default=20, help="Beam size") group.add_argument("--penalty", type=float, default=0.0, help="Insertion penalty") group.add_argument( "--maxlenratio", type=float, default=0.0, help="Input length ratio to obtain max output length. " "If maxlenratio=0.0 (default), it uses a end-detect " "function " "to automatically find maximum hypothesis lengths." "If maxlenratio<0.0, its absolute value is interpreted" "as a constant max output length", ) group.add_argument( "--minlenratio", type=float, default=0.0, help="Input length ratio to obtain min output length", ) group.add_argument( "--ctc_weight", type=float, default=0.5, help="CTC weight in joint decoding", ) group.add_argument("--lm_weight", type=float, default=1.0, help="RNNLM weight") group.add_argument("--ngram_weight", type=float, default=0.9, help="ngram weight") group.add_argument("--streaming", type=str2bool, default=False) group.add_argument( "--frontend_conf", default=None, help="", ) group.add_argument("--raw_inputs", type=list, default=None) # example=[{'key':'EdevDEWdIYQ_0021','file':'/mnt/data/jiangyu.xzy/test_data/speech_io/SPEECHIO_ASR_ZH00007_zhibodaihuo/wav/EdevDEWdIYQ_0021.wav'}]) group = parser.add_argument_group("Text converter related") group.add_argument( "--token_type", type=str_or_none, default=None, choices=["char", "bpe", None], help="The token type for ASR model. " "If not given, refers from the training args", ) group.add_argument( "--bpemodel", type=str_or_none, default=None, help="The model path of sentencepiece. " "If not given, refers from the training args", ) return parser def main(cmd=None): print(get_commandline_args(), file=sys.stderr) parser = get_parser() args = parser.parse_args(cmd) param_dict = {'hotword': args.hotword} kwargs = vars(args) kwargs.pop("config", None) kwargs['param_dict'] = param_dict inference(**kwargs) if __name__ == "__main__": main() # from modelscope.pipelines import pipeline # from modelscope.utils.constant import Tasks # # inference_16k_pipline = pipeline( # task=Tasks.auto_speech_recognition, # model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch') # # rec_result = inference_16k_pipline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav') # print(rec_result)