mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
375 lines
15 KiB
Python
375 lines
15 KiB
Python
#!/usr/bin/env python3
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import argparse
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import logging
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from pathlib import Path
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import sys
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import os
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from typing import Optional
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from typing import Sequence
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from typing import Tuple
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from typing import Union
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from typing import Dict
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from typing import Any
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from typing import List
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import numpy as np
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import torch
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from torch.nn.parallel import data_parallel
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from typeguard import check_argument_types
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from funasr.datasets.preprocessor import CommonPreprocessor
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from funasr.utils.cli_utils import get_commandline_args
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from funasr.fileio.datadir_writer import DatadirWriter
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from funasr.tasks.punctuation import PunctuationTask
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from funasr.torch_utils.device_funcs import to_device
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from funasr.torch_utils.forward_adaptor import ForwardAdaptor
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from funasr.torch_utils.set_all_random_seed import set_all_random_seed
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from funasr.utils import config_argparse
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from funasr.utils.types import float_or_none
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from funasr.utils.types import str2bool
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from funasr.utils.types import str2triple_str
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from funasr.utils.types import str_or_none
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from funasr.punctuation.text_preprocessor import split_words, split_to_mini_sentence
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def inference(
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batch_size: int,
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dtype: str,
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ngpu: int,
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seed: int,
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num_workers: int,
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output_dir: str,
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log_level: Union[int, str],
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train_config: Optional[str],
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model_file: Optional[str],
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key_file: Optional[str] = None,
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data_path_and_name_and_type: Sequence[Tuple[str, str, str]] = None,
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raw_inputs: Union[List[Any], bytes, str] = None,
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**kwargs,
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):
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inference_pipeline = inference_modelscope(
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output_dir=output_dir,
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raw_inputs=raw_inputs,
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batch_size=batch_size,
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dtype=dtype,
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ngpu=ngpu,
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seed=seed,
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num_workers=num_workers,
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log_level=log_level,
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key_file=key_file,
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train_config=train_config,
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model_file=model_file,
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**kwargs,
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)
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return inference_pipeline(data_path_and_name_and_type, raw_inputs)
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def inference_modelscope(
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batch_size: int,
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dtype: str,
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ngpu: int,
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seed: int,
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num_workers: int,
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log_level: Union[int, str],
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key_file: Optional[str],
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train_config: Optional[str],
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model_file: Optional[str],
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output_dir: Optional[str] = None,
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**kwargs,
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):
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assert check_argument_types()
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logging.basicConfig(
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level=log_level,
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format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
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)
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if ngpu >= 1 and torch.cuda.is_available():
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device = "cuda"
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else:
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device = "cpu"
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# 1. Set random-seed
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set_all_random_seed(seed)
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# 2. Build Model
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model, train_args = PunctuationTask.build_model_from_file(
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train_config, model_file, device)
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# Wrape model to make model.nll() data-parallel
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wrapped_model = ForwardAdaptor(model, "inference")
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wrapped_model.to(dtype=getattr(torch, dtype)).to(device=device).eval()
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logging.info(f"Model:\n{model}")
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punc_list = train_args.punc_list
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period = 0
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for i in range(len(punc_list)):
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if punc_list[i] == ",":
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punc_list[i] = "，"
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elif punc_list[i] == "?":
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punc_list[i] = "？"
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elif punc_list[i] == "。":
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period = i
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preprocessor = CommonPreprocessor(
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train=False,
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token_type="word",
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token_list=train_args.token_list,
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bpemodel=train_args.bpemodel,
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text_cleaner=train_args.cleaner,
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g2p_type=train_args.g2p,
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text_name="text",
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non_linguistic_symbols=train_args.non_linguistic_symbols,
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)
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print("start decoding!!!")
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def _forward(
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data_path_and_name_and_type,
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raw_inputs: Union[List[Any], bytes, str] = None,
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output_dir_v2: Optional[str] = None,
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):
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results = []
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split_size = 20
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if raw_inputs != None:
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line = raw_inputs.strip()
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key = "demo"
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if line=="":
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item = {'key': key, 'value': ""}
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results.append(item)
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return results
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cache_sent = []
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words = split_words(line)
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new_mini_sentence = ""
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new_mini_sentence_punc = ""
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cache_pop_trigger_limit = 200
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mini_sentences = split_to_mini_sentence(words, split_size)
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for mini_sentence_i in range(len(mini_sentences)):
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mini_sentence = mini_sentences[mini_sentence_i]
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mini_sentence = cache_sent + mini_sentence
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data = {"text": " ".join(mini_sentence)}
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batch = preprocessor(data=data, uid="12938712838719")
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batch["text_lengths"] = torch.from_numpy(
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np.array([len(batch["text"])], dtype='int32'))
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batch["text"] = torch.from_numpy(batch["text"])
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# Extend one dimension to fake a batch dim.
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batch["text"] = torch.unsqueeze(batch["text"], 0)
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batch = to_device(batch, device)
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y, _ = wrapped_model(**batch)
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_, indices = y.view(-1, y.shape[-1]).topk(1, dim=1)
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punctuations = indices
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if indices.size()[0] != 1:
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punctuations = torch.squeeze(indices)
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assert punctuations.size()[0] == len(mini_sentence)
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# Search for the last Period/QuestionMark as cache
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if mini_sentence_i < len(mini_sentences)-1:
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sentenceEnd = -1
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last_comma_index = -1
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for i in range(len(punctuations)-2,1,-1):
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if punc_list[punctuations[i]] == "。" or punc_list[punctuations[i]] == "？":
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sentenceEnd = i
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break
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if last_comma_index < 0 and punc_list[punctuations[i]] == "，":
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last_comma_index = i
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if sentenceEnd < 0 and len(mini_sentence) > cache_pop_trigger_limit and last_comma_index >= 0:
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# The sentence it too long, cut off at a comma.
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sentenceEnd = last_comma_index
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punctuations[sentenceEnd] = period
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cache_sent = mini_sentence[sentenceEnd+1:]
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mini_sentence = mini_sentence[0:sentenceEnd+1]
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punctuations = punctuations[0:sentenceEnd+1]
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punctuations_np = punctuations.cpu().numpy()
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new_mini_sentence_punc += "".join([str(x) for x in punctuations_np])
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words_with_punc = []
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for i in range(len(mini_sentence)):
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if i>0:
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if len(mini_sentence[i][0].encode()) == 1 and len(mini_sentence[i-1][0].encode()) == 1:
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mini_sentence[i] = " "+ mini_sentence[i]
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words_with_punc.append(mini_sentence[i])
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if punc_list[punctuations[i]] != "_":
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words_with_punc.append(punc_list[punctuations[i]])
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new_mini_sentence += "".join(words_with_punc)
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# Add Period for the end of the sentence
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new_mini_sentence_out = new_mini_sentence
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new_mini_sentence_punc_out = new_mini_sentence_punc
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if mini_sentence_i == len(mini_sentences)-1:
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if new_mini_sentence[-1]=="，" or new_mini_sentence[-1]=="、":
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new_mini_sentence_out = new_mini_sentence[:-1] + "。"
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new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + str(period)
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elif new_mini_sentence[-1]!="。" and new_mini_sentence[-1]!="？":
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new_mini_sentence_out=new_mini_sentence+"。"
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new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + str(period)
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item = {'key': key, 'value': new_mini_sentence_out}
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results.append(item)
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return results
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for inference_text, _, _ in data_path_and_name_and_type:
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with open(inference_text, "r", encoding="utf-8") as fin:
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for line in fin:
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line = line.strip()
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segs = line.split("\t")
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if len(segs) != 2:
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continue
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key = segs[0]
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if len(segs[1]) == 0:
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continue
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cache_sent = []
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words = split_words(segs[1])
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new_mini_sentence = ""
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new_mini_sentence_punc = ""
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cache_pop_trigger_limit = 200
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mini_sentences = split_to_mini_sentence(words, split_size)
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for mini_sentence_i in range(len(mini_sentences)):
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mini_sentence = mini_sentences[mini_sentence_i]
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mini_sentence = cache_sent + mini_sentence
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data = {"text": " ".join(mini_sentence)}
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batch = preprocessor(data=data, uid="12938712838719")
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batch["text_lengths"] = torch.from_numpy(
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np.array([len(batch["text"])], dtype='int32'))
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batch["text"] = torch.from_numpy(batch["text"])
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# Extend one dimension to fake a batch dim.
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batch["text"] = torch.unsqueeze(batch["text"], 0)
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batch = to_device(batch, device)
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y, _ = wrapped_model(**batch)
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_, indices = y.view(-1, y.shape[-1]).topk(1, dim=1)
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punctuations = indices
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if indices.size()[0] != 1:
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punctuations = torch.squeeze(indices)
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assert punctuations.size()[0] == len(mini_sentence)
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# Search for the last Period/QuestionMark as cache
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if mini_sentence_i < len(mini_sentences)-1:
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sentenceEnd = -1
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last_comma_index = -1
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for i in range(len(punctuations)-2,1,-1):
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if punc_list[punctuations[i]] == "。" or punc_list[punctuations[i]] == "？":
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sentenceEnd = i
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break
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if last_comma_index < 0 and punc_list[punctuations[i]] == "，":
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last_comma_index = i
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if sentenceEnd < 0 and len(mini_sentence) > cache_pop_trigger_limit and last_comma_index >= 0:
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# The sentence it too long, cut off at a comma.
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sentenceEnd = last_comma_index
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punctuations[sentenceEnd] = period
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cache_sent = mini_sentence[sentenceEnd+1:]
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mini_sentence = mini_sentence[0:sentenceEnd+1]
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punctuations = punctuations[0:sentenceEnd+1]
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punctuations_np = punctuations.cpu().numpy()
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new_mini_sentence_punc += "".join([str(x) for x in punctuations_np])
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words_with_punc = []
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for i in range(len(mini_sentence)):
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if i>0:
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if len(mini_sentence[i][0].encode()) == 1 and len(mini_sentence[i-1][0].encode()) == 1:
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mini_sentence[i] = " "+ mini_sentence[i]
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words_with_punc.append(mini_sentence[i])
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if punc_list[punctuations[i]] != "_":
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words_with_punc.append(punc_list[punctuations[i]])
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new_mini_sentence += "".join(words_with_punc)
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# Add Period for the end of the sentence
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new_mini_sentence_out = new_mini_sentence
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new_mini_sentence_punc_out = new_mini_sentence_punc
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if mini_sentence_i == len(mini_sentences)-1:
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if new_mini_sentence[-1]=="，" or new_mini_sentence[-1]=="、":
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new_mini_sentence_out = new_mini_sentence[:-1] + "。"
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new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + str(period)
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elif new_mini_sentence[-1]!="。" and new_mini_sentence[-1]!="？":
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new_mini_sentence_out=new_mini_sentence+"。"
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new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + str(period)
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item = {'key': key, 'value': new_mini_sentence_out}
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results.append(item)
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output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
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if output_path != None:
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output_file_name = "infer.out"
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Path(output_path).mkdir(parents=True, exist_ok=True)
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output_file_path = (Path(output_path) / output_file_name).absolute()
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with open(output_file_path, "w", encoding="utf-8") as fout:
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for item_i in results:
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key_out = item_i["key"]
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value_out = item_i["value"]
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fout.write(f"{key_out}\t{value_out}\n")
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return results
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return _forward
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def get_parser():
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parser = config_argparse.ArgumentParser(
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description="Punctuation inference",
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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)
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parser.add_argument(
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"--log_level",
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type=lambda x: x.upper(),
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default="INFO",
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choices=("CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"),
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help="The verbose level of logging",
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)
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parser.add_argument("--output_dir", type=str, required=False)
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parser.add_argument(
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"--ngpu",
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type=int,
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default=0,
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help="The number of gpus. 0 indicates CPU mode",
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)
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parser.add_argument("--seed", type=int, default=0, help="Random seed")
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parser.add_argument(
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"--dtype",
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default="float32",
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choices=["float16", "float32", "float64"],
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help="Data type",
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)
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parser.add_argument(
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"--num_workers",
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type=int,
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default=1,
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help="The number of workers used for DataLoader",
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)
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parser.add_argument(
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"--batch_size",
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type=int,
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default=1,
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help="The batch size for inference",
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)
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group = parser.add_argument_group("Input data related")
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group.add_argument(
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"--data_path_and_name_and_type",
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type=str2triple_str,
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action="append",
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required=False
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)
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group.add_argument(
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"--raw_inputs",
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type=str,
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required=False
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)
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group.add_argument("--key_file", type=str_or_none)
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group = parser.add_argument_group("The model configuration related")
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group.add_argument("--train_config", type=str)
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group.add_argument("--model_file", type=str)
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return parser
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def main(cmd=None):
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print(get_commandline_args(), file=sys.stderr)
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parser = get_parser()
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args = parser.parse_args(cmd)
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kwargs = vars(args)
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# kwargs.pop("config", None)
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inference(**kwargs)
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if __name__ == "__main__":
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main()
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