funasr1.0

游雁

2023-12-26 bdc7a17c1f3efccb437517e74c780f64923ea647

funasr1.0

 examples/industrial_data_pretraining/punc/infer.sh

New file
@@ -0,0 +1,9 @@

cmd="funasr/bin/inference.py"

python $cmd \
+model="/Users/zhifu/Downloads/modelscope_models/punc_ct-transformer_zh-cn-common-vocab272727-pytorch" \
+input="/Users/zhifu/FunASR/egs_modelscope/punctuation/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/data/punc_example.txt" \
+output_dir="/Users/zhifu/Downloads/ckpt/funasr2/exp2_punc" \
+device="cpu" \
+debug="true"

 funasr/download/download_from_hub.py

@@ -26,12 +26,15 @@
    kwargs["init_param"] = init_param
    if os.path.exists(os.path.join(model_or_path, "tokens.txt")):
        kwargs["tokenizer_conf"]["token_list"] = os.path.join(model_or_path, "tokens.txt")
    if os.path.exists(os.path.join(model_or_path, "tokens.json")):
        kwargs["tokenizer_conf"]["token_list"] = os.path.join(model_or_path, "tokens.json")
    if os.path.exists(os.path.join(model_or_path, "seg_dict")):
        kwargs["tokenizer_conf"]["seg_dict"] = os.path.join(model_or_path, "seg_dict")
    if os.path.exists(os.path.join(model_or_path, "bpe.model")):
        kwargs["tokenizer_conf"]["bpemodel"] = os.path.join(model_or_path, "bpe.model")
    kwargs["model"] = cfg["model"]
    kwargs["frontend_conf"]["cmvn_file"] = os.path.join(model_or_path, "am.mvn")
    if os.path.exists(os.path.join(model_or_path, "am.mvn")):
        kwargs["frontend_conf"]["cmvn_file"] = os.path.join(model_or_path, "am.mvn")
    
    return OmegaConf.to_container(kwargs, resolve=True)


 funasr/models/ct_transformer/model.py

@@ -1,9 +1,16 @@
from typing import Any
from typing import List
from typing import Tuple
from typing import Optional
import numpy as np
import torch.nn.functional as F

from funasr.models.transformer.utils.nets_utils import make_pad_mask
from funasr.train_utils.device_funcs import force_gatherable
from funasr.train_utils.device_funcs import to_device
import torch
import torch.nn as nn
from funasr.models.ct_transformer.utils import split_to_mini_sentence

from funasr.register import tables

@@ -17,7 +24,7 @@
    def __init__(
        self,
        encoder: str = None,
        encoder_conf: str = None,
        encoder_conf: dict = None,
        vocab_size: int = -1,
        punc_list: list = None,
        punc_weight: list = None,
@@ -191,7 +198,7 @@
        punc_lengths: torch.Tensor,
        vad_indexes: Optional[torch.Tensor] = None,
        vad_indexes_lengths: Optional[torch.Tensor] = None,
    ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
    ):
        nll, y_lengths = self.nll(text, punc, text_lengths, punc_lengths, vad_indexes=vad_indexes)
        ntokens = y_lengths.sum()
        loss = nll.sum() / ntokens
@@ -202,11 +209,115 @@
        return loss, stats, weight
    
    def generate(self,
                  text: torch.Tensor,
                  text_lengths: torch.Tensor,
                  vad_indexes: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, None]:
        if self.with_vad():
            assert vad_indexes is not None
            return self.punc_forward(text, text_lengths, vad_indexes)
        else:
            return self.punc_forward(text, text_lengths)
                 data_in,
                 data_lengths=None,
                 key: list = None,
                 tokenizer=None,
                 frontend=None,
                 **kwargs,
                 ):
        vad_indexes = kwargs.get("vad_indexes", None)
        text = data_in
        text_lengths = data_lengths
        split_size = kwargs.get("split_size", 20)
        
        data = {"text": text}
        result = self.preprocessor(data=data, uid="12938712838719")
        split_text = self.preprocessor.pop_split_text_data(result)
        mini_sentences = split_to_mini_sentence(split_text, split_size)
        mini_sentences_id = split_to_mini_sentence(data["text"], split_size)
        assert len(mini_sentences) == len(mini_sentences_id)
        cache_sent = []
        cache_sent_id = torch.from_numpy(np.array([], dtype='int32'))
        new_mini_sentence = ""
        new_mini_sentence_punc = []
        cache_pop_trigger_limit = 200
        for mini_sentence_i in range(len(mini_sentences)):
            mini_sentence = mini_sentences[mini_sentence_i]
            mini_sentence_id = mini_sentences_id[mini_sentence_i]
            mini_sentence = cache_sent + mini_sentence
            mini_sentence_id = np.concatenate((cache_sent_id, mini_sentence_id), axis=0)
            data = {
                "text": torch.unsqueeze(torch.from_numpy(mini_sentence_id), 0),
                "text_lengths": torch.from_numpy(np.array([len(mini_sentence_id)], dtype='int32')),
            }
            data = to_device(data, self.device)
            # y, _ = self.wrapped_model(**data)
            y, _ = self.punc_forward(text, text_lengths)
            _, indices = y.view(-1, y.shape[-1]).topk(1, dim=1)
            punctuations = indices
            if indices.size()[0] != 1:
                punctuations = torch.squeeze(indices)
            assert punctuations.size()[0] == len(mini_sentence)

            # Search for the last Period/QuestionMark as cache
            if mini_sentence_i < len(mini_sentences) - 1:
                sentenceEnd = -1
                last_comma_index = -1
                for i in range(len(punctuations) - 2, 1, -1):
                    if self.punc_list[punctuations[i]] == "。" or self.punc_list[punctuations[i]] == "？":
                        sentenceEnd = i
                        break
                    if last_comma_index < 0 and self.punc_list[punctuations[i]] == "，":
                        last_comma_index = i

                if sentenceEnd < 0 and len(mini_sentence) > cache_pop_trigger_limit and last_comma_index >= 0:
                    # The sentence it too long, cut off at a comma.
                    sentenceEnd = last_comma_index
                    punctuations[sentenceEnd] = self.period
                cache_sent = mini_sentence[sentenceEnd + 1:]
                cache_sent_id = mini_sentence_id[sentenceEnd + 1:]
                mini_sentence = mini_sentence[0:sentenceEnd + 1]
                punctuations = punctuations[0:sentenceEnd + 1]

            # if len(punctuations) == 0:
            #    continue

            punctuations_np = punctuations.cpu().numpy()
            new_mini_sentence_punc += [int(x) for x in punctuations_np]
            words_with_punc = []
            for i in range(len(mini_sentence)):
                if (i==0 or self.punc_list[punctuations[i-1]] == "。" or self.punc_list[punctuations[i-1]] == "？") and len(mini_sentence[i][0].encode()) == 1:
                    mini_sentence[i] = mini_sentence[i].capitalize()
                if i == 0:
                    if len(mini_sentence[i][0].encode()) == 1:
                        mini_sentence[i] = " " + mini_sentence[i]
                if i > 0:
                    if len(mini_sentence[i][0].encode()) == 1 and len(mini_sentence[i - 1][0].encode()) == 1:
                        mini_sentence[i] = " " + mini_sentence[i]
                words_with_punc.append(mini_sentence[i])
                if self.punc_list[punctuations[i]] != "_":
                    punc_res = self.punc_list[punctuations[i]]
                    if len(mini_sentence[i][0].encode()) == 1:
                        if punc_res == "，":
                            punc_res = ","
                        elif punc_res == "。":
                            punc_res = "."
                        elif punc_res == "？":
                            punc_res = "?"
                    words_with_punc.append(punc_res)
            new_mini_sentence += "".join(words_with_punc)
            # Add Period for the end of the sentence
            new_mini_sentence_out = new_mini_sentence
            new_mini_sentence_punc_out = new_mini_sentence_punc
            if mini_sentence_i == len(mini_sentences) - 1:
                if new_mini_sentence[-1] == "，" or new_mini_sentence[-1] == "、":
                    new_mini_sentence_out = new_mini_sentence[:-1] + "。"
                    new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + [self.period]
                elif new_mini_sentence[-1] == ",":
                    new_mini_sentence_out = new_mini_sentence[:-1] + "."
                    new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + [self.period]
                elif new_mini_sentence[-1] != "。" and new_mini_sentence[-1] != "？" and len(new_mini_sentence[-1].encode())==0:
                    new_mini_sentence_out = new_mini_sentence + "。"
                    new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + [self.period]
                elif new_mini_sentence[-1] != "." and new_mini_sentence[-1] != "?" and len(new_mini_sentence[-1].encode())==1:
                    new_mini_sentence_out = new_mini_sentence + "."
                    new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + [self.period]
        
        return new_mini_sentence_out, new_mini_sentence_punc_out
        
        # if self.with_vad():
        #     assert vad_indexes is not None
        #     return self.punc_forward(text, text_lengths, vad_indexes)
        # else:
        #     return self.punc_forward(text, text_lengths)

 funasr/models/ct_transformer/utils.py

New file
@@ -0,0 +1,14 @@


def split_to_mini_sentence(words: list, word_limit: int = 20):
    assert word_limit > 1
    if len(words) <= word_limit:
        return [words]
    sentences = []
    length = len(words)
    sentence_len = length // word_limit
    for i in range(sentence_len):
        sentences.append(words[i * word_limit:(i + 1) * word_limit])
    if length % word_limit > 0:
        sentences.append(words[sentence_len * word_limit:])
    return sentences