vad_realtime onnx runnable

九耳

2023-04-07 79007d36f1636eb51e0cead6bc0e6b18ff1f8253

vad_realtime onnx runnable

 funasr/runtime/python/onnxruntime/demo_punc_online.py

New file
@@ -0,0 +1,15 @@
from funasr_onnx import CT_Transformer_VadRealtime

model_dir = "/disk1/mengzhe.cmz/workspace/FunASR/funasr/export/damo/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727"
model = CT_Transformer_VadRealtime(model_dir)

text_in  = "跨境河流是养育沿岸|人民的生命之源长期以来为帮助下游地区防灾减灾中方技术人员|在上游地区极为恶劣的自然条件下克服巨大困难甚至冒着生命危险|向印方提供汛期水文资料处理紧急事件中方重视印方在跨境河流>问题上的关切|愿意进一步完善双方联合工作机制|凡是|中方能做的我们|都会去做而且会做得更好我请印度朋友们放心中国在上游的|任何开发利用都会经过科学|规划和论证兼顾上下游的利益"

vads = text_in.split("|")
rec_result_all="outputs:"
param_dict = {"cache": []}
for vad in vads:
    result = model(vad, param_dict=param_dict)
    rec_result_all += result[0]

print(rec_result_all)

 funasr/runtime/python/onnxruntime/funasr_onnx/__init__.py

@@ -2,4 +2,4 @@
from .paraformer_bin import Paraformer
from .vad_bin import Fsmn_vad
from .punc_bin import CT_Transformer
#from .punc_bin import VadRealtimeCT_Transformer
from .punc_bin import CT_Transformer_VadRealtime

 funasr/runtime/python/onnxruntime/funasr_onnx/punc_bin.py

@@ -117,3 +117,133 @@
        outputs = self.ort_infer([feats, feats_len])
        return outputs


class CT_Transformer_VadRealtime(CT_Transformer):
    def __init__(self, model_dir: Union[str, Path] = None,
                 batch_size: int = 1,
                 device_id: Union[str, int] = "-1",
                 quantize: bool = False,
                 intra_op_num_threads: int = 4
                 ):
        super(CT_Transformer_VadRealtime, self).__init__(model_dir, batch_size, device_id, quantize, intra_op_num_threads)

    def __call__(self, text: str, param_dict: map, split_size=20):
        cache_key = "cache"
        assert cache_key in param_dict
        cache = param_dict[cache_key]
        if cache is not None and len(cache) > 0:
            precache = "".join(cache)
        else:
            precache = ""
            cache = []
        full_text = precache + text
        split_text = code_mix_split_words(full_text)
        split_text_id = self.converter.tokens2ids(split_text)
        mini_sentences = split_to_mini_sentence(split_text, split_size)
        mini_sentences_id = split_to_mini_sentence(split_text_id, split_size)
        new_mini_sentence_punc = []
        assert len(mini_sentences) == len(mini_sentences_id)

        cache_sent = []
        cache_sent_id = np.array([], dtype='int32')
        sentence_punc_list = []
        sentence_words_list = []
        cache_pop_trigger_limit = 200
        skip_num = 0
        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)
            text_length = len(mini_sentence_id)
            data = {
                "input": mini_sentence_id[None,:],
                "text_lengths": np.array([text_length], dtype='int32'),
                "vad_mask": self.vad_mask(text_length, len(cache) - 1)[None, None, :, :].astype(np.float32),
                "sub_masks": np.tril(np.ones((text_length, text_length), dtype=np.float32))[None, None, :, :].astype(np.float32)
            }
            try:
                outputs = self.infer(data['input'], data['text_lengths'], data['vad_mask'], data["sub_masks"])
                y = outputs[0]
                punctuations = np.argmax(y,axis=-1)[0]
                assert punctuations.size == len(mini_sentence)
            except ONNXRuntimeError:
                logging.warning("error")

            # 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]

            punctuations_np = [int(x) for x in punctuations]
            new_mini_sentence_punc += punctuations_np
            sentence_punc_list += [self.punc_list[int(x)] for x in punctuations_np]
            sentence_words_list += mini_sentence

        assert len(sentence_punc_list) == len(sentence_words_list)
        words_with_punc = []
        sentence_punc_list_out = []
        for i in range(0, len(sentence_words_list)):
            if i > 0:
                if len(sentence_words_list[i][0].encode()) == 1 and len(sentence_words_list[i - 1][-1].encode()) == 1:
                    sentence_words_list[i] = " " + sentence_words_list[i]
            if skip_num < len(cache):
                skip_num += 1
            else:
                words_with_punc.append(sentence_words_list[i])
            if skip_num >= len(cache):
                sentence_punc_list_out.append(sentence_punc_list[i])
                if sentence_punc_list[i] != "_":
                    words_with_punc.append(sentence_punc_list[i])
        sentence_out = "".join(words_with_punc)

        sentenceEnd = -1
        for i in range(len(sentence_punc_list) - 2, 1, -1):
            if sentence_punc_list[i] == "。" or sentence_punc_list[i] == "？":
                sentenceEnd = i
                break
        cache_out = sentence_words_list[sentenceEnd + 1:]
        if sentence_out[-1] in self.punc_list:
            sentence_out = sentence_out[:-1]
            sentence_punc_list_out[-1] = "_"
        param_dict[cache_key] = cache_out
        return sentence_out, sentence_punc_list_out, cache_out

    def vad_mask(self, size, vad_pos, dtype=np.bool):
        """Create mask for decoder self-attention.

        :param int size: size of mask
        :param int vad_pos: index of vad index
        :param torch.dtype dtype: result dtype
        :rtype: torch.Tensor (B, Lmax, Lmax)
        """
        ret = np.ones((size, size), dtype=dtype)
        if vad_pos <= 0 or vad_pos >= size:
            return ret
        sub_corner = np.zeros(
            (vad_pos - 1, size - vad_pos), dtype=dtype)
        ret[0:vad_pos - 1, vad_pos:] = sub_corner
        return ret

    def infer(self, feats: np.ndarray,
              feats_len: np.ndarray,
              vad_mask: np.ndarray,
              sub_masks: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
        outputs = self.ort_infer([feats, feats_len, vad_mask, sub_masks])
        return outputs