update paraformer streaming code

funasr/bin/asr_inference_paraformer_streaming.py

@@ -19,7 +19,6 @@ from typing import List
 
 import numpy as np
 import torch
-import torchaudio
 from typeguard import check_argument_types
 
 from funasr.fileio.datadir_writer import DatadirWriter
@@ -40,11 +39,12 @@ 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.models.frontend.wav_frontend import WavFrontend, WavFrontendOnline
 from funasr.export.models.e2e_asr_paraformer import Paraformer as Paraformer_export
+
 np.set_printoptions(threshold=np.inf)
 
+
 class Speech2Text:
     """Speech2Text class
 
@@ -89,7 +89,7 @@ class Speech2Text:
         )
         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)
+            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))
@@ -189,8 +189,7 @@ class Speech2Text:
 
     @torch.no_grad()
     def __call__(
-            self, cache: dict, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None,
-            begin_time: int = 0, end_time: int = None,
+            self, cache: dict, speech: Union[torch.Tensor], speech_lengths: Union[torch.Tensor] = None
     ):
         """Inference
 
@@ -201,38 +200,57 @@ class Speech2Text:
 
         """
         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
+        results = []
+        cache_en = cache["encoder"]
+        if speech.shape[1] < 16 * 60 and cache["is_final"]:
+            cache["last_chunk"] = True
+            feats = cache["feats"]
+            feats_len = torch.tensor([feats.shape[1]])
         else:
-            feats = speech
-            feats_len = speech_lengths
-        lfr_factor = max(1, (feats.size()[-1] // 80) - 1)
-        feats_len = cache["encoder"]["stride"] + cache["encoder"]["pad_left"] + cache["encoder"]["pad_right"]
-        feats = feats[:,cache["encoder"]["start_idx"]:cache["encoder"]["start_idx"]+feats_len,:]
-        feats_len = torch.tensor([feats_len])
-        batch = {"speech": feats, "speech_lengths": feats_len, "cache": cache}
-
-        # a. To device
-        batch = to_device(batch, device=self.device)
+            if self.frontend is not None:
+                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:
+                if cache_en["is_final"]:
+                    if feats.shape[1] + cache_en["chunk_size"][2] < cache_en["chunk_size"][1]:
+                        cache_en["last_chunk"] = True
+                    else:
+                        # first chunk
+                        feats_chunk1 = feats[:, :cache_en["chunk_size"][1], :]
+                        feats_len = torch.tensor([feats_chunk1.shape[1]])
+                        results_chunk1 = self.infer(feats_chunk1, feats_len, cache)
+
+                        # last chunk
+                        cache_en["last_chunk"] = True
+                        feats_chunk2 = feats[:, -(feats.shape[1] + cache_en["chunk_size"][2] - cache_en["chunk_size"][1]):, :]
+                        feats_len = torch.tensor([feats_chunk2.shape[1]])
+                        results_chunk2 = self.infer(feats_chunk2, feats_len, cache)
+
+                        return results_chunk1 + results_chunk2
+
+                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)
+        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, alphas, pre_peak_index = predictor_outs[0], predictor_outs[1], \
-                                                                        predictor_outs[2], predictor_outs[3]
-        pre_token_length = pre_token_length.floor().long()
+        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)
@@ -279,166 +297,12 @@ class Speech2Text:
                     text = self.tokenizer.tokens2text(token)
                 else:
                     text = None
-
-                results.append((text, token, token_int, hyp, enc_len_batch_total, lfr_factor))
+                results.append(text)
 
         # assert check_return_type(results)
         return results
 
 
-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,
@@ -536,8 +400,6 @@ def inference_modelscope(
         **kwargs,
 ):
     assert check_argument_types()
-    ncpu = kwargs.get("ncpu", 1)
-    torch.set_num_threads(ncpu)
 
     if word_lm_train_config is not None:
         raise NotImplementedError("Word LM is not implemented")
@@ -580,11 +442,9 @@ def inference_modelscope(
         penalty=penalty,
         nbest=nbest,
     )
-    if export_mode:
-        speech2text = Speech2TextExport(**speech2text_kwargs)
-    else:
-        speech2text = Speech2Text(**speech2text_kwargs)
-        
+
+    speech2text = Speech2Text(**speech2text_kwargs)
+
     def _load_bytes(input):
         middle_data = np.frombuffer(input, dtype=np.int16)
         middle_data = np.asarray(middle_data)
@@ -599,7 +459,33 @@ def inference_modelscope(
         offset = i.min + abs_max
         array = np.frombuffer((middle_data.astype(dtype) - offset) / abs_max, dtype=np.float32)
         return array
-    
+
+    def _prepare_cache(cache: dict = {}, chunk_size=[5,10,5], batch_size=1):
+        if len(cache) > 0:
+            return cache
+
+        cache_en = {"start_idx": 0, "cif_hidden": torch.zeros((batch_size, 1, 320)),
+                    "cif_alphas": torch.zeros((batch_size, 1)), "chunk_size": chunk_size, "last_chunk": False,
+                    "feats": torch.zeros((batch_size, chunk_size[0] + chunk_size[2], 560))}
+        cache["encoder"] = cache_en
+
+        cache_de = {"decode_fsmn": None}
+        cache["decoder"] = cache_de
+
+        return cache
+
+    def _cache_reset(cache: dict = {}, chunk_size=[5,10,5], batch_size=1):
+        if len(cache) > 0:
+            cache_en = {"start_idx": 0, "cif_hidden": torch.zeros((batch_size, 1, 320)),
+                        "cif_alphas": torch.zeros((batch_size, 1)), "chunk_size": chunk_size, "last_chunk": False,
+                        "feats": torch.zeros((batch_size, chunk_size[0] + chunk_size[2], 560))}
+            cache["encoder"] = cache_en
+
+            cache_de = {"decode_fsmn": None}
+            cache["decoder"] = cache_de
+
+        return cache
+
     def _forward(
             data_path_and_name_and_type,
             raw_inputs: Union[np.ndarray, torch.Tensor] = None,
@@ -610,123 +496,35 @@ def inference_modelscope(
     ):
 
         # 3. Build data-iterator
+        if data_path_and_name_and_type is not None and data_path_and_name_and_type[2] == "bytes":
+            raw_inputs = _load_bytes(data_path_and_name_and_type[0])
+            raw_inputs = torch.tensor(raw_inputs)
+        if data_path_and_name_and_type is None and raw_inputs is not None:
+            if isinstance(raw_inputs, np.ndarray):
+                raw_inputs = torch.tensor(raw_inputs)
         is_final = False
         cache = {}
+        chunk_size = [5, 10, 5]
         if param_dict is not None and "cache" in param_dict:
             cache = param_dict["cache"]
         if param_dict is not None and "is_final" in param_dict:
             is_final = param_dict["is_final"]
+        if param_dict is not None and "chunk_size" in param_dict:
+            chunk_size = param_dict["chunk_size"]
 
-        if data_path_and_name_and_type is not None and data_path_and_name_and_type[2] == "bytes":
-            raw_inputs = _load_bytes(data_path_and_name_and_type[0])
-            raw_inputs = torch.tensor(raw_inputs)
-        if data_path_and_name_and_type is not None and data_path_and_name_and_type[2] == "sound":
-            raw_inputs = torchaudio.load(data_path_and_name_and_type[0])[0][0]
-            is_final = True
-        if data_path_and_name_and_type is None and raw_inputs is not None:
-            if isinstance(raw_inputs, np.ndarray):
-                raw_inputs = torch.tensor(raw_inputs)
         # 7 .Start for-loop
         # FIXME(kamo): The output format should be discussed about
+        raw_inputs = torch.unsqueeze(raw_inputs, axis=0)
+        input_lens = torch.tensor([raw_inputs.shape[1]])
         asr_result_list = []
-        results = []
-        asr_result = ""
-        wait = True
-        if len(cache) == 0:
-            cache["encoder"] = {"start_idx": 0, "pad_left": 0, "stride": 10, "pad_right": 5, "cif_hidden": None, "cif_alphas": None, "is_final": is_final, "left": 0, "right": 0}
-            cache_de = {"decode_fsmn": None}
-            cache["decoder"] = cache_de
-            cache["first_chunk"] = True
-            cache["speech"] = []
-            cache["accum_speech"] = 0
 
-        if raw_inputs is not None:
-            if len(cache["speech"]) == 0:
-                cache["speech"] = raw_inputs
-            else:
-                cache["speech"] = torch.cat([cache["speech"], raw_inputs], dim=0)
-            cache["accum_speech"] += len(raw_inputs)
-            while cache["accum_speech"] >= 960:
-                if cache["first_chunk"]:
-                    if cache["accum_speech"] >= 14400:
-                        speech = torch.unsqueeze(cache["speech"], axis=0)
-                        speech_length = torch.tensor([len(cache["speech"])])
-                        cache["encoder"]["pad_left"] = 5 
-                        cache["encoder"]["pad_right"] = 5 
-                        cache["encoder"]["stride"] = 10
-                        cache["encoder"]["left"] = 5
-                        cache["encoder"]["right"] = 0
-                        results = speech2text(cache, speech, speech_length)
-                        cache["accum_speech"] -= 4800
-                        cache["first_chunk"] = False
-                        cache["encoder"]["start_idx"] = -5
-                        cache["encoder"]["is_final"] = False
-                        wait = False
-                    else:
-                        if is_final:
-                            cache["encoder"]["stride"] = len(cache["speech"]) // 960
-                            cache["encoder"]["pad_left"] = 0
-                            cache["encoder"]["pad_right"] = 0
-                            speech = torch.unsqueeze(cache["speech"], axis=0)
-                            speech_length = torch.tensor([len(cache["speech"])])
-                            results = speech2text(cache, speech, speech_length)
-                            cache["accum_speech"] = 0
-                            wait = False
-                        else:
-                            break
-                else:
-                    if cache["accum_speech"] >= 19200:
-                        cache["encoder"]["start_idx"] += 10
-                        cache["encoder"]["stride"] = 10
-                        cache["encoder"]["pad_left"] = 5
-                        cache["encoder"]["pad_right"] = 5
-                        cache["encoder"]["left"] = 0
-                        cache["encoder"]["right"] = 0
-                        speech = torch.unsqueeze(cache["speech"], axis=0)
-                        speech_length = torch.tensor([len(cache["speech"])])
-                        results = speech2text(cache, speech, speech_length)
-                        cache["accum_speech"] -= 9600
-                        wait = False
-                    else:
-                        if is_final:
-                            cache["encoder"]["is_final"] = True
-                            if cache["accum_speech"] >= 14400:
-                                cache["encoder"]["start_idx"] += 10
-                                cache["encoder"]["stride"] = 10
-                                cache["encoder"]["pad_left"] = 5
-                                cache["encoder"]["pad_right"] = 5
-                                cache["encoder"]["left"] = 0
-                                cache["encoder"]["right"] = cache["accum_speech"] // 960 - 15
-                                speech = torch.unsqueeze(cache["speech"], axis=0)
-                                speech_length = torch.tensor([len(cache["speech"])])
-                                results = speech2text(cache, speech, speech_length)
-                                cache["accum_speech"] -= 9600
-                                wait = False
-                            else:
-                                cache["encoder"]["start_idx"] += 10
-                                cache["encoder"]["stride"] = cache["accum_speech"] // 960 - 5
-                                cache["encoder"]["pad_left"] = 5
-                                cache["encoder"]["pad_right"] = 0
-                                cache["encoder"]["left"] = 0
-                                cache["encoder"]["right"] = 0
-                                speech = torch.unsqueeze(cache["speech"], axis=0)
-                                speech_length = torch.tensor([len(cache["speech"])])
-                                results = speech2text(cache, speech, speech_length)
-                                cache["accum_speech"] = 0
-                                wait = False
-                        else:
-                            break
-                
-                if len(results) >= 1:
-                    asr_result += results[0][0]
-            if asr_result == "":
-                asr_result = "sil"
-            if wait:
-                asr_result = "waiting_for_more_voice"
-            item = {'key': "utt", 'value': asr_result}
-            asr_result_list.append(item)
-        else:
-            return []
+        cache = _prepare_cache(cache, chunk_size=chunk_size, batch_size=1)
+        cache["encoder"]["is_final"] = is_final
+        asr_result = speech2text(cache, raw_inputs, input_lens)
+        item = {'key': "utt", 'value': asr_result}
+        asr_result_list.append(item)
+        if is_final:
+            cache = _cache_reset(cache, chunk_size=chunk_size, batch_size=1)
         return asr_result_list
 
     return _forward
@@ -921,4 +719,3 @@ if __name__ == "__main__":
     # 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)
 
-

funasr/models/e2e_asr_paraformer.py

@@ -712,9 +712,9 @@ class ParaformerOnline(Paraformer):
 
     def calc_predictor_chunk(self, encoder_out, cache=None):
 
-        pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = \
+        pre_acoustic_embeds, pre_token_length = \
             self.predictor.forward_chunk(encoder_out, cache["encoder"])
-        return pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index
+        return pre_acoustic_embeds, pre_token_length
 
     def cal_decoder_with_predictor_chunk(self, encoder_out, sematic_embeds, cache=None):
         decoder_outs = self.decoder.forward_chunk(

funasr/models/encoder/sanm_encoder.py

@@ -6,9 +6,11 @@ from typing import Union
 import logging
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
 from funasr.modules.streaming_utils.chunk_utilis import overlap_chunk
 from typeguard import check_argument_types
 import numpy as np
+from funasr.torch_utils.device_funcs import to_device
 from funasr.modules.nets_utils import make_pad_mask
 from funasr.modules.attention import MultiHeadedAttention, MultiHeadedAttentionSANM, MultiHeadedAttentionSANMwithMask
 from funasr.modules.embedding import SinusoidalPositionEncoder, StreamSinusoidalPositionEncoder
@@ -349,6 +351,23 @@ class SANMEncoder(AbsEncoder):
             return (xs_pad, intermediate_outs), olens, None
         return xs_pad, olens, None
 
+    def _add_overlap_chunk(self, feats: np.ndarray, cache: dict = {}):
+        if len(cache) == 0:
+            return feats
+        # process last chunk
+        cache["feats"] = to_device(cache["feats"], device=feats.device)
+        overlap_feats = torch.cat((cache["feats"], feats), dim=1)
+        if cache["is_final"]:
+            cache["feats"] = overlap_feats[:, -cache["chunk_size"][0]:, :]
+            if not cache["last_chunk"]:
+               padding_length = sum(cache["chunk_size"]) - overlap_feats.shape[1]
+               overlap_feats = overlap_feats.transpose(1, 2)
+               overlap_feats = F.pad(overlap_feats, (0, padding_length))
+               overlap_feats = overlap_feats.transpose(1, 2)
+        else:
+            cache["feats"] = overlap_feats[:, -(cache["chunk_size"][0] + cache["chunk_size"][2]):, :]
+        return overlap_feats
+
     def forward_chunk(self,
                       xs_pad: torch.Tensor,
                       ilens: torch.Tensor,
@@ -360,7 +379,7 @@ class SANMEncoder(AbsEncoder):
             xs_pad = xs_pad
         else:
             xs_pad = self.embed(xs_pad, cache)
-
+        xs_pad = self._add_overlap_chunk(xs_pad, cache)
         encoder_outs = self.encoders0(xs_pad, None, None, None, None)
         xs_pad, masks = encoder_outs[0], encoder_outs[1]
         intermediate_outs = []

funasr/models/predictor/cif.py

@@ -2,6 +2,7 @@ import torch
 from torch import nn
 import logging
 import numpy as np
+from funasr.torch_utils.device_funcs import to_device
 from funasr.modules.nets_utils import make_pad_mask
 from funasr.modules.streaming_utils.utils import sequence_mask
 
@@ -200,7 +201,7 @@ class CifPredictorV2(nn.Module):
         return acoustic_embeds, token_num, alphas, cif_peak
 
     def forward_chunk(self, hidden, cache=None):
-        b, t, d = hidden.size()
+        batch_size, len_time, hidden_size = hidden.shape
         h = hidden
         context = h.transpose(1, 2)
         queries = self.pad(context)
@@ -211,58 +212,81 @@ class CifPredictorV2(nn.Module):
         alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold)
 
         alphas = alphas.squeeze(-1)
-        mask_chunk_predictor = None
-        if cache is not None:
-            mask_chunk_predictor = None
-            mask_chunk_predictor = torch.zeros_like(alphas)
-            mask_chunk_predictor[:, cache["pad_left"]:cache["stride"] + cache["pad_left"]] = 1.0
-       
-        if mask_chunk_predictor is not None:
-            alphas = alphas * mask_chunk_predictor
-      
-        if cache is not None:
-            if cache["is_final"]:
-                alphas[:, cache["stride"] + cache["pad_left"] - 1] += 0.45
-            if cache["cif_hidden"] is not None:
-                hidden = torch.cat((cache["cif_hidden"], hidden), 1)
-            if cache["cif_alphas"] is not None:
-                alphas = torch.cat((cache["cif_alphas"], alphas), -1)
 
-        token_num = alphas.sum(-1)
-        acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)
-        len_time = alphas.size(-1)
-        last_fire_place = len_time - 1
-        last_fire_remainds = 0.0
-        pre_alphas_length = 0
-        last_fire = False
- 
-        mask_chunk_peak_predictor = None
-        if cache is not None:
-            mask_chunk_peak_predictor = None
-            mask_chunk_peak_predictor = torch.zeros_like(cif_peak)
-            if cache["cif_alphas"] is not None:
-                pre_alphas_length = cache["cif_alphas"].size(-1)
-                mask_chunk_peak_predictor[:, :pre_alphas_length] = 1.0
-            mask_chunk_peak_predictor[:, pre_alphas_length + cache["pad_left"]:pre_alphas_length + cache["stride"] + cache["pad_left"]] = 1.0
-            
-        if mask_chunk_peak_predictor is not None:
-            cif_peak = cif_peak * mask_chunk_peak_predictor.squeeze(-1)
-        
-        for i in range(len_time):
-            if cif_peak[0][len_time - 1 - i] > self.threshold or cif_peak[0][len_time - 1 - i] == self.threshold:
-                last_fire_place = len_time - 1 - i
-                last_fire_remainds = cif_peak[0][len_time - 1 - i] - self.threshold
-                last_fire = True
-                break
-        if last_fire:
-           last_fire_remainds = torch.tensor([last_fire_remainds], dtype=alphas.dtype).to(alphas.device)
-           cache["cif_hidden"] = hidden[:, last_fire_place:, :]
-           cache["cif_alphas"] = torch.cat((last_fire_remainds.unsqueeze(0), alphas[:, last_fire_place+1:]), -1)
-        else:
-           cache["cif_hidden"] = hidden
-           cache["cif_alphas"] = alphas
-        token_num_int = token_num.floor().type(torch.int32).item()
-        return acoustic_embeds[:, 0:token_num_int, :], token_num, alphas, cif_peak
+        token_length = []
+        list_fires = []
+        list_frames = []
+        cache_alphas = []
+        cache_hiddens = []
+
+        if cache is not None and "chunk_size" in cache:
+            alphas[:, :cache["chunk_size"][0]] = 0.0
+            alphas[:, sum(cache["chunk_size"][:2]):] = 0.0
+        if cache is not None and "cif_alphas" in cache and "cif_hidden" in cache:
+            cache["cif_hidden"] = to_device(cache["cif_hidden"], device=hidden.device)
+            cache["cif_alphas"] = to_device(cache["cif_alphas"], device=alphas.device)
+            hidden = torch.cat((cache["cif_hidden"], hidden), dim=1)
+            alphas = torch.cat((cache["cif_alphas"], alphas), dim=1)
+        if cache is not None and "last_chunk" in cache and cache["last_chunk"]:
+            tail_hidden = torch.zeros((batch_size, 1, hidden_size), device=hidden.device)
+            tail_alphas = torch.tensor([[self.tail_threshold]], device=alphas.device)
+            tail_alphas = torch.tile(tail_alphas, (batch_size, 1))
+            hidden = torch.cat((hidden, tail_hidden), dim=1)
+            alphas = torch.cat((alphas, tail_alphas), dim=1)
+
+        len_time = alphas.shape[1]
+        for b in range(batch_size):
+            integrate = 0.0
+            frames = torch.zeros((hidden_size), device=hidden.device)
+            list_frame = []
+            list_fire = []
+            for t in range(len_time):
+                alpha = alphas[b][t]
+                if alpha + integrate < self.threshold:
+                    integrate += alpha
+                    list_fire.append(integrate)
+                    frames += alpha * hidden[b][t]
+                else:
+                    frames += (self.threshold - integrate) * hidden[b][t]
+                    list_frame.append(frames)
+                    integrate += alpha
+                    list_fire.append(integrate)
+                    integrate -= self.threshold
+                    frames = integrate * hidden[b][t]
+
+            cache_alphas.append(integrate)
+            if integrate > 0.0:
+                cache_hiddens.append(frames / integrate)
+            else:
+                cache_hiddens.append(frames)
+
+            token_length.append(torch.tensor(len(list_frame), device=alphas.device))
+            list_fires.append(list_fire)
+            list_frames.append(list_frame)
+
+        cache["cif_alphas"] = torch.stack(cache_alphas, axis=0)
+        cache["cif_alphas"] = torch.unsqueeze(cache["cif_alphas"], axis=0)
+        cache["cif_hidden"] = torch.stack(cache_hiddens, axis=0)
+        cache["cif_hidden"] = torch.unsqueeze(cache["cif_hidden"], axis=0)
+
+        max_token_len = max(token_length)
+        if max_token_len == 0:
+             return hidden, torch.stack(token_length, 0)
+        list_ls = []
+        for b in range(batch_size):
+            pad_frames = torch.zeros((max_token_len - token_length[b], hidden_size), device=alphas.device)
+            if token_length[b] == 0:
+                list_ls.append(pad_frames)
+            else:
+                list_frames[b] = torch.stack(list_frames[b])
+                list_ls.append(torch.cat((list_frames[b], pad_frames), dim=0))
+
+        cache["cif_alphas"] = torch.stack(cache_alphas, axis=0)
+        cache["cif_alphas"] = torch.unsqueeze(cache["cif_alphas"], axis=0)
+        cache["cif_hidden"] = torch.stack(cache_hiddens, axis=0)
+        cache["cif_hidden"] = torch.unsqueeze(cache["cif_hidden"], axis=0)
+        return torch.stack(list_ls, 0), torch.stack(token_length, 0)
+
 
     def tail_process_fn(self, hidden, alphas, token_num=None, mask=None):
         b, t, d = hidden.size()

funasr/modules/embedding.py

@@ -425,21 +425,14 @@ class StreamSinusoidalPositionEncoder(torch.nn.Module):
         return encoding.type(dtype)
 
     def forward(self, x, cache=None):
-        start_idx = 0
-        pad_left = 0
-        pad_right = 0
         batch_size, timesteps, input_dim = x.size()
+        start_idx = 0
         if cache is not None:
             start_idx = cache["start_idx"]
-            pad_left = cache["left"]
-            pad_right = cache["right"]
+            cache["start_idx"] += timesteps
         positions = torch.arange(1, timesteps+start_idx+1)[None, :]
         position_encoding = self.encode(positions, input_dim, x.dtype).to(x.device)
-        outputs = x + position_encoding[:, start_idx: start_idx + timesteps]
-        outputs = outputs.transpose(1, 2)
-        outputs = F.pad(outputs, (pad_left, pad_right))
-        outputs = outputs.transpose(1, 2)
-        return outputs
+        return x + position_encoding[:, start_idx: start_idx + timesteps]
 
 class StreamingRelPositionalEncoding(torch.nn.Module):
     """Relative positional encoding.