From ce92fde1b754ae56aec7f62ff910c205a84bf159 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E6=B8=B8=E9=9B=81?= <zhifu.gzf@alibaba-inc.com>
Date: Tue, 16 Jan 2024 10:41:16 +0800
Subject: [PATCH 1/2] funasr1.0 auto/ auto_model auto_frontend auto_tokenizer

---
 funasr/__init__.py            |   3 +-
 funasr/auto/__init__.py       |   0
 funasr/auto/auto_frontend.py  |  95 +++++++
 funasr/auto/auto_model.py     | 416 +++++++++++++++++++++++++++++++
 funasr/auto/auto_tokenizer.py |   8 +
 funasr/bin/inference.py       | 453 +---------------------------------
 6 files changed, 522 insertions(+), 453 deletions(-)
 create mode 100644 funasr/auto/__init__.py
 create mode 100644 funasr/auto/auto_frontend.py
 create mode 100644 funasr/auto/auto_model.py
 create mode 100644 funasr/auto/auto_tokenizer.py

diff --git a/funasr/__init__.py b/funasr/__init__.py
index 669bdac48..a5011bf79 100644
--- a/funasr/__init__.py
+++ b/funasr/__init__.py
@@ -30,4 +30,5 @@ def import_submodules(package, recursive=True):
 
 import_submodules(__name__)
 
-from funasr.bin.inference import AutoModel, AutoFrontend
\ No newline at end of file
+from funasr.auto.auto_model import AutoModel
+from funasr.auto.auto_frontend import AutoFrontend
\ No newline at end of file
diff --git a/funasr/auto/__init__.py b/funasr/auto/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/funasr/auto/auto_frontend.py b/funasr/auto/auto_frontend.py
new file mode 100644
index 000000000..661f94939
--- /dev/null
+++ b/funasr/auto/auto_frontend.py
@@ -0,0 +1,95 @@
+import json
+import time
+import torch
+import hydra
+import random
+import string
+import logging
+import os.path
+from tqdm import tqdm
+from omegaconf import DictConfig, OmegaConf, ListConfig
+
+from funasr.register import tables
+from funasr.utils.load_utils import load_bytes
+from funasr.download.file import download_from_url
+from funasr.download.download_from_hub import download_model
+from funasr.utils.vad_utils import slice_padding_audio_samples
+from funasr.train_utils.set_all_random_seed import set_all_random_seed
+from funasr.train_utils.load_pretrained_model import load_pretrained_model
+from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
+from funasr.utils.timestamp_tools import timestamp_sentence
+from funasr.models.campplus.utils import sv_chunk, postprocess, distribute_spk
+from funasr.models.campplus.cluster_backend import ClusterBackend
+from funasr.auto.auto_model import prepare_data_iterator
+
+
+
+class AutoFrontend:
+    def __init__(self, **kwargs):
+        assert "model" in kwargs
+        if "model_conf" not in kwargs:
+            logging.info("download models from model hub: {}".format(kwargs.get("model_hub", "ms")))
+            kwargs = download_model(**kwargs)
+        
+        # build frontend
+        frontend = kwargs.get("frontend", None)
+        if frontend is not None:
+            frontend_class = tables.frontend_classes.get(frontend)
+            frontend = frontend_class(**kwargs["frontend_conf"])
+
+        self.frontend = frontend
+        if "frontend" in kwargs:
+            del kwargs["frontend"]
+        self.kwargs = kwargs
+
+    
+    def __call__(self, input, input_len=None, kwargs=None, **cfg):
+        
+        kwargs = self.kwargs if kwargs is None else kwargs
+        kwargs.update(cfg)
+
+
+        key_list, data_list = prepare_data_iterator(input, input_len=input_len)
+        batch_size = kwargs.get("batch_size", 1)
+        device = kwargs.get("device", "cpu")
+        if device == "cpu":
+            batch_size = 1
+        
+        meta_data = {}
+        
+        result_list = []
+        num_samples = len(data_list)
+        pbar = tqdm(colour="blue", total=num_samples + 1, dynamic_ncols=True)
+        
+        time0 = time.perf_counter()
+        for beg_idx in range(0, num_samples, batch_size):
+            end_idx = min(num_samples, beg_idx + batch_size)
+            data_batch = data_list[beg_idx:end_idx]
+            key_batch = key_list[beg_idx:end_idx]
+
+            # extract fbank feats
+            time1 = time.perf_counter()
+            audio_sample_list = load_audio_text_image_video(data_batch, fs=self.frontend.fs, audio_fs=kwargs.get("fs", 16000))
+            time2 = time.perf_counter()
+            meta_data["load_data"] = f"{time2 - time1:0.3f}"
+            speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
+                                                   frontend=self.frontend, **kwargs)
+            time3 = time.perf_counter()
+            meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
+            meta_data["batch_data_time"] = speech_lengths.sum().item() * self.frontend.frame_shift * self.frontend.lfr_n / 1000
+            
+            speech.to(device=device), speech_lengths.to(device=device)
+            batch = {"input": speech, "input_len": speech_lengths, "key": key_batch}
+            result_list.append(batch)
+            
+            pbar.update(1)
+            description = (
+                f"{meta_data}, "
+            )
+            pbar.set_description(description)
+        
+        time_end = time.perf_counter()
+        pbar.set_description(f"time escaped total: {time_end - time0:0.3f}")
+        
+        return result_list
+
diff --git a/funasr/auto/auto_model.py b/funasr/auto/auto_model.py
new file mode 100644
index 000000000..25edeb708
--- /dev/null
+++ b/funasr/auto/auto_model.py
@@ -0,0 +1,416 @@
+import json
+import time
+import torch
+import hydra
+import random
+import string
+import logging
+import os.path
+from tqdm import tqdm
+from omegaconf import DictConfig, OmegaConf, ListConfig
+
+from funasr.register import tables
+from funasr.utils.load_utils import load_bytes
+from funasr.download.file import download_from_url
+from funasr.download.download_from_hub import download_model
+from funasr.utils.vad_utils import slice_padding_audio_samples
+from funasr.train_utils.set_all_random_seed import set_all_random_seed
+from funasr.train_utils.load_pretrained_model import load_pretrained_model
+from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
+from funasr.utils.timestamp_tools import timestamp_sentence
+from funasr.models.campplus.utils import sv_chunk, postprocess, distribute_spk
+from funasr.models.campplus.cluster_backend import ClusterBackend
+
+
+def prepare_data_iterator(data_in, input_len=None, data_type=None, key=None):
+    """
+    
+    :param input:
+    :param input_len:
+    :param data_type:
+    :param frontend:
+    :return:
+    """
+    data_list = []
+    key_list = []
+    filelist = [".scp", ".txt", ".json", ".jsonl"]
+    
+    chars = string.ascii_letters + string.digits
+    if isinstance(data_in, str) and data_in.startswith('http'): # url
+        data_in = download_from_url(data_in)
+    if isinstance(data_in, str) and os.path.exists(data_in): # wav_path; filelist: wav.scp, file.jsonl;text.txt;
+        _, file_extension = os.path.splitext(data_in)
+        file_extension = file_extension.lower()
+        if file_extension in filelist: #filelist: wav.scp, file.jsonl;text.txt;
+            with open(data_in, encoding='utf-8') as fin:
+                for line in fin:
+                    key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
+                    if data_in.endswith(".jsonl"): #file.jsonl: json.dumps({"source": data})
+                        lines = json.loads(line.strip())
+                        data = lines["source"]
+                        key = data["key"] if "key" in data else key
+                    else: # filelist, wav.scp, text.txt: id \t data or data
+                        lines = line.strip().split(maxsplit=1)
+                        data = lines[1] if len(lines)>1 else lines[0]
+                        key = lines[0] if len(lines)>1 else key
+                    
+                    data_list.append(data)
+                    key_list.append(key)
+        else:
+            key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
+            data_list = [data_in]
+            key_list = [key]
+    elif isinstance(data_in, (list, tuple)):
+        if data_type is not None and isinstance(data_type, (list, tuple)): # mutiple inputs
+            data_list_tmp = []
+            for data_in_i, data_type_i in zip(data_in, data_type):
+                key_list, data_list_i = prepare_data_iterator(data_in=data_in_i, data_type=data_type_i)
+                data_list_tmp.append(data_list_i)
+            data_list = []
+            for item in zip(*data_list_tmp):
+                data_list.append(item)
+        else:
+            # [audio sample point, fbank, text]
+            data_list = data_in
+            key_list = ["rand_key_" + ''.join(random.choice(chars) for _ in range(13)) for _ in range(len(data_in))]
+    else: # raw text; audio sample point, fbank; bytes
+        if isinstance(data_in, bytes): # audio bytes
+            data_in = load_bytes(data_in)
+        if key is None:
+            key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
+        data_list = [data_in]
+        key_list = [key]
+    
+    return key_list, data_list
+
+
+class AutoModel:
+    
+    def __init__(self, **kwargs):
+        tables.print()
+        
+        model, kwargs = self.build_model(**kwargs)
+        
+        # if vad_model is not None, build vad model else None
+        vad_model = kwargs.get("vad_model", None)
+        vad_kwargs = kwargs.get("vad_model_revision", None)
+        if vad_model is not None:
+            logging.info("Building VAD model.")
+            vad_kwargs = {"model": vad_model, "model_revision": vad_kwargs}
+            vad_model, vad_kwargs = self.build_model(**vad_kwargs)
+
+        # if punc_model is not None, build punc model else None
+        punc_model = kwargs.get("punc_model", None)
+        punc_kwargs = kwargs.get("punc_model_revision", None)
+        if punc_model is not None:
+            logging.info("Building punc model.")
+            punc_kwargs = {"model": punc_model, "model_revision": punc_kwargs}
+            punc_model, punc_kwargs = self.build_model(**punc_kwargs)
+
+        # if spk_model is not None, build spk model else None
+        spk_model = kwargs.get("spk_model", None)
+        spk_kwargs = kwargs.get("spk_model_revision", None)
+        if spk_model is not None:
+            logging.info("Building SPK model.")
+            spk_kwargs = {"model": spk_model, "model_revision": spk_kwargs}
+            spk_model, spk_kwargs = self.build_model(**spk_kwargs)
+            self.cb_model = ClusterBackend()
+            spk_mode = kwargs.get("spk_mode", 'punc_segment')
+            if spk_mode not in ["default", "vad_segment", "punc_segment"]:
+                logging.error("spk_mode should be one of default, vad_segment and punc_segment.")
+            self.spk_mode = spk_mode
+            self.preset_spk_num = kwargs.get("preset_spk_num", None)
+            if self.preset_spk_num:
+                logging.warning("Using preset speaker number: {}".format(self.preset_spk_num))
+            logging.warning("Many to print when using speaker model...")
+            
+        self.kwargs = kwargs
+        self.model = model
+        self.vad_model = vad_model
+        self.vad_kwargs = vad_kwargs
+        self.punc_model = punc_model
+        self.punc_kwargs = punc_kwargs
+        self.spk_model = spk_model
+        self.spk_kwargs = spk_kwargs
+        self.model_path = kwargs["model_path"]
+  
+        
+    def build_model(self, **kwargs):
+        assert "model" in kwargs
+        if "model_conf" not in kwargs:
+            logging.info("download models from model hub: {}".format(kwargs.get("model_hub", "ms")))
+            kwargs = download_model(**kwargs)
+        
+        set_all_random_seed(kwargs.get("seed", 0))
+        
+        device = kwargs.get("device", "cuda")
+        if not torch.cuda.is_available() or kwargs.get("ngpu", 0):
+            device = "cpu"
+            # kwargs["batch_size"] = 1
+        kwargs["device"] = device
+        
+        if kwargs.get("ncpu", None):
+            torch.set_num_threads(kwargs.get("ncpu"))
+        
+        # build tokenizer
+        tokenizer = kwargs.get("tokenizer", None)
+        if tokenizer is not None:
+            tokenizer_class = tables.tokenizer_classes.get(tokenizer)
+            tokenizer = tokenizer_class(**kwargs["tokenizer_conf"])
+            kwargs["tokenizer"] = tokenizer
+            kwargs["token_list"] = tokenizer.token_list
+            vocab_size = len(tokenizer.token_list)
+        else:
+            vocab_size = -1
+        
+        # build frontend
+        frontend = kwargs.get("frontend", None)
+        if frontend is not None:
+            frontend_class = tables.frontend_classes.get(frontend)
+            frontend = frontend_class(**kwargs["frontend_conf"])
+            kwargs["frontend"] = frontend
+            kwargs["input_size"] = frontend.output_size()
+        
+        # build model
+        model_class = tables.model_classes.get(kwargs["model"])
+        model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=vocab_size)
+        model.eval()
+        model.to(device)
+        
+        # init_param
+        init_param = kwargs.get("init_param", None)
+        if init_param is not None:
+            logging.info(f"Loading pretrained params from {init_param}")
+            load_pretrained_model(
+                model=model,
+                init_param=init_param,
+                ignore_init_mismatch=kwargs.get("ignore_init_mismatch", False),
+                oss_bucket=kwargs.get("oss_bucket", None),
+            )
+        
+        return model, kwargs
+    
+    def __call__(self, *args, **cfg):
+        kwargs = self.kwargs
+        kwargs.update(cfg)
+        res = self.model(*args, kwargs)
+        return res
+
+        
+
+    def generate(self, input, input_len=None, **cfg):
+        if self.vad_model is None:
+            return self.inference(input, input_len=input_len, **cfg)
+    
+        else:
+            return self.inference_with_vad(input, input_len=input_len, **cfg)
+        
+    def inference(self, input, input_len=None, model=None, kwargs=None, key=None, **cfg):
+        kwargs = self.kwargs if kwargs is None else kwargs
+        kwargs.update(cfg)
+        model = self.model if model is None else model
+
+        batch_size = kwargs.get("batch_size", 1)
+        # if kwargs.get("device", "cpu") == "cpu":
+        #     batch_size = 1
+        
+        key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=kwargs.get("data_type", None), key=key)
+        
+        speed_stats = {}
+        asr_result_list = []
+        num_samples = len(data_list)
+        pbar = tqdm(colour="blue", total=num_samples+1, dynamic_ncols=True)
+        time_speech_total = 0.0
+        time_escape_total = 0.0
+        for beg_idx in range(0, num_samples, batch_size):
+            end_idx = min(num_samples, beg_idx + batch_size)
+            data_batch = data_list[beg_idx:end_idx]
+            key_batch = key_list[beg_idx:end_idx]
+            batch = {"data_in": data_batch, "key": key_batch}
+            if (end_idx - beg_idx) == 1 and isinstance(data_batch[0], torch.Tensor): # fbank
+                batch["data_in"] = data_batch[0]
+                batch["data_lengths"] = input_len
+        
+            time1 = time.perf_counter()
+            with torch.no_grad():
+                results, meta_data = model.inference(**batch, **kwargs)
+            time2 = time.perf_counter()
+            
+            asr_result_list.extend(results)
+            pbar.update(1)
+            
+            # batch_data_time = time_per_frame_s * data_batch_i["speech_lengths"].sum().item()
+            batch_data_time = meta_data.get("batch_data_time", -1)
+            time_escape = time2 - time1
+            speed_stats["load_data"] = meta_data.get("load_data", 0.0)
+            speed_stats["extract_feat"] = meta_data.get("extract_feat", 0.0)
+            speed_stats["forward"] = f"{time_escape:0.3f}"
+            speed_stats["batch_size"] = f"{len(results)}"
+            speed_stats["rtf"] = f"{(time_escape) / batch_data_time:0.3f}"
+            description = (
+                f"{speed_stats}, "
+            )
+            pbar.set_description(description)
+            time_speech_total += batch_data_time
+            time_escape_total += time_escape
+            
+        pbar.update(1)
+        pbar.set_description(f"rtf_avg: {time_escape_total/time_speech_total:0.3f}")
+        torch.cuda.empty_cache()
+        return asr_result_list
+    
+    def inference_with_vad(self, input, input_len=None, **cfg):
+        
+        # step.1: compute the vad model
+        self.vad_kwargs.update(cfg)
+        beg_vad = time.time()
+        res = self.generate(input, input_len=input_len, model=self.vad_model, kwargs=self.vad_kwargs, **cfg)
+        end_vad = time.time()
+        print(f"time cost vad: {end_vad - beg_vad:0.3f}")
+
+
+        # step.2 compute asr model
+        model = self.model
+        kwargs = self.kwargs
+        kwargs.update(cfg)
+        batch_size = int(kwargs.get("batch_size_s", 300))*1000
+        batch_size_threshold_ms = int(kwargs.get("batch_size_threshold_s", 60))*1000
+        kwargs["batch_size"] = batch_size
+        
+        key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=kwargs.get("data_type", None))
+        results_ret_list = []
+        time_speech_total_all_samples = 0.0
+
+        beg_total = time.time()
+        pbar_total = tqdm(colour="red", total=len(res) + 1, dynamic_ncols=True)
+        for i in range(len(res)):
+            key = res[i]["key"]
+            vadsegments = res[i]["value"]
+            input_i = data_list[i]
+            speech = load_audio_text_image_video(input_i, fs=kwargs["frontend"].fs, audio_fs=kwargs.get("fs", 16000))
+            speech_lengths = len(speech)
+            n = len(vadsegments)
+            data_with_index = [(vadsegments[i], i) for i in range(n)]
+            sorted_data = sorted(data_with_index, key=lambda x: x[0][1] - x[0][0])
+            results_sorted = []
+            
+            if not len(sorted_data):
+                logging.info("decoding, utt: {}, empty speech".format(key))
+                continue
+
+            if len(sorted_data) > 0 and len(sorted_data[0]) > 0:
+                batch_size = max(batch_size, sorted_data[0][0][1] - sorted_data[0][0][0])
+            
+            batch_size_ms_cum = 0
+            beg_idx = 0
+            beg_asr_total = time.time()
+            time_speech_total_per_sample = speech_lengths/16000
+            time_speech_total_all_samples += time_speech_total_per_sample
+
+            for j, _ in enumerate(range(0, n)):
+                batch_size_ms_cum += (sorted_data[j][0][1] - sorted_data[j][0][0])
+                if j < n - 1 and (
+                    batch_size_ms_cum + sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size and (
+                    sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size_threshold_ms:
+                    continue
+                batch_size_ms_cum = 0
+                end_idx = j + 1
+                speech_j, speech_lengths_j = slice_padding_audio_samples(speech, speech_lengths, sorted_data[beg_idx:end_idx])       
+                results = self.generate(speech_j, input_len=None, model=model, kwargs=kwargs, **cfg)
+                if self.spk_model is not None:
+                    all_segments = []
+                    # compose vad segments: [[start_time_sec, end_time_sec, speech], [...]]
+                    for _b in range(len(speech_j)):
+                        vad_segments = [[sorted_data[beg_idx:end_idx][_b][0][0]/1000.0, \
+                                        sorted_data[beg_idx:end_idx][_b][0][1]/1000.0, \
+                                        speech_j[_b]]]
+                        segments = sv_chunk(vad_segments)
+                        all_segments.extend(segments)
+                        speech_b = [i[2] for i in segments]
+                        spk_res = self.generate(speech_b, input_len=None, model=self.spk_model, kwargs=kwargs, **cfg)
+                        results[_b]['spk_embedding'] = spk_res[0]['spk_embedding']
+                beg_idx = end_idx
+                if len(results) < 1:
+                    continue
+                results_sorted.extend(results)
+
+
+            pbar_total.update(1)
+            end_asr_total = time.time()
+            time_escape_total_per_sample = end_asr_total - beg_asr_total
+            pbar_total.set_description(f"rtf_avg_per_sample: {time_escape_total_per_sample / time_speech_total_per_sample:0.3f}, "
+                                 f"time_speech_total_per_sample: {time_speech_total_per_sample: 0.3f}, "
+                                 f"time_escape_total_per_sample: {time_escape_total_per_sample:0.3f}")
+
+            restored_data = [0] * n
+            for j in range(n):
+                index = sorted_data[j][1]
+                restored_data[index] = results_sorted[j]
+            result = {}
+            
+            # results combine for texts, timestamps, speaker embeddings and others
+            # TODO: rewrite for clean code
+            for j in range(n):
+                for k, v in restored_data[j].items():
+                    if k.startswith("timestamp"):
+                        if k not in result:
+                            result[k] = []
+                        for t in restored_data[j][k]:
+                            t[0] += vadsegments[j][0]
+                            t[1] += vadsegments[j][0]
+                        result[k].extend(restored_data[j][k])
+                    elif k == 'spk_embedding':
+                        if k not in result:
+                            result[k] = restored_data[j][k]
+                        else:
+                            result[k] = torch.cat([result[k], restored_data[j][k]], dim=0)
+                    elif k == 'text':
+                        if k not in result:
+                            result[k] = restored_data[j][k]
+                        else:
+                            result[k] += " " + restored_data[j][k]
+                    else:
+                        if k not in result:
+                            result[k] = restored_data[j][k]
+                        else:
+                            result[k] += restored_data[j][k]
+                            
+            # step.3 compute punc model
+            if self.punc_model is not None:
+                self.punc_kwargs.update(cfg)
+                punc_res = self.generate(result["text"], model=self.punc_model, kwargs=self.punc_kwargs, **cfg)
+                result["text_with_punc"] = punc_res[0]["text"]
+                     
+            # speaker embedding cluster after resorted
+            if self.spk_model is not None:
+                all_segments = sorted(all_segments, key=lambda x: x[0])
+                spk_embedding = result['spk_embedding']
+                labels = self.cb_model(spk_embedding, oracle_num=self.preset_spk_num)
+                del result['spk_embedding']
+                sv_output = postprocess(all_segments, None, labels, spk_embedding.cpu())
+                if self.spk_mode == 'vad_segment':
+                    sentence_list = []
+                    for res, vadsegment in zip(restored_data, vadsegments):
+                        sentence_list.append({"start": vadsegment[0],\
+                                                "end": vadsegment[1],
+                                                "sentence": res['text'],
+                                                "timestamp": res['timestamp']})
+                else: # punc_segment
+                    sentence_list = timestamp_sentence(punc_res[0]['punc_array'], \
+                                                        result['timestamp'], \
+                                                        result['text'])
+                distribute_spk(sentence_list, sv_output)
+                result['sentence_info'] = sentence_list
+                    
+            result["key"] = key
+            results_ret_list.append(result)
+            pbar_total.update(1)
+            
+        pbar_total.update(1)
+        end_total = time.time()
+        time_escape_total_all_samples = end_total - beg_total
+        pbar_total.set_description(f"rtf_avg_all_samples: {time_escape_total_all_samples / time_speech_total_all_samples:0.3f}, "
+                             f"time_speech_total_all_samples: {time_speech_total_all_samples: 0.3f}, "
+                             f"time_escape_total_all_samples: {time_escape_total_all_samples:0.3f}")
+        return results_ret_list
+
diff --git a/funasr/auto/auto_tokenizer.py b/funasr/auto/auto_tokenizer.py
new file mode 100644
index 000000000..d5082e2e8
--- /dev/null
+++ b/funasr/auto/auto_tokenizer.py
@@ -0,0 +1,8 @@
+
+
+class AutoTokenizer:
+	"""
+	Undo
+	"""
+	def __init__(self):
+		pass
\ No newline at end of file
diff --git a/funasr/bin/inference.py b/funasr/bin/inference.py
index 7368d161e..bc435c43c 100644
--- a/funasr/bin/inference.py
+++ b/funasr/bin/inference.py
@@ -20,69 +20,9 @@ from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
 from funasr.utils.timestamp_tools import timestamp_sentence
 from funasr.models.campplus.utils import sv_chunk, postprocess, distribute_spk
 from funasr.models.campplus.cluster_backend import ClusterBackend
+from funasr.auto.auto_model import AutoModel
 
 
-def prepare_data_iterator(data_in, input_len=None, data_type=None, key=None):
-    """
-    
-    :param input:
-    :param input_len:
-    :param data_type:
-    :param frontend:
-    :return:
-    """
-    data_list = []
-    key_list = []
-    filelist = [".scp", ".txt", ".json", ".jsonl"]
-    
-    chars = string.ascii_letters + string.digits
-    if isinstance(data_in, str) and data_in.startswith('http'): # url
-        data_in = download_from_url(data_in)
-    if isinstance(data_in, str) and os.path.exists(data_in): # wav_path; filelist: wav.scp, file.jsonl;text.txt;
-        _, file_extension = os.path.splitext(data_in)
-        file_extension = file_extension.lower()
-        if file_extension in filelist: #filelist: wav.scp, file.jsonl;text.txt;
-            with open(data_in, encoding='utf-8') as fin:
-                for line in fin:
-                    key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
-                    if data_in.endswith(".jsonl"): #file.jsonl: json.dumps({"source": data})
-                        lines = json.loads(line.strip())
-                        data = lines["source"]
-                        key = data["key"] if "key" in data else key
-                    else: # filelist, wav.scp, text.txt: id \t data or data
-                        lines = line.strip().split(maxsplit=1)
-                        data = lines[1] if len(lines)>1 else lines[0]
-                        key = lines[0] if len(lines)>1 else key
-                    
-                    data_list.append(data)
-                    key_list.append(key)
-        else:
-            key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
-            data_list = [data_in]
-            key_list = [key]
-    elif isinstance(data_in, (list, tuple)):
-        if data_type is not None and isinstance(data_type, (list, tuple)): # mutiple inputs
-            data_list_tmp = []
-            for data_in_i, data_type_i in zip(data_in, data_type):
-                key_list, data_list_i = prepare_data_iterator(data_in=data_in_i, data_type=data_type_i)
-                data_list_tmp.append(data_list_i)
-            data_list = []
-            for item in zip(*data_list_tmp):
-                data_list.append(item)
-        else:
-            # [audio sample point, fbank, text]
-            data_list = data_in
-            key_list = ["rand_key_" + ''.join(random.choice(chars) for _ in range(13)) for _ in range(len(data_in))]
-    else: # raw text; audio sample point, fbank; bytes
-        if isinstance(data_in, bytes): # audio bytes
-            data_in = load_bytes(data_in)
-        if key is None:
-            key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
-        data_list = [data_in]
-        key_list = [key]
-    
-    return key_list, data_list
-
 @hydra.main(config_name=None, version_base=None)
 def main_hydra(cfg: DictConfig):
     def to_plain_list(cfg_item):
@@ -104,397 +44,6 @@ def main_hydra(cfg: DictConfig):
     res = model(input=kwargs["input"])
     print(res)
 
-class AutoModel:
-    
-    def __init__(self, **kwargs):
-        tables.print()
-        
-        model, kwargs = self.build_model(**kwargs)
-        
-        # if vad_model is not None, build vad model else None
-        vad_model = kwargs.get("vad_model", None)
-        vad_kwargs = kwargs.get("vad_model_revision", None)
-        if vad_model is not None:
-            logging.info("Building VAD model.")
-            vad_kwargs = {"model": vad_model, "model_revision": vad_kwargs}
-            vad_model, vad_kwargs = self.build_model(**vad_kwargs)
-
-        # if punc_model is not None, build punc model else None
-        punc_model = kwargs.get("punc_model", None)
-        punc_kwargs = kwargs.get("punc_model_revision", None)
-        if punc_model is not None:
-            logging.info("Building punc model.")
-            punc_kwargs = {"model": punc_model, "model_revision": punc_kwargs}
-            punc_model, punc_kwargs = self.build_model(**punc_kwargs)
-
-        # if spk_model is not None, build spk model else None
-        spk_model = kwargs.get("spk_model", None)
-        spk_kwargs = kwargs.get("spk_model_revision", None)
-        if spk_model is not None:
-            logging.info("Building SPK model.")
-            spk_kwargs = {"model": spk_model, "model_revision": spk_kwargs}
-            spk_model, spk_kwargs = self.build_model(**spk_kwargs)
-            self.cb_model = ClusterBackend()
-            spk_mode = kwargs.get("spk_mode", 'punc_segment')
-            if spk_mode not in ["default", "vad_segment", "punc_segment"]:
-                logging.error("spk_mode should be one of default, vad_segment and punc_segment.")
-            self.spk_mode = spk_mode
-            self.preset_spk_num = kwargs.get("preset_spk_num", None)
-            if self.preset_spk_num:
-                logging.warning("Using preset speaker number: {}".format(self.preset_spk_num))
-            logging.warning("Many to print when using speaker model...")
-            
-        self.kwargs = kwargs
-        self.model = model
-        self.vad_model = vad_model
-        self.vad_kwargs = vad_kwargs
-        self.punc_model = punc_model
-        self.punc_kwargs = punc_kwargs
-        self.spk_model = spk_model
-        self.spk_kwargs = spk_kwargs
-        self.model_path = kwargs["model_path"]
-  
-        
-    def build_model(self, **kwargs):
-        assert "model" in kwargs
-        if "model_conf" not in kwargs:
-            logging.info("download models from model hub: {}".format(kwargs.get("model_hub", "ms")))
-            kwargs = download_model(**kwargs)
-        
-        set_all_random_seed(kwargs.get("seed", 0))
-        
-        device = kwargs.get("device", "cuda")
-        if not torch.cuda.is_available() or kwargs.get("ngpu", 0):
-            device = "cpu"
-            # kwargs["batch_size"] = 1
-        kwargs["device"] = device
-        
-        if kwargs.get("ncpu", None):
-            torch.set_num_threads(kwargs.get("ncpu"))
-        
-        # build tokenizer
-        tokenizer = kwargs.get("tokenizer", None)
-        if tokenizer is not None:
-            tokenizer_class = tables.tokenizer_classes.get(tokenizer)
-            tokenizer = tokenizer_class(**kwargs["tokenizer_conf"])
-            kwargs["tokenizer"] = tokenizer
-            kwargs["token_list"] = tokenizer.token_list
-            vocab_size = len(tokenizer.token_list)
-        else:
-            vocab_size = -1
-        
-        # build frontend
-        frontend = kwargs.get("frontend", None)
-        if frontend is not None:
-            frontend_class = tables.frontend_classes.get(frontend)
-            frontend = frontend_class(**kwargs["frontend_conf"])
-            kwargs["frontend"] = frontend
-            kwargs["input_size"] = frontend.output_size()
-        
-        # build model
-        model_class = tables.model_classes.get(kwargs["model"])
-        model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=vocab_size)
-        model.eval()
-        model.to(device)
-        
-        # init_param
-        init_param = kwargs.get("init_param", None)
-        if init_param is not None:
-            logging.info(f"Loading pretrained params from {init_param}")
-            load_pretrained_model(
-                model=model,
-                init_param=init_param,
-                ignore_init_mismatch=kwargs.get("ignore_init_mismatch", False),
-                oss_bucket=kwargs.get("oss_bucket", None),
-            )
-        
-        return model, kwargs
-    
-    def __call__(self, input, input_len=None, **cfg):
-        if self.vad_model is None:
-            return self.generate(input, input_len=input_len, **cfg)
-            
-        else:
-            return self.generate_with_vad(input, input_len=input_len, **cfg)
-        
-    def generate(self, input, input_len=None, model=None, kwargs=None, key=None, **cfg):
-        kwargs = self.kwargs if kwargs is None else kwargs
-        kwargs.update(cfg)
-        model = self.model if model is None else model
-
-        batch_size = kwargs.get("batch_size", 1)
-        # if kwargs.get("device", "cpu") == "cpu":
-        #     batch_size = 1
-        
-        key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=kwargs.get("data_type", None), key=key)
-        
-        speed_stats = {}
-        asr_result_list = []
-        num_samples = len(data_list)
-        pbar = tqdm(colour="blue", total=num_samples+1, dynamic_ncols=True)
-        time_speech_total = 0.0
-        time_escape_total = 0.0
-        for beg_idx in range(0, num_samples, batch_size):
-            end_idx = min(num_samples, beg_idx + batch_size)
-            data_batch = data_list[beg_idx:end_idx]
-            key_batch = key_list[beg_idx:end_idx]
-            batch = {"data_in": data_batch, "key": key_batch}
-            if (end_idx - beg_idx) == 1 and isinstance(data_batch[0], torch.Tensor): # fbank
-                batch["data_in"] = data_batch[0]
-                batch["data_lengths"] = input_len
-        
-            time1 = time.perf_counter()
-            with torch.no_grad():
-                results, meta_data = model.inference(**batch, **kwargs)
-            time2 = time.perf_counter()
-            
-            asr_result_list.extend(results)
-            pbar.update(1)
-            
-            # batch_data_time = time_per_frame_s * data_batch_i["speech_lengths"].sum().item()
-            batch_data_time = meta_data.get("batch_data_time", -1)
-            time_escape = time2 - time1
-            speed_stats["load_data"] = meta_data.get("load_data", 0.0)
-            speed_stats["extract_feat"] = meta_data.get("extract_feat", 0.0)
-            speed_stats["forward"] = f"{time_escape:0.3f}"
-            speed_stats["batch_size"] = f"{len(results)}"
-            speed_stats["rtf"] = f"{(time_escape) / batch_data_time:0.3f}"
-            description = (
-                f"{speed_stats}, "
-            )
-            pbar.set_description(description)
-            time_speech_total += batch_data_time
-            time_escape_total += time_escape
-            
-        pbar.update(1)
-        pbar.set_description(f"rtf_avg: {time_escape_total/time_speech_total:0.3f}")
-        torch.cuda.empty_cache()
-        return asr_result_list
-    
-    def generate_with_vad(self, input, input_len=None, **cfg):
-        
-        # step.1: compute the vad model
-        self.vad_kwargs.update(cfg)
-        beg_vad = time.time()
-        res = self.generate(input, input_len=input_len, model=self.vad_model, kwargs=self.vad_kwargs, **cfg)
-        end_vad = time.time()
-        print(f"time cost vad: {end_vad - beg_vad:0.3f}")
-
-
-        # step.2 compute asr model
-        model = self.model
-        kwargs = self.kwargs
-        kwargs.update(cfg)
-        batch_size = int(kwargs.get("batch_size_s", 300))*1000
-        batch_size_threshold_ms = int(kwargs.get("batch_size_threshold_s", 60))*1000
-        kwargs["batch_size"] = batch_size
-        
-        key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=kwargs.get("data_type", None))
-        results_ret_list = []
-        time_speech_total_all_samples = 0.0
-
-        beg_total = time.time()
-        pbar_total = tqdm(colour="red", total=len(res) + 1, dynamic_ncols=True)
-        for i in range(len(res)):
-            key = res[i]["key"]
-            vadsegments = res[i]["value"]
-            input_i = data_list[i]
-            speech = load_audio_text_image_video(input_i, fs=kwargs["frontend"].fs, audio_fs=kwargs.get("fs", 16000))
-            speech_lengths = len(speech)
-            n = len(vadsegments)
-            data_with_index = [(vadsegments[i], i) for i in range(n)]
-            sorted_data = sorted(data_with_index, key=lambda x: x[0][1] - x[0][0])
-            results_sorted = []
-            
-            if not len(sorted_data):
-                logging.info("decoding, utt: {}, empty speech".format(key))
-                continue
-
-            if len(sorted_data) > 0 and len(sorted_data[0]) > 0:
-                batch_size = max(batch_size, sorted_data[0][0][1] - sorted_data[0][0][0])
-            
-            batch_size_ms_cum = 0
-            beg_idx = 0
-            beg_asr_total = time.time()
-            time_speech_total_per_sample = speech_lengths/16000
-            time_speech_total_all_samples += time_speech_total_per_sample
-
-            for j, _ in enumerate(range(0, n)):
-                batch_size_ms_cum += (sorted_data[j][0][1] - sorted_data[j][0][0])
-                if j < n - 1 and (
-                    batch_size_ms_cum + sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size and (
-                    sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size_threshold_ms:
-                    continue
-                batch_size_ms_cum = 0
-                end_idx = j + 1
-                speech_j, speech_lengths_j = slice_padding_audio_samples(speech, speech_lengths, sorted_data[beg_idx:end_idx])       
-                results = self.generate(speech_j, input_len=None, model=model, kwargs=kwargs, **cfg)
-                if self.spk_model is not None:
-                    all_segments = []
-                    # compose vad segments: [[start_time_sec, end_time_sec, speech], [...]]
-                    for _b in range(len(speech_j)):
-                        vad_segments = [[sorted_data[beg_idx:end_idx][_b][0][0]/1000.0, \
-                                        sorted_data[beg_idx:end_idx][_b][0][1]/1000.0, \
-                                        speech_j[_b]]]
-                        segments = sv_chunk(vad_segments)
-                        all_segments.extend(segments)
-                        speech_b = [i[2] for i in segments]
-                        spk_res = self.generate(speech_b, input_len=None, model=self.spk_model, kwargs=kwargs, **cfg)
-                        results[_b]['spk_embedding'] = spk_res[0]['spk_embedding']
-                beg_idx = end_idx
-                if len(results) < 1:
-                    continue
-                results_sorted.extend(results)
-
-
-            pbar_total.update(1)
-            end_asr_total = time.time()
-            time_escape_total_per_sample = end_asr_total - beg_asr_total
-            pbar_total.set_description(f"rtf_avg_per_sample: {time_escape_total_per_sample / time_speech_total_per_sample:0.3f}, "
-                                 f"time_speech_total_per_sample: {time_speech_total_per_sample: 0.3f}, "
-                                 f"time_escape_total_per_sample: {time_escape_total_per_sample:0.3f}")
-
-            restored_data = [0] * n
-            for j in range(n):
-                index = sorted_data[j][1]
-                restored_data[index] = results_sorted[j]
-            result = {}
-            
-            # results combine for texts, timestamps, speaker embeddings and others
-            # TODO: rewrite for clean code
-            for j in range(n):
-                for k, v in restored_data[j].items():
-                    if k.startswith("timestamp"):
-                        if k not in result:
-                            result[k] = []
-                        for t in restored_data[j][k]:
-                            t[0] += vadsegments[j][0]
-                            t[1] += vadsegments[j][0]
-                        result[k].extend(restored_data[j][k])
-                    elif k == 'spk_embedding':
-                        if k not in result:
-                            result[k] = restored_data[j][k]
-                        else:
-                            result[k] = torch.cat([result[k], restored_data[j][k]], dim=0)
-                    elif k == 'text':
-                        if k not in result:
-                            result[k] = restored_data[j][k]
-                        else:
-                            result[k] += " " + restored_data[j][k]
-                    else:
-                        if k not in result:
-                            result[k] = restored_data[j][k]
-                        else:
-                            result[k] += restored_data[j][k]
-                            
-            # step.3 compute punc model
-            if self.punc_model is not None:
-                self.punc_kwargs.update(cfg)
-                punc_res = self.generate(result["text"], model=self.punc_model, kwargs=self.punc_kwargs, **cfg)
-                result["text_with_punc"] = punc_res[0]["text"]
-                     
-            # speaker embedding cluster after resorted
-            if self.spk_model is not None:
-                all_segments = sorted(all_segments, key=lambda x: x[0])
-                spk_embedding = result['spk_embedding']
-                labels = self.cb_model(spk_embedding, oracle_num=self.preset_spk_num)
-                del result['spk_embedding']
-                sv_output = postprocess(all_segments, None, labels, spk_embedding.cpu())
-                if self.spk_mode == 'vad_segment':
-                    sentence_list = []
-                    for res, vadsegment in zip(restored_data, vadsegments):
-                        sentence_list.append({"start": vadsegment[0],\
-                                                "end": vadsegment[1],
-                                                "sentence": res['text'],
-                                                "timestamp": res['timestamp']})
-                else: # punc_segment
-                    sentence_list = timestamp_sentence(punc_res[0]['punc_array'], \
-                                                        result['timestamp'], \
-                                                        result['text'])
-                distribute_spk(sentence_list, sv_output)
-                result['sentence_info'] = sentence_list
-                    
-            result["key"] = key
-            results_ret_list.append(result)
-            pbar_total.update(1)
-            
-        pbar_total.update(1)
-        end_total = time.time()
-        time_escape_total_all_samples = end_total - beg_total
-        pbar_total.set_description(f"rtf_avg_all_samples: {time_escape_total_all_samples / time_speech_total_all_samples:0.3f}, "
-                             f"time_speech_total_all_samples: {time_speech_total_all_samples: 0.3f}, "
-                             f"time_escape_total_all_samples: {time_escape_total_all_samples:0.3f}")
-        return results_ret_list
-
-
-class AutoFrontend:
-    def __init__(self, **kwargs):
-        assert "model" in kwargs
-        if "model_conf" not in kwargs:
-            logging.info("download models from model hub: {}".format(kwargs.get("model_hub", "ms")))
-            kwargs = download_model(**kwargs)
-        
-        # build frontend
-        frontend = kwargs.get("frontend", None)
-        if frontend is not None:
-            frontend_class = tables.frontend_classes.get(frontend)
-            frontend = frontend_class(**kwargs["frontend_conf"])
-
-        self.frontend = frontend
-        if "frontend" in kwargs:
-            del kwargs["frontend"]
-        self.kwargs = kwargs
-
-    
-    def __call__(self, input, input_len=None, kwargs=None, **cfg):
-        
-        kwargs = self.kwargs if kwargs is None else kwargs
-        kwargs.update(cfg)
-
-
-        key_list, data_list = prepare_data_iterator(input, input_len=input_len)
-        batch_size = kwargs.get("batch_size", 1)
-        device = kwargs.get("device", "cpu")
-        if device == "cpu":
-            batch_size = 1
-        
-        meta_data = {}
-        
-        result_list = []
-        num_samples = len(data_list)
-        pbar = tqdm(colour="blue", total=num_samples + 1, dynamic_ncols=True)
-        
-        time0 = time.perf_counter()
-        for beg_idx in range(0, num_samples, batch_size):
-            end_idx = min(num_samples, beg_idx + batch_size)
-            data_batch = data_list[beg_idx:end_idx]
-            key_batch = key_list[beg_idx:end_idx]
-
-            # extract fbank feats
-            time1 = time.perf_counter()
-            audio_sample_list = load_audio_text_image_video(data_batch, fs=self.frontend.fs, audio_fs=kwargs.get("fs", 16000))
-            time2 = time.perf_counter()
-            meta_data["load_data"] = f"{time2 - time1:0.3f}"
-            speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
-                                                   frontend=self.frontend, **kwargs)
-            time3 = time.perf_counter()
-            meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
-            meta_data["batch_data_time"] = speech_lengths.sum().item() * self.frontend.frame_shift * self.frontend.lfr_n / 1000
-            
-            speech.to(device=device), speech_lengths.to(device=device)
-            batch = {"input": speech, "input_len": speech_lengths, "key": key_batch}
-            result_list.append(batch)
-            
-            pbar.update(1)
-            description = (
-                f"{meta_data}, "
-            )
-            pbar.set_description(description)
-        
-        time_end = time.perf_counter()
-        pbar.set_description(f"time escaped total: {time_end - time0:0.3f}")
-        
-        return result_list
 
 
 if __name__ == '__main__':

From b7cb19b01a1454f7a1388e24dcd4e10fc654bd7c Mon Sep 17 00:00:00 2001
From: "shixian.shi" <shixian.shi@alibaba-inc.com>
Date: Tue, 16 Jan 2024 11:30:25 +0800
Subject: [PATCH 2/2] update demo, readme

---
 README.md                                     | 24 ++++++++++++-------
 README_zh.md                                  | 18 +++++++++-----
 .../bicif_paraformer/demo.py                  | 16 ++++++-------
 .../campplus_sv/demo.py                       |  2 +-
 .../contextual_paraformer/demo.py             |  2 +-
 .../ct_transformer/demo.py                    |  4 ++--
 .../ct_transformer_streaming/demo.py          |  2 +-
 .../emotion2vec/demo.py                       |  2 +-
 .../fsmn_vad_streaming/demo.py                |  4 ++--
 .../monotonic_aligner/demo.py                 |  2 +-
 .../paraformer-zh-spk/demo.py                 |  4 ++--
 .../paraformer/demo.py                        |  4 ++--
 .../paraformer_streaming/demo.py              | 16 ++++++-------
 .../seaco_paraformer/demo.py                  |  4 ++--
 funasr/auto/auto_model.py                     |  8 +++----
 funasr/bin/inference.py                       | 21 +---------------
 16 files changed, 63 insertions(+), 70 deletions(-)

diff --git a/README.md b/README.md
index a53ce4d3e..2bd28e218 100644
--- a/README.md
+++ b/README.md
@@ -95,9 +95,9 @@ model = AutoModel(model="paraformer-zh", model_revision="v2.0.2", \
                   vad_model="fsmn-vad", vad_model_revision="v2.0.2", \
                   punc_model="ct-punc-c", punc_model_revision="v2.0.2", \
                   spk_model="cam++", spk_model_revision="v2.0.2")
-res = model(input=f"{model.model_path}/example/asr_example.wav", 
-            batch_size=64, 
-            hotword='魔搭')
+res = model.generate(input=f"{model.model_path}/example/asr_example.wav", 
+                     batch_size=64, 
+                     hotword='魔搭')
 print(res)
 ```
 Note: `model_hub`: represents the model repository, `ms` stands for selecting ModelScope download, `hf` stands for selecting Huggingface download.
@@ -124,7 +124,7 @@ total_chunk_num = int(len((speech)-1)/chunk_stride+1)
 for i in range(total_chunk_num):
     speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
     is_final = i == total_chunk_num - 1
-    res = model(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back)
+    res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back)
     print(res)
 ```
 Note: `chunk_size` is the configuration for streaming latency.` [0,10,5]` indicates that the real-time display granularity is `10*60=600ms`, and the lookahead information is `5*60=300ms`. Each inference input is `600ms` (sample points are `16000*0.6=960`), and the output is the corresponding text. For the last speech segment input, `is_final=True` needs to be set to force the output of the last word.
@@ -135,7 +135,7 @@ from funasr import AutoModel
 
 model = AutoModel(model="fsmn-vad", model_revision="v2.0.2")
 wav_file = f"{model.model_path}/example/asr_example.wav"
-res = model(input=wav_file)
+res = model.generate(input=wav_file)
 print(res)
 ```
 ### Voice Activity Detection (Non-streaming)
@@ -156,7 +156,7 @@ total_chunk_num = int(len((speech)-1)/chunk_stride+1)
 for i in range(total_chunk_num):
     speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
     is_final = i == total_chunk_num - 1
-    res = model(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size)
+    res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size)
     if len(res[0]["value"]):
         print(res)
 ```
@@ -165,7 +165,7 @@ for i in range(total_chunk_num):
 from funasr import AutoModel
 
 model = AutoModel(model="ct-punc", model_revision="v2.0.2")
-res = model(input="那今天的会就到这里吧 happy new year 明年见")
+res = model.generate(input="那今天的会就到这里吧 happy new year 明年见")
 print(res)
 ```
 ### Timestamp Prediction
@@ -175,7 +175,7 @@ from funasr import AutoModel
 model = AutoModel(model="fa-zh", model_revision="v2.0.2")
 wav_file = f"{model.model_path}/example/asr_example.wav"
 text_file = f"{model.model_path}/example/text.txt"
-res = model(input=(wav_file, text_file), data_type=("sound", "text"))
+res = model.generate(input=(wav_file, text_file), data_type=("sound", "text"))
 print(res)
 ```
 [//]: # (FunASR supports inference and fine-tuning of models trained on industrial datasets of tens of thousands of hours. For more details, please refer to &#40;[modelscope_egs]&#40;https://alibaba-damo-academy.github.io/FunASR/en/modelscope_pipeline/quick_start.html&#41;&#41;. It also supports training and fine-tuning of models on academic standard datasets. For more details, please refer to&#40;[egs]&#40;https://alibaba-damo-academy.github.io/FunASR/en/academic_recipe/asr_recipe.html&#41;&#41;. The models include speech recognition &#40;ASR&#41;, speech activity detection &#40;VAD&#41;, punctuation recovery, language model, speaker verification, speaker separation, and multi-party conversation speech recognition. For a detailed list of models, please refer to the [Model Zoo]&#40;https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md&#41;:)
@@ -229,10 +229,16 @@ The use of pretraining model is subject to [model license](./MODEL_LICENSE)
 }
 @inproceedings{gao22b_interspeech,
   author={Zhifu Gao and ShiLiang Zhang and Ian McLoughlin and Zhijie Yan},
-  title={{Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition}},
+  title={Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition},
   year=2022,
   booktitle={Proc. Interspeech 2022},
   pages={2063--2067},
   doi={10.21437/Interspeech.2022-9996}
 }
+@inproceedings{shi2023seaco,
+  author={Xian Shi and Yexin Yang and Zerui Li and Yanni Chen and Zhifu Gao and Shiliang Zhang},
+  title={SeACo-Paraformer: A Non-Autoregressive ASR System with Flexible and Effective Hotword Customization Ability},
+  year={2023},
+  booktitle={ICASSP2024}
+}
 ```
diff --git a/README_zh.md b/README_zh.md
index 861e61c2e..dc2030222 100644
--- a/README_zh.md
+++ b/README_zh.md
@@ -91,7 +91,7 @@ model = AutoModel(model="paraformer-zh", model_revision="v2.0.2", \
                   vad_model="fsmn-vad", vad_model_revision="v2.0.2", \
                   punc_model="ct-punc-c", punc_model_revision="v2.0.2", \
                   spk_model="cam++", spk_model_revision="v2.0.2")
-res = model(input=f"{model.model_path}/example/asr_example.wav", 
+res = model.generate(input=f"{model.model_path}/example/asr_example.wav", 
             batch_size=64, 
             hotword='魔搭')
 print(res)
@@ -121,7 +121,7 @@ total_chunk_num = int(len((speech)-1)/chunk_stride+1)
 for i in range(total_chunk_num):
     speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
     is_final = i == total_chunk_num - 1
-    res = model(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back)
+    res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back)
     print(res)
 ```
 
@@ -134,7 +134,7 @@ from funasr import AutoModel
 model = AutoModel(model="fsmn-vad", model_revision="v2.0.2")
 
 wav_file = f"{model.model_path}/example/asr_example.wav"
-res = model(input=wav_file)
+res = model.generate(input=wav_file)
 print(res)
 ```
 
@@ -156,7 +156,7 @@ total_chunk_num = int(len((speech)-1)/chunk_stride+1)
 for i in range(total_chunk_num):
     speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
     is_final = i == total_chunk_num - 1
-    res = model(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size)
+    res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size)
     if len(res[0]["value"]):
         print(res)
 ```
@@ -167,7 +167,7 @@ from funasr import AutoModel
 
 model = AutoModel(model="ct-punc", model_revision="v2.0.2")
 
-res = model(input="那今天的会就到这里吧 happy new year 明年见")
+res = model.generate(input="那今天的会就到这里吧 happy new year 明年见")
 print(res)
 ```
 
@@ -179,7 +179,7 @@ model = AutoModel(model="fa-zh", model_revision="v2.0.0")
 
 wav_file = f"{model.model_path}/example/asr_example.wav"
 text_file = f"{model.model_path}/example/text.txt"
-res = model(input=(wav_file, text_file), data_type=("sound", "text"))
+res = model.generate(input=(wav_file, text_file), data_type=("sound", "text"))
 print(res)
 ```
 更多详细用法（[示例](examples/industrial_data_pretraining)）
@@ -242,4 +242,10 @@ FunASR支持预训练或者进一步微调的模型进行服务部署。目前
   pages={2063--2067},
   doi={10.21437/Interspeech.2022-9996}
 }
+@article{shi2023seaco,
+  author={Xian Shi and Yexin Yang and Zerui Li and Yanni Chen and Zhifu Gao and Shiliang Zhang},
+  title={{SeACo-Paraformer: A Non-Autoregressive ASR System with Flexible and Effective Hotword Customization Ability}},
+  year=2023,
+  journal={arXiv preprint arXiv:2308.03266(accepted by ICASSP2024)},
+}
 ```
diff --git a/examples/industrial_data_pretraining/bicif_paraformer/demo.py b/examples/industrial_data_pretraining/bicif_paraformer/demo.py
index 60718de08..a06b308d1 100644
--- a/examples/industrial_data_pretraining/bicif_paraformer/demo.py
+++ b/examples/industrial_data_pretraining/bicif_paraformer/demo.py
@@ -6,14 +6,14 @@
 from funasr import AutoModel
 
 model = AutoModel(model="damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
-                    model_revision="v2.0.2",
-                    vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
-                    vad_model_revision="v2.0.2",
-                    punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
-                    punc_model_revision="v2.0.2",
-                    spk_model="damo/speech_campplus_sv_zh-cn_16k-common",
-                    spk_model_revision="v2.0.2",
+                  model_revision="v2.0.2",
+                  vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
+                  vad_model_revision="v2.0.2",
+                  punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
+                  punc_model_revision="v2.0.2",
+                  spk_model="damo/speech_campplus_sv_zh-cn_16k-common",
+                  spk_model_revision="v2.0.2",
                   )
 
-res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_vad_punc_example.wav", batch_size_s=300, batch_size_threshold_s=60)
+res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_vad_punc_example.wav", batch_size_s=300, batch_size_threshold_s=60)
 print(res)
diff --git a/examples/industrial_data_pretraining/campplus_sv/demo.py b/examples/industrial_data_pretraining/campplus_sv/demo.py
index 6a7f10548..16d629b29 100644
--- a/examples/industrial_data_pretraining/campplus_sv/demo.py
+++ b/examples/industrial_data_pretraining/campplus_sv/demo.py
@@ -9,5 +9,5 @@ model = AutoModel(model="damo/speech_campplus_sv_zh-cn_16k-common",
                   model_revision="v2.0.2",
                   )
 
-res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav")
+res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav")
 print(res)
\ No newline at end of file
diff --git a/examples/industrial_data_pretraining/contextual_paraformer/demo.py b/examples/industrial_data_pretraining/contextual_paraformer/demo.py
index 78693c527..d1378ca3a 100644
--- a/examples/industrial_data_pretraining/contextual_paraformer/demo.py
+++ b/examples/industrial_data_pretraining/contextual_paraformer/demo.py
@@ -7,6 +7,6 @@ from funasr import AutoModel
 
 model = AutoModel(model="damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404", model_revision="v2.0.2")
 
-res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
+res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
             hotword='达摩院 魔搭')
 print(res)
\ No newline at end of file
diff --git a/examples/industrial_data_pretraining/ct_transformer/demo.py b/examples/industrial_data_pretraining/ct_transformer/demo.py
index d648f3d27..f547f0342 100644
--- a/examples/industrial_data_pretraining/ct_transformer/demo.py
+++ b/examples/industrial_data_pretraining/ct_transformer/demo.py
@@ -7,7 +7,7 @@ from funasr import AutoModel
 
 model = AutoModel(model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch", model_revision="v2.0.2")
 
-res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/punc_example.txt")
+res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/punc_example.txt")
 print(res)
 
 
@@ -15,5 +15,5 @@ from funasr import AutoModel
 
 model = AutoModel(model="damo/punc_ct-transformer_cn-en-common-vocab471067-large", model_revision="v2.0.2")
 
-res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/punc_example.txt")
+res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/punc_example.txt")
 print(res)
\ No newline at end of file
diff --git a/examples/industrial_data_pretraining/ct_transformer_streaming/demo.py b/examples/industrial_data_pretraining/ct_transformer_streaming/demo.py
index 5ef83813e..081fd1928 100644
--- a/examples/industrial_data_pretraining/ct_transformer_streaming/demo.py
+++ b/examples/industrial_data_pretraining/ct_transformer_streaming/demo.py
@@ -12,7 +12,7 @@ vads = inputs.split("|")
 rec_result_all = "outputs: "
 cache = {}
 for vad in vads:
-    rec_result = model(input=vad, cache=cache)
+    rec_result = model.generate(input=vad, cache=cache)
     print(rec_result)
     rec_result_all += rec_result[0]['text']
 
diff --git a/examples/industrial_data_pretraining/emotion2vec/demo.py b/examples/industrial_data_pretraining/emotion2vec/demo.py
index abaa9f40e..ea8da99dd 100644
--- a/examples/industrial_data_pretraining/emotion2vec/demo.py
+++ b/examples/industrial_data_pretraining/emotion2vec/demo.py
@@ -7,5 +7,5 @@ from funasr import AutoModel
 
 model = AutoModel(model="damo/emotion2vec_base", model_revision="v2.0.1")
 
-res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav", output_dir="./outputs")
+res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav", output_dir="./outputs")
 print(res)
\ No newline at end of file
diff --git a/examples/industrial_data_pretraining/fsmn_vad_streaming/demo.py b/examples/industrial_data_pretraining/fsmn_vad_streaming/demo.py
index 459dfff41..8084dec26 100644
--- a/examples/industrial_data_pretraining/fsmn_vad_streaming/demo.py
+++ b/examples/industrial_data_pretraining/fsmn_vad_streaming/demo.py
@@ -9,7 +9,7 @@ wav_file = "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audi
 chunk_size = 60000 # ms
 model = AutoModel(model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch", model_revision="v2.0.2")
 
-res = model(input=wav_file, chunk_size=chunk_size, )
+res = model.generate(input=wav_file, chunk_size=chunk_size, )
 print(res)
 
 
@@ -28,7 +28,7 @@ total_chunk_num = int(len((speech)-1)/chunk_stride+1)
 for i in range(total_chunk_num):
     speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
     is_final = i == total_chunk_num - 1
-    res = model(input=speech_chunk,
+    res = model.generate(input=speech_chunk,
                 cache=cache,
                 is_final=is_final,
                 chunk_size=chunk_size,
diff --git a/examples/industrial_data_pretraining/monotonic_aligner/demo.py b/examples/industrial_data_pretraining/monotonic_aligner/demo.py
index def6b7de8..cad9aab91 100644
--- a/examples/industrial_data_pretraining/monotonic_aligner/demo.py
+++ b/examples/industrial_data_pretraining/monotonic_aligner/demo.py
@@ -7,7 +7,7 @@ from funasr import AutoModel
 
 model = AutoModel(model="damo/speech_timestamp_prediction-v1-16k-offline", model_revision="v2.0.2")
 
-res = model(input=("https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
+res = model.generate(input=("https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
                    "欢迎大家来到魔搭社区进行体验"),
             data_type=("sound", "text"),
             batch_size=2,
diff --git a/examples/industrial_data_pretraining/paraformer-zh-spk/demo.py b/examples/industrial_data_pretraining/paraformer-zh-spk/demo.py
index aa895eb85..b4453e927 100644
--- a/examples/industrial_data_pretraining/paraformer-zh-spk/demo.py
+++ b/examples/industrial_data_pretraining/paraformer-zh-spk/demo.py
@@ -15,6 +15,6 @@ model = AutoModel(model="damo/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-co
                   spk_model_revision="v2.0.2"
                   )
 
-res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
-            hotword='达摩院 磨搭')
+res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
+                     hotword='达摩院 磨搭')
 print(res)
\ No newline at end of file
diff --git a/examples/industrial_data_pretraining/paraformer/demo.py b/examples/industrial_data_pretraining/paraformer/demo.py
index 6dbe33d06..ef33bf40d 100644
--- a/examples/industrial_data_pretraining/paraformer/demo.py
+++ b/examples/industrial_data_pretraining/paraformer/demo.py
@@ -7,7 +7,7 @@ from funasr import AutoModel
 
 model = AutoModel(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", model_revision="v2.0.2")
 
-res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav")
+res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav")
 print(res)
 
 
@@ -18,5 +18,5 @@ frontend = AutoFrontend(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-co
 fbanks = frontend(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav", batch_size=2)
 
 for batch_idx, fbank_dict in enumerate(fbanks):
-    res = model(**fbank_dict)
+    res = model.generate(**fbank_dict)
     print(res)
\ No newline at end of file
diff --git a/examples/industrial_data_pretraining/paraformer_streaming/demo.py b/examples/industrial_data_pretraining/paraformer_streaming/demo.py
index 8f7eef350..07efde67c 100644
--- a/examples/industrial_data_pretraining/paraformer_streaming/demo.py
+++ b/examples/industrial_data_pretraining/paraformer_streaming/demo.py
@@ -11,7 +11,7 @@ decoder_chunk_look_back = 1 #number of encoder chunks to lookback for decoder cr
 
 model = AutoModel(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online", model_revision="v2.0.2")
 cache = {}
-res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
+res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
             chunk_size=chunk_size,
             encoder_chunk_look_back=encoder_chunk_look_back,
             decoder_chunk_look_back=decoder_chunk_look_back,
@@ -32,11 +32,11 @@ total_chunk_num = int(len((speech)-1)/chunk_stride+1)
 for i in range(total_chunk_num):
     speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
     is_final = i == total_chunk_num - 1
-    res = model(input=speech_chunk,
-                cache=cache,
-                is_final=is_final,
-                chunk_size=chunk_size,
-                encoder_chunk_look_back=encoder_chunk_look_back,
-                decoder_chunk_look_back=decoder_chunk_look_back,
-                )
+    res = model.generate(input=speech_chunk,
+                         cache=cache,
+                         is_final=is_final,
+                         chunk_size=chunk_size,
+                         encoder_chunk_look_back=encoder_chunk_look_back,
+                         decoder_chunk_look_back=decoder_chunk_look_back,
+                         )
     print(res)
diff --git a/examples/industrial_data_pretraining/seaco_paraformer/demo.py b/examples/industrial_data_pretraining/seaco_paraformer/demo.py
index cf49e42c4..5f17252f9 100644
--- a/examples/industrial_data_pretraining/seaco_paraformer/demo.py
+++ b/examples/industrial_data_pretraining/seaco_paraformer/demo.py
@@ -15,6 +15,6 @@ model = AutoModel(model="damo/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-co
                   spk_model_revision="v2.0.2",
                   )
 
-res = model(input=f"{model.model_path}/example/asr_example.wav",
-            hotword='达摩院 魔搭')
+res = model.generate(input=f"{model.model_path}/example/asr_example.wav",
+                     hotword='达摩院 魔搭')
 print(res)
\ No newline at end of file
diff --git a/funasr/auto/auto_model.py b/funasr/auto/auto_model.py
index 25edeb708..580cca8d4 100644
--- a/funasr/auto/auto_model.py
+++ b/funasr/auto/auto_model.py
@@ -264,7 +264,7 @@ class AutoModel:
         # step.1: compute the vad model
         self.vad_kwargs.update(cfg)
         beg_vad = time.time()
-        res = self.generate(input, input_len=input_len, model=self.vad_model, kwargs=self.vad_kwargs, **cfg)
+        res = self.inference(input, input_len=input_len, model=self.vad_model, kwargs=self.vad_kwargs, **cfg)
         end_vad = time.time()
         print(f"time cost vad: {end_vad - beg_vad:0.3f}")
 
@@ -316,7 +316,7 @@ class AutoModel:
                 batch_size_ms_cum = 0
                 end_idx = j + 1
                 speech_j, speech_lengths_j = slice_padding_audio_samples(speech, speech_lengths, sorted_data[beg_idx:end_idx])       
-                results = self.generate(speech_j, input_len=None, model=model, kwargs=kwargs, **cfg)
+                results = self.inference(speech_j, input_len=None, model=model, kwargs=kwargs, **cfg)
                 if self.spk_model is not None:
                     all_segments = []
                     # compose vad segments: [[start_time_sec, end_time_sec, speech], [...]]
@@ -327,7 +327,7 @@ class AutoModel:
                         segments = sv_chunk(vad_segments)
                         all_segments.extend(segments)
                         speech_b = [i[2] for i in segments]
-                        spk_res = self.generate(speech_b, input_len=None, model=self.spk_model, kwargs=kwargs, **cfg)
+                        spk_res = self.inference(speech_b, input_len=None, model=self.spk_model, kwargs=kwargs, **cfg)
                         results[_b]['spk_embedding'] = spk_res[0]['spk_embedding']
                 beg_idx = end_idx
                 if len(results) < 1:
@@ -378,7 +378,7 @@ class AutoModel:
             # step.3 compute punc model
             if self.punc_model is not None:
                 self.punc_kwargs.update(cfg)
-                punc_res = self.generate(result["text"], model=self.punc_model, kwargs=self.punc_kwargs, **cfg)
+                punc_res = self.inference(result["text"], model=self.punc_model, kwargs=self.punc_kwargs, **cfg)
                 result["text_with_punc"] = punc_res[0]["text"]
                      
             # speaker embedding cluster after resorted
diff --git a/funasr/bin/inference.py b/funasr/bin/inference.py
index bc435c43c..d2f0c149d 100644
--- a/funasr/bin/inference.py
+++ b/funasr/bin/inference.py
@@ -1,25 +1,7 @@
-import json
-import time
-import torch
 import hydra
-import random
-import string
 import logging
-import os.path
-from tqdm import tqdm
 from omegaconf import DictConfig, OmegaConf, ListConfig
 
-from funasr.register import tables
-from funasr.utils.load_utils import load_bytes
-from funasr.download.file import download_from_url
-from funasr.download.download_from_hub import download_model
-from funasr.utils.vad_utils import slice_padding_audio_samples
-from funasr.train_utils.set_all_random_seed import set_all_random_seed
-from funasr.train_utils.load_pretrained_model import load_pretrained_model
-from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
-from funasr.utils.timestamp_tools import timestamp_sentence
-from funasr.models.campplus.utils import sv_chunk, postprocess, distribute_spk
-from funasr.models.campplus.cluster_backend import ClusterBackend
 from funasr.auto.auto_model import AutoModel
 
 
@@ -41,10 +23,9 @@ def main_hydra(cfg: DictConfig):
     if kwargs.get("debug", False):
         import pdb; pdb.set_trace()
     model = AutoModel(**kwargs)
-    res = model(input=kwargs["input"])
+    res = model.generate(input=kwargs["input"])
     print(res)
 
 
-
 if __name__ == '__main__':
     main_hydra()
\ No newline at end of file