Dev gzf (#1467)

zhifu gao

2024-03-11 a7d7a0f3a2e7cd44a337ced34e3536b12ccb534e

 funasr/auto/auto_model.py

@@ -1,3 +1,8 @@
#!/usr/bin/env python3
# -*- encoding: utf-8 -*-
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
#  MIT License  (https://opensource.org/licenses/MIT)

import json
import time
import copy
@@ -9,21 +14,23 @@
import numpy as np
from tqdm import tqdm

from funasr.utils.misc import deep_update
from funasr.register import tables
from funasr.utils.load_utils import load_bytes
from funasr.download.file import download_from_url
from funasr.utils.timestamp_tools import timestamp_sentence
from funasr.download.download_from_hub import download_model
from funasr.utils.vad_utils import slice_padding_audio_samples
from funasr.utils.load_utils import load_audio_text_image_video
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
from funasr.utils.timestamp_tools import timestamp_sentence
from funasr.models.campplus.utils import sv_chunk, postprocess, distribute_spk
from funasr.utils import export_utils
try:
    from funasr.models.campplus.utils import sv_chunk, postprocess, distribute_spk
    from funasr.models.campplus.cluster_backend import ClusterBackend
except:
    print("If you want to use the speaker diarization, please `pip install hdbscan`")
import pdb


def prepare_data_iterator(data_in, input_len=None, data_type=None, key=None):
    """
@@ -36,11 +43,12 @@
    """
    data_list = []
    key_list = []
    filelist = [".scp", ".txt", ".json", ".jsonl"]
    filelist = [".scp", ".txt", ".json", ".jsonl", ".text"]
    
    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()
@@ -90,9 +98,9 @@
class AutoModel:
    
    def __init__(self, **kwargs):
        if not kwargs.get("disable_log", False):
        if not kwargs.get("disable_log", True):
            tables.print()
        

        model, kwargs = self.build_model(**kwargs)
        
        # if vad_model is not None, build vad model else None
@@ -137,18 +145,18 @@
    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")))
            logging.info("download models from model hub: {}".format(kwargs.get("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", 1) == 0:
            device = "cpu"
            kwargs["batch_size"] = 1
        kwargs["device"] = device
        
        if kwargs.get("ncpu", None):
        if kwargs.get("ncpu", 4):
            torch.set_num_threads(kwargs.get("ncpu"))
        
        # build tokenizer
@@ -157,43 +165,47 @@
            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)

            kwargs["token_list"] = tokenizer.token_list if hasattr(tokenizer, "token_list") else None
            kwargs["token_list"] = tokenizer.get_vocab() if hasattr(tokenizer, "get_vocab") else kwargs["token_list"]
            vocab_size = len(kwargs["token_list"]) if kwargs["token_list"] is not None else -1
        else:
            vocab_size = -1
        
        # build frontend
        frontend = kwargs.get("frontend", None)
        kwargs["input_size"] = 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()
            kwargs["input_size"] = frontend.output_size() if hasattr(frontend, "output_size") else None
        
        # build model
        model_class = tables.model_classes.get(kwargs["model"])
        model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=vocab_size)
        
        model = model_class(**kwargs, **kwargs.get("model_conf", {}), vocab_size=vocab_size)
        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,
                path=init_param,
                ignore_init_mismatch=kwargs.get("ignore_init_mismatch", False),
                oss_bucket=kwargs.get("oss_bucket", None),
                scope_map=kwargs.get("scope_map", None),
                excludes=kwargs.get("excludes", None),
            )
            if os.path.exists(init_param):
                logging.info(f"Loading pretrained params from {init_param}")
                load_pretrained_model(
                    model=model,
                    path=init_param,
                    ignore_init_mismatch=kwargs.get("ignore_init_mismatch", False),
                    oss_bucket=kwargs.get("oss_bucket", None),
                    scope_map=kwargs.get("scope_map", []),
                    excludes=kwargs.get("excludes", None),
                )
            else:
                print(f"error, init_param does not exist!: {init_param}")
        
        return model, kwargs
    
    def __call__(self, *args, **cfg):
        kwargs = self.kwargs
        kwargs.update(cfg)
        deep_update(kwargs, cfg)
        res = self.model(*args, kwargs)
        return res

@@ -206,20 +218,20 @@
        
    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)
        deep_update(kwargs, cfg)
        model = self.model if model is None else model
        model.eval()

        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)
        disable_pbar = kwargs.get("disable_pbar", False)
        disable_pbar = self.kwargs.get("disable_pbar", False)
        pbar = tqdm(colour="blue", total=num_samples, dynamic_ncols=True) if not disable_pbar else None
        time_speech_total = 0.0
        time_escape_total = 0.0
@@ -232,14 +244,15 @@
            if (end_idx - beg_idx) == 1 and kwargs.get("data_type", None) == "fbank": # fbank
                batch["data_in"] = data_batch[0]
                batch["data_lengths"] = input_len
        

            time1 = time.perf_counter()
            with torch.no_grad():
                pdb.set_trace()
                results, meta_data = model.inference(**batch, **kwargs)
                 res = model.inference(**batch, **kwargs)
                 if isinstance(res, (list, tuple)):
                    results = res[0]
                    meta_data = res[1] if len(res) > 1 else {}
            time2 = time.perf_counter()
            
            pdb.set_trace()

            asr_result_list.extend(results)

            # batch_data_time = time_per_frame_s * data_batch_i["speech_lengths"].sum().item()
@@ -264,31 +277,29 @@
            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):
        
        kwargs = self.kwargs
        # step.1: compute the vad model
        self.vad_kwargs.update(cfg)
        deep_update(self.vad_kwargs, cfg)
        beg_vad = time.time()
        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}")


        # step.2 compute asr model
        model = self.model
        kwargs = self.kwargs
        kwargs.update(cfg)
        deep_update(kwargs, 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 = 1e-6

        beg_total = time.time()
        pbar_total = tqdm(colour="red", total=len(res), dynamic_ncols=True)
        pbar_total = tqdm(colour="red", total=len(res), dynamic_ncols=True) if not kwargs.get("disable_pbar", False) else None
        for i in range(len(res)):
            key = res[i]["key"]
            vadsegments = res[i]["value"]
@@ -299,14 +310,14 @@
            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()
@@ -325,8 +336,8 @@
                    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.inference(speech_j, input_len=None, model=model, kwargs=kwargs, disable_pbar=True, **cfg)
                speech_j, speech_lengths_j = slice_padding_audio_samples(speech, speech_lengths, sorted_data[beg_idx:end_idx])
                results = self.inference(speech_j, input_len=None, model=model, kwargs=kwargs, **cfg)
                if self.spk_model is not None:
                    # compose vad segments: [[start_time_sec, end_time_sec, speech], [...]]
                    for _b in range(len(speech_j)):
@@ -336,26 +347,26 @@
                        segments = sv_chunk(vad_segments)
                        all_segments.extend(segments)
                        speech_b = [i[2] for i in segments]
                        spk_res = self.inference(speech_b, input_len=None, model=self.spk_model, kwargs=kwargs, disable_pbar=True, **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:
                    continue
                results_sorted.extend(results)
            

            # end_asr_total = time.time()
            # time_escape_total_per_sample = end_asr_total - beg_asr_total
            # pbar_sample.update(1)
            # pbar_sample.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):
@@ -382,18 +393,22 @@
                            result[k] = restored_data[j][k]
                        else:
                            result[k] += restored_data[j][k]
            
            return_raw_text = kwargs.get('return_raw_text', False)            

            return_raw_text = kwargs.get('return_raw_text', False)
            # step.3 compute punc model
            if self.punc_model is not None:
                self.punc_kwargs.update(cfg)
                punc_res = self.inference(result["text"], model=self.punc_model, kwargs=self.punc_kwargs, disable_pbar=True, **cfg)
                raw_text = copy.copy(result["text"])
                if return_raw_text: result['raw_text'] = raw_text
                result["text"] = punc_res[0]["text"]
                if not len(result["text"]):
                    if return_raw_text:
                        result['raw_text'] = ''
                else:
                    deep_update(self.punc_kwargs, cfg)
                    punc_res = self.inference(result["text"], model=self.punc_model, kwargs=self.punc_kwargs, **cfg)
                    raw_text = copy.copy(result["text"])
                    if return_raw_text: result['raw_text'] = raw_text
                    result["text"] = punc_res[0]["text"]
            else:
                raw_text = None
                

            # speaker embedding cluster after resorted
            if self.spk_model is not None and kwargs.get('return_spk_res', True):
                if raw_text is None:
@@ -426,19 +441,23 @@
                distribute_spk(sentence_list, sv_output)
                result['sentence_info'] = sentence_list
            elif kwargs.get("sentence_timestamp", False):
                sentence_list = timestamp_sentence(punc_res[0]['punc_array'],
                                                   result['timestamp'],
                                                   raw_text,
                                                   return_raw_text=return_raw_text)
                if not len(result['text']):
                    sentence_list = []
                else:
                    sentence_list = timestamp_sentence(punc_res[0]['punc_array'],
                                                       result['timestamp'],
                                                       raw_text,
                                                       return_raw_text=return_raw_text)
                result['sentence_info'] = sentence_list
            if "spk_embedding" in result: del result['spk_embedding']
                    

            result["key"] = key
            results_ret_list.append(result)
            end_asr_total = time.time()
            time_escape_total_per_sample = end_asr_total - beg_asr_total
            pbar_total.update(1)
            pbar_total.set_description(f"rtf_avg: {time_escape_total_per_sample / time_speech_total_per_sample:0.3f}, "
            if pbar_total:
                pbar_total.update(1)
                pbar_total.set_description(f"rtf_avg: {time_escape_total_per_sample / time_speech_total_per_sample:0.3f}, "
                                 f"time_speech: {time_speech_total_per_sample: 0.3f}, "
                                 f"time_escape: {time_escape_total_per_sample:0.3f}")

@@ -450,3 +469,37 @@
        #                      f"time_escape_all: {time_escape_total_all_samples:0.3f}")
        return results_ret_list

    def export(self, input=None,
               type : str = "onnx",
               quantize: bool = False,
               fallback_num: int = 5,
               calib_num: int = 100,
               opset_version: int = 14,
               **cfg):
    
        device = cfg.get("device", "cpu")
        model = self.model.to(device=device)
        kwargs = self.kwargs
        deep_update(kwargs, cfg)
        kwargs["device"] = device
        del kwargs["model"]
        model.eval()

        batch_size = 1

        key_list, data_list = prepare_data_iterator(input, input_len=None, data_type=kwargs.get("data_type", None), key=None)

        with torch.no_grad():
            
            if type == "onnx":
                export_dir = export_utils.export_onnx(
                                        model=model,
                                        data_in=data_list,
                                        **kwargs)
            else:
                export_dir = export_utils.export_torchscripts(
                                        model=model,
                                        data_in=data_list,
                                        **kwargs)

        return export_dir