mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
451 lines
15 KiB
Python
451 lines
15 KiB
Python
import os.path
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import torch
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import numpy as np
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import hydra
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import json
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from omegaconf import DictConfig, OmegaConf, ListConfig
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import logging
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from funasr.download.download_from_hub import download_model
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from funasr.train_utils.set_all_random_seed import set_all_random_seed
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from funasr.utils.load_utils import load_bytes
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from funasr.train_utils.device_funcs import to_device
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from tqdm import tqdm
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from funasr.train_utils.load_pretrained_model import load_pretrained_model
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import time
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import random
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import string
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from funasr.register import tables
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from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
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from funasr.utils.vad_utils import slice_padding_audio_samples
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from funasr.utils.timestamp_tools import time_stamp_sentence
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from funasr.download.file import download_from_url
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def prepare_data_iterator(data_in, input_len=None, data_type=None, key=None):
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"""
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:param input:
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:param input_len:
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:param data_type:
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:param frontend:
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:return:
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"""
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data_list = []
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key_list = []
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filelist = [".scp", ".txt", ".json", ".jsonl"]
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chars = string.ascii_letters + string.digits
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if isinstance(data_in, str) and data_in.startswith('http'): # url
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data_in = download_from_url(data_in)
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if isinstance(data_in, str) and os.path.exists(data_in): # wav_path; filelist: wav.scp, file.jsonl;text.txt;
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_, file_extension = os.path.splitext(data_in)
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file_extension = file_extension.lower()
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if file_extension in filelist: #filelist: wav.scp, file.jsonl;text.txt;
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with open(data_in, encoding='utf-8') as fin:
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for line in fin:
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key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
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if data_in.endswith(".jsonl"): #file.jsonl: json.dumps({"source": data})
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lines = json.loads(line.strip())
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data = lines["source"]
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key = data["key"] if "key" in data else key
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else: # filelist, wav.scp, text.txt: id \t data or data
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lines = line.strip().split(maxsplit=1)
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data = lines[1] if len(lines)>1 else lines[0]
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key = lines[0] if len(lines)>1 else key
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data_list.append(data)
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key_list.append(key)
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else:
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key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
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data_list = [data_in]
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key_list = [key]
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elif isinstance(data_in, (list, tuple)):
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if data_type is not None and isinstance(data_type, (list, tuple)): # mutiple inputs
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data_list_tmp = []
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for data_in_i, data_type_i in zip(data_in, data_type):
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key_list, data_list_i = prepare_data_iterator(data_in=data_in_i, data_type=data_type_i)
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data_list_tmp.append(data_list_i)
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data_list = []
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for item in zip(*data_list_tmp):
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data_list.append(item)
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else:
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# [audio sample point, fbank, text]
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data_list = data_in
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key_list = ["rand_key_" + ''.join(random.choice(chars) for _ in range(13)) for _ in range(len(data_in))]
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else: # raw text; audio sample point, fbank; bytes
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if isinstance(data_in, bytes): # audio bytes
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data_in = load_bytes(data_in)
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if key is None:
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key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
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data_list = [data_in]
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key_list = [key]
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return key_list, data_list
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@hydra.main(config_name=None, version_base=None)
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def main_hydra(cfg: DictConfig):
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def to_plain_list(cfg_item):
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if isinstance(cfg_item, ListConfig):
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return OmegaConf.to_container(cfg_item, resolve=True)
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elif isinstance(cfg_item, DictConfig):
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return {k: to_plain_list(v) for k, v in cfg_item.items()}
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else:
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return cfg_item
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kwargs = to_plain_list(cfg)
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log_level = getattr(logging, kwargs.get("log_level", "INFO").upper())
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logging.basicConfig(level=log_level)
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if kwargs.get("debug", False):
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import pdb; pdb.set_trace()
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model = AutoModel(**kwargs)
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res = model(input=kwargs["input"])
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print(res)
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class AutoModel:
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def __init__(self, **kwargs):
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tables.print()
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model, kwargs = self.build_model(**kwargs)
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# if vad_model is not None, build vad model else None
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vad_model = kwargs.get("vad_model", None)
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vad_kwargs = kwargs.get("vad_model_revision", None)
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if vad_model is not None:
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print("build vad model")
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vad_kwargs = {"model": vad_model, "model_revision": vad_kwargs}
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vad_model, vad_kwargs = self.build_model(**vad_kwargs)
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# if punc_model is not None, build punc model else None
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punc_model = kwargs.get("punc_model", None)
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punc_kwargs = kwargs.get("punc_model_revision", None)
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if punc_model is not None:
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punc_kwargs = {"model": punc_model, "model_revision": punc_kwargs}
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punc_model, punc_kwargs = self.build_model(**punc_kwargs)
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self.kwargs = kwargs
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self.model = model
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self.vad_model = vad_model
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self.vad_kwargs = vad_kwargs
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self.punc_model = punc_model
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self.punc_kwargs = punc_kwargs
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def build_model(self, **kwargs):
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assert "model" in kwargs
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if "model_conf" not in kwargs:
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logging.info("download models from model hub: {}".format(kwargs.get("model_hub", "ms")))
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kwargs = download_model(**kwargs)
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set_all_random_seed(kwargs.get("seed", 0))
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device = kwargs.get("device", "cuda")
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if not torch.cuda.is_available() or kwargs.get("ngpu", 0):
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device = "cpu"
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# kwargs["batch_size"] = 1
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kwargs["device"] = device
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if kwargs.get("ncpu", None):
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torch.set_num_threads(kwargs.get("ncpu"))
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# build tokenizer
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tokenizer = kwargs.get("tokenizer", None)
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if tokenizer is not None:
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tokenizer_class = tables.tokenizer_classes.get(tokenizer.lower())
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tokenizer = tokenizer_class(**kwargs["tokenizer_conf"])
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kwargs["tokenizer"] = tokenizer
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kwargs["token_list"] = tokenizer.token_list
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# build frontend
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frontend = kwargs.get("frontend", None)
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if frontend is not None:
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frontend_class = tables.frontend_classes.get(frontend.lower())
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frontend = frontend_class(**kwargs["frontend_conf"])
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kwargs["frontend"] = frontend
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kwargs["input_size"] = frontend.output_size()
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# build model
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model_class = tables.model_classes.get(kwargs["model"].lower())
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model = model_class(**kwargs, **kwargs["model_conf"],
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vocab_size=len(tokenizer.token_list) if tokenizer is not None else -1)
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model.eval()
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model.to(device)
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# init_param
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init_param = kwargs.get("init_param", None)
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if init_param is not None:
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logging.info(f"Loading pretrained params from {init_param}")
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load_pretrained_model(
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model=model,
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init_param=init_param,
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ignore_init_mismatch=kwargs.get("ignore_init_mismatch", False),
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oss_bucket=kwargs.get("oss_bucket", None),
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)
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return model, kwargs
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def __call__(self, input, input_len=None, **cfg):
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if self.vad_model is None:
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return self.generate(input, input_len=input_len, **cfg)
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else:
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return self.generate_with_vad(input, input_len=input_len, **cfg)
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def generate(self, input, input_len=None, model=None, kwargs=None, key=None, **cfg):
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# import pdb; pdb.set_trace()
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kwargs = self.kwargs if kwargs is None else kwargs
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kwargs.update(cfg)
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model = self.model if model is None else model
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batch_size = kwargs.get("batch_size", 1)
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# if kwargs.get("device", "cpu") == "cpu":
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# batch_size = 1
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key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=kwargs.get("data_type", None), key=key)
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speed_stats = {}
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asr_result_list = []
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num_samples = len(data_list)
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pbar = tqdm(colour="blue", total=num_samples+1, dynamic_ncols=True)
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time_speech_total = 0.0
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time_escape_total = 0.0
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for beg_idx in range(0, num_samples, batch_size):
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end_idx = min(num_samples, beg_idx + batch_size)
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data_batch = data_list[beg_idx:end_idx]
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key_batch = key_list[beg_idx:end_idx]
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batch = {"data_in": data_batch, "key": key_batch}
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if (end_idx - beg_idx) == 1 and isinstance(data_batch[0], torch.Tensor): # fbank
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batch["data_in"] = data_batch[0]
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batch["data_lengths"] = input_len
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time1 = time.perf_counter()
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with torch.no_grad():
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results, meta_data = model.generate(**batch, **kwargs)
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time2 = time.perf_counter()
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asr_result_list.extend(results)
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pbar.update(1)
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# batch_data_time = time_per_frame_s * data_batch_i["speech_lengths"].sum().item()
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batch_data_time = meta_data.get("batch_data_time", -1)
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time_escape = time2 - time1
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speed_stats["load_data"] = meta_data.get("load_data", 0.0)
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speed_stats["extract_feat"] = meta_data.get("extract_feat", 0.0)
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speed_stats["forward"] = f"{time_escape:0.3f}"
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speed_stats["batch_size"] = f"{len(results)}"
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speed_stats["rtf"] = f"{(time_escape) / batch_data_time:0.3f}"
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description = (
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f"{speed_stats}, "
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)
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pbar.set_description(description)
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time_speech_total += batch_data_time
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time_escape_total += time_escape
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pbar.update(1)
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pbar.set_description(f"rtf_avg: {time_escape_total/time_speech_total:0.3f}")
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torch.cuda.empty_cache()
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return asr_result_list
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def generate_with_vad(self, input, input_len=None, **cfg):
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# step.1: compute the vad model
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model = self.vad_model
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kwargs = self.vad_kwargs
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kwargs.update(cfg)
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beg_vad = time.time()
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res = self.generate(input, input_len=input_len, model=model, kwargs=kwargs, **cfg)
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end_vad = time.time()
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print(f"time cost vad: {end_vad - beg_vad:0.3f}")
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# step.2 compute asr model
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model = self.model
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kwargs = self.kwargs
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kwargs.update(cfg)
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batch_size = int(kwargs.get("batch_size_s", 300))*1000
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batch_size_threshold_ms = int(kwargs.get("batch_size_threshold_s", 60))*1000
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kwargs["batch_size"] = batch_size
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key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=kwargs.get("data_type", None))
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results_ret_list = []
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time_speech_total_all_samples = 0.0
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beg_total = time.time()
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pbar_total = tqdm(colour="red", total=len(res) + 1, dynamic_ncols=True)
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for i in range(len(res)):
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key = res[i]["key"]
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vadsegments = res[i]["value"]
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input_i = data_list[i]
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speech = load_audio_text_image_video(input_i, fs=kwargs["frontend"].fs, audio_fs=kwargs.get("fs", 16000))
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speech_lengths = len(speech)
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n = len(vadsegments)
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data_with_index = [(vadsegments[i], i) for i in range(n)]
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sorted_data = sorted(data_with_index, key=lambda x: x[0][1] - x[0][0])
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results_sorted = []
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if not len(sorted_data):
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logging.info("decoding, utt: {}, empty speech".format(key))
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continue
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# if kwargs["device"] == "cpu":
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# batch_size = 0
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if len(sorted_data) > 0 and len(sorted_data[0]) > 0:
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batch_size = max(batch_size, sorted_data[0][0][1] - sorted_data[0][0][0])
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batch_size_ms_cum = 0
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beg_idx = 0
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beg_asr_total = time.time()
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time_speech_total_per_sample = speech_lengths/16000
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time_speech_total_all_samples += time_speech_total_per_sample
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for j, _ in enumerate(range(0, n)):
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batch_size_ms_cum += (sorted_data[j][0][1] - sorted_data[j][0][0])
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if j < n - 1 and (
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batch_size_ms_cum + sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size and (
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sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size_threshold_ms:
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continue
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batch_size_ms_cum = 0
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end_idx = j + 1
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speech_j, speech_lengths_j = slice_padding_audio_samples(speech, speech_lengths, sorted_data[beg_idx:end_idx])
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beg_idx = end_idx
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results = self.generate(speech_j, input_len=None, model=model, kwargs=kwargs, **cfg)
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if len(results) < 1:
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continue
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results_sorted.extend(results)
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pbar_total.update(1)
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end_asr_total = time.time()
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time_escape_total_per_sample = end_asr_total - beg_asr_total
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pbar_total.set_description(f"rtf_avg_per_sample: {time_escape_total_per_sample / time_speech_total_per_sample:0.3f}, "
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f"time_speech_total_per_sample: {time_speech_total_per_sample: 0.3f}, "
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f"time_escape_total_per_sample: {time_escape_total_per_sample:0.3f}")
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restored_data = [0] * n
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for j in range(n):
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index = sorted_data[j][1]
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restored_data[index] = results_sorted[j]
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result = {}
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for j in range(n):
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for k, v in restored_data[j].items():
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if not k.startswith("timestamp"):
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if k not in result:
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result[k] = restored_data[j][k]
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else:
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result[k] += restored_data[j][k]
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else:
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result[k] = []
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for t in restored_data[j][k]:
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t[0] += vadsegments[j][0]
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t[1] += vadsegments[j][0]
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result[k] += restored_data[j][k]
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result["key"] = key
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results_ret_list.append(result)
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pbar_total.update(1)
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# step.3 compute punc model
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model = self.punc_model
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kwargs = self.punc_kwargs
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kwargs.update(cfg)
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for i, result in enumerate(results_ret_list):
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beg_punc = time.time()
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res = self.generate(result["text"], model=model, kwargs=kwargs, **cfg)
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end_punc = time.time()
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print(f"time punc: {end_punc - beg_punc:0.3f}")
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# sentences = time_stamp_sentence(model.punc_list, model.sentence_end_id, results_ret_list[i]["timestamp"], res[i]["text"])
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# results_ret_list[i]["time_stamp"] = res[0]["text_postprocessed_punc"]
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# results_ret_list[i]["sentences"] = sentences
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results_ret_list[i]["text_with_punc"] = res[i]["text"]
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pbar_total.update(1)
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end_total = time.time()
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time_escape_total_all_samples = end_total - beg_total
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pbar_total.set_description(f"rtf_avg_all_samples: {time_escape_total_all_samples / time_speech_total_all_samples:0.3f}, "
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f"time_speech_total_all_samples: {time_speech_total_all_samples: 0.3f}, "
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f"time_escape_total_all_samples: {time_escape_total_all_samples:0.3f}")
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return results_ret_list
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class AutoFrontend:
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def __init__(self, **kwargs):
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assert "model" in kwargs
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if "model_conf" not in kwargs:
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logging.info("download models from model hub: {}".format(kwargs.get("model_hub", "ms")))
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kwargs = download_model(**kwargs)
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# build frontend
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frontend = kwargs.get("frontend", None)
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if frontend is not None:
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frontend_class = tables.frontend_classes.get(frontend.lower())
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frontend = frontend_class(**kwargs["frontend_conf"])
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self.frontend = frontend
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if "frontend" in kwargs:
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del kwargs["frontend"]
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self.kwargs = kwargs
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def __call__(self, input, input_len=None, kwargs=None, **cfg):
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kwargs = self.kwargs if kwargs is None else kwargs
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kwargs.update(cfg)
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key_list, data_list = prepare_data_iterator(input, input_len=input_len)
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batch_size = kwargs.get("batch_size", 1)
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device = kwargs.get("device", "cpu")
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if device == "cpu":
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batch_size = 1
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meta_data = {}
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result_list = []
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num_samples = len(data_list)
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pbar = tqdm(colour="blue", total=num_samples + 1, dynamic_ncols=True)
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time0 = time.perf_counter()
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for beg_idx in range(0, num_samples, batch_size):
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end_idx = min(num_samples, beg_idx + batch_size)
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data_batch = data_list[beg_idx:end_idx]
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key_batch = key_list[beg_idx:end_idx]
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# extract fbank feats
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time1 = time.perf_counter()
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audio_sample_list = load_audio_text_image_video(data_batch, fs=self.frontend.fs, audio_fs=kwargs.get("fs", 16000))
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time2 = time.perf_counter()
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meta_data["load_data"] = f"{time2 - time1:0.3f}"
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speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
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frontend=self.frontend, **kwargs)
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time3 = time.perf_counter()
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meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
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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__':
|
|
main_hydra() |