mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
667 lines
28 KiB
Python
667 lines
28 KiB
Python
#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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import json
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import time
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import copy
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import torch
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import random
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import string
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import logging
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import os.path
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import numpy as np
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from tqdm import tqdm
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from omegaconf import DictConfig, ListConfig
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from funasr.utils.misc import deep_update
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from funasr.register import tables
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from funasr.utils.load_utils import load_bytes
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from funasr.download.file import download_from_url
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from funasr.utils.timestamp_tools import timestamp_sentence
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from funasr.utils.timestamp_tools import timestamp_sentence_en
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from funasr.download.download_model_from_hub import download_model
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from funasr.utils.vad_utils import slice_padding_audio_samples
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from funasr.utils.vad_utils import merge_vad
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from funasr.utils.load_utils import load_audio_text_image_video
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from funasr.train_utils.set_all_random_seed import set_all_random_seed
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from funasr.train_utils.load_pretrained_model import load_pretrained_model
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from funasr.utils import export_utils
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from funasr.utils import misc
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try:
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from funasr.models.campplus.utils import sv_chunk, postprocess, distribute_spk
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from funasr.models.campplus.cluster_backend import ClusterBackend
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except:
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pass
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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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data_list = []
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key_list = []
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filelist = [".scp", ".txt", ".json", ".jsonl", ".text"]
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chars = string.ascii_letters + string.digits
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if isinstance(data_in, str):
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if data_in.startswith("http://") or data_in.startswith("https://"): # 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(
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data_in
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): # 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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if key is None:
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# key = "rand_key_" + "".join(random.choice(chars) for _ in range(13))
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key = misc.extract_filename_without_extension(data_in)
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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(
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data_in=data_in_i, data_type=data_type_i
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)
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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 = []
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for data_i in data_in:
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if isinstance(data_i, str) and os.path.exists(data_i):
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key = misc.extract_filename_without_extension(data_i)
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else:
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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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key_list.append(key)
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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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class AutoModel:
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def __init__(self, **kwargs):
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try:
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from funasr.utils.version_checker import check_for_update
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check_for_update(disable=kwargs.get("disable_update", False))
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except:
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pass
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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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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 = {} if kwargs.get("vad_kwargs", {}) is None else kwargs.get("vad_kwargs", {})
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if vad_model is not None:
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logging.info("Building VAD model.")
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vad_kwargs["model"] = vad_model
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vad_kwargs["model_revision"] = kwargs.get("vad_model_revision", "master")
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vad_kwargs["device"] = kwargs["device"]
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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 = {} if kwargs.get("punc_kwargs", {}) is None else kwargs.get("punc_kwargs", {})
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if punc_model is not None:
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logging.info("Building punc model.")
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punc_kwargs["model"] = punc_model
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punc_kwargs["model_revision"] = kwargs.get("punc_model_revision", "master")
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punc_kwargs["device"] = kwargs["device"]
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punc_model, punc_kwargs = self.build_model(**punc_kwargs)
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# if spk_model is not None, build spk model else None
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spk_model = kwargs.get("spk_model", None)
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spk_kwargs = {} if kwargs.get("spk_kwargs", {}) is None else kwargs.get("spk_kwargs", {})
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cb_kwargs = {} if spk_kwargs.get("cb_kwargs", {}) is None else spk_kwargs.get("cb_kwargs", {})
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if spk_model is not None:
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logging.info("Building SPK model.")
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spk_kwargs["model"] = spk_model
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spk_kwargs["model_revision"] = kwargs.get("spk_model_revision", "master")
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spk_kwargs["device"] = kwargs["device"]
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spk_model, spk_kwargs = self.build_model(**spk_kwargs)
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self.cb_model = ClusterBackend(**cb_kwargs).to(kwargs["device"])
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spk_mode = kwargs.get("spk_mode", "punc_segment")
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if spk_mode not in ["default", "vad_segment", "punc_segment"]:
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logging.error("spk_mode should be one of default, vad_segment and punc_segment.")
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self.spk_mode = spk_mode
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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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self.spk_model = spk_model
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self.spk_kwargs = spk_kwargs
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self.model_path = kwargs.get("model_path")
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@staticmethod
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def build_model(**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("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", 1) == 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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torch.set_num_threads(kwargs.get("ncpu", 4))
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# build tokenizer
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tokenizer = kwargs.get("tokenizer", None)
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kwargs["tokenizer"] = tokenizer
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kwargs["vocab_size"] = -1
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if tokenizer is not None:
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tokenizers = (
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tokenizer.split(",") if isinstance(tokenizer, str) else tokenizer
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) # type of tokenizers is list!!!
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tokenizers_conf = kwargs.get("tokenizer_conf", {})
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tokenizers_build = []
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vocab_sizes = []
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token_lists = []
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### === only for kws ===
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token_list_files = kwargs.get("token_lists", [])
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seg_dicts = kwargs.get("seg_dicts", [])
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### === only for kws ===
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if not isinstance(tokenizers_conf, (list, tuple, ListConfig)):
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tokenizers_conf = [tokenizers_conf] * len(tokenizers)
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for i, tokenizer in enumerate(tokenizers):
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tokenizer_class = tables.tokenizer_classes.get(tokenizer)
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tokenizer_conf = tokenizers_conf[i]
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### === only for kws ===
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if len(token_list_files) > 1:
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tokenizer_conf["token_list"] = token_list_files[i]
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if len(seg_dicts) > 1:
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tokenizer_conf["seg_dict"] = seg_dicts[i]
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### === only for kws ===
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tokenizer = tokenizer_class(**tokenizer_conf)
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tokenizers_build.append(tokenizer)
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token_list = tokenizer.token_list if hasattr(tokenizer, "token_list") else None
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token_list = (
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tokenizer.get_vocab() if hasattr(tokenizer, "get_vocab") else token_list
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)
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vocab_size = -1
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if token_list is not None:
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vocab_size = len(token_list)
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if vocab_size == -1 and hasattr(tokenizer, "get_vocab_size"):
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vocab_size = tokenizer.get_vocab_size()
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token_lists.append(token_list)
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vocab_sizes.append(vocab_size)
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if len(tokenizers_build) <= 1:
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tokenizers_build = tokenizers_build[0]
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token_lists = token_lists[0]
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vocab_sizes = vocab_sizes[0]
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kwargs["tokenizer"] = tokenizers_build
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kwargs["vocab_size"] = vocab_sizes
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kwargs["token_list"] = token_lists
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# build frontend
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frontend = kwargs.get("frontend", None)
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kwargs["input_size"] = None
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if frontend is not None:
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frontend_class = tables.frontend_classes.get(frontend)
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frontend = frontend_class(**kwargs.get("frontend_conf", {}))
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kwargs["input_size"] = (
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frontend.output_size() if hasattr(frontend, "output_size") else None
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)
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kwargs["frontend"] = frontend
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# build model
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model_class = tables.model_classes.get(kwargs["model"])
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assert model_class is not None, f'{kwargs["model"]} is not registered'
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model_conf = {}
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deep_update(model_conf, kwargs.get("model_conf", {}))
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deep_update(model_conf, kwargs)
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model = model_class(**model_conf)
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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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if os.path.exists(init_param):
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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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path=init_param,
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ignore_init_mismatch=kwargs.get("ignore_init_mismatch", True),
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oss_bucket=kwargs.get("oss_bucket", None),
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scope_map=kwargs.get("scope_map", []),
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excludes=kwargs.get("excludes", None),
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)
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else:
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print(f"error, init_param does not exist!: {init_param}")
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# fp16
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if kwargs.get("fp16", False):
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model.to(torch.float16)
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elif kwargs.get("bf16", False):
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model.to(torch.bfloat16)
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model.to(device)
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if not kwargs.get("disable_log", True):
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tables.print()
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return model, kwargs
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def __call__(self, *args, **cfg):
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kwargs = self.kwargs
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deep_update(kwargs, cfg)
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res = self.model(*args, kwargs)
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return res
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def generate(self, input, input_len=None, **cfg):
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if self.vad_model is None:
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return self.inference(input, input_len=input_len, **cfg)
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else:
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return self.inference_with_vad(input, input_len=input_len, **cfg)
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def inference(self, input, input_len=None, model=None, kwargs=None, key=None, **cfg):
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kwargs = self.kwargs if kwargs is None else kwargs
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if "cache" in kwargs:
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kwargs.pop("cache")
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deep_update(kwargs, cfg)
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model = self.model if model is None else model
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model.eval()
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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(
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input, input_len=input_len, data_type=kwargs.get("data_type", None), key=key
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)
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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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disable_pbar = self.kwargs.get("disable_pbar", False)
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pbar = (
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tqdm(colour="blue", total=num_samples, dynamic_ncols=True) if not disable_pbar else None
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)
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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 kwargs.get("data_type", None) == "fbank": # 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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res = model.inference(**batch, **kwargs)
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if isinstance(res, (list, tuple)):
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results = res[0] if len(res) > 0 else [{"text": ""}]
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meta_data = res[1] if len(res) > 1 else {}
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time2 = time.perf_counter()
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asr_result_list.extend(results)
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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 = f"{speed_stats}, "
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if pbar:
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pbar.update(end_idx - beg_idx)
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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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if pbar:
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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 inference_with_vad(self, input, input_len=None, **cfg):
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kwargs = self.kwargs
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# step.1: compute the vad model
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deep_update(self.vad_kwargs, cfg)
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beg_vad = time.time()
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res = self.inference(
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input, input_len=input_len, model=self.vad_model, kwargs=self.vad_kwargs, **cfg
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)
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end_vad = time.time()
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# FIX(gcf): concat the vad clips for sense vocie model for better aed
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if cfg.get("merge_vad", False):
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for i in range(len(res)):
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res[i]["value"] = merge_vad(
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res[i]["value"], kwargs.get("merge_length_s", 15) * 1000
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)
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# step.2 compute asr model
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model = self.model
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deep_update(kwargs, cfg)
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batch_size = max(int(kwargs.get("batch_size_s", 300)) * 1000, 1)
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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(
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input, input_len=input_len, data_type=kwargs.get("data_type", None)
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)
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results_ret_list = []
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time_speech_total_all_samples = 1e-6
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beg_total = time.time()
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pbar_total = (
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tqdm(colour="red", total=len(res), dynamic_ncols=True)
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if not kwargs.get("disable_pbar", False)
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else None
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)
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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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fs = kwargs["frontend"].fs if hasattr(kwargs["frontend"], "fs") else 16000
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speech = load_audio_text_image_video(input_i, fs=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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results_ret_list.append({"key": key, "text": "", "timestamp": []})
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logging.info("decoding, utt: {}, empty speech".format(key))
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continue
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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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if kwargs["device"] == "cpu":
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batch_size = 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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# pbar_sample = tqdm(colour="blue", total=n, dynamic_ncols=True)
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all_segments = []
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max_len_in_batch = 0
|
|
end_idx = 1
|
|
for j, _ in enumerate(range(0, n)):
|
|
# pbar_sample.update(1)
|
|
sample_length = sorted_data[j][0][1] - sorted_data[j][0][0]
|
|
potential_batch_length = max(max_len_in_batch, sample_length) * (j + 1 - beg_idx)
|
|
# batch_size_ms_cum += sorted_data[j][0][1] - sorted_data[j][0][0]
|
|
if (
|
|
j < n - 1
|
|
and sample_length < batch_size_threshold_ms
|
|
and potential_batch_length < batch_size
|
|
):
|
|
max_len_in_batch = max(max_len_in_batch, sample_length)
|
|
end_idx += 1
|
|
continue
|
|
|
|
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)):
|
|
vad_segments = [
|
|
[
|
|
sorted_data[beg_idx:end_idx][_b][0][0] / 1000.0,
|
|
sorted_data[beg_idx:end_idx][_b][0][1] / 1000.0,
|
|
np.array(speech_j[_b]),
|
|
]
|
|
]
|
|
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, **cfg
|
|
)
|
|
results[_b]["spk_embedding"] = spk_res[0]["spk_embedding"]
|
|
beg_idx = end_idx
|
|
end_idx += 1
|
|
max_len_in_batch = sample_length
|
|
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}")
|
|
|
|
if len(results_sorted) != n:
|
|
results_ret_list.append({"key": key, "text": "", "timestamp": []})
|
|
logging.info("decoding, utt: {}, empty result".format(key))
|
|
continue
|
|
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 "text" in k:
|
|
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]
|
|
|
|
if not len(result["text"].strip()):
|
|
continue
|
|
return_raw_text = kwargs.get("return_raw_text", False)
|
|
# step.3 compute punc model
|
|
raw_text = None
|
|
if self.punc_model is not None:
|
|
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"]
|
|
|
|
# speaker embedding cluster after resorted
|
|
if self.spk_model is not None and kwargs.get("return_spk_res", True):
|
|
if raw_text is None:
|
|
logging.error("Missing punc_model, which is required by spk_model.")
|
|
all_segments = sorted(all_segments, key=lambda x: x[0])
|
|
spk_embedding = result["spk_embedding"]
|
|
labels = self.cb_model(
|
|
spk_embedding.cpu(), oracle_num=kwargs.get("preset_spk_num", None)
|
|
)
|
|
# del result['spk_embedding']
|
|
sv_output = postprocess(all_segments, None, labels, spk_embedding.cpu())
|
|
if self.spk_mode == "vad_segment": # recover sentence_list
|
|
sentence_list = []
|
|
for rest, vadsegment in zip(restored_data, vadsegments):
|
|
if "timestamp" not in rest:
|
|
logging.error(
|
|
"Only 'iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch' \
|
|
and 'iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'\
|
|
can predict timestamp, and speaker diarization relies on timestamps."
|
|
)
|
|
sentence_list.append(
|
|
{
|
|
"start": vadsegment[0],
|
|
"end": vadsegment[1],
|
|
"sentence": rest["text"],
|
|
"timestamp": rest["timestamp"],
|
|
}
|
|
)
|
|
elif self.spk_mode == "punc_segment":
|
|
if "timestamp" not in result:
|
|
logging.error(
|
|
"Only 'iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch' \
|
|
and 'iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'\
|
|
can predict timestamp, and speaker diarization relies on timestamps."
|
|
)
|
|
if kwargs.get("en_post_proc", False):
|
|
sentence_list = timestamp_sentence_en(
|
|
punc_res[0]["punc_array"],
|
|
result["timestamp"],
|
|
raw_text,
|
|
return_raw_text=return_raw_text,
|
|
)
|
|
else:
|
|
sentence_list = timestamp_sentence(
|
|
punc_res[0]["punc_array"],
|
|
result["timestamp"],
|
|
raw_text,
|
|
return_raw_text=return_raw_text,
|
|
)
|
|
distribute_spk(sentence_list, sv_output)
|
|
result["sentence_info"] = sentence_list
|
|
elif kwargs.get("sentence_timestamp", False):
|
|
if not len(result["text"].strip()):
|
|
sentence_list = []
|
|
else:
|
|
if kwargs.get("en_post_proc", False):
|
|
sentence_list = timestamp_sentence_en(
|
|
punc_res[0]["punc_array"],
|
|
result["timestamp"],
|
|
raw_text,
|
|
return_raw_text=return_raw_text,
|
|
)
|
|
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
|
|
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}"
|
|
)
|
|
|
|
# end_total = time.time()
|
|
# time_escape_total_all_samples = end_total - beg_total
|
|
# print(f"rtf_avg_all: {time_escape_total_all_samples / time_speech_total_all_samples:0.3f}, "
|
|
# f"time_speech_all: {time_speech_total_all_samples: 0.3f}, "
|
|
# f"time_escape_all: {time_escape_total_all_samples:0.3f}")
|
|
return results_ret_list
|
|
|
|
def export(self, input=None, **cfg):
|
|
"""
|
|
|
|
:param input:
|
|
:param type:
|
|
:param quantize:
|
|
:param fallback_num:
|
|
:param calib_num:
|
|
:param opset_version:
|
|
:param cfg:
|
|
:return:
|
|
"""
|
|
|
|
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()
|
|
|
|
type = kwargs.get("type", "onnx")
|
|
|
|
key_list, data_list = prepare_data_iterator(
|
|
input, input_len=None, data_type=kwargs.get("data_type", None), key=None
|
|
)
|
|
|
|
with torch.no_grad():
|
|
export_dir = export_utils.export(model=model, data_in=data_list, **kwargs)
|
|
|
|
return export_dir
|