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+#!/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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+
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+import copy
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+import json
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+import logging
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+import os.path
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+import random
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+import re
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+import string
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+import time
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+
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+import numpy as np
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+import torch
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+from funasr.download.download_model_from_hub import download_model
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+from funasr.download.file import download_from_url
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+from funasr.register import tables
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+from funasr.train_utils.load_pretrained_model import load_pretrained_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 import export_utils, misc
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+from funasr.utils.load_utils import load_audio_text_image_video, load_bytes
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+from funasr.utils.misc import deep_update
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+from funasr.utils.timestamp_tools import timestamp_sentence, timestamp_sentence_en
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+from tqdm import tqdm
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+
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+from .vad_utils import merge_vad, slice_padding_audio_samples
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+
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+try:
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+ from funasr.models.campplus.cluster_backend import ClusterBackend
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+ from funasr.models.campplus.utils import distribute_spk, postprocess, sv_chunk
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+except:
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+ pass
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+
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+
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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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+
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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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+
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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(
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+ ".jsonl"
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+ ): # 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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+
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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(
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+ data_type, (list, tuple)
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+ ): # 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(
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+ random.choice(chars) for _ in range(13)
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+ )
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+ key_list.append(key)
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+
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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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+
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+ return key_list, data_list
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+
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+
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+class AutoModel:
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+
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+ def __init__(self, **kwargs):
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+
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+ try:
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+ from funasr.utils.version_checker import check_for_update
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+
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+ print(
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+ "Check update of funasr, and it would cost few times. You may disable it by set `disable_update=True` in AutoModel"
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+ )
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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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+
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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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+
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+ model, kwargs = self.build_model(**kwargs)
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+
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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 = (
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+ {} if kwargs.get("vad_kwargs", {}) is None else kwargs.get("vad_kwargs", {})
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+ )
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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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+
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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 = (
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+ {}
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+ if kwargs.get("punc_kwargs", {}) is None
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+ else kwargs.get("punc_kwargs", {})
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+ )
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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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+
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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 = (
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+ {} if kwargs.get("spk_kwargs", {}) is None else kwargs.get("spk_kwargs", {})
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+ )
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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().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(
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+ "spk_mode should be one of default, vad_segment and punc_segment."
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+ )
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+ self.spk_mode = spk_mode
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+
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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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+
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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(
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+ "download models from model hub: {}".format(kwargs.get("hub", "ms"))
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+ )
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+ kwargs = download_model(**kwargs)
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+
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+ set_all_random_seed(kwargs.get("seed", 0))
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+
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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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+
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+ torch.set_num_threads(kwargs.get("ncpu", 4))
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+
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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)
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+ tokenizer = tokenizer_class(**kwargs.get("tokenizer_conf", {}))
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+ kwargs["token_list"] = (
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+ tokenizer.token_list if hasattr(tokenizer, "token_list") else None
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+ )
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+ kwargs["token_list"] = (
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+ tokenizer.get_vocab()
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+ if hasattr(tokenizer, "get_vocab")
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+ else kwargs["token_list"]
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+ )
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+ vocab_size = (
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+ len(kwargs["token_list"]) if kwargs["token_list"] is not None else -1
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+ )
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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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+ else:
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+ vocab_size = -1
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+ kwargs["tokenizer"] = tokenizer
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+
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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, vocab_size=vocab_size)
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+
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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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+
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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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+
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+ if not kwargs.get("disable_log", True):
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+ tables.print()
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+
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+ return model, kwargs
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+
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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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+
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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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+
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+ else:
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+ return self.inference_with_vad(input, input_len=input_len, **cfg)
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+
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+ def inference(
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+ self, input, input_len=None, model=None, kwargs=None, key=None, **cfg
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+ ):
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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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+
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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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+
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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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+
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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)
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+ if not disable_pbar
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+ 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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+
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+ if (end_idx - beg_idx) == 1 and kwargs.get(
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+ "data_type", None
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+ ) == "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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+
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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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+
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+ asr_result_list.extend(results)
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+
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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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+
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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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+
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+ def 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,
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+ input_len=input_len,
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+ model=self.vad_model,
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+ kwargs=self.vad_kwargs,
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+ **cfg,
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+ )
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+ end_vad = time.time()
|
|
|
+ # FIX(gcf): concat the vad clips for sense vocie model for better aed
|
|
|
+ if cfg.get("merge_vad", False):
|
|
|
+ for i in range(len(res)):
|
|
|
+ res[i]["value"] = merge_vad(
|
|
|
+ res[i]["value"], kwargs.get("merge_length_s", 15) * 1000
|
|
|
+ )
|
|
|
+ elapsed = end_vad - beg_vad
|
|
|
+ return elapsed, res
|
|
|
+
|
|
|
+ def inference_with_vadres(self, input, vad_res, input_len=None, **cfg):
|
|
|
+
|
|
|
+ kwargs = self.kwargs
|
|
|
+
|
|
|
+ # step.2 compute asr model
|
|
|
+ model = self.model
|
|
|
+ deep_update(kwargs, cfg)
|
|
|
+ batch_size = max(int(kwargs.get("batch_size_s", 300)) * 1000, 1)
|
|
|
+ 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(vad_res), dynamic_ncols=True)
|
|
|
+ if not kwargs.get("disable_pbar", False)
|
|
|
+ else None
|
|
|
+ )
|
|
|
+
|
|
|
+ for i in range(len(vad_res)):
|
|
|
+ key = vad_res[i]["key"]
|
|
|
+ vadsegments = vad_res[i]["value"]
|
|
|
+ input_i = data_list[i]
|
|
|
+ fs = kwargs["frontend"].fs if hasattr(kwargs["frontend"], "fs") else 16000
|
|
|
+ speech = load_audio_text_image_video(
|
|
|
+ input_i, fs=fs, audio_fs=kwargs.get("fs", 16000)
|
|
|
+ )
|
|
|
+ speech_lengths = len(speech)
|
|
|
+ n = len(vadsegments)
|
|
|
+ data_with_index = [(vadsegments[i], i) for i in range(n)]
|
|
|
+ sorted_data = sorted(data_with_index, key=lambda x: x[0][1] - x[0][0])
|
|
|
+ results_sorted = []
|
|
|
+
|
|
|
+ if not len(sorted_data):
|
|
|
+ results_ret_list.append({"key": key, "text": "", "timestamp": []})
|
|
|
+ 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]
|
|
|
+ )
|
|
|
+
|
|
|
+ if kwargs["device"] == "cpu":
|
|
|
+ batch_size = 0
|
|
|
+
|
|
|
+ beg_idx = 0
|
|
|
+ beg_asr_total = time.time()
|
|
|
+ time_speech_total_per_sample = speech_lengths / 16000
|
|
|
+ time_speech_total_all_samples += time_speech_total_per_sample
|
|
|
+
|
|
|
+ # pbar_sample = tqdm(colour="blue", total=n, dynamic_ncols=True)
|
|
|
+
|
|
|
+ all_segments = []
|
|
|
+ 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, intervals = 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
|
|
|
+ )
|
|
|
+
|
|
|
+ for _b in range(len(speech_j)):
|
|
|
+ results[_b]["interval"] = intervals[_b]
|
|
|
+
|
|
|
+ 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}")
|
|
|
+
|
|
|
+ restored_data = [0] * n
|
|
|
+ for j in range(n):
|
|
|
+ index = sorted_data[j][1]
|
|
|
+ cur = results_sorted[j]
|
|
|
+ pattern = r"<\|([^|]+)\|>"
|
|
|
+ emotion_string = re.findall(pattern, cur["text"])
|
|
|
+ cur["text"] = re.sub(pattern, "", cur["text"])
|
|
|
+ cur["emo"] = "".join([f"<|{t}|>" for t in emotion_string])
|
|
|
+ if self.punc_model is not None and len(cur["text"].strip()) > 0:
|
|
|
+ deep_update(self.punc_kwargs, cfg)
|
|
|
+ punc_res = self.inference(
|
|
|
+ cur["text"],
|
|
|
+ model=self.punc_model,
|
|
|
+ kwargs=self.punc_kwargs,
|
|
|
+ **cfg,
|
|
|
+ )
|
|
|
+ cur["text"] = punc_res[0]["text"]
|
|
|
+
|
|
|
+ restored_data[index] = cur
|
|
|
+
|
|
|
+ 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 restored_data
|
|
|
+
|
|
|
+ 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
|
spicysama