wehub-resource-sync
1043 lines
45 KiB
Python
1043 lines
45 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.postprocess_hotwords import apply_postprocess_hotwords_to_results
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from funasr.utils import misc
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def is_npu_available():
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"""检查NPU是否可用。"""
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try:
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import torch_npu
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return torch_npu.npu.is_available()
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except ImportError:
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return False
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def _resolve_ncpu(config, fallback=4):
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"""Return a positive integer representing CPU threads from config."""
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value = config.get("ncpu", fallback)
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try:
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value = int(value)
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except (TypeError, ValueError):
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value = fallback
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return max(value, 1)
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def _get_import_errors():
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"""Internal: get import errors."""
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try:
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import funasr
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except Exception:
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return {}
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get_import_errors = getattr(funasr, "get_import_errors", None)
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if get_import_errors is not None:
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return get_import_errors()
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return dict(getattr(funasr, "_IMPORT_ERRORS", {}))
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def _format_unregistered_component_error(component_type, component_name, registry):
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"""Internal: format unregistered component error.
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Args:
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component_type: TODO.
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component_name: TODO.
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registry: TODO.
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"""
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registered = sorted(registry.keys())
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preview = ", ".join(registered[:80])
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if len(registered) > 80:
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preview += f", ... ({len(registered)} total)"
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if not preview:
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preview = "(none)"
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import_errors = _get_import_errors()
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if import_errors:
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lines = [
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f" - {name}: {error}"
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for name, error in sorted(import_errors.items())[:50]
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]
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remaining = len(import_errors) - len(lines)
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if remaining > 0:
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lines.append(f" ... {remaining} more import failures hidden")
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import_error_text = "\n".join(lines)
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else:
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import_error_text = " (none recorded)"
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return (
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f"{component_type} '{component_name}' is not registered.\n"
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f"Registered {component_type} keys ({len(registered)}): {preview}\n"
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"Some modules may have failed to import during auto-registration. "
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"Set FUNASR_IMPORT_DEBUG=1 to print failures during import, or "
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"FUNASR_STRICT_IMPORT=1 to fail fast.\n"
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f"Recorded import failures:\n{import_error_text}"
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)
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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 = lines.get("key", 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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"""Initialize AutoModel with ASR model and optional sub-models.
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Args:
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model (str): Model name (hub alias or full ID) or local path.
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device (str): Device for inference. "cuda:0", "cpu", "mps", "npu:0".
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Falls back to CPU if specified device is unavailable.
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vad_model (str, optional): VAD model for long audio segmentation.
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Enables processing of any-length audio.
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vad_kwargs (dict, optional): VAD config, e.g. {"max_single_segment_time": 60000}.
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punc_model (str, optional): Punctuation restoration model.
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Not needed for Fun-ASR-Nano/SenseVoice/Qwen3-ASR (they output punctuation natively).
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spk_model (str, optional): Speaker model for diarization ("cam++" or full model ID).
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Requires vad_model. For Qwen3-ASR, also requires forced_aligner.
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spk_mode (str, optional): Speaker diarization mode. "punc_segment" (default) or "vad_segment".
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hub (str): Model hub. "ms" (ModelScope, default) or "hf" (HuggingFace).
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ncpu (int): CPU threads (default: 4).
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disable_update (bool): Skip version check on startup.
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disable_pbar (bool): Disable tqdm progress bars.
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**kwargs: Additional model-specific parameters (passed to config.yaml overrides).
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Examples:
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>>> model = AutoModel(model="paraformer-zh", vad_model="fsmn-vad", punc_model="ct-punc")
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>>> model = AutoModel(model="FunAudioLLM/Fun-ASR-Nano-2512", trust_remote_code=True,
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... remote_code="./model.py", vad_model="fsmn-vad", spk_model="cam++", hub="hf")
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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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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_kwargs.setdefault("ncpu", kwargs.get("ncpu", 4))
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if "hub" in kwargs:
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vad_kwargs.setdefault("hub", kwargs["hub"])
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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_kwargs.setdefault("ncpu", kwargs.get("ncpu", 4))
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if "hub" in kwargs:
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punc_kwargs.setdefault("hub", kwargs["hub"])
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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 = (
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{} if spk_kwargs.get("cb_kwargs", {}) is None else spk_kwargs.get("cb_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_kwargs.setdefault("ncpu", kwargs.get("ncpu", 4))
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if "hub" in kwargs:
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spk_kwargs.setdefault("hub", kwargs["hub"])
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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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self._store_base_configs()
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@staticmethod
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def build_model(**kwargs):
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"""Download model from hub, build all components, and load pretrained weights.
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This method handles the full model construction pipeline:
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1. Download model files from ModelScope/HuggingFace (if not local)
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2. Parse config.yaml to determine model class, tokenizer, frontend
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3. Instantiate tokenizer, frontend, and model via the registry
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4. Load pretrained weights from model.pt
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Args:
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**kwargs: Must include 'model' (str). All other config.yaml fields can be overridden.
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Returns:
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tuple: (model, kwargs) where model is the instantiated nn.Module and
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kwargs contains the resolved configuration.
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"""
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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 (
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(device.startswith("cuda") and not torch.cuda.is_available())
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or (device.startswith("xpu") and not torch.xpu.is_available())
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or (device.startswith("mps") and not torch.backends.mps.is_available())
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or (device.startswith("npu") and not is_npu_available())
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or kwargs.get("ngpu", 1) == 0
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):
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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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ncpu = _resolve_ncpu(kwargs, 4)
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kwargs["ncpu"] = ncpu
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if torch.get_num_threads() != ncpu:
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torch.set_num_threads(ncpu)
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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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if model_class is None:
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raise RuntimeError(
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_format_unregistered_component_error(
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"model", kwargs["model"], tables.model_classes
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)
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)
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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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model.eval()
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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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"""Internal: call .
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Args:
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*args: Variable positional arguments.
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**cfg: Configuration overrides.
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"""
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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, progress_callback=None, **cfg):
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"""Run speech recognition on input audio.
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This is the primary user-facing method. It automatically routes to:
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- inference() if no vad_model is configured (single utterance)
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- inference_with_vad() if vad_model is configured (long audio with segmentation)
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Args:
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input: Audio input. Accepts:
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- File path (str): "audio.wav", "audio.mp3"
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- URL (str): "https://..."
|
|
- numpy array: raw audio samples (float32, 16kHz)
|
|
- list: batch of file paths or arrays
|
|
- bytes: raw audio bytes
|
|
input_len (tensor, optional): Length of each input sample.
|
|
progress_callback (callable, optional): fn(current, total) called during processing.
|
|
**cfg: Runtime parameters:
|
|
- cache (dict): State cache for streaming mode. Pass {} for first call.
|
|
- hotword (str/list): Keywords to boost recognition accuracy.
|
|
- postprocess_hotwords (str/list/dict): Text-level hotword correction after
|
|
decoding. Unlike model-level ``hotword``, this runs on the final text.
|
|
- postprocess_hotword_file (str): Hotword file path. Each line is a target
|
|
word or an explicit mapping like ``错误词=>目标词``.
|
|
- postprocess_hotword_threshold (float): Fuzzy match threshold in [0, 1].
|
|
- return_postprocess_hotword_matches (bool): Include replacement details.
|
|
- language (str): Language hint ("auto", "zh", "en", "Chinese", etc.)
|
|
- batch_size_s (int): Dynamic batch total duration in seconds.
|
|
- is_final (bool): Last chunk flag for streaming mode.
|
|
- return_spk_res (bool): Return speaker diarization results.
|
|
- sentence_timestamp (bool): Return sentence-level timestamps.
|
|
- use_itn (bool): Apply inverse text normalization (SenseVoice).
|
|
|
|
Returns:
|
|
list[dict]: Results for each input sample. Common fields:
|
|
- "key" (str): Sample identifier
|
|
- "text" (str): Recognized text
|
|
- "timestamp" (list): [[start_ms, end_ms], ...] per character/word
|
|
- "sentence_info" (list): [{text, start, end, spk, timestamp}, ...] when spk enabled
|
|
"""
|
|
self._reset_runtime_configs()
|
|
if self.vad_model is None:
|
|
results = self.inference(
|
|
input, input_len=input_len, progress_callback=progress_callback, **cfg
|
|
)
|
|
if self.punc_model is not None:
|
|
deep_update(self.punc_kwargs, cfg)
|
|
for result in results:
|
|
punc_res = self.inference(
|
|
result["text"], model=self.punc_model, kwargs=self.punc_kwargs, **cfg
|
|
)
|
|
if cfg.get("return_raw_text", self.kwargs.get("return_raw_text", False)):
|
|
result["raw_text"] = copy.copy(result["text"])
|
|
result["text"] = punc_res[0]["text"]
|
|
return apply_postprocess_hotwords_to_results(results, cfg)
|
|
|
|
else:
|
|
results = self.inference_with_vad(
|
|
input, input_len=input_len, progress_callback=progress_callback, **cfg
|
|
)
|
|
return apply_postprocess_hotwords_to_results(results, cfg)
|
|
|
|
def inference(
|
|
self,
|
|
input,
|
|
input_len=None,
|
|
model=None,
|
|
kwargs=None,
|
|
key=None,
|
|
progress_callback=None,
|
|
**cfg,
|
|
):
|
|
"""Run model inference on input data (internal method).
|
|
|
|
Handles batching, timing, and progress reporting. Called by generate()
|
|
and inference_with_vad(). Typically not called directly by users.
|
|
|
|
Args:
|
|
input: Audio data, file path, or text (for punc model).
|
|
input_len (tensor, optional): Input lengths for batch.
|
|
model (nn.Module, optional): Override model (used for VAD/PUNC/SPK sub-models).
|
|
kwargs (dict, optional): Override kwargs (used for sub-model configs).
|
|
key (list, optional): Sample identifiers.
|
|
progress_callback (callable, optional): Progress reporting function.
|
|
**cfg: Additional config merged into kwargs.
|
|
|
|
Returns:
|
|
list[dict]: Model inference results.
|
|
"""
|
|
if kwargs is None:
|
|
self._reset_runtime_configs()
|
|
kwargs = self.kwargs if kwargs is None else kwargs
|
|
if "cache" in kwargs:
|
|
kwargs.pop("cache")
|
|
deep_update(kwargs, cfg)
|
|
model = self.model if model is None else model
|
|
|
|
batch_size = kwargs.get("batch_size", 1)
|
|
# if kwargs.get("device", "cpu") == "cpu":
|
|
# batch_size = 1
|
|
|
|
key_list, data_list = prepare_data_iterator(
|
|
input, input_len=input_len, data_type=kwargs.get("data_type", None), key=key
|
|
)
|
|
|
|
speed_stats = {}
|
|
asr_result_list = []
|
|
num_samples = len(data_list)
|
|
disable_pbar = self.kwargs.get("disable_pbar", False)
|
|
pbar = (
|
|
tqdm(colour="blue", total=num_samples, dynamic_ncols=True) if not disable_pbar else None
|
|
)
|
|
time_speech_total = 0.0
|
|
time_escape_total = 0.0
|
|
for beg_idx in range(0, num_samples, batch_size):
|
|
end_idx = min(num_samples, beg_idx + batch_size)
|
|
data_batch = data_list[beg_idx:end_idx]
|
|
key_batch = key_list[beg_idx:end_idx]
|
|
batch = {"data_in": data_batch, "key": key_batch}
|
|
|
|
if (end_idx - beg_idx) == 1 and kwargs.get("data_type", None) == "fbank": # fbank
|
|
batch["data_in"] = data_batch[0]
|
|
batch["data_lengths"] = input_len
|
|
|
|
time1 = time.perf_counter()
|
|
with torch.no_grad():
|
|
res = model.inference(**batch, **kwargs)
|
|
if isinstance(res, (list, tuple)):
|
|
results = res[0] if len(res) > 0 else [{"text": ""}]
|
|
meta_data = res[1] if len(res) > 1 else {}
|
|
time2 = time.perf_counter()
|
|
|
|
asr_result_list.extend(results)
|
|
|
|
# batch_data_time = time_per_frame_s * data_batch_i["speech_lengths"].sum().item()
|
|
batch_data_time = meta_data.get("batch_data_time", -1)
|
|
time_escape = time2 - time1
|
|
speed_stats["load_data"] = meta_data.get("load_data", 0.0)
|
|
speed_stats["extract_feat"] = meta_data.get("extract_feat", 0.0)
|
|
speed_stats["forward"] = f"{time_escape:0.3f}"
|
|
speed_stats["batch_size"] = f"{len(results)}"
|
|
speed_stats["rtf"] = f"{(time_escape) / batch_data_time:0.3f}"
|
|
description = f"{speed_stats}, "
|
|
if pbar:
|
|
pbar.update(end_idx - beg_idx)
|
|
pbar.set_description(description)
|
|
if progress_callback:
|
|
try:
|
|
progress_callback(end_idx, num_samples)
|
|
except Exception as e:
|
|
logging.error(f"progress_callback error: {e}")
|
|
time_speech_total += batch_data_time
|
|
time_escape_total += time_escape
|
|
|
|
if pbar:
|
|
# pbar.update(1)
|
|
pbar.set_description(f"rtf_avg: {time_escape_total/time_speech_total:0.3f}")
|
|
|
|
device = next(model.parameters()).device
|
|
if device.type == "cuda":
|
|
with torch.cuda.device(device):
|
|
torch.cuda.empty_cache()
|
|
return asr_result_list
|
|
|
|
def inference_with_vad(self, input, input_len=None, **cfg):
|
|
"""Run ASR with VAD segmentation, punctuation, and optional speaker diarization.
|
|
|
|
Pipeline:
|
|
1. VAD: Segment audio into speech regions
|
|
2. ASR: Recognize each segment (sorted by length for efficient batching)
|
|
3. Timestamp merge: Combine per-segment timestamps with VAD offsets
|
|
4. Punctuation: Add punctuation to combined text (if punc_model configured)
|
|
5. Speaker diarization: Cluster speaker embeddings and assign labels (if spk_model configured)
|
|
|
|
Args:
|
|
input: Audio file path, URL, or numpy array.
|
|
input_len: Not used (kept for interface consistency).
|
|
**cfg: Runtime parameters (same as generate()).
|
|
|
|
Returns:
|
|
list[dict]: Results with fields: key, text, timestamp, sentence_info, raw_text.
|
|
"""
|
|
self._reset_runtime_configs()
|
|
if self.spk_model is not None and "output_timestamp" not in cfg:
|
|
cfg["output_timestamp"] = True
|
|
cfg["return_time_stamps"] = True
|
|
kwargs = self.kwargs
|
|
# step.1: compute the vad model
|
|
deep_update(self.vad_kwargs, cfg)
|
|
beg_vad = time.time()
|
|
res = self.inference(
|
|
input, input_len=input_len, model=self.vad_model, kwargs=self.vad_kwargs, **cfg
|
|
)
|
|
end_vad = time.time()
|
|
|
|
# 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
|
|
)
|
|
|
|
# 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(res), dynamic_ncols=True)
|
|
if not kwargs.get("disable_pbar", False)
|
|
else None
|
|
)
|
|
for i in range(len(res)):
|
|
key = res[i]["key"]
|
|
vadsegments = res[i]["value"]
|
|
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 = 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
|
|
)
|
|
spk_embs = torch.cat([r["spk_embedding"] for r in spk_res], dim=0)
|
|
results[_b]["spk_embedding"] = spk_embs
|
|
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]:
|
|
if isinstance(t, dict):
|
|
t["start_time"] = (
|
|
float(t["start_time"]) * 1000 + int(vadsegments[j][0])
|
|
) / 1000
|
|
t["end_time"] = (
|
|
float(t["end_time"]) * 1000 + int(vadsegments[j][0])
|
|
) / 1000
|
|
else:
|
|
t[0] = int(t[0]) + int(vadsegments[j][0])
|
|
t[1] = int(t[1]) + int(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]
|
|
|
|
# Convert dict-format timestamps (Fun-ASR-Nano) to list-format for downstream compatibility
|
|
if "timestamps" in result and "timestamp" not in result:
|
|
result["timestamp"] = [
|
|
[int(t["start_time"] * 1000), int(t["end_time"] * 1000)]
|
|
for t in result["timestamps"]
|
|
]
|
|
|
|
if not len(result["text"].strip()):
|
|
continue
|
|
return_raw_text = kwargs.get("return_raw_text", False)
|
|
# step.3 compute punc model
|
|
raw_text = None
|
|
punc_res = None
|
|
if self.punc_model is not None and "timestamps" not in result:
|
|
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 and self.spk_mode == "punc_segment":
|
|
logging.warning("punc_model is missing, falling back to vad_segment mode for speaker diarization.")
|
|
self.spk_mode = "vad_segment"
|
|
elif 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']
|
|
# postprocess expects np.ndarray embeddings (per its type hint).
|
|
spk_embedding_np = spk_embedding.detach().cpu().numpy()
|
|
if kwargs.get("return_spk_center", False):
|
|
sv_output, spk_center = postprocess(
|
|
all_segments, None, labels, spk_embedding_np, return_spk_center=True
|
|
)
|
|
# Per-speaker ERes2NetV2 centroids, indexed by the `spk` id in
|
|
# sentence_info. Kept on the result for downstream voiceprint use
|
|
# (the per-chunk spk_embedding below is still deleted to keep output small).
|
|
result["spk_embedding_center"] = spk_center
|
|
else:
|
|
sv_output = postprocess(all_segments, None, labels, spk_embedding_np)
|
|
if self.spk_mode == "punc_segment" and "timestamp" not in result and "timestamps" not in result:
|
|
logging.warning("No timestamps in ASR result (e.g. SenseVoice), falling back to vad_segment mode for speaker diarization.")
|
|
self.spk_mode = "vad_segment"
|
|
if self.spk_mode == "vad_segment": # recover sentence_list
|
|
sentence_list = []
|
|
for rest, vadsegment in zip(restored_data, vadsegments):
|
|
if "timestamp" in rest:
|
|
ts = rest["timestamp"]
|
|
elif "timestamps" in rest:
|
|
ts = [
|
|
[int(t["start_time"] * 1000), int(t["end_time"] * 1000)]
|
|
for t in rest["timestamps"]
|
|
]
|
|
else:
|
|
logging.error("No timestamp found in ASR result. Speaker diarization relies on timestamps.")
|
|
ts = []
|
|
sentence_list.append(
|
|
{
|
|
"start": vadsegment[0],
|
|
"end": vadsegment[1],
|
|
"sentence": rest["text"],
|
|
"timestamp": ts,
|
|
}
|
|
)
|
|
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 punc_res is None:
|
|
logging.error(
|
|
"Missing punc_model, which is required for punc_segment speaker diarization."
|
|
)
|
|
sentence_list = []
|
|
elif 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 = []
|
|
elif punc_res is None:
|
|
logging.warning(
|
|
"punc_model is required for sentence_timestamp, skipping sentence segmentation."
|
|
)
|
|
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):
|
|
"""Export model to ONNX format.
|
|
|
|
Creates a deep copy of the model to isolate ONNX operator monkey-patching,
|
|
then runs torch.onnx.export. The original model remains usable after export.
|
|
|
|
Args:
|
|
input: Sample input for tracing (auto-generated if None).
|
|
**cfg: Export parameters:
|
|
- type (str): Export format, "onnx" (default).
|
|
- quantize (bool): Whether to quantize the model.
|
|
- device (str): Device for export.
|
|
|
|
Returns:
|
|
str: Path to the exported model directory.
|
|
"""
|
|
"""
|
|
|
|
:param input:
|
|
:param type:
|
|
:param quantize:
|
|
:param fallback_num:
|
|
:param calib_num:
|
|
:param opset_version:
|
|
:param cfg:
|
|
:return:
|
|
"""
|
|
|
|
device = cfg.get("device", "cpu")
|
|
|
|
# 对模型进行深拷贝，隔离 ONNX 算子替换（Monkey-patching）对原模型的破坏
|
|
# Implement deep copy of the model to isolate ONNX operator monkey-patching
|
|
# and prevent corruption of the original model
|
|
model = copy.deepcopy(self.model).to(device=device)
|
|
|
|
# 对配置参数进行深拷贝，隔离 deep_update 和 del 的引用污染
|
|
# Implement deep copy of configuration parameters to isolate reference pollution caused by deep_update and del.
|
|
kwargs = copy.deepcopy(self.kwargs)
|
|
|
|
deep_update(kwargs, cfg)
|
|
kwargs["device"] = device
|
|
|
|
# Safely delete keys that may cause issues during export
|
|
if "model" in kwargs:
|
|
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():
|
|
# 这里的导出操作只会魔改 model 副本，原实例的 self.model 依然是纯洁的 PyTorch 图
|
|
# This export operation only mutates the model copy;
|
|
# the original self.model instance remains an intact PyTorch graph.
|
|
export_dir = export_utils.export(model=model, data_in=data_list, **kwargs)
|
|
|
|
return export_dir
|
|
|
|
def _store_base_configs(self):
|
|
"""Snapshot base kwargs for all submodules to allow reset before inference."""
|
|
baseline = {}
|
|
for name in dir(self):
|
|
if not name.endswith("kwargs"):
|
|
continue
|
|
value = getattr(self, name, None)
|
|
if isinstance(value, dict):
|
|
baseline[name] = copy.deepcopy(value)
|
|
# include primary kwargs explicitly
|
|
baseline["kwargs"] = copy.deepcopy(self.kwargs)
|
|
self._base_kwargs_map = baseline
|
|
|
|
_IMMUTABLE_KWARGS_KEYS = frozenset([
|
|
"token_list", "tokenizer", "frontend", "model", "init_param", "model_path",
|
|
])
|
|
|
|
def _reset_runtime_configs(self):
|
|
"""Ensure runtime kwargs reset to baseline defaults before inference."""
|
|
base_map = getattr(self, "_base_kwargs_map", None)
|
|
if not base_map:
|
|
return
|
|
|
|
for name, base in base_map.items():
|
|
restored = {}
|
|
for k, v in base.items():
|
|
if k in self._IMMUTABLE_KWARGS_KEYS or not isinstance(v, (dict, list)):
|
|
restored[k] = v
|
|
else:
|
|
restored[k] = copy.deepcopy(v)
|
|
setattr(self, name, restored)
|
|
|
|
ncpu = _resolve_ncpu(self.kwargs, 4)
|
|
self.kwargs["ncpu"] = ncpu
|
|
for name, value in base_map.items():
|
|
if name == "kwargs":
|
|
continue
|
|
config = getattr(self, name, None)
|
|
if isinstance(config, dict):
|
|
config.setdefault("ncpu", ncpu)
|
|
if torch.get_num_threads() != ncpu:
|
|
torch.set_num_threads(ncpu)
|