wehub-resource-sync
312 lines
11 KiB
Python
312 lines
11 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 time
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import torch
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import logging
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from torch.cuda.amp import autocast
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from typing import Union, Dict, List, Tuple, Optional
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from funasr.register import tables
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from funasr.models.ctc.ctc import CTC
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from funasr.utils import postprocess_utils
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from funasr.metrics.compute_acc import th_accuracy
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from funasr.train_utils.device_funcs import to_device
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from funasr.utils.datadir_writer import DatadirWriter
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from funasr.models.paraformer.search import Hypothesis
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from funasr.models.paraformer.cif_predictor import mae_loss
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from funasr.train_utils.device_funcs import force_gatherable
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from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
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from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
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from funasr.models.transformer.utils.nets_utils import make_pad_mask
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from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
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@tables.register("model_classes", "SanmKWS")
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class SanmKWS(torch.nn.Module):
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"""SANM-KWS: Self-Attention Neural Memory based Keyword Spotting.
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Advanced keyword spotting using self-attention mechanism
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for better context modeling of keyword patterns.
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Output: {"key": str, "value": detected_keyword_info}
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"""
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def __init__(
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self,
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specaug: Optional[str] = None,
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specaug_conf: Optional[Dict] = None,
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normalize: str = None,
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normalize_conf: Optional[Dict] = None,
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encoder: str = None,
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encoder_conf: Optional[Dict] = None,
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ctc: str = None,
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ctc_conf: Optional[Dict] = None,
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ctc_weight: float = 1.0,
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input_size: int = 360,
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vocab_size: int = -1,
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ignore_id: int = -1,
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blank_id: int = 0,
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sos: int = 1,
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eos: int = 2,
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**kwargs,
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):
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"""Initialize SanmKWS.
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Args:
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specaug: TODO.
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specaug_conf: Configuration dict for specaug.
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normalize: TODO.
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normalize_conf: Configuration dict for normalize.
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encoder: TODO.
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encoder_conf: Configuration dict for encoder.
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ctc: TODO.
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ctc_conf: Configuration dict for ctc.
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ctc_weight: TODO.
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input_size: Size/dimension parameter.
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vocab_size: Size/dimension parameter.
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ignore_id: TODO.
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blank_id: TODO.
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sos: TODO.
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eos: TODO.
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**kwargs: Additional keyword arguments.
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"""
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super().__init__()
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if specaug is not None:
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specaug_class = tables.specaug_classes.get(specaug)
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specaug = specaug_class(**specaug_conf)
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if normalize is not None:
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normalize_class = tables.normalize_classes.get(normalize)
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normalize = normalize_class(**normalize_conf)
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encoder_class = tables.encoder_classes.get(encoder)
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encoder = encoder_class(input_size=input_size, **encoder_conf)
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encoder_output_size = encoder.output_size()
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if ctc_conf is None:
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ctc_conf = {}
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ctc = CTC(odim=vocab_size, encoder_output_size=encoder_output_size, **ctc_conf)
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# note that eos is the same as sos (equivalent ID)
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self.blank_id = blank_id
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self.sos = sos if sos is not None else vocab_size - 1
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self.eos = eos if eos is not None else vocab_size - 1
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self.vocab_size = vocab_size
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self.ignore_id = ignore_id
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self.ctc_weight = ctc_weight
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# self.token_list = token_list.copy()
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#
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# self.frontend = frontend
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self.specaug = specaug
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self.normalize = normalize
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self.encoder = encoder
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self.ctc = ctc
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self.error_calculator = None
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def forward(
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self,
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speech: torch.Tensor,
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speech_lengths: torch.Tensor,
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text: torch.Tensor,
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text_lengths: torch.Tensor,
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**kwargs,
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) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
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"""Encoder + Decoder + Calc loss
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Args:
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speech: (Batch, Length, ...)
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speech_lengths: (Batch, )
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text: (Batch, Length)
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text_lengths: (Batch,)
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"""
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if len(text_lengths.size()) > 1:
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text_lengths = text_lengths[:, 0]
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if len(speech_lengths.size()) > 1:
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speech_lengths = speech_lengths[:, 0]
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batch_size = speech.shape[0]
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# Encoder
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encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
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# decoder: CTC branch
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loss_ctc, cer_ctc = self._calc_ctc_loss(
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encoder_out, encoder_out_lens, text, text_lengths
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)
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# Collect CTC branch stats
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stats = dict()
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stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None
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stats["cer_ctc"] = cer_ctc
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stats["cer"] = cer_ctc
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loss = loss_ctc
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stats["loss"] = torch.clone(loss.detach())
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stats["batch_size"] = batch_size
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# force_gatherable: to-device and to-tensor if scalar for DataParallel
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loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
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return loss, stats, weight
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def encode(
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self,
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speech: torch.Tensor,
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speech_lengths: torch.Tensor,
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**kwargs,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Encoder. Note that this method is used by asr_inference.py
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Args:
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speech: (Batch, Length, ...)
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speech_lengths: (Batch, )
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ind: int
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"""
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with autocast(False):
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# Data augmentation
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if self.specaug is not None and self.training:
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speech, speech_lengths = self.specaug(speech, speech_lengths)
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# Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
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if self.normalize is not None:
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speech, speech_lengths = self.normalize(speech, speech_lengths)
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# Forward encoder
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encoder_out, encoder_out_lens, _ = self.encoder(speech, speech_lengths)
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if isinstance(encoder_out, tuple):
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encoder_out = encoder_out[0]
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return encoder_out, encoder_out_lens
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def _calc_ctc_loss(
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self,
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encoder_out: torch.Tensor,
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encoder_out_lens: torch.Tensor,
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ys_pad: torch.Tensor,
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ys_pad_lens: torch.Tensor,
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):
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# Calc CTC loss
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"""Internal: calc ctc loss.
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Args:
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encoder_out: Encoder output tensor.
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encoder_out_lens: Encoder output lengths.
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ys_pad: TODO.
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ys_pad_lens: Lengths of ys_pad.
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"""
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loss_ctc = self.ctc(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens)
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# Calc CER using CTC
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cer_ctc = None
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if not self.training and self.error_calculator is not None:
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ys_hat = self.ctc.argmax(encoder_out).data
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cer_ctc = self.error_calculator(ys_hat.cpu(), ys_pad.cpu(), is_ctc=True)
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return loss_ctc, cer_ctc
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def inference(
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self,
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data_in,
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data_lengths=None,
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key: list = None,
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tokenizer=None,
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frontend=None,
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**kwargs,
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):
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"""Run inference on input data.
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Args:
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data_in: Input data (audio samples, file paths, or text).
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data_lengths: Lengths of each input sample in the batch.
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key: Sample identifiers.
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tokenizer: Tokenizer instance for text encoding/decoding.
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frontend: Audio frontend for feature extraction.
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**kwargs: Additional keyword arguments.
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"""
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keywords = kwargs.get("keywords")
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from funasr.utils.kws_utils import KwsCtcPrefixDecoder
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self.kws_decoder = KwsCtcPrefixDecoder(
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ctc=self.ctc,
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keywords=keywords,
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token_list=tokenizer.token_list,
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seg_dict=tokenizer.seg_dict,
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)
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meta_data = {}
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if (
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isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank"
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): # fbank
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speech, speech_lengths = data_in, data_lengths
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if len(speech.shape) < 3:
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speech = speech[None, :, :]
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if speech_lengths is not None:
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speech_lengths = speech_lengths.squeeze(-1)
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else:
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speech_lengths = speech.shape[1]
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else:
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# extract fbank feats
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time1 = time.perf_counter()
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audio_sample_list = load_audio_text_image_video(
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data_in,
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fs=frontend.fs,
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audio_fs=kwargs.get("fs", 16000),
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data_type=kwargs.get("data_type", "sound"),
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tokenizer=tokenizer,
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)
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time2 = time.perf_counter()
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meta_data["load_data"] = f"{time2 - time1:0.3f}"
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speech, speech_lengths = extract_fbank(
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audio_sample_list, data_type=kwargs.get("data_type", "sound"), frontend=frontend
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)
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time3 = time.perf_counter()
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meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
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meta_data["batch_data_time"] = (
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speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
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)
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speech = speech.to(device=kwargs["device"])
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speech_lengths = speech_lengths.to(device=kwargs["device"])
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# Encoder
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if kwargs.get("fp16", False):
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speech = speech.half()
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encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
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if isinstance(encoder_out, tuple):
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encoder_out = encoder_out[0]
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results = []
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if kwargs.get("output_dir") is not None:
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if not hasattr(self, "writer"):
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self.writer = DatadirWriter(kwargs.get("output_dir"))
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for i in range(encoder_out.size(0)):
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x = encoder_out[i, : encoder_out_lens[i], :]
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detect_result = self.kws_decoder.decode(x)
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is_deted, det_keyword, det_score = detect_result[0], detect_result[1], detect_result[2]
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if is_deted:
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self.writer["detect"][key[i]] = "detected " + det_keyword + " " + str(det_score)
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det_info = "detected " + det_keyword + " " + str(det_score)
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else:
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self.writer["detect"][key[i]] = "rejected"
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det_info = "rejected"
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result_i = {"key": key[i], "text": det_info}
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results.append(result_i)
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return results, meta_data
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def export(self, **kwargs):
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"""Export.
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Args:
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**kwargs: Additional keyword arguments.
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"""
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from .export_meta import export_rebuild_model
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if "max_seq_len" not in kwargs:
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kwargs["max_seq_len"] = 512
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models = export_rebuild_model(model=self, **kwargs)
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return models
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