wehub-resource-sync
500 lines
18 KiB
Python
500 lines
18 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 typing import Dict, Tuple
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from contextlib import contextmanager
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from distutils.version import LooseVersion
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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.utils.datadir_writer import DatadirWriter
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from funasr.models.sanm_kws.model import SanmKWS
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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, pad_list
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from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
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if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
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from torch.cuda.amp import autocast
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else:
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# Nothing to do if torch<1.6.0
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@contextmanager
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def autocast(enabled=True):
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"""Autocast.
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Args:
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enabled: TODO.
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"""
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yield
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@tables.register("model_classes", "SanmKWSStreaming")
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class SanmKWSStreaming(SanmKWS):
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"""
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Author: Speech Lab of DAMO Academy, Alibaba Group
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Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
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https://arxiv.org/abs/2206.08317
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"""
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def __init__(
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self,
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*args,
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**kwargs,
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):
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"""Initialize SanmKWSStreaming.
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Args:
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*args: Variable positional arguments.
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**kwargs: Additional keyword arguments.
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"""
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super().__init__(*args, **kwargs)
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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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decoding_ind = kwargs.get("decoding_ind")
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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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if hasattr(self.encoder, "overlap_chunk_cls"):
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ind = self.encoder.overlap_chunk_cls.random_choice(self.training, decoding_ind)
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encoder_out, encoder_out_lens = self.encode(speech, speech_lengths, ind=ind)
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else:
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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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if hasattr(self.encoder, "overlap_chunk_cls"):
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encoder_out_ctc, encoder_out_lens_ctc = self.encoder.overlap_chunk_cls.remove_chunk(
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encoder_out, encoder_out_lens, chunk_outs=None
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)
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else:
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encoder_out_ctc, encoder_out_lens_ctc = encoder_out, encoder_out_lens
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loss_ctc, cer_ctc = self._calc_ctc_loss(
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encoder_out_ctc, encoder_out_lens_ctc, 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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loss = loss_ctc
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stats["cer"] = cer_ctc
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stats["loss"] = torch.clone(loss.detach())
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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_chunk(
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self,
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speech: torch.Tensor,
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speech_lengths: torch.Tensor,
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cache: dict = None,
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**kwargs,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Encode chunk.
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Args:
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speech: Speech audio tensor, shape (batch, time).
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speech_lengths: Length of each speech sample.
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cache: State cache dict for streaming inference.
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**kwargs: Additional keyword arguments.
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"""
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if cache is None:
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cache = {}
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"""Frontend + 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.forward_chunk(
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speech, speech_lengths, cache=cache["encoder"]
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)
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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, torch.tensor([encoder_out.size(1)])
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def init_cache(self, cache: dict = None, **kwargs):
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"""Init cache.
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Args:
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cache: State cache dict for streaming inference.
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**kwargs: Additional keyword arguments.
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"""
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if cache is None:
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cache = {}
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chunk_size = kwargs.get("chunk_size", [0, 10, 5])
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encoder_chunk_look_back = kwargs.get("encoder_chunk_look_back", 0)
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decoder_chunk_look_back = kwargs.get("decoder_chunk_look_back", 0)
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batch_size = 1
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enc_output_size = kwargs["encoder_conf"]["output_size"]
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feats_dims = kwargs["frontend_conf"]["n_mels"] * kwargs["frontend_conf"]["lfr_m"]
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cache_encoder = {
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"start_idx": 0,
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"cif_hidden": torch.zeros((batch_size, 1, enc_output_size)),
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"cif_alphas": torch.zeros((batch_size, 1)),
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"encoder_out": None,
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"encoder_out_lens": None,
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"chunk_size": chunk_size,
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"encoder_chunk_look_back": encoder_chunk_look_back,
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"last_chunk": False,
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"opt": None,
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"feats": torch.zeros((batch_size, chunk_size[0] + chunk_size[2], feats_dims)),
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"tail_chunk": False,
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}
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cache["encoder"] = cache_encoder
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cache_decoder = {
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"decode_fsmn": None,
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"decoder_chunk_look_back": decoder_chunk_look_back,
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"opt": None,
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"chunk_size": chunk_size,
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}
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cache["decoder"] = cache_decoder
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cache["frontend"] = {}
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cache["prev_samples"] = torch.empty(0)
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return cache
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def generate_chunk(
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self,
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speech,
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speech_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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"""Generate chunk.
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Args:
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speech: Speech audio tensor, shape (batch, time).
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speech_lengths: Length of each speech sample.
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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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cache = kwargs.get("cache", {})
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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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is_final = kwargs.get("is_final", False)
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encoder_out, encoder_out_lens = self.encode_chunk(
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speech, speech_lengths, cache=cache, is_final=is_final
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)
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if isinstance(encoder_out, tuple):
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encoder_out = encoder_out[0]
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chunk_size = cache["encoder"]["chunk_size"]
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real_start_pos = chunk_size[0]
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if encoder_out_lens[0] > chunk_size[0] + chunk_size[1] + chunk_size[2]:
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assert False, print("impossible case 1 !")
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if encoder_out_lens[0] == chunk_size[0] + chunk_size[1] + chunk_size[2]:
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real_end_pos = chunk_size[0] + chunk_size[1]
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elif encoder_out_lens[0] > chunk_size[0] + chunk_size[1]:
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real_end_pos = chunk_size[0] + chunk_size[1]
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elif encoder_out_lens[0] > chunk_size[0]:
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real_end_pos = encoder_out_lens[0]
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else:
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assert False, print("impossible case 2 !")
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encoder_out_accum = cache["encoder"]["encoder_out"]
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if encoder_out_accum is not None:
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encoder_out_accum = torch.cat((encoder_out_accum, encoder_out[:, real_start_pos:real_end_pos, :]), dim=1)
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else:
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encoder_out_accum = encoder_out[:, real_start_pos:real_end_pos, :]
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cache["encoder"]["encoder_out"] = encoder_out_accum
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if cache["encoder"]["encoder_out_lens"] is not None:
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cache["encoder"]["encoder_out_lens"][0] += real_end_pos - real_start_pos
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else:
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cache["encoder"]["encoder_out_lens"] = encoder_out_lens
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cache["encoder"]["encoder_out_lens"][0] = real_end_pos - real_start_pos
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if is_final:
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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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results = []
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for i in range(encoder_out_accum.size(0)):
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x = encoder_out_accum[i, : cache["encoder"]["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
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else:
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return None
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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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cache: dict = 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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cache: State cache dict for streaming inference.
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**kwargs: Additional keyword arguments.
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"""
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if cache is None:
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cache = {}
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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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chunk_size = kwargs["chunk_size"]
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chunk_stride_samples = int(chunk_size[1] * 960) # 600ms
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first_chunk_padding_samples = int(chunk_size[2] * 960) # 600ms
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if len(cache) == 0:
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self.init_cache(cache, **kwargs)
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time1 = time.perf_counter()
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cfg = {"is_final": kwargs.get("is_final", False)}
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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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cache=cfg,
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)
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_is_final = cfg["is_final"] # if data_in is a file or url, set is_final=True
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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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assert len(audio_sample_list) == 1, "batch_size must be set 1"
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audio_sample = torch.cat((cache["prev_samples"], audio_sample_list[0]))
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if len(audio_sample) < first_chunk_padding_samples:
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print("key: {}, audio is too short for inference {}".format(key, len(audio_sample)))
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audio_sample_pre = audio_sample[0 : first_chunk_padding_samples]
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feat_pre, feat_pre_lengths = extract_fbank(
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[audio_sample_pre],
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data_type=kwargs.get("data_type", "sound"),
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frontend=frontend,
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cache=cache["frontend"],
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is_final=False,
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)
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audio_sample = audio_sample[first_chunk_padding_samples:]
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audio_chunks = int(len(audio_sample) // chunk_stride_samples)
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for i in range(audio_chunks):
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if i == 0:
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cache["encoder"]["feats"][:, chunk_size[2] :, :] = feat_pre
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kwargs["is_final"] = False
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audio_sample_i = audio_sample[i * chunk_stride_samples : (i + 1) * chunk_stride_samples]
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if kwargs["is_final"] and len(audio_sample_i) < 960:
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cache["encoder"]["tail_chunk"] = True
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speech = cache["encoder"]["feats"]
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speech_lengths = torch.tensor([speech.shape[1]], dtype=torch.int64).to(
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speech.device
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)
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else:
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# extract fbank feats
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speech, speech_lengths = extract_fbank(
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[audio_sample_i],
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data_type=kwargs.get("data_type", "sound"),
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frontend=frontend,
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cache=cache["frontend"],
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is_final=kwargs["is_final"],
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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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results_chunk_i = self.generate_chunk(
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speech,
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speech_lengths,
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key=key,
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tokenizer=tokenizer,
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cache=cache,
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frontend=frontend,
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**kwargs,
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)
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# results_chunk_i must be None when is_final=False
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assert results_chunk_i is None
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# process tail samples
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tail_audio_sample = audio_sample[ audio_chunks * chunk_stride_samples: ]
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if len(tail_audio_sample) < 960:
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kwargs["is_final"] = _is_final
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cache["encoder"]["tail_chunk"] = True
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speech = cache["encoder"]["feats"]
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speech_lengths = torch.tensor([speech.shape[1]], dtype=torch.int64).to(
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speech.device
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)
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results_chunk_tail = self.generate_chunk(
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speech,
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speech_lengths,
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key=key,
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tokenizer=tokenizer,
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cache=cache,
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frontend=frontend,
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**kwargs,
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)
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elif len(tail_audio_sample) <= first_chunk_padding_samples:
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kwargs["is_final"] = _is_final
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# extract fbank feats
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# cache["encoder"]["tail_chunk"] = True # cannot be true
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speech, speech_lengths = extract_fbank(
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[ tail_audio_sample ],
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data_type=kwargs.get("data_type", "sound"),
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frontend=frontend,
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cache=cache["frontend"],
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is_final=kwargs["is_final"],
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)
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results_chunk_tail = self.generate_chunk(
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speech,
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speech_lengths,
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key=key,
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tokenizer=tokenizer,
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cache=cache,
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frontend=frontend,
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**kwargs,
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)
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elif len(tail_audio_sample) > first_chunk_padding_samples and \
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len(tail_audio_sample) < chunk_stride_samples:
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kwargs["is_final"] = False
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# extract fbank feats
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speech, speech_lengths = extract_fbank(
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[ tail_audio_sample ],
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data_type=kwargs.get("data_type", "sound"),
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frontend=frontend,
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cache=cache["frontend"],
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is_final=kwargs["is_final"],
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)
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results_chunk = self.generate_chunk(
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speech,
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speech_lengths,
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key=key,
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tokenizer=tokenizer,
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cache=cache,
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frontend=frontend,
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**kwargs,
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)
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# results_chunk must be None when is_final=False
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assert results_chunk is None
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# push tail chunk
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kwargs["is_final"] = _is_final
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cache["encoder"]["tail_chunk"] = True
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speech = cache["encoder"]["feats"]
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speech_lengths = torch.tensor([speech.shape[1]], dtype=torch.int64).to(
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speech.device
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)
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results_chunk_tail = self.generate_chunk(
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speech,
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speech_lengths,
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key=key,
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tokenizer=tokenizer,
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cache=cache,
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frontend=frontend,
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**kwargs,
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)
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result = results_chunk_tail
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if _is_final:
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self.init_cache(cache, **kwargs)
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if kwargs.get("output_dir"):
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if not hasattr(self, "writer"):
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self.writer = DatadirWriter(kwargs.get("output_dir"))
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return result, 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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models = export_rebuild_model(model=self, **kwargs)
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return models
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