funasr1.0.5 (#1328)

zhifu gao

2024-01-31 3f487c42904a27deeae4ab48cf8ccc45537263d1

 funasr/models/transformer/model.py

@@ -24,18 +24,16 @@
    
    def __init__(
        self,
        frontend: Optional[str] = None,
        frontend_conf: Optional[Dict] = None,
        specaug: Optional[str] = None,
        specaug_conf: Optional[Dict] = None,
        specaug: str = None,
        specaug_conf: dict = None,
        normalize: str = None,
        normalize_conf: Optional[Dict] = None,
        normalize_conf: dict = None,
        encoder: str = None,
        encoder_conf: Optional[Dict] = None,
        encoder_conf: dict = None,
        decoder: str = None,
        decoder_conf: Optional[Dict] = None,
        decoder_conf: dict = None,
        ctc: str = None,
        ctc_conf: Optional[Dict] = None,
        ctc_conf: dict = None,
        ctc_weight: float = 0.5,
        interctc_weight: float = 0.0,
        input_size: int = 80,
@@ -59,20 +57,17 @@

        super().__init__()

        if frontend is not None:
            frontend_class = tables.frontend_classes.get_class(frontend)
            frontend = frontend_class(**frontend_conf)
        if specaug is not None:
            specaug_class = tables.specaug_classes.get_class(specaug)
            specaug_class = tables.specaug_classes.get(specaug)
            specaug = specaug_class(**specaug_conf)
        if normalize is not None:
            normalize_class = tables.normalize_classes.get_class(normalize)
            normalize_class = tables.normalize_classes.get(normalize)
            normalize = normalize_class(**normalize_conf)
        encoder_class = tables.encoder_classes.get_class(encoder)
        encoder_class = tables.encoder_classes.get(encoder)
        encoder = encoder_class(input_size=input_size, **encoder_conf)
        encoder_output_size = encoder.output_size()
        if decoder is not None:
            decoder_class = tables.decoder_classes.get_class(decoder)
            decoder_class = tables.decoder_classes.get(decoder)
            decoder = decoder_class(
                vocab_size=vocab_size,
                encoder_output_size=encoder_output_size,
@@ -93,7 +88,6 @@
        self.vocab_size = vocab_size
        self.ignore_id = ignore_id
        self.ctc_weight = ctc_weight
        self.frontend = frontend
        self.specaug = specaug
        self.normalize = normalize
        self.encoder = encoder
@@ -338,6 +332,7 @@
            )
        token_list = kwargs.get("token_list")
        scorers.update(
            decoder=self.decoder,
            length_bonus=LengthBonus(len(token_list)),
        )

@@ -348,14 +343,14 @@
        scorers["ngram"] = ngram
        
        weights = dict(
            decoder=1.0 - kwargs.get("decoding_ctc_weight", 0.0),
            ctc=kwargs.get("decoding_ctc_weight", 0.0),
            decoder=1.0 - kwargs.get("decoding_ctc_weight", 0.5),
            ctc=kwargs.get("decoding_ctc_weight", 0.5),
            lm=kwargs.get("lm_weight", 0.0),
            ngram=kwargs.get("ngram_weight", 0.0),
            length_bonus=kwargs.get("penalty", 0.0),
        )
        beam_search = BeamSearch(
            beam_size=kwargs.get("beam_size", 2),
            beam_size=kwargs.get("beam_size", 10),
            weights=weights,
            scorers=scorers,
            sos=self.sos,
@@ -364,17 +359,15 @@
            token_list=token_list,
            pre_beam_score_key=None if self.ctc_weight == 1.0 else "full",
        )
        # beam_search.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval()
        # for scorer in scorers.values():
        #     if isinstance(scorer, torch.nn.Module):
        #         scorer.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval()

        self.beam_search = beam_search
        
    def generate(self,
             data_in: list,
             data_lengths: list=None,
    def inference(self,
             data_in,
             data_lengths=None,
             key: list=None,
             tokenizer=None,
             frontend=None,
             **kwargs,
             ):
        
@@ -382,27 +375,34 @@
            raise NotImplementedError("batch decoding is not implemented")
        
        # init beamsearch
        is_use_ctc = kwargs.get("decoding_ctc_weight", 0.0) > 0.00001 and self.ctc != None
        is_use_lm = kwargs.get("lm_weight", 0.0) > 0.00001 and kwargs.get("lm_file", None) is not None
        if self.beam_search is None and (is_use_lm or is_use_ctc):
        if self.beam_search is None:
            logging.info("enable beam_search")
            self.init_beam_search(**kwargs)
            self.nbest = kwargs.get("nbest", 1)
        

        meta_data = {}
        # extract fbank feats
        time1 = time.perf_counter()
        audio_sample_list = load_audio_text_image_video(data_in, fs=self.frontend.fs, audio_fs=kwargs.get("fs", 16000))
        time2 = time.perf_counter()
        meta_data["load_data"] = f"{time2 - time1:0.3f}"
        speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"), frontend=self.frontend)
        time3 = time.perf_counter()
        meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
        meta_data["batch_data_time"] = speech_lengths.sum().item() * self.frontend.frame_shift * self.frontend.lfr_n / 1000
        
        if isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank":  # fbank
            speech, speech_lengths = data_in, data_lengths
            if len(speech.shape) < 3:
                speech = speech[None, :, :]
            if speech_lengths is None:
                speech_lengths = speech.shape[1]
        else:
            # extract fbank feats
            time1 = time.perf_counter()
            audio_sample_list = load_audio_text_image_video(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000),
                                                            data_type=kwargs.get("data_type", "sound"),
                                                            tokenizer=tokenizer)
            time2 = time.perf_counter()
            meta_data["load_data"] = f"{time2 - time1:0.3f}"
            speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
                                                   frontend=frontend)
            time3 = time.perf_counter()
            meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
            meta_data["batch_data_time"] = speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000

        speech = speech.to(device=kwargs["device"])
        speech_lengths = speech_lengths.to(device=kwargs["device"])

        # Encoder
        encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
        if isinstance(encoder_out, tuple):
@@ -439,14 +439,13 @@
                token = tokenizer.ids2tokens(token_int)
                text = tokenizer.tokens2text(token)
                
                text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
                result_i = {"key": key[i], "token": token, "text": text, "text_postprocessed": text_postprocessed}
                # text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
                result_i = {"key": key[i], "token": token, "text": text}
                results.append(result_i)
                
                if ibest_writer is not None:
                    ibest_writer["token"][key[i]] = " ".join(token)
                    ibest_writer["text"][key[i]] = text
                    ibest_writer["text_postprocessed"][key[i]] = text_postprocessed
        
        return results, meta_data