update docs

雾聪

2024-03-05 585a4d3e5ff8b77ee89c2ec2a9ff1e7cacd79319

 funasr/models/llm_asr/model.py

@@ -20,8 +20,8 @@
from funasr.register import tables


@tables.register("model_classes", "LLMASRNAR")
class LLMASRNAR(nn.Module):
@tables.register("model_classes", "LLMASR")
class LLMASR(nn.Module):
    """ """
    
    def __init__(
@@ -73,7 +73,7 @@
        hub = encoder_conf.get("hub", None)
        if hub == "funasr":
            from funasr import AutoModel
            init_param_path = encoder_conf.get("hub", "iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch")
            init_param_path = encoder_conf.get("init_param_path", "iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch")
            model = AutoModel(model=init_param_path, model_revision="v2.0.4")
            # frontend = model.kwargs.get("frontend")
            model.model.decoder = None
@@ -179,6 +179,7 @@

        if input_ids is not None:
            input_ids[input_ids == -1] = 0
            input_ids[input_ids == -100] = 0
            if hasattr(self.llm.model, "embed_tokens"):
                inputs_embeds = self.llm.model.embed_tokens(input_ids)
            elif hasattr(self.llm.model.model, "embed_tokens"):
@@ -190,7 +191,7 @@
                batch_size, token_num, dims = inputs_embeds.shape
                _, l, _ = encoder_out.shape
                encoder_outs_pad = F.pad(encoder_out, (0, 0, token_num-l-1, 1, 0, 0), value=0.0)
                inputs_embeds = encoder_outs_pad * audio_mask[:, :, None] + inputs_embeds * (~audio_mask[:, :, None])
                inputs_embeds = encoder_outs_pad * audio_mask[:, :, None] + inputs_embeds * (1.0-audio_mask[:, :, None])
                inputs_embeds = F.pad(inputs_embeds[:, 1:, :], (0, 0, 0, 1, 0, 0), value=0.0)

        model_outputs = self.llm(inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels_ids)
@@ -198,11 +199,10 @@


        stats = {}
        if self.metric:
            with torch.no_grad():
                preds = torch.argmax(model_outputs.logits, -1)
                acc_att = compute_accuracy(preds[:, :-1], labels_ids[:, 1:], ignore_label=-100)
                stats["acc"] = acc_att
        with torch.no_grad():
            preds = torch.argmax(model_outputs.logits, -1)
            acc_att = compute_accuracy(preds[:, :-1], labels_ids[:, 1:], ignore_label=-100)
            stats["acc"] = acc_att

        stats["loss"] = torch.clone(loss.detach())

@@ -216,16 +216,17 @@
        self, speech: torch.Tensor, speech_lengths: torch.Tensor, **kwargs,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
    
        audio_mask = kwargs.get("audio_mask")
        audio_token_lengths = audio_mask.sum(-1)
        audio_mask = kwargs.get("audio_mask", None)
        audio_token_lengths = audio_mask.sum(-1) if audio_mask is not None else None

        batch = {"speech": speech, "speech_lengths": speech_lengths}
        enc, enc_lens = self.audio_encoder.encode(**batch)
        enc_mask = sequence_mask(enc_lens, enc.size(1), device=enc.device)[:, None, :]
        pre_acoustic_embeds, pre_token_length, _, _ = self.audio_encoder.predictor(enc,
                                                                           mask=enc_mask,
                                                                           target_label_length=audio_token_lengths,
                                                                           )
        with autocast(False):
            enc_mask = sequence_mask(enc_lens, enc.size(1), device=enc.device)[:, None, :]
            pre_acoustic_embeds, pre_token_length, _, _ = self.audio_encoder.predictor(enc,
                                                                               mask=enc_mask,
                                                                               target_label_length=audio_token_lengths,
                                                                               )

        return pre_acoustic_embeds, pre_token_length

@@ -239,14 +240,12 @@
                  **kwargs,
                  ):
        
        prompt = kwargs.get("prompt", "Transcribe speech to text.")
        
        if kwargs.get("batch_size", 1) > 1:
            raise NotImplementedError("batch decoding is not implemented")
        
        # init beamsearch
        if self.beam_search is None:
            logging.info("enable beam_search")
            self.init_beam_search(**kwargs)
            self.nbest = kwargs.get("nbest", 1)


        
        meta_data = {}
        if isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank":  # fbank
@@ -271,50 +270,72 @@
        
        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):
            encoder_out = encoder_out[0]

        # adaptor
        encoder_out = self.adaptor(encoder_out)
        
        # c. Passed the encoder result and the beam search
        nbest_hyps = self.beam_search(
            x=encoder_out[0], maxlenratio=kwargs.get("maxlenratio", 0.0), minlenratio=kwargs.get("minlenratio", 0.0)
        )
    
        prompt_pre = "USER: \nINSTRUCTION: {}\nINPUT: ".format(prompt)
        prompt_ids = tokenizer.encode(prompt_pre)
        prompt_length = len(prompt_ids)
        prompt_ids = torch.tensor(prompt_ids, dtype=torch.int64).to(kwargs["device"])


        if hasattr(self.llm.model, "embed_tokens"):
            inputs_embeds = self.llm.model.embed_tokens(prompt_ids)
        elif hasattr(self.llm.model.model, "embed_tokens"):
            inputs_embeds = self.llm.model.model.embed_tokens(prompt_ids)
        else:
            inputs_embeds = self.llm.model.model.model.embed_tokens(prompt_ids)

        inputs_embeds = torch.cat((inputs_embeds[None, :, :], encoder_out), dim=1)  # [prompt, audio]
        attention_mask = torch.ones(inputs_embeds.size()[:-1], dtype=torch.long).to(kwargs["device"])
        
        nbest_hyps = nbest_hyps[: self.nbest]
        # model_outputs = self.llm.generate(
        #     inputs_embeds=inputs_embeds,
        #     max_length=kwargs.get("max_length", 200),
        #     max_new_tokens=kwargs.get("max_new_tokens", 200),
        #     num_beams=kwargs.get("num_beams", 4),
        #     do_sample=kwargs.get("do_sample", False),
        #     min_length=kwargs.get("min_length", 1),
        #     top_p=kwargs.get("top_p", 1.0),
        #     repetition_penalty=kwargs.get("repetition_penalty", 1.0),
        #     length_penalty=kwargs.get("length_penalty", 1.0),
        #     temperature=kwargs.get("temperature", 1.0),
        #     attention_mask=attention_mask,
        #     bos_token_id=tokenizer.bos_token_id,
        #     eos_token_id=tokenizer.eos_token_id,
        #     pad_token_id=tokenizer.pad_token_id
        # )


        model_outputs = self.llm(inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=None)
        preds = torch.argmax(model_outputs.logits, -1)
        text = tokenizer.batch_decode(preds, add_special_tokens=False, skip_special_tokens=True)

        text = text[0].split(': ')[-1]
        text = text.strip()
        
        # preds = torch.argmax(model_outputs.logits, -1)
        
        ibest_writer = None
        if kwargs.get("output_dir") is not None:
            if not hasattr(self, "writer"):
                self.writer = DatadirWriter(kwargs.get("output_dir"))
            ibest_writer = self.writer[f"{0 + 1}best_recog"]

        results = []
        b, n, d = encoder_out.size()
        for i in range(b):
            
            for nbest_idx, hyp in enumerate(nbest_hyps):
                ibest_writer = None
                if kwargs.get("output_dir") is not None:
                    if not hasattr(self, "writer"):
                        self.writer = DatadirWriter(kwargs.get("output_dir"))
                    ibest_writer = self.writer[f"{nbest_idx + 1}best_recog"]
                
                # remove sos/eos and get results
                last_pos = -1
                if isinstance(hyp.yseq, list):
                    token_int = hyp.yseq[1:last_pos]
                else:
                    token_int = hyp.yseq[1:last_pos].tolist()
                
                # remove blank symbol id, which is assumed to be 0
                token_int = list(filter(lambda x: x != self.eos and x != self.sos and x != self.blank_id, token_int))
                
                # Change integer-ids to tokens
                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_postprocessed}
                results.append(result_i)
                
                if ibest_writer is not None:
                    ibest_writer["token"][key[i]] = " ".join(token)
                    ibest_writer["text"][key[i]] = text_postprocessed
        result_i = {"key": key[0], "text": text}
        results.append(result_i)

        if ibest_writer is not None:
            ibest_writer["text"][key[0]] = text
        
        
        
        
        return results, meta_data