Update demo.py

shixian.shi

2024-01-25 fa56f36921c6bcb4608a28ab76686822033b728e

 funasr/auto/auto_model.py

@@ -123,7 +123,6 @@
            self.preset_spk_num = kwargs.get("preset_spk_num", None)
            if self.preset_spk_num:
                logging.warning("Using preset speaker number: {}".format(self.preset_spk_num))
            logging.warning("Many to print when using speaker model...")
            
        self.kwargs = kwargs
        self.model = model
@@ -146,7 +145,7 @@
        set_all_random_seed(kwargs.get("seed", 0))
        
        device = kwargs.get("device", "cuda")
        if not torch.cuda.is_available() or kwargs.get("ngpu", 0):
        if not torch.cuda.is_available() or kwargs.get("ngpu", 0) == 0:
            device = "cpu"
            kwargs["batch_size"] = 1
        kwargs["device"] = device
@@ -224,7 +223,7 @@
        asr_result_list = []
        num_samples = len(data_list)
        disable_pbar = kwargs.get("disable_pbar", False)
        pbar = tqdm(colour="blue", total=num_samples+1, dynamic_ncols=True) if not disable_pbar else None
        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):
@@ -311,7 +310,7 @@
            batch_size_ms_cum = 0
            beg_idx = 0
            beg_asr_total = time.time()
            time_speech_total_per_sample = speech_lengths/16000
            time_speech_total_per_sample = speech_lengths/16000 + 1e-6
            time_speech_total_all_samples += time_speech_total_per_sample

            pbar_sample = tqdm(colour="blue", total=n + 1, dynamic_ncols=True)
@@ -329,8 +328,6 @@
                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, disable_pbar=True, **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,
@@ -345,16 +342,14 @@
                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}")
            

            restored_data = [0] * n
            for j in range(n):
                index = sorted_data[j][1]
@@ -377,7 +372,7 @@
                            result[k] = restored_data[j][k]
                        else:
                            result[k] = torch.cat([result[k], restored_data[j][k]], dim=0)
                    elif k == 'text':
                    elif 'text' in k:
                        if k not in result:
                            result[k] = restored_data[j][k]
                        else:
@@ -392,8 +387,9 @@
            if self.punc_model is not None:
                self.punc_kwargs.update(cfg)
                punc_res = self.inference(result["text"], model=self.punc_model, kwargs=self.punc_kwargs, **cfg)
                import copy; raw_text = copy.copy(result["text"])
                result["text"] = punc_res[0]["text"]
                     
                
            # speaker embedding cluster after resorted
            if self.spk_model is not None:
                all_segments = sorted(all_segments, key=lambda x: x[0])
@@ -401,19 +397,24 @@
                labels = self.cb_model(spk_embedding.cpu(), oracle_num=self.preset_spk_num)
                del result['spk_embedding']
                sv_output = postprocess(all_segments, None, labels, spk_embedding.cpu())
                if self.spk_mode == 'vad_segment':
                if self.spk_mode == 'vad_segment':  # recover sentence_list
                    sentence_list = []
                    for res, vadsegment in zip(restored_data, vadsegments):
                        sentence_list.append({"start": vadsegment[0],\
                                                "end": vadsegment[1],
                                                "sentence": res['text'],
                                                "sentence": res['raw_text'],
                                                "timestamp": res['timestamp']})
                else: # punc_segment
                elif self.spk_mode == 'punc_segment':
                    sentence_list = timestamp_sentence(punc_res[0]['punc_array'], \
                                                        result['timestamp'], \
                                                        result['text'])
                                                        result['raw_text'])
                distribute_spk(sentence_list, sv_output)
                result['sentence_info'] = sentence_list
            elif kwargs.get("sentence_timestamp", False):
                sentence_list = timestamp_sentence(punc_res[0]['punc_array'], \
                                                        result['timestamp'], \
                                                        result['raw_text'])
                result['sentence_info'] = sentence_list
                    
            result["key"] = key
            results_ret_list.append(result)