Merge pull request #939 from alibaba-damo-academy/dev_sxfix

Xian Shi

2023-09-12 57ccdf04e0dc17af86ae9b2f2d6155440989f450

Merge pull request #939 from alibaba-damo-academy/dev_sxfix

Bug fix

 funasr/bin/asr_inference_launch.py

@@ -415,7 +415,7 @@
                        ibest_writer["rtf"][key] = rtf_cur

                    if text is not None:
                        if use_timestamp and timestamp is not None:
                        if use_timestamp and timestamp is not None and len(timestamp):
                            postprocessed_result = postprocess_utils.sentence_postprocess(token, timestamp)
                        else:
                            postprocessed_result = postprocess_utils.sentence_postprocess(token)
@@ -427,7 +427,7 @@
                        else:
                            text_postprocessed, word_lists = postprocessed_result[0], postprocessed_result[1]
                        item = {'key': key, 'value': text_postprocessed}
                        if timestamp_postprocessed != "":
                        if timestamp_postprocessed != "" or len(timestamp) == 0:
                            item['timestamp'] = timestamp_postprocessed
                        asr_result_list.append(item)
                        finish_count += 1
@@ -692,7 +692,7 @@
            text, token, token_int = result[0], result[1], result[2]
            time_stamp = result[4] if len(result[4]) > 0 else None

            if use_timestamp and time_stamp is not None:
            if use_timestamp and time_stamp is not None and len(time_stamp):
                postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp)
            else:
                postprocessed_result = postprocess_utils.sentence_postprocess(token)
@@ -717,7 +717,7 @@
            item = {'key': key, 'value': text_postprocessed_punc}
            if text_postprocessed != "":
                item['text_postprocessed'] = text_postprocessed
            if time_stamp_postprocessed != "":
            if time_stamp_postprocessed != "" or len(time_stamp) == 0:
                item['time_stamp'] = time_stamp_postprocessed

            item['sentences'] = time_stamp_sentence(punc_id_list, time_stamp_postprocessed, text_postprocessed)

 funasr/export/export_model.py

@@ -254,7 +254,7 @@
            if not os.path.exists(quant_model_path):
                onnx_model = onnx.load(model_path)
                nodes = [n.name for n in onnx_model.graph.node]
                nodes_to_exclude = [m for m in nodes if 'output' in m]
                nodes_to_exclude = [m for m in nodes if 'output' in m or 'bias_encoder' in m  or 'bias_decoder' in m]
                quantize_dynamic(
                    model_input=model_path,
                    model_output=quant_model_path,

 funasr/runtime/python/onnxruntime/demo_contextual_paraformer.py

@@ -5,7 +5,7 @@
model = ContextualParaformer(model_dir, batch_size=1)

wav_path = ['{}/.cache/modelscope/hub/damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/example/asr_example.wav'.format(Path.home())]
hotwords = '随机热词 各种热词 魔搭 阿里巴巴'
hotwords = '随机热词 各种热词 魔搭 阿里巴巴 仏'

result = model(wav_path, hotwords)
print(result)

 funasr/runtime/python/onnxruntime/funasr_onnx/paraformer_bin.py

@@ -314,7 +314,14 @@
        hotwords_length = torch.Tensor(hotwords_length).to(torch.int32)
        # hotwords.append('<s>')
        def word_map(word):
            return torch.tensor([self.vocab[i] for i in word])
            hotwords = []
            for c in word:
                if c not in self.vocab.keys():
                    hotwords.append(8403)
                    logging.warning("oov character {} found in hotword {}, replaced by <unk>".format(c, word))
                else:
                    hotwords.append(self.vocab[c])
            return torch.tensor(hotwords)
        hotword_int = [word_map(i) for i in hotwords]
        # import pdb; pdb.set_trace()
        hotword_int.append(torch.tensor([1]))