funasr1.0 fix punc model

游雁

2024-01-13 835369d6315e96c1820326ed11ea4b999793720f

funasr1.0 fix punc model

 examples/industrial_data_pretraining/bicif_paraformer/demo.py

@@ -10,7 +10,7 @@
                    vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
                    vad_model_revision="v2.0.1",
                    punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
                    punc_model_revision="v2.0.0",
                    punc_model_revision="v2.0.1",
                    spk_model="/Users/shixian/code/modelscope_models/speech_campplus_sv_zh-cn_16k-common",
                  )

@@ -23,7 +23,7 @@
                    vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
                    vad_model_revision="v2.0.1",
                    punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
                    punc_model_revision="v2.0.0",
                    punc_model_revision="v2.0.1",
                    spk_model="/Users/shixian/code/modelscope_models/speech_campplus_sv_zh-cn_16k-common",
                    spk_mode='punc_segment',
                  )

 examples/industrial_data_pretraining/bicif_paraformer/infer.sh

@@ -4,7 +4,7 @@
vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch"
vad_model_revision="v2.0.0"
punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
punc_model_revision="v2.0.0"
punc_model_revision="v2.0.1"

python funasr/bin/inference.py \
+model=${model} \

 examples/industrial_data_pretraining/ct_transformer/demo.py

@@ -5,7 +5,15 @@

from funasr import AutoModel

model = AutoModel(model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch", model_revision="v2.0.0")
model = AutoModel(model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch", model_revision="v2.0.1")

res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/punc_example.txt")
print(res)


from funasr import AutoModel

model = AutoModel(model="damo/punc_ct-transformer_cn-en-common-vocab471067-large", model_revision="v2.0.1")

res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/punc_example.txt")
print(res)

 examples/industrial_data_pretraining/ct_transformer/infer.sh

@@ -1,6 +1,9 @@

model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
model_revision="v2.0.0"
model_revision="v2.0.1"

model="damo/punc_ct-transformer_cn-en-common-vocab471067-large"
model_revision="v2.0.1"

python funasr/bin/inference.py \
+model=${model} \

 examples/industrial_data_pretraining/paraformer-zh-spk/demo.py

@@ -10,7 +10,7 @@
                  vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
                  vad_model_revision="v2.0.1",
                  punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
                  punc_model_revision="v2.0.0",
                  punc_model_revision="v2.0.1",
                  spk_model="damo/speech_campplus_sv_zh-cn_16k-common",
                  spk_model_revision="v2.0.0"
                  )

 examples/industrial_data_pretraining/paraformer-zh-spk/infer.sh

@@ -4,7 +4,7 @@
vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch"
vad_model_revision="v2.0.1"
punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
punc_model_revision="v2.0.0"
punc_model_revision="v2.0.1"
spk_model="damo/speech_campplus_sv_zh-cn_16k-common"
spk_model_revision="v2.0.0"


 examples/industrial_data_pretraining/seaco_paraformer/demo.py

@@ -10,7 +10,7 @@
                  vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
                  vad_model_revision="v2.0.1",
                  punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
                  punc_model_revision="v2.0.0",
                  punc_model_revision="v2.0.1",
                  )

res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",

 examples/industrial_data_pretraining/seaco_paraformer/infer.sh

@@ -4,7 +4,7 @@
vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch"
vad_model_revision="v2.0.1"
punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
punc_model_revision="v2.0.0"
punc_model_revision="v2.0.1"

python funasr/bin/inference.py \
+model=${model} \

 funasr/download/download_from_hub.py

@@ -37,6 +37,8 @@
        kwargs["model"] = cfg["model"]
        if os.path.exists(os.path.join(model_or_path, "am.mvn")):
            kwargs["frontend_conf"]["cmvn_file"] = os.path.join(model_or_path, "am.mvn")
        if os.path.exists(os.path.join(model_or_path, "jieba_usr_dict")):
            kwargs["jieba_usr_dict"] = os.path.join(model_or_path, "jieba_usr_dict")
    else:# configuration.json
        assert os.path.exists(os.path.join(model_or_path, "configuration.json"))
        with open(os.path.join(model_or_path, "configuration.json"), 'r', encoding='utf-8') as f:

 funasr/models/ct_transformer/model.py

@@ -225,8 +225,14 @@
        # text = data_in[0]
        # text_lengths = data_lengths[0] if data_lengths is not None else None
        split_size = kwargs.get("split_size", 20)
        
        tokens = split_words(text)

        jieba_usr_dict = kwargs.get("jieba_usr_dict", None)
        if jieba_usr_dict and isinstance(jieba_usr_dict, str):
            import jieba
            jieba.load_userdict(jieba_usr_dict)
            jieba_usr_dict = jieba
            kwargs["jieba_usr_dict"] = "jieba_usr_dict"
        tokens = split_words(text, jieba_usr_dict=jieba_usr_dict)
        tokens_int = tokenizer.encode(tokens)

        mini_sentences = split_to_mini_sentence(tokens, split_size)

 funasr/models/ct_transformer/utils.py

@@ -1,4 +1,4 @@

import re

def split_to_mini_sentence(words: list, word_limit: int = 20):
    assert word_limit > 1
@@ -14,23 +14,98 @@
    return sentences


def split_words(text: str):
    words = []
    segs = text.split()
    for seg in segs:
        # There is no space in seg.
        current_word = ""
        for c in seg:
            if len(c.encode()) == 1:
                # This is an ASCII char.
                current_word += c
# def split_words(text: str, **kwargs):
#     words = []
#     segs = text.split()
#     for seg in segs:
#         # There is no space in seg.
#         current_word = ""
#         for c in seg:
#             if len(c.encode()) == 1:
#                 # This is an ASCII char.
#                 current_word += c
#             else:
#                 # This is a Chinese char.
#                 if len(current_word) > 0:
#                     words.append(current_word)
#                     current_word = ""
#                 words.append(c)
#         if len(current_word) > 0:
#             words.append(current_word)
#
#     return words

def split_words(text: str, jieba_usr_dict=None, **kwargs):
    if jieba_usr_dict:
        input_list = text.split()
        token_list_all = []
        langauge_list = []
        token_list_tmp = []
        language_flag = None
        for token in input_list:
            if isEnglish(token) and language_flag == 'Chinese':
                token_list_all.append(token_list_tmp)
                langauge_list.append('Chinese')
                token_list_tmp = []
            elif not isEnglish(token) and language_flag == 'English':
                token_list_all.append(token_list_tmp)
                langauge_list.append('English')
                token_list_tmp = []

            token_list_tmp.append(token)

            if isEnglish(token):
                language_flag = 'English'
            else:
                # This is a Chinese char.
                if len(current_word) > 0:
                    words.append(current_word)
                    current_word = ""
                words.append(c)
        if len(current_word) > 0:
            words.append(current_word)
    
    return words
                language_flag = 'Chinese'

        if token_list_tmp:
            token_list_all.append(token_list_tmp)
            langauge_list.append(language_flag)

        result_list = []
        for token_list_tmp, language_flag in zip(token_list_all, langauge_list):
            if language_flag == 'English':
                result_list.extend(token_list_tmp)
            else:
                seg_list = jieba_usr_dict.cut(join_chinese_and_english(token_list_tmp), HMM=False)
                result_list.extend(seg_list)

        return result_list

    else:
        words = []
        segs = text.split()
        for seg in segs:
            # There is no space in seg.
            current_word = ""
            for c in seg:
                if len(c.encode()) == 1:
                    # This is an ASCII char.
                    current_word += c
                else:
                    # This is a Chinese char.
                    if len(current_word) > 0:
                        words.append(current_word)
                        current_word = ""
                    words.append(c)
            if len(current_word) > 0:
                words.append(current_word)
        return words

def isEnglish(text:str):
    if re.search('^[a-zA-Z\']+$', text):
        return True
    else:
        return False

def join_chinese_and_english(input_list):
    line = ''
    for token in input_list:
        if isEnglish(token):
            line = line + ' ' + token
        else:
            line = line + token

    line = line.strip()
    return line