FUNASR训练
parent: 2ac38adb | 补丁 | 提交 | ignore whitespace
zhifu gao
2024-04-23 8795bf5bf1daac5a839f856a748d7e92cc4c5015 
Dev gzf exp (#1649)

* sensevoice finetune

* sensevoice finetune

* sensevoice finetune

* sensevoice finetune

* sensevoice finetune

* sensevoice finetune

* sensevoice finetune

* sensevoice finetune

* sensevoice finetune

* sensevoice finetune

* bugfix

* update with main (#1631)

* update seaco finetune

* v1.0.24

---------

Co-authored-by: 维石 <shixian.shi@alibaba-inc.com>

* sensevoice

* sensevoice

* sensevoice

* update with main (#1638)

* update seaco finetune

* v1.0.24

* update rwkv template

---------

Co-authored-by: 维石 <shixian.shi@alibaba-inc.com>

* sensevoice

* sensevoice

* sensevoice

* sensevoice

* sensevoice

* sensevoice

* sensevoice

* sensevoice

* sensevoice

* sensevoice

* sensevoice

* sensevoice

* sensevoice

* sensevoice

* sensevoice

* sense voice

* sense voice

* sense voice

* sense voice

* sense voice

* sense voice

* sense voice

* sense voice

* sense voice

* sense voice

* sense voice

* sense voice

* sense voice

* sense voice

* sense voice

* sense voice

* sense voice

* sense voice

---------

Co-authored-by: 维石 <shixian.shi@alibaba-inc.com>
3个文件已修改
229 ■■■■■ 已修改文件
funasr/datasets/audio_datasets/espnet_samplers.py 8 ●●●●● 补丁 | 查看 | 原始文档 | blame | 历史
funasr/datasets/audio_datasets/index_ds.py 213 ●●●●● 补丁 | 查看 | 原始文档 | blame | 历史
funasr/datasets/audio_datasets/samplers.py 8 ●●●●● 补丁 | 查看 | 原始文档 | blame | 历史 
 funasr/datasets/audio_datasets/espnet_samplers.py


@@ -48,10 +48,10 @@


        except:


            rank = 0


            num_replicas = 1


        if rank_split:


            logging.info(f"Warning, rank_split: {rank_split}, batch and shuffle data in local rank")


            rank = 0


            num_replicas = 1


        # if rank_split:


        #     logging.info(f"Warning, rank_split: {rank_split}, batch and shuffle data in local rank")


        #     rank = 0


        #     num_replicas = 1


        self.rank = rank


        self.num_replicas = num_replicas


        self.dataset = dataset




 funasr/datasets/audio_datasets/index_ds.py


@@ -10,67 +10,6 @@


from funasr.register import tables






# @tables.register("index_ds_classes", "IndexDSJsonlRankSplit")


# class IndexDSJsonlRankSplit(torch.utils.data.Dataset):


#


#     def __init__(self, path):


#         super().__init__()


#


#         contents = []


#         with open(path, encoding='utf-8') as fin:


#             for line in fin:


#                 data = json.loads(line.strip())


#                 if "text" in data:  # for sft


#                     self.contents.append(data['text'])


#                 if "source" in data:  # for speech lab pretrain


#                     prompt = data["prompt"]


#                     source = data["source"]


#                     target = data["target"]


#                     source_len = data["source_len"]


#                     target_len = data["target_len"]


#


#                     contents.append({"source": source,


#                                      "prompt": prompt,


#                                      "target": target,


#                                      "source_len": source_len,


#                                      "target_len": target_len,


#                                      }


#                                     )


#


#         self.contents = []


#         total_num = len(contents)


#         try:


#             rank = dist.get_rank()


#             world_size = dist.get_world_size()


#         except:


#             rank = 0


#             world_size = 1


#             logging.info("distributed is not initialized, only single shard")


#         num_per_rank = total_num // world_size


#


#         # rank = 0


#         # import ipdb; ipdb.set_trace()


#         self.contents = contents[rank * num_per_rank:(rank + 1) * num_per_rank]


#


#         logging.info("in rank: {}, num of samplers: {}, total_num of samplers across ranks: {}".format(rank, len(self.contents), len(contents)))


#


#     def __len__(self):


#         return len(self.contents)


#


#     def __getitem__(self, index):


#         try:


#             data = self.contents[index]


#         except:


#             print(index)


#         return data


#


#     def get_source_len(self, data_dict):


#         return data_dict["source_len"]


#


#     def get_target_len(self, data_dict):


#


#         return data_dict["target_len"] if "target_len" in data_dict else 0




@tables.register("index_ds_classes", "IndexDSJsonl")


@tables.register("index_ds_classes", "IndexDSJsonlRankFull")


@tables.register("index_ds_classes", "IndexDSJsonlRankSplit")


@@ -104,37 +43,39 @@


            file_list = [path]


            




        total_num = len(file_list)


        try:


            rank = dist.get_rank()


            world_size = dist.get_world_size()


        except:


            rank = 0


            world_size = 1


            logging.info("distributed is not initialized, only single shard")


        


        if not kwargs.get("rank_split", False):


            logging.info(f"Warning, rank_split disenabled, batch and shuffle data in global")


            rank = 0


            world_size = 1


        


        num_per_rank = total_num // world_size


        if num_per_rank * world_size < total_num:


            logging.info(f"Warning, jsonl file:{total_num} could not be divided by world_size: {world_size}, {path}")


            total_num_needed = num_per_rank * world_size


        # total_num = len(file_list)


        # try:


        #     rank = dist.get_rank()


        #     world_size = dist.get_world_size()


        # except:


        #     rank = 0


        #     world_size = 1


        #     logging.info("distributed is not initialized, only single shard")


        #


        # if not kwargs.get("rank_split", False):


        #     logging.info(f"Warning, rank_split disenabled, batch and shuffle data in global")


        #     rank = 0


        #     world_size = 1


        #


        # num_per_rank = total_num // world_size


        # if num_per_rank * world_size < total_num:


        #     logging.info(f"Warning, jsonl file:{total_num} could not be divided by world_size: {world_size}, {path}")


        #     total_num_needed = num_per_rank * world_size


        #


        #     extra_num = total_num_needed - total_num


        #     file_list_tmp = random.choices(file_list, k=extra_num)


        #     file_list += file_list_tmp


        #     logging.info(f"Warning, after random choices: {file_list}")


        #


        # file_list_rank = file_list[rank * num_per_rank:(rank + 1) * num_per_rank]


        #


        # logging.info(


        #     f"is_training: {is_training}, file_list_rank: {file_list_rank}")




            extra_num = total_num_needed - total_num


            file_list_tmp = random.choices(file_list, k=extra_num)


            file_list += file_list_tmp


            logging.info(f"Warning, after random choices: {file_list}")




        file_list_rank = file_list[rank * num_per_rank:(rank + 1) * num_per_rank]




        logging.info(


            f"is_training: {is_training}, file_list_rank: {file_list_rank}")




        # contents = []


        # for file_json in file_list_rank:


        contents = []


        for file_json in file_list_rank:


        for file_json in file_list:


            with open(file_json.strip(), encoding='utf-8') as fin:


                for line in fin:


                    data = json.loads(line.strip())


@@ -187,95 +128,3 @@


        


        return data_dict.get("target_len", 0)




# 


# @tables.register("index_ds_classes", "IndexDSJsonlRankSplit")


# class IndexDSJsonlRankSplit(torch.utils.data.Dataset):


# 


#     def __init__(self, path: str, **kwargs):


#         super().__init__()


#         logging.info("building IndexDS")


#         self.max_source_length = kwargs.get("max_source_length", 2048)


#         self.min_source_length = kwargs.get("min_source_length", 0)


#         self.max_target_length = kwargs.get("max_target_length", 2048)


#         self.min_target_length = kwargs.get("min_target_length", 0)


# 


#         data_split_num = kwargs.get("data_split_num", 1)


#         data_split_i = kwargs.get("data_split_i", 0)


#         if not kwargs.get("is_training", True):


#             data_split_num = 1


#             data_split_i = 0


#         with open(path, encoding='utf-8') as fin:


#             file_list_all = fin.readlines()


# 


#             num_per_slice = len(file_list_all) // data_split_num


#             file_list = file_list_all[data_split_i * num_per_slice:(data_split_i + 1) * num_per_slice]


#             logging.info(f"data_split_num: {data_split_num}, data_split_i: {data_split_i}, file_list: {file_list}, file_list_all: {file_list_all}")


# 


# 


#         total_num = len(file_list)


#         try:


#             rank = dist.get_rank()


#             world_size = dist.get_world_size()


#         except:


#             rank = 0


#             world_size = 1


#             logging.info("distributed is not initialized, only single shard")


#         num_per_rank = total_num // world_size


#         if num_per_rank * world_size < total_num:


#             logging.info(f"Warning, jsonl file:{total_num} could not be divided by world_size: {world_size}, {path}")


# 


#         file_list_rank = file_list[rank * num_per_rank:(rank + 1) * num_per_rank]


# 


#         contents = []


#         for file_json in file_list_rank:


# 


#             with open(file_json.strip(), encoding='utf-8') as fin:


#                 for line in fin:


#                     data = json.loads(line.strip())


#                     if "text" in data:  # for sft


#                         contents.append(data['text'])


#                     if "source" in data:  # for speech lab pretrain


#                         prompt = data.get("prompt", "<ASR>")


#                         source = data["source"].replace("/cpfs01", "/cpfs_speech/data")


#                         target = data["target"]


#                         source_len = data.get("source_len", 1)


#                         target_len = data.get("target_len", 0)


# 


#                         if source_len < self.min_source_length or source_len > self.max_source_length:


#                             continue


#                         if target_len < self.min_target_length or target_len > self.max_target_length:


#                             continue


#                         contents_i = {"source": source,


#                                       "prompt": prompt,


#                                       "target": target,


#                                       "source_len": source_len,


#                                       "target_len": target_len,


#                                       }


#                         text_language = data.get("text_language", None)


#                         if text_language is not None:


#                             contents_i["text_language"] = text_language


#                         # audio_language = data.get("audio_language", None)


#                         # if audio_language is not None:


#                         #     contents_i["audio_language"] = audio_language


#                         contents.append(contents_i)


# 


#         self.contents = contents


# 


#         logging.info(f"total_num: {len(self.contents)} of samplers in ranks: {rank}, file_list_rank: {file_list_rank}")


# 


#     def __len__(self):


#         return len(self.contents)


# 


#     def __getitem__(self, index):


#         try:


#             data = self.contents[index]


#         except:


#             print(index)


#         return data


# 


#     def get_source_len(self, data_dict):


#         return data_dict.get("source_len", 1)


# 


#     def get_target_len(self, data_dict):


# 


#         return data_dict.get("target_len", 0)




 funasr/datasets/audio_datasets/samplers.py


@@ -316,10 +316,10 @@


            rank = 0


            num_replicas = 1




        if rank_split:


            logging.info(f"Warning, rank_split: {rank_split}, batch and shuffle data in local rank")


            rank = 0


            num_replicas = 1


        # if rank_split:


        #     logging.info(f"Warning, rank_split: {rank_split}, batch and shuffle data in local rank")


        #     rank = 0


        #     num_replicas = 1


            


        self.rank = rank


        self.num_replicas = num_replicas