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https://github.com/modelscope/FunASR
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嘉渊
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@ -1,5 +1,5 @@
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# -*- encoding: utf-8 -*-
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#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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@ -7,40 +7,25 @@ import argparse
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import logging
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import os
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import sys
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from typing import Union, Dict, Any
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from funasr.utils import config_argparse
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from funasr.utils.cli_utils import get_commandline_args
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from funasr.utils.types import str2bool
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from funasr.utils.types import str2triple_str
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from funasr.utils.types import str_or_none
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from funasr.utils.types import float_or_none
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import argparse
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import logging
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from pathlib import Path
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import sys
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import os
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from typing import Optional
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from typing import Sequence
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from typing import Tuple
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from typing import Union
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from typing import Dict
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from typing import Any
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from typing import List
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from typing import Optional
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from typing import Union
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import numpy as np
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import torch
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from torch.nn.parallel import data_parallel
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from typeguard import check_argument_types
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from funasr.tasks.lm import LMTask
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from funasr.build_utils.build_model_from_file import build_model_from_file
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from funasr.build_utils.build_streaming_iterator import build_streaming_iterator
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from funasr.datasets.preprocessor import LMPreprocessor
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from funasr.utils.cli_utils import get_commandline_args
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from funasr.fileio.datadir_writer import DatadirWriter
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from funasr.torch_utils.device_funcs import to_device
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from funasr.torch_utils.forward_adaptor import ForwardAdaptor
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from funasr.torch_utils.set_all_random_seed import set_all_random_seed
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from funasr.utils import config_argparse
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from funasr.utils.cli_utils import get_commandline_args
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from funasr.utils.types import float_or_none
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from funasr.utils.types import str2bool
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from funasr.utils.types import str2triple_str
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@ -48,42 +33,42 @@ from funasr.utils.types import str_or_none
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def inference_lm(
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batch_size: int,
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dtype: str,
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ngpu: int,
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seed: int,
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num_workers: int,
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log_level: Union[int, str],
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key_file: Optional[str],
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train_config: Optional[str],
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model_file: Optional[str],
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log_base: Optional[float] = 10,
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allow_variable_data_keys: bool = False,
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split_with_space: Optional[bool] = False,
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seg_dict_file: Optional[str] = None,
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output_dir: Optional[str] = None,
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param_dict: dict = None,
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**kwargs,
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batch_size: int,
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dtype: str,
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ngpu: int,
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seed: int,
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num_workers: int,
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log_level: Union[int, str],
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key_file: Optional[str],
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train_config: Optional[str],
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model_file: Optional[str],
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log_base: Optional[float] = 10,
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allow_variable_data_keys: bool = False,
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split_with_space: Optional[bool] = False,
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seg_dict_file: Optional[str] = None,
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output_dir: Optional[str] = None,
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param_dict: dict = None,
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**kwargs,
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):
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assert check_argument_types()
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ncpu = kwargs.get("ncpu", 1)
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torch.set_num_threads(ncpu)
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if ngpu >= 1 and torch.cuda.is_available():
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device = "cuda"
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else:
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device = "cpu"
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# 1. Set random-seed
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set_all_random_seed(seed)
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# 2. Build Model
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model, train_args = LMTask.build_model_from_file(
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train_config, model_file, device)
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model, train_args = build_model_from_file(
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train_config, model_file, None, device, "lm")
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wrapped_model = ForwardAdaptor(model, "nll")
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wrapped_model.to(dtype=getattr(torch, dtype)).to(device=device).eval()
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logging.info(f"Model:\n{model}")
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preprocessor = LMPreprocessor(
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train=False,
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token_type=train_args.token_type,
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@ -96,12 +81,12 @@ def inference_lm(
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split_with_space=split_with_space,
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seg_dict_file=seg_dict_file
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)
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def _forward(
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data_path_and_name_and_type,
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raw_inputs: Union[List[Any], bytes, str] = None,
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output_dir_v2: Optional[str] = None,
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param_dict: dict = None,
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data_path_and_name_and_type,
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raw_inputs: Union[List[Any], bytes, str] = None,
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output_dir_v2: Optional[str] = None,
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param_dict: dict = None,
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):
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results = []
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output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
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@ -109,7 +94,7 @@ def inference_lm(
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writer = DatadirWriter(output_path)
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else:
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writer = None
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if raw_inputs != None:
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line = raw_inputs.strip()
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key = "lm demo"
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@ -121,7 +106,7 @@ def inference_lm(
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batch['text'] = line
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if preprocessor != None:
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batch = preprocessor(key, batch)
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# Force data-precision
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for name in batch:
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value = batch[name]
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@ -138,11 +123,11 @@ def inference_lm(
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else:
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raise NotImplementedError(f"Not supported dtype: {value.dtype}")
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batch[name] = value
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batch["text_lengths"] = torch.from_numpy(
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np.array([len(batch["text"])], dtype='int32'))
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batch["text"] = np.expand_dims(batch["text"], axis=0)
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with torch.no_grad():
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batch = to_device(batch, device)
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if ngpu <= 1:
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@ -173,7 +158,7 @@ def inference_lm(
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word_nll=round(word_nll.item(), 8)
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)
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pre_word = cur_word
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sent_nll_mean = sent_nll.mean().cpu().numpy()
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sent_nll_sum = sent_nll.sum().cpu().numpy()
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if log_base is None:
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@ -189,22 +174,20 @@ def inference_lm(
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if writer is not None:
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writer["ppl"][key + ":\n"] = ppl_out
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results.append(item)
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return results
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# 3. Build data-iterator
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loader = LMTask.build_streaming_iterator(
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data_path_and_name_and_type,
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loader = build_streaming_iterator(
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task_name="lm",
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preprocess_args=train_args,
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data_path_and_name_and_type=data_path_and_name_and_type,
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dtype=dtype,
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batch_size=batch_size,
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key_file=key_file,
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num_workers=num_workers,
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preprocess_fn=preprocessor,
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collate_fn=LMTask.build_collate_fn(train_args, False),
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allow_variable_data_keys=allow_variable_data_keys,
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inference=True,
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)
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# 4. Start for-loop
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total_nll = 0.0
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total_ntokens = 0
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@ -214,7 +197,7 @@ def inference_lm(
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assert all(isinstance(s, str) for s in keys), keys
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_bs = len(next(iter(batch.values())))
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assert len(keys) == _bs, f"{len(keys)} != {_bs}"
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ppl_out_batch = ""
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with torch.no_grad():
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batch = to_device(batch, device)
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@ -247,7 +230,7 @@ def inference_lm(
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word_nll=round(word_nll.item(), 8)
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)
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pre_word = cur_word
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sent_nll_mean = sent_nll.mean().cpu().numpy()
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sent_nll_sum = sent_nll.sum().cpu().numpy()
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if log_base is None:
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@ -265,9 +248,9 @@ def inference_lm(
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writer["ppl"][key + ":\n"] = ppl_out
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writer["utt2nll"][key] = str(utt2nll)
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results.append(item)
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ppl_out_all += ppl_out_batch
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assert _bs == len(nll) == len(lengths), (_bs, len(nll), len(lengths))
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# nll: (B, L) -> (B,)
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nll = nll.detach().cpu().numpy().sum(1)
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@ -275,12 +258,12 @@ def inference_lm(
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lengths = lengths.detach().cpu().numpy()
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total_nll += nll.sum()
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total_ntokens += lengths.sum()
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if log_base is None:
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ppl = np.exp(total_nll / total_ntokens)
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else:
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ppl = log_base ** (total_nll / total_ntokens / np.log(log_base))
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avg_ppl = 'logprob= {total_nll} ppl= {total_ppl}\n'.format(
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total_nll=round(-total_nll.item(), 4),
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total_ppl=round(ppl.item(), 4)
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@ -290,9 +273,9 @@ def inference_lm(
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if writer is not None:
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writer["ppl"]["AVG PPL : "] = avg_ppl
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results.append(item)
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return results
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return _forward
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@ -302,7 +285,8 @@ def inference_launch(mode, **kwargs):
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else:
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logging.info("Unknown decoding mode: {}".format(mode))
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return None
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def get_parser():
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parser = config_argparse.ArgumentParser(
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description="Calc perplexity",
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@ -407,4 +391,3 @@ def main(cmd=None):
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if __name__ == "__main__":
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main()
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@ -26,6 +26,7 @@ def build_streaming_iterator(
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# preprocess
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if preprocess_args is not None:
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preprocess_args.task_name = task_name
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preprocess_fn = build_preprocess(preprocess_args, train)
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else:
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preprocess_fn = None
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