From 2a66366be4c2715870e4859fd5a5db6e8a9dc00a Mon Sep 17 00:00:00 2001
From: chenmengzheAAA <123789350+chenmengzheAAA@users.noreply.github.com>
Date: 星期四, 14 九月 2023 19:00:17 +0800
Subject: [PATCH] Merge pull request #956 from alibaba-damo-academy/chenmengzheAAA-patch-4
---
funasr/bin/tp_inference_launch.py | 165 +++++++++++++++++++++++++++++++++++++++++++++++++++---
1 files changed, 155 insertions(+), 10 deletions(-)
diff --git a/funasr/bin/tp_inference_launch.py b/funasr/bin/tp_inference_launch.py
index 6cdff05..6c10254 100644
--- a/funasr/bin/tp_inference_launch.py
+++ b/funasr/bin/tp_inference_launch.py
@@ -1,17 +1,169 @@
#!/usr/bin/env python3
+# -*- encoding: utf-8 -*-
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+# MIT License (https://opensource.org/licenses/MIT)
import argparse
import logging
import os
import sys
-from typing import Union, Dict, Any
+from typing import Optional
+from typing import Union
+import numpy as np
+import torch
+
+from funasr.bin.tp_infer import Speech2Timestamp
+from funasr.build_utils.build_streaming_iterator import build_streaming_iterator
+from funasr.datasets.preprocessor import LMPreprocessor
+from funasr.fileio.datadir_writer import DatadirWriter
+from funasr.torch_utils.set_all_random_seed import set_all_random_seed
from funasr.utils import config_argparse
from funasr.utils.cli_utils import get_commandline_args
+from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard
from funasr.utils.types import str2bool
from funasr.utils.types import str2triple_str
from funasr.utils.types import str_or_none
+
+
+def inference_tp(
+ batch_size: int,
+ ngpu: int,
+ log_level: Union[int, str],
+ # data_path_and_name_and_type,
+ timestamp_infer_config: Optional[str],
+ timestamp_model_file: Optional[str],
+ timestamp_cmvn_file: Optional[str] = None,
+ # raw_inputs: Union[np.ndarray, torch.Tensor] = None,
+ key_file: Optional[str] = None,
+ allow_variable_data_keys: bool = False,
+ output_dir: Optional[str] = None,
+ dtype: str = "float32",
+ seed: int = 0,
+ num_workers: int = 1,
+ split_with_space: bool = True,
+ seg_dict_file: Optional[str] = None,
+ **kwargs,
+):
+ ncpu = kwargs.get("ncpu", 1)
+ torch.set_num_threads(ncpu)
+
+ if batch_size > 1:
+ raise NotImplementedError("batch decoding is not implemented")
+ if ngpu > 1:
+ raise NotImplementedError("only single GPU decoding is supported")
+
+ logging.basicConfig(
+ level=log_level,
+ format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
+ )
+
+ if ngpu >= 1 and torch.cuda.is_available():
+ device = "cuda"
+ else:
+ device = "cpu"
+ # 1. Set random-seed
+ set_all_random_seed(seed)
+
+ # 2. Build speech2vadsegment
+ speechtext2timestamp_kwargs = dict(
+ timestamp_infer_config=timestamp_infer_config,
+ timestamp_model_file=timestamp_model_file,
+ timestamp_cmvn_file=timestamp_cmvn_file,
+ device=device,
+ dtype=dtype,
+ )
+ logging.info("speechtext2timestamp_kwargs: {}".format(speechtext2timestamp_kwargs))
+ speechtext2timestamp = Speech2Timestamp(**speechtext2timestamp_kwargs)
+
+ preprocessor = LMPreprocessor(
+ train=False,
+ token_type=speechtext2timestamp.tp_train_args.token_type,
+ token_list=speechtext2timestamp.tp_train_args.token_list,
+ bpemodel=None,
+ text_cleaner=None,
+ g2p_type=None,
+ text_name="text",
+ non_linguistic_symbols=speechtext2timestamp.tp_train_args.non_linguistic_symbols,
+ split_with_space=split_with_space,
+ seg_dict_file=seg_dict_file,
+ )
+
+ if output_dir is not None:
+ writer = DatadirWriter(output_dir)
+ tp_writer = writer[f"timestamp_prediction"]
+ # ibest_writer["token_list"][""] = " ".join(speech2text.asr_train_args.token_list)
+ else:
+ tp_writer = None
+
+ def _forward(
+ data_path_and_name_and_type,
+ raw_inputs: Union[np.ndarray, torch.Tensor] = None,
+ output_dir_v2: Optional[str] = None,
+ fs: dict = None,
+ param_dict: dict = None,
+ **kwargs
+ ):
+ output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
+ writer = None
+ if output_path is not None:
+ writer = DatadirWriter(output_path)
+ tp_writer = writer[f"timestamp_prediction"]
+ else:
+ tp_writer = None
+ # 3. Build data-iterator
+ if data_path_and_name_and_type is None and raw_inputs is not None:
+ if isinstance(raw_inputs, torch.Tensor):
+ raw_inputs = raw_inputs.numpy()
+ data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]
+
+ loader = build_streaming_iterator(
+ task_name="asr",
+ preprocess_args=speechtext2timestamp.tp_train_args,
+ data_path_and_name_and_type=data_path_and_name_and_type,
+ dtype=dtype,
+ batch_size=batch_size,
+ key_file=key_file,
+ num_workers=num_workers,
+ preprocess_fn=preprocessor,
+ )
+
+ tp_result_list = []
+ for keys, batch in loader:
+ assert isinstance(batch, dict), type(batch)
+ assert all(isinstance(s, str) for s in keys), keys
+ _bs = len(next(iter(batch.values())))
+ assert len(keys) == _bs, f"{len(keys)} != {_bs}"
+
+ logging.info("timestamp predicting, utt_id: {}".format(keys))
+ _batch = {'speech': batch['speech'],
+ 'speech_lengths': batch['speech_lengths'],
+ 'text_lengths': batch['text_lengths']}
+ us_alphas, us_cif_peak = speechtext2timestamp(**_batch)
+
+ for batch_id in range(_bs):
+ key = keys[batch_id]
+ token = speechtext2timestamp.converter.ids2tokens(batch['text'][batch_id])
+ ts_str, ts_list = ts_prediction_lfr6_standard(us_alphas[batch_id], us_cif_peak[batch_id], token,
+ force_time_shift=-3.0)
+ logging.warning(ts_str)
+ item = {'key': key, 'value': ts_str, 'timestamp': ts_list}
+ if tp_writer is not None:
+ tp_writer["tp_sync"][key + '#'] = ts_str
+ tp_writer["tp_time"][key + '#'] = str(ts_list)
+ tp_result_list.append(item)
+ return tp_result_list
+
+ return _forward
+
+
+def inference_launch(mode, **kwargs):
+ if mode == "tp_norm":
+ return inference_tp(**kwargs)
+ else:
+ logging.info("Unknown decoding mode: {}".format(mode))
+ return None
def get_parser():
@@ -100,14 +252,6 @@
return parser
-def inference_launch(mode, **kwargs):
- if mode == "tp_norm":
- from funasr.bin.tp_inference import inference_modelscope
- return inference_modelscope(**kwargs)
- else:
- logging.info("Unknown decoding mode: {}".format(mode))
- return None
-
def main(cmd=None):
print(get_commandline_args(), file=sys.stderr)
parser = get_parser()
@@ -135,7 +279,8 @@
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = gpuid
- inference_launch(**kwargs)
+ inference_pipeline = inference_launch(**kwargs)
+ return inference_pipeline(kwargs["data_path_and_name_and_type"])
if __name__ == "__main__":
--
Gitblit v1.9.1