From 580b11b57ac4b62f7e2acda73813a4e10e8e4cd3 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期二, 10 十月 2023 17:17:29 +0800
Subject: [PATCH] v0.8.0
---
funasr/bin/vad_inference_launch.py | 256 ++++++++++++++++++++++++++++++++++++++++++++++++---
1 files changed, 241 insertions(+), 15 deletions(-)
diff --git a/funasr/bin/vad_inference_launch.py b/funasr/bin/vad_inference_launch.py
index 42c5c1e..0cde570 100644
--- a/funasr/bin/vad_inference_launch.py
+++ b/funasr/bin/vad_inference_launch.py
@@ -1,18 +1,254 @@
#!/usr/bin/env python3
-# Copyright ESPnet (https://github.com/espnet/espnet). All Rights Reserved.
-# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
+# -*- encoding: utf-8 -*-
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+# MIT License (https://opensource.org/licenses/MIT)
+
+
+import torch
+
+torch.set_num_threads(1)
import argparse
import logging
import os
import sys
-from typing import Union, Dict, Any
+import json
+from typing import Optional
+from typing import Union
+import numpy as np
+import torch
+from funasr.build_utils.build_streaming_iterator import build_streaming_iterator
+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.types import str2bool
from funasr.utils.types import str2triple_str
from funasr.utils.types import str_or_none
+from funasr.bin.vad_infer import Speech2VadSegment, Speech2VadSegmentOnline
+
+
+def inference_vad(
+ batch_size: int,
+ ngpu: int,
+ log_level: Union[int, str],
+ # data_path_and_name_and_type,
+ vad_infer_config: Optional[str],
+ vad_model_file: Optional[str],
+ vad_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,
+ **kwargs,
+):
+ if batch_size > 1:
+ raise NotImplementedError("batch decoding is not implemented")
+
+ 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"
+ batch_size = 1
+ # 1. Set random-seed
+ set_all_random_seed(seed)
+
+ # 2. Build speech2vadsegment
+ speech2vadsegment_kwargs = dict(
+ vad_infer_config=vad_infer_config,
+ vad_model_file=vad_model_file,
+ vad_cmvn_file=vad_cmvn_file,
+ device=device,
+ dtype=dtype,
+ )
+ logging.info("speech2vadsegment_kwargs: {}".format(speech2vadsegment_kwargs))
+ speech2vadsegment = Speech2VadSegment(**speech2vadsegment_kwargs)
+
+ 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
+ ):
+ # 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="vad",
+ preprocess_args=None,
+ 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,
+ )
+
+ finish_count = 0
+ file_count = 1
+ # 7 .Start for-loop
+ # FIXME(kamo): The output format should be discussed about
+ output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
+ if output_path is not None:
+ writer = DatadirWriter(output_path)
+ ibest_writer = writer[f"1best_recog"]
+ else:
+ writer = None
+ ibest_writer = None
+
+ vad_results = []
+ 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}"
+
+ # do vad segment
+ _, results = speech2vadsegment(**batch)
+ for i, _ in enumerate(keys):
+ if "MODELSCOPE_ENVIRONMENT" in os.environ and os.environ["MODELSCOPE_ENVIRONMENT"] == "eas":
+ results[i] = json.dumps(results[i])
+ item = {'key': keys[i], 'value': results[i]}
+ vad_results.append(item)
+ if writer is not None:
+ ibest_writer["text"][keys[i]] = "{}".format(results[i])
+ torch.cuda.empty_cache()
+ return vad_results
+
+ return _forward
+
+
+def inference_vad_online(
+ batch_size: int,
+ ngpu: int,
+ log_level: Union[int, str],
+ # data_path_and_name_and_type,
+ vad_infer_config: Optional[str],
+ vad_model_file: Optional[str],
+ vad_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,
+ **kwargs,
+):
+
+ 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"
+ batch_size = 1
+
+ # 1. Set random-seed
+ set_all_random_seed(seed)
+
+ # 2. Build speech2vadsegment
+ speech2vadsegment_kwargs = dict(
+ vad_infer_config=vad_infer_config,
+ vad_model_file=vad_model_file,
+ vad_cmvn_file=vad_cmvn_file,
+ device=device,
+ dtype=dtype,
+ )
+ logging.info("speech2vadsegment_kwargs: {}".format(speech2vadsegment_kwargs))
+ speech2vadsegment = Speech2VadSegmentOnline(**speech2vadsegment_kwargs)
+
+ 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,
+ ):
+ # 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="vad",
+ preprocess_args=None,
+ 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,
+ )
+
+ finish_count = 0
+ file_count = 1
+ # 7 .Start for-loop
+ # FIXME(kamo): The output format should be discussed about
+ output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
+ if output_path is not None:
+ writer = DatadirWriter(output_path)
+ ibest_writer = writer[f"1best_recog"]
+ else:
+ writer = None
+ ibest_writer = None
+
+ vad_results = []
+ if param_dict is None:
+ param_dict = dict()
+ param_dict['in_cache'] = dict()
+ param_dict['is_final'] = True
+ batch_in_cache = param_dict.get('in_cache', dict())
+ is_final = param_dict.get('is_final', False)
+ max_end_sil = param_dict.get('max_end_sil', 800)
+ 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}"
+ batch['in_cache'] = batch_in_cache
+ batch['is_final'] = is_final
+ batch['max_end_sil'] = max_end_sil
+
+ # do vad segment
+ _, results, param_dict['in_cache'] = speech2vadsegment(**batch)
+ # param_dict['in_cache'] = batch['in_cache']
+ if results:
+ for i, _ in enumerate(keys):
+ if results[i]:
+ if "MODELSCOPE_ENVIRONMENT" in os.environ and os.environ["MODELSCOPE_ENVIRONMENT"] == "eas":
+ results[i] = json.dumps(results[i])
+ item = {'key': keys[i], 'value': results[i]}
+ vad_results.append(item)
+ if writer is not None:
+ ibest_writer["text"][keys[i]] = "{}".format(results[i])
+
+ return vad_results
+
+ return _forward
+
+
+def inference_launch(mode, **kwargs):
+ if mode == "offline":
+ return inference_vad(**kwargs)
+ elif mode == "online":
+ return inference_vad_online(**kwargs)
+ else:
+ logging.info("Unknown decoding mode: {}".format(mode))
+ return None
def get_parser():
@@ -106,17 +342,6 @@
return parser
-def inference_launch(mode, **kwargs):
- if mode == "offline":
- from funasr.bin.vad_inference import inference_modelscope
- return inference_modelscope(**kwargs)
- elif mode == "online":
- from funasr.bin.vad_inference_online 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()
@@ -144,7 +369,8 @@
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