From 4ace5a95b052d338947fc88809a440ccd55cf6b4 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 16 十一月 2023 16:39:52 +0800
Subject: [PATCH] funasr pages
---
funasr/bin/diar_inference_launch.py | 379 +++++++++++++++++++++++++++++++++++++++++++++++++----
1 files changed, 349 insertions(+), 30 deletions(-)
diff --git a/funasr/bin/diar_inference_launch.py b/funasr/bin/diar_inference_launch.py
index 85e4518..b655df5 100755
--- a/funasr/bin/diar_inference_launch.py
+++ b/funasr/bin/diar_inference_launch.py
@@ -1,18 +1,356 @@
-#!/usr/bin/env python3
+# !/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 List
+from typing import Optional
+from typing import Sequence
+from typing import Tuple
+from typing import Union
+import numpy as np
+import soundfile
+import torch
+from scipy.signal import medfilt
+
+from funasr.bin.diar_infer import Speech2DiarizationSOND, Speech2DiarizationEEND
+from funasr.datasets.iterable_dataset import load_bytes
+from funasr.build_utils.build_streaming_iterator import build_streaming_iterator
+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
+
+
+def inference_sond(
+ diar_train_config: str,
+ diar_model_file: str,
+ output_dir: Optional[str] = None,
+ batch_size: int = 1,
+ dtype: str = "float32",
+ ngpu: int = 0,
+ seed: int = 0,
+ num_workers: int = 0,
+ log_level: Union[int, str] = "INFO",
+ key_file: Optional[str] = None,
+ model_tag: Optional[str] = None,
+ allow_variable_data_keys: bool = True,
+ streaming: bool = False,
+ smooth_size: int = 83,
+ dur_threshold: int = 10,
+ out_format: str = "vad",
+ param_dict: Optional[dict] = None,
+ mode: str = "sond",
+ **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",
+ )
+ logging.info("param_dict: {}".format(param_dict))
+
+ if ngpu >= 1 and torch.cuda.is_available():
+ device = "cuda"
+ else:
+ device = "cpu"
+
+ # 1. Set random-seed
+ set_all_random_seed(seed)
+
+ # 2a. Build speech2xvec [Optional]
+ if mode == "sond_demo" and param_dict is not None and "extract_profile" in param_dict and param_dict[
+ "extract_profile"]:
+ assert "sv_train_config" in param_dict, "sv_train_config must be provided param_dict."
+ assert "sv_model_file" in param_dict, "sv_model_file must be provided in param_dict."
+ sv_train_config = param_dict["sv_train_config"]
+ sv_model_file = param_dict["sv_model_file"]
+ if "model_dir" in param_dict:
+ sv_train_config = os.path.join(param_dict["model_dir"], sv_train_config)
+ sv_model_file = os.path.join(param_dict["model_dir"], sv_model_file)
+ from funasr.bin.sv_infer import Speech2Xvector
+ speech2xvector_kwargs = dict(
+ sv_train_config=sv_train_config,
+ sv_model_file=sv_model_file,
+ device=device,
+ dtype=dtype,
+ streaming=streaming,
+ embedding_node="resnet1_dense"
+ )
+ logging.info("speech2xvector_kwargs: {}".format(speech2xvector_kwargs))
+ speech2xvector = Speech2Xvector(**speech2xvector_kwargs)
+ speech2xvector.sv_model.eval()
+
+ # 2b. Build speech2diar
+ speech2diar_kwargs = dict(
+ diar_train_config=diar_train_config,
+ diar_model_file=diar_model_file,
+ device=device,
+ dtype=dtype,
+ streaming=streaming,
+ smooth_size=smooth_size,
+ dur_threshold=dur_threshold,
+ )
+ logging.info("speech2diarization_kwargs: {}".format(speech2diar_kwargs))
+ speech2diar = Speech2DiarizationSOND(**speech2diar_kwargs)
+ speech2diar.diar_model.eval()
+
+ def output_results_str(results: dict, uttid: str):
+ rst = []
+ mid = uttid.rsplit("-", 1)[0]
+ for key in results:
+ results[key] = [(x[0] / 100, x[1] / 100) for x in results[key]]
+ if out_format == "vad":
+ for spk, segs in results.items():
+ rst.append("{} {}".format(spk, segs))
+ else:
+ template = "SPEAKER {} 0 {:.2f} {:.2f} <NA> <NA> {} <NA> <NA>"
+ for spk, segs in results.items():
+ rst.extend([template.format(mid, st, ed, spk) for st, ed in segs])
+
+ return "\n".join(rst)
+
+ def _forward(
+ data_path_and_name_and_type: Sequence[Tuple[str, str, str]] = None,
+ raw_inputs: List[List[Union[np.ndarray, torch.Tensor, str, bytes]]] = None,
+ output_dir_v2: Optional[str] = None,
+ param_dict: Optional[dict] = None,
+ ):
+ logging.info("param_dict: {}".format(param_dict))
+ if data_path_and_name_and_type is None and raw_inputs is not None:
+ if isinstance(raw_inputs, (list, tuple)):
+ if not isinstance(raw_inputs[0], List):
+ raw_inputs = [raw_inputs]
+
+ assert all([len(example) >= 2 for example in raw_inputs]), \
+ "The length of test case in raw_inputs must larger than 1 (>=2)."
+
+ def prepare_dataset():
+ for idx, example in enumerate(raw_inputs):
+ # read waveform file
+ example = [load_bytes(x) if isinstance(x, bytes) else x
+ for x in example]
+ example = [soundfile.read(x)[0] if isinstance(x, str) else x
+ for x in example]
+ # convert torch tensor to numpy array
+ example = [x.numpy() if isinstance(example[0], torch.Tensor) else x
+ for x in example]
+ speech = example[0]
+ logging.info("Extracting profiles for {} waveforms".format(len(example) - 1))
+ profile = [speech2xvector.calculate_embedding(x) for x in example[1:]]
+ profile = torch.cat(profile, dim=0)
+ yield ["test{}".format(idx)], {"speech": [speech], "profile": [profile]}
+
+ loader = prepare_dataset()
+ else:
+ raise TypeError("raw_inputs must be a list or tuple in [speech, profile1, profile2, ...] ")
+ else:
+ # 3. Build data-iterator
+ loader = build_streaming_iterator(
+ task_name="diar",
+ 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,
+ use_collate_fn=False,
+ )
+
+ # 7. Start for-loop
+ output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
+ if output_path is not None:
+ os.makedirs(output_path, exist_ok=True)
+ output_writer = open("{}/result.txt".format(output_path), "w")
+ pse_label_writer = open("{}/labels.txt".format(output_path), "w")
+ logging.info("Start to diarize...")
+ result_list = []
+ for idx, (keys, batch) in enumerate(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 = {k: v[0] for k, v in batch.items() if not k.endswith("_lengths")}
+
+ results, pse_labels = speech2diar(**batch)
+ # Only supporting batch_size==1
+ key, value = keys[0], output_results_str(results, keys[0])
+ item = {"key": key, "value": value}
+ result_list.append(item)
+ if output_path is not None:
+ output_writer.write(value)
+ output_writer.flush()
+ pse_label_writer.write("{} {}\n".format(key, " ".join(pse_labels)))
+ pse_label_writer.flush()
+
+ if idx % 100 == 0:
+ logging.info("Processing {:5d}: {}".format(idx, key))
+
+ if output_path is not None:
+ output_writer.close()
+ pse_label_writer.close()
+
+ return result_list
+
+ return _forward
+
+
+def inference_eend(
+ diar_train_config: str,
+ diar_model_file: str,
+ output_dir: Optional[str] = None,
+ batch_size: int = 1,
+ dtype: str = "float32",
+ ngpu: int = 1,
+ num_workers: int = 0,
+ log_level: Union[int, str] = "INFO",
+ key_file: Optional[str] = None,
+ model_tag: Optional[str] = None,
+ allow_variable_data_keys: bool = True,
+ streaming: bool = False,
+ param_dict: Optional[dict] = 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",
+ )
+ logging.info("param_dict: {}".format(param_dict))
+
+ if ngpu >= 1 and torch.cuda.is_available():
+ device = "cuda"
+ else:
+ device = "cpu"
+
+ # 1. Build speech2diar
+ speech2diar_kwargs = dict(
+ diar_train_config=diar_train_config,
+ diar_model_file=diar_model_file,
+ device=device,
+ dtype=dtype,
+ )
+ logging.info("speech2diarization_kwargs: {}".format(speech2diar_kwargs))
+ speech2diar = Speech2DiarizationEEND(**speech2diar_kwargs)
+ speech2diar.diar_model.eval()
+
+ def output_results_str(results: dict, uttid: str):
+ rst = []
+ mid = uttid.rsplit("-", 1)[0]
+ for key in results:
+ results[key] = [(x[0] / 100, x[1] / 100) for x in results[key]]
+ template = "SPEAKER {} 0 {:.2f} {:.2f} <NA> <NA> {} <NA> <NA>"
+ for spk, segs in results.items():
+ rst.extend([template.format(mid, st, ed, spk) for st, ed in segs])
+
+ return "\n".join(rst)
+
+ def _forward(
+ data_path_and_name_and_type: Sequence[Tuple[str, str, str]] = None,
+ raw_inputs: List[List[Union[np.ndarray, torch.Tensor, str, bytes]]] = None,
+ output_dir_v2: Optional[str] = None,
+ param_dict: Optional[dict] = None,
+ ):
+ # 2. 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[0], "speech", "sound"]
+ loader = build_streaming_iterator(
+ task_name="diar",
+ 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,
+ )
+
+ # 3. Start for-loop
+ output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
+ if output_path is not None:
+ os.makedirs(output_path, exist_ok=True)
+ output_writer = open("{}/result.txt".format(output_path), "w")
+ 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}"
+ # batch = {k: v[0] for k, v in batch.items() if not k.endswith("_lengths")}
+
+ results = speech2diar(**batch)
+
+ # post process
+ a = results[0][0].cpu().numpy()
+ a = medfilt(a, (11, 1))
+ rst = []
+ for spkid, frames in enumerate(a.T):
+ frames = np.pad(frames, (1, 1), 'constant')
+ changes, = np.where(np.diff(frames, axis=0) != 0)
+ fmt = "SPEAKER {:s} 1 {:7.2f} {:7.2f} <NA> <NA> {:s} <NA>"
+ for s, e in zip(changes[::2], changes[1::2]):
+ st = s / 10.
+ dur = (e - s) / 10.
+ rst.append(fmt.format(keys[0], st, dur, "{}_{}".format(keys[0], str(spkid))))
+
+ # Only supporting batch_size==1
+ value = "\n".join(rst)
+ item = {"key": keys[0], "value": value}
+ result_list.append(item)
+ if output_path is not None:
+ output_writer.write(value)
+ output_writer.flush()
+
+ if output_path is not None:
+ output_writer.close()
+
+ return result_list
+
+ return _forward
+
+
+def inference_launch(mode, **kwargs):
+ if mode == "sond":
+ return inference_sond(mode=mode, **kwargs)
+ elif mode == "sond_demo":
+ param_dict = {
+ "extract_profile": True,
+ "sv_train_config": "sv.yaml",
+ "sv_model_file": "sv.pb",
+ }
+ if "param_dict" in kwargs and kwargs["param_dict"] is not None:
+ for key in param_dict:
+ if key not in kwargs["param_dict"]:
+ kwargs["param_dict"][key] = param_dict[key]
+ else:
+ kwargs["param_dict"] = param_dict
+ return inference_sond(mode=mode, **kwargs)
+ elif mode == "eend-ola":
+ return inference_eend(mode=mode, **kwargs)
+ else:
+ logging.info("Unknown decoding mode: {}".format(mode))
+ return None
def get_parser():
@@ -115,39 +453,19 @@
help="The batch size for inference",
)
group.add_argument(
- "--diar_smooth_size",
+ "--smooth_size",
type=int,
default=121,
help="The smoothing size for post-processing"
)
+ group.add_argument(
+ "--dur_threshold",
+ type=int,
+ default=10,
+ help="The threshold of minimum duration"
+ )
return parser
-
-
-def inference_launch(mode, **kwargs):
- if mode == "sond":
- from funasr.bin.sond_inference import inference_modelscope
- return inference_modelscope(mode=mode, **kwargs)
- elif mode == "sond_demo":
- from funasr.bin.sond_inference import inference_modelscope
- param_dict = {
- "extract_profile": True,
- "sv_train_config": "sv.yaml",
- "sv_model_file": "sv.pb",
- }
- if "param_dict" in kwargs and kwargs["param_dict"] is not None:
- for key in param_dict:
- if key not in kwargs["param_dict"]:
- kwargs["param_dict"][key] = param_dict[key]
- else:
- kwargs["param_dict"] = param_dict
- return inference_modelscope(mode=mode, **kwargs)
- elif mode == "eend-ola":
- from funasr.bin.eend_ola_inference import inference_modelscope
- return inference_modelscope(mode=mode, **kwargs)
- else:
- logging.info("Unknown decoding mode: {}".format(mode))
- return None
def main(cmd=None):
@@ -177,7 +495,8 @@
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__":
--
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