From 28a19dbc4e85d3b8a4ec2ef7483bba64d422b43f Mon Sep 17 00:00:00 2001
From: aky15 <ankeyu.aky@11.17.44.249>
Date: 星期三, 12 四月 2023 18:03:06 +0800
Subject: [PATCH] Merge remote-tracking branch 'origin/main' into dev_aky
---
funasr/bin/sv_inference.py | 62 +++++++++++++++++--------------
1 files changed, 34 insertions(+), 28 deletions(-)
diff --git a/funasr/bin/sv_inference.py b/funasr/bin/sv_inference.py
index b0fae38..7e63bbd 100755
--- a/funasr/bin/sv_inference.py
+++ b/funasr/bin/sv_inference.py
@@ -1,4 +1,7 @@
#!/usr/bin/env python3
+# 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
@@ -26,14 +29,14 @@
from funasr.utils.types import str2bool
from funasr.utils.types import str2triple_str
from funasr.utils.types import str_or_none
-
+from funasr.utils.misc import statistic_model_parameters
class Speech2Xvector:
"""Speech2Xvector class
Examples:
>>> import soundfile
- >>> speech2xvector = Speech2Xvector("sv_config.yml", "sv.pth")
+ >>> speech2xvector = Speech2Xvector("sv_config.yml", "sv.pb")
>>> audio, rate = soundfile.read("speech.wav")
>>> speech2xvector(audio)
[(text, token, token_int, hypothesis object), ...]
@@ -59,6 +62,7 @@
device=device
)
logging.info("sv_model: {}".format(sv_model))
+ logging.info("model parameter number: {}".format(statistic_model_parameters(sv_model)))
logging.info("sv_train_args: {}".format(sv_train_args))
sv_model.to(dtype=getattr(torch, dtype)).eval()
@@ -156,21 +160,22 @@
def inference_modelscope(
- output_dir: Optional[str],
- batch_size: int,
- dtype: str,
- ngpu: int,
- seed: int,
- num_workers: int,
- log_level: Union[int, str],
- key_file: Optional[str],
- sv_train_config: Optional[str],
- sv_model_file: Optional[str],
- model_tag: Optional[str],
+ output_dir: Optional[str] = None,
+ batch_size: int = 1,
+ dtype: str = "float32",
+ ngpu: int = 1,
+ seed: int = 0,
+ num_workers: int = 0,
+ log_level: Union[int, str] = "INFO",
+ key_file: Optional[str] = None,
+ sv_train_config: Optional[str] = "sv.yaml",
+ sv_model_file: Optional[str] = "sv.pb",
+ model_tag: Optional[str] = None,
allow_variable_data_keys: bool = True,
streaming: bool = False,
embedding_node: str = "resnet1_dense",
sv_threshold: float = 0.9465,
+ param_dict: Optional[dict] = None,
**kwargs,
):
assert check_argument_types()
@@ -183,6 +188,7 @@
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"
@@ -212,7 +218,9 @@
data_path_and_name_and_type: Sequence[Tuple[str, str, str]] = None,
raw_inputs: Union[np.ndarray, torch.Tensor] = 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, torch.Tensor):
raw_inputs = raw_inputs.numpy()
@@ -233,11 +241,10 @@
# 7 .Start for-loop
output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
- embd_fd, ref_emb_fd, score_fd = None, None, None
+ embd_writer, ref_embd_writer, score_writer = None, None, None
if output_path is not None:
os.makedirs(output_path, exist_ok=True)
- embd_writer = WriteHelper("ark:{}/xvector.ark".format(output_path))
- # embd_fd = open(os.path.join(output_path, "xvector.ark"), "wb")
+ embd_writer = WriteHelper("ark,scp:{}/xvector.ark,{}/xvector.scp".format(output_path, output_path))
sv_result_list = []
for keys, batch in loader:
assert isinstance(batch, dict), type(batch)
@@ -249,6 +256,7 @@
embedding, ref_embedding, score = speech2xvector(**batch)
# Only supporting batch_size==1
key = keys[0]
+ normalized_score = 0.0
if score is not None:
score = score.item()
normalized_score = max(score - sv_threshold, 0.0) / (1.0 - sv_threshold) * 100.0
@@ -257,23 +265,21 @@
item = {"key": key, "value": embedding.squeeze(0).cpu().numpy()}
sv_result_list.append(item)
if output_path is not None:
- # kaldiio.save_mat(embd_fd, embedding[0].cpu().numpy(), key)
embd_writer(key, embedding[0].cpu().numpy())
if ref_embedding is not None:
- if ref_emb_fd is None:
- # ref_emb_fd = open(os.path.join(output_path, "ref_xvector.ark"), "wb")
- ref_embd_writer = WriteHelper("ark:{}/ref_xvector.ark".format(output_path))
- score_fd = open(os.path.join(output_path, "score.txt"), "w")
- # kaldiio.save_mat(ref_emb_fd, ref_embedding[0].cpu().numpy(), key)
+ if ref_embd_writer is None:
+ ref_embd_writer = WriteHelper(
+ "ark,scp:{}/ref_xvector.ark,{}/ref_xvector.scp".format(output_path, output_path)
+ )
+ score_writer = open(os.path.join(output_path, "score.txt"), "w")
ref_embd_writer(key, ref_embedding[0].cpu().numpy())
- score_fd.write("{:.6f}\n".format(score.item()))
+ score_writer.write("{} {:.6f}\n".format(key, normalized_score))
+
if output_path is not None:
- # embd_fd.close()
embd_writer.close()
- if ref_emb_fd is not None:
- # ref_emb_fd.close()
- ref_emb_fd.close()
- score_fd.close()
+ if ref_embd_writer is not None:
+ ref_embd_writer.close()
+ score_writer.close()
return sv_result_list
--
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