From 8d519b9dc66e0df35c15110ef23a26d07bc7f7c3 Mon Sep 17 00:00:00 2001
From: shixian.shi <shixian.shi@alibaba-inc.com>
Date: 星期二, 27 六月 2023 19:48:51 +0800
Subject: [PATCH] update
---
funasr/bin/asr_inference_launch.py | 44 ++++++++++++++++++++++++--------------------
1 files changed, 24 insertions(+), 20 deletions(-)
diff --git a/funasr/bin/asr_inference_launch.py b/funasr/bin/asr_inference_launch.py
index 539e823..026874e 100644
--- a/funasr/bin/asr_inference_launch.py
+++ b/funasr/bin/asr_inference_launch.py
@@ -19,6 +19,7 @@
import numpy as np
import torch
import torchaudio
+import soundfile
import yaml
from typeguard import check_argument_types
@@ -35,8 +36,6 @@
from funasr.fileio.datadir_writer import DatadirWriter
from funasr.modules.beam_search.beam_search import Hypothesis
from funasr.modules.subsampling import TooShortUttError
-from funasr.tasks.asr import ASRTask
-from funasr.tasks.vad import VADTask
from funasr.torch_utils.device_funcs import to_device
from funasr.torch_utils.set_all_random_seed import set_all_random_seed
from funasr.utils import asr_utils, postprocess_utils
@@ -261,6 +260,7 @@
export_mode = param_dict.get("export_mode", False)
else:
hotword_list_or_file = None
+ clas_scale = param_dict.get('clas_scale', 1.0)
if kwargs.get("device", None) == "cpu":
ngpu = 0
@@ -293,6 +293,7 @@
penalty=penalty,
nbest=nbest,
hotword_list_or_file=hotword_list_or_file,
+ clas_sacle=clas_scale,
)
speech2text = Speech2TextParaformer(**speech2text_kwargs)
@@ -621,7 +622,12 @@
data_with_index = [(vadsegments[i], i) for i in range(n)]
sorted_data = sorted(data_with_index, key=lambda x: x[0][1] - x[0][0])
results_sorted = []
- batch_size_token_ms = batch_size_token * 60
+
+ batch_size_token_ms = batch_size_token*60
+ if speech2text.device == "cpu":
+ batch_size_token_ms = 0
+ batch_size_token_ms = max(batch_size_token_ms, sorted_data[0][0][1] - sorted_data[0][0][0])
+
batch_size_token_ms_cum = 0
beg_idx = 0
for j, _ in enumerate(range(0, n)):
@@ -860,7 +866,13 @@
raw_inputs = _load_bytes(data_path_and_name_and_type[0])
raw_inputs = torch.tensor(raw_inputs)
if data_path_and_name_and_type is not None and data_path_and_name_and_type[2] == "sound":
- raw_inputs = torchaudio.load(data_path_and_name_and_type[0])[0][0]
+ try:
+ raw_inputs = torchaudio.load(data_path_and_name_and_type[0])[0][0]
+ except:
+ raw_inputs = soundfile.read(data_path_and_name_and_type[0], dtype='float32')[0]
+ if raw_inputs.ndim == 2:
+ raw_inputs = raw_inputs[:, 0]
+ raw_inputs = torch.tensor(raw_inputs)
if data_path_and_name_and_type is None and raw_inputs is not None:
if isinstance(raw_inputs, np.ndarray):
raw_inputs = torch.tensor(raw_inputs)
@@ -1360,20 +1372,14 @@
**kwargs,
):
# 3. Build data-iterator
- loader = ASRTask.build_streaming_iterator(
- data_path_and_name_and_type,
+ loader = build_streaming_iterator(
+ task_name="asr",
+ preprocess_args=speech2text.asr_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=ASRTask.build_preprocess_fn(
- speech2text.asr_train_args, False
- ),
- collate_fn=ASRTask.build_collate_fn(
- speech2text.asr_train_args, False
- ),
- allow_variable_data_keys=allow_variable_data_keys,
- inference=True,
)
# 4 .Start for-loop
@@ -1520,18 +1526,16 @@
if isinstance(raw_inputs, torch.Tensor):
raw_inputs = raw_inputs.numpy()
data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]
- loader = ASRTask.build_streaming_iterator(
- data_path_and_name_and_type,
+ loader = build_streaming_iterator(
+ task_name="asr",
+ preprocess_args=speech2text.asr_train_args,
+ data_path_and_name_and_type=data_path_and_name_and_type,
dtype=dtype,
fs=fs,
mc=mc,
batch_size=batch_size,
key_file=key_file,
num_workers=num_workers,
- preprocess_fn=ASRTask.build_preprocess_fn(speech2text.asr_train_args, False),
- collate_fn=ASRTask.build_collate_fn(speech2text.asr_train_args, False),
- allow_variable_data_keys=allow_variable_data_keys,
- inference=True,
)
finish_count = 0
--
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