From fb176404cfeb40c053f4f42d01eb45c185d21ce2 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 08 一月 2024 16:20:45 +0800
Subject: [PATCH] funasr1.0 emotion2vec
---
funasr/bin/inference.py | 19 ++++++++++---------
1 files changed, 10 insertions(+), 9 deletions(-)
diff --git a/funasr/bin/inference.py b/funasr/bin/inference.py
index 1fac92e..5b58907 100644
--- a/funasr/bin/inference.py
+++ b/funasr/bin/inference.py
@@ -17,11 +17,11 @@
import string
from funasr.register import tables
-from funasr.utils.load_utils import load_audio_and_text_image_video, extract_fbank
+from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
from funasr.utils.vad_utils import slice_padding_audio_samples
from funasr.utils.timestamp_tools import time_stamp_sentence
-def build_iter_for_infer(data_in, input_len=None, data_type=None, key=None):
+def prepare_data_iterator(data_in, input_len=None, data_type=None, key=None):
"""
:param input:
@@ -62,7 +62,7 @@
if data_type is not None and isinstance(data_type, (list, tuple)):
data_list_tmp = []
for data_in_i, data_type_i in zip(data_in, data_type):
- key_list, data_list_i = build_iter_for_infer(data_in=data_in_i, data_type=data_type_i)
+ key_list, data_list_i = prepare_data_iterator(data_in=data_in_i, data_type=data_type_i)
data_list_tmp.append(data_list_i)
data_list = []
for item in zip(*data_list_tmp):
@@ -204,7 +204,7 @@
# if kwargs.get("device", "cpu") == "cpu":
# batch_size = 1
- key_list, data_list = build_iter_for_infer(input, input_len=input_len, data_type=data_type, key=key)
+ key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=data_type, key=key)
speed_stats = {}
asr_result_list = []
@@ -222,7 +222,8 @@
batch["data_lengths"] = input_len
time1 = time.perf_counter()
- results, meta_data = model.generate(**batch, **kwargs)
+ with torch.no_grad():
+ results, meta_data = model.generate(**batch, **kwargs)
time2 = time.perf_counter()
asr_result_list.extend(results)
@@ -268,7 +269,7 @@
batch_size_threshold_ms = int(kwargs.get("batch_size_threshold_s", 60))*1000
kwargs["batch_size"] = batch_size
data_type = kwargs.get("data_type", "sound")
- key_list, data_list = build_iter_for_infer(input, input_len=input_len, data_type=data_type)
+ key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=data_type)
results_ret_list = []
time_speech_total_all_samples = 0.0
@@ -278,7 +279,7 @@
key = res[i]["key"]
vadsegments = res[i]["value"]
input_i = data_list[i]
- speech = load_audio_and_text_image_video(input_i, fs=kwargs["frontend"].fs, audio_fs=kwargs.get("fs", 16000))
+ speech = load_audio_text_image_video(input_i, fs=kwargs["frontend"].fs, audio_fs=kwargs.get("fs", 16000))
speech_lengths = len(speech)
n = len(vadsegments)
data_with_index = [(vadsegments[i], i) for i in range(n)]
@@ -397,7 +398,7 @@
kwargs.update(cfg)
- key_list, data_list = build_iter_for_infer(input, input_len=input_len)
+ key_list, data_list = prepare_data_iterator(input, input_len=input_len)
batch_size = kwargs.get("batch_size", 1)
device = kwargs.get("device", "cpu")
if device == "cpu":
@@ -417,7 +418,7 @@
# extract fbank feats
time1 = time.perf_counter()
- audio_sample_list = load_audio_and_text_image_video(data_batch, fs=self.frontend.fs, audio_fs=kwargs.get("fs", 16000))
+ audio_sample_list = load_audio_text_image_video(data_batch, fs=self.frontend.fs, audio_fs=kwargs.get("fs", 16000))
time2 = time.perf_counter()
meta_data["load_data"] = f"{time2 - time1:0.3f}"
speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
--
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