From d79287c37e4e7ae2694a992cbbfb03a5ca4f7670 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期二, 20 二月 2024 14:05:58 +0800
Subject: [PATCH] Merge branch 'main' of github.com:alibaba-damo-academy/FunASR merge
---
funasr/models/bicif_paraformer/model.py | 34 +++++++++++++++++-----------------
1 files changed, 17 insertions(+), 17 deletions(-)
diff --git a/funasr/models/bicif_paraformer/model.py b/funasr/models/bicif_paraformer/model.py
index 01f19c6..0069b8c 100644
--- a/funasr/models/bicif_paraformer/model.py
+++ b/funasr/models/bicif_paraformer/model.py
@@ -235,23 +235,23 @@
self.nbest = kwargs.get("nbest", 1)
meta_data = {}
- if isinstance(data_in, torch.Tensor): # fbank
- speech, speech_lengths = data_in, data_lengths
- if len(speech.shape) < 3:
- speech = speech[None, :, :]
- if speech_lengths is None:
- speech_lengths = speech.shape[1]
- else:
- # extract fbank feats
- time1 = time.perf_counter()
- audio_sample_list = load_audio_text_image_video(data_in, fs=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"),
- frontend=frontend)
- time3 = time.perf_counter()
- meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
- meta_data["batch_data_time"] = speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
+ # if isinstance(data_in, torch.Tensor): # fbank
+ # speech, speech_lengths = data_in, data_lengths
+ # if len(speech.shape) < 3:
+ # speech = speech[None, :, :]
+ # if speech_lengths is None:
+ # speech_lengths = speech.shape[1]
+ # else:
+ # extract fbank feats
+ time1 = time.perf_counter()
+ audio_sample_list = load_audio_text_image_video(data_in, fs=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"),
+ frontend=frontend)
+ time3 = time.perf_counter()
+ meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
+ meta_data["batch_data_time"] = speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
speech = speech.to(device=kwargs["device"])
speech_lengths = speech_lengths.to(device=kwargs["device"])
--
Gitblit v1.9.1