From d783b24ba7d8a03dabfa2139fcbf40c216e0ea3d Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 16 三月 2023 19:34:52 +0800
Subject: [PATCH] Merge pull request #199 from alibaba-damo-academy/dev_xw
---
funasr/bin/asr_inference_uniasr.py | 27 ++++++++++++++-------------
1 files changed, 14 insertions(+), 13 deletions(-)
diff --git a/funasr/bin/asr_inference_uniasr.py b/funasr/bin/asr_inference_uniasr.py
index c50bf17..ac71538 100644
--- a/funasr/bin/asr_inference_uniasr.py
+++ b/funasr/bin/asr_inference_uniasr.py
@@ -46,7 +46,7 @@
Examples:
>>> import soundfile
- >>> speech2text = Speech2Text("asr_config.yml", "asr.pth")
+ >>> speech2text = Speech2Text("asr_config.yml", "asr.pb")
>>> audio, rate = soundfile.read("speech.wav")
>>> speech2text(audio)
[(text, token, token_int, hypothesis object), ...]
@@ -398,6 +398,19 @@
else:
device = "cpu"
+ if param_dict is not None and "decoding_model" in param_dict:
+ if param_dict["decoding_model"] == "fast":
+ decoding_ind = 0
+ decoding_mode = "model1"
+ elif param_dict["decoding_model"] == "normal":
+ decoding_ind = 0
+ decoding_mode = "model2"
+ elif param_dict["decoding_model"] == "offline":
+ decoding_ind = 1
+ decoding_mode = "model2"
+ else:
+ raise NotImplementedError("unsupported decoding model {}".format(param_dict["decoding_model"]))
+
# 1. Set random-seed
set_all_random_seed(seed)
@@ -440,18 +453,6 @@
if isinstance(raw_inputs, torch.Tensor):
raw_inputs = raw_inputs.numpy()
data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]
- if param_dict is not None and "decoding_model" in param_dict:
- if param_dict["decoding_model"] == "fast":
- speech2text.decoding_ind = 0
- speech2text.decoding_mode = "model1"
- elif param_dict["decoding_model"] == "normal":
- speech2text.decoding_ind = 0
- speech2text.decoding_mode = "model2"
- elif param_dict["decoding_model"] == "offline":
- speech2text.decoding_ind = 1
- speech2text.decoding_mode = "model2"
- else:
- raise NotImplementedError("unsupported decoding model {}".format(param_dict["decoding_model"]))
loader = ASRTask.build_streaming_iterator(
data_path_and_name_and_type,
dtype=dtype,
--
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