From c2dee5e3c29eba79e591d9e9caebaef15ea4e56b Mon Sep 17 00:00:00 2001
From: hnluo <haoneng.lhn@alibaba-inc.com>
Date: 星期四, 29 六月 2023 11:09:28 +0800
Subject: [PATCH] Merge pull request #687 from alibaba-damo-academy/dev_lhn
---
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/infer.py | 31 +++++++++++++++++++++++++++++++
1 files changed, 31 insertions(+), 0 deletions(-)
diff --git a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/infer.py b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/infer.py
index 07a6233..a0f0965 100644
--- a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/infer.py
+++ b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/infer.py
@@ -1,3 +1,33 @@
+<<<<<<< HEAD
+import os
+import shutil
+import argparse
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+def modelscope_infer(args):
+ os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpuid)
+ inference_pipeline = pipeline(
+ task=Tasks.auto_speech_recognition,
+ model=args.model,
+ output_dir=args.output_dir,
+ batch_size=args.batch_size,
+ param_dict={"decoding_model": args.decoding_mode, "hotword": args.hotword_txt}
+ )
+ inference_pipeline(audio_in=args.audio_in)
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--model', type=str, default="damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch")
+ parser.add_argument('--audio_in', type=str, default="./data/test/wav.scp")
+ parser.add_argument('--output_dir', type=str, default="./results/")
+ parser.add_argument('--decoding_mode', type=str, default="normal")
+ parser.add_argument('--hotword_txt', type=str, default=None)
+ parser.add_argument('--batch_size', type=int, default=64)
+ parser.add_argument('--gpuid', type=str, default="0")
+ args = parser.parse_args()
+ modelscope_infer(args)
+=======
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
@@ -11,3 +41,4 @@
)
rec_result = inference_pipeline(audio_in=audio_in, param_dict={"decoding_model":"offline"})
print(rec_result)
+>>>>>>> main
--
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