From 41b5d06e51c96f197b9db9b353da61ea8378a4f8 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 21 四月 2023 17:13:39 +0800
Subject: [PATCH] docs
---
egs/aishell/transformer/utils/prepare_checkpoint.py | 29 -----------------------------
1 files changed, 0 insertions(+), 29 deletions(-)
diff --git a/egs/aishell/transformer/utils/prepare_checkpoint.py b/egs/aishell/transformer/utils/prepare_checkpoint.py
index 01763d4..1c732e4 100644
--- a/egs/aishell/transformer/utils/prepare_checkpoint.py
+++ b/egs/aishell/transformer/utils/prepare_checkpoint.py
@@ -5,35 +5,6 @@
from modelscope.utils.constant import Tasks
from modelscope.hub.snapshot_download import snapshot_download
-def modelscope_infer_after_finetune(params):
- # prepare for decoding
-
- try:
- pretrained_model_path = snapshot_download(params["modelscope_model_name"], cache_dir=params["output_dir"])
- except BaseException:
- raise BaseException(f"Please download pretrain model from ModelScope firstly.")
- shutil.copy(os.path.join(params["output_dir"], params["decoding_model_name"]), os.path.join(pretrained_model_path, "model.pb"))
- decoding_path = os.path.join(params["output_dir"], "decode_results")
- if os.path.exists(decoding_path):
- shutil.rmtree(decoding_path)
- os.mkdir(decoding_path)
-
- # decoding
- inference_pipeline = pipeline(
- task=Tasks.auto_speech_recognition,
- model=pretrained_model_path,
- output_dir=decoding_path,
- batch_size=params["batch_size"]
- )
- audio_in = os.path.join(params["data_dir"], "wav.scp")
- inference_pipeline(audio_in=audio_in)
-
- # computer CER if GT text is set
- text_in = os.path.join(params["data_dir"], "text")
- if os.path.exists(text_in):
- text_proc_file = os.path.join(decoding_path, "1best_recog/text")
- compute_wer(text_in, text_proc_file, os.path.join(decoding_path, "text.cer"))
-
if __name__ == '__main__':
import sys
--
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