From 59a791121fccd3c9ca177c4f6d33105a82d23ef3 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 10 二月 2023 15:30:49 +0800
Subject: [PATCH] Merge branch 'main' of github.com:alibaba-damo-academy/FunASR add
---
egs_modelscope/asr/data2vec/speech_data2vec_pretrain-paraformer-zh-cn-aishell2-16k/infer_after_finetune.py | 52 ++++++++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 52 insertions(+), 0 deletions(-)
diff --git a/egs_modelscope/asr/data2vec/speech_data2vec_pretrain-paraformer-zh-cn-aishell2-16k/infer_after_finetune.py b/egs_modelscope/asr/data2vec/speech_data2vec_pretrain-paraformer-zh-cn-aishell2-16k/infer_after_finetune.py
new file mode 100644
index 0000000..56c282c
--- /dev/null
+++ b/egs_modelscope/asr/data2vec/speech_data2vec_pretrain-paraformer-zh-cn-aishell2-16k/infer_after_finetune.py
@@ -0,0 +1,52 @@
+import json
+import os
+import shutil
+
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+from funasr.utils.compute_wer import compute_wer
+
+
+def modelscope_infer_after_finetune(params):
+ # prepare for decoding
+ pretrained_model_path = os.path.join(os.environ["HOME"], ".cache/modelscope/hub", params["modelscope_model_name"])
+ for file_name in params["required_files"]:
+ if file_name == "configuration.json":
+ with open(os.path.join(pretrained_model_path, file_name)) as f:
+ config_dict = json.load(f)
+ config_dict["model"]["am_model_name"] = params["decoding_model_name"]
+ with open(os.path.join(params["output_dir"], "configuration.json"), "w") as f:
+ json.dump(config_dict, f, indent=4, separators=(',', ': '))
+ else:
+ shutil.copy(os.path.join(pretrained_model_path, file_name),
+ os.path.join(params["output_dir"], file_name))
+ 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=params["output_dir"],
+ output_dir=decoding_path,
+ )
+ 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/token")
+ compute_wer(text_in, text_proc_file, os.path.join(decoding_path, "text.cer"))
+
+
+if __name__ == '__main__':
+ params = {}
+ params["modelscope_model_name"] = "damo/speech_data2vec_pretrain-paraformer-zh-cn-aishell2-16k"
+ params["required_files"] = ["am.mvn", "decoding.yaml", "configuration.json"]
+ params["output_dir"] = "./checkpoint"
+ params["data_dir"] = "./data/test"
+ params["decoding_model_name"] = "valid.cer_ctc.ave.pth"
+ modelscope_infer_after_finetune(params)
--
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