From c7d8fc0c586e231c3b71229e08f640328ddc4cc1 Mon Sep 17 00:00:00 2001
From: lzr265946 <lzr265946@alibaba-inc.com>
Date: 星期一, 06 二月 2023 17:43:27 +0800
Subject: [PATCH] add infer_after_finetune in paraformer-large-vad-punc-model
---
egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/README.md | 16 ++++++++
egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer_after_finetune.py | 57 ++++++++++++++++++++++++++++
2 files changed, 73 insertions(+), 0 deletions(-)
diff --git a/egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/README.md b/egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/README.md
index c68a8cd..1094bb5 100644
--- a/egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/README.md
+++ b/egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/README.md
@@ -28,3 +28,19 @@
```python
python infer.py
```
+
+### Inference using local finetuned model
+
+- Modify inference related parameters in `infer_after_finetune.py`
+ - <strong>output_dir:</strong> # result dir
+ - <strong>data_dir:</strong> # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed
+ - <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pth`
+
+- Then you can run the pipeline to finetune with:
+```python
+ python infer_after_finetune.py
+```
+
+- Results
+
+The decoding results can be found in `$output_dir/decoding_results/text.cer`, which includes recognition results of each sample and the CER metric of the whole test set.
diff --git a/egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer_after_finetune.py b/egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer_after_finetune.py
new file mode 100644
index 0000000..5f171b4
--- /dev/null
+++ b/egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer_after_finetune.py
@@ -0,0 +1,57 @@
+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
+ if not os.path.exists(os.path.join(params["output_dir"], "punc")):
+ os.makedirs(os.path.join(params["output_dir"], "punc"))
+ if not os.path.exists(os.path.join(params["output_dir"], "vad")):
+ os.makedirs(os.path.join(params["output_dir"], "vad"))
+ 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,
+ batch_size=64
+ )
+ 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 text_in is not None:
+ 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_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
+ params["required_files"] = ["am.mvn", "decoding.yaml", "configuration.json", "punc/punc.pb", "punc/punc.yaml", "vad/vad.mvn", "vad/vad.pb", "vad/vad.yaml"]
+ params["output_dir"] = "./checkpoint"
+ params["data_dir"] = "./data/test"
+ params["decoding_model_name"] = "valid.acc.ave_10best.pth"
+ modelscope_infer_after_finetune(params)
--
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