From 0efa2aa971f3a1365ea488a8b05f89b3ea9bce0b Mon Sep 17 00:00:00 2001
From: haoneng.lhn <haoneng.lhn@alibaba-inc.com>
Date: 星期一, 10 四月 2023 15:51:21 +0800
Subject: [PATCH] update uniasr infer recipe
---
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/infer_after_finetune.py | 93 ++++++++++++++++++++++++++++++++++++----------
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/infer.py | 3 -
2 files changed, 73 insertions(+), 23 deletions(-)
diff --git a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/infer.py b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/infer.py
index 96db5f9..ce8988e 100644
--- a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/infer.py
+++ b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/infer.py
@@ -23,8 +23,7 @@
batch_size=1
)
audio_in = os.path.join(split_dir, "wav.{}.scp".format(idx))
- inference_pipline(audio_in=audio_in, param_dict={"decoding_model": "offline"})
-
+ inference_pipline(audio_in=audio_in)
def modelscope_infer(params):
# prepare for multi-GPU decoding
diff --git a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/infer_after_finetune.py b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/infer_after_finetune.py
index 74691f0..1e9c4d1 100644
--- a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/infer_after_finetune.py
+++ b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/infer_after_finetune.py
@@ -2,52 +2,103 @@
import os
import shutil
+from multiprocessing import Pool
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from funasr.utils.compute_wer import compute_wer
+def modelscope_infer_after_finetune_core(model_dir, output_dir, split_dir, njob, idx):
+ output_dir_job = os.path.join(output_dir, "output.{}".format(idx))
+ gpu_id = (int(idx) - 1) // njob
+ if "CUDA_VISIBLE_DEVICES" in os.environ.keys():
+ gpu_list = os.environ['CUDA_VISIBLE_DEVICES'].split(",")
+ os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_list[gpu_id])
+ else:
+ os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_id)
+ inference_pipeline = pipeline(
+ task=Tasks.auto_speech_recognition,
+ model=model_dir,
+ output_dir=output_dir_job,
+ batch_size=1
+ )
+ audio_in = os.path.join(split_dir, "wav.{}.scp".format(idx))
+ inference_pipeline(audio_in=audio_in)
+
def modelscope_infer_after_finetune(params):
- # prepare for decoding
+ # prepare for multi-GPU decoding
+ model_dir = params["model_dir"]
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:
+ with open(os.path.join(model_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)
+ os.path.join(model_dir, file_name))
+ ngpu = params["ngpu"]
+ njob = params["njob"]
+ output_dir = params["output_dir"]
+ if os.path.exists(output_dir):
+ shutil.rmtree(output_dir)
+ os.mkdir(output_dir)
+ split_dir = os.path.join(output_dir, "split")
+ os.mkdir(split_dir)
+ nj = ngpu * njob
+ wav_scp_file = os.path.join(params["data_dir"], "wav.scp")
+ with open(wav_scp_file) as f:
+ lines = f.readlines()
+ num_lines = len(lines)
+ num_job_lines = num_lines // nj
+ start = 0
+ for i in range(nj):
+ end = start + num_job_lines
+ file = os.path.join(split_dir, "wav.{}.scp".format(str(i + 1)))
+ with open(file, "w") as f:
+ if i == nj - 1:
+ f.writelines(lines[start:])
+ else:
+ f.writelines(lines[start:end])
+ start = end
- # decoding
- inference_pipeline = pipeline(
- task=Tasks.auto_speech_recognition,
- model=params["output_dir"],
- output_dir=decoding_path,
- batch_size=1
- )
- audio_in = os.path.join(params["data_dir"], "wav.scp")
- inference_pipeline(audio_in=audio_in, param_dict={"decoding_model": "offline"})
+ p = Pool(nj)
+ for i in range(nj):
+ p.apply_async(modelscope_infer_after_finetune_core,
+ args=(model_dir, output_dir, split_dir, njob, str(i + 1)))
+ p.close()
+ p.join()
- # computer CER if GT text is set
+ # combine decoding results
+ best_recog_path = os.path.join(output_dir, "1best_recog")
+ os.mkdir(best_recog_path)
+ files = ["text", "token", "score"]
+ for file in files:
+ with open(os.path.join(best_recog_path, file), "w") as f:
+ for i in range(nj):
+ job_file = os.path.join(output_dir, "output.{}/1best_recog".format(str(i + 1)), file)
+ with open(job_file) as f_job:
+ lines = f_job.readlines()
+ f.writelines(lines)
+
+ # If text exists, compute CER
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"))
-
+ text_proc_file = os.path.join(best_recog_path, "token")
+ compute_wer(text_in, text_proc_file, os.path.join(best_recog_path, "text.cer"))
if __name__ == '__main__':
params = {}
params["modelscope_model_name"] = "damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline"
params["required_files"] = ["am.mvn", "decoding.yaml", "configuration.json"]
- params["output_dir"] = "./checkpoint"
+ params["model_dir"] = "./checkpoint"
+ params["output_dir"] = "./results"
params["data_dir"] = "./data/test"
params["decoding_model_name"] = "20epoch.pb"
+ params["ngpu"] = 1
+ params["njob"] = 1
modelscope_infer_after_finetune(params)
+
--
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