From fdf74bb85cfe3dd0ce6cbaf51ec8d5b3ca3d2039 Mon Sep 17 00:00:00 2001
From: 仁迷 <haoneng.lhn@alibaba-inc.com>
Date: 星期四, 09 二月 2023 17:18:43 +0800
Subject: [PATCH] update persian model recipe
---
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-fa-16k-common-vocab1257-pytorch-online/infer.py | 88 +++++++++++++++++++++++++++++++++++++++++---
1 files changed, 82 insertions(+), 6 deletions(-)
diff --git a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-fa-16k-common-vocab1257-pytorch-online/infer.py b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-fa-16k-common-vocab1257-pytorch-online/infer.py
index 960c393..ecb1381 100644
--- a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-fa-16k-common-vocab1257-pytorch-online/infer.py
+++ b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-fa-16k-common-vocab1257-pytorch-online/infer.py
@@ -1,13 +1,89 @@
+import os
+import shutil
+from multiprocessing import Pool
+
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
-if __name__ == "__main__":
- audio_in = "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_fa.wav"
- output_dir = "./results"
+from funasr.utils.compute_wer import compute_wer
+
+
+def modelscope_infer_core(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_pipline = pipeline(
task=Tasks.auto_speech_recognition,
model="damo/speech_UniASR_asr_2pass-fa-16k-common-vocab1257-pytorch-online",
- output_dir=output_dir,
+ output_dir=output_dir_job,
+ batch_size=1
)
- rec_result = inference_pipline(audio_in=audio_in)
- print(rec_result)
+ audio_in = os.path.join(split_dir, "wav.{}.scp".format(idx))
+ inference_pipline(audio_in=audio_in)
+
+
+def modelscope_infer(params):
+ # prepare for multi-GPU decoding
+ 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
+
+ p = Pool(nj)
+ for i in range(nj):
+ p.apply_async(modelscope_infer_core,
+ args=(output_dir, split_dir, njob, str(i + 1)))
+ p.close()
+ p.join()
+
+ # 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(best_recog_path, "token")
+ compute_wer(text_in, text_proc_file, os.path.join(best_recog_path, "text.cer"))
+ os.system("tail -n 3 {}".format(os.path.join(best_recog_path, "text.cer")))
+
+
+if __name__ == "__main__":
+ params = {}
+ params["data_dir"] = "./data/test"
+ params["output_dir"] = "./results"
+ params["ngpu"] = 1
+ params["njob"] = 8
+ modelscope_infer(params)
--
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