Delete infer.py

egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online/infer.py

@@ -1,39 +0,0 @@
-import os
-import logging
-import torch
-import soundfile
-
-from modelscope.pipelines import pipeline
-from modelscope.utils.constant import Tasks
-from modelscope.utils.logger import get_logger
-
-logger = get_logger(log_level=logging.CRITICAL)
-logger.setLevel(logging.CRITICAL)
-
-os.environ["MODELSCOPE_CACHE"] = "./"
-inference_pipeline = pipeline(
-    task=Tasks.auto_speech_recognition,
-    model='damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online',
-    model_revision='v1.0.4'
-)
-
-model_dir = os.path.join(os.environ["MODELSCOPE_CACHE"], "damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online")
-speech, sample_rate = soundfile.read(os.path.join(model_dir, "example/asr_example.wav"))
-speech_length = speech.shape[0]
-
-sample_offset = 0
-chunk_size = [8, 8, 4] #[5, 10, 5] 600ms, [8, 8, 4] 480ms
-stride_size =  chunk_size[1] * 960
-param_dict = {"cache": dict(), "is_final": False, "chunk_size": chunk_size}
-final_result = ""
-
-for sample_offset in range(0, speech_length, min(stride_size, speech_length - sample_offset)):
-    if sample_offset + stride_size >= speech_length - 1:
-        stride_size = speech_length - sample_offset
-        param_dict["is_final"] = True
-    rec_result = inference_pipeline(audio_in=speech[sample_offset: sample_offset + stride_size],
-                                    param_dict=param_dict)
-    if len(rec_result) != 0:
-        final_result += rec_result['text'] + " "
-        print(rec_result)
-print(final_result.strip())