update paraformer online inference recipe

egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/demo_online_v2.py

@@ -0,0 +1,41 @@
+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-large_asr_nat-zh-cn-16k-common-vocab8404-online',
+    model_revision='v1.0.7',
+    update_model=False,
+    mode="paraformer_streaming"
+)
+
+model_dir = os.path.join(os.environ["MODELSCOPE_CACHE"], "damo/speech_paraformer-large_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 = [0, 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, "encoder_chunk_look_back": 4, "decoder_chunk_look_back": 1}
+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)