Update benchmark_hf_pipeline.py

benchmark_hf_pipeline.py

@@ -0,0 +1,25 @@
+from pprint import pprint
+from transformers import pipeline
+from datasets import load_dataset
+
+# config
+model_id = "kotoba-tech/kotoba-whisper-v1.0"
+generate_kwargs = {"language": "japanese", "task": "transcribe"}
+
+# load model
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model=model_id,
+    chunk_length_s=15,
+    batch_size=64
+)
+
+# load sample audio (concatenate instances to create a long audio)
+dataset = load_dataset("kotoba-tech/kotoba-whisper-eval", split="train")
+x = dataset['audio'][0]
+elapsed = {}
+for x in dataset['audio']:
+    start = time()
+    transcription = pipe(x.copy(), generate_kwargs=generate_kwargs)
+    elapsed[x['path']] = time() - start
+pprint(elapsed)