chore: import upstream snapshot with attribution

deployment/benchmarks/infer_acoustic.py

@@ -0,0 +1,32 @@
+import numpy as np
+import onnxruntime as ort
+import tqdm
+
+n_tokens = 10
+n_frames = 100
+n_runs = 20
+speedup = 20
+provider = 'DmlExecutionProvider'
+
+tokens = np.array([[1] * n_tokens], dtype=np.int64)
+durations = np.array([[n_frames // n_tokens] * n_tokens], dtype=np.int64)
+f0 = np.array([[440.] * n_frames], dtype=np.float32)
+speedup = np.array(speedup, dtype=np.int64)
+
+session = ort.InferenceSession('model1.onnx', providers=[provider])
+for _ in tqdm.tqdm(range(n_runs)):
+    session.run(['mel'], {
+        'tokens': tokens,
+        'durations': durations,
+        'f0': f0,
+        'speedup': speedup
+    })
+
+session = ort.InferenceSession('model2.onnx', providers=[provider])
+for _ in tqdm.tqdm(range(n_runs)):
+    session.run(['mel'], {
+        'tokens': tokens,
+        'durations': durations,
+        'f0': f0,
+        'speedup': speedup
+    })

deployment/benchmarks/infer_nsf_hifigan.py

@@ -0,0 +1,16 @@
+import numpy as np
+import onnxruntime as ort
+import tqdm
+
+n_frames = 1000
+n_runs = 20
+mel = np.random.randn(1, n_frames, 128).astype(np.float32)
+f0 = np.random.randn(1, n_frames).astype(np.float32) + 440.
+provider = 'DmlExecutionProvider'
+
+session = ort.InferenceSession('nsf_hifigan.onnx', providers=[provider])
+for _ in tqdm.tqdm(range(n_runs)):
+    session.run(['waveform'], {
+        'mel': mel,
+        'f0': f0
+    })