chore: import upstream snapshot with attribution

python/example/test_gpu_batch.py

@@ -0,0 +1,61 @@
+#!/usr/bin/env python3
+
+import sys
+import json
+
+from vosk import BatchModel, BatchRecognizer, GpuInit
+from timeit import default_timer as timer
+
+TOT_SAMPLES = 0
+
+GpuInit()
+
+model = BatchModel("model")
+
+with open(sys.argv[1]) as fn:
+    fnames = fn.readlines()
+    fds = [open(x.strip(), "rb") for x in fnames]
+    uids = [fname.strip().split("/")[-1][:-4] for fname in fnames]
+    recs = [BatchRecognizer(model, 16000) for x in fnames]
+    results = [""] * len(fnames)
+
+ended = set()
+
+start_time = timer()
+
+while True:
+
+    # Feed in the data
+    for i, fd in enumerate(fds):
+        if i in ended:
+            continue
+        data = fd.read(8000)
+        if len(data) == 0:
+            recs[i].FinishStream()
+            ended.add(i)
+            continue
+        recs[i].AcceptWaveform(data)
+        TOT_SAMPLES += len(data)
+
+    # Wait for results from CUDA
+    model.Wait()
+
+    # Retrieve and add results
+    for i, fd in enumerate(fds):
+        res = recs[i].Result()
+        if len(res) != 0:
+            results[i] = results[i] + " " + json.loads(res)["text"]
+
+    if len(ended) == len(fds):
+        break
+
+end_time = timer()
+
+for i, res in enumerate(results):
+    print(uids[i], res.strip())
+
+print("Processed %.3f seconds of audio in %.3f seconds (%.3f xRT)"
+    % (TOT_SAMPLES / 16000.0 / 2,
+    end_time - start_time,
+    (TOT_SAMPLES / 16000.0 / 2 / (end_time - start_time))),
+    file=sys.stderr)