init

download_audio.py

@@ -114,7 +114,18 @@ def get_audio(dataframe: pd.DataFrame):
                 try:
                     wav = audio_loader.decode_example({"path": features[f"{side}.path"], "bytes": None})
                     if wav["sampling_rate"] != sampling_rate:
-                        wav["array"] = julius.resample_frac(torch.as_tensor(wav["array"]), wav["sampling_rate"], sampling_rate)
+                        print(f"RESAMPLING: {wav['array']} length audio")
+                        if torch.cuda.is_available():
+                            wav["array"] = julius.resample_frac(
+                                torch.as_tensor(wav["array"]).to("cuda"),
+                                wav["sampling_rate"],
+                                sampling_rate
+                            ).cpu().numpy()
+                        else:
+                            wav["array"] = julius.resample_frac(
+                                torch.as_tensor(wav["array"]),
+                                wav["sampling_rate"], sampling_rate
+                            ).numpy()
                         wav["sampling_rate"] = sampling_rate
                     if start < end <= len(wav["array"]):
                         sf.write(features[f"{side}.path"], wav["array"][start:end], wav["sampling_rate"])

main.sh

@@ -1,5 +1,7 @@
 rm -rf download/audio
 rm -rf download/feature
+python -c 'n=41; import os; from glob import glob; tmp = [int(os.path.basename(i).split(".")[0]) for i in glob("download/feature/enA-jaA/*.json")]; print(len([x for x in tmp if (n-1) * 2500 <= x < n * 2500]))'
+python -c 'n=41; import os; from glob import glob; tmp = [int(os.path.basename(i).split(".")[0]) for i in glob("download/audio/enA-jaA/enA/*")]; print(len([x for x in tmp if (n-1) * 2500 <= x < n * 2500]))'
 ####################
 # enA-jaA: 718_606 #
 ####################