Add model file and checkpoint

.gitattributes

@@ -15,3 +15,4 @@
 *.pt filter=lfs diff=lfs merge=lfs -text
 *.pth filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+hubert_sd.ckpt filter=lfs diff=lfs merge=lfs -text

hubert_sd.ckpt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7a6414fe26484dd73d1696a3f8dffa1499747307eb7541ea715ffea4c678fba8
+size 31526316

model.py

@@ -0,0 +1,55 @@
+"""
+This is just an example of what people would submit for inference.
+"""
+
+import os
+from typing import Dict
+
+import torch
+from s3prl.downstream.runner import Runner
+
+
+class PreTrainedModel(Runner):
+    def __init__(self, path=""):
+        """
+        Initialize downstream model.
+        """
+        ckp_file = os.path.join(path, "hubert_sd.ckpt")
+        ckp = torch.load(ckp_file, map_location="cpu")
+        ckp["Args"].init_ckpt = ckp_file
+        ckp["Args"].mode = "inference"
+        ckp["Args"].device = "cpu"  # Just to try in my computer
+
+        Runner.__init__(self, ckp["Args"], ckp["Config"])
+
+    def __call__(self, inputs) -> Dict[str, str]:
+        """
+        Args: inputs (:obj:`np.array`): The raw waveform of audio received. By
+            default at 16KHz.
+
+        Return: A :obj:`dict`:. The object should return a dictionary {"frames":
+            "XXX"} containing the frames where one, both, or none of the
+            speakers are speaking.
+        """
+        for entry in self.all_entries:
+            entry.model.eval()
+
+        inputs = [torch.FloatTensor(inputs)]
+
+        with torch.no_grad():
+            features = self.upstream.model(inputs)
+            features = self.featurizer.model(inputs, features)
+            preds = self.downstream.model.inference(features, [])
+        return preds[0]
+
+
+"""
+import io
+import soundfile as sf
+from urllib.request import urlopen
+
+model = PreTrainedModel()
+url = "https://huggingface.co/datasets/lewtun/s3prl-sd-dummy/raw/main/audio.wav"
+data, samplerate = sf.read(io.BytesIO(urlopen(url).read()))
+print(model(data))
+"""