init

tokenize_dataset_s2s.py

@@ -19,10 +19,10 @@ tokenizer = EncodecTokenizer.from_pretrained()
 
 def tokenize(example):
     for side in sides:
-        wav = torch.as_tensor(example[f"{side}.audio"]["array"].reshape(1, 1, -1), dtype=torch.FloatTensor)
+        wav = torch.as_tensor(example[f"{side}.audio"]["array"].reshape(1, 1, -1), dtype=torch.float32)
         example[f"{side}.audio.tokens"] = tokenizer.wav_to_tokens(
             wav=wav, sample_rate=example[f"{side}.audio"]["sampling_rate"]
-        ).numpy().tolist()
+        ).numpy().tolist()[0]
     return example
 
 
@@ -32,7 +32,4 @@ dataset = dataset.map(
     num_proc=num_proc,
     desc="tokenize dataset"
 )
-DatasetDict({"train": dataset}).push_to_hub(
-        f"{hf_org}/{hf_dataset}.tokenized",
-        config_name=f"subset_{dataset_id}"
-    )
+DatasetDict({"train": dataset}).push_to_hub(f"{hf_org}/{hf_dataset}.tokenized", config_name=f"subset_{dataset_id}")