init

encodec_audio_tokenizer.py

@@ -34,7 +34,7 @@ class BaseEncodecTokenizer:
     @torch.no_grad()
     def wav_to_tokens(self,
                       wav: torch.Tensor,
-                      sample_rate: List[int],
+                      sample_rate: int,
                       cpu_offload: bool = True,
                       chunk_length: Optional[int] = None,
                       stride: Optional[int] = None,
@@ -54,13 +54,8 @@ class BaseEncodecTokenizer:
             raise ValueError(f"wav should be (batch, channel, time): {wav.ndim} dims")
         original_device = wav.device
         # sampling audio
-        assert len(sample_rate) == len(wav)
-        new_wav = []
-        for sr, single_wav in zip(sample_rate, wav):
-            if sr != self.sample_rate:
-                single_wav = julius.resample_frac(single_wav, sr, self.sample_rate)
-            new_wav.append(single_wav)
-        wav = torch.concat(new_wav)
+        if sample_rate != self.sample_rate:
+            wav = julius.resample_frac(wav, sample_rate, self.sample_rate)
         batch_size, channels, input_length = wav.shape
         if channels > 1:
             logging.warning("Audio has more than one channel but encoder takes the first channel only.")

tokenize_dataset_s2s.py

@@ -17,21 +17,18 @@ dataset = load_dataset(f"{hf_org}/{hf_dataset}", f"subset_{dataset_id}", split="
 tokenizer = EncodecTokenizer.from_pretrained()
 
 
-def tokenize(batch):
+def tokenize(example):
     for side in sides:
-        wav = torch.as_tensor(np.concatenate([i["array"] for i in batch[f"{side}.audio"]]))
-        print(wav.shape)
-
-        sr = [i["sampling_rate"] for i in batch[f"{side}.audio"]]
-        batch[f"{side}.audio.tokens"] = tokenizer.wav_to_tokens(wav=wav, sample_rate=sr).numpy().tolist()
-    return batch
+        wav = torch.as_tensor(example[f"{side}.audio"]["array"].reshape(1, 1, -1))
+        example[f"{side}.audio.tokens"] = tokenizer.wav_to_tokens(
+            wav=wav, sample_rate=[example[f"{side}.audio"]["sampling_rate"]]
+        ).numpy().tolist()
+    return example
 
 
 dataset = dataset.map(
     function=tokenize,
     remove_columns=[f"{s}.audio" for s in sides] + [f"{s}.url" for s in sides] + [f"{s}.duration_start" for s in sides] + [f"{s}.duration_end" for s in sides],
-    batched=True,
-    batch_size=batch_size,
     num_proc=num_proc,
     desc="tokenize dataset"
 )