init

attach_speaker_embedding_s2s.py

@@ -60,7 +60,6 @@ print(f"Num examples (after filtering): {len(dataset)}")
 
 def speaker_embedding(example):
     for side in sides:
-        print(example[f"{side}.audio"]["array"].shape)
         embedding = speaker_embedder.get_speaker_embedding(
             example[f"{side}.audio"]["array"], example[f"{side}.audio"]["sampling_rate"]
         )

main_s2s.sh

@@ -404,10 +404,12 @@ do
   python attach_speaker_embedding_s2s.py
 done
 
-export SE_MODEL="hubert-xl"  # running
-export DATASET_ID=167
-export DIRECTION="enA-zhA"
-python attach_speaker_embedding_s2s.py
+for i in 162 163 164 165 83 8;
+do
+  export SE_MODEL="xlsr-2b"  # running
+  export DIRECTION="enA-zhA"
+  python attach_speaker_embedding_s2s.py
+done
 
 export SE_MODEL="w2vbert-600m"  # running
 export DATASET_ID=167

tokenize_dataset_s2s.py

@@ -17,6 +17,7 @@ hf_dataset = f"seamless-align-{direction}"
 dataset = load_dataset(f"{hf_org}/{hf_dataset}", f"subset_{dataset_id}", split="train")
 tokenizer = EncodecTokenizer.from_pretrained()
 max_seq_length = 10000000
+min_seq_length = 50000
 audio_loader = Audio()
 
 
@@ -24,7 +25,7 @@ def error_file(example):
     for side in sides:
         try:
             wav = audio_loader.decode_example(example[f"{side}.audio"])
-            if len(wav["array"]) == 0 or len(wav["array"]) > max_seq_length:
+            if len(wav["array"]) < min_seq_length or len(wav["array"]) > max_seq_length:
                 return False
         except ValueError:
             return False