init

attach_speaker_embedding_s2s.py

@@ -21,7 +21,7 @@ sides = set(direction.split("-"))
 dataset_id = os.getenv("DATASET_ID", 0)
 num_proc = int(os.getenv("NUM_PROC", 1))
 hf_org = os.getenv("HF_ORG", "asahi417")
-hf_dataset = f"seamless-align-{direction}"
+hf_dataset = os.getenv("HF_DATASET", f"seamless-align-{direction}")
 dataset = load_dataset(f"{hf_org}/{hf_dataset}", f"subset_{dataset_id}", split="train")
 audio_loader = Audio()
 

fetch_dataset_s2s.py

@@ -41,7 +41,7 @@ line_no_start = int(os.getenv("LINE_NO_START", 0))
 line_no_end = int(os.getenv("LINE_NO_END", 10000))
 dataset_id = os.getenv("DATASET_ID", 0)
 hf_org = os.getenv("HF_ORG", "asahi417")
-hf_dataset = f"seamless-align-{direction}"
+hf_dataset = os.getenv("HF_DATASET", f"seamless-align-{direction}")
 skip_download = bool(int(os.getenv("SKIP_DOWNLOAD", 0)))
 sampling_rate = 16000  # seamless-align aligns audio in 16kHz
 

main_s2s.sh

@@ -25,6 +25,13 @@ do
   export DIRECTION="enA-jaA"
   python tokenize_dataset_s2s.py
 done
+# speaker embedding
+for i in $(seq 1 144);
+do
+  export DATASET_ID=${i}
+  export DIRECTION="enA-jaA"
+  python attach_speaker_embedding_s2s.py
+done
 
 ####################
 # enA-hiA: 454_942 #

test_s2s.sh

@@ -0,0 +1,17 @@
+# audio
+export DATASET_ID=test
+export DIRECTION="enA-jaA"
+export LINE_NO_START=0
+export LINE_NO_END=10
+export HF_DATASET="experiment-process-seamless-align"
+python fetch_dataset_s2s.py
+# tokenize
+export DATASET_ID=test
+export DIRECTION="enA-jaA"
+export HF_DATASET="experiment-process-seamless-align"
+python tokenize_dataset_s2s.py
+# speaker embedding
+export DATASET_ID=test
+export DIRECTION="enA-jaA"
+export HF_DATASET="experiment-process-seamless-align"
+python attach_speaker_embedding_s2s.py

tokenize_dataset_s2s.py

@@ -13,7 +13,7 @@ sides = set(direction.split("-"))
 dataset_id = os.getenv("DATASET_ID", 0)
 num_proc = int(os.getenv("NUM_PROC", 1))
 hf_org = os.getenv("HF_ORG", "asahi417")
-hf_dataset = f"seamless-align-{direction}"
+hf_dataset = os.getenv("HF_DATASET", f"seamless-align-{direction}")
 dataset = load_dataset(f"{hf_org}/{hf_dataset}", f"subset_{dataset_id}", split="train")
 tokenizer = EncodecTokenizer.from_pretrained()