init

delete_audio.py

@@ -9,7 +9,7 @@ cache_dir_feature = p_join("download", "feature", direction)
 line_no_start = int(os.getenv("LINE_NO_START", 0))
 line_no_end = int(os.getenv("LINE_NO_END", 10000))
 for i in tqdm(range(line_no_start, line_no_end), total=line_no_end-line_no_start):
-    for audio_file in glob(p_join(cache_dir_audio, "*", "*")):
+    for audio_file in glob(p_join(cache_dir_audio, "*", f"*{i}*")):
         os.remove(audio_file)
     if os.path.exists(p_join(cache_dir_feature, f"{i}.json")):
         os.remove(p_join(cache_dir_feature, f"{i}.json"))

download_audio.py

@@ -4,7 +4,6 @@ import tarfile
 import zipfile
 import gzip
 import subprocess
-import time
 from os.path import join as p_join
 from tqdm import tqdm
 from multiprocessing import Pool
@@ -37,7 +36,7 @@ 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}"
-skip_wget = bool(int(os.getenv("SKIP_WGET", 0)))
+skip_wget = bool(int(os.getenv("SKIP_WGET", 1)))
 
 
 def wget(url: str, output_file: Optional[str] = None):
@@ -127,21 +126,21 @@ def get_audio(dataframe: pd.DataFrame):
 
 
 if __name__ == '__main__':
-    df_metadata = get_metadata()
-    print(f"metadata: {len(df_metadata)}, {line_no_start} --> {line_no_end}")
-    # inputs = [
-    #     g for line_no, g in df_metadata.groupby("line_no")
-    #     if line_no_start <= line_no < line_no_end and not os.path.exists(
-    #         p_join(cache_dir_feature, f'{int(line_no)}.json')
-    #     )
-    # ]
-    inputs = [g for line_no, g in df_metadata.groupby("line_no") if line_no_start <= line_no < line_no_end]
-
-    print(f"filtered unique lines: {len(inputs)}")
-    if direction == "enA-jaA":
-        inputs = [g for g in inputs if len(g["side"].unique()) == 2 and set(g["side"].unique()) == sides]
-        print(f"removed side != 2: {len(inputs)}")
     if not skip_wget:
+        df_metadata = get_metadata()
+        print(f"metadata: {len(df_metadata)}, {line_no_start} --> {line_no_end}")
+        # inputs = [
+        #     g for line_no, g in df_metadata.groupby("line_no")
+        #     if line_no_start <= line_no < line_no_end and not os.path.exists(
+        #         p_join(cache_dir_feature, f'{int(line_no)}.json')
+        #     )
+        # ]
+        inputs = [g for line_no, g in df_metadata.groupby("line_no") if line_no_start <= line_no < line_no_end]
+        print(f"filtered unique lines: {len(inputs)}")
+        if direction == "enA-jaA":
+            inputs = [g for g in inputs if len(g["side"].unique()) == 2 and set(g["side"].unique()) == sides]
+            print(f"removed side != 2: {len(inputs)}")
+
         if n_pool == 1:
             for g in tqdm(inputs, total=len(inputs)):
                 line_no = get_audio(g)
@@ -166,9 +165,34 @@ if __name__ == '__main__':
     audio_dataset = Dataset.from_dict(data_dict)
     for side in sides:
         audio_dataset = audio_dataset.cast_column(f"{side}.audio", Audio())
-    dataset_to_push = DatasetDict({"train": audio_dataset})
-    repo_name = f"{hf_org}/{hf_dataset}"
-    dataset_to_push.push_to_hub(repo_name, config_name=f"subset_{dataset_id}")
+    # DatasetDict({"train": audio_dataset}).push_to_hub(
+    #     f"{hf_org}/{hf_dataset}",
+    #     config_name=f"subset_{dataset_id}"
+    # )
+
+
+    DatasetDict({"train": audio_dataset.select(list(range(1000)))}).push_to_hub(
+        f"{hf_org}/{hf_dataset}",
+        config_name=f"subset_{dataset_id}"
+    )
+
+    # # 2 panel
+    # dataset_id = 75
+    DatasetDict({"train": audio_dataset.select(list(range(3000, len(audio_dataset))))}).push_to_hub(
+        f"{hf_org}/{hf_dataset}",
+        config_name=f"subset_{dataset_id}"
+    )
+    #
+    #
+
+
+    # audio_dataset = audio_dataset.select(list(range(2500)))
+    # dataset_to_push = DatasetDict({"train": audio_dataset})
+    # repo_name = f"{hf_org}/{hf_dataset}"
+    # dataset_to_push.push_to_hub(repo_name, config_name=f"subset_{dataset_id}")
+    # dataset_to_push.push_to_hub(repo_name, config_name=f"subset_{dataset_id}", max_shard_size="2GiB")
+    # dataset_to_push.push_to_hub(repo_name, config_name=f"subset_{dataset_id}", num_shards={"train": 1})
+
     # while True:
     #     try:
     #         dataset_to_push.push_to_hub(repo_name, config_name=f"subset_{dataset_id}")

main.sh

@@ -1,12 +1,7 @@
 ####################
 # enA-jaA: 718_606 #
 ####################
-export DATASET_ID=2
-export DIRECTION="enA-jaA"
-export LINE_NO_START=$(((DATASET_ID-1) * 5000))
-export LINE_NO_END=$((DATASET_ID * 5000))
-python download_audio.py
-
+# TODO: 4, 6, 24, 25, 26
 export DATASET_ID=4
 export DIRECTION="enA-jaA"
 export LINE_NO_START=$(((DATASET_ID-1) * 5000))
@@ -76,7 +71,7 @@ export LINE_NO_START=$(((DATASET_ID-1) * 5000))
 export LINE_NO_END=$((DATASET_ID * 5000))
 python download_audio.py
 
-export DATASET_ID=7
+export DATASET_ID=25
 export DIRECTION="enA-jaA"
 export LINE_NO_START=$(((DATASET_ID-1) * 5000))
 export LINE_NO_END=$((DATASET_ID * 5000))