init

download_audio.py

@@ -1,6 +1,7 @@
 """
 https://stackoverflow.com/questions/31353244/how-to-config-wget-to-retry-more-than-20
 """
+import json
 import os
 import tarfile
 import zipfile
@@ -9,20 +10,23 @@ import subprocess
 from os.path import join as p_join
 from tqdm import tqdm
 from multiprocessing import Pool
-from typing import Optional
+from typing import Optional, List
 
 import pandas as pd
 
-
-url_metadata_s2s = "https://dl.fbaipublicfiles.com/seamless/data/seamless_align_nov2023_extension/seamless.dataset.metadata.public.enA-jaA.tsv.gz"
-url_metadata_s2t = "https://dl.fbaipublicfiles.com/seamless/data/seamless.dataset.metadata.public.enA-jpn.withduration.tsv.gz"
-cache_dir_root = "download"
-cache_dir_audio = p_join(cache_dir_root, "audio")
-cache_dir_metadata = p_join(cache_dir_root, "meta")
+# dataset config
+url_metadata_dict = {
+    "enA-jaA": "https://dl.fbaipublicfiles.com/seamless/data/seamless_align_nov2023_extension/seamless.dataset.metadata.public.enA-jaA.tsv.gz",
+    "enA-jpn": "https://dl.fbaipublicfiles.com/seamless/data/seamless.dataset.metadata.public.enA-jpn.withduration.tsv.gz"
+}
+direction = os.getenv("DIRECTION", "enA-jaA")
+sides = set(direction.split("-"))
+# processor config
 n_pool = int(os.getenv("N_POOL", 8))
 wget_max_retry = os.getenv("MAX_RETRY", "1")
 wget_timeout = os.getenv("TIMEOUT", "20")
-data = os.getenv("DATA", "s2s")
+line_no_start = int(os.getenv("LINE_NO_START", 0))
+line_no_end = int(os.getenv("LINE_NO_END", 500000))
 
 
 def wget(url: str, cache_dir: str, filename: Optional[str] = None):
@@ -52,44 +56,53 @@ def wget(url: str, cache_dir: str, filename: Optional[str] = None):
     return True
 
 
-def get_metadata(url: str):
-    filename = os.path.basename(url).replace(".gz", "")
-    if not os.path.exists(filename):
-        assert wget(url, cache_dir=cache_dir_metadata)
-    df = pd.read_csv(p_join(cache_dir_metadata, filename), sep=r'[\t\s]', header=None)[[0, 2, 6, 9, 10, 11, 12]]
+def get_metadata():
+    url_metadata = url_metadata_dict[direction]
+    meta_data_filename = os.path.basename(url_metadata).replace(".gz", "")
+    cache_dir_metadata = p_join("download", "meta", meta_data_filename)
+    if not os.path.exists(cache_dir_metadata):
+        assert wget(url_metadata, cache_dir=cache_dir_metadata)
+    df = pd.read_csv(meta_data_filename, sep=r'[\t\s]', header=None)[[0, 2, 6, 9, 10, 11, 12]]
     df.columns = ["id", "url", "text_lid_score", "laser_score", "direction", "side", "line_no"]
-    return df
+    assert len(df["direction"].unique()) == 1
+    df.pop("direction")
+    return df.sort_values(by=["line_no", "side", "direction"])
 
 
-def get_audio(url: str, filename: str):
-    if not os.path.exists(p_join(cache_dir_audio, filename)):
-        return wget(url, filename=filename, cache_dir=cache_dir_audio)
+def get_audio(dataframe: pd.DataFrame):
+    cache_dir_audio = p_join("download", "audio", direction)
+    cache_dir_feature = p_join("download", "feature", direction)
+    features = {"line_no": dataframe.pop('line_no').values[0]}
+    for side, df in dataframe.groupby("side"):
+        df.pop("side")
+        features.update({f"{side}.{k}": v for k, v in df.iloc[0].to_dict().items()})
+        features[f"{side}.path"] = p_join(cache_dir_audio, os.path.basename(features[f"{side}.url"]))
+        if not os.path.exists(features[f"{side}.path"]):
+            if not wget(features[f"{side}.url"], filename=features[f"{side}.path"], cache_dir=cache_dir_audio):
+                return False
+    with open(cache_dir_feature, "w") as f:
+        json.dump(features, f)
     return True
 
 
-def process_dataset(url_metadata):
-    df_metadata = get_metadata(url_metadata)
-    print(f"load metadata: {url_metadata}, ({len(df_metadata)} rows)")
-    inputs = [(
-        r['url'], f"{r['id']}.{r['direction']}.{r['side']}.{os.path.basename(r['url'])}"
-    ) for _, r in df_metadata.iterrows()]
-    inputs = [x for x in inputs if not os.path.exists(p_join(cache_dir_audio, x[1]))]
-    print(f"{len(inputs)} urls to download")
+def process_dataset():
+    df_metadata = get_metadata()
+    print(f"metadata: {len(df_metadata)}")
+    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)}")
+    inputs = [g for g in inputs if len(g) == 2]
+    print(f"removed != 2: {len(inputs)}")
+    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 url, filename in tqdm(inputs, total=len(inputs)):
-            flag = get_audio(url, filename)
-            if not flag:
-                print(f"failed:\n{url}")
+        for g in tqdm(inputs, total=len(inputs)):
+            if not get_audio(g):
+                print(f"failed:\n{g['url']}")
     else:
         with Pool(n_pool) as pool:
             pool.starmap(get_audio, tqdm(inputs, total=len(inputs)))
 
 
 if __name__ == '__main__':
-    if data == "s2s":
-        process_dataset(url_metadata_s2s)
-    elif data == "s2t":
-        process_dataset(url_metadata_s2t)
-    else:
-        raise ValueError(f"unknown data type {data}")
+    process_dataset()