init

fetch_dataset_s2s.py

@@ -107,7 +107,6 @@ def cleanup(features, feature_file):
 
 
 def get_audio(dataframe: pd.DataFrame):
-    resampler = {}
     features = {"line_no": int(dataframe.pop('line_no').values[0])}
     feature_file = p_join(cache_dir_feature, f'{features["line_no"]}.json')
     for side, df in dataframe.groupby("side"):

fetch_dataset_s2t.py

@@ -19,18 +19,17 @@ from datasets import Dataset, Audio, DatasetDict
 audio_loader = Audio()
 # 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_speech = os.getenv("DIRECTION_SPEECH", "enA")
 direction_text = os.getenv("DIRECTION_TEXT", "jpn")
-direction = os.getenv("DIRECTION", "enA-jpn")
-sides = set(direction.split("-"))
-cache_dir_audio = p_join("download", "audio", direction)
+direction = f"{direction_speech}-{direction_text}"
+if direction not in url_metadata_dict:
+    url_metadata_dict[direction] = f"https://dl.fbaipublicfiles.com/seamless/data/seamless.dataset.metadata.public.{direction}.withduration.tsv.gz"
 cache_dir_feature = p_join("download", "feature", direction)
+cache_dir_audio = p_join("download", "audio", direction)
 os.makedirs(cache_dir_feature, exist_ok=True)
-for s in sides:
-    os.makedirs(p_join(cache_dir_audio, s), exist_ok=True)
+os.makedirs(p_join(cache_dir_audio, direction_speech), exist_ok=True)
 # processor config
 n_pool = int(os.getenv("N_POOL", 1))
 wget_max_retry = os.getenv("MAX_RETRY", "2")
@@ -43,6 +42,11 @@ hf_dataset = f"seamless-align-{direction}"
 skip_download = bool(int(os.getenv("SKIP_DOWNLOAD", 0)))
 sampling_rate = 16000  # seamless-align aligns audio in 16kHz
 
+text_corpus = p_join("text_corpus", f"text.{direction_speech}-{direction_text}.json")
+assert os.path.exists(text_corpus)
+with open(text_corpus) as f:
+    line_no_to_text = json.load(f)
+
 
 def wget(url: str, output_file: Optional[str] = None):
     os.makedirs(os.path.dirname(output_file), exist_ok=True)
@@ -96,52 +100,49 @@ def to_json_serializable(val):
 def cleanup(features, feature_file):
     if os.path.exists(feature_file):
         os.remove(feature_file)
-    for _side in sides:
-        for _unrelated_audio_file in glob(p_join(cache_dir_audio, _side, f"{features['line_no']}.*")):
-            os.remove(_unrelated_audio_file)
+    for _unrelated_audio_file in glob(p_join(cache_dir_audio, direction_speech, f"{features['line_no']}.*")):
+        os.remove(_unrelated_audio_file)
     # create a dummy so that we can skip from next run
     with open(feature_file, "w") as f:
         json.dump({"dummy": "dummy"}, f)
 
 
 def get_audio(dataframe: pd.DataFrame):
-    resampler = {}
     features = {"line_no": int(dataframe.pop('line_no').values[0])}
+    if features["line_no"] not in text_corpus:
+        return None
+    features[f"{direction_text}.text"] = text_corpus[features["line_no"]]
     feature_file = p_join(cache_dir_feature, f'{features["line_no"]}.json')
-    for side, df in dataframe.groupby("side"):
-        df.pop("side")
-        features.update({f"{side}.{k}": to_json_serializable(v) for k, v in df.iloc[0].to_dict().items()})
-        identifier = os.path.basename(features[f"{side}.url"]).split(".")[-1]
-        features[f"{side}.path"] = str(p_join(cache_dir_audio, side, f"{features['line_no']}.{identifier}"))
-        start, end = features[f"{side}.duration_start"], features[f"{side}.duration_end"]
-        if not os.path.exists(features[f"{side}.path"]):
-            print(f"WGET {features[f'{side}.url']}")
-            flag = wget(features[f"{side}.url"], output_file=features[f"{side}.path"])
-            if not flag:
-                print("\n#### ERROR: wget failure ####\n")
+    features.update({f"{direction_speech}.{k}": to_json_serializable(v) for k, v in dataframe.iloc[0].to_dict().items()})
+    identifier = os.path.basename(features[f"{direction_speech}.url"]).split(".")[-1]
+    features[f"{direction_speech}.path"] = p_join(
+        cache_dir_audio, direction_speech, f"{features['line_no']}.{identifier}"
+    )
+    start, end = features[f"{direction_speech}.duration_start"], features[f"{direction_speech}.duration_end"]
+    if not os.path.exists(features[f"{direction_speech}.path"]):
+        print(f"WGET {features[f'{direction_speech}.url']}")
+        flag = wget(features[f"{direction_speech}.url"], output_file=features[f"{direction_speech}.path"])
+        if not flag:
+            print("\n#### ERROR: wget failure ####\n")
+            cleanup(features, feature_file)
+            return None
+        else:
+            try:
+                print(f"LOAD AUDIO FROM {features[f'{direction_speech}.path']}")
+                wav, sr = sf.read(features[f"{direction_speech}.path"])
+                print(f"wav shape:{wav.shape}")
+                if wav.ndim > 1:
+                    wav = wav[:, 0]
+                wav = wav[floor(start / sampling_rate * sr):ceil(end / sampling_rate * sr)]
+                print(f"wav shape (after truncate):{wav.shape}")
+                wav = wav[:int(end/sampling_rate * sr) + sr]
+                print(f"SAVING: {features[f'{direction_speech}.path']}")
+                sf.write(features[f"{direction_speech}.path"], wav, sr)
+
+            except Exception as e:
+                print(f"\n#### ERROR ####\n {e}")
                 cleanup(features, feature_file)
                 return None
-            else:
-                try:
-                    print(f"LOAD AUDIO FROM {features[f'{side}.path']}")
-                    wav, sr = sf.read(features[f"{side}.path"])
-                    print(f"wav shape:{wav.shape}")
-                    if wav.ndim > 1:
-                        wav = wav[:, 0]
-                    wav = wav[floor(start / sampling_rate * sr):ceil(end / sampling_rate * sr)]
-                    print(f"wav shape (after truncate):{wav.shape}")
-                    wav = wav[:int(end/sampling_rate * sr) + sr]
-                    print(f"SAVING: {features[f'{side}.path']}")
-                    sf.write(features[f"{side}.path"], wav, sr)
-                    # if sr != sampling_rate:
-                    #     print(f"RESAMPLING: {wav.shape} length audio")
-                    #     wav = librosa.resample(wav, orig_sr=sr, target_sr=sampling_rate)
-                    # sf.write(features[f"{side}.path"], wav[start:end], sampling_rate)
-
-                except Exception as e:
-                    print(f"\n#### ERROR ####\n {e}")
-                    cleanup(features, feature_file)
-                    return None
     print(f"\n### SUCCESS! ###\n:{features['line_no']}")
     with open(feature_file, "w") as f:
         json.dump(features, f)
@@ -164,10 +165,7 @@ if __name__ == '__main__':
             )
         ]
         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)}")
-
+        inputs = [g for g in inputs if len(g) == 1]
         if n_pool == 1:
             for g in tqdm(inputs, total=len(inputs)):
                 line_no = get_audio(g)
@@ -187,46 +185,11 @@ if __name__ == '__main__':
     print(f"- dummy removed: {len(features)}")
     print(f"push {len(features)} records to hub")
     data_dict = {}
-    for side in sides:
-        data_dict.update({f"{side}.audio": [i.pop(f"{side}.path") for i in features]})
+    data_dict.update({f"{direction_speech}.audio": [i.pop(f"{direction_speech}.path") for i in features]})
     data_dict.update({k: [i[k] for i in features] for k in features[0].keys()})
     audio_dataset = Dataset.from_dict(data_dict)
-    for side in sides:
-        audio_dataset = audio_dataset.cast_column(f"{side}.audio", Audio())
+    audio_dataset = audio_dataset.cast_column(f"{direction_speech}.audio", Audio())
     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}")
-    #         break
-    #     except Exception:
-    #         print(f"FAILED: push_to_hub on {repo_name} failed. wait 60 sec and retry soon...")
-    #         time.sleep(60)
-
-

format_text.py

@@ -7,7 +7,6 @@ import pandas as pd
 
 direction_speech = os.getenv("DIRECTION_SPEECH", "enA")
 direction_text = os.getenv("DIRECTION_TEXT", "jpn")
-direction = os.getenv("DIRECTION", "enA-jpn")
 
 df = pd.concat([
     pd.read_csv(i, quoting=csv.QUOTE_NONE, encoding='utf-8', sep='\t', header=None, on_bad_lines='skip')

main_s2t.sh

@@ -47,7 +47,7 @@ done
 # text
 export DIRECTION_SPEECH="enA"
 export DIRECTION_TEXT="jpn"
-export CHUNK_SIZE=10
+export CHUNK_SIZE=20
 python download_s2t_metadata.py
 for i in $(seq 1 ${CHUNK_SIZE});
 do