test streaming

.gitignore

@@ -1,3 +1,4 @@
 .idea
 build
-preprocess
+preprocess
+download

format_text.py

@@ -0,0 +1,10 @@
+import csv
+import json
+from glob import glob
+import pandas as pd
+
+df = pd.concat([pd.read_csv(i, quoting=csv.QUOTE_NONE, encoding='utf-8', sep='\t', header=None, on_bad_lines='skip') for i in glob('seamless.dataset.metadata.public.jpn.batch_*.tsv')])
+line_no = [i.split(" ")[3] for i in df[0]]
+text = df[1].values.tolist()
+with open("text.enA-jpn.json", "w") as f:
+    json.dump({l: t for l, t in zip(line_no, text)}, f)

main.sh

@@ -130,7 +130,10 @@ cat seamless.dataset.metadata.public.enA-jpn.withduration.reordered.batch_8.tsv
 cat seamless.dataset.metadata.public.enA-jpn.withduration.reordered.batch_9.tsv | egrep ^crawl-data | tr '\t' ' ' | wet_lines | tee seamless.dataset.metadata.public.jpn.batch_9.tsv
 cat seamless.dataset.metadata.public.enA-jpn.withduration.reordered.batch_10.tsv | egrep ^crawl-data | tr '\t' ' ' | wet_lines | tee seamless.dataset.metadata.public.jpn.batch_10.tsv
 cat seamless.dataset.metadata.public.enA-jpn.withduration.reordered.batch_11.tsv | egrep ^crawl-data | tr '\t' ' ' | wet_lines | tee seamless.dataset.metadata.public.jpn.batch_11.tsv
-
+cp ../format_text.py ./
+python format_text.py
+mv text.enA-jpn.json ../
+cd ../
 
 ########
 # NLLB #

push_s2s_translation.py

@@ -21,7 +21,7 @@ line_no_start = int(os.getenv("LINE_NO_START", 0))
 line_no_end = int(os.getenv("LINE_NO_END", 10000))
 dataset_id = int(os.getenv("DATASET_ID", 0))
 hf_org = "kotoba-tech"
-hf_dataset = f"seamless-align-{direction}-{dataset_id}"
+hf_dataset = f"seamless-align-{direction}"
 
 
 def loader(feature: str) -> Dict:
@@ -50,37 +50,37 @@ data_dict.update(
 audio_dataset = Dataset.from_dict(data_dict)
 audio_dataset = audio_dataset.cast_column(f"{sides_rev[1]}.audio", Audio())
 audio_dataset = audio_dataset.cast_column(f"{sides_rev[2]}.audio", Audio())
-#
-# # remove instances with broken audio files
-# broken_files = []
-# for i in tqdm(range(len(audio_dataset))):
-#     try:
-#         a = audio_dataset[i]
-#         flag = True
-#         for side_id in sides_rev.keys():
-#             start = a[f"{sides_rev[side_id]}.duration_start"]
-#             end = a[f"{sides_rev[side_id]}.duration_end"]
-#             array = a[f"{sides_rev[side_id]}.audio"]["array"]
-#             flag = 0 < start < end < len(array)
-#         if not flag:
-#             broken_files.append(i)
-#     except LibsndfileError:
-#         broken_files.append(i)
-#         continue
-# print(f"features (removed broken audio): {len(audio_dataset) - len(broken_files)}")
-# if len(broken_files) > 0:
-#     print(f"found {len(broken_files)} broken files:")
-#     flag = input("delete the broken files? (y/n): ")
-#     if flag == "y":
-#         # remove broken files
-#         for i in broken_files:
-#             if os.path.exists(files[file_ids[i]]):
-#                 os.remove(files[file_ids[i]])
-#             for side_id in sides_rev.keys():
-#                 if os.path.exists(data_dict[f"{sides_rev[side_id]}.audio"][i]):
-#                     os.remove(data_dict[f"{sides_rev[side_id]}.audio"][i])
-# valid_data_id = [i for i in range(len(audio_dataset)) if i not in broken_files]
-# audio_dataset_valid = audio_dataset.select(valid_data_id)
+
+# remove instances with broken audio files
+broken_files = []
+for i in tqdm(range(len(audio_dataset))):
+    try:
+        a = audio_dataset[i]
+        flag = True
+        for side_id in sides_rev.keys():
+            start = a[f"{sides_rev[side_id]}.duration_start"]
+            end = a[f"{sides_rev[side_id]}.duration_end"]
+            array = a[f"{sides_rev[side_id]}.audio"]["array"]
+            flag = 0 < start < end < len(array)
+        if not flag:
+            broken_files.append(i)
+    except LibsndfileError:
+        broken_files.append(i)
+        continue
+print(f"features (removed broken audio): {len(audio_dataset) - len(broken_files)}")
+if len(broken_files) > 0:
+    print(f"found {len(broken_files)} broken files:")
+    flag = input("delete the broken files? (y/n): ")
+    if flag == "y":
+        # remove broken files
+        for i in broken_files:
+            if os.path.exists(files[file_ids[i]]):
+                os.remove(files[file_ids[i]])
+            for side_id in sides_rev.keys():
+                if os.path.exists(data_dict[f"{sides_rev[side_id]}.audio"][i]):
+                    os.remove(data_dict[f"{sides_rev[side_id]}.audio"][i])
+valid_data_id = [i for i in range(len(audio_dataset)) if i not in broken_files]
+audio_dataset_valid = audio_dataset.select(valid_data_id)
 
 
 # trim the audio according to the duration
@@ -108,7 +108,7 @@ dataset_to_push = DatasetDict({"train": audio_dataset_valid})
 repo_name = f"{hf_org}/{hf_dataset}"
 while True:
     try:
-        dataset_to_push.push_to_hub(repo_name)
+        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...")

push_s2t_translation.py

@@ -0,0 +1,114 @@
+import json
+import os
+import time
+from os.path import join as p_join
+from tqdm import tqdm
+from typing import Dict
+from glob import glob
+
+from soundfile import LibsndfileError
+from datasets import Dataset, Audio, DatasetDict
+
+# dataset config
+direction_speech = os.getenv("DIRECTION_SPEECH", "enA")
+direction_text = os.getenv("DIRECTION_TEXT", "jpn")
+direction = f"{direction_speech}-{direction_text}"
+with open(f"text.{direction}.json") as f:
+    line2text = json.load(f)
+cache_dir_audio = p_join("download", "audio", direction)
+cache_dir_feature = p_join("download", "feature", direction)
+os.makedirs(cache_dir_audio, exist_ok=True)
+os.makedirs(cache_dir_feature, exist_ok=True)
+line_no_start = int(os.getenv("LINE_NO_START", 0))
+line_no_end = int(os.getenv("LINE_NO_END", 10000))
+dataset_id = int(os.getenv("DATASET_ID", 0))
+hf_org = "kotoba-tech"
+hf_dataset = f"seamless-align-{direction}"
+
+
+def loader(feature: str) -> Dict:
+    with open(feature) as f:
+        return json.load(f)
+
+
+# create a dataset instance
+
+files = {
+    int(os.path.basename(i).replace(".json", "")): i for i in glob(p_join(cache_dir_feature, "*.json"))
+}
+
+
+def delete_audio(target_audio_file):
+    if os.path.exists(target_audio_file):
+        os.remove(target_audio_file)
+    line_no = os.path.basename(target_audio_file).split(".")[0]
+    try:
+        feature_file = files[int(line_no)]
+        if os.path.exists(feature_file):
+            os.remove(feature_file)
+    except Exception as e:
+        print(e)
+
+
+# remove broken audio files
+features = []
+audio_loader = Audio()
+for i in tqdm(list(range(line_no_start, line_no_end))):
+    if i in files:
+        continue
+    i = loader(files[i])
+    i[f"{direction_text}.text"] = line2text[str(i)]
+    audio_file = i[f"{direction_speech}.path"]
+    start, end = i[f"{direction_speech}.duration_start"], i[f"{direction_speech}.duration_end"]
+    if os.path.exists(audio_file):
+        try:
+            wav = audio_loader.decode_example({"path": audio_file, "bytes": None})
+            if start < end < len(wav["array"]):
+                features.append(i)
+            else:
+                delete_audio(audio_file)
+        except Exception as e:
+            print(e)
+            delete_audio(audio_file)
+
+
+print(f"features (filtered): {len(features)}")
+data_dict = {f"{direction_speech}.audio": [i.pop(f"{direction_speech}.path") for i in features]}
+keys = features[0].keys()
+data_dict.update({k: [i[k] for i in features] for k in keys})
+audio_dataset = Dataset.from_dict(data_dict)
+audio_dataset = audio_dataset.cast_column(f"{direction_speech}.audio", Audio())
+
+
+# trim the audio according to the duration
+def clip_audio(batch):
+    start = batch[f"{direction_speech}.duration_start"]
+    end = batch[f"{direction_speech}.duration_end"]
+    audio = batch[f"{direction_speech}.audio"]
+    batch[f"{direction_speech}.audio"] = [
+        {"array": a["array"][s:e], "sampling_rate": a["sampling_rate"]}
+        for a, s, e in zip(audio, start, end)
+    ]
+    return batch
+
+
+audio_dataset_valid = audio_dataset_valid.map(
+    function=clip_audio,
+    batched=True,
+    batch_size=128,
+    num_proc=1,
+    desc="clipping audio based on the duration:"
+)
+
+dataset_to_push = DatasetDict({"train": audio_dataset_valid})
+repo_name = f"{hf_org}/{hf_dataset}"
+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)
+os.makedirs("log", exist_ok=True)
+with open(f"log/pushed.line_no.{dataset_id}.json", "w") as f:
+    json.dump(data_dict["line_no"], f)