add huggingface to pretrained

examples/grpo/cosyvoice2/prepare_data.py

@@ -0,0 +1,88 @@
+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Preprocess the Text to Speech dataset to parquet format
+"""
+
+import argparse
+import os
+import re
+
+import datasets
+
+from verl.utils.hdfs_io import copy, makedirs
+
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--train_file", required=True, help="Path to training JSON/JSONL file")
+    parser.add_argument("--test_file", required=True, help="Path to test JSON/JSONL file")
+    parser.add_argument("--local_dir", default=None, required=True)
+    parser.add_argument("--hdfs_dir", default=None)
+
+    args = parser.parse_args()
+
+    # Load datasets from local JSON files
+    train_dataset = datasets.load_dataset("json", data_files=args.train_file)['train']
+    test_dataset = datasets.load_dataset("json", data_files=args.test_file)['train']
+
+    # add a row to each data item that represents a unique id
+    def make_map_fn(split):
+        def process_fn(example, idx):
+            text = example.pop("text")
+
+            # use cosyvoice2 official huggingface compatible checkpoint template
+            question = text
+            answer = ""
+
+            data = {
+                "data_source": f"{args.train_file}_{args.test_file}",  # Use file names as data source
+                "prompt": [
+                    {
+                        "role": "user",
+                        "content": question,
+                    },
+                    {
+                        "role": "assistant",
+                        "content": answer,
+                    },
+                ],
+                "ability": "text-to-speech",
+                "reward_model": {"style": "rule", "ground_truth": text},
+                "extra_info": {
+                    "split": split,
+                    "index": idx,
+                    "text": text,
+                },
+            }
+            return data
+
+        return process_fn
+
+    train_dataset = train_dataset.map(function=make_map_fn("train"), with_indices=True)
+    test_dataset = test_dataset.map(function=make_map_fn("test"), with_indices=True)
+
+    local_dir = args.local_dir
+    hdfs_dir = args.hdfs_dir
+
+    print(train_dataset)
+    print(test_dataset)
+    train_dataset.to_parquet(os.path.join(local_dir, "train.parquet"))
+    test_dataset.to_parquet(os.path.join(local_dir, "test.parquet"))
+
+    if hdfs_dir is not None:
+        makedirs(hdfs_dir)
+
+        copy(src=local_dir, dst=hdfs_dir)