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Browse files- checkpoint-1120-epoch-6/optimizer.bin +3 -0
- checkpoint-1120-epoch-6/pytorch_model.bin +3 -0
- checkpoint-1120-epoch-6/random_states_0.pkl +3 -0
- checkpoint-1120-epoch-6/scheduler.bin +3 -0
- checkpoint-1280-epoch-7/optimizer.bin +3 -0
- checkpoint-1280-epoch-7/pytorch_model.bin +3 -0
- checkpoint-1280-epoch-7/random_states_0.pkl +3 -0
- checkpoint-1280-epoch-7/scheduler.bin +3 -0
- checkpoint-640-epoch-3/optimizer.bin +3 -0
- checkpoint-640-epoch-3/pytorch_model.bin +3 -0
- checkpoint-640-epoch-3/random_states_0.pkl +3 -0
- checkpoint-640-epoch-3/scheduler.bin +3 -0
- checkpoint-800-epoch-4/optimizer.bin +3 -0
- checkpoint-800-epoch-4/pytorch_model.bin +3 -0
- checkpoint-800-epoch-4/random_states_0.pkl +3 -0
- checkpoint-800-epoch-4/scheduler.bin +3 -0
- checkpoint-960-epoch-5/optimizer.bin +3 -0
- checkpoint-960-epoch-5/pytorch_model.bin +3 -0
- checkpoint-960-epoch-5/random_states_0.pkl +3 -0
- checkpoint-960-epoch-5/scheduler.bin +3 -0
- config.json +276 -0
- finetuning_config.json +54 -0
- generation_config.json +12 -0
- model.safetensors +3 -0
- preprocessor_config.json +10 -0
- run_parler_tts_training.py +1763 -0
- special_tokens_map.json +125 -0
- spiece.model +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +941 -0
checkpoint-1120-epoch-6/optimizer.bin
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checkpoint-1120-epoch-6/pytorch_model.bin
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checkpoint-1120-epoch-6/random_states_0.pkl
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checkpoint-1120-epoch-6/scheduler.bin
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checkpoint-1280-epoch-7/optimizer.bin
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checkpoint-1280-epoch-7/pytorch_model.bin
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checkpoint-640-epoch-3/optimizer.bin
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checkpoint-640-epoch-3/pytorch_model.bin
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checkpoint-800-epoch-4/optimizer.bin
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checkpoint-960-epoch-5/scheduler.bin
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config.json
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|
finetuning_config.json
ADDED
@@ -0,0 +1,54 @@
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|
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|
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|
|
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|
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|
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|
|
|
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|
|
|
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|
|
|
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|
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|
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|
|
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|
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|
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|
1 |
+
{
|
2 |
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"model_name_or_path": "parler-tts/parler_tts_mini_v0.1",
|
3 |
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"feature_extractor_name": "parler-tts/dac_44khZ_8kbps",
|
4 |
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"description_tokenizer_name": "parler-tts/parler_tts_mini_v0.1",
|
5 |
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|
6 |
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|
7 |
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|
8 |
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"train_dataset_name": "ylacombe/expresso+reach-vb/jenny_tts_dataset+sanchit-gandhi/libritts_r_test+sanchit-gandhi/libritts_r_test",
|
9 |
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"train_metadata_dataset_name": "reach-vb/expresso-tagged-w-speech-mistral-v3+ylacombe/jenny-tts-10k-tagged+parler-tts/libritts_r_tags_tagged_10k_generated+parler-tts/libritts_r_tags_tagged_10k_generated",
|
10 |
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|
11 |
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|
12 |
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"eval_dataset_name": "ylacombe/expresso+reach-vb/jenny_tts_dataset+sanchit-gandhi/libritts_r_test+sanchit-gandhi/libritts_r_test",
|
13 |
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"eval_metadata_dataset_name": "reach-vb/expresso-tagged-w-speech-mistral-v3+ylacombe/jenny-tts-10k-tagged+parler-tts/libritts_r_tags_tagged_10k_generated+parler-tts/libritts_r_tags_tagged_10k_generated",
|
14 |
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"eval_dataset_config_name": "read+default+clean+other",
|
15 |
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|
16 |
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
27 |
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|
28 |
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
40 |
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|
41 |
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|
42 |
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"output_dir": "./",
|
43 |
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"temporary_save_to_disk": "../audio_code_tmp_constant/",
|
44 |
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"save_to_disk": "../tmp_dataset_audio_constant/",
|
45 |
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"dataloader_num_workers": 4,
|
46 |
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"do_eval": true,
|
47 |
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|
48 |
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|
49 |
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"save_strategy": "epoch",
|
50 |
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"evaluation_strategy": "epoch",
|
51 |
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"save_total_limit": 5,
|
52 |
+
"group_by_length": true
|
53 |
+
}
|
54 |
+
|
generation_config.json
ADDED
@@ -0,0 +1,12 @@
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|
|
|
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|
|
|
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|
|
|
|
|
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|
1 |
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{
|
2 |
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"_from_model_config": true,
|
3 |
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"bos_token_id": 1025,
|
4 |
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"decoder_start_token_id": 1025,
|
5 |
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|
6 |
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|
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|
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|
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|
10 |
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|
11 |
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|
12 |
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}
|
model.safetensors
ADDED
@@ -0,0 +1,3 @@
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|
1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:45937d8dfa9f232e79f76f536602301dde6f2b5be7200414d69ff07bff1228c3
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3 |
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size 2588215392
|
preprocessor_config.json
ADDED
@@ -0,0 +1,10 @@
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1 |
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{
|
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|
3 |
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"feature_extractor_type": "EncodecFeatureExtractor",
|
4 |
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|
6 |
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|
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|
9 |
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"sampling_rate": 44100
|
10 |
+
}
|
run_parler_tts_training.py
ADDED
@@ -0,0 +1,1763 @@
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1 |
+
#!/usr/bin/env python
|
2 |
+
# coding=utf-8
|
3 |
+
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Train Parler-TTS using 🤗 Accelerate"""
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import logging
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import os
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import re
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import shutil
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import sys
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import time
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from dataclasses import dataclass, field
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from datetime import timedelta
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from pathlib import Path
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from typing import Dict, List, Optional, Set, Union
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import datasets
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import evaluate
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import numpy as np
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import torch
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import transformers
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from accelerate import Accelerator
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from accelerate.utils import AutocastKwargs, InitProcessGroupKwargs, TorchDynamoPlugin, set_seed
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from accelerate.utils.memory import release_memory
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from datasets import Dataset, DatasetDict, IterableDataset, concatenate_datasets, interleave_datasets, load_dataset
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from huggingface_hub import Repository, create_repo
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from multiprocess import set_start_method
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from torch.utils.data import DataLoader
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from tqdm import tqdm
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from transformers import (
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AutoFeatureExtractor,
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AutoModel,
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AutoProcessor,
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AutoTokenizer,
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HfArgumentParser,
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Seq2SeqTrainingArguments,
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pipeline,
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)
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from transformers.optimization import get_scheduler
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from transformers.trainer_pt_utils import LengthGroupedSampler
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from transformers.utils import send_example_telemetry
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from wandb import Audio
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from parler_tts import (
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ParlerTTSConfig,
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ParlerTTSForConditionalGeneration,
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build_delay_pattern_mask,
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)
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logger = logging.getLogger(__name__)
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def list_field(default=None, metadata=None):
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return field(default_factory=lambda: default, metadata=metadata)
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_RE_CHECKPOINT = re.compile(r"^checkpoint-(\d+)-epoch-(\d+)$")
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def get_last_checkpoint(folder):
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content = os.listdir(folder)
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checkpoints = [
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path
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for path in content
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if _RE_CHECKPOINT.search(path) is not None and os.path.isdir(os.path.join(folder, path))
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]
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if len(checkpoints) == 0:
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return
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return os.path.join(folder, max(checkpoints, key=lambda x: int(_RE_CHECKPOINT.search(x).groups()[0])))
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def sorted_checkpoints(output_dir=None, checkpoint_prefix="checkpoint") -> List[str]:
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"""Helper function to sort saved checkpoints from oldest to newest."""
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ordering_and_checkpoint_path = []
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glob_checkpoints = [str(x) for x in Path(output_dir).glob(f"{checkpoint_prefix}-*") if os.path.isdir(x)]
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for path in glob_checkpoints:
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regex_match = re.match(f".*{checkpoint_prefix}-([0-9]+)", path)
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if regex_match is not None and regex_match.groups() is not None:
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ordering_and_checkpoint_path.append((int(regex_match.groups()[0]), path))
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checkpoints_sorted = sorted(ordering_and_checkpoint_path)
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checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted]
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return checkpoints_sorted
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def rotate_checkpoints(save_total_limit=None, output_dir=None, checkpoint_prefix="checkpoint") -> None:
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"""Helper function to delete old checkpoints."""
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if save_total_limit is None or save_total_limit <= 0:
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return
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# Check if we should delete older checkpoint(s)
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checkpoints_sorted = sorted_checkpoints(output_dir=output_dir, checkpoint_prefix=checkpoint_prefix)
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if len(checkpoints_sorted) <= save_total_limit:
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return
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number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - save_total_limit)
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checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete]
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for checkpoint in checkpoints_to_be_deleted:
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logger.info(f"Deleting older checkpoint [{checkpoint}] due to args.save_total_limit")
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shutil.rmtree(checkpoint, ignore_errors=True)
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def log_metric(
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accelerator,
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metrics: Dict,
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train_time: float,
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step: int,
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epoch: int,
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learning_rate: float = None,
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prefix: str = "train",
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):
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"""Helper function to log all training/evaluation metrics with the correct prefixes and styling."""
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log_metrics = {}
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for k, v in metrics.items():
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log_metrics[f"{prefix}/{k}"] = v
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log_metrics[f"{prefix}/time"] = train_time
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log_metrics[f"{prefix}/epoch"] = epoch
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if learning_rate is not None:
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log_metrics[f"{prefix}/learning_rate"] = learning_rate
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accelerator.log(log_metrics, step=step)
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def log_pred(
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accelerator,
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pred_descriptions: List[str],
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pred_prompts: List[str],
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transcriptions: List[str],
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audios: List[torch.Tensor],
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sampling_rate: int,
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step: int,
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prefix: str = "eval",
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num_lines: int = 200000,
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):
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"""Helper function to log target/predicted transcriptions to weights and biases (wandb)."""
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if accelerator.is_main_process:
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wandb_tracker = accelerator.get_tracker("wandb")
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# pretty name for current step: step 50000 -> step 50k
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cur_step_pretty = f"{int(step // 1000)}k" if step > 1000 else step
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prefix_pretty = prefix.replace("/", "-")
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# convert str data to a wandb compatible format
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str_data = [[pred_descriptions[i], pred_prompts[i], transcriptions[i]] for i in range(len(pred_descriptions))]
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# log as a table with the appropriate headers
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wandb_tracker.log_table(
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table_name=f"predictions/{prefix_pretty}-step-{cur_step_pretty}",
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columns=["Target descriptions", "Target prompts", "Predicted transcriptions"],
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data=str_data[:num_lines],
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step=step,
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commit=False,
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)
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# wandb can only loads 100 audios per step
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wandb_tracker.log(
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{
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f"Speech samples/{prefix}": [
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Audio(
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audio,
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caption=f"{pred_prompts[i]} --- DESCRIPTION: {pred_descriptions[i]}",
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sample_rate=sampling_rate,
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)
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for (i, audio) in enumerate(audios[: min(len(audios), 100)])
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]
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},
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step=step,
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)
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@dataclass
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class ModelArguments:
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"""
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Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
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"""
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model_name_or_path: str = field(
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metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
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)
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config_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
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)
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feature_extractor_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained feature extractor name or path if not the same as model_name"}
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)
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description_tokenizer_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained description tokenizer name or path if not the same as model_name"}
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)
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prompt_tokenizer_name: Optional[str] = field(
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default=None,
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metadata={"help": "Pretrained prompt tokenizer name or path if not the same as description_tokenizer_name"},
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)
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cache_dir: Optional[str] = field(
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default=None,
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metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
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)
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use_fast_tokenizer: bool = field(
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default=True,
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metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
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)
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model_revision: str = field(
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default="main",
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metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
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)
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pad_token_id: int = field(
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default=None,
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metadata={"help": "If specified, change the model pad token id."},
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)
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decoder_start_token_id: int = field(
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default=None,
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metadata={"help": "If specified, change the model decoder start token id."},
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)
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freeze_text_encoder: bool = field(
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default=False,
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metadata={"help": "Whether to freeze the text encoder."},
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)
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do_sample: bool = field(
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default=True,
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metadata={"help": "Whether to do sampling or greedy decoding."},
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)
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temperature: float = field(
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default=1.0,
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metadata={"help": "Temperature if sampling."},
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)
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max_length: int = field(
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default=2580,
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metadata={"help": "Generation max length."},
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)
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bandwidth: float = field(
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default=6,
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metadata={"help": "Audio encoder bandwidth."},
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)
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asr_model_name_or_path: str = field(
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default="distil-whisper/distil-large-v2",
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metadata={
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"help": "Used to compute WER during evaluation. Path to pretrained model or model identifier from huggingface.co/models"
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},
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)
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clap_model_name_or_path: str = field(
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default="laion/larger_clap_music_and_speech",
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metadata={
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"help": "Used to compute audio similarity during evaluation. Path to pretrained model or model identifier from huggingface.co/models"
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},
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)
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+
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@dataclass
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class DataTrainingArguments:
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"""
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Arguments pertaining to what data we are going to input our model for training and eval.
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+
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+
Using `HfArgumentParser` we can turn this class
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into argparse arguments to be able to specify them on
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the command line.
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"""
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+
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train_dataset_name: str = field(
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default=None,
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metadata={
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"help": "The name of the training dataset to use (via the datasets library). Load and combine "
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"multiple datasets by separating dataset ids by a '+' symbol. For example, to load and combine "
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" librispeech and common voice, set `train_dataset_name='librispeech_asr+common_voice'`."
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},
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)
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train_dataset_config_name: Optional[str] = field(
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default=None,
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metadata={
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"help": "The configuration name of the training dataset to use (via the datasets library). Load and combine "
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"multiple datasets by separating dataset configs by a '+' symbol."
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},
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)
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train_split_name: str = field(
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default="train",
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metadata={
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"help": ("The name of the training data set split to use (via the datasets library). Defaults to 'train'")
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},
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)
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train_dataset_samples: str = field(
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default=None,
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metadata={
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"help": "Number of samples in the training data. Load and combine "
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"multiple datasets by separating dataset samples by a '+' symbol."
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},
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)
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train_metadata_dataset_name: str = field(
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default=None,
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metadata={
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"help": "The name of the metadata training dataset to use (via the datasets library). Load and combine "
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+
"multiple datasets by separating dataset ids by a '+' symbol. For example, to load and combine "
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" librispeech and common voice, set `train_dataset_name='librispeech_asr+common_voice'`."
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},
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)
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eval_dataset_name: str = field(
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default=None,
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metadata={
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"help": "The name of the evaluation dataset to use (via the datasets library). Defaults to the training dataset name if unspecified."
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},
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)
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eval_dataset_config_name: Optional[str] = field(
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default=None,
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metadata={
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"help": "The configuration name of the evaluation dataset to use (via the datasets library). Defaults to the training dataset config name if unspecified"
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},
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)
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eval_split_name: str = field(
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default="test",
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metadata={
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"help": "The name of the evaluation data set split to use (via the datasets library). Defaults to 'test'"
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+
},
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)
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+
eval_metadata_dataset_name: str = field(
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default=None,
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+
metadata={
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+
"help": "The name of the metadata training dataset to use (via the datasets library). Load and combine "
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+
"multiple datasets by separating dataset ids by a '+' symbol. For example, to load and combine "
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+
" librispeech and common voice, set `train_dataset_name='librispeech_asr+common_voice'`."
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+
},
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)
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+
target_audio_column_name: str = field(
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default="audio",
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metadata={"help": "The name of the dataset column containing the target audio data. Defaults to 'audio'"},
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+
)
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+
description_column_name: str = field(
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default=None,
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+
metadata={"help": "The name of the dataset column containing the description text data. Defaults to 'None'."},
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+
)
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339 |
+
prompt_column_name: str = field(
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+
default=None,
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+
metadata={"help": "The name of the dataset column containing the prompt text data. Defaults to 'None'."},
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+
)
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343 |
+
overwrite_cache: bool = field(
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+
default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
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+
)
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346 |
+
preprocessing_num_workers: Optional[int] = field(
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+
default=None,
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+
metadata={"help": "The number of processes to use for the preprocessing."},
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349 |
+
)
|
350 |
+
max_train_samples: Optional[int] = field(
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+
default=None,
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352 |
+
metadata={
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353 |
+
"help": (
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354 |
+
"For debugging purposes or quicker training, truncate the number of training examples to this "
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+
"value if set."
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356 |
+
)
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357 |
+
},
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358 |
+
)
|
359 |
+
max_eval_samples: Optional[int] = field(
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360 |
+
default=None,
|
361 |
+
metadata={
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362 |
+
"help": (
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363 |
+
"For debugging purposes or quicker training, truncate the number of validation examples to this "
|
364 |
+
"value if set."
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365 |
+
)
|
366 |
+
},
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367 |
+
)
|
368 |
+
max_duration_in_seconds: float = field(
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+
default=35.0,
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+
metadata={
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371 |
+
"help": (
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+
"Filter audio files that are longer than `max_duration_in_seconds` seconds to 'max_duration_in_seconds`."
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373 |
+
"Also, used to set maximum audio length if `pad_to_max_length=True`."
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+
)
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+
},
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+
)
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377 |
+
min_duration_in_seconds: float = field(
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378 |
+
default=0.0, metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"}
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379 |
+
)
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380 |
+
max_text_length: int = field(
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381 |
+
default=500, metadata={"help": "If set, max description lengths in number of characters."}
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382 |
+
)
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383 |
+
max_prompt_token_length: int = field(
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+
default=None,
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385 |
+
metadata={
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+
"help": (
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387 |
+
"If set, filter samples with prompts that are longer than `max_prompt_token_length` tokens."
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388 |
+
"Also, used to set maximum prompt token length if `pad_to_max_length=True`."
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389 |
+
)
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390 |
+
},
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391 |
+
)
|
392 |
+
max_description_token_length: int = field(
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393 |
+
default=None,
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394 |
+
metadata={
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395 |
+
"help": (
|
396 |
+
"If set, filter samples with descriptions that are longer than `max_description_token_length` tokens."
|
397 |
+
"Also, used to set maximum desription token length if `pad_to_max_length=True`."
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398 |
+
)
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399 |
+
},
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400 |
+
)
|
401 |
+
pad_to_max_length: bool = field(
|
402 |
+
default=False,
|
403 |
+
metadata={
|
404 |
+
"help": (
|
405 |
+
"If `True`, pad audio, prompt and description to a maximum length set with respectively "
|
406 |
+
"`max_duration_in_seconds`, `max_prompt_token_length`, `max_description_token_length`."
|
407 |
+
)
|
408 |
+
},
|
409 |
+
)
|
410 |
+
preprocessing_only: bool = field(
|
411 |
+
default=False,
|
412 |
+
metadata={
|
413 |
+
"help": (
|
414 |
+
"Whether to only do data preprocessing and skip training. This is especially useful when data"
|
415 |
+
" preprocessing errors out in distributed training due to timeout. In this case, one should run the"
|
416 |
+
" preprocessing in a non-distributed setup with `preprocessing_only=True` so that the cached datasets"
|
417 |
+
" can consequently be loaded in distributed training."
|
418 |
+
" In this training script, `save_to_disk` must be set to the path in which the dataset should be saved. "
|
419 |
+
)
|
420 |
+
},
|
421 |
+
)
|
422 |
+
token: str = field(
|
423 |
+
default=None,
|
424 |
+
metadata={
|
425 |
+
"help": (
|
426 |
+
"The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
|
427 |
+
"generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
|
428 |
+
)
|
429 |
+
},
|
430 |
+
)
|
431 |
+
use_auth_token: bool = field(
|
432 |
+
default=None,
|
433 |
+
metadata={
|
434 |
+
"help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead."
|
435 |
+
},
|
436 |
+
)
|
437 |
+
trust_remote_code: bool = field(
|
438 |
+
default=False,
|
439 |
+
metadata={
|
440 |
+
"help": (
|
441 |
+
"Whether or not to allow for custom models defined on the Hub in their own modeling files. This option "
|
442 |
+
"should only be set to `True` for repositories you trust and in which you have read the code, as it will "
|
443 |
+
"execute code present on the Hub on your local machine."
|
444 |
+
)
|
445 |
+
},
|
446 |
+
)
|
447 |
+
add_audio_samples_to_wandb: bool = field(
|
448 |
+
default=False,
|
449 |
+
metadata={"help": "If set and if `wandb` in args.report_to, will add generated audio samples to wandb logs."},
|
450 |
+
)
|
451 |
+
id_column_name: str = field(default=None, metadata={"help": "id column name."})
|
452 |
+
wandb_project: str = field(
|
453 |
+
default="parler-speech",
|
454 |
+
metadata={"help": "The name of the wandb project."},
|
455 |
+
)
|
456 |
+
save_to_disk: str = field(
|
457 |
+
default=None,
|
458 |
+
metadata={
|
459 |
+
"help": "If set, will save the dataset to this path if this is an empyt folder. If not empty, will load the datasets from it."
|
460 |
+
},
|
461 |
+
)
|
462 |
+
temporary_save_to_disk: str = field(default=None, metadata={"help": "Temporarily save audio labels here."})
|
463 |
+
pad_to_multiple_of: Optional[int] = field(
|
464 |
+
default=2,
|
465 |
+
metadata={"help": ("Pad to multiple of for tokenizers.")},
|
466 |
+
)
|
467 |
+
|
468 |
+
|
469 |
+
@dataclass
|
470 |
+
class ParlerTTSTrainingArguments(Seq2SeqTrainingArguments):
|
471 |
+
dtype: Optional[str] = field(
|
472 |
+
default="float32",
|
473 |
+
metadata={
|
474 |
+
"help": (
|
475 |
+
"The data type (dtype) in which to run training. One of `float32` (full-precision), "
|
476 |
+
"`float16` or `bfloat16` (both half-precision)."
|
477 |
+
)
|
478 |
+
},
|
479 |
+
)
|
480 |
+
audio_encoder_per_device_batch_size: int = field(
|
481 |
+
default=8,
|
482 |
+
metadata={"help": ("Specify the batch size of the audio encoding pre-processing steps.")},
|
483 |
+
)
|
484 |
+
|
485 |
+
|
486 |
+
@dataclass
|
487 |
+
class DataCollatorEncodecWithPadding:
|
488 |
+
"""
|
489 |
+
Data collator that will dynamically pad the inputs received to the longest sequence in the batch or
|
490 |
+
to `max_length` if `max_length` is set and `padding=max_length`.
|
491 |
+
"""
|
492 |
+
|
493 |
+
feature_extractor: AutoFeatureExtractor
|
494 |
+
audio_column_name: str
|
495 |
+
feature_extractor_input_name: Optional[str] = "input_values"
|
496 |
+
max_length: Optional[int] = None
|
497 |
+
padding: Optional[str] = "longest"
|
498 |
+
|
499 |
+
def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
|
500 |
+
# split inputs and labels since they have to be of different lengths and need
|
501 |
+
# different padding methods
|
502 |
+
audios = [feature[self.audio_column_name]["array"] for feature in features]
|
503 |
+
len_audio = [len(audio) for audio in audios]
|
504 |
+
|
505 |
+
batch = self.feature_extractor(audios, return_tensors="pt", padding=self.padding, max_length=self.max_length, sampling_rate=self.feature_extractor.sampling_rate)
|
506 |
+
batch["len_audio"] = torch.tensor(len_audio).unsqueeze(1)
|
507 |
+
return batch
|
508 |
+
|
509 |
+
|
510 |
+
@dataclass
|
511 |
+
class DataCollatorParlerTTSWithPadding:
|
512 |
+
"""
|
513 |
+
Data collator that will dynamically pad the inputs received.
|
514 |
+
Args:
|
515 |
+
prompt_tokenizer (:class:`~transformers.AutoTokenizer`)
|
516 |
+
The prompt_tokenizer used for proccessing the data.
|
517 |
+
description_tokenizer (:class:`~transformers.AutoTokenizer`)
|
518 |
+
The description_tokenizer used for proccessing the data.
|
519 |
+
padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
|
520 |
+
Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
|
521 |
+
among:
|
522 |
+
* :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
|
523 |
+
sequence if provided).
|
524 |
+
* :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
|
525 |
+
maximum acceptable input length for the model if that argument is not provided.
|
526 |
+
* :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
|
527 |
+
different lengths).
|
528 |
+
pad_to_multiple_of (:obj:`int`, `optional`):
|
529 |
+
If set will pad the sequence to a multiple of the provided value.
|
530 |
+
This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
|
531 |
+
7.5 (Volta).
|
532 |
+
"""
|
533 |
+
|
534 |
+
prompt_tokenizer: AutoTokenizer
|
535 |
+
description_tokenizer: AutoTokenizer
|
536 |
+
padding: Union[bool, str] = "longest"
|
537 |
+
pad_to_multiple_of: Optional[int] = None
|
538 |
+
prompt_max_length: Optional[int] = None
|
539 |
+
description_max_length: Optional[int] = None
|
540 |
+
audio_max_length: Optional[int] = None
|
541 |
+
|
542 |
+
def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
|
543 |
+
# split inputs and labels since they have to be of different lengths and need
|
544 |
+
# different padding methods
|
545 |
+
|
546 |
+
labels = [torch.tensor(feature["labels"]).transpose(0, 1) for feature in features]
|
547 |
+
# (bsz, seq_len, num_codebooks)
|
548 |
+
labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True, padding_value=-100)
|
549 |
+
if self.audio_max_length is not None and self.padding == "max_length":
|
550 |
+
labels = torch.nn.functional.pad(labels, pad=(0, 0, 0, max(self.audio_max_length - labels.shape[1], 0)))
|
551 |
+
|
552 |
+
input_ids = [{"input_ids": feature["input_ids"]} for feature in features]
|
553 |
+
|
554 |
+
input_ids = self.description_tokenizer.pad(
|
555 |
+
input_ids,
|
556 |
+
return_tensors="pt",
|
557 |
+
padding=self.padding,
|
558 |
+
pad_to_multiple_of=self.pad_to_multiple_of,
|
559 |
+
max_length=self.description_max_length,
|
560 |
+
)
|
561 |
+
|
562 |
+
batch = {"labels": labels, **input_ids}
|
563 |
+
|
564 |
+
if self.audio_max_length is not None and self.padding == "max_length":
|
565 |
+
# if we do torch.compile, we need to also specify the attention_mask
|
566 |
+
decoder_attention_mask = torch.ones(labels.shape[:2], dtype=input_ids["attention_mask"].dtype)
|
567 |
+
batch["decoder_attention_mask"] = decoder_attention_mask
|
568 |
+
|
569 |
+
prompt_input_ids = [{"input_ids": feature["prompt_input_ids"]} for feature in features]
|
570 |
+
prompt_input_ids = self.prompt_tokenizer.pad(
|
571 |
+
prompt_input_ids,
|
572 |
+
return_tensors="pt",
|
573 |
+
padding=self.padding,
|
574 |
+
pad_to_multiple_of=self.pad_to_multiple_of,
|
575 |
+
max_length=self.prompt_max_length,
|
576 |
+
)
|
577 |
+
|
578 |
+
batch["prompt_input_ids"] = prompt_input_ids["input_ids"]
|
579 |
+
if "attention_mask" in prompt_input_ids:
|
580 |
+
batch["prompt_attention_mask"] = prompt_input_ids["attention_mask"]
|
581 |
+
|
582 |
+
return batch
|
583 |
+
|
584 |
+
|
585 |
+
def convert_dataset_str_to_list(
|
586 |
+
dataset_names,
|
587 |
+
dataset_config_names,
|
588 |
+
metadata_dataset_names=None,
|
589 |
+
splits=None,
|
590 |
+
dataset_samples=None,
|
591 |
+
default_split="train",
|
592 |
+
):
|
593 |
+
if isinstance(dataset_names, str):
|
594 |
+
dataset_names = dataset_names.split("+")
|
595 |
+
dataset_config_names = dataset_config_names.split("+")
|
596 |
+
splits = splits.split("+") if splits is not None else None
|
597 |
+
dataset_samples = dataset_samples.split("+") if dataset_samples is not None else None
|
598 |
+
metadata_dataset_names = metadata_dataset_names.split("+") if metadata_dataset_names is not None else None
|
599 |
+
|
600 |
+
# basic checks to ensure we've got the right number of datasets/configs/splits/columns/probs
|
601 |
+
if len(dataset_names) != len(dataset_config_names):
|
602 |
+
raise ValueError(
|
603 |
+
f"Ensure one config is passed for each dataset, got {len(dataset_names)} datasets and"
|
604 |
+
f" {len(dataset_config_names)} configs."
|
605 |
+
)
|
606 |
+
|
607 |
+
if splits is not None and len(splits) != len(dataset_names):
|
608 |
+
raise ValueError(
|
609 |
+
f"Ensure one split is passed for each dataset, got {len(dataset_names)} datasets and {len(splits)} splits."
|
610 |
+
)
|
611 |
+
|
612 |
+
if metadata_dataset_names is not None and len(metadata_dataset_names) != len(dataset_names):
|
613 |
+
raise ValueError(
|
614 |
+
f"Ensure one metadata dataset is passed for each dataset, got {len(dataset_names)} datasets and {len(metadata_dataset_names)} metadata datasets."
|
615 |
+
)
|
616 |
+
|
617 |
+
if dataset_samples is not None:
|
618 |
+
if len(dataset_samples) != len(dataset_names):
|
619 |
+
raise ValueError(
|
620 |
+
f"Ensure one sample is passed for each dataset, got {len(dataset_names)} datasets and "
|
621 |
+
f"{len(dataset_samples)} samples."
|
622 |
+
)
|
623 |
+
dataset_samples = [float(ds_sample) for ds_sample in dataset_samples]
|
624 |
+
else:
|
625 |
+
dataset_samples = [None] * len(dataset_names)
|
626 |
+
|
627 |
+
splits = splits if splits is not None else [default_split for _ in range(len(dataset_names))]
|
628 |
+
|
629 |
+
dataset_names_dict = []
|
630 |
+
for i, ds_name in enumerate(dataset_names):
|
631 |
+
dataset_names_dict.append(
|
632 |
+
{
|
633 |
+
"name": ds_name,
|
634 |
+
"config": dataset_config_names[i],
|
635 |
+
"split": splits[i],
|
636 |
+
"metadata_dataset_name": metadata_dataset_names[i],
|
637 |
+
"samples": dataset_samples[i],
|
638 |
+
}
|
639 |
+
)
|
640 |
+
return dataset_names_dict
|
641 |
+
|
642 |
+
|
643 |
+
def load_multiple_datasets(
|
644 |
+
accelerator: Accelerator,
|
645 |
+
dataset_names: Union[List, str],
|
646 |
+
dataset_config_names: Union[List, str],
|
647 |
+
metadata_dataset_names: Optional[str] = None,
|
648 |
+
splits: Optional[Union[List, str]] = None,
|
649 |
+
label_column_names: Optional[List] = None,
|
650 |
+
stopping_strategy: Optional[str] = "first_exhausted",
|
651 |
+
dataset_samples: Optional[Union[List, np.array]] = None,
|
652 |
+
streaming: Optional[bool] = False,
|
653 |
+
seed: Optional[int] = None,
|
654 |
+
id_column_name: Optional[str] = None,
|
655 |
+
columns_to_keep: Optional[Set[str]] = None,
|
656 |
+
prompt_column_name: Optional[str] = None,
|
657 |
+
sampling_rate: Optional[int] = None,
|
658 |
+
audio_column_name: Optional[str] = None,
|
659 |
+
**kwargs,
|
660 |
+
) -> Union[Dataset, IterableDataset]:
|
661 |
+
dataset_names_dict = convert_dataset_str_to_list(
|
662 |
+
dataset_names, dataset_config_names, metadata_dataset_names, splits, label_column_names, dataset_samples
|
663 |
+
)
|
664 |
+
|
665 |
+
if dataset_samples is not None:
|
666 |
+
dataset_samples = [ds_dict["samples"] for ds_dict in dataset_names_dict]
|
667 |
+
probabilities = np.array(dataset_samples) / np.sum(dataset_samples)
|
668 |
+
else:
|
669 |
+
probabilities = None
|
670 |
+
|
671 |
+
all_datasets = []
|
672 |
+
# iterate over the datasets we want to interleave
|
673 |
+
for dataset_dict in tqdm(dataset_names_dict, desc="Combining datasets..."):
|
674 |
+
with accelerator.main_process_first():
|
675 |
+
dataset = load_dataset(
|
676 |
+
dataset_dict["name"],
|
677 |
+
dataset_dict["config"],
|
678 |
+
split=dataset_dict["split"],
|
679 |
+
streaming=streaming,
|
680 |
+
**kwargs,
|
681 |
+
)
|
682 |
+
dataset_features = dataset.features.keys()
|
683 |
+
|
684 |
+
if sampling_rate is not None and audio_column_name is not None:
|
685 |
+
# resample target audio
|
686 |
+
dataset = dataset.cast_column(audio_column_name, datasets.features.Audio(sampling_rate=sampling_rate))
|
687 |
+
|
688 |
+
metadata_dataset_name = dataset_dict["metadata_dataset_name"]
|
689 |
+
if metadata_dataset_name is not None:
|
690 |
+
logger.info(
|
691 |
+
f'Merging {dataset_dict["name"]} - {dataset_dict["split"]} with {metadata_dataset_name} - {dataset_dict["split"]}'
|
692 |
+
)
|
693 |
+
metadata_dataset = load_dataset(
|
694 |
+
metadata_dataset_name,
|
695 |
+
dataset_dict["config"],
|
696 |
+
split=dataset_dict["split"],
|
697 |
+
streaming=streaming,
|
698 |
+
**kwargs,
|
699 |
+
)
|
700 |
+
|
701 |
+
# TODO(YL): I forgot to create unique ids for MLS english.
|
702 |
+
# To iterate faster, I bypass the original id check and do another one. - Done once because assuming it won't change next time
|
703 |
+
# if dataset_dict["name"] == "parler-tts/mls_eng_10k":
|
704 |
+
# def concat_ids(book_id, speaker_id, begin_time):
|
705 |
+
# return {"id": f"{book_id}_{speaker_id}_{str(begin_time).replace('.', '_')}"}
|
706 |
+
# dataset = dataset.map(concat_ids, input_columns=["book_id", "speaker_id", "begin_time"], num_proc=24)
|
707 |
+
# metadata_dataset = metadata_dataset.map(concat_ids, input_columns=["book_id", "speaker_id", "begin_time"], num_proc=24)
|
708 |
+
# metadata_dataset = metadata_dataset.rename_column(id_column_name, f"metadata_{id_column_name}")
|
709 |
+
|
710 |
+
if dataset_dict["name"] != "parler-tts/mls_eng_10k":
|
711 |
+
if id_column_name is not None and id_column_name not in dataset.column_names:
|
712 |
+
raise ValueError(
|
713 |
+
f"id_column_name={id_column_name} but has not been found in the dataset columns"
|
714 |
+
f"- one of {', '.join(list(dataset.column_names))}."
|
715 |
+
)
|
716 |
+
if id_column_name is not None and id_column_name not in metadata_dataset.column_names:
|
717 |
+
raise ValueError(
|
718 |
+
f"id_column_name={id_column_name} but has not been found in the metadata dataset columns"
|
719 |
+
f"- one of {', '.join(list(metadata_dataset.column_names))}."
|
720 |
+
)
|
721 |
+
elif id_column_name is not None:
|
722 |
+
metadata_dataset = metadata_dataset.rename_column(id_column_name, f"metadata_{id_column_name}")
|
723 |
+
|
724 |
+
metadata_columns_to_remove = set(metadata_dataset.column_names).intersection(set(dataset.column_names))
|
725 |
+
|
726 |
+
if prompt_column_name is not None:
|
727 |
+
# We might have applied some transformations to the prompts (e.g punctuation restoration)
|
728 |
+
# so we make sure to remove it from the original dataset
|
729 |
+
if prompt_column_name in dataset.column_names:
|
730 |
+
logger.info(
|
731 |
+
f"REMOVE {prompt_column_name} from dataset {dataset_dict['name']} - dataset_dict['split']"
|
732 |
+
)
|
733 |
+
dataset.remove_columns(prompt_column_name)
|
734 |
+
|
735 |
+
metadata_columns_to_remove = set(metadata_dataset.column_names).intersection(set(dataset.column_names))
|
736 |
+
metadata_dataset = metadata_dataset.remove_columns(metadata_columns_to_remove)
|
737 |
+
|
738 |
+
dataset = concatenate_datasets([dataset, metadata_dataset], axis=1)
|
739 |
+
|
740 |
+
if id_column_name is not None and dataset_dict["name"] != "parler-tts/mls_eng_10k":
|
741 |
+
if (
|
742 |
+
len(
|
743 |
+
dataset.filter(
|
744 |
+
lambda id1, id2: id1 != id2,
|
745 |
+
input_columns=[id_column_name, f"metadata_{id_column_name}"],
|
746 |
+
)
|
747 |
+
)
|
748 |
+
!= 0
|
749 |
+
):
|
750 |
+
raise ValueError(
|
751 |
+
f"Concatenate didn't work. Some ids don't correspond on dataset {dataset_dict['name']}"
|
752 |
+
)
|
753 |
+
|
754 |
+
dataset_features = dataset.features.keys()
|
755 |
+
|
756 |
+
if columns_to_keep is not None:
|
757 |
+
dataset = dataset.remove_columns(set(dataset_features - columns_to_keep))
|
758 |
+
all_datasets.append(dataset)
|
759 |
+
|
760 |
+
if len(all_datasets) == 1:
|
761 |
+
# we have a single dataset so just return it as is
|
762 |
+
return all_datasets[0]
|
763 |
+
|
764 |
+
if streaming:
|
765 |
+
interleaved_dataset = interleave_datasets(
|
766 |
+
all_datasets,
|
767 |
+
stopping_strategy=stopping_strategy,
|
768 |
+
probabilities=probabilities,
|
769 |
+
seed=seed,
|
770 |
+
)
|
771 |
+
else:
|
772 |
+
with accelerator.main_process_first():
|
773 |
+
interleaved_dataset = concatenate_datasets(all_datasets)
|
774 |
+
|
775 |
+
return interleaved_dataset
|
776 |
+
|
777 |
+
|
778 |
+
def main():
|
779 |
+
# See all possible arguments in src/transformers/training_args.py
|
780 |
+
# or by passing the --help flag to this script.
|
781 |
+
# We now keep distinct sets of args, for a cleaner separation of concerns.
|
782 |
+
|
783 |
+
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, ParlerTTSTrainingArguments))
|
784 |
+
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
|
785 |
+
# If we pass only one argument to the script and it's the path to a json file,
|
786 |
+
# let's parse it to get our arguments.
|
787 |
+
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
|
788 |
+
else:
|
789 |
+
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
|
790 |
+
|
791 |
+
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
|
792 |
+
# information sent is the one passed as arguments along with your Python/PyTorch versions.
|
793 |
+
send_example_telemetry("run_parler_tts", model_args, data_args)
|
794 |
+
|
795 |
+
if training_args.dtype == "float16":
|
796 |
+
mixed_precision = "fp16"
|
797 |
+
elif training_args.dtype == "bfloat16":
|
798 |
+
mixed_precision = "bf16"
|
799 |
+
else:
|
800 |
+
mixed_precision = "no"
|
801 |
+
|
802 |
+
if data_args.pad_to_max_length and (
|
803 |
+
data_args.max_duration_in_seconds is None
|
804 |
+
or data_args.max_prompt_token_length is None
|
805 |
+
or data_args.max_description_token_length is None
|
806 |
+
):
|
807 |
+
raise ValueError(
|
808 |
+
"`pad_to_max_length` is `True` but one of the following parameters has not been set: `max_duration_in_seconds`, `max_prompt_token_length`, `max_description_token_length`"
|
809 |
+
)
|
810 |
+
|
811 |
+
padding = "max_length" if data_args.pad_to_max_length else "longest"
|
812 |
+
|
813 |
+
####### A. Preparation
|
814 |
+
kwargs_handlers = [InitProcessGroupKwargs(timeout=timedelta(minutes=60))]
|
815 |
+
if training_args.torch_compile:
|
816 |
+
# TODO(YL): add more compile modes?
|
817 |
+
kwargs_handlers.append(TorchDynamoPlugin(backend="inductor", mode="default")) # reduce-overhead
|
818 |
+
|
819 |
+
accelerator = Accelerator(
|
820 |
+
gradient_accumulation_steps=training_args.gradient_accumulation_steps,
|
821 |
+
mixed_precision=mixed_precision,
|
822 |
+
log_with=training_args.report_to,
|
823 |
+
project_dir=training_args.output_dir,
|
824 |
+
kwargs_handlers=kwargs_handlers,
|
825 |
+
)
|
826 |
+
|
827 |
+
accelerator.init_trackers(
|
828 |
+
project_name=data_args.wandb_project,
|
829 |
+
config={
|
830 |
+
"learning_rate": training_args.learning_rate,
|
831 |
+
"model_name_or_path": model_args.model_name_or_path,
|
832 |
+
"num_train_epochs": training_args.num_train_epochs,
|
833 |
+
"gradient_accumulation_steps": training_args.gradient_accumulation_steps,
|
834 |
+
"per_device_train_batch_size": training_args.per_device_train_batch_size,
|
835 |
+
"global_batch_size": training_args.per_device_train_batch_size * accelerator.num_processes,
|
836 |
+
"mixed_precision": mixed_precision,
|
837 |
+
"lr_scheduler_type": training_args.lr_scheduler_type,
|
838 |
+
"warmup_steps": training_args.warmup_steps,
|
839 |
+
"freeze_text_encoder": model_args.freeze_text_encoder,
|
840 |
+
"max_duration_in_seconds": data_args.max_duration_in_seconds,
|
841 |
+
"weight_decay": training_args.weight_decay,
|
842 |
+
"adam_beta1": training_args.adam_beta1,
|
843 |
+
"adam_beta2": training_args.adam_beta2,
|
844 |
+
"temperature": model_args.temperature,
|
845 |
+
},
|
846 |
+
)
|
847 |
+
|
848 |
+
# Detecting last checkpoint and eventually continue from last checkpoint
|
849 |
+
last_checkpoint = None
|
850 |
+
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
|
851 |
+
last_checkpoint = get_last_checkpoint(training_args.output_dir)
|
852 |
+
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
|
853 |
+
raise ValueError(
|
854 |
+
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
|
855 |
+
"Use --overwrite_output_dir to overcome."
|
856 |
+
)
|
857 |
+
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
|
858 |
+
logger.info(
|
859 |
+
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
|
860 |
+
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
|
861 |
+
)
|
862 |
+
|
863 |
+
# Setup logging
|
864 |
+
logging.basicConfig(
|
865 |
+
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
866 |
+
datefmt="%m/%d/%Y %H:%M:%S",
|
867 |
+
handlers=[logging.StreamHandler(sys.stdout)],
|
868 |
+
)
|
869 |
+
logger.setLevel(logging.INFO if accelerator.is_main_process else logging.WARN)
|
870 |
+
|
871 |
+
# Log a small summary on each proces
|
872 |
+
logger.warning(
|
873 |
+
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
|
874 |
+
f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
|
875 |
+
)
|
876 |
+
|
877 |
+
# Set the verbosity to info of the Transformers logger (on main process only)
|
878 |
+
if accelerator.is_local_main_process:
|
879 |
+
datasets.utils.logging.set_verbosity_warning()
|
880 |
+
transformers.utils.logging.set_verbosity_info()
|
881 |
+
else:
|
882 |
+
datasets.utils.logging.set_verbosity_error()
|
883 |
+
transformers.utils.logging.set_verbosity_error()
|
884 |
+
|
885 |
+
logger.info("Training/evaluation parameters %s", training_args)
|
886 |
+
|
887 |
+
# Set seed before initializing model.
|
888 |
+
set_seed(training_args.seed)
|
889 |
+
num_workers = data_args.preprocessing_num_workers
|
890 |
+
|
891 |
+
# 1. First, lett's instantiate the feature extractor, tokenizers and model
|
892 |
+
# Note for distributed training, the .from_pretrained methods guarantee that only
|
893 |
+
# one local process can concurrently download model & vocab.
|
894 |
+
|
895 |
+
# load feature extractor
|
896 |
+
feature_extractor = AutoFeatureExtractor.from_pretrained(
|
897 |
+
model_args.feature_extractor_name or model_args.model_name_or_path,
|
898 |
+
cache_dir=model_args.cache_dir,
|
899 |
+
token=data_args.token,
|
900 |
+
trust_remote_code=data_args.trust_remote_code,
|
901 |
+
)
|
902 |
+
sampling_rate = feature_extractor.sampling_rate
|
903 |
+
|
904 |
+
# load prompt tokenizer
|
905 |
+
prompt_tokenizer = AutoTokenizer.from_pretrained(
|
906 |
+
model_args.prompt_tokenizer_name or model_args.description_tokenizer_name or model_args.model_name_or_path,
|
907 |
+
cache_dir=model_args.cache_dir,
|
908 |
+
token=data_args.token,
|
909 |
+
trust_remote_code=data_args.trust_remote_code,
|
910 |
+
use_fast=model_args.use_fast_tokenizer,
|
911 |
+
padding_side="left", # prompt has to be padded on the left bc it's preprend to codebooks hidden states
|
912 |
+
)
|
913 |
+
|
914 |
+
# load description tokenizer
|
915 |
+
description_tokenizer = AutoTokenizer.from_pretrained(
|
916 |
+
model_args.description_tokenizer_name or model_args.model_name_or_path,
|
917 |
+
cache_dir=model_args.cache_dir,
|
918 |
+
token=data_args.token,
|
919 |
+
trust_remote_code=data_args.trust_remote_code,
|
920 |
+
use_fast=model_args.use_fast_tokenizer,
|
921 |
+
)
|
922 |
+
|
923 |
+
if model_args.use_fast_tokenizer:
|
924 |
+
logger.warning(
|
925 |
+
"Disabling fast tokenizer warning: https://github.com/huggingface/transformers/blob/main/src/transformers/tokenization_utils_base.py#L3231-L3235"
|
926 |
+
)
|
927 |
+
prompt_tokenizer.deprecation_warnings["Asking-to-pad-a-fast-tokenizer"] = True
|
928 |
+
description_tokenizer.deprecation_warnings["Asking-to-pad-a-fast-tokenizer"] = True
|
929 |
+
|
930 |
+
# 2. Now, let's load the dataset
|
931 |
+
|
932 |
+
if data_args.save_to_disk is not None:
|
933 |
+
os.makedirs(data_args.save_to_disk, exist_ok=True)
|
934 |
+
|
935 |
+
# assume that the dataset has been saved to `save_to_disk` if the latter is not empty
|
936 |
+
dataset_was_precomputed = len(os.listdir(data_args.save_to_disk)) > 0
|
937 |
+
if dataset_was_precomputed:
|
938 |
+
vectorized_datasets = datasets.load_from_disk(data_args.save_to_disk)
|
939 |
+
else:
|
940 |
+
raw_datasets = DatasetDict()
|
941 |
+
|
942 |
+
columns_to_keep = {
|
943 |
+
"target_audio_column_name": data_args.target_audio_column_name,
|
944 |
+
"prompt_column_name": data_args.prompt_column_name,
|
945 |
+
}
|
946 |
+
if data_args.description_column_name is not None:
|
947 |
+
columns_to_keep["description_column_name"] = data_args.description_column_name
|
948 |
+
|
949 |
+
if training_args.do_train:
|
950 |
+
raw_datasets["train"] = load_multiple_datasets(
|
951 |
+
accelerator,
|
952 |
+
data_args.train_dataset_name,
|
953 |
+
data_args.train_dataset_config_name,
|
954 |
+
metadata_dataset_names=data_args.train_metadata_dataset_name,
|
955 |
+
splits=data_args.train_split_name,
|
956 |
+
dataset_samples=data_args.train_dataset_samples,
|
957 |
+
seed=training_args.seed,
|
958 |
+
cache_dir=model_args.cache_dir,
|
959 |
+
num_proc=data_args.preprocessing_num_workers,
|
960 |
+
id_column_name=data_args.id_column_name,
|
961 |
+
columns_to_keep=columns_to_keep.values(),
|
962 |
+
prompt_column_name=data_args.prompt_column_name,
|
963 |
+
audio_column_name=data_args.target_audio_column_name,
|
964 |
+
sampling_rate=sampling_rate,
|
965 |
+
# streaming=data_args.streaming, TODO(SG): optionally enable streaming mode
|
966 |
+
)
|
967 |
+
|
968 |
+
for key in columns_to_keep:
|
969 |
+
if columns_to_keep[key] not in raw_datasets["train"].column_names:
|
970 |
+
raise ValueError(
|
971 |
+
f"--{key} '{columns_to_keep[key]}' not found in dataset '{data_args.train_dataset_name}'."
|
972 |
+
f" Make sure to set `--{key}` to the correct audio column - one of"
|
973 |
+
f" {', '.join(raw_datasets['train'].column_names)}."
|
974 |
+
)
|
975 |
+
|
976 |
+
if data_args.max_train_samples is not None:
|
977 |
+
raw_datasets["train"] = raw_datasets["train"].select(range(data_args.max_train_samples))
|
978 |
+
|
979 |
+
if training_args.do_eval:
|
980 |
+
raw_datasets["eval"] = load_multiple_datasets(
|
981 |
+
accelerator,
|
982 |
+
data_args.eval_dataset_name if data_args.eval_dataset_name else data_args.train_dataset_name,
|
983 |
+
data_args.eval_dataset_config_name
|
984 |
+
if data_args.eval_dataset_config_name
|
985 |
+
else data_args.train_dataset_config_name,
|
986 |
+
metadata_dataset_names=data_args.eval_metadata_dataset_name,
|
987 |
+
splits=data_args.eval_split_name,
|
988 |
+
cache_dir=model_args.cache_dir,
|
989 |
+
num_proc=data_args.preprocessing_num_workers,
|
990 |
+
id_column_name=data_args.id_column_name,
|
991 |
+
columns_to_keep=columns_to_keep.values(),
|
992 |
+
prompt_column_name=data_args.prompt_column_name,
|
993 |
+
audio_column_name=data_args.target_audio_column_name,
|
994 |
+
sampling_rate=sampling_rate,
|
995 |
+
# streaming=data_args.streaming, TODO(SG): optionally enable streaming mode
|
996 |
+
)
|
997 |
+
|
998 |
+
if data_args.max_eval_samples is not None:
|
999 |
+
raw_datasets["eval"] = (
|
1000 |
+
raw_datasets["eval"].shuffle(seed=training_args.seed).select(range(data_args.max_eval_samples))
|
1001 |
+
)
|
1002 |
+
|
1003 |
+
# 3. Next, let's load the config.
|
1004 |
+
config = ParlerTTSConfig.from_pretrained(
|
1005 |
+
model_args.model_name_or_path,
|
1006 |
+
cache_dir=model_args.cache_dir,
|
1007 |
+
token=data_args.token,
|
1008 |
+
trust_remote_code=data_args.trust_remote_code,
|
1009 |
+
)
|
1010 |
+
|
1011 |
+
# update pad token id and decoder_start_token_id
|
1012 |
+
config.update(
|
1013 |
+
{
|
1014 |
+
"pad_token_id": model_args.pad_token_id if model_args.pad_token_id is not None else config.pad_token_id,
|
1015 |
+
"decoder_start_token_id": (
|
1016 |
+
model_args.decoder_start_token_id
|
1017 |
+
if model_args.decoder_start_token_id is not None
|
1018 |
+
else config.decoder_start_token_id
|
1019 |
+
),
|
1020 |
+
}
|
1021 |
+
)
|
1022 |
+
|
1023 |
+
# create model
|
1024 |
+
model = ParlerTTSForConditionalGeneration.from_pretrained(
|
1025 |
+
model_args.model_name_or_path,
|
1026 |
+
cache_dir=model_args.cache_dir,
|
1027 |
+
config=config,
|
1028 |
+
token=data_args.token,
|
1029 |
+
trust_remote_code=data_args.trust_remote_code,
|
1030 |
+
)
|
1031 |
+
|
1032 |
+
# enable gradient checkpointing if necessary
|
1033 |
+
if training_args.gradient_checkpointing:
|
1034 |
+
model.gradient_checkpointing_enable()
|
1035 |
+
|
1036 |
+
# 4. Now we preprocess the datasets including loading the audio, resampling and normalization
|
1037 |
+
# Thankfully, `datasets` takes care of automatically loading and resampling the audio,
|
1038 |
+
# so that we just need to set the correct target sampling rate and normalize the input
|
1039 |
+
# via the `feature_extractor`
|
1040 |
+
|
1041 |
+
# derive max & min input length for sample rate & max duration
|
1042 |
+
sampling_rate = feature_extractor.sampling_rate
|
1043 |
+
max_target_length = data_args.max_duration_in_seconds * sampling_rate
|
1044 |
+
min_target_length = data_args.min_duration_in_seconds * sampling_rate
|
1045 |
+
target_audio_column_name = data_args.target_audio_column_name
|
1046 |
+
description_column_name = data_args.description_column_name
|
1047 |
+
prompt_column_name = data_args.prompt_column_name
|
1048 |
+
feature_extractor_input_name = feature_extractor.model_input_names[0]
|
1049 |
+
audio_encoder_pad_token_id = config.decoder.pad_token_id
|
1050 |
+
audio_encoder_eos_token_id = config.decoder.eos_token_id
|
1051 |
+
audio_encoder_bos_token_id = model.generation_config.decoder_start_token_id
|
1052 |
+
max_length = model.generation_config.max_length
|
1053 |
+
num_codebooks = model.decoder.config.num_codebooks
|
1054 |
+
bandwidth = model_args.bandwidth
|
1055 |
+
|
1056 |
+
# Freeze Encoders
|
1057 |
+
model.freeze_encoders(model_args.freeze_text_encoder)
|
1058 |
+
|
1059 |
+
# Test all gather - used for warmout and avoiding timeout
|
1060 |
+
test_tensor = torch.tensor([accelerator.process_index], device=accelerator.device)
|
1061 |
+
gathered_tensor = accelerator.gather(test_tensor)
|
1062 |
+
print("gathered_tensor", gathered_tensor)
|
1063 |
+
accelerator.wait_for_everyone()
|
1064 |
+
|
1065 |
+
if not dataset_was_precomputed:
|
1066 |
+
# Filter on text length
|
1067 |
+
if description_column_name is not None and data_args.max_text_length is not None:
|
1068 |
+
with accelerator.main_process_first():
|
1069 |
+
# filter description that is shorter than max_text_length
|
1070 |
+
raw_datasets = raw_datasets.filter(
|
1071 |
+
lambda x: len(x) < data_args.max_text_length,
|
1072 |
+
num_proc=num_workers,
|
1073 |
+
input_columns=[description_column_name],
|
1074 |
+
)
|
1075 |
+
|
1076 |
+
# Preprocessing the dataset.
|
1077 |
+
# We need to tokenize the texts.
|
1078 |
+
def pass_through_processors(description, prompt):
|
1079 |
+
batch = {}
|
1080 |
+
|
1081 |
+
batch["input_ids"] = description_tokenizer(description.strip())["input_ids"]
|
1082 |
+
batch["prompt_input_ids"] = prompt_tokenizer(prompt.strip())["input_ids"]
|
1083 |
+
|
1084 |
+
return batch
|
1085 |
+
|
1086 |
+
with accelerator.main_process_first():
|
1087 |
+
# this is a trick to avoid to rewrite the entire audio column which takes ages
|
1088 |
+
vectorized_datasets = raw_datasets.map(
|
1089 |
+
pass_through_processors,
|
1090 |
+
remove_columns=next(iter(raw_datasets.values())).column_names,
|
1091 |
+
input_columns=[description_column_name, prompt_column_name],
|
1092 |
+
num_proc=num_workers,
|
1093 |
+
desc="preprocess datasets",
|
1094 |
+
)
|
1095 |
+
|
1096 |
+
# We use Accelerate to perform distributed inference
|
1097 |
+
# T5 doesn't support fp16
|
1098 |
+
autocast_kwargs = AutocastKwargs(enabled=(mixed_precision != "fp16"))
|
1099 |
+
|
1100 |
+
# Now we encode the audio labels with encodec.
|
1101 |
+
####### B. Encode audio
|
1102 |
+
|
1103 |
+
logger.info("*** Encode target audio with encodec ***")
|
1104 |
+
|
1105 |
+
# no need to prepare audio_decoder because used for inference without mixed precision
|
1106 |
+
# see: https://huggingface.co/docs/accelerate/main/en/package_reference/accelerator#accelerate.Accelerator.prepare
|
1107 |
+
if training_args.torch_compile:
|
1108 |
+
audio_decoder = accelerator.prepare_model(model.audio_encoder, evaluation_mode=True)
|
1109 |
+
else:
|
1110 |
+
audio_decoder = model.audio_encoder
|
1111 |
+
|
1112 |
+
encoder_data_collator = DataCollatorEncodecWithPadding(
|
1113 |
+
feature_extractor,
|
1114 |
+
audio_column_name=target_audio_column_name,
|
1115 |
+
feature_extractor_input_name=feature_extractor_input_name,
|
1116 |
+
max_length=max_target_length,
|
1117 |
+
padding=padding,
|
1118 |
+
)
|
1119 |
+
|
1120 |
+
def apply_audio_decoder(batch):
|
1121 |
+
len_audio = batch.pop("len_audio")
|
1122 |
+
audio_decoder.to(batch["input_values"].device).eval()
|
1123 |
+
with torch.no_grad():
|
1124 |
+
labels = audio_decoder.encode(**batch, bandwidth=bandwidth)["audio_codes"]
|
1125 |
+
output = {}
|
1126 |
+
output["len_audio"] = len_audio
|
1127 |
+
# (1, bsz, codebooks, seq_len) -> (bsz, seq_len, codebooks)
|
1128 |
+
output["labels"] = labels.squeeze(0).transpose(1, 2)
|
1129 |
+
output["ratio"] = torch.ones_like(len_audio) * labels.shape[-1] / len_audio.max()
|
1130 |
+
return output
|
1131 |
+
|
1132 |
+
for split in vectorized_datasets:
|
1133 |
+
data_loader = DataLoader(
|
1134 |
+
raw_datasets[split],
|
1135 |
+
batch_size=training_args.audio_encoder_per_device_batch_size,
|
1136 |
+
collate_fn=encoder_data_collator,
|
1137 |
+
num_workers=training_args.dataloader_num_workers,
|
1138 |
+
pin_memory=True,
|
1139 |
+
)
|
1140 |
+
data_loader = accelerator.prepare(data_loader)
|
1141 |
+
|
1142 |
+
all_generated_labels = []
|
1143 |
+
all_lens = []
|
1144 |
+
for batch in tqdm(data_loader, disable=not accelerator.is_local_main_process):
|
1145 |
+
generate_labels = apply_audio_decoder(batch)
|
1146 |
+
generate_labels = accelerator.pad_across_processes(generate_labels, dim=1, pad_index=0)
|
1147 |
+
generate_labels = accelerator.gather_for_metrics(generate_labels)
|
1148 |
+
|
1149 |
+
if accelerator.is_main_process:
|
1150 |
+
lab = generate_labels["labels"].cpu().transpose(1, 2).to(torch.int16)
|
1151 |
+
rat = generate_labels["ratio"].cpu().squeeze()
|
1152 |
+
lens = generate_labels["len_audio"].cpu().squeeze()
|
1153 |
+
lab = [l[:, : int(ratio * length)] for (l, ratio, length) in zip(lab, rat, lens)]
|
1154 |
+
|
1155 |
+
all_generated_labels.extend(lab)
|
1156 |
+
all_lens.extend(lens)
|
1157 |
+
|
1158 |
+
# (1, codebooks, seq_len) where seq_len=1
|
1159 |
+
bos_labels = torch.ones((1, num_codebooks, 1)) * audio_encoder_bos_token_id
|
1160 |
+
|
1161 |
+
if accelerator.is_main_process:
|
1162 |
+
tmp_labels = Dataset.from_dict({"labels": all_generated_labels, "target_length": all_lens})
|
1163 |
+
tmp_labels.save_to_disk(
|
1164 |
+
os.path.join(data_args.temporary_save_to_disk, split),
|
1165 |
+
num_proc=1 if split == "eval" else data_args.preprocessing_num_workers,
|
1166 |
+
)
|
1167 |
+
accelerator.wait_for_everyone()
|
1168 |
+
del all_generated_labels
|
1169 |
+
|
1170 |
+
tmp_labels = datasets.load_from_disk(os.path.join(data_args.temporary_save_to_disk, split))
|
1171 |
+
with accelerator.main_process_first():
|
1172 |
+
vectorized_datasets[split] = concatenate_datasets([vectorized_datasets[split], tmp_labels], axis=1)
|
1173 |
+
|
1174 |
+
def postprocess_dataset(labels):
|
1175 |
+
# (1, codebooks, seq_len)
|
1176 |
+
labels = torch.tensor(labels).unsqueeze(0)
|
1177 |
+
# add bos
|
1178 |
+
labels = torch.cat([bos_labels, labels], dim=-1)
|
1179 |
+
|
1180 |
+
labels, delay_pattern_mask = build_delay_pattern_mask(
|
1181 |
+
labels,
|
1182 |
+
bos_token_id=audio_encoder_bos_token_id,
|
1183 |
+
pad_token_id=audio_encoder_eos_token_id,
|
1184 |
+
max_length=labels.shape[-1] + num_codebooks,
|
1185 |
+
num_codebooks=num_codebooks,
|
1186 |
+
)
|
1187 |
+
|
1188 |
+
# the first ids of the delay pattern mask are precisely labels, we use the rest of the labels mask
|
1189 |
+
# to take care of EOS
|
1190 |
+
# we want labels to look like this:
|
1191 |
+
# - [B, a, b, E, E, E, E]
|
1192 |
+
# - [B, B, c, d, E, E, E]
|
1193 |
+
# - [B, B, B, e, f, E, E]
|
1194 |
+
# - [B, B, B, B, g, h, E]
|
1195 |
+
labels = torch.where(delay_pattern_mask == -1, audio_encoder_eos_token_id, delay_pattern_mask)
|
1196 |
+
|
1197 |
+
# the first timestamp is associated to a row full of BOS, let's get rid of it
|
1198 |
+
# we also remove the last timestampts (full of PAD)
|
1199 |
+
output = {"labels": labels[:, 1:]}
|
1200 |
+
return output
|
1201 |
+
|
1202 |
+
with accelerator.main_process_first():
|
1203 |
+
vectorized_datasets[split] = vectorized_datasets[split].map(
|
1204 |
+
postprocess_dataset,
|
1205 |
+
num_proc=data_args.preprocessing_num_workers, # this one is resource consuming if many processor.
|
1206 |
+
input_columns=["labels"],
|
1207 |
+
desc="Postprocessing labeling",
|
1208 |
+
)
|
1209 |
+
|
1210 |
+
accelerator.free_memory()
|
1211 |
+
del generate_labels, all_lens
|
1212 |
+
|
1213 |
+
with accelerator.main_process_first():
|
1214 |
+
# NOTE: filtering is done at the end because in the `datasets` library, caching audio files is done after most operations
|
1215 |
+
# caching audio files is time and disk-space consuming, so we want to avoid it at all costs, especially for large (>1Kh) audio datasets.
|
1216 |
+
# That's also why we avoid to concat the processed datasets (vectorized_datasets) with the audio column present in raw_datasets.
|
1217 |
+
|
1218 |
+
def is_audio_in_length_range(length):
|
1219 |
+
return length > min_target_length and length < max_target_length
|
1220 |
+
|
1221 |
+
# filter data that is shorter than min_target_length
|
1222 |
+
vectorized_datasets = vectorized_datasets.filter(
|
1223 |
+
is_audio_in_length_range,
|
1224 |
+
num_proc=num_workers,
|
1225 |
+
input_columns=["target_length"],
|
1226 |
+
)
|
1227 |
+
|
1228 |
+
if description_column_name is not None and data_args.max_description_token_length is not None:
|
1229 |
+
with accelerator.main_process_first():
|
1230 |
+
# filter description that is shorter than max_text_length
|
1231 |
+
vectorized_datasets = vectorized_datasets.filter(
|
1232 |
+
lambda x: len(x) < data_args.max_description_token_length,
|
1233 |
+
num_proc=num_workers,
|
1234 |
+
input_columns=["input_ids"],
|
1235 |
+
)
|
1236 |
+
|
1237 |
+
if data_args.max_prompt_token_length is not None:
|
1238 |
+
with accelerator.main_process_first():
|
1239 |
+
# filter description that is shorter than max_text_length
|
1240 |
+
vectorized_datasets = vectorized_datasets.filter(
|
1241 |
+
lambda x: len(x) < data_args.max_prompt_token_length,
|
1242 |
+
num_proc=num_workers,
|
1243 |
+
input_columns=["prompt_input_ids"],
|
1244 |
+
)
|
1245 |
+
|
1246 |
+
if data_args.save_to_disk is not None and not dataset_was_precomputed:
|
1247 |
+
if accelerator.is_main_process:
|
1248 |
+
vectorized_datasets.save_to_disk(
|
1249 |
+
data_args.save_to_disk,
|
1250 |
+
num_proc=min(data_args.preprocessing_num_workers, len(vectorized_datasets["eval"]) - 1),
|
1251 |
+
)
|
1252 |
+
logger.info(f"Dataset saved at {data_args.save_to_disk}")
|
1253 |
+
|
1254 |
+
audio_max_length = None
|
1255 |
+
if training_args.torch_compile:
|
1256 |
+
audio_max_length = max(vectorized_datasets["train"]["target_length"])
|
1257 |
+
with accelerator.main_process_first():
|
1258 |
+
max_sample = vectorized_datasets["train"].filter(
|
1259 |
+
lambda x: x == audio_max_length,
|
1260 |
+
num_proc=num_workers,
|
1261 |
+
input_columns=["target_length"],
|
1262 |
+
)
|
1263 |
+
audio_max_length = torch.tensor(max_sample[0]["labels"]).shape[1]
|
1264 |
+
|
1265 |
+
# for large datasets it is advised to run the preprocessing on a
|
1266 |
+
# single machine first with ``args.preprocessing_only`` since there will mostly likely
|
1267 |
+
# be a timeout when running the script in distributed mode.
|
1268 |
+
# In a second step ``args.preprocessing_only`` can then be set to `False` to load the
|
1269 |
+
# cached dataset
|
1270 |
+
if data_args.preprocessing_only and data_args.save_to_disk is None:
|
1271 |
+
raise ValueError(
|
1272 |
+
"`preprocessing_only=True` but `save_to_disk` is not set. The latter should indicates where to save the dataset locally."
|
1273 |
+
)
|
1274 |
+
elif data_args.preprocessing_only:
|
1275 |
+
logger.info(f"Data preprocessing finished. Files save at {data_args.save_to_disk}")
|
1276 |
+
return
|
1277 |
+
|
1278 |
+
# 6. Next, we can prepare the training.
|
1279 |
+
|
1280 |
+
# Let's use word CLAP similary and WER metrics as our evaluation metrics,
|
1281 |
+
|
1282 |
+
# Define evaluation metrics during training, *i.e.* CLAP similarity
|
1283 |
+
clap = AutoModel.from_pretrained(model_args.clap_model_name_or_path)
|
1284 |
+
clap_processor = AutoProcessor.from_pretrained(model_args.clap_model_name_or_path)
|
1285 |
+
metric = evaluate.load("wer")
|
1286 |
+
|
1287 |
+
def clap_similarity(texts, audios, device):
|
1288 |
+
clap_inputs = clap_processor(text=texts, audios=audios, padding=True, return_tensors="pt").to(device)
|
1289 |
+
clap.to(device)
|
1290 |
+
with torch.no_grad():
|
1291 |
+
text_features = clap.get_text_features(
|
1292 |
+
clap_inputs["input_ids"], attention_mask=clap_inputs.get("attention_mask", None)
|
1293 |
+
)
|
1294 |
+
audio_features = clap.get_audio_features(clap_inputs["input_features"])
|
1295 |
+
|
1296 |
+
cosine_sim = torch.nn.functional.cosine_similarity(audio_features, text_features, dim=1, eps=1e-8)
|
1297 |
+
|
1298 |
+
clap.to("cpu")
|
1299 |
+
clap_inputs.to("cpu")
|
1300 |
+
return cosine_sim.mean().to("cpu")
|
1301 |
+
|
1302 |
+
def wer(prompts, audios, device):
|
1303 |
+
asr_pipeline = pipeline(model=model_args.asr_model_name_or_path, device=device)
|
1304 |
+
transcriptions = asr_pipeline(
|
1305 |
+
[{"raw": audio, "sampling_rate": sampling_rate} for audio in audios],
|
1306 |
+
batch_size=int(training_args.per_device_eval_batch_size),
|
1307 |
+
)
|
1308 |
+
|
1309 |
+
word_error = 100 * metric.compute(
|
1310 |
+
predictions=[t["text"].lower() for t in transcriptions], references=[t.lower() for t in prompts]
|
1311 |
+
)
|
1312 |
+
|
1313 |
+
return word_error, [t["text"] for t in transcriptions]
|
1314 |
+
|
1315 |
+
eval_methods = {"clap": clap_similarity, "wer": wer}
|
1316 |
+
|
1317 |
+
def compute_metrics(audios, descriptions, prompts, device="cpu"):
|
1318 |
+
input_ids = descriptions
|
1319 |
+
texts = description_tokenizer.batch_decode(input_ids, skip_special_tokens=True)
|
1320 |
+
prompts = prompt_tokenizer.batch_decode(prompts, skip_special_tokens=True)
|
1321 |
+
audios = [a.cpu().numpy() for a in audios]
|
1322 |
+
results = {"clap": eval_methods["clap"](texts, audios, device)}
|
1323 |
+
word_error, transcriptions = eval_methods["wer"](prompts, audios, device)
|
1324 |
+
results["wer"] = word_error
|
1325 |
+
|
1326 |
+
return results, texts, prompts, audios, transcriptions
|
1327 |
+
|
1328 |
+
# Define Training Schedule
|
1329 |
+
# Store some constants
|
1330 |
+
per_device_train_batch_size = int(training_args.per_device_train_batch_size)
|
1331 |
+
train_batch_size = per_device_train_batch_size * accelerator.num_processes
|
1332 |
+
gradient_accumulation_steps = int(training_args.gradient_accumulation_steps)
|
1333 |
+
per_device_eval_batch_size = int(training_args.per_device_eval_batch_size)
|
1334 |
+
|
1335 |
+
if training_args.max_steps < 0:
|
1336 |
+
num_epochs = int(training_args.num_train_epochs)
|
1337 |
+
steps_per_epoch = len(vectorized_datasets["train"]) // (train_batch_size * gradient_accumulation_steps)
|
1338 |
+
total_train_steps = steps_per_epoch * num_epochs
|
1339 |
+
elif training_args.max_steps > 0:
|
1340 |
+
logger.info("max_steps is given, it will override any value given in num_train_epochs")
|
1341 |
+
total_train_steps = int(training_args.max_steps)
|
1342 |
+
# Setting a very large number of epochs so we go as many times as necessary over the iterator.
|
1343 |
+
num_epochs = sys.maxsize
|
1344 |
+
steps_per_epoch = total_train_steps
|
1345 |
+
|
1346 |
+
if training_args.evaluation_strategy == "epoch":
|
1347 |
+
eval_steps = steps_per_epoch
|
1348 |
+
elif training_args.eval_steps is None:
|
1349 |
+
logger.info(f"eval_steps is not set, evaluating at the end of each epoch")
|
1350 |
+
eval_steps = steps_per_epoch
|
1351 |
+
else:
|
1352 |
+
eval_steps = training_args.eval_steps
|
1353 |
+
|
1354 |
+
if training_args.save_strategy == "epoch":
|
1355 |
+
save_steps = steps_per_epoch
|
1356 |
+
elif training_args.save_strategy == "steps":
|
1357 |
+
save_steps = training_args.save_steps
|
1358 |
+
else:
|
1359 |
+
save_steps = sys.maxsize
|
1360 |
+
|
1361 |
+
# T5 doesn't support fp16
|
1362 |
+
autocast_kwargs = AutocastKwargs(enabled=(mixed_precision != "fp16"))
|
1363 |
+
|
1364 |
+
# Define optimizer, LR scheduler, collator
|
1365 |
+
optimizer = torch.optim.AdamW(
|
1366 |
+
params=model.parameters(),
|
1367 |
+
lr=training_args.learning_rate,
|
1368 |
+
betas=(training_args.adam_beta1, training_args.adam_beta2),
|
1369 |
+
eps=training_args.adam_epsilon,
|
1370 |
+
weight_decay=training_args.weight_decay,
|
1371 |
+
)
|
1372 |
+
|
1373 |
+
# LR scheduler gets stepped by `num_processes` each time -> account for this in warmup / total steps
|
1374 |
+
lr_scheduler = get_scheduler(
|
1375 |
+
name=training_args.lr_scheduler_type,
|
1376 |
+
optimizer=optimizer,
|
1377 |
+
num_warmup_steps=training_args.get_warmup_steps(total_train_steps) * accelerator.num_processes,
|
1378 |
+
num_training_steps=total_train_steps * accelerator.num_processes,
|
1379 |
+
)
|
1380 |
+
|
1381 |
+
# Instantiate custom data collator
|
1382 |
+
data_collator = DataCollatorParlerTTSWithPadding(
|
1383 |
+
prompt_tokenizer=prompt_tokenizer,
|
1384 |
+
description_tokenizer=description_tokenizer,
|
1385 |
+
pad_to_multiple_of=data_args.pad_to_multiple_of,
|
1386 |
+
padding=padding,
|
1387 |
+
prompt_max_length=data_args.max_prompt_token_length,
|
1388 |
+
description_max_length=data_args.max_description_token_length,
|
1389 |
+
audio_max_length=audio_max_length,
|
1390 |
+
)
|
1391 |
+
|
1392 |
+
# Prepare everything with accelerate
|
1393 |
+
model, optimizer, lr_scheduler = accelerator.prepare(model, optimizer, lr_scheduler)
|
1394 |
+
|
1395 |
+
logger.info("***** Running training *****")
|
1396 |
+
logger.info(f" Num examples = {total_train_steps * train_batch_size * gradient_accumulation_steps}")
|
1397 |
+
logger.info(" Instantaneous batch size per device =" f" {per_device_train_batch_size}")
|
1398 |
+
logger.info(" Gradient accumulation steps =" f" {gradient_accumulation_steps}")
|
1399 |
+
logger.info(
|
1400 |
+
f" Total train batch size (w. parallel & distributed) = {train_batch_size * gradient_accumulation_steps}"
|
1401 |
+
)
|
1402 |
+
logger.info(f" Total optimization steps = {total_train_steps}")
|
1403 |
+
|
1404 |
+
# ======================== Training ================================
|
1405 |
+
train_time = 0
|
1406 |
+
train_start = time.time()
|
1407 |
+
steps_trained_progress_bar = tqdm(
|
1408 |
+
range(total_train_steps), desc="Train steps ... ", position=0, disable=not accelerator.is_local_main_process
|
1409 |
+
)
|
1410 |
+
continue_training = True
|
1411 |
+
epochs_trained = 0
|
1412 |
+
cur_step = 0
|
1413 |
+
|
1414 |
+
checkpoint = None
|
1415 |
+
if training_args.resume_from_checkpoint is not None:
|
1416 |
+
checkpoint = training_args.resume_from_checkpoint
|
1417 |
+
elif last_checkpoint is not None:
|
1418 |
+
checkpoint = last_checkpoint
|
1419 |
+
|
1420 |
+
if accelerator.is_main_process:
|
1421 |
+
if training_args.push_to_hub:
|
1422 |
+
# Retrieve of infer repo_name
|
1423 |
+
repo_name = training_args.hub_model_id
|
1424 |
+
if repo_name is None:
|
1425 |
+
repo_name = Path(training_args.output_dir).absolute().name
|
1426 |
+
# Create repo and retrieve repo_id
|
1427 |
+
repo_id = create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
|
1428 |
+
# Clone repo locally
|
1429 |
+
repo = Repository(training_args.output_dir, clone_from=repo_id, token=training_args.hub_token)
|
1430 |
+
|
1431 |
+
with open(os.path.join(training_args.output_dir, ".gitignore"), "w+") as gitignore:
|
1432 |
+
if "wandb" not in gitignore:
|
1433 |
+
gitignore.write("wandb\n")
|
1434 |
+
elif training_args.output_dir is not None:
|
1435 |
+
os.makedirs(training_args.output_dir, exist_ok=True)
|
1436 |
+
accelerator.wait_for_everyone()
|
1437 |
+
|
1438 |
+
# Now save everything to be able to create a single processor later
|
1439 |
+
# make sure all processes wait until data is saved
|
1440 |
+
with accelerator.main_process_first():
|
1441 |
+
# only the main process saves them
|
1442 |
+
if accelerator.is_main_process:
|
1443 |
+
# save feature extractor, tokenizer and config
|
1444 |
+
if (
|
1445 |
+
model_args.prompt_tokenizer_name is None
|
1446 |
+
and model_args.description_tokenizer_name
|
1447 |
+
or (model_args.prompt_tokenizer_name == model_args.description_tokenizer_name)
|
1448 |
+
):
|
1449 |
+
prompt_tokenizer.save_pretrained(training_args.output_dir)
|
1450 |
+
else:
|
1451 |
+
logger.warning(
|
1452 |
+
"Prompt tokenizer ('{model_args.prompt_tokenizer_name}') and description tokenizer ('{model_args.description_tokenizer_name}') are not the same. Saving only the prompt tokenizer."
|
1453 |
+
)
|
1454 |
+
prompt_tokenizer.save_pretrained(training_args.output_dir)
|
1455 |
+
|
1456 |
+
feature_extractor.save_pretrained(training_args.output_dir)
|
1457 |
+
config.save_pretrained(training_args.output_dir)
|
1458 |
+
|
1459 |
+
if checkpoint is not None:
|
1460 |
+
accelerator.load_state(checkpoint)
|
1461 |
+
# Find num steps and epoch from saved state string pattern
|
1462 |
+
pattern = r"checkpoint-(\d+)-epoch-(\d+)"
|
1463 |
+
match = re.search(pattern, checkpoint)
|
1464 |
+
cur_step = int(match.group(1))
|
1465 |
+
epochs_trained = int(match.group(2))
|
1466 |
+
|
1467 |
+
logger.info(" Continuing training from checkpoint, will skip to saved global_step")
|
1468 |
+
logger.info(f" Continuing training from epoch {epochs_trained}")
|
1469 |
+
logger.info(f" Continuing training from global step {cur_step}")
|
1470 |
+
|
1471 |
+
steps_trained_progress_bar.update(cur_step)
|
1472 |
+
|
1473 |
+
for epoch in range(0, epochs_trained):
|
1474 |
+
vectorized_datasets["train"] = vectorized_datasets["train"].shuffle(training_args.seed)
|
1475 |
+
|
1476 |
+
if training_args.max_steps < 0:
|
1477 |
+
# we know exactly the number of steps per epoch, so can skip through the required number of batches
|
1478 |
+
resume_step = (cur_step - epochs_trained * steps_per_epoch) * gradient_accumulation_steps
|
1479 |
+
else:
|
1480 |
+
# Currently we don't know how many steps we've taken in the current epoch
|
1481 |
+
# So we just shuffle the dataset one extra time and start from a fresh epoch
|
1482 |
+
# This is "good enough" for our purposes but not fully correct
|
1483 |
+
resume_step = None
|
1484 |
+
vectorized_datasets["train"] = vectorized_datasets["train"].shuffle(training_args.seed)
|
1485 |
+
else:
|
1486 |
+
resume_step = None
|
1487 |
+
|
1488 |
+
gen_kwargs = {
|
1489 |
+
"do_sample": model_args.do_sample,
|
1490 |
+
"temperature": model_args.temperature,
|
1491 |
+
"max_length": model_args.max_length,
|
1492 |
+
}
|
1493 |
+
|
1494 |
+
# Define gradient update step fn
|
1495 |
+
def train_step(
|
1496 |
+
batch,
|
1497 |
+
accelerator,
|
1498 |
+
autocast_kwargs,
|
1499 |
+
):
|
1500 |
+
model.train()
|
1501 |
+
|
1502 |
+
if mixed_precision == "fp16":
|
1503 |
+
# fp16 doesn't work with T5-like models
|
1504 |
+
with accelerator.autocast(autocast_handler=autocast_kwargs):
|
1505 |
+
if training_args.parallel_mode.value != "distributed":
|
1506 |
+
encoder_outputs = model.text_encoder(
|
1507 |
+
input_ids=batch.get("input_ids"), attention_mask=batch.get("attention_mask", None)
|
1508 |
+
)
|
1509 |
+
else:
|
1510 |
+
encoder_outputs = model.module.text_encoder(
|
1511 |
+
input_ids=batch.get("input_ids"), attention_mask=batch.get("attention_mask", None)
|
1512 |
+
)
|
1513 |
+
batch["encoder_outputs"] = encoder_outputs
|
1514 |
+
|
1515 |
+
outputs = model(**batch)
|
1516 |
+
# CE (data) loss
|
1517 |
+
ce_loss = outputs.loss
|
1518 |
+
|
1519 |
+
metrics = {"loss": ce_loss}
|
1520 |
+
return ce_loss, metrics
|
1521 |
+
|
1522 |
+
# Define eval fn
|
1523 |
+
def eval_step(
|
1524 |
+
batch,
|
1525 |
+
accelerator,
|
1526 |
+
autocast_kwargs,
|
1527 |
+
):
|
1528 |
+
eval_model = model if not training_args.torch_compile else model._orig_mod
|
1529 |
+
eval_model.eval()
|
1530 |
+
|
1531 |
+
if mixed_precision == "fp16":
|
1532 |
+
# fp16 doesn't work with T5-like models
|
1533 |
+
with accelerator.autocast(autocast_handler=autocast_kwargs):
|
1534 |
+
with torch.no_grad():
|
1535 |
+
if training_args.parallel_mode.value != "distributed" or training_args.torch_compile:
|
1536 |
+
encoder_outputs = eval_model.text_encoder(
|
1537 |
+
input_ids=batch.get("input_ids"), attention_mask=batch.get("attention_mask", None)
|
1538 |
+
)
|
1539 |
+
else:
|
1540 |
+
encoder_outputs = eval_model.module.text_encoder(
|
1541 |
+
input_ids=batch.get("input_ids"), attention_mask=batch.get("attention_mask", None)
|
1542 |
+
)
|
1543 |
+
batch["encoder_outputs"] = encoder_outputs
|
1544 |
+
|
1545 |
+
with torch.no_grad():
|
1546 |
+
outputs = eval_model(**batch)
|
1547 |
+
# CE (data) loss
|
1548 |
+
ce_loss = outputs.loss
|
1549 |
+
metrics = {"loss": ce_loss}
|
1550 |
+
return metrics
|
1551 |
+
|
1552 |
+
def generate_step(batch):
|
1553 |
+
batch.pop("decoder_attention_mask", None)
|
1554 |
+
eval_model = accelerator.unwrap_model(model, keep_fp32_wrapper=mixed_precision != "fp16").eval()
|
1555 |
+
if training_args.torch_compile:
|
1556 |
+
eval_model = model._orig_mod
|
1557 |
+
|
1558 |
+
output_audios = eval_model.generate(**batch, **gen_kwargs)
|
1559 |
+
output_audios = accelerator.pad_across_processes(output_audios, dim=1, pad_index=0)
|
1560 |
+
return output_audios
|
1561 |
+
|
1562 |
+
for epoch in range(epochs_trained, num_epochs):
|
1563 |
+
vectorized_datasets["train"] = vectorized_datasets["train"].shuffle(training_args.seed)
|
1564 |
+
sampler = None
|
1565 |
+
if training_args.group_by_length:
|
1566 |
+
sampler = LengthGroupedSampler(train_batch_size, lengths=vectorized_datasets["train"]["target_length"])
|
1567 |
+
train_dataloader = DataLoader(
|
1568 |
+
vectorized_datasets["train"],
|
1569 |
+
collate_fn=data_collator,
|
1570 |
+
batch_size=per_device_train_batch_size,
|
1571 |
+
sampler=sampler,
|
1572 |
+
num_workers=training_args.dataloader_num_workers,
|
1573 |
+
pin_memory=training_args.dataloader_pin_memory,
|
1574 |
+
)
|
1575 |
+
train_dataloader = accelerator.prepare(train_dataloader)
|
1576 |
+
if hasattr(train_dataloader, "dataset") and isinstance(train_dataloader.dataset, IterableDataset):
|
1577 |
+
train_dataloader.dataset.set_epoch(epoch)
|
1578 |
+
|
1579 |
+
if resume_step is not None:
|
1580 |
+
# Skip the first N batches in the dataloader when resuming from a checkpoint
|
1581 |
+
train_dataloader = accelerator.skip_first_batches(train_dataloader, resume_step)
|
1582 |
+
resume_step = None
|
1583 |
+
|
1584 |
+
for batch in train_dataloader:
|
1585 |
+
with accelerator.accumulate(model):
|
1586 |
+
loss, train_metric = train_step(batch, accelerator, autocast_kwargs)
|
1587 |
+
accelerator.backward(loss)
|
1588 |
+
if accelerator.sync_gradients:
|
1589 |
+
accelerator.clip_grad_norm_(model.parameters(), training_args.max_grad_norm)
|
1590 |
+
optimizer.step()
|
1591 |
+
lr_scheduler.step()
|
1592 |
+
optimizer.zero_grad()
|
1593 |
+
|
1594 |
+
# Check if the accelerator has performed an optimization step behind the scenes
|
1595 |
+
if accelerator.sync_gradients:
|
1596 |
+
steps_trained_progress_bar.update(1)
|
1597 |
+
cur_step += 1
|
1598 |
+
|
1599 |
+
if cur_step % training_args.logging_steps == 0:
|
1600 |
+
steps_trained_progress_bar.write(
|
1601 |
+
f"Step... ({cur_step} / {total_train_steps} | Loss:"
|
1602 |
+
f" {train_metric['loss']}, Learning Rate:"
|
1603 |
+
f" {lr_scheduler.get_last_lr()[0]})"
|
1604 |
+
)
|
1605 |
+
log_metric(
|
1606 |
+
accelerator,
|
1607 |
+
metrics=train_metric,
|
1608 |
+
learning_rate=lr_scheduler.get_last_lr()[0],
|
1609 |
+
train_time=train_time + time.time() - train_start,
|
1610 |
+
step=cur_step,
|
1611 |
+
epoch=epoch + (cur_step - epoch * steps_per_epoch) / steps_per_epoch,
|
1612 |
+
prefix="train",
|
1613 |
+
)
|
1614 |
+
|
1615 |
+
# save checkpoint and weights after each save_steps and at the end of training
|
1616 |
+
if (cur_step % save_steps == 0) or cur_step == total_train_steps:
|
1617 |
+
intermediate_dir = os.path.join(training_args.output_dir, f"checkpoint-{cur_step}-epoch-{epoch}")
|
1618 |
+
# safe_serialization=False to avoid shared tensors saving issue (TODO(YL): it's a temporary fix)
|
1619 |
+
# https://github.com/huggingface/transformers/issues/27293#issuecomment-1872560074
|
1620 |
+
accelerator.save_state(output_dir=intermediate_dir, safe_serialization=False)
|
1621 |
+
accelerator.wait_for_everyone()
|
1622 |
+
if accelerator.is_main_process:
|
1623 |
+
rotate_checkpoints(training_args.save_total_limit, output_dir=training_args.output_dir)
|
1624 |
+
|
1625 |
+
if cur_step == total_train_steps:
|
1626 |
+
# un-wrap student model for save
|
1627 |
+
unwrapped_model = accelerator.unwrap_model(model)
|
1628 |
+
unwrapped_model.save_pretrained(training_args.output_dir)
|
1629 |
+
|
1630 |
+
if training_args.push_to_hub:
|
1631 |
+
repo.push_to_hub(
|
1632 |
+
commit_message=f"Saving train state of step {cur_step}",
|
1633 |
+
blocking=False,
|
1634 |
+
)
|
1635 |
+
|
1636 |
+
if training_args.do_eval and (cur_step % eval_steps == 0 or cur_step == total_train_steps):
|
1637 |
+
train_time += time.time() - train_start
|
1638 |
+
# ======================== Evaluating ==============================
|
1639 |
+
eval_metrics = []
|
1640 |
+
eval_preds = []
|
1641 |
+
eval_descriptions = []
|
1642 |
+
eval_prompts = []
|
1643 |
+
eval_start = time.time()
|
1644 |
+
|
1645 |
+
# release training input batch
|
1646 |
+
batch = release_memory(batch)
|
1647 |
+
|
1648 |
+
validation_dataloader = DataLoader(
|
1649 |
+
vectorized_datasets["eval"],
|
1650 |
+
collate_fn=data_collator,
|
1651 |
+
batch_size=per_device_eval_batch_size,
|
1652 |
+
drop_last=False,
|
1653 |
+
num_workers=training_args.dataloader_pin_memory,
|
1654 |
+
pin_memory=training_args.dataloader_pin_memory,
|
1655 |
+
)
|
1656 |
+
validation_dataloader = accelerator.prepare(validation_dataloader)
|
1657 |
+
|
1658 |
+
for batch in tqdm(
|
1659 |
+
validation_dataloader,
|
1660 |
+
desc="Evaluating - Inference ...",
|
1661 |
+
position=2,
|
1662 |
+
disable=not accelerator.is_local_main_process,
|
1663 |
+
):
|
1664 |
+
# Model forward
|
1665 |
+
eval_metric = eval_step(batch, accelerator, autocast_kwargs)
|
1666 |
+
eval_metric = accelerator.gather_for_metrics(eval_metric)
|
1667 |
+
eval_metrics.append(eval_metric)
|
1668 |
+
|
1669 |
+
if training_args.predict_with_generate:
|
1670 |
+
validation_dataloader = DataLoader(
|
1671 |
+
vectorized_datasets["eval"],
|
1672 |
+
collate_fn=data_collator,
|
1673 |
+
batch_size=per_device_eval_batch_size,
|
1674 |
+
drop_last=False,
|
1675 |
+
num_workers=training_args.dataloader_pin_memory,
|
1676 |
+
pin_memory=training_args.dataloader_pin_memory,
|
1677 |
+
)
|
1678 |
+
validation_dataloader = accelerator.prepare(validation_dataloader)
|
1679 |
+
# generation
|
1680 |
+
for batch in tqdm(
|
1681 |
+
validation_dataloader,
|
1682 |
+
desc="Evaluating - Generation ...",
|
1683 |
+
position=2,
|
1684 |
+
disable=not accelerator.is_local_main_process,
|
1685 |
+
):
|
1686 |
+
generated_audios = generate_step(batch)
|
1687 |
+
# Gather all predictions and targets
|
1688 |
+
generated_audios, input_ids, prompts = accelerator.pad_across_processes(
|
1689 |
+
(generated_audios, batch["input_ids"], batch["prompt_input_ids"]), dim=1, pad_index=0
|
1690 |
+
)
|
1691 |
+
generated_audios, input_ids, prompts = accelerator.gather_for_metrics(
|
1692 |
+
(generated_audios, input_ids, prompts)
|
1693 |
+
)
|
1694 |
+
eval_preds.extend(generated_audios.to("cpu"))
|
1695 |
+
eval_descriptions.extend(input_ids.to("cpu"))
|
1696 |
+
eval_prompts.extend(prompts.to("cpu"))
|
1697 |
+
|
1698 |
+
eval_time = time.time() - eval_start
|
1699 |
+
# normalize eval metrics
|
1700 |
+
eval_metrics = {
|
1701 |
+
key: torch.mean(torch.cat([d[key].unsqueeze(0) for d in eval_metrics]))
|
1702 |
+
for key in eval_metrics[0]
|
1703 |
+
}
|
1704 |
+
|
1705 |
+
# compute metrics
|
1706 |
+
metrics_desc = ""
|
1707 |
+
if training_args.predict_with_generate:
|
1708 |
+
metric_values, pred_descriptions, pred_prompts, audios, transcriptions = compute_metrics(
|
1709 |
+
eval_preds, eval_descriptions, eval_prompts, accelerator.device
|
1710 |
+
)
|
1711 |
+
eval_metrics.update(metric_values)
|
1712 |
+
metrics_desc = " ".join([f"Eval {key}: {value} |" for key, value in metric_values.items()])
|
1713 |
+
if "wandb" in training_args.report_to:
|
1714 |
+
log_pred(
|
1715 |
+
accelerator,
|
1716 |
+
pred_descriptions,
|
1717 |
+
pred_prompts,
|
1718 |
+
transcriptions,
|
1719 |
+
audios,
|
1720 |
+
sampling_rate=sampling_rate,
|
1721 |
+
step=cur_step,
|
1722 |
+
prefix="eval",
|
1723 |
+
)
|
1724 |
+
|
1725 |
+
# Print metrics and update progress bar
|
1726 |
+
steps_trained_progress_bar.write(
|
1727 |
+
f"Eval results for step ({cur_step} / {total_train_steps} | Eval Loss: {eval_metrics['loss']} |"
|
1728 |
+
f" {metrics_desc})"
|
1729 |
+
)
|
1730 |
+
|
1731 |
+
log_metric(
|
1732 |
+
accelerator,
|
1733 |
+
metrics=eval_metrics,
|
1734 |
+
train_time=eval_time,
|
1735 |
+
step=cur_step,
|
1736 |
+
epoch=epoch + (cur_step - epoch * steps_per_epoch) / steps_per_epoch,
|
1737 |
+
prefix="eval",
|
1738 |
+
)
|
1739 |
+
|
1740 |
+
# release eval batch and relax metrics
|
1741 |
+
eval_metrics = []
|
1742 |
+
eval_preds = []
|
1743 |
+
eval_descriptions = []
|
1744 |
+
eval_prompts = []
|
1745 |
+
batch = release_memory(batch)
|
1746 |
+
|
1747 |
+
# flush the train metrics
|
1748 |
+
train_start = time.time()
|
1749 |
+
|
1750 |
+
# break condition
|
1751 |
+
if cur_step == total_train_steps:
|
1752 |
+
continue_training = False
|
1753 |
+
break
|
1754 |
+
|
1755 |
+
if not continue_training:
|
1756 |
+
break
|
1757 |
+
|
1758 |
+
accelerator.end_training()
|
1759 |
+
|
1760 |
+
|
1761 |
+
if __name__ == "__main__":
|
1762 |
+
set_start_method("spawn")
|
1763 |
+
main()
|
special_tokens_map.json
ADDED
@@ -0,0 +1,125 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"additional_special_tokens": [
|
3 |
+
"<extra_id_0>",
|
4 |
+
"<extra_id_1>",
|
5 |
+
"<extra_id_2>",
|
6 |
+
"<extra_id_3>",
|
7 |
+
"<extra_id_4>",
|
8 |
+
"<extra_id_5>",
|
9 |
+
"<extra_id_6>",
|
10 |
+
"<extra_id_7>",
|
11 |
+
"<extra_id_8>",
|
12 |
+
"<extra_id_9>",
|
13 |
+
"<extra_id_10>",
|
14 |
+
"<extra_id_11>",
|
15 |
+
"<extra_id_12>",
|
16 |
+
"<extra_id_13>",
|
17 |
+
"<extra_id_14>",
|
18 |
+
"<extra_id_15>",
|
19 |
+
"<extra_id_16>",
|
20 |
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"<extra_id_17>",
|
21 |
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"<extra_id_18>",
|
22 |
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|
23 |
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"<extra_id_20>",
|
24 |
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"<extra_id_21>",
|
25 |
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"<extra_id_22>",
|
26 |
+
"<extra_id_23>",
|
27 |
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"<extra_id_24>",
|
28 |
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|
29 |
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|
30 |
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|
31 |
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|
32 |
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|
33 |
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|
34 |
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|
35 |
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|
36 |
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|
37 |
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
72 |
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|
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|
74 |
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|
75 |
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|
76 |
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|
77 |
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|
78 |
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|
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|
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|
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|
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|
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|
84 |
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|
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|
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|
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|
88 |
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|
89 |
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|
90 |
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|
91 |
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|
92 |
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|
93 |
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|
94 |
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|
95 |
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|
96 |
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|
97 |
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|
98 |
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|
99 |
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|
100 |
+
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|
101 |
+
"<extra_id_98>",
|
102 |
+
"<extra_id_99>"
|
103 |
+
],
|
104 |
+
"eos_token": {
|
105 |
+
"content": "</s>",
|
106 |
+
"lstrip": false,
|
107 |
+
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|
108 |
+
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|
109 |
+
"single_word": false
|
110 |
+
},
|
111 |
+
"pad_token": {
|
112 |
+
"content": "<pad>",
|
113 |
+
"lstrip": false,
|
114 |
+
"normalized": false,
|
115 |
+
"rstrip": false,
|
116 |
+
"single_word": false
|
117 |
+
},
|
118 |
+
"unk_token": {
|
119 |
+
"content": "<unk>",
|
120 |
+
"lstrip": false,
|
121 |
+
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|
122 |
+
"rstrip": false,
|
123 |
+
"single_word": false
|
124 |
+
}
|
125 |
+
}
|
spiece.model
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d60acb128cf7b7f2536e8f38a5b18a05535c9e14c7a355904270e15b0945ea86
|
3 |
+
size 791656
|
tokenizer.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
tokenizer_config.json
ADDED
@@ -0,0 +1,941 @@
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|
1 |
+
{
|
2 |
+
"add_prefix_space": true,
|
3 |
+
"added_tokens_decoder": {
|
4 |
+
"0": {
|
5 |
+
"content": "<pad>",
|
6 |
+
"lstrip": false,
|
7 |
+
"normalized": false,
|
8 |
+
"rstrip": false,
|
9 |
+
"single_word": false,
|
10 |
+
"special": true
|
11 |
+
},
|
12 |
+
"1": {
|
13 |
+
"content": "</s>",
|
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925 |
+
"<extra_id_95>",
|
926 |
+
"<extra_id_96>",
|
927 |
+
"<extra_id_97>",
|
928 |
+
"<extra_id_98>",
|
929 |
+
"<extra_id_99>"
|
930 |
+
],
|
931 |
+
"clean_up_tokenization_spaces": true,
|
932 |
+
"eos_token": "</s>",
|
933 |
+
"extra_ids": 100,
|
934 |
+
"legacy": true,
|
935 |
+
"model_max_length": 512,
|
936 |
+
"pad_token": "<pad>",
|
937 |
+
"padding_side": "left",
|
938 |
+
"sp_model_kwargs": {},
|
939 |
+
"tokenizer_class": "T5Tokenizer",
|
940 |
+
"unk_token": "<unk>"
|
941 |
+
}
|