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import logging
import os
import shutil
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import torch
from accelerate import Accelerator
from accelerate.logging import get_logger
from datasets import DatasetDict, load_dataset
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
HfArgumentParser,
)
logger = get_logger(__name__, log_level="INFO")
@dataclass
class ModelArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
model_name_or_path: str = field(
metadata={"help": "The name of the model to use (via the transformers library) for the prompt annotation."},
)
per_device_eval_batch_size: int = field(
metadata={"help": "The per-device batch size to use for inference."},
)
model_variant: str = field(
default=None,
metadata={"help": "If specified load weights from `variant` filename, *e.g.* pytorch_model.<variant>.bin. "},
)
model_revision: str = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
)
torch_dtype: Optional[str] = field(
default="float16",
metadata={
"help": (
"Floating-point format in which the model weights should be initialized"
" and the computations run. Choose one of `[float32, float16, bfloat16]`."
)
},
)
attn_implementation: Optional[str] = field(
default="sdpa",
metadata={"help": "Which attn type to use: ['eager', 'sdpa', 'flash_attention_2']"},
)
load_in_8bit: Optional[bool] = field(
default=False, metadata={"help": "Whether to use 8-bit precision for inference."}
)
load_in_4bit: Optional[bool] = field(
default=False, metadata={"help": "Whether to use 4-bit precision for inference."}
)
bnb_4bit_quant_type: Optional[str] = field(
default="nf4", metadata={"help": "precise the quantization type (fp4 or nf4)"}
)
use_bnb_nested_quant: Optional[bool] = field(default=False, metadata={"help": "use nested quantization"})
trust_remote_code: Optional[bool] = field(
default=False,
metadata={
"help": (
"Whether or not to allow for custom models defined on the Hub in their own modeling files. This option "
"should only be set to `True` for repositories you trust and in which you have read the code, as it will "
"execute code present on the Hub on your local machine."
)
},
)
use_fast_tokenizer: Optional[bool] = field(
default=True, metadata={"help": "Use fast tokenizer for encoding/decoding input ids"}
)
token: Optional[bool] = field(
default=True,
metadata={
"help": "Whether or not to use an authentication token when loading/uploading from the Hugging Face Hub"
},
)
do_sample: Optional[bool] = field(default=True, metadata={"help": "Whether to use sampling mode for generation"})
temperature: Optional[float] = field(default=0.6, metadata={"help": "Temperature for sampling-based generation"})
max_new_tokens: Optional[int] = field(
default=256, metadata={"help": "Maximum number of new tokens during generation"}
)
compile_generate: Optional[bool] = field(
default=False, metadata={"help": "Whether to compile the forward pass (not sampling) in generate."}
)
@dataclass
class DataArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
output_dir: str = field(
metadata={
"help": "Where to save the processed dataset to disk. If unspecified, uses a 'pretty' version of the "
"original dataset name. E.g. 'facebook/voxpopuli' will be saved under 'voxpopuli'."
},
)
dataset_name: str = field(
default=None,
metadata={"help": "The name of the dataset to use (via the datasets library)"},
)
dataset_config_name: Optional[str] = field(
default=None,
metadata={"help": "The configuration name of the dataset to use (via the datasets library)."},
)
dataset_split_name: Optional[str] = field(
default=None,
metadata={"help": "The split name of the dataset to use (via the datasets library)."},
)
dataset_cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Path to cache directory for saving and loading datasets"},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={"help": "Maximum number of samples for generation - use for debugging purposes."},
)
overwrite_cache: bool = field(
default=False,
metadata={"help": "Overwrite the cached training and evaluation sets"},
)
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={"help": "The number of processes to use for the preprocessing."},
)
dataloader_num_workers: Optional[int] = field(
default=0,
metadata={"help": "The number of processes to use for the dataloader."},
)
push_to_hub: Optional[bool] = field(
default=False,
metadata={"help": "Whether or not to push the processed dataset to the Hub."},
)
hub_dataset_id: Optional[str] = field(
default=None,
metadata={"help": "Repository namespace if pushing to the Hugging Face Hub."},
)
overwrite_output_dir: Optional[bool] = field(
default=False,
metadata={"help": "Overwrite the content of the output directory each time the script is run."},
)
def __post_init__(self):
if self.push_to_hub and self.hub_dataset_id is None:
raise ValueError("You must specify the `hub_dataset_id` when setting `--push_to_hub=True`")
def get_quantization_config(model_args: ModelArguments) -> Union[BitsAndBytesConfig, None]:
if model_args.load_in_4bit:
compute_dtype = torch.float16
if model_args.torch_dtype not in {"auto", None}:
compute_dtype = getattr(torch, model_args.torch_dtype)
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=compute_dtype,
bnb_4bit_quant_type=model_args.bnb_4bit_quant_type,
bnb_4bit_use_double_quant=model_args.use_bnb_nested_quant,
)
elif model_args.load_in_8bit:
quantization_config = BitsAndBytesConfig(
load_in_8bit=True,
)
else:
quantization_config = None
return quantization_config
def get_current_device() -> int:
"""Get the current device. For GPU we return the local process index to enable multiple GPU training."""
return Accelerator().local_process_index if torch.cuda.is_available() else "cpu"
def get_kbit_device_map() -> Union[Dict[str, int], None]:
"""Useful for running inference with quantized models by setting `device_map=get_peft_device_map()`"""
return {"": get_current_device()} if torch.cuda.is_available() else None
@dataclass
class DataCollatorWithPadding:
"""
Data collator that will dynamically pad the inputs received to the longest sequence in the batch.
"""
tokenizer: Any
def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
# split inputs and labels since they have to be of different lengths and need
# different padding methods
input_ids = {"input_ids": [feature["input_ids"] for feature in features]}
batch = self.tokenizer.pad(input_ids, return_tensors="pt", padding="longest", return_attention_mask=True)
return batch
# TODO(SG): add accent keyword
PROMPT = """You will be given six descriptive keywords related to an audio sample of a person's speech. These keywords include:
1. The gender (e.g., male, female)
2. The level of reverberation (e.g., very roomy sounding, quite roomy sounding, slightly roomy sounding, moderate reverberation, slightly confined sounding, quite confined sounding, very confined sounding)
3. The amount of noise the sample (e.g., very noisy, quite noisy, slightly noisy, moderate ambient sound, slightly clear, quite clear, very clear)
4. The tone of the speaker's voice (e.g., very monotone, quite monotone, slightly monotone, moderate intonation, slightly expressive, quite expressive, very expressive)
5. The pace of the speaker's delivery (e.g., very slowly, quite slowly, slightly slowly, moderate speed, slightly fast, quite fast, very fast)
6. The pitch of the speaker's voice (e.g., very low pitch, quite low pitch, slightly low pitch, moderate pitch, slightly high pitch, quite high pitch, very high pitch)
Your task is to create a text description using these keywords that accurately describes the speech sample while ensuring the description remains grammatically correct and easy to understand. You should rearrange the keyword order as necessary, and substitute synonymous terms where appropriate. If the amount of noise is 'very noisy' and the level of reverberation is 'very roomy sounding', include terms like 'very bad recording' in the description. Likewise, if the amount of noise is 'very clear' and the level of reverberation is 'very confined sounding', include terms like 'very good recording' in the description. Otherwise, do not add extra details beyond what has been provided, and only return the generated description.
For example, given the following keywords: 'female', 'slightly roomy sounding', 'slightly noisy', 'very expressive', 'slightly low pitch', 'very slowly', a valid description would be: 'a woman with a deep voice speaks slowly but has an animated delivery in an echoey room with some background noise'.
For the keywords: '[gender]', '[reverberation]', '[noise]', '[speech_monotony]', '[pitch]', '[speaking_rate]', the corresponding description is:"
"""
def main():
# 1. Parse input arguments
parser = HfArgumentParser((ModelArguments, DataArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args = parser.parse_args_into_dataclasses()
# 2. Setup logging
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
accelerator = Accelerator()
if data_args.overwrite_output_dir and os.path.exists(data_args.output_dir) and os.path.isdir(data_args.output_dir):
logger.info("Cleaning output dir from previous run...")
shutil.rmtree(data_args.output_dir)
# 3. Load annotated dataset
logger.info("*** Load annotated dataset ***")
if data_args.dataset_split_name is not None:
raw_datasets = DatasetDict()
data_splits = data_args.dataset_split_name.split("+")
# load on a split-wise basis
for split in data_splits:
raw_datasets[split] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=split,
cache_dir=model_args.cache_dir,
token=model_args.token,
num_proc=data_args.preprocessing_num_workers,
)
else:
# load all splits for annotation
raw_datasets = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
cache_dir=model_args.cache_dir,
token=model_args.token,
num_proc=data_args.preprocessing_num_workers,
)
raw_datasets_features = set(raw_datasets[next(iter(raw_datasets))].features.keys())
if data_args.max_eval_samples is not None:
for split in raw_datasets:
raw_datasets[split] = raw_datasets[split].select(range(data_args.max_eval_samples))
# TODO(SG): add accent
EXPECTED_COLUMNS = {"gender", "pitch", "noise", "reverberation", "speech_monotony", "speaking_rate"}
if not EXPECTED_COLUMNS.issubset(raw_datasets_features):
missing_columns = EXPECTED_COLUMNS - raw_datasets_features
raise ValueError(
f"Missing columns {missing_columns} from the dataset features. Got dataset features {raw_datasets_features}"
)
# 4. Load pre-trained model
logger.info("*** Load pretrained model ***")
torch_dtype = (
model_args.torch_dtype if model_args.torch_dtype in ["auto", None] else getattr(torch, model_args.torch_dtype)
)
quantization_config = get_quantization_config(model_args)
model = AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
revision=model_args.model_revision,
variant=model_args.model_variant,
trust_remote_code=model_args.trust_remote_code,
attn_implementation=model_args.attn_implementation,
torch_dtype=torch_dtype,
device_map=get_kbit_device_map() if quantization_config is not None else None,
quantization_config=quantization_config,
low_cpu_mem_usage=True,
token=model_args.token,
).eval()
if model_args.compile_generate:
if not callable(getattr(model, "_setup_cache", None)):
raise ValueError(
f"Static k/v cache is not compatible with the model {model.__class__.__name__}. Set `--compile_generate=False"
"for dynamic k/v cache"
)
model.generation_config.cache_implementation = "static"
model._forward = model.forward
compiled_forward = torch.compile(model.forward)
def compiled(func, input_ids, **kwargs):
return func(input_ids, **kwargs)
def call(input_ids, **kwargs):
if input_ids.shape[-1] == 1:
return compiled(compiled_forward, input_ids, **kwargs)
return model._forward(input_ids, **kwargs)
model.forward = call
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
revision=model_args.model_revision,
trust_remote_code=model_args.trust_remote_code,
use_fast=model_args.use_fast_tokenizer,
padding_side="left",
)
if tokenizer.pad_token_id is None:
tokenizer.pad_token_id = tokenizer.bos_token_id
model.generation_config.pad_token_id = model.generation_config.eos_token_id
def prepare_dataset(sample):
sample_prompt = PROMPT
for key in EXPECTED_COLUMNS:
sample_prompt = sample_prompt.replace(f"[{key}]", sample[key])
sample_prompt = [{"role": "user", "content": sample_prompt}]
token_ids = tokenizer.apply_chat_template(sample_prompt)
sample["input_ids"] = token_ids
return sample
with accelerator.main_process_first():
vectorized_datasets = raw_datasets.map(
prepare_dataset, num_proc=data_args.preprocessing_num_workers, desc="Preparing prompts"
)
# Prepare everything with our `accelerator`
model = accelerator.prepare(model)
data_collator = DataCollatorWithPadding(tokenizer)
def generate_step(batch):
output_ids = accelerator.unwrap_model(model).generate(
batch["input_ids"],
attention_mask=batch["attention_mask"],
do_sample=model_args.do_sample,
temperature=model_args.temperature,
max_new_tokens=model_args.max_new_tokens,
)
output_ids = accelerator.pad_across_processes(output_ids, dim=1, pad_index=tokenizer.pad_token_id)
return output_ids
def postprocess_dataset(sample):
prompt_text = tokenizer.decode(sample["input_ids"], skip_special_tokens=True)
generated_text = tokenizer.decode(sample["generated_ids"], skip_special_tokens=True)
sample["text_description"] = generated_text[len(prompt_text) :]
return sample
for split in vectorized_datasets:
data_loader = DataLoader(
vectorized_datasets[split],
batch_size=model_args.per_device_eval_batch_size,
collate_fn=data_collator,
num_workers=data_args.dataloader_num_workers,
pin_memory=True,
)
data_loader = accelerator.prepare(data_loader)
all_generated_ids = []
for batch in tqdm(data_loader, disable=not accelerator.is_local_main_process):
generated_ids = generate_step(batch)
generated_ids = accelerator.gather_for_metrics(generated_ids)
all_generated_ids.extend(generated_ids.cpu().numpy())
vectorized_datasets[split] = vectorized_datasets[split].add_column("generated_ids", all_generated_ids)
if accelerator.is_main_process:
vectorized_datasets[split] = vectorized_datasets[split].map(
postprocess_dataset,
num_proc=data_args.preprocessing_num_workers,
desc="Postprocessing dataset",
remove_columns=["input_ids", "generated_ids"],
)
if accelerator.is_main_process:
vectorized_datasets.save_to_disk(data_args.output_dir)
if data_args.push_to_hub:
vectorized_datasets.push_to_hub(
data_args.hub_dataset_id,
config_name=data_args.dataset_config_name if data_args.dataset_config_name is not None else "default",
token=model_args.token,
)
accelerator.end_training()
if __name__ == "__main__":
main()
setup.py
View file @ a53577f2
# Copyright 2023 The HuggingFace Team. All rights reserved.
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
......@@ -12,16 +12,15 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import setuptools
_deps = [
"transformers>=4.34.0",
"datasets[audio]>=2.14.5",
"torch",
"sentencepiece",
"descript-audio-codec",
]
_extras_dev_deps = [
......@@ -30,13 +29,21 @@ _extras_dev_deps = [
"ruff>=0.0.241,<=0.0.259",
]
_extras_training_deps = [
"jiwer",
"wandb",
"accelerate",
"evaluate",
"datasets[audio]>=2.14.5",
]
here = os.path.abspath(os.path.dirname(__file__))
with open(os.path.join(here, "README.md"), encoding="utf-8") as f:
long_description = f.read()
# read version
with open(os.path.join(here, "stable_speech", "__init__.py"), encoding="utf-8") as f:
with open(os.path.join(here, "parler_tts", "__init__.py"), encoding="utf-8") as f:
for line in f:
if line.startswith("__version__"):
version = line.split("=")[1].strip().strip('"')
......@@ -45,14 +52,15 @@ with open(os.path.join(here, "stable_speech", "__init__.py"), encoding="utf-8")
raise RuntimeError("Unable to find version string.")
setuptools.setup(
name="stable_speech",
name="parler_tts",
version=version,
description="Toolkit for reproducing Stability AI's text-to-speech model.",
description="Toolkit for using and training Parler-TTS, a high-quality text-to-speech model.",
long_description=long_description,
long_description_content_type="text/markdown",
packages=setuptools.find_packages(),
install_requires=_deps,
extras_require={
"dev": [_extras_dev_deps],
"train": [_extras_training_deps],
},
)
training/README.md 0 → 100644
View file @ a53577f2
# Training Parler-TTS
**TL;DR:** After having followed the [installation steps](#requirements), you can reproduce the Parler-TTS v0.1 training recipe with the following command line:
```sh
accelerate launch ./training/run_parler_tts_training.py ./helpers/training_configs/starting_point_0.01.json
```
-------------
This sub-folder contains all the information to train or fine-tune your own Parler-TTS model. It consists of:
- [1. An introduction to the Parler-TTS architecture](#a-architecture)
- [2. First steps to get started](#b-getting-started)
- [3. Training guide](#c-training)
## 1. Architecture
At the moment, Parler-TTS architecture is a carbon copy of the [MusicGen architecture](https://huggingface.co/docs/transformers/v4.39.3/en/model_doc/musicgen#model-structure) and can be decomposed into three distinct stages:
1. Text encoder: maps the text descriptions to a sequence of hidden-state representations. Parler-TTS uses a frozen text encoder initialised entirely from Flan-T5
2. Parler-TTS decoder: a language model (LM) that auto-regressively generates audio tokens (or codes) conditional on the encoder hidden-state representations
3. Audio codec: used to recover the audio waveform from the audio tokens predicted by the decoder. We use the [DAC model](https://github.com/descriptinc/descript-audio-codec) from Descript, although other codec models, such as [EnCodec](https://huggingface.co/facebook/encodec_48khz), can also be used
Parler-TTS however introduces some small tweaks:
- The text **description** is passed through the text encoder and used in the cross-attention layers of the decoder.
- The text **prompt** is simply passed through an embedding layer and concatenated to the decoder input hidden states.
- The audio encoder used is [**DAC**](https://descript.notion.site/Descript-Audio-Codec-11389fce0ce2419891d6591a68f814d5) instead of [Encodec](https://github.com/facebookresearch/encodec), as it exhibits better quality.
## 2. Getting started
To get started, you need to follow a few steps:
1. Install the requirements.
2. Find or initialize the model you'll train on.
3. Find and/or annotate the dataset you'll train your model on.
### Requirements
The Parler-TTS code is written in [PyTorch](https://pytorch.org) and [Accelerate](https://huggingface.co/docs/accelerate/index). It uses some additional requirements, like [wandb](https://wandb.ai/), especially for logging and evaluation.
To install the package for training, you need to clone the repository from source...
```bash
git clone https://github.com/huggingface/parler-tts.git
cd parler-tts
```
... And then install the requirements:
```bash
pip install -e .[train]
```
Optionally, you can create a wandb account and login to it by following [this guide](https://docs.wandb.ai/quickstart). [`wandb`](https://docs.wandb.ai/) allows for better tracking of the experiments metrics and losses.
You also have the option to configure Accelerate by running the following command. Note that you should set the number of GPUs you wish to use for training, and also the data type (dtype) to your preferred dtype for training/inference (e.g. `bfloat16` on A100 GPUs, `float16` on V100 GPUs, etc.):
```bash
accelerate config
```
Lastly, you can link you Hugging Face account so that you can push model repositories on the Hub. This will allow you to save your trained models on the Hub so that you can share them with the community. Run the command:
```bash
git config --global credential.helper store
huggingface-cli login
```
And then enter an authentication token from https://huggingface.co/settings/tokens. Create a new token if you do not have one already. You should make sure that this token has "write" privileges.
### Initialize a model from scratch or use a pre-trained one.
Depending on your compute resources and your dataset, you need to choose between fine-tuning a pre-trained model and training a new model from scratch.
In that sense, we released a 300M checkpoint trained on 10.5K hours of annotated data under the repository id: [`parler-tts/parler_tts_300M_v0.1`](https://huggingface.co/parler-tts/parler_tts_300M_v0.1), that you can fine-tune for your own use-case.
You can also train you own model from scratch. You can find [here](/helpers/model_init_scripts/) examples on how to initialize a model from scratch. For example, you can initialize a dummy model with:
```sh
python helpers/model_init_scripts/init_dummy_model.py ./parler-tts-untrained-dummy --text_model "google-t5/t5-small" --audio_model "parler-tts/dac_44khZ_8kbps"
```
In the rest of this guide, and to reproduce the Parler-TTS v0.1 training recipe, we'll use a 300-M parameters that we'll initialize with:
```sh
python helpers/model_init_scripts/init_model_300M.py ./parler-tts-untrained-300M --text_model "google/flan-t5-base" --audio_model "parler-tts/dac_44khZ_8kbps"
```
### Create or find datasets
To train your own Parler-TTS, you need datasets with 3 main features:
- speech data
- text transcription of the speech data
- conditionning text description - that you can create using [Data-Speech](https://github.com/huggingface/dataspeech), a library that allows you to annotate the speaker and utterance characteristics with natural language description.
Note that we made the choice to use description of the main speech characteristics (speaker pitch, speaking rate, level of noise, etc.) but that you are free to use any handmade or generated text description that makes sense.
To train Parler-TTS v0.1, we used:
* The full [LibriTTS-R dataset](https://huggingface.co/datasets/blabble-io/libritts_r), a 1K hours high-quality speech dataset.
* A [10K hours subset](https://huggingface.co/datasets/parler-tts/mls_eng_10k) of [Multilingual LibriSpeech](https://huggingface.co/datasets/facebook/multilingual_librispeech).
Both datasets have been annotated using the [Data-Speech](https://github.com/huggingface/dataspeech) recipe, respectively [here](https://huggingface.co/datasets/parler-tts/libritts_r_tags_tagged_10k_generated) and [here](https://huggingface.co/datasets/parler-tts/mls-eng-10k-tags_tagged_10k_generated).
## 3. Training
The script [`run_parler_tts_training.py`](/training/run_parler_tts_training.py) is an end-to-end script that:
1. load dataset(s) and merge them to the annotation dataset(s) if necessary
2. pre-compute audio tokens
3. train Parler-TTS
To train Parler-TTS v0.1, we roughly used:
```sh
accelerate launch ./training/run_parler_tts_training.py \
--model_name_or_path "./parler-tts-untrained-300M/parler-tts-untrained-300M/" \
--feature_extractor_name "parler-tts/dac_44khZ_8kbps" \
--description_tokenizer_name "google/flan-t5-base" \
--prompt_tokenizer_name "google/flan-t5-base" \
--report_to "wandb" \
--overwrite_output_dir true \
--train_dataset_name "blabble-io/libritts_r+blabble-io/libritts_r+blabble-io/libritts_r+parler-tts/mls_eng_10k" \
--train_metadata_dataset_name "parler-tts/libritts_r_tags_tagged_10k_generated+parler-tts/libritts_r_tags_tagged_10k_generated+parler-tts/libritts_r_tags_tagged_10k_generated+parler-tts/mls-eng-10k-tags_tagged_10k_generated" \
--train_dataset_config_name "clean+clean+other+default" \
--train_split_name "train.clean.360+train.clean.100+train.other.500+train" \
--eval_dataset_name "blabble-io/libritts_r+parler-tts/mls_eng_10k" \
--eval_metadata_dataset_name "parler-tts/libritts_r_tags_tagged_10k_generated+parler-tts/mls-eng-10k-tags_tagged_10k_generated" \
--eval_dataset_config_name "other+default" \
--eval_split_name "test.other+test" \
--target_audio_column_name "audio" \
--description_column_name "text_description" \
--prompt_column_name "text" \
--max_duration_in_seconds 30 \
--min_duration_in_seconds 2.0 \
--max_text_length 400 \
--add_audio_samples_to_wandb true \
--id_column_name "id" \
--preprocessing_num_workers 8 \
--do_train true \
--num_train_epochs 40 \
--gradient_accumulation_steps 8 \
--gradient_checkpointing false \
--per_device_train_batch_size 3 \
--learning_rate 0.00095 \
--adam_beta1 0.9 \
--adam_beta2 0.99 \
--weight_decay 0.01 \
--lr_scheduler_type "constant_with_warmup" \
--warmup_steps 20000 \
--logging_steps 1000 \
--freeze_text_encoder true \
--do_eval true \
--predict_with_generate true \
--include_inputs_for_metrics true \
--evaluation_strategy steps \
--eval_steps 10000 \
--save_steps 10000 \
--per_device_eval_batch_size 12 \
--audio_encoder_per_device_batch_size 20 \
--dtype "bfloat16" \
--seed 456 \
--output_dir "./output_dir_training/" \
--temporary_save_to_disk "./audio_code_tmp/" \
--save_to_disk "./tmp_dataset_audio/" \
--max_eval_samples 96 \
--dataloader_num_workers 8 \
--group_by_length true
```
In particular, note how multiple training datasets, metadataset, configurations and splits can be loaded by separating the dataset arguments by + symbols:
```sh
"train_dataset_name": "blabble-io/libritts_r+blabble-io/libritts_r+blabble-io/libritts_r+parler-tts/mls_eng_10k",
"train_metadata_dataset_name": "parler-tts/libritts_r_tags_tagged_10k_generated+parler-tts/libritts_r_tags_tagged_10k_generated+parler-tts/libritts_r_tags_tagged_10k_generated+parler-tts/mls-eng-10k-tags_tagged_10k_generated",
"train_dataset_config_name": "clean+clean+other+default",
"train_split_name": "train.clean.360+train.clean.100+train.other.500+train",
```
Additionally, you can also write a JSON config file. Here, [starting_point_0.01.json](helpers/training_configs/starting_point_0.01.json) contains the exact same hyper-parameters than above and can be launched like that:
```sh
accelerate launch ./training/run_parler_tts_training.py ./helpers/training_configs/starting_point_0.01.json
```
Training logs will be reported to wandb, provided that you passed `--report_to "wandb"` to the arguments. An example of what a training log from the above training looks like can be found [here](https://wandb.ai/ylacombe/parler-tts-300M-punctuated/runs/q6h7hspc?nw=nwuserylacombe).
> [!TIP]
> Starting training a new model from scratch can easily be overwhelming, so here's what training looked like for v0.1: [logs](https://api.wandb.ai/links/ylacombe/ea449l81)
Scaling to multiple GPUs using [distributed data parallelism (DDP)](https://pytorch.org/tutorials/beginner/ddp_series_theory.html) is trivial: simply run `accelerate config` and select the multi-GPU option, specifying the IDs of the GPUs you wish to use. The above script can then be run using DDP with no code changes. In our case, we used a node of 8 H100 80GB to train Parler-TTS v0.1 for around 4 days.
There are a few other noteworthy arguments:
1. `train_metadata_dataset_name` and `eval_metadata_dataset_name` specify, if necessary, the names of the dataset(s) that contain(s) the conditionning text descriptions. For example, this [dataset resulting from the Data-Speech annotation process](https://huggingface.co/datasets/parler-tts/libritts_r_tags_tagged_10k_generated) is saved without the audio column, as it's costly to write and push audio data, so it needs to be concatenated back to the original LibriTTS-R dataset.
2. As noted above, the script pre-computes audio tokens as computing audio codes is costly and only needs to be done once, since we're freezing the audio encoder. `audio_encoder_per_device_batch_size` is used to precise the per devie batch size for this pre-processing step.
3. Additionnally, when scaling up the training data and iterating on the hyper-parameters or the model architecture, we might want to avoid recomputing the audio tokens at each training run. That's why we introduced two additional parameters, `save_to_disk` and `temporary_save_to_disk` that serves as temporary buffers to save intermediary datasets. Note that processed data is made of text and audio tokens which are much more memory efficient, so the additional required space is negligible.
4. `predict_with_generate` and `add_audio_samples_to_wandb` are required to store generated audios and to compute WER and CLAP similarity.
5. `freeze_text_encoder`: which allows to freeze the text encoder, to save compute resources.
And finally, two additional comments:
1. `lr_scheduler_stype`: defines the learning rate schedule, one of `constant_with_warmup` or `cosine`. When experimenting with a training set-up or training for very few epochs, using `constant_with_warmup` is typically beneficial, since the learning rate remains high over the short training run. When performing longer training runs, using a `cosine` schedule shoud give better results.
2. `dtype`: data type (dtype) in which the model computation should be performed. Note that this only controls the dtype of the computations (forward and backward pass), and not the dtype of the parameters or optimiser states.
> [!TIP]
> Fine-tuning is as easy as modifying `model_name_or_path` to a pre-trained model.
> For example: `--model_name_or_path parler-tts/parler_tts_300M_v0.1`.
training/run_parler_tts_training.py 0 → 100644
View file @ a53577f2
#!/usr/bin/env python
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Train Parler-TTS using 🤗 Accelerate"""
import logging
import os
import re
import sys
import shutil
import time
from multiprocess import set_start_method
from datetime import timedelta
import evaluate
from tqdm import tqdm
from pathlib import Path
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Union, Set
import datasets
import numpy as np
import torch
from torch.utils.data import DataLoader
from datasets import DatasetDict, load_dataset, Dataset, IterableDataset, interleave_datasets, concatenate_datasets
from huggingface_hub import Repository, create_repo
import transformers
from transformers import (
AutoFeatureExtractor,
AutoModel,
AutoProcessor,
AutoTokenizer,
HfArgumentParser,
Seq2SeqTrainingArguments,
)
from transformers.trainer_pt_utils import LengthGroupedSampler
from transformers import pipeline
from transformers.optimization import get_scheduler
from transformers.utils import send_example_telemetry
from transformers import AutoModel
from accelerate import Accelerator
from accelerate.utils import set_seed, AutocastKwargs, InitProcessGroupKwargs, TorchDynamoPlugin
from accelerate.utils.memory import release_memory
from parler_tts import (
ParlerTTSForConditionalGeneration,
ParlerTTSConfig,
build_delay_pattern_mask,
)
from wandb import Audio
logger = logging.getLogger(__name__)
def list_field(default=None, metadata=None):
return field(default_factory=lambda: default, metadata=metadata)
_RE_CHECKPOINT = re.compile(r"^checkpoint-(\d+)-epoch-(\d+)$")
def get_last_checkpoint(folder):
content = os.listdir(folder)
checkpoints = [
path
for path in content
if _RE_CHECKPOINT.search(path) is not None and os.path.isdir(os.path.join(folder, path))
]
if len(checkpoints) == 0:
return
return os.path.join(folder, max(checkpoints, key=lambda x: int(_RE_CHECKPOINT.search(x).groups()[0])))
def sorted_checkpoints(output_dir=None, checkpoint_prefix="checkpoint") -> List[str]:
"""Helper function to sort saved checkpoints from oldest to newest."""
ordering_and_checkpoint_path = []
glob_checkpoints = [str(x) for x in Path(output_dir).glob(f"{checkpoint_prefix}-*") if os.path.isdir(x)]
for path in glob_checkpoints:
regex_match = re.match(f".*{checkpoint_prefix}-([0-9]+)", path)
if regex_match is not None and regex_match.groups() is not None:
ordering_and_checkpoint_path.append((int(regex_match.groups()[0]), path))
checkpoints_sorted = sorted(ordering_and_checkpoint_path)
checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted]
return checkpoints_sorted
def rotate_checkpoints(save_total_limit=None, output_dir=None, checkpoint_prefix="checkpoint") -> None:
"""Helper function to delete old checkpoints."""
if save_total_limit is None or save_total_limit <= 0:
return
# Check if we should delete older checkpoint(s)
checkpoints_sorted = sorted_checkpoints(output_dir=output_dir, checkpoint_prefix=checkpoint_prefix)
if len(checkpoints_sorted) <= save_total_limit:
return
number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - save_total_limit)
checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete]
for checkpoint in checkpoints_to_be_deleted:
logger.info(f"Deleting older checkpoint [{checkpoint}] due to args.save_total_limit")
shutil.rmtree(checkpoint, ignore_errors=True)
def log_metric(
accelerator,
metrics: Dict,
train_time: float,
step: int,
epoch: int,
learning_rate: float = None,
prefix: str = "train",
):
"""Helper function to log all training/evaluation metrics with the correct prefixes and styling."""
log_metrics = {}
for k, v in metrics.items():
log_metrics[f"{prefix}/{k}"] = v
log_metrics[f"{prefix}/time"] = train_time
log_metrics[f"{prefix}/epoch"] = epoch
if learning_rate is not None:
log_metrics[f"{prefix}/learning_rate"] = learning_rate
accelerator.log(log_metrics, step=step)
def log_pred(
accelerator,
pred_descriptions: List[str],
pred_prompts: List[str],
transcriptions: List[str],
audios: List[torch.Tensor],
sampling_rate: int,
step: int,
prefix: str = "eval",
num_lines: int = 200000,
):
"""Helper function to log target/predicted transcriptions to weights and biases (wandb)."""
if accelerator.is_main_process:
wandb_tracker = accelerator.get_tracker("wandb")
# pretty name for current step: step 50000 -> step 50k
cur_step_pretty = f"{int(step // 1000)}k" if step > 1000 else step
prefix_pretty = prefix.replace("/", "-")
# convert str data to a wandb compatible format
str_data = [[pred_descriptions[i], pred_prompts[i], transcriptions[i]] for i in range(len(pred_descriptions))]
# log as a table with the appropriate headers
wandb_tracker.log_table(
table_name=f"predictions/{prefix_pretty}-step-{cur_step_pretty}",
columns=["Target descriptions", "Target prompts", "Predicted transcriptions"],
data=str_data[:num_lines],
step=step,
commit=False,
)
# wandb can only loads 100 audios per step
wandb_tracker.log(
{
"Speech samples": [
Audio(
audio,
caption=f"{pred_prompts[i]} --- DESCRIPTION: {pred_descriptions[i]}",
sample_rate=sampling_rate,
)
for (i, audio) in enumerate(audios[: min(len(audios), 100)])
]
},
step=step,
)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
feature_extractor_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained feature extractor name or path if not the same as model_name"}
)
description_tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained description tokenizer name or path if not the same as model_name"}
)
prompt_tokenizer_name: Optional[str] = field(
default=None,
metadata={"help": "Pretrained prompt tokenizer name or path if not the same as description_tokenizer_name"},
)
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
)
use_fast_tokenizer: bool = field(
default=True,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
model_revision: str = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
pad_token_id: int = field(
default=None,
metadata={"help": "If specified, change the model pad token id."},
)
decoder_start_token_id: int = field(
default=None,
metadata={"help": "If specified, change the model decoder start token id."},
)
freeze_text_encoder: bool = field(
default=False,
metadata={"help": "Whether to freeze the text encoder."},
)
do_sample: bool = field(
default=True,
metadata={"help": "Whether to do sampling or greedy decoding."},
)
temperature: float = field(
default=1.0,
metadata={"help": "Temperature if sampling."},
)
max_length: int = field(
default=2580,
metadata={"help": "Generation max length."},
)
bandwidth: float = field(
default=6,
metadata={"help": "Audio encoder bandwidth."},
)
asr_model_name_or_path: str = field(
default="distil-whisper/distil-large-v2",
metadata={"help": "Used to compute WER during evaluation. Path to pretrained model or model identifier from huggingface.co/models"}
)
clap_model_name_or_path: str = field(
default="laion/larger_clap_music_and_speech",
metadata={"help": "Used to compute audio similarity during evaluation. Path to pretrained model or model identifier from huggingface.co/models"}
)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
Using `HfArgumentParser` we can turn this class
into argparse arguments to be able to specify them on
the command line.
"""
train_dataset_name: str = field(
default=None,
metadata={
"help": "The name of the training dataset to use (via the datasets library). Load and combine "
"multiple datasets by separating dataset ids by a '+' symbol. For example, to load and combine "
" librispeech and common voice, set `train_dataset_name='librispeech_asr+common_voice'`."
},
)
train_dataset_config_name: Optional[str] = field(
default=None,
metadata={
"help": "The configuration name of the training dataset to use (via the datasets library). Load and combine "
"multiple datasets by separating dataset configs by a '+' symbol."
},
)
train_split_name: str = field(
default="train",
metadata={
"help": ("The name of the training data set split to use (via the datasets library). Defaults to 'train'")
},
)
train_dataset_samples: str = field(
default=None,
metadata={
"help": "Number of samples in the training data. Load and combine "
"multiple datasets by separating dataset samples by a '+' symbol."
},
)
train_metadata_dataset_name: str = field(
default=None,
metadata={
"help": "The name of the metadata training dataset to use (via the datasets library). Load and combine "
"multiple datasets by separating dataset ids by a '+' symbol. For example, to load and combine "
" librispeech and common voice, set `train_dataset_name='librispeech_asr+common_voice'`."
},
)
eval_dataset_name: str = field(
default=None,
metadata={
"help": "The name of the evaluation dataset to use (via the datasets library). Defaults to the training dataset name if unspecified."
},
)
eval_dataset_config_name: Optional[str] = field(
default=None,
metadata={
"help": "The configuration name of the evaluation dataset to use (via the datasets library). Defaults to the training dataset config name if unspecified"
},
)
eval_split_name: str = field(
default="test",
metadata={
"help": "The name of the evaluation data set split to use (via the datasets library). Defaults to 'test'"
},
)
eval_metadata_dataset_name: str = field(
default=None,
metadata={
"help": "The name of the metadata training dataset to use (via the datasets library). Load and combine "
"multiple datasets by separating dataset ids by a '+' symbol. For example, to load and combine "
" librispeech and common voice, set `train_dataset_name='librispeech_asr+common_voice'`."
},
)
target_audio_column_name: str = field(
default="audio",
metadata={"help": "The name of the dataset column containing the target audio data. Defaults to 'audio'"},
)
description_column_name: str = field(
default=None,
metadata={"help": "The name of the dataset column containing the description text data. Defaults to 'None'."},
)
prompt_column_name: str = field(
default=None,
metadata={"help": "The name of the dataset column containing the prompt text data. Defaults to 'None'."},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
)
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={"help": "The number of processes to use for the preprocessing."},
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of validation examples to this "
"value if set."
)
},
)
max_duration_in_seconds: float = field(
default=35.0,
metadata={
"help": (
"Filter audio files that are longer than `max_duration_in_seconds` seconds to 'max_duration_in_seconds`."
"Also, used to set maximum audio length if `pad_to_max_length=True`."
)
},
)
min_duration_in_seconds: float = field(
default=0.0, metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"}
)
max_text_length: int = field(
default=500, metadata={"help": "If set, max description lengths in number of characters."}
)
max_prompt_token_length: int = field(
default=None,
metadata={
"help": (
"If set, filter samples with prompts that are longer than `max_prompt_token_length` tokens."
"Also, used to set maximum prompt token length if `pad_to_max_length=True`."
)
},
)
max_description_token_length: int = field(
default=None,
metadata={
"help": (
"If set, filter samples with descriptions that are longer than `max_description_token_length` tokens."
"Also, used to set maximum desription token length if `pad_to_max_length=True`."
)
},
)
pad_to_max_length: bool = field(
default=False,
metadata={
"help": (
"If `True`, pad audio, prompt and description to a maximum length set with respectively "
"`max_duration_in_seconds`, `max_prompt_token_length`, `max_description_token_length`."
)
},
)
preprocessing_only: bool = field(
default=False,
metadata={
"help": (
"Whether to only do data preprocessing and skip training. This is especially useful when data"
" preprocessing errors out in distributed training due to timeout. In this case, one should run the"
" preprocessing in a non-distributed setup with `preprocessing_only=True` so that the cached datasets"
" can consequently be loaded in distributed training."
" In this training script, `save_to_disk` must be set to the path in which the dataset should be saved. "
)
},
)
token: str = field(
default=None,
metadata={
"help": (
"The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
"generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
)
},
)
use_auth_token: bool = field(
default=None,
metadata={
"help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead."
},
)
trust_remote_code: bool = field(
default=False,
metadata={
"help": (
"Whether or not to allow for custom models defined on the Hub in their own modeling files. This option "
"should only be set to `True` for repositories you trust and in which you have read the code, as it will "
"execute code present on the Hub on your local machine."
)
},
)
add_audio_samples_to_wandb: bool = field(
default=False,
metadata={"help": "If set and if `wandb` in args.report_to, will add generated audio samples to wandb logs."},
)
id_column_name: str = field(default=None, metadata={"help": "id column name."})
wandb_project: str = field(
default="parler-speech",
metadata={"help": "The name of the wandb project."},
)
save_to_disk: str = field(
default=None,
metadata={
"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."
},
)
temporary_save_to_disk: str = field(default=None, metadata={"help": "Temporarily save audio labels here."})
pad_to_multiple_of: Optional[int] = field(
default=2,
metadata={"help": ("Pad to multiple of for tokenizers.")},
)
@dataclass
class ParlerTTSTrainingArguments(Seq2SeqTrainingArguments):
dtype: Optional[str] = field(
default="float32",
metadata={
"help": (
"The data type (dtype) in which to run training. One of `float32` (full-precision), "
"`float16` or `bfloat16` (both half-precision)."
)
},
)
audio_encoder_per_device_batch_size: int = field(
default=8,
metadata={"help": ("Specify the batch size of the audio encoding pre-processing steps.")},
)
@dataclass
class DataCollatorEncodecWithPadding:
"""
Data collator that will dynamically pad the inputs received to the longest sequence in the batch or
to `max_length` if `max_length` is set and `padding=max_length`.
"""
feature_extractor: AutoFeatureExtractor
audio_column_name: str
feature_extractor_input_name: Optional[str] = "input_values"
max_length: Optional[int] = None
padding: Optional[str] = "longest"
def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
# split inputs and labels since they have to be of different lengths and need
# different padding methods
audios = [feature[self.audio_column_name]["array"] for feature in features]
len_audio = [len(audio) for audio in audios]
batch = self.feature_extractor(audios, return_tensors="pt", padding=self.padding, max_length=self.max_length)
batch["len_audio"] = torch.tensor(len_audio).unsqueeze(1)
return batch
@dataclass
class DataCollatorParlerTTSWithPadding:
"""
Data collator that will dynamically pad the inputs received.
Args:
prompt_tokenizer (:class:`~transformers.AutoTokenizer`)
The prompt_tokenizer used for proccessing the data.
description_tokenizer (:class:`~transformers.AutoTokenizer`)
The description_tokenizer used for proccessing the data.
padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
among:
* :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
* :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
maximum acceptable input length for the model if that argument is not provided.
* :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
different lengths).
pad_to_multiple_of (:obj:`int`, `optional`):
If set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
7.5 (Volta).
"""
prompt_tokenizer: AutoTokenizer
description_tokenizer: AutoTokenizer
padding: Union[bool, str] = "longest"
pad_to_multiple_of: Optional[int] = None
prompt_max_length: Optional[int] = None
description_max_length: Optional[int] = None
audio_max_length: Optional[int] = None
def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
# split inputs and labels since they have to be of different lengths and need
# different padding methods
labels = [torch.tensor(feature["labels"]).transpose(0, 1) for feature in features]
# (bsz, seq_len, num_codebooks)
labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True, padding_value=-100)
if self.audio_max_length is not None and self.padding == "max_length":
labels = torch.nn.functional.pad(labels, pad=(0, 0, 0, max(self.audio_max_length - labels.shape[1], 0)))
input_ids = [{"input_ids": feature["input_ids"]} for feature in features]
input_ids = self.description_tokenizer.pad(
input_ids,
return_tensors="pt",
padding=self.padding,
pad_to_multiple_of=self.pad_to_multiple_of,
max_length=self.description_max_length,
)
batch = {"labels": labels, **input_ids}
if self.audio_max_length is not None and self.padding == "max_length":
# if we do torch.compile, we need to also specify the attention_mask
decoder_attention_mask = torch.ones(labels.shape[:2], dtype=input_ids["attention_mask"].dtype)
batch["decoder_attention_mask"] = decoder_attention_mask
prompt_input_ids = [{"input_ids": feature["prompt_input_ids"]} for feature in features]
prompt_input_ids = self.prompt_tokenizer.pad(
prompt_input_ids,
return_tensors="pt",
padding=self.padding,
pad_to_multiple_of=self.pad_to_multiple_of,
max_length=self.prompt_max_length,
)
batch["prompt_input_ids"] = prompt_input_ids["input_ids"]
if "attention_mask" in prompt_input_ids:
batch["prompt_attention_mask"] = prompt_input_ids["attention_mask"]
return batch
def convert_dataset_str_to_list(
dataset_names,
dataset_config_names,
metadata_dataset_names=None,
splits=None,
dataset_samples=None,
default_split="train",
):
if isinstance(dataset_names, str):
dataset_names = dataset_names.split("+")
dataset_config_names = dataset_config_names.split("+")
splits = splits.split("+") if splits is not None else None
dataset_samples = dataset_samples.split("+") if dataset_samples is not None else None
metadata_dataset_names = metadata_dataset_names.split("+") if metadata_dataset_names is not None else None
# basic checks to ensure we've got the right number of datasets/configs/splits/columns/probs
if len(dataset_names) != len(dataset_config_names):
raise ValueError(
f"Ensure one config is passed for each dataset, got {len(dataset_names)} datasets and"
f" {len(dataset_config_names)} configs."
)
if splits is not None and len(splits) != len(dataset_names):
raise ValueError(
f"Ensure one split is passed for each dataset, got {len(dataset_names)} datasets and {len(splits)} splits."
)
if metadata_dataset_names is not None and len(metadata_dataset_names) != len(dataset_names):
raise ValueError(
f"Ensure one metadata dataset is passed for each dataset, got {len(dataset_names)} datasets and {len(metadata_dataset_names)} metadata datasets."
)
if dataset_samples is not None:
if len(dataset_samples) != len(dataset_names):
raise ValueError(
f"Ensure one sample is passed for each dataset, got {len(dataset_names)} datasets and "
f"{len(dataset_samples)} samples."
)
dataset_samples = [float(ds_sample) for ds_sample in dataset_samples]
else:
dataset_samples = [None] * len(dataset_names)
splits = splits if splits is not None else [default_split for _ in range(len(dataset_names))]
dataset_names_dict = []
for i, ds_name in enumerate(dataset_names):
dataset_names_dict.append(
{
"name": ds_name,
"config": dataset_config_names[i],
"split": splits[i],
"metadata_dataset_name": metadata_dataset_names[i],
"samples": dataset_samples[i],
}
)
return dataset_names_dict
def load_multiple_datasets(
accelerator: Accelerator,
dataset_names: Union[List, str],
dataset_config_names: Union[List, str],
metadata_dataset_names: Optional[str] = None,
splits: Optional[Union[List, str]] = None,
label_column_names: Optional[List] = None,
stopping_strategy: Optional[str] = "first_exhausted",
dataset_samples: Optional[Union[List, np.array]] = None,
streaming: Optional[bool] = False,
seed: Optional[int] = None,
id_column_name: Optional[str] = None,
columns_to_keep: Optional[Set[str]] = None,
prompt_column_name: Optional[str] = None,
sampling_rate: Optional[int] = None,
audio_column_name: Optional[str] = None,
**kwargs,
) -> Union[Dataset, IterableDataset]:
dataset_names_dict = convert_dataset_str_to_list(
dataset_names, dataset_config_names, metadata_dataset_names, splits, label_column_names, dataset_samples
)
if dataset_samples is not None:
dataset_samples = [ds_dict["samples"] for ds_dict in dataset_names_dict]
probabilities = np.array(dataset_samples) / np.sum(dataset_samples)
else:
probabilities = None
all_datasets = []
# iterate over the datasets we want to interleave
for dataset_dict in tqdm(dataset_names_dict, desc="Combining datasets..."):
with accelerator.main_process_first():
dataset = load_dataset(
dataset_dict["name"],
dataset_dict["config"],
split=dataset_dict["split"],
streaming=streaming,
**kwargs,
)
dataset_features = dataset.features.keys()
if sampling_rate is not None and audio_column_name is not None:
# resample target audio
dataset = dataset.cast_column(audio_column_name, datasets.features.Audio(sampling_rate=sampling_rate))
metadata_dataset_name = dataset_dict["metadata_dataset_name"]
if metadata_dataset_name is not None:
logger.info(
f'Merging {dataset_dict["name"]} - {dataset_dict["split"]} with {metadata_dataset_name} - {dataset_dict["split"]}'
)
metadata_dataset = load_dataset(
metadata_dataset_name,
dataset_dict["config"],
split=dataset_dict["split"],
streaming=streaming,
**kwargs,
)
# TODO(YL): I forgot to create unique ids for MLS english.
# To iterate faster, I bypass the original id check and do another one. - Done once because assuming it won't change next time
# if dataset_dict["name"] == "parler-tts/mls_eng_10k":
# def concat_ids(book_id, speaker_id, begin_time):
# return {"id": f"{book_id}_{speaker_id}_{str(begin_time).replace('.', '_')}"}
# dataset = dataset.map(concat_ids, input_columns=["book_id", "speaker_id", "begin_time"], num_proc=24)
# metadata_dataset = metadata_dataset.map(concat_ids, input_columns=["book_id", "speaker_id", "begin_time"], num_proc=24)
# metadata_dataset = metadata_dataset.rename_column(id_column_name, f"metadata_{id_column_name}")
if dataset_dict["name"] != "parler-tts/mls_eng_10k":
if id_column_name is not None and id_column_name not in dataset.column_names:
raise ValueError(
f"id_column_name={id_column_name} but has not been found in the dataset columns"
f"- one of {', '.join(list(dataset.column_names))}."
)
if id_column_name is not None and id_column_name not in metadata_dataset.column_names:
raise ValueError(
f"id_column_name={id_column_name} but has not been found in the metadata dataset columns"
f"- one of {', '.join(list(metadata_dataset.column_names))}."
)
elif id_column_name is not None:
metadata_dataset = metadata_dataset.rename_column(id_column_name, f"metadata_{id_column_name}")
metadata_columns_to_remove = set(metadata_dataset.column_names).intersection(set(dataset.column_names))
if prompt_column_name is not None:
# We might have applied some transformations to the prompts (e.g punctuation restoration)
# so we make sure to remove it from the original dataset
if prompt_column_name in dataset.column_names:
logger.info(
f"REMOVE {prompt_column_name} from dataset {dataset_dict['name']} - dataset_dict['split']"
)
dataset.remove_columns(prompt_column_name)
metadata_columns_to_remove = set(metadata_dataset.column_names).intersection(set(dataset.column_names))
metadata_dataset = metadata_dataset.remove_columns(metadata_columns_to_remove)
dataset = concatenate_datasets([dataset, metadata_dataset], axis=1)
if id_column_name is not None and dataset_dict["name"] != "parler-tts/mls_eng_10k":
if (
len(
dataset.filter(
lambda id1, id2: id1 != id2,
input_columns=[id_column_name, f"metadata_{id_column_name}"],
)
)
!= 0
):
raise ValueError(
f"Concatenate didn't work. Some ids don't correspond on dataset {dataset_dict['name']}"
)
dataset_features = dataset.features.keys()
if columns_to_keep is not None:
dataset = dataset.remove_columns(set(dataset_features - columns_to_keep))
all_datasets.append(dataset)
if len(all_datasets) == 1:
# we have a single dataset so just return it as is
return all_datasets[0]
if streaming:
interleaved_dataset = interleave_datasets(
all_datasets,
stopping_strategy=stopping_strategy,
probabilities=probabilities,
seed=seed,
)
else:
with accelerator.main_process_first():
interleaved_dataset = concatenate_datasets(all_datasets)
return interleaved_dataset
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, ParlerTTSTrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_parler_tts", model_args, data_args)
if training_args.dtype == "float16":
mixed_precision = "fp16"
elif training_args.dtype == "bfloat16":
mixed_precision = "bf16"
else:
mixed_precision = "no"
if data_args.pad_to_max_length and (
data_args.max_duration_in_seconds is None
or data_args.max_prompt_token_length is None
or data_args.max_description_token_length is None
):
raise ValueError(
"`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`"
)
padding = "max_length" if data_args.pad_to_max_length else "longest"
####### A. Preparation
kwargs_handlers = [InitProcessGroupKwargs(timeout=timedelta(minutes=60))]
if training_args.torch_compile:
# TODO(YL): add more compile modes?
kwargs_handlers.append(TorchDynamoPlugin(backend="inductor", mode="default")) # reduce-overhead
accelerator = Accelerator(
gradient_accumulation_steps=training_args.gradient_accumulation_steps,
mixed_precision=mixed_precision,
log_with=training_args.report_to,
project_dir=training_args.output_dir,
kwargs_handlers=kwargs_handlers,
)
accelerator.init_trackers(
project_name=data_args.wandb_project,
config={
"learning_rate": training_args.learning_rate,
"model_name_or_path": model_args.model_name_or_path,
"num_train_epochs": training_args.num_train_epochs,
"gradient_accumulation_steps": training_args.gradient_accumulation_steps,
"per_device_train_batch_size": training_args.per_device_train_batch_size,
"global_batch_size": training_args.per_device_train_batch_size * accelerator.num_processes,
"mixed_precision": mixed_precision,
"lr_scheduler_type": training_args.lr_scheduler_type,
"warmup_steps": training_args.warmup_steps,
"freeze_text_encoder": model_args.freeze_text_encoder,
"max_duration_in_seconds": data_args.max_duration_in_seconds,
"weight_decay": training_args.weight_decay,
"adam_beta1": training_args.adam_beta1,
"adam_beta2": training_args.adam_beta2,
"temperature": model_args.temperature,
},
)
# Detecting last checkpoint and eventually continue from last checkpoint
last_checkpoint = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome."
)
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
logger.setLevel(logging.INFO if accelerator.is_main_process else logging.WARN)
# Log a small summary on each proces
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
)
# Set the verbosity to info of the Transformers logger (on main process only)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
logger.info("Training/evaluation parameters %s", training_args)
# Set seed before initializing model.
set_seed(training_args.seed)
num_workers = data_args.preprocessing_num_workers
# 1. First, lett's instantiate the feature extractor, tokenizers and model
# Note for distributed training, the .from_pretrained methods guarantee that only
# one local process can concurrently download model & vocab.
# load feature extractor
feature_extractor = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor_name or model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
token=data_args.token,
trust_remote_code=data_args.trust_remote_code,
)
sampling_rate = feature_extractor.sampling_rate
# load prompt tokenizer
prompt_tokenizer = AutoTokenizer.from_pretrained(
model_args.prompt_tokenizer_name or model_args.description_tokenizer_name or model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
token=data_args.token,
trust_remote_code=data_args.trust_remote_code,
use_fast=model_args.use_fast_tokenizer,
padding_side="left", # prompt has to be padded on the left bc it's preprend to codebooks hidden states
)
# load description tokenizer
description_tokenizer = AutoTokenizer.from_pretrained(
model_args.description_tokenizer_name or model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
token=data_args.token,
trust_remote_code=data_args.trust_remote_code,
use_fast=model_args.use_fast_tokenizer,
)
if model_args.use_fast_tokenizer:
logger.warning(
"Disabling fast tokenizer warning: https://github.com/huggingface/transformers/blob/main/src/transformers/tokenization_utils_base.py#L3231-L3235"
)
prompt_tokenizer.deprecation_warnings["Asking-to-pad-a-fast-tokenizer"] = True
description_tokenizer.deprecation_warnings["Asking-to-pad-a-fast-tokenizer"] = True
# 2. Now, let's load the dataset
if data_args.save_to_disk is not None:
os.makedirs(data_args.save_to_disk, exist_ok=True)
# assume that the dataset has been saved to `save_to_disk` if the latter is not empty
dataset_was_precomputed = len(os.listdir(data_args.save_to_disk)) > 0
if dataset_was_precomputed:
vectorized_datasets = datasets.load_from_disk(data_args.save_to_disk)
else:
raw_datasets = DatasetDict()
columns_to_keep = {
"target_audio_column_name": data_args.target_audio_column_name,
"prompt_column_name": data_args.prompt_column_name,
}
if data_args.description_column_name is not None:
columns_to_keep["description_column_name"] = data_args.description_column_name
if training_args.do_train:
raw_datasets["train"] = load_multiple_datasets(
accelerator,
data_args.train_dataset_name,
data_args.train_dataset_config_name,
metadata_dataset_names=data_args.train_metadata_dataset_name,
splits=data_args.train_split_name,
dataset_samples=data_args.train_dataset_samples,
seed=training_args.seed,
cache_dir=model_args.cache_dir,
num_proc=data_args.preprocessing_num_workers,
id_column_name=data_args.id_column_name,
columns_to_keep=columns_to_keep.values(),
prompt_column_name=data_args.prompt_column_name,
audio_column_name=data_args.target_audio_column_name,
sampling_rate=sampling_rate,
# streaming=data_args.streaming, TODO(SG): optionally enable streaming mode
)
for key in columns_to_keep:
if columns_to_keep[key] not in raw_datasets["train"].column_names:
raise ValueError(
f"--{key} '{columns_to_keep[key]}' not found in dataset '{data_args.train_dataset_name}'."
f" Make sure to set `--{key}` to the correct audio column - one of"
f" {', '.join(raw_datasets['train'].column_names)}."
)
if data_args.max_train_samples is not None:
raw_datasets["train"] = raw_datasets["train"].select(range(data_args.max_train_samples))
if training_args.do_eval:
raw_datasets["eval"] = load_multiple_datasets(
accelerator,
data_args.eval_dataset_name if data_args.eval_dataset_name else data_args.train_dataset_name,
data_args.eval_dataset_config_name
if data_args.eval_dataset_config_name
else data_args.train_dataset_config_name,
metadata_dataset_names=data_args.eval_metadata_dataset_name,
splits=data_args.eval_split_name,
cache_dir=model_args.cache_dir,
num_proc=data_args.preprocessing_num_workers,
id_column_name=data_args.id_column_name,
columns_to_keep=columns_to_keep.values(),
prompt_column_name=data_args.prompt_column_name,
audio_column_name=data_args.target_audio_column_name,
sampling_rate=sampling_rate,
# streaming=data_args.streaming, TODO(SG): optionally enable streaming mode
)
if data_args.max_eval_samples is not None:
raw_datasets["eval"] = (
raw_datasets["eval"].shuffle(seed=training_args.seed).select(range(data_args.max_eval_samples))
)
# 3. Next, let's load the config.
config = ParlerTTSConfig.from_pretrained(
model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
token=data_args.token,
trust_remote_code=data_args.trust_remote_code,
)
# update pad token id and decoder_start_token_id
config.update(
{
"pad_token_id": model_args.pad_token_id
if model_args.pad_token_id is not None
else config.pad_token_id,
"decoder_start_token_id": model_args.decoder_start_token_id
if model_args.decoder_start_token_id is not None
else config.decoder_start_token_id,
}
)
# create model
model = ParlerTTSForConditionalGeneration.from_pretrained(
model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
config=config,
token=data_args.token,
trust_remote_code=data_args.trust_remote_code,
)
# enable gradient checkpointing if necessary
if training_args.gradient_checkpointing:
model.gradient_checkpointing_enable()
# 4. Now we preprocess the datasets including loading the audio, resampling and normalization
# Thankfully, `datasets` takes care of automatically loading and resampling the audio,
# so that we just need to set the correct target sampling rate and normalize the input
# via the `feature_extractor`
# derive max & min input length for sample rate & max duration
sampling_rate = feature_extractor.sampling_rate
max_target_length = data_args.max_duration_in_seconds * sampling_rate
min_target_length = data_args.min_duration_in_seconds * sampling_rate
target_audio_column_name = data_args.target_audio_column_name
description_column_name = data_args.description_column_name
prompt_column_name = data_args.prompt_column_name
feature_extractor_input_name = feature_extractor.model_input_names[0]
audio_encoder_pad_token_id = config.decoder.pad_token_id
audio_encoder_eos_token_id = config.decoder.eos_token_id
audio_encoder_bos_token_id = model.generation_config.decoder_start_token_id
max_length = model.generation_config.max_length
num_codebooks = model.decoder.config.num_codebooks
bandwidth = model_args.bandwidth
# Freeze Encoders
model.freeze_encoders(model_args.freeze_text_encoder)
# Test all gather - used for warmout and avoiding timeout
test_tensor = torch.tensor([accelerator.process_index], device=accelerator.device)
gathered_tensor = accelerator.gather(test_tensor)
print("gathered_tensor", gathered_tensor)
accelerator.wait_for_everyone()
if not dataset_was_precomputed:
# Filter on text length
if description_column_name is not None and data_args.max_text_length is not None:
with accelerator.main_process_first():
# filter description that is shorter than max_text_length
raw_datasets = raw_datasets.filter(
lambda x: len(x) < data_args.max_text_length,
num_proc=num_workers,
input_columns=[description_column_name],
)
# Preprocessing the dataset.
# We need to tokenize the texts.
def pass_through_processors(description, prompt):
batch = {}
batch["input_ids"] = description_tokenizer(description.strip())["input_ids"]
batch["prompt_input_ids"] = prompt_tokenizer(prompt.strip())["input_ids"]
return batch
with accelerator.main_process_first():
# this is a trick to avoid to rewrite the entire audio column which takes ages
vectorized_datasets = raw_datasets.map(
pass_through_processors,
remove_columns=next(iter(raw_datasets.values())).column_names,
input_columns=[description_column_name, prompt_column_name],
num_proc=num_workers,
desc="preprocess datasets",
)
# We use Accelerate to perform distributed inference
# T5 doesn't support fp16
autocast_kwargs = AutocastKwargs(enabled=(mixed_precision != "fp16"))
# Now we encode the audio labels with encodec.
####### B. Encode audio
logger.info("*** Encode target audio with encodec ***")
# no need to prepare audio_decoder because used for inference without mixed precision
# see: https://huggingface.co/docs/accelerate/main/en/package_reference/accelerator#accelerate.Accelerator.prepare
if training_args.torch_compile:
audio_decoder = accelerator.prepare_model(model.audio_encoder, evaluation_mode=True)
else:
audio_decoder = model.audio_encoder
encoder_data_collator = DataCollatorEncodecWithPadding(
feature_extractor,
audio_column_name=target_audio_column_name,
feature_extractor_input_name=feature_extractor_input_name,
max_length=max_target_length,
padding=padding,
)
def apply_audio_decoder(batch):
len_audio = batch.pop("len_audio")
audio_decoder.to(batch["input_values"].device).eval()
with torch.no_grad():
labels = audio_decoder.encode(**batch, bandwidth=bandwidth)["audio_codes"]
output = {}
output["len_audio"] = len_audio
# (1, bsz, codebooks, seq_len) -> (bsz, seq_len, codebooks)
output["labels"] = labels.squeeze(0).transpose(1, 2)
output["ratio"] = torch.ones_like(len_audio) * labels.shape[-1] / len_audio.max()
return output
for split in vectorized_datasets:
data_loader = DataLoader(
raw_datasets[split],
batch_size=training_args.audio_encoder_per_device_batch_size,
collate_fn=encoder_data_collator,
num_workers=training_args.dataloader_num_workers,
pin_memory=True,
)
data_loader = accelerator.prepare(data_loader)
all_generated_labels = []
all_lens = []
for batch in tqdm(data_loader, disable=not accelerator.is_local_main_process):
generate_labels = apply_audio_decoder(batch)
generate_labels = accelerator.pad_across_processes(generate_labels, dim=1, pad_index=0)
generate_labels = accelerator.gather_for_metrics(generate_labels)
if accelerator.is_main_process:
lab = generate_labels["labels"].cpu().transpose(1, 2).to(torch.int16)
rat = generate_labels["ratio"].cpu().squeeze()
lens = generate_labels["len_audio"].cpu().squeeze()
lab = [l[:, : int(ratio * length)] for (l, ratio, length) in zip(lab, rat, lens)]
all_generated_labels.extend(lab)
all_lens.extend(lens)
# (1, codebooks, seq_len) where seq_len=1
bos_labels = torch.ones((1, num_codebooks, 1)) * audio_encoder_bos_token_id
if accelerator.is_main_process:
tmp_labels = Dataset.from_dict({"labels": all_generated_labels, "target_length": all_lens})
tmp_labels.save_to_disk(
os.path.join(data_args.temporary_save_to_disk, split),
num_proc=1 if split == "eval" else data_args.preprocessing_num_workers,
)
accelerator.wait_for_everyone()
del all_generated_labels
tmp_labels = datasets.load_from_disk(os.path.join(data_args.temporary_save_to_disk, split))
with accelerator.main_process_first():
vectorized_datasets[split] = concatenate_datasets([vectorized_datasets[split], tmp_labels], axis=1)
def postprocess_dataset(labels):
# (1, codebooks, seq_len)
labels = torch.tensor(labels).unsqueeze(0)
# add bos
labels = torch.cat([bos_labels, labels], dim=-1)
labels, delay_pattern_mask = build_delay_pattern_mask(
labels,
bos_token_id=audio_encoder_bos_token_id,
pad_token_id=audio_encoder_eos_token_id,
max_length=labels.shape[-1] + num_codebooks,
num_codebooks=num_codebooks,
)
# the first ids of the delay pattern mask are precisely labels, we use the rest of the labels mask
# to take care of EOS
# we want labels to look like this:
# - [B, a, b, E, E, E, E]
# - [B, B, c, d, E, E, E]
# - [B, B, B, e, f, E, E]
# - [B, B, B, B, g, h, E]
labels = torch.where(delay_pattern_mask == -1, audio_encoder_eos_token_id, delay_pattern_mask)
# the first timestamp is associated to a row full of BOS, let's get rid of it
# we also remove the last timestampts (full of PAD)
output = {"labels": labels[:, 1:]}
return output
with accelerator.main_process_first():
vectorized_datasets[split] = vectorized_datasets[split].map(
postprocess_dataset,
num_proc=data_args.preprocessing_num_workers, # this one is resource consuming if many processor.
input_columns=["labels"],
desc="Postprocessing labeling",
)
accelerator.free_memory()
del generate_labels, all_lens
with accelerator.main_process_first():
# NOTE: filtering is done at the end because in the `datasets` library, caching audio files is done after most operations
# caching audio files is time and disk-space consuming, so we want to avoid it at all costs, especially for large (>1Kh) audio datasets.
# That's also why we avoid to concat the processed datasets (vectorized_datasets) with the audio column present in raw_datasets.
def is_audio_in_length_range(length):
return length > min_target_length and length < max_target_length
# filter data that is shorter than min_target_length
vectorized_datasets = vectorized_datasets.filter(
is_audio_in_length_range,
num_proc=num_workers,
input_columns=["target_length"],
)
if description_column_name is not None and data_args.max_description_token_length is not None:
with accelerator.main_process_first():
# filter description that is shorter than max_text_length
vectorized_datasets = vectorized_datasets.filter(
lambda x: len(x) < data_args.max_description_token_length,
num_proc=num_workers,
input_columns=["input_ids"],
)
if data_args.max_prompt_token_length is not None:
with accelerator.main_process_first():
# filter description that is shorter than max_text_length
vectorized_datasets = vectorized_datasets.filter(
lambda x: len(x) < data_args.max_prompt_token_length,
num_proc=num_workers,
input_columns=["prompt_input_ids"],
)
if data_args.save_to_disk is not None and not dataset_was_precomputed:
if accelerator.is_main_process:
vectorized_datasets.save_to_disk(
data_args.save_to_disk,
num_proc=min(data_args.preprocessing_num_workers, len(vectorized_datasets["eval"]) - 1),
)
logger.info(f"Dataset saved at {data_args.save_to_disk}")
audio_max_length = None
if training_args.torch_compile:
audio_max_length = max(vectorized_datasets["train"]["target_length"])
with accelerator.main_process_first():
max_sample = vectorized_datasets["train"].filter(
lambda x: x == audio_max_length,
num_proc=num_workers,
input_columns=["target_length"],
)
audio_max_length = torch.tensor(max_sample[0]["labels"]).shape[1]
# for large datasets it is advised to run the preprocessing on a
# single machine first with ``args.preprocessing_only`` since there will mostly likely
# be a timeout when running the script in distributed mode.
# In a second step ``args.preprocessing_only`` can then be set to `False` to load the
# cached dataset
if data_args.preprocessing_only and data_args.save_to_disk is None:
raise ValueError(
"`preprocessing_only=True` but `save_to_disk` is not set. The latter should indicates where to save the dataset locally."
)
elif data_args.preprocessing_only:
logger.info(f"Data preprocessing finished. Files save at {data_args.save_to_disk}")
return
# 6. Next, we can prepare the training.
# Let's use word CLAP similary and WER metrics as our evaluation metrics,
# Define evaluation metrics during training, *i.e.* CLAP similarity
clap = AutoModel.from_pretrained(model_args.clap_model_name_or_path)
clap_processor = AutoProcessor.from_pretrained(model_args.clap_model_name_or_path)
metric = evaluate.load("wer")
def clap_similarity(texts, audios, device):
clap_inputs = clap_processor(text=texts, audios=audios, padding=True, return_tensors="pt").to(device)
clap.to(device)
with torch.no_grad():
text_features = clap.get_text_features(
clap_inputs["input_ids"], attention_mask=clap_inputs.get("attention_mask", None)
)
audio_features = clap.get_audio_features(clap_inputs["input_features"])
cosine_sim = torch.nn.functional.cosine_similarity(audio_features, text_features, dim=1, eps=1e-8)
clap.to("cpu")
clap_inputs.to("cpu")
return cosine_sim.mean().to("cpu")
def wer(prompts, audios, device):
asr_pipeline = pipeline(model=model_args.asr_model_name_or_path, device=device)
transcriptions = asr_pipeline(
[{"raw": audio, "sampling_rate": sampling_rate} for audio in audios],
batch_size=int(training_args.per_device_eval_batch_size),
)
word_error = 100 * metric.compute(
predictions=[t["text"].lower() for t in transcriptions], references=[t.lower() for t in prompts]
)
return word_error, [t["text"] for t in transcriptions]
eval_methods = {"clap": clap_similarity, "wer": wer}
def compute_metrics(audios, descriptions, prompts, device="cpu"):
input_ids = descriptions
texts = description_tokenizer.batch_decode(input_ids, skip_special_tokens=True)
prompts = prompt_tokenizer.batch_decode(prompts, skip_special_tokens=True)
audios = [a.cpu().numpy() for a in audios]
results = {"clap": eval_methods["clap"](texts, audios, device)}
word_error, transcriptions = eval_methods["wer"](prompts, audios, device)
results["wer"] = word_error
return results, texts, prompts, audios, transcriptions
# Define Training Schedule
# Store some constants
per_device_train_batch_size = int(training_args.per_device_train_batch_size)
train_batch_size = per_device_train_batch_size * accelerator.num_processes
gradient_accumulation_steps = int(training_args.gradient_accumulation_steps)
per_device_eval_batch_size = int(training_args.per_device_eval_batch_size)
if training_args.max_steps < 0:
num_epochs = int(training_args.num_train_epochs)
steps_per_epoch = len(vectorized_datasets["train"]) // (train_batch_size * gradient_accumulation_steps)
total_train_steps = steps_per_epoch * num_epochs
elif training_args.max_steps > 0:
logger.info("max_steps is given, it will override any value given in num_train_epochs")
total_train_steps = int(training_args.max_steps)
# Setting a very large number of epochs so we go as many times as necessary over the iterator.
num_epochs = sys.maxsize
steps_per_epoch = total_train_steps
if training_args.eval_steps is None:
logger.info(f"eval_steps is not set, evaluating at the end of each epoch")
eval_steps = steps_per_epoch
else:
eval_steps = training_args.eval_steps
# T5 doesn't support fp16
autocast_kwargs = AutocastKwargs(enabled=(mixed_precision != "fp16"))
# Define optimizer, LR scheduler, collator
optimizer = torch.optim.AdamW(
params=model.parameters(),
lr=training_args.learning_rate,
betas=(training_args.adam_beta1, training_args.adam_beta2),
eps=training_args.adam_epsilon,
weight_decay=training_args.weight_decay,
)
# LR scheduler gets stepped by `num_processes` each time -> account for this in warmup / total steps
lr_scheduler = get_scheduler(
name=training_args.lr_scheduler_type,
optimizer=optimizer,
num_warmup_steps=training_args.get_warmup_steps(total_train_steps) * accelerator.num_processes,
num_training_steps=total_train_steps * accelerator.num_processes,
)
# Instantiate custom data collator
data_collator = DataCollatorParlerTTSWithPadding(
prompt_tokenizer=prompt_tokenizer,
description_tokenizer=description_tokenizer,
pad_to_multiple_of=data_args.pad_to_multiple_of,
padding=padding,
prompt_max_length=data_args.max_prompt_token_length,
description_max_length=data_args.max_description_token_length,
audio_max_length=audio_max_length,
)
# Prepare everything with accelerate
model, optimizer, lr_scheduler = accelerator.prepare(model, optimizer, lr_scheduler)
logger.info("***** Running training *****")
logger.info(f" Num examples = {total_train_steps * train_batch_size * gradient_accumulation_steps}")
logger.info(" Instantaneous batch size per device =" f" {per_device_train_batch_size}")
logger.info(" Gradient accumulation steps =" f" {gradient_accumulation_steps}")
logger.info(
f" Total train batch size (w. parallel & distributed) = {train_batch_size * gradient_accumulation_steps}"
)
logger.info(f" Total optimization steps = {total_train_steps}")
# ======================== Training ================================
train_time = 0
train_start = time.time()
steps_trained_progress_bar = tqdm(
range(total_train_steps), desc="Train steps ... ", position=0, disable=not accelerator.is_local_main_process
)
continue_training = True
epochs_trained = 0
cur_step = 0
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
checkpoint = last_checkpoint
if accelerator.is_main_process:
if training_args.push_to_hub:
# Retrieve of infer repo_name
repo_name = training_args.hub_model_id
if repo_name is None:
repo_name = Path(training_args.output_dir).absolute().name
# Create repo and retrieve repo_id
repo_id = create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
# Clone repo locally
repo = Repository(training_args.output_dir, clone_from=repo_id, token=training_args.hub_token)
with open(os.path.join(training_args.output_dir, ".gitignore"), "w+") as gitignore:
if "wandb" not in gitignore:
gitignore.write("wandb\n")
elif training_args.output_dir is not None:
os.makedirs(training_args.output_dir, exist_ok=True)
accelerator.wait_for_everyone()
# Now save everything to be able to create a single processor later
# make sure all processes wait until data is saved
with accelerator.main_process_first():
# only the main process saves them
if accelerator.is_main_process:
# save feature extractor, tokenizer and config
if (
model_args.prompt_tokenizer_name is None
and model_args.description_tokenizer_name
or (model_args.prompt_tokenizer_name == model_args.description_tokenizer_name)
):
prompt_tokenizer.save_pretrained(training_args.output_dir)
else:
logger.warning(
"Prompt tokenizer ('{model_args.prompt_tokenizer_name}') and description tokenizer ('{model_args.description_tokenizer_name}') are not the same. Saving only the prompt tokenizer."
)
prompt_tokenizer.save_pretrained(training_args.output_dir)
feature_extractor.save_pretrained(training_args.output_dir)
config.save_pretrained(training_args.output_dir)
if checkpoint is not None:
accelerator.load_state(checkpoint)
# Find num steps and epoch from saved state string pattern
pattern = r"checkpoint-(\d+)-epoch-(\d+)"
match = re.search(pattern, checkpoint)
cur_step = int(match.group(1))
epochs_trained = int(match.group(2))
logger.info(" Continuing training from checkpoint, will skip to saved global_step")
logger.info(f" Continuing training from epoch {epochs_trained}")
logger.info(f" Continuing training from global step {cur_step}")
steps_trained_progress_bar.update(cur_step)
for epoch in range(0, epochs_trained):
vectorized_datasets["train"] = vectorized_datasets["train"].shuffle(training_args.seed)
if training_args.max_steps < 0:
# we know exactly the number of steps per epoch, so can skip through the required number of batches
resume_step = (cur_step - epochs_trained * steps_per_epoch) * gradient_accumulation_steps
else:
# Currently we don't know how many steps we've taken in the current epoch
# So we just shuffle the dataset one extra time and start from a fresh epoch
# This is "good enough" for our purposes but not fully correct
resume_step = None
vectorized_datasets["train"] = vectorized_datasets["train"].shuffle(training_args.seed)
else:
resume_step = None
gen_kwargs = {
"do_sample": model_args.do_sample,
"temperature": model_args.temperature,
"max_length": model_args.max_length,
}
# Define gradient update step fn
def train_step(
batch,
accelerator,
autocast_kwargs,
):
model.train()
if mixed_precision == "fp16":
# fp16 doesn't work with T5-like models
with accelerator.autocast(autocast_handler=autocast_kwargs):
if training_args.parallel_mode.value != "distributed":
encoder_outputs = model.text_encoder(
input_ids=batch.get("input_ids"), attention_mask=batch.get("attention_mask", None)
)
else:
encoder_outputs = model.module.text_encoder(
input_ids=batch.get("input_ids"), attention_mask=batch.get("attention_mask", None)
)
batch["encoder_outputs"] = encoder_outputs
outputs = model(**batch)
# CE (data) loss
ce_loss = outputs.loss
metrics = {"loss": ce_loss}
return ce_loss, metrics
# Define eval fn
def eval_step(
batch,
accelerator,
autocast_kwargs,
):
eval_model = model if not training_args.torch_compile else model._orig_mod
eval_model.eval()
if mixed_precision == "fp16":
# fp16 doesn't work with T5-like models
with accelerator.autocast(autocast_handler=autocast_kwargs):
with torch.no_grad():
if training_args.parallel_mode.value != "distributed" or training_args.torch_compile:
encoder_outputs = eval_model.text_encoder(
input_ids=batch.get("input_ids"), attention_mask=batch.get("attention_mask", None)
)
else:
encoder_outputs = eval_model.module.text_encoder(
input_ids=batch.get("input_ids"), attention_mask=batch.get("attention_mask", None)
)
batch["encoder_outputs"] = encoder_outputs
with torch.no_grad():
outputs = eval_model(**batch)
# CE (data) loss
ce_loss = outputs.loss
metrics = {"loss": ce_loss}
return metrics
def generate_step(batch):
batch.pop("decoder_attention_mask", None)
eval_model = accelerator.unwrap_model(model, keep_fp32_wrapper=mixed_precision != "fp16").eval()
if training_args.torch_compile:
eval_model = model._orig_mod
output_audios = eval_model.generate(**batch, **gen_kwargs)
output_audios = accelerator.pad_across_processes(output_audios, dim=1, pad_index=0)
return output_audios
for epoch in range(epochs_trained, num_epochs):
vectorized_datasets["train"] = vectorized_datasets["train"].shuffle(training_args.seed)
sampler = None
if training_args.group_by_length:
sampler = LengthGroupedSampler(train_batch_size, lengths=vectorized_datasets["train"]["target_length"])
train_dataloader = DataLoader(
vectorized_datasets["train"],
collate_fn=data_collator,
batch_size=per_device_train_batch_size,
sampler=sampler,
num_workers=training_args.dataloader_num_workers,
pin_memory=training_args.dataloader_pin_memory,
)
train_dataloader = accelerator.prepare(train_dataloader)
if hasattr(train_dataloader, "dataset") and isinstance(train_dataloader.dataset, IterableDataset):
train_dataloader.dataset.set_epoch(epoch)
if resume_step is not None:
# Skip the first N batches in the dataloader when resuming from a checkpoint
train_dataloader = accelerator.skip_first_batches(train_dataloader, resume_step)
resume_step = None
for batch in train_dataloader:
with accelerator.accumulate(model):
loss, train_metric = train_step(batch, accelerator, autocast_kwargs)
accelerator.backward(loss)
if accelerator.sync_gradients:
accelerator.clip_grad_norm_(model.parameters(), training_args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Check if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
steps_trained_progress_bar.update(1)
cur_step += 1
if cur_step % training_args.logging_steps == 0:
steps_trained_progress_bar.write(
f"Step... ({cur_step} / {total_train_steps} | Loss:"
f" {train_metric['loss']}, Learning Rate:"
f" {lr_scheduler.get_last_lr()[0]})"
)
log_metric(
accelerator,
metrics=train_metric,
learning_rate=lr_scheduler.get_last_lr()[0],
train_time=train_time + time.time() - train_start,
step=cur_step,
epoch=epoch,
prefix="train",
)
# save checkpoint and weights after each save_steps and at the end of training
if (cur_step % training_args.save_steps == 0) or cur_step == total_train_steps:
intermediate_dir = os.path.join(training_args.output_dir, f"checkpoint-{cur_step}-epoch-{epoch}")
# safe_serialization=False to avoid shared tensors saving issue (TODO(YL): it's a temporary fix)
# https://github.com/huggingface/transformers/issues/27293#issuecomment-1872560074
accelerator.save_state(output_dir=intermediate_dir, safe_serialization=False)
accelerator.wait_for_everyone()
if accelerator.is_main_process:
rotate_checkpoints(training_args.save_total_limit, output_dir=training_args.output_dir)
if cur_step == total_train_steps:
# un-wrap student model for save
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(training_args.output_dir)
if training_args.push_to_hub:
repo.push_to_hub(
commit_message=f"Saving train state of step {cur_step}",
blocking=False,
)
if training_args.do_eval and (cur_step % eval_steps == 0 or cur_step == total_train_steps):
train_time += time.time() - train_start
# ======================== Evaluating ==============================
eval_metrics = []
eval_preds = []
eval_descriptions = []
eval_prompts = []
eval_start = time.time()
# release training input batch
batch = release_memory(batch)
validation_dataloader = DataLoader(
vectorized_datasets["eval"],
collate_fn=data_collator,
batch_size=per_device_eval_batch_size,
drop_last=False,
num_workers=training_args.dataloader_pin_memory,
pin_memory=training_args.dataloader_pin_memory,
)
validation_dataloader = accelerator.prepare(validation_dataloader)
for batch in tqdm(
validation_dataloader,
desc=f"Evaluating - Inference ...",
position=2,
disable=not accelerator.is_local_main_process,
):
# Model forward
eval_metric = eval_step(batch, accelerator, autocast_kwargs)
eval_metric = accelerator.gather_for_metrics(eval_metric)
eval_metrics.append(eval_metric)
if training_args.predict_with_generate:
validation_dataloader = DataLoader(
vectorized_datasets["eval"],
collate_fn=data_collator,
batch_size=per_device_eval_batch_size,
drop_last=False,
num_workers=training_args.dataloader_pin_memory,
pin_memory=training_args.dataloader_pin_memory,
)
validation_dataloader = accelerator.prepare(validation_dataloader)
# generation
for batch in tqdm(
validation_dataloader,
desc=f"Evaluating - Generation ...",
position=2,
disable=not accelerator.is_local_main_process,
):
generated_audios = generate_step(batch)
# Gather all predictions and targets
generated_audios, input_ids, prompts = accelerator.pad_across_processes(
(generated_audios, batch["input_ids"], batch["prompt_input_ids"]), dim=1, pad_index=0
)
generated_audios, input_ids, prompts = accelerator.gather_for_metrics(
(generated_audios, input_ids, prompts)
)
eval_preds.extend(generated_audios.to("cpu"))
eval_descriptions.extend(input_ids.to("cpu"))
eval_prompts.extend(prompts.to("cpu"))
eval_time = time.time() - eval_start
# normalize eval metrics
eval_metrics = {
key: torch.mean(torch.cat([d[key].unsqueeze(0) for d in eval_metrics]))
for key in eval_metrics[0]
}
# compute metrics
metrics_desc = ""
if training_args.predict_with_generate:
metric_values, pred_descriptions, pred_prompts, audios, transcriptions = compute_metrics(
eval_preds, eval_descriptions, eval_prompts, accelerator.device
)
eval_metrics.update(metric_values)
metrics_desc = " ".join([f"Eval {key}: {value} |" for key, value in metric_values.items()])
if "wandb" in training_args.report_to:
log_pred(
accelerator,
pred_descriptions,
pred_prompts,
transcriptions,
audios,
sampling_rate=sampling_rate,
step=cur_step,
prefix="eval",
)
# Print metrics and update progress bar
steps_trained_progress_bar.write(
f"Eval results for step ({cur_step} / {total_train_steps} | Eval Loss: {eval_metrics['loss']} |"
f" {metrics_desc})"
)
log_metric(
accelerator,
metrics=eval_metrics,
train_time=eval_time,
step=cur_step,
epoch=epoch,
prefix="eval",
)
# release eval batch and relax metrics
eval_metrics = []
eval_preds = []
eval_descriptions = []
eval_prompts = []
batch = release_memory(batch)
# flush the train metrics
train_start = time.time()
# break condition
if cur_step == total_train_steps:
continue_training = False
break
if not continue_training:
break
accelerator.end_training()
if __name__ == "__main__":
set_start_method("spawn")
main()
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