run_audio_classification.py

#!/usr/bin/env python
# Copyright 2021 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.

import io
import logging
import os
import sys
from dataclasses import dataclass, field
from random import randint
from typing import Optional

import evaluate
import numpy as np
import soundfile as sf
import transformers
from datasets import Audio, DatasetDict, load_dataset
from transformers import AutoConfig, AutoFeatureExtractor, AutoModelForAudioClassification, HfArgumentParser
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version

from optimum.habana import GaudiConfig, GaudiTrainer, GaudiTrainingArguments
from optimum.habana.utils import set_seed


try:
    from optimum.habana.utils import check_optimum_habana_min_version
except ImportError:

    def check_optimum_habana_min_version(*a, **b):
        return ()


logger = logging.getLogger(__name__)

# Will error if the minimal version of Transformers and Optimum Habana are not installed. Remove at your own risks.
check_min_version("4.55.0")
check_optimum_habana_min_version("1.19.0.dev0")

require_version("datasets>=4.0.0", "To fix: pip install -r examples/pytorch/audio-classification/requirements.txt")

# Disable torchcodec decoding in datasets before any dataset ops
os.environ.setdefault("HF_DATASETS_DISABLE_TORCHCODEC", "1")


def random_subsample(wav: np.ndarray, max_length: float, sample_rate: int = 16000):
    """Randomly sample chunks of `max_length` seconds from the input audio"""
    sample_length = int(round(sample_rate * max_length))
    if len(wav) <= sample_length:
        return wav
    random_offset = randint(0, len(wav) - sample_length - 1)
    return wav[random_offset : random_offset + sample_length]


@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.
    """

    dataset_name: Optional[str] = field(default=None, metadata={"help": "Name of a dataset from the datasets package"})
    dataset_config_name: Optional[str] = field(
        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
    )
    train_file: Optional[str] = field(
        default=None, metadata={"help": "A file containing the training audio paths and labels."}
    )
    eval_file: Optional[str] = field(
        default=None, metadata={"help": "A file containing the validation audio paths and labels."}
    )
    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'"
        },
    )
    eval_split_name: str = field(
        default="validation",
        metadata={
            "help": (
                "The name of the training data set split to use (via the datasets library). Defaults to 'validation'"
            )
        },
    )
    audio_column_name: str = field(
        default="audio",
        metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
    )
    label_column_name: str = field(
        default="label", metadata={"help": "The name of the dataset column containing the labels. Defaults to 'label'"}
    )
    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 evaluation examples to this "
                "value if set."
            )
        },
    )
    max_length_seconds: float = field(
        default=20,
        metadata={"help": "Audio clips will be randomly cut to this length during training if the value is set."},
    )


@dataclass
class ModelArguments:
    """
    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
    """

    model_name_or_path: str = field(
        default="facebook/wav2vec2-base",
        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"}
    )
    cache_dir: Optional[str] = field(
        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from the Hub"}
    )
    model_revision: str = field(
        default="main",
        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
    )
    feature_extractor_name: Optional[str] = field(
        default=None, metadata={"help": "Name or path of preprocessor config."}
    )
    freeze_feature_encoder: bool = field(
        default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."}
    )
    attention_mask: bool = field(
        default=True, metadata={"help": "Whether to generate an attention mask in the feature extractor."}
    )
    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 `hf auth login` (stored in `~/.huggingface`)."
            )
        },
    )
    trust_remote_code: bool = field(
        default=False,
        metadata={
            "help": (
                "Whether to trust the execution of code from datasets/models defined on the Hub."
                " 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."
            )
        },
    )
    ignore_mismatched_sizes: bool = field(
        default=False,
        metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."},
    )


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, GaudiTrainingArguments))
    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_audio_classification", model_args, data_args)

    # 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)],
    )

    if training_args.should_log:
        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
        transformers.utils.logging.set_verbosity_info()

    log_level = training_args.get_process_log_level()
    logger.setLevel(log_level)
    transformers.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    gaudi_config = GaudiConfig.from_pretrained(
        training_args.gaudi_config_name,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        token=model_args.token,
    )

    # Log on each process the small summary:
    mixed_precision = training_args.bf16 or gaudi_config.use_torch_autocast
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, "
        + f"distributed training: {training_args.parallel_mode.value == 'distributed'}, "
        + f"mixed-precision training: {mixed_precision}"
    )
    logger.info(f"Training/evaluation parameters {training_args}")

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # Detecting 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 train from scratch."
            )
        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."
            )

    # Initialize our dataset and prepare it for the audio classification task.
    raw_datasets = DatasetDict()
    raw_datasets["train"] = load_dataset(
        data_args.dataset_name,
        data_args.dataset_config_name,
        split=data_args.train_split_name,
        token=model_args.token,
    )
    raw_datasets["eval"] = load_dataset(
        data_args.dataset_name,
        data_args.dataset_config_name,
        split=data_args.eval_split_name,
        token=model_args.token,
    )

    if data_args.audio_column_name not in raw_datasets["train"].column_names:
        raise ValueError(
            f"--audio_column_name {data_args.audio_column_name} not found in dataset '{data_args.dataset_name}'. "
            "Make sure to set `--audio_column_name` to the correct audio column - one of "
            f"{', '.join(raw_datasets['train'].column_names)}."
        )

    if data_args.label_column_name not in raw_datasets["train"].column_names:
        raise ValueError(
            f"--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. "
            "Make sure to set `--label_column_name` to the correct text column - one of "
            f"{', '.join(raw_datasets['train'].column_names)}."
        )

    # Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
    # transformer outputs in the classifier, but it doesn't always lead to better accuracy
    feature_extractor = AutoFeatureExtractor.from_pretrained(
        model_args.feature_extractor_name or model_args.model_name_or_path,
        return_attention_mask=model_args.attention_mask,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        token=model_args.token,
        trust_remote_code=model_args.trust_remote_code,
    )

    # Make sure datasets does not auto-decode audio (we'll open via soundfile in prepare_dataset).
    raw_datasets = raw_datasets.cast_column(
        data_args.audio_column_name,
        Audio(sampling_rate=feature_extractor.sampling_rate, decode=False),
    )

    # Max input length
    max_length = int(round(feature_extractor.sampling_rate * data_args.max_length_seconds))

    model_input_name = feature_extractor.model_input_names[0]

    def load_and_validate_audio(sample, feature_extractor, subsample: bool = False, max_length: float = None):
        """
        Open audio via soundfile, downmix to mono if needed, validate sample rate,
        and optionally apply random subsampling.
        """
        path = sample.get("path")
        wav, sr = None, None

        if isinstance(path, str):
            try:
                wav, sr = sf.read(path, dtype="float32", always_2d=False)
            except Exception:
                wav, sr = None, None

        if wav is None:
            raw = sample.get("bytes")
            if not raw:
                raise RuntimeError(f"Cannot open audio sample: {sample}")
            fileobj = io.BytesIO(raw)
            wav, sr = sf.read(fileobj, dtype="float32", always_2d=False)

        if wav.ndim > 1:
            wav = wav.mean(axis=1)

        if sr != feature_extractor.sampling_rate:
            raise RuntimeError(f"Expected {feature_extractor.sampling_rate} Hz, but got {sr} Hz for {path}")

        if subsample and max_length is not None:
            wav = random_subsample(wav, max_length=max_length, sample_rate=sr)

        return wav

    def train_transforms(batch):
        """Apply train_transforms across a batch."""
        subsampled_wavs = [
            load_and_validate_audio(sample, feature_extractor, subsample=True, max_length=data_args.max_length_seconds)
            for sample in batch[data_args.audio_column_name]
        ]
        inputs = feature_extractor(
            subsampled_wavs,
            max_length=max_length,
            sampling_rate=feature_extractor.sampling_rate,
            padding="max_length",
            truncation=True,
        )
        return {
            model_input_name: inputs.get(model_input_name),
            "labels": list(batch[data_args.label_column_name]),
        }

    def val_transforms(batch):
        """Apply val_transforms across a batch."""
        wavs = [
            load_and_validate_audio(sample, feature_extractor, subsample=False)
            for sample in batch[data_args.audio_column_name]
        ]
        inputs = feature_extractor(
            wavs,
            max_length=max_length,
            sampling_rate=feature_extractor.sampling_rate,
            padding="max_length",
            truncation=True,
        )
        return {
            model_input_name: inputs.get(model_input_name),
            "labels": list(batch[data_args.label_column_name]),
        }

    # Prepare label mappings.
    # We'll include these in the model's config to get human readable labels in the Inference API.
    labels = raw_datasets["train"].features[data_args.label_column_name].names
    label2id, id2label = {}, {}
    for i, label in enumerate(labels):
        label2id[label] = str(i)
        id2label[str(i)] = label

    # Load the accuracy metric from the datasets package
    metric = evaluate.load("accuracy", cache_dir=model_args.cache_dir)

    # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
    # `predictions` and `label_ids` fields) and has to return a dictionary string to float.
    def compute_metrics(eval_pred):
        """Computes accuracy on a batch of predictions"""
        predictions = np.argmax(eval_pred.predictions, axis=1)
        return metric.compute(predictions=predictions, references=eval_pred.label_ids)

    config = AutoConfig.from_pretrained(
        model_args.config_name or model_args.model_name_or_path,
        num_labels=len(labels),
        label2id=label2id,
        id2label=id2label,
        finetuning_task="audio-classification",
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        token=model_args.token,
        trust_remote_code=model_args.trust_remote_code,
    )
    model = AutoModelForAudioClassification.from_pretrained(
        model_args.model_name_or_path,
        from_tf=bool(".ckpt" in model_args.model_name_or_path),
        config=config,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        token=model_args.token,
        trust_remote_code=model_args.trust_remote_code,
        ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
        attn_implementation=training_args.attn_implementation,
    )

    # freeze the convolutional waveform encoder if supported by model
    if hasattr(model, "freeze_feature_encoder") and model_args.freeze_feature_encoder:
        model.freeze_feature_encoder()

    if training_args.do_train:
        if data_args.max_train_samples is not None:
            raw_datasets["train"] = (
                raw_datasets["train"].shuffle(seed=training_args.seed).select(range(data_args.max_train_samples))
            )
        # Set the training transforms
        raw_datasets["train"].set_transform(train_transforms, output_all_columns=False)

    if training_args.do_eval:
        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))
            )
        # Set the validation transforms
        raw_datasets["eval"].set_transform(val_transforms, output_all_columns=False)

    # Initialize our trainer
    trainer = GaudiTrainer(
        model=model,
        gaudi_config=gaudi_config,
        args=training_args,
        train_dataset=raw_datasets["train"] if training_args.do_train else None,
        eval_dataset=raw_datasets["eval"] if training_args.do_eval else None,
        compute_metrics=compute_metrics,
        processing_class=feature_extractor,
    )

    # Training
    if training_args.do_train:
        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
        train_result = trainer.train(resume_from_checkpoint=checkpoint)
        trainer.save_model()
        trainer.log_metrics("train", train_result.metrics)
        trainer.save_metrics("train", train_result.metrics)
        trainer.save_state()

    # Evaluation
    if training_args.do_eval:
        metrics = trainer.evaluate()
        trainer.log_metrics("eval", metrics)
        trainer.save_metrics("eval", metrics)

    # Write model card and (optionally) push to hub
    kwargs = {
        "finetuned_from": model_args.model_name_or_path,
        "tasks": "audio-classification",
        "dataset": data_args.dataset_name,
        "tags": ["audio-classification"],
    }
    if training_args.push_to_hub:
        trainer.push_to_hub(**kwargs)
    else:
        trainer.create_model_card(**kwargs)


if __name__ == "__main__":
    main()