Added training scripts

run.sh

@@ -0,0 +1,32 @@
+python run_distil_audio_multilabel_classification.py \
+    --model_name_or_path MIT/ast-finetuned-audioset-10-10-0.4593 \
+    --dataset_name bookbot/audioset \
+    --output_dir distil-ast-audioset-2 \
+    --overwrite_output_dir \
+    --remove_unused_columns False \
+    --freeze_feature_encoder False \
+    --do_train --do_eval \
+    --fp16 \
+    --learning_rate 3e-5 \
+    --alpha 0.5 \
+    --temperature 2.0 \
+    --layer_prefix audio_spectrogram_transformer.encoder.layer \
+    --delimiter . \
+    --teacher_blocks 0 2 4 6 8 10 \
+    --attention_mask False \
+    --warmup_ratio 0.1 \
+    --num_train_epochs 10 \
+    --per_device_train_batch_size 32 \
+    --gradient_accumulation_steps 4 \
+    --per_device_eval_batch_size 32 \
+    --dataloader_num_workers 4 \
+    --logging_strategy epoch \
+    --evaluation_strategy epoch \
+    --save_strategy epoch \
+    --save_total_limit 3 \
+    --seed 0 \
+    --report_to tensorboard \
+    --push_to_hub \
+    --hub_model_id bookbot/distil-ast-audioset-2 \
+    --hub_private_repo True \
+    --use_auth_token True

run_distil_audio_multilabel_classification.py

@@ -0,0 +1,579 @@
+#!/usr/bin/env python
+# coding=utf-8
+# 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 logging
+import os
+import sys
+import warnings
+from dataclasses import dataclass, field, asdict
+from typing import Optional, List
+
+import datasets
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from datasets import DatasetDict, load_dataset
+
+import transformers
+from transformers import (
+    AutoConfig,
+    AutoFeatureExtractor,
+    AutoModelForAudioClassification,
+    EvalPrediction,
+    HfArgumentParser,
+    Trainer,
+    TrainingArguments,
+    set_seed,
+)
+from transformers.trainer_utils import get_last_checkpoint
+from transformers.utils import send_example_telemetry
+from transformers.utils.versions import require_version
+
+from sklearn.metrics import (
+    accuracy_score,
+    average_precision_score,
+    f1_score,
+    roc_auc_score,
+)
+
+logger = logging.getLogger(__name__)
+
+require_version(
+    "datasets>=1.14.0",
+    "To fix: pip install -r examples/pytorch/audio-classification/requirements.txt",
+)
+
+
+def list_field(default=None, metadata=None):
+    return field(default_factory=lambda: default, metadata=metadata)
+
+
+@dataclass
+class DistillationTrainingArguments:
+    """
+    Arguments pertaining to distillation settings.
+    """
+
+    alpha: float = field(
+        default=0.5,
+        metadata={
+            "help": "Hyperparameter to control the relative strength of each loss."
+        },
+    )
+    temperature: float = field(
+        default=2.0,
+        metadata={"help": "Scale factor of logits to soften the probabilities."},
+    )
+    layer_prefix: str = field(
+        default=None,
+        metadata={
+            "help": "Layer name prefix to copy from teacher model. E.g. `wav2vec2.encoder.layers`."
+        },
+    )
+    delimiter: str = field(
+        default=".", metadata={"help": "Layer name components delimiter."}
+    )
+    teacher_blocks: List[str] = list_field(
+        default=None,
+        metadata={
+            "help": "A list of teacher block indices to copy from. E.g. `'0 2 4 6 8 10'`"
+        },
+    )
+
+
+class MultiLabelDistillationTrainer(Trainer):
+    def __init__(self, *args, teacher_model=None, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.teacher_model = teacher_model
+
+    def compute_loss(self, model, inputs, return_outputs=False):
+        labels = inputs.pop("labels")
+        outputs_stu = model(**inputs)
+        logits_stu = outputs_stu.logits
+        bce_loss_fct = torch.nn.BCEWithLogitsLoss()
+        loss_bce = bce_loss_fct(
+            logits_stu.view(-1, self.model.config.num_labels),
+            labels.float().view(-1, self.model.config.num_labels),
+        )
+        with torch.no_grad():
+            outputs_tea = self.teacher_model(**inputs)
+            logits_tea = outputs_tea.logits
+        kd_loss_fct = nn.KLDivLoss(reduction="batchmean")
+        loss_kd = self.args.temperature**2 * kd_loss_fct(
+            F.log_softmax(logits_stu / self.args.temperature, dim=-1),
+            F.softmax(logits_tea / self.args.temperature, dim=-1),
+        )
+        loss = self.args.alpha * loss_bce + (1.0 - self.args.alpha) * loss_kd
+        return (loss, outputs_stu) if return_outputs else loss
+
+
+@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: Optional[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."
+        },
+    )
+    use_auth_token: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
+                "with private models)."
+            )
+        },
+    )
+    freeze_feature_extractor: Optional[bool] = field(
+        default=None,
+        metadata={
+            "help": "Whether to freeze the feature extractor layers of the model."
+        },
+    )
+    ignore_mismatched_sizes: bool = field(
+        default=False,
+        metadata={
+            "help": "Will enable to load a pretrained model whose head dimensions are different."
+        },
+    )
+
+    def __post_init__(self):
+        if not self.freeze_feature_extractor and self.freeze_feature_encoder:
+            warnings.warn(
+                "The argument `--freeze_feature_extractor` is deprecated and "
+                "will be removed in a future version. Use `--freeze_feature_encoder`"
+                "instead. Setting `freeze_feature_encoder==True`.",
+                FutureWarning,
+            )
+        if self.freeze_feature_extractor and not self.freeze_feature_encoder:
+            raise ValueError(
+                "The argument `--freeze_feature_extractor` is deprecated and "
+                "should not be used in combination with `--freeze_feature_encoder`."
+                "Only make use of `--freeze_feature_encoder`."
+            )
+
+
+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,
+            TrainingArguments,
+            DistillationTrainingArguments,
+        )
+    )
+    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, distil_args = parser.parse_json_file(
+            json_file=os.path.abspath(sys.argv[1])
+        )
+    else:
+        (
+            model_args,
+            data_args,
+            training_args,
+            distil_args,
+        ) = parser.parse_args_into_dataclasses()
+
+    # copy alpha and temperature values from DistillationTrainingArguments to TrainingArguments
+    for key, value in asdict(distil_args).items():
+        setattr(training_args, key, value)
+
+    # 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()
+
+    # Log on each process the small summary:
+    logger.warning(
+        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu} "
+        + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
+    )
+    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,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+    raw_datasets["eval"] = load_dataset(
+        data_args.dataset_name,
+        data_args.dataset_config_name,
+        split=data_args.eval_split_name,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+
+    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)}."
+        )
+
+    # 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,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+
+    # `datasets` takes care of automatically loading and resampling the audio,
+    # so we just need to set the correct target sampling rate.
+    raw_datasets = raw_datasets.cast_column(
+        data_args.audio_column_name,
+        datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate),
+    )
+
+    model_input_name = feature_extractor.model_input_names[0]
+
+    def preprocess_data(examples):
+        # get audio arrays
+        audio_arrays = [x["array"] for x in examples[data_args.audio_column_name]]
+        # encode batch of audio
+        inputs = feature_extractor(
+            audio_arrays, sampling_rate=feature_extractor.sampling_rate
+        )
+        # add labels
+        labels_batch = {k: examples[k] for k in examples.keys() if k in labels}
+        # create numpy array of shape (batch_size, num_labels)
+        labels_matrix = np.zeros((len(audio_arrays), len(labels)))
+        # fill numpy array
+        for idx, label in enumerate(labels):
+            labels_matrix[:, idx] = labels_batch[label]
+
+        output_batch = {model_input_name: inputs.get(model_input_name)}
+        output_batch["labels"] = labels_matrix.tolist()
+
+        return output_batch
+
+    def multi_label_metrics(predictions, labels, threshold=0.5):
+        # first, apply sigmoid on predictions which are of shape (batch_size, num_labels)
+        sigmoid = torch.nn.Sigmoid()
+        probs = sigmoid(torch.Tensor(predictions)).cpu().numpy()
+        # next, use threshold to turn them into integer predictions
+        y_pred = np.zeros(probs.shape)
+        y_pred[np.where(probs >= threshold)] = 1
+        # finally, compute metrics
+        f1_micro_average = f1_score(y_true=labels, y_pred=y_pred, average="micro")
+        roc_auc = roc_auc_score(labels, y_pred, average="micro")
+        accuracy = accuracy_score(labels, y_pred)
+        mAP = average_precision_score(labels, probs, average="micro")
+        # return as dictionary
+        metrics = {
+            "f1": f1_micro_average,
+            "roc_auc": roc_auc,
+            "accuracy": accuracy,
+            "mAP": mAP,
+        }
+        return metrics
+
+    def compute_metrics(p: EvalPrediction):
+        """Computes mean average precision (mAP) score"""
+        preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
+        result = multi_label_metrics(predictions=preds, labels=p.label_ids)
+        return result
+
+    teacher_config = AutoConfig.from_pretrained(
+        model_args.config_name or model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+    teacher_model = AutoModelForAudioClassification.from_pretrained(
+        model_args.model_name_or_path,
+        from_tf=bool(".ckpt" in model_args.model_name_or_path),
+        config=teacher_config,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+        ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
+    ).to(training_args.device)
+
+    labels = list(teacher_config.id2label.values())
+
+    layer_num_idx: int = len(distil_args.layer_prefix.split(distil_args.delimiter))
+    num_hidden_layers: int = len(distil_args.teacher_blocks)
+    assert num_hidden_layers <= teacher_model.config.num_hidden_layers
+
+    student_config = AutoConfig.from_pretrained(
+        model_args.config_name or model_args.model_name_or_path,
+        num_hidden_layers=num_hidden_layers,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+    student_model = AutoModelForAudioClassification.from_pretrained(
+        model_args.model_name_or_path,
+        from_tf=bool(".ckpt" in model_args.model_name_or_path),
+        config=student_config,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+        ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
+    )
+
+    # initialize student's weights from teacher's
+    teacher_weights = teacher_model.state_dict()
+    student_weights = student_model.state_dict()
+
+    for name, param in student_weights.items():
+        if name.startswith(distil_args.layer_prefix):
+            # split layer name to its components
+            student_layer_name_comps = name.split(distil_args.delimiter)
+            student_layer_num = student_layer_name_comps[layer_num_idx]
+            # replace the layer num with teacher's layer num
+            student_layer_name_comps[layer_num_idx] = distil_args.teacher_blocks[
+                int(student_layer_num)
+            ]
+            # join to get teacher's layer name
+            teacher_layer_name = distil_args.delimiter.join(student_layer_name_comps)
+            # in-place copy to student params
+            param.copy_(teacher_weights[teacher_layer_name])
+
+    # freeze the convolutional waveform encoder
+    if model_args.freeze_feature_encoder:
+        student_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(preprocess_data, 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(preprocess_data, output_all_columns=False)
+
+    # Initialize our trainer
+    trainer = MultiLabelDistillationTrainer(
+        model=student_model,
+        teacher_model=teacher_model,
+        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,
+        tokenizer=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()