Training in progress, step 500

.gitattributes

@@ -27,3 +27,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 wandb/run-20220503_172048-zotxt8wa/run-zotxt8wa.wandb filter=lfs diff=lfs merge=lfs -text
 wandb/run-20220503_172048-zotxt8wa/logs/debug-internal.log filter=lfs diff=lfs merge=lfs -text
+wandb/run-20220504_095140-cwhobv6l/run-cwhobv6l.wandb filter=lfs diff=lfs merge=lfs -text
+wandb/run-20220504_142129-w4rlzz90/run-w4rlzz90.wandb filter=lfs diff=lfs merge=lfs -text

config.json

@@ -182,7 +182,7 @@
     "forced_eos_token_id": null,
     "gradient_checkpointing": false,
     "hidden_act": "gelu",
-    "hidden_dropout": 0.1742341660721257,
+    "hidden_dropout": 0.17305159310134854,
     "hidden_size": 1024,
     "id2label": {
       "0": "LABEL_0",

pytorch_model.bin

@@ -1,3 +1,3 @@
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-oid sha256:60197bcce895c667b06077bcd384a0a7cbcb92374123b43cedf794ae7fde6ad8
+oid sha256:db6d4e2df5d05af63a5d5063df5d529ce8f7390a16a5ecafaa4c5dd1164a828e
 size 2353867057

run_xtreme_s.py

@@ -1,948 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2022 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
-
-""" Fine-tuning a 🤗 Transformers pretrained speech model on the XTREME-S benchmark tasks"""
-
-import json
-import logging
-import os
-import re
-import sys
-from collections import OrderedDict, defaultdict
-from dataclasses import dataclass, field
-from typing import Dict, List, Optional, Union
-
-import datasets
-import numpy as np
-import torch
-from datasets import DatasetDict, load_dataset, load_metric
-
-import transformers
-from transformers import (
-    AutoConfig,
-    AutoFeatureExtractor,
-    AutoModelForAudioClassification,
-    AutoModelForCTC,
-    AutoModelForSpeechSeq2Seq,
-    AutoProcessor,
-    AutoTokenizer,
-    HfArgumentParser,
-    Seq2SeqTrainer,
-    Seq2SeqTrainingArguments,
-    SpeechEncoderDecoderModel,
-    Trainer,
-    set_seed,
-)
-from transformers.trainer_utils import get_last_checkpoint, is_main_process
-from transformers.utils import check_min_version
-from transformers.utils.versions import require_version
-
-
-# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.18.0.dev0")
-
-require_version("datasets>=1.18.0", "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt")
-
-
-logger = logging.getLogger(__name__)
-
-
-def list_field(default=None, metadata=None):
-    return field(default_factory=lambda: default, metadata=metadata)
-
-
-TASK_TO_TARGET_COLUMN_NAME = {
-    "fleurs-asr": "transcription",
-    "fleurs-lang_id": "lang_id",
-    "mls": "transcription",
-    "voxpopuli": "transcription",
-    "covost2": "translation",
-    "minds14": "intent_class",
-    "babel": "transcription",
-}
-
-
-@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"}
-    )
-    tokenizer_name_or_path: Optional[str] = field(
-        default=None,
-        metadata={"help": "Path to pretrained tokenizer or tokenizer identifier from huggingface.co/models"},
-    )
-    cache_dir: Optional[str] = field(
-        default=None,
-        metadata={
-            "help": "Where do you want to store the pretrained models and datasets downloaded from " "huggingface.co"
-        },
-    )
-    freeze_feature_encoder: bool = field(
-        default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."}
-    )
-    attention_dropout: float = field(
-        default=0.0, metadata={"help": "The dropout ratio for the attention probabilities."}
-    )
-    activation_dropout: float = field(
-        default=0.0, metadata={"help": "The dropout ratio for activations inside the fully connected layer."}
-    )
-    feat_proj_dropout: float = field(default=0.0, metadata={"help": "The dropout ratio for the projected features."})
-    hidden_dropout: float = field(
-        default=0.0,
-        metadata={
-            "help": "The dropout probability for all fully connected layers in the embeddings, encoder, and pooler."
-        },
-    )
-    final_dropout: float = field(
-        default=0.0,
-        metadata={"help": "The dropout probability for the final projection layer."},
-    )
-    mask_time_prob: float = field(
-        default=0.05,
-        metadata={
-            "help": "Probability of each feature vector along the time axis to be chosen as the start of the vector"
-            "span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"
-            "vectors will be masked along the time axis."
-        },
-    )
-    mask_time_length: int = field(
-        default=10,
-        metadata={"help": "Length of vector span to mask along the time axis."},
-    )
-    mask_feature_prob: float = field(
-        default=0.0,
-        metadata={
-            "help": "Probability of each feature vector along the feature axis to be chosen as the start of the vector"
-            "span to be masked. Approximately ``mask_feature_prob * sequence_length // mask_feature_length`` feature bins will be masked along the time axis."
-        },
-    )
-    mask_feature_length: int = field(
-        default=10,
-        metadata={"help": "Length of vector span to mask along the feature axis."},
-    )
-    layerdrop: float = field(default=0.0, metadata={"help": "The LayerDrop probability."})
-    ctc_zero_infinity: bool = field(
-        default=False,
-        metadata={"help": "Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`."},
-    )
-    ctc_loss_reduction: Optional[str] = field(
-        default="mean", metadata={"help": "The way the ctc loss should be reduced. Should be one of 'mean' or 'sum'."}
-    )
-
-
-@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: str = field(
-        default="google/xtreme_s",
-        metadata={"help": "The name of the dataset to use (via the datasets library). Defaults to 'google/xtreme_s'"},
-    )
-    task: str = field(
-        default=None,
-        metadata={
-            "help": "The task name of the benchmark to use (via the datasets library). Should be on of: "
-            "'fleurs-asr', 'mls', 'voxpopuli', 'covost2', 'minds14', 'fleurs-lang_id', 'babel'."
-        },
-    )
-    language: str = field(
-        default="all",
-        metadata={"help": "The language id as defined in the datasets config name or `all` for all languages."},
-    )
-    language_group: str = field(
-        default=None,
-        metadata={
-            "help": "The language group to select a subset of languages to train on. "
-            "This option is only used the 'fleurs-asr' task. Should be one of: "
-            "'western_european_we', 'eastern_european_ee', 'central_asia_middle_north_african_cmn', "
-            "'sub_saharan_african_ssa', 'south_asian_sa', 'south_east_asian_sea', 'chinese_japanase_korean_cjk'."
-        },
-    )
-    train_split_name: str = field(
-        default="train",
-        metadata={
-            "help": "The name of the training dataset split to use (via the datasets library). Defaults to 'train'"
-        },
-    )
-    eval_split_name: str = field(
-        default="validation",
-        metadata={
-            "help": "The name of the evaluation dataset split to use (via the datasets library). "
-            "Defaults to 'validation'"
-        },
-    )
-    predict_split_name: str = field(
-        default="test",
-        metadata={
-            "help": "The name of the prediction dataset split to use (via the datasets library). " "Defaults to 'test'"
-        },
-    )
-    audio_column_name: str = field(
-        default="audio",
-        metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
-    )
-    target_column_name: str = field(
-        default=None,
-        metadata={
-            "help": "The name of the dataset column containing the target data "
-            "(transcription/translation/label). If None, the name will be inferred from the task. 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_predict_samples: Optional[int] = field(
-        default=None,
-        metadata={
-            "help": "For debugging purposes or quicker training, truncate the number of prediction examples to this "
-            "value if set."
-        },
-    )
-    chars_to_ignore: Optional[List[str]] = list_field(
-        default=', ? . ! - ; : " “ % ‘ ” �'.split(" "),
-        metadata={"help": "A list of characters to remove from the transcripts."},
-    )
-    max_duration_in_seconds: float = field(
-        default=30.0,
-        metadata={
-            "help": "Filter audio files that are longer than `max_duration_in_seconds` seconds to 'max_duration_in_seconds`"
-        },
-    )
-    min_duration_in_seconds: float = field(
-        default=0.0, metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"}
-    )
-    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"
-        },
-    )
-    use_auth_token: bool = field(
-        default=False,
-        metadata={
-            "help": "If :obj:`True`, will use the token generated when running"
-            ":obj:`transformers-cli login` as HTTP bearer authorization for remote files."
-        },
-    )
-    unk_token: str = field(
-        default="[UNK]",
-        metadata={"help": "The unk token for the tokenizer"},
-    )
-    pad_token: str = field(
-        default="[PAD]",
-        metadata={"help": "The padding token for the tokenizer"},
-    )
-    word_delimiter_token: str = field(
-        default="|",
-        metadata={"help": "The word delimiter token for the tokenizer"},
-    )
-    phoneme_language: Optional[str] = field(
-        default=None,
-        metadata={
-            "help": "The target language that should be used be"
-            " passed to the tokenizer for tokenization. Note that"
-            " this is only relevant if the model classifies the"
-            " input audio to a sequence of phoneme sequences."
-        },
-    )
-    per_lang_metrics: bool = field(
-        default=True,
-        metadata={
-            "help": "If `True`, compute the test metrics separately for each language, and average the results. "
-            "If `False` compute the average test metrics in a single pass for all languages at once."
-        },
-    )
-
-
-@dataclass
-class SpeechDataCollatorWithPadding:
-
-    processor: AutoProcessor
-    decoder_start_token_id: Optional[int] = None
-    padding: Union[bool, str] = "longest"
-    pad_labels: Optional[int] = True
-    pad_to_multiple_of: Optional[int] = None
-    pad_to_multiple_of_labels: 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 lenghts and need
-        # different padding methods
-        input_features = [{"input_values": feature["input_values"]} for feature in features]
-
-        batch = self.processor.pad(
-            input_features,
-            padding=self.padding,
-            pad_to_multiple_of=self.pad_to_multiple_of,
-            return_tensors="pt",
-        )
-
-        if self.pad_labels:
-            label_features = [{"input_ids": feature["labels"]} for feature in features]
-            with self.processor.as_target_processor():
-                labels_batch = self.processor.pad(
-                    label_features,
-                    padding=self.padding,
-                    pad_to_multiple_of=self.pad_to_multiple_of_labels,
-                    return_tensors="pt",
-                )
-
-            # replace padding with -100 to ignore loss correctly
-            labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
-
-            # if bos token is appended in previous tokenization step,
-            # cut bos token here as it's append later anyways
-            if (
-                self.decoder_start_token_id is not None
-                and (labels[:, 0] == self.decoder_start_token_id).all().cpu().item()
-            ):
-                labels = labels[:, 1:]
-
-            batch["labels"] = labels
-        else:
-            batch["labels"] = torch.tensor([feature["labels"] for feature in features])
-
-        return batch
-
-
-def create_vocabulary_from_data(
-    datasets: DatasetDict,
-    word_delimiter_token: Optional[str] = None,
-    unk_token: Optional[str] = None,
-    pad_token: Optional[str] = None,
-):
-    # Given training and test labels create vocabulary
-    def extract_all_chars(batch):
-        all_text = " ".join(batch["target_text"])
-        vocab = list(set(all_text))
-        return {"vocab": [vocab], "all_text": [all_text]}
-
-    vocabs = datasets.map(
-        extract_all_chars,
-        batched=True,
-        batch_size=-1,
-        keep_in_memory=True,
-        remove_columns=datasets["train"].column_names,
-    )
-
-    # take union of all unique characters in each dataset
-    vocab_set = (
-        (set(vocabs["train"]["vocab"][0]) if "train" in vocabs else set())
-        | (set(vocabs["eval"]["vocab"][0]) if "eval" in vocabs else set())
-        | (set(vocabs["predict"]["vocab"][0]) if "predict" in vocabs else set())
-    )
-
-    vocab_dict = {v: k for k, v in enumerate(sorted(list(vocab_set)))}
-
-    # replace white space with delimiter token
-    if word_delimiter_token is not None:
-        vocab_dict[word_delimiter_token] = vocab_dict[" "]
-        del vocab_dict[" "]
-
-    # add unk and pad token
-    if unk_token is not None:
-        vocab_dict[unk_token] = len(vocab_dict)
-
-    if pad_token is not None:
-        vocab_dict[pad_token] = len(vocab_dict)
-
-    return vocab_dict
-
-
-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, Seq2SeqTrainingArguments))
-    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()
-
-    # 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 overcome."
-            )
-        elif last_checkpoint is not 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 is_main_process(training_args.local_rank) else logging.WARN)
-
-    # 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}"
-    )
-    # Set the verbosity to info of the Transformers logger (on main process only):
-    if is_main_process(training_args.local_rank):
-        transformers.utils.logging.set_verbosity_info()
-    logger.info("Training/evaluation parameters %s", training_args)
-
-    # Set seed before initializing model.
-    set_seed(training_args.seed)
-
-    # 1. First, let's load the dataset
-    raw_datasets = DatasetDict()
-    task_name = data_args.task
-    lang_id = data_args.language
-
-    if task_name is None:
-        raise ValueError(
-            "Set --task should be set to '<xtreme_s_task>' " "(e.g. 'fleurs-asr', 'mls', 'covost2', 'minds14') "
-        )
-    if lang_id is None:
-        raise ValueError(
-            "Set --language should be set to the language id of the sub dataset "
-            "config to be used (e.g. 'pl', 'en.tr', 'fr-FR') or 'all'"
-            " for multi-lingual fine-tuning."
-        )
-    if data_args.language_group is not None:
-        if data_args.task != "fleurs-asr":
-            raise ValueError("--language_group should only be used with --task=fleurs-asr")
-        if data_args.language != "all":
-            raise ValueError("--language_group should only be used with --language=all")
-
-    if data_args.target_column_name is None:
-        target_column_name = TASK_TO_TARGET_COLUMN_NAME[task_name]
-    else:
-        target_column_name = data_args.target_column_name
-
-    # here we differentiate between tasks with text as the target and classification tasks
-    is_text_target = target_column_name in ("transcription", "translation")
-
-    config_name = ".".join([task_name.split("-")[0], lang_id])
-
-    if training_args.do_train:
-        raw_datasets["train"] = load_dataset(
-            data_args.dataset_name,
-            config_name,
-            split=data_args.train_split_name,
-            use_auth_token=data_args.use_auth_token,
-            cache_dir=model_args.cache_dir,
-        )
-
-        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 target_column_name not in raw_datasets["train"].column_names:
-            raise ValueError(
-                f"--target_column_name {target_column_name} not found in dataset '{data_args.dataset_name}'. "
-                "Make sure to set `--target_column_name` to the correct text 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_dataset(
-            data_args.dataset_name,
-            config_name,
-            split=data_args.eval_split_name,
-            use_auth_token=data_args.use_auth_token,
-            cache_dir=model_args.cache_dir,
-        )
-
-        if data_args.max_eval_samples is not None:
-            raw_datasets["eval"] = raw_datasets["eval"].select(range(data_args.max_eval_samples))
-
-    if training_args.do_predict:
-        raw_datasets["predict"] = load_dataset(
-            data_args.dataset_name,
-            config_name,
-            split=data_args.predict_split_name,
-            use_auth_token=data_args.use_auth_token,
-            cache_dir=model_args.cache_dir,
-        )
-
-        if data_args.max_predict_samples is not None:
-            raw_datasets["predict"] = raw_datasets["predict"].select(range(data_args.max_predict_samples))
-
-    lang_list = next(iter(raw_datasets.values())).features["lang_id"].names
-    if not is_text_target:
-        label_list = next(iter(raw_datasets.values())).features[target_column_name].names
-        num_labels = len(label_list)
-
-    num_workers = data_args.preprocessing_num_workers
-
-    lang_group = data_args.language_group
-    if lang_group is not None:
-        with training_args.main_process_first(desc="language group filter"):
-            lang_group_id = next(iter(raw_datasets.values())).features["lang_group_id"].str2int(lang_group)
-            raw_datasets = raw_datasets.filter(
-                lambda lang_group: lang_group == lang_group_id,
-                num_proc=num_workers,
-                input_columns=["lang_group_id"],
-            )
-
-    # 2. We remove some special characters from the datasets
-    # that make training complicated and do not help in transcribing the speech
-    # E.g. characters, such as `,` and `.` do not really have an acoustic characteristic
-    # that could be easily picked up by the model
-    chars_to_ignore_regex = (
-        f'[{"".join(data_args.chars_to_ignore)}]' if data_args.chars_to_ignore is not None else None
-    )
-
-    def remove_special_characters(batch):
-        if chars_to_ignore_regex is not None:
-            batch["target_text"] = re.sub(chars_to_ignore_regex, "", batch[target_column_name]).lower()
-        else:
-            batch["target_text"] = batch[target_column_name].lower()
-        return batch
-
-    if is_text_target:
-        with training_args.main_process_first(desc="dataset map special characters removal"):
-            raw_datasets = raw_datasets.map(
-                remove_special_characters,
-                remove_columns=[target_column_name],
-                desc="remove special characters from datasets",
-            )
-
-        # save special tokens for tokenizer
-        word_delimiter_token = data_args.word_delimiter_token
-        unk_token = data_args.unk_token
-        pad_token = data_args.pad_token
-
-
-    encoder_id = "facebook/wav2vec2-xls-r-300m"
-    decoder_id = "facebook/bart-large"
-
-    model = SpeechEncoderDecoderModel.from_encoder_decoder_pretrained(encoder_id, decoder_id, encoder_add_adapter=True)
-    model.config.encoder.feat_proj_dropout = 0.0
-    model.config.encoder.final_dropout = 0.0
-    model.config.encoder.mask_time_prob = 0.1
-    model.config.decoder_start_token_id = model.decoder.config.bos_token_id
-    model.config.pad_token_id = model.decoder.config.pad_token_id
-    model.config.eos_token_id = model.decoder.config.eos_token_id
-    model.config.max_length = 40
-    model.config.num_beams = 1
-    model.config.encoder.layerdrop = 0.0
-    model.config.use_cache = False
-    model.config.processor_class = "Wav2Vec2Processor"
-
-    model.save_pretrained(model_args.model_name_or_path)
-
-    feature_etxractor = AutoFeatureExtractor.from_pretrained(encoder_id)
-    feature_etxractor.save_pretrained(model_args.model_name_or_path)
-    tokenizer = AutoTokenizer.from_pretrained(decoder_id)
-    tokenizer.save_pretrained(model_args.model_name_or_path)
-    
-    # 3. Next, let's load the config as we might need it to create
-    # the tokenizer
-    config = AutoConfig.from_pretrained(
-        model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_auth_token=data_args.use_auth_token
-    )
-
-    if is_text_target:
-        # 4. (Optional, for ASR and translation) If no tokenizer file is defined,
-        # we create the vocabulary of the model by extracting all unique characters from
-        # the training and evaluation datasets
-        # We need to make sure that only first rank saves vocabulary
-        # make sure all processes wait until vocab is created
-        tokenizer_name_or_path = model_args.tokenizer_name_or_path
-        tokenizer_kwargs = {}
-        if tokenizer_name_or_path is None:
-            # save vocab in training output dir
-            tokenizer_name_or_path = training_args.output_dir
-
-            vocab_file = os.path.join(tokenizer_name_or_path, "vocab.json")
-
-            with training_args.main_process_first():
-                if training_args.overwrite_output_dir and os.path.isfile(vocab_file):
-                    os.remove(vocab_file)
-
-            with training_args.main_process_first(desc="dataset map vocabulary creation"):
-                if not os.path.isfile(vocab_file):
-                    os.makedirs(tokenizer_name_or_path, exist_ok=True)
-                    vocab_dict = create_vocabulary_from_data(
-                        raw_datasets,
-                        word_delimiter_token=word_delimiter_token,
-                        unk_token=unk_token,
-                        pad_token=pad_token,
-                    )
-
-                    # save vocab dict to be loaded into tokenizer
-                    with open(vocab_file, "w") as file:
-                        json.dump(vocab_dict, file)
-
-            # if tokenizer has just been created
-            # it is defined by `tokenizer_class` if present in config else by `model_type`
-            if not config.is_encoder_decoder:
-                tokenizer_kwargs = {
-                    "config": config if config.tokenizer_class is not None else None,
-                    "tokenizer_type": config.model_type if config.tokenizer_class is None else None,
-                    "unk_token": unk_token,
-                    "pad_token": pad_token,
-                    "word_delimiter_token": word_delimiter_token,
-                }
-            else:
-                tokenizer_kwargs = {}
-
-    # 5. Now we can instantiate the feature extractor, tokenizer and model
-    # Note for distributed training, the .from_pretrained methods guarantee that only
-    # one local process can concurrently download model & vocab.
-
-    # load feature_extractor and tokenizer
-    if is_text_target:
-        tokenizer = AutoTokenizer.from_pretrained(
-            tokenizer_name_or_path,
-            use_auth_token=data_args.use_auth_token,
-            **tokenizer_kwargs,
-        )
-    feature_extractor = AutoFeatureExtractor.from_pretrained(
-        model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_auth_token=data_args.use_auth_token
-    )
-
-    # adapt config
-    # (speech translation requires pre-configured seq2seq models)
-    if task_name != "covost2":
-        config.update(
-            {
-                "feat_proj_dropout": model_args.feat_proj_dropout,
-                "attention_dropout": model_args.attention_dropout,
-                "hidden_dropout": model_args.hidden_dropout,
-                "final_dropout": model_args.final_dropout,
-                "mask_time_prob": model_args.mask_time_prob,
-                "mask_time_length": model_args.mask_time_length,
-                "mask_feature_prob": model_args.mask_feature_prob,
-                "mask_feature_length": model_args.mask_feature_length,
-                "gradient_checkpointing": training_args.gradient_checkpointing,
-                "layerdrop": model_args.layerdrop,
-                "ctc_zero_infinity": model_args.ctc_zero_infinity,
-                "ctc_loss_reduction": model_args.ctc_loss_reduction,
-                "activation_dropout": model_args.activation_dropout,
-            }
-        )
-        if training_args.do_train:
-            if is_text_target:
-                config.pad_token_id = tokenizer.pad_token_id
-                config.vocab_size = len(tokenizer)
-            else:
-                label_to_id = {v: i for i, v in enumerate(label_list)}
-                config.label2id = label_to_id
-                config.id2label = {id: label for label, id in label_to_id.items()}
-                config.num_labels = num_labels
-    else:
-        config.encoder.update({"hidden_dropout": model_args.hidden_dropout})
-
-    # create model
-    if target_column_name == "transcription":
-        model = AutoModelForCTC.from_pretrained(
-            model_args.model_name_or_path,
-            cache_dir=model_args.cache_dir,
-            config=config,
-            use_auth_token=data_args.use_auth_token,
-        )
-    elif config.is_encoder_decoder:
-        model = AutoModelForSpeechSeq2Seq.from_pretrained(
-            model_args.model_name_or_path,
-            cache_dir=model_args.cache_dir,
-            config=config,
-            use_auth_token=data_args.use_auth_token,
-        )
-        if model.config.decoder_start_token_id is None:
-            raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
-    else:
-        model = AutoModelForAudioClassification.from_pretrained(
-            model_args.model_name_or_path,
-            cache_dir=model_args.cache_dir,
-            config=config,
-            use_auth_token=data_args.use_auth_token,
-        )
-
-    # freeze encoder
-    if model_args.freeze_feature_encoder:
-        model.freeze_feature_encoder()
-
-    # 6. 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`
-
-    # make sure that dataset decodes audio with correct sampling rate
-    dataset_sampling_rate = next(iter(raw_datasets.values())).features[data_args.audio_column_name].sampling_rate
-    if dataset_sampling_rate != feature_extractor.sampling_rate:
-        raw_datasets = raw_datasets.cast_column(
-            data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)
-        )
-
-    # derive max & min input length for sample rate & max duration
-    max_input_length = data_args.max_duration_in_seconds * feature_extractor.sampling_rate
-    min_input_length = data_args.min_duration_in_seconds * feature_extractor.sampling_rate
-    audio_column_name = data_args.audio_column_name
-
-    # `phoneme_language` is only relevant if the model is fine-tuned on phoneme classification
-    phoneme_language = data_args.phoneme_language
-
-    # Preprocessing the datasets.
-    # We need to read the audio files as arrays and tokenize the targets.
-    def prepare_dataset(batch):
-        # load audio
-        sample = batch[audio_column_name]
-
-        inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
-        batch["input_values"] = inputs.input_values[0]
-        batch["length"] = len(batch["input_values"])
-
-        # encode targets
-        additional_kwargs = {}
-        if phoneme_language is not None:
-            additional_kwargs["phonemizer_lang"] = phoneme_language
-
-        if is_text_target:
-            batch["labels"] = tokenizer(batch["target_text"], **additional_kwargs).input_ids
-        else:
-            batch["labels"] = batch[target_column_name]
-
-        batch["lang"] = batch["lang_id"]
-
-        return batch
-
-    with training_args.main_process_first(desc="dataset map preprocessing"):
-        vectorized_datasets = raw_datasets.map(
-            prepare_dataset,
-            remove_columns=next(iter(raw_datasets.values())).column_names,
-            num_proc=num_workers,
-            desc="preprocess datasets",
-        )
-
-        if training_args.do_train:
-
-            def is_audio_in_length_range(length):
-                return length > min_input_length and length < max_input_length
-
-            # filter data that is shorter than min_input_length
-            vectorized_datasets["train"] = vectorized_datasets["train"].filter(
-                is_audio_in_length_range,
-                num_proc=num_workers,
-                input_columns=["length"],
-            )
-
-    # 7. Next, we can prepare for the training step.
-    # Let's use the appropriate XTREME-S evaluation metric,
-    # instantiate a data collator and the trainer
-
-    # Define evaluation metrics during training, *i.e.* word error rate, character error rate
-    eval_metric = load_metric("xtreme_s", task_name)
-
-    # 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:
-        logger.info(f"Data preprocessing finished. Files cached at {vectorized_datasets.cache_files}")
-        return
-
-    def asr_logits_argmax(logits, labels):
-        return logits.argmax(dim=-1)
-
-    def compute_asr_metric(pred):
-        pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id
-
-        pred_str = tokenizer.batch_decode(pred.predictions)
-        # we do not want to group tokens when computing the metrics
-        label_str = tokenizer.batch_decode(pred.label_ids, group_tokens=False)
-
-        metric = eval_metric.compute(predictions=pred_str, references=label_str)
-        return metric
-
-    def compute_classification_metric(pred):
-        pred_ids = np.argmax(pred.predictions, axis=1)
-        metric = eval_metric.compute(predictions=pred_ids, references=pred.label_ids)
-        return metric
-
-    # Now save everything to be able to create a single processor later
-    if is_main_process(training_args.local_rank):
-        # save feature extractor, tokenizer and config
-        feature_extractor.save_pretrained(training_args.output_dir)
-        if is_text_target:
-            tokenizer.save_pretrained(training_args.output_dir)
-        config.save_pretrained(training_args.output_dir)
-    # wait until configs are saved in the main process before loading the processor
-    if training_args.local_rank != -1:
-        torch.distributed.barrier()
-
-    if is_text_target:
-        processor = AutoProcessor.from_pretrained(training_args.output_dir)
-    else:
-        processor = AutoFeatureExtractor.from_pretrained(training_args.output_dir)
-
-    # Instantiate custom data collator
-    data_collator = SpeechDataCollatorWithPadding(processor=processor, pad_labels=is_text_target)
-
-    # Initialize Trainer
-    if target_column_name == "translation":
-        trainer = Seq2SeqTrainer(
-            model=model,
-            data_collator=data_collator,
-            args=training_args,
-            preprocess_logits_for_metrics=asr_logits_argmax if training_args.predict_with_generate else None,
-            compute_metrics=compute_asr_metric if training_args.predict_with_generate else None,
-            train_dataset=vectorized_datasets["train"] if training_args.do_train else None,
-            eval_dataset=vectorized_datasets["eval"] if training_args.do_eval else None,
-            tokenizer=feature_extractor,
-        )
-    else:
-        trainer = Trainer(
-            model=model,
-            data_collator=data_collator,
-            args=training_args,
-            preprocess_logits_for_metrics=asr_logits_argmax if is_text_target else None,
-            compute_metrics=compute_asr_metric if is_text_target else compute_classification_metric,
-            train_dataset=vectorized_datasets["train"] if training_args.do_train else None,
-            eval_dataset=vectorized_datasets["eval"] if training_args.do_eval else None,
-            tokenizer=feature_extractor,
-        )
-
-    # 8. Finally, we can start training
-
-    # Training
-    if training_args.do_train:
-
-        # use last checkpoint if exist
-        if last_checkpoint is not None:
-            checkpoint = last_checkpoint
-        elif os.path.isdir(model_args.model_name_or_path):
-            checkpoint = model_args.model_name_or_path
-        else:
-            checkpoint = None
-
-        train_result = trainer.train(resume_from_checkpoint=checkpoint)
-        trainer.save_model()
-
-        metrics = train_result.metrics
-        max_train_samples = (
-            data_args.max_train_samples
-            if data_args.max_train_samples is not None
-            else len(vectorized_datasets["train"])
-        )
-        metrics["train_samples"] = min(max_train_samples, len(vectorized_datasets["train"]))
-
-        trainer.log_metrics("train", metrics)
-        trainer.save_metrics("train", metrics)
-        trainer.save_state()
-
-    # Evaluation on the test set
-    results = {}
-    if training_args.do_predict:
-        logger.info(f"*** Evaluating on the `{data_args.predict_split_name}` set ***")
-        if data_args.per_lang_metrics:
-            # separate the `test` dataset into language-specific subsets and compute metrics for each of them
-            metrics = {}
-            average_metrics = defaultdict(list)
-            for lang_id in range(len(lang_list)):
-                lang_name = lang_list[lang_id]
-                with training_args.main_process_first(desc="per-language dataset filter"):
-                    lang_dataset = vectorized_datasets["predict"].filter(
-                        lambda lang: lang == lang_id,
-                        num_proc=num_workers,
-                        input_columns=["lang"],
-                    )
-                lang_metrics = trainer.evaluate(lang_dataset)
-                redundant_metrics = ["eval_runtime", "eval_samples_per_second", "eval_steps_per_second", "eval_epoch"]
-                for metric_name, value in lang_metrics.items():
-                    average_metrics[metric_name].append(value)
-                    if metric_name not in redundant_metrics:
-                        metrics[f"{metric_name}_{lang_name}"] = value
-            for metric_name, value in average_metrics.items():
-                metrics[metric_name] = np.mean(value)
-        else:
-            metrics = trainer.evaluate(vectorized_datasets["predict"])
-        max_predict_samples = (
-            data_args.max_predict_samples
-            if data_args.max_predict_samples is not None
-            else len(vectorized_datasets["predict"])
-        )
-        metrics["predict_samples"] = min(max_predict_samples, len(vectorized_datasets["predict"]))
-
-        # make sure that the `predict` metrics end up in the log history for the model card
-        trainer.log(OrderedDict(sorted(metrics.items())))
-
-        trainer.log_metrics("predict", metrics)
-        trainer.save_metrics("predict", metrics)
-
-    # Write model card and (optionally) push to hub
-    kwargs = {
-        "finetuned_from": model_args.model_name_or_path,
-        "tasks": task_name,
-        "tags": [task_name, data_args.dataset_name],
-        "dataset_args": f"Config: {config_name}, Training split: {data_args.train_split_name}, Eval split: {data_args.eval_split_name}, Predict split: {data_args.predict_split_name}",
-        "dataset": f"{data_args.dataset_name.upper()} - {config_name.upper()}",
-        "language": data_args.language,
-    }
-
-    if training_args.push_to_hub:
-        trainer.push_to_hub(**kwargs)
-    else:
-        trainer.create_model_card(**kwargs)
-
-    return results
-
-
-if __name__ == "__main__":
-    main()
-

run_xtreme_s.py

@@ -0,0 +1 @@
+/home/sanchit_huggingface_co/run_xtreme_s.py

runs/May03_17-16-03_sanchit--v100/events.out.tfevents.1651598448.sanchit--v100.42221.0

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training_args.bin

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wandb/debug-cli.log

@@ -0,0 +1,26 @@
+2022-05-04 13:30:45 INFO Running runs: []
+2022-05-04 13:30:45 INFO Agent received command: run
+2022-05-04 13:30:45 INFO Agent starting run with config:
+	eval_split_name: test
+	eval_steps: 500
+	evaluation_strategy: steps
+	generation_max_length: 40
+	generation_num_beams: 1
+	gradient_accumulation_steps: 8
+	greater_is_better: True
+	hidden_dropout: 0.17305159310134854
+	language: fr.en
+	learning_rate: 0.00012335092351490598
+	logging_steps: 1
+	max_duration_in_seconds: 20
+	metric_for_best_model: bleu
+	model_name_or_path: ./
+	num_train_epochs: 3
+	output_dir: ./
+	per_device_eval_batch_size: 8
+	per_device_train_batch_size: 8
+	save_steps: 500
+	task: covost2
+	warmup_steps: 500
+2022-05-04 13:30:45 INFO About to run command: python3 run_xtreme_s.py --overwrite_output_dir --freeze_feature_encoder --gradient_checkpointing --predict_with_generate --fp16 --group_by_length --do_train --do_eval --load_best_model_at_end --push_to_hub --use_auth_token --eval_split_name=test --eval_steps=500 --evaluation_strategy=steps --generation_max_length=40 --generation_num_beams=1 --gradient_accumulation_steps=8 --greater_is_better=True --hidden_dropout=0.17305159310134854 --language=fr.en --learning_rate=0.00012335092351490598 --logging_steps=1 --max_duration_in_seconds=20 --metric_for_best_model=bleu --model_name_or_path=./ --num_train_epochs=3 --output_dir=./ --per_device_eval_batch_size=8 --per_device_train_batch_size=8 --save_steps=500 --task=covost2 --warmup_steps=500
+2022-05-04 13:30:50 INFO Running runs: ['w4rlzz90']

wandb/debug-internal.log

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+run-20220504_142129-w4rlzz90/logs/debug-internal.log

wandb/debug.log

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wandb/latest-run

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-run-20220503_172048-zotxt8wa
+run-20220504_142129-w4rlzz90

wandb/run-20220503_172048-zotxt8wa/logs/debug-internal.log

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wandb/run-20220503_172048-zotxt8wa/logs/debug.log

@@ -26,3 +26,7 @@ config: {}
 2022-05-03 17:20:52,207 INFO    MainThread:42221 [wandb_init.py:init():651] run started, returning control to user process
 2022-05-03 17:20:52,210 INFO    MainThread:42221 [wandb_run.py:_config_callback():966] config_cb None None {'return_dict': True, 'output_hidden_states': False, 'output_attentions': False, 'torchscript': False, 'torch_dtype': 'torch.float32', 'use_bfloat16': False, 'pruned_heads': {}, 'tie_word_embeddings': False, 'is_encoder_decoder': True, 'is_decoder': False, 'cross_attention_hidden_size': None, 'add_cross_attention': False, 'tie_encoder_decoder': False, 'max_length': 40, 'min_length': 0, 'do_sample': False, 'early_stopping': False, 'num_beams': 1, 'num_beam_groups': 1, 'diversity_penalty': 0.0, 'temperature': 1.0, 'top_k': 50, 'top_p': 1.0, 'typical_p': 1.0, 'repetition_penalty': 1.0, 'length_penalty': 1.0, 'no_repeat_ngram_size': 0, 'encoder_no_repeat_ngram_size': 0, 'bad_words_ids': None, 'num_return_sequences': 1, 'chunk_size_feed_forward': 0, 'output_scores': False, 'return_dict_in_generate': False, 'forced_bos_token_id': None, 'forced_eos_token_id': None, 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+}

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