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pytorch_model.bin

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run_speech_recognition_seq2seq_mixed_mgb2_wandb.py

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+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2022 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.
+# 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.
+"""
+Fine-tuning the library models for sequence to sequence speech recognition
+with 🤗 Datasets' streaming mode.
+"""
+# You can also adapt this script for your own sequence to sequence speech
+# recognition task. Pointers for this are left as comments.
+
+import logging
+import os
+import sys
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional, Union
+
+import datasets
+import torch
+from datasets import DatasetDict, IterableDatasetDict, interleave_datasets, load_dataset
+from torch.utils.data import IterableDataset
+
+import evaluate
+import transformers
+from transformers import (
+    AutoConfig,
+    AutoFeatureExtractor,
+    AutoModelForSpeechSeq2Seq,
+    AutoProcessor,
+    AutoTokenizer,
+    HfArgumentParser,
+    Seq2SeqTrainer,
+    Seq2SeqTrainingArguments,
+    TrainerCallback,
+    set_seed,
+)
+from transformers.models.whisper.english_normalizer import BasicTextNormalizer
+from transformers.trainer_pt_utils import IterableDatasetShard
+from transformers.trainer_utils import get_last_checkpoint, is_main_process
+from transformers.utils import check_min_version, send_example_telemetry
+from transformers.utils.versions import require_version
+import wandb
+
+run = wandb.init(project="whisper_finetuning", job_type="fine-tuning", group="medium", resume="must", id="2k10w4qq" )
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.25.0.dev0")
+
+require_version(
+    "datasets>=1.18.2",
+    "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt",
+)
+
+logger = logging.getLogger(__name__)
+
+
+def load_samples_dataset(dataset, num_samples=10):
+    samples = []
+    for i, item in enumerate(dataset):
+        samples.append(item)
+        if i == (num_samples-1):
+            break
+    sample_dataset = Dataset.from_list(samples)
+    return sample_dataset
+
+def compute_spectrograms(example):
+    waveform =  example["audio"]["array"]
+    specs = feature_extractor(waveform, sampling_rate=16000, padding="do_not_pad").input_features[0]
+    return {"spectrogram": specs}
+
+
+def record_to_html(sample_record):
+    audio_array = np.array(sample_record["audio"]["array"])
+    audio_sr = sample_record["audio"]["sampling_rate"]
+    audio_duration = sample_record["length"]
+    audio_spectrogram = np.array(sample_record["spectrogram"])
+
+    bounds = (0,0, audio_duration, audio_spectrogram.max())
+
+    waveform_int = np.int16(audio_array * 32767)
+
+    
+    
+    hv_audio = pn.pane.Audio(waveform_int, sample_rate=audio_sr, name='Audio', throttle=500)
+    
+    slider = pn.widgets.FloatSlider(end=audio_duration, visible=False, step=0.001)
+    line_audio = hv.VLine(0).opts(color='black')
+    line_spec = hv.VLine(0).opts(color='red')
+    
+    
+    slider.jslink(hv_audio, value='time', bidirectional=True)
+    slider.jslink(line_audio, value='glyph.location')
+    slider.jslink(line_spec, value='glyph.location')
+    
+    time = np.linspace(0, audio_duration, num=len(audio_array))
+    line_plot_hv = hv.Curve(
+        (time, audio_array), ["Time (s)", "amplitude"]).opts(
+        width=500, height=150, axiswise=True) * line_audio
+    
+    hv_spec_gram = hv.Image(
+        audio_spectrogram, bounds=(bounds), kdims=["Time (s)", "Frequency (hz)"]).opts(
+        width=500, height=150, labelled=[], axiswise=True, color_levels=512)* line_spec
+    
+    
+    combined = pn.Row(hv_audio, hv_spec_gram, line_plot_hv, slider)
+    audio_html = StringIO()
+    combined.save(audio_html)
+    return audio_html
+
+
+def dataset_to_records(dataset):
+    records = []
+    for item in dataset:
+        record = {}
+        record["audio_with_spec"] = wandb.Html(record_to_html(item))
+        record["sentence"] = item["sentence"]
+        record["length"] = item["length"]
+        records.append(record)
+    records = pd.DataFrame(records)
+    return records
+    
+def decode_predictions(trainer, predictions):
+    pred_ids = predictions.predictions
+    pred_str = trainer.tokenizer.batch_decode(pred_ids, skip_special_tokens=True, )
+    return pred_str
+
+
+def compute_measures(predictions, labels):
+    measures = [jiwer.compute_measures(ls, ps,) for ps, ls in zip(predictions, labels)]
+    measures_df = pd.DataFrame(measures)[["wer", "hits", "substitutions", "deletions", "insertions"]]
+    return measures_df
+
+class WandbProgressResultsCallback(WandbCallback):
+    def __init__(self, trainer, sample_dataset): 
+        super().__init__()
+        self.trainer = trainer
+        self.sample_dataset = sample_dataset
+        self.records_df = dataset_to_records(sample_dataset)
+        
+    def on_log(self, args, state, control, model=None, logs=None, **kwargs):
+        super().on_log(args, state, control, model, logs)
+        predictions = trainer.predict(self.sample_dataset)
+        predictions = decode_predictions(self.trainer, predictions)
+        measures_df = compute_measures(predictions, self.records_df["sentence"].tolist())
+        records_df = pd.concat([self.records_df, measures_df], axis=1)
+        records_df["prediction"] = predictions
+        records_df["step"] = state.global_step
+        records_table = self._wandb.Table(dataframe=records_df)
+        self._wandb.log({"sample_predictions": records_table})
+        
+    def on_save(self, args, state, control, model=None, tokenizer=None, **kwargs):
+        if self._wandb is None:
+            return
+        if self._log_model and self._initialized and state.is_world_process_zero:
+            with tempfile.TemporaryDirectory() as temp_dir:
+                self.trainer.save_model(temp_dir)
+                metadata = (
+                    {
+                        k: v
+                        for k, v in dict(self._wandb.summary).items()
+                        if isinstance(v, numbers.Number) and not k.startswith("_")
+                    }
+                    if not args.load_best_model_at_end
+                    else {
+                        f"eval/{args.metric_for_best_model}": state.best_metric,
+                        "train/total_floss": state.total_flos,
+                    }
+                )
+                artifact = self._wandb.Artifact(
+                    name=f"model-{self._wandb.run.id}",
+                    type="model", metadata=metadata)
+                for f in Path(temp_dir).glob("*"):
+                    if f.is_file():
+                        with artifact.new_file(f.name, mode="wb") as fa:
+                            fa.write(f.read_bytes())
+                self._wandb.run.log_artifact(artifact)
+
+@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"
+        },
+    )
+    tokenizer_name: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "Pretrained tokenizer name or path if not the same as model_name"
+        },
+    )
+    feature_extractor_name: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "feature extractor name or path if not the same as model_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)."
+        },
+    )
+    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_encoder: bool = field(
+        default=True,
+        metadata={"help": "Whether to freeze the feature encoder layers of the model."},
+    )
+    freeze_encoder: bool = field(
+        default=False,
+        metadata={"help": "Whether to freeze the entire encoder of the seq2seq model."},
+    )
+    forced_decoder_ids: List[List[int]] = field(
+        default=None,
+        metadata={
+            "help": (
+                "A list of pairs of integers which indicates a mapping from generation indices to token indices "
+                "that will be forced before sampling. For example, [[0, 123]] means the first generated token "
+                "will always be a token of index 123."
+            )
+        },
+    )
+    suppress_tokens: List[int] = field(
+        default=None,
+        metadata={"help": "A list of tokens that will be suppressed at generation."},
+    )
+    model_index_name: str = field(
+        default=None, metadata={"help": "Pretty name for the model card."}
+    )
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    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)."
+        },
+    )
+    text_column: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "The name of the column in the datasets containing the full texts (for summarization)."
+        },
+    )
+    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."
+            )
+        },
+    )
+    audio_column_name: str = field(
+        default="audio",
+        metadata={
+            "help": "The name of the dataset column containing the audio data. Defaults to 'audio'"
+        },
+    )
+    text_column_name: str = field(
+        default="text",
+        metadata={
+            "help": "The name of the dataset column containing the text data. Defaults to 'text'"
+        },
+    )
+    max_duration_in_seconds: float = field(
+        default=20.0,
+        metadata={
+            "help": (
+                "Truncate 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"
+        },
+    )
+    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="test",
+        metadata={
+            "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
+        },
+    )
+    do_lower_case: bool = field(
+        default=False,
+        metadata={"help": "Whether the target text should be lower cased."},
+    )
+    do_remove_punctuation: bool = field(
+        default=False,
+        metadata={"help": "Whether the target text should be striped of punctuation."},
+    )
+    do_normalize_eval: bool = field(
+        default=True,
+        metadata={
+            "help": "Whether to normalise the references and predictions in the eval WER calculation."
+        },
+    )
+    language: str = field(
+        default=None,
+        metadata={
+            "help": (
+                "Language for multilingual fine-tuning. This argument should be set for multilingual fine-tuning "
+                "only. For English speech recognition, it should be set to `None`."
+            )
+        },
+    )
+    task: str = field(
+        default="transcribe",
+        metadata={
+            "help": "Task, either `transcribe` for speech recognition or `translate` for speech translation."
+        },
+    )
+    shuffle_buffer_size: Optional[int] = field(
+        default=500,
+        metadata={
+            "help": (
+                "The number of streamed examples to download before shuffling them. The large the buffer, "
+                "the closer it is to real offline shuffling."
+            )
+        },
+    )
+    streaming: bool = field(
+        default=True,
+        metadata={
+            "help": "Whether to use streaming mode to load and pre-process the data."
+        },
+    )
+
+
+@dataclass
+class DataCollatorSpeechSeq2SeqWithPadding:
+    """
+    Data collator that will dynamically pad the inputs received.
+    Args:
+        processor ([`WhisperProcessor`])
+            The processor used for processing the data.
+        decoder_start_token_id (`int`)
+            The begin-of-sentence of the decoder.
+    """
+
+    processor: Any
+    decoder_start_token_id: int
+
+    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
+        model_input_name = self.processor.model_input_names[0]
+        input_features = [
+            {model_input_name: feature[model_input_name]} for feature in features
+        ]
+        label_features = [{"input_ids": feature["labels"]} for feature in features]
+
+        batch = self.processor.feature_extractor.pad(
+            input_features, return_tensors="pt"
+        )
+
+        labels_batch = self.processor.tokenizer.pad(label_features, 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 (labels[:, 0] == self.decoder_start_token_id).all().cpu().item():
+            labels = labels[:, 1:]
+
+        batch["labels"] = labels
+
+        return batch
+
+
+def load_maybe_streaming_dataset(
+    dataset_name, dataset_config_name, split="train", streaming=True, **kwargs
+):
+    """
+    Utility function to load a dataset in streaming mode. For datasets with multiple splits,
+    each split is loaded individually and then splits combined by taking alternating examples from
+    each (interleaving).
+    """
+    if "+" in split:
+        # load multiple splits separated by the `+` symbol with streaming mode
+        dataset_splits = [
+            load_dataset(
+                dataset_name,
+                dataset_config_name,
+                split=split_name,
+                streaming=streaming,
+                **kwargs,
+            )
+            for split_name in split.split("+")
+        ]
+        # interleave multiple splits to form one dataset
+        interleaved_dataset = interleave_datasets(dataset_splits)
+        return interleaved_dataset
+    else:
+        # load a single split *with* streaming mode
+        dataset = load_dataset(
+            dataset_name,
+            dataset_config_name,
+            split=split,
+            streaming=streaming,
+            **kwargs,
+        )
+        return dataset
+
+
+def main():
+    # 1. Parse input arguments
+    # 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()
+
+    # 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_speech_recognition_seq2seq_streaming", model_args, data_args
+    )
+
+    # 2. 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)],
+    )
+    log_level = training_args.get_process_log_level()
+    logger.setLevel(log_level)
+    datasets.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.enable_default_handler()
+    transformers.utils.logging.enable_explicit_format()
+
+    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}"
+    )
+    logger.info(f"Training/evaluation parameters {training_args}")
+
+    # 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)
+
+    # 3. 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."
+            )
+
+    # Set seed before initializing model.
+    set_seed(training_args.seed)
+
+    # 4. Load dataset
+    raw_datasets = IterableDatasetDict()
+
+    if training_args.do_train:
+        raw_datasets["train"] = load_maybe_streaming_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            split=data_args.train_split_name,
+            streaming=True,
+            use_auth_token=True if model_args.use_auth_token else None,
+        )
+
+    if training_args.do_eval:
+        raw_datasets["eval"] = load_maybe_streaming_dataset(
+            "arbml/mgb3",
+            data_args.dataset_config_name,
+            split="train",
+            streaming=False,
+            use_auth_token=True if model_args.use_auth_token else None,
+        )
+
+    raw_datasets_features = list(next(iter(raw_datasets.values())).features.keys())
+
+    if data_args.audio_column_name not in raw_datasets_features:
+        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_features)}."
+        )
+
+    if data_args.text_column_name not in raw_datasets_features:
+        raise ValueError(
+            f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. "
+            "Make sure to set `--text_column_name` to the correct text column - one of "
+            f"{', '.join(raw_datasets_features)}."
+        )
+
+    # 5. Load pretrained model, tokenizer, and feature extractor
+    #
+    # Distributed training:
+    # The .from_pretrained methods guarantee that only one local process can concurrently
+    config = AutoConfig.from_pretrained(
+        model_args.config_name
+        if model_args.config_name
+        else 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,
+    )
+
+    config.update(
+        {
+            "forced_decoder_ids": model_args.forced_decoder_ids,
+            "suppress_tokens": model_args.suppress_tokens,
+        }
+    )
+
+    if training_args.gradient_checkpointing:
+        config.update({"use_cache": False})
+
+    feature_extractor = AutoFeatureExtractor.from_pretrained(
+        model_args.feature_extractor_name
+        if model_args.feature_extractor_name
+        else 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,
+    )
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_args.tokenizer_name
+        if model_args.tokenizer_name
+        else model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        use_fast=model_args.use_fast_tokenizer,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+    model = AutoModelForSpeechSeq2Seq.from_pretrained(
+        model_args.model_name_or_path,
+        config=config,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+
+    if model.config.decoder_start_token_id is None:
+        raise ValueError(
+            "Make sure that `config.decoder_start_token_id` is correctly defined"
+        )
+
+    max_label_length = model.config.max_length
+
+    if model_args.freeze_feature_encoder:
+        model.freeze_feature_encoder()
+
+    if model_args.freeze_encoder:
+        model.freeze_encoder()
+        model.model.encoder.gradient_checkpointing = False
+
+    if data_args.language is not None:
+        # We only need to set the task id when the language is specified (i.e. in a multilingual setting)
+        tokenizer.set_prefix_tokens(language=data_args.language, task=data_args.task)
+
+    # 6. Resample speech dataset if necessary
+    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),
+        )
+
+    # 7. Preprocessing the datasets.
+    # We need to read the audio files as arrays and tokenize the targets.
+    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
+    text_column_name = data_args.text_column_name
+    model_input_name = feature_extractor.model_input_names[0]
+    do_lower_case = data_args.do_lower_case
+    do_remove_punctuation = data_args.do_remove_punctuation
+    normalizer = BasicTextNormalizer()  # 'official' text normalizer from OpenAI
+
+    if data_args.max_train_samples is not None:
+        raw_datasets["train"] = raw_datasets["train"].take(data_args.max_train_samples)
+
+    if data_args.max_eval_samples is not None:
+        raw_datasets["eval"] = raw_datasets["eval"].select(
+            range(data_args.max_eval_samples)
+        )
+
+    def prepare_dataset(batch):
+        # process audio
+        sample = batch[audio_column_name]
+        inputs = feature_extractor(
+            sample["array"], sampling_rate=sample["sampling_rate"]
+        )
+        # process audio length
+        batch[model_input_name] = inputs.get(model_input_name)[0]
+        batch["input_length"] = len(sample["array"])
+
+        # process targets
+        input_str = (
+            batch[text_column_name].lower()
+            if do_lower_case
+            else batch[text_column_name]
+        )
+        if do_remove_punctuation:
+            input_str = normalizer(input_str).strip()
+        batch["labels"] = tokenizer(input_str).input_ids
+        return batch
+
+    with training_args.main_process_first(desc="dataset map pre-processing"):
+        vectorized_datasets = raw_datasets.map(
+            prepare_dataset,
+            remove_columns=raw_datasets_features,
+        ).with_format("torch")
+
+        if training_args.do_train:
+            vectorized_datasets["train"] = vectorized_datasets["train"].shuffle(
+                buffer_size=data_args.shuffle_buffer_size,
+                seed=training_args.seed,
+            )
+
+    # filter training data that is shorter than min_input_length or longer than
+    # max_input_length
+    def is_audio_in_length_range(length):
+        return min_input_length < length < max_input_length
+
+    vectorized_datasets["train"] = vectorized_datasets["train"].filter(
+        is_audio_in_length_range,
+        input_columns=["input_length"],
+    )
+
+    def filter_labels(labels):
+        """Filter label sequences longer than max length"""
+        return len(labels) < max_label_length
+
+    vectorized_datasets = vectorized_datasets.filter(filter_labels, input_columns=["labels"])
+    
+    # 8. Load Metric
+    metric = evaluate.load("wer")
+    do_normalize_eval = data_args.do_normalize_eval
+
+    def compute_metrics(pred):
+        pred_ids = pred.predictions
+
+        pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id
+
+        pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
+        # we do not want to group tokens when computing the metrics
+        label_str = tokenizer.batch_decode(pred.label_ids, skip_special_tokens=True)
+
+        if do_normalize_eval:
+            pred_str = [normalizer(pred) for pred in pred_str]
+            label_str = [normalizer(label) for label in label_str]
+            # filtering step to only evaluate the samples that correspond to non-zero references:
+            pred_str = [
+                pred_str[i] for i in range(len(pred_str)) if len(label_str[i]) > 0
+            ]
+            label_str = [
+                label_str[i] for i in range(len(label_str)) if len(label_str[i]) > 0
+            ]
+
+        wer = 100 * metric.compute(predictions=pred_str, references=label_str)
+
+        return {"wer": wer}
+
+    # 9. Create a single speech processor
+    if is_main_process(training_args.local_rank):
+        # save feature extractor, tokenizer and config
+        feature_extractor.save_pretrained(training_args.output_dir)
+        tokenizer.save_pretrained(training_args.output_dir)
+        config.save_pretrained(training_args.output_dir)
+
+    processor = AutoProcessor.from_pretrained(training_args.output_dir)
+
+    # 10. Define data collator
+    data_collator = DataCollatorSpeechSeq2SeqWithPadding(
+        processor=processor,
+        decoder_start_token_id=model.config.decoder_start_token_id,
+    )
+
+    # 11. Configure Trainer
+    # Trainer callback to reinitialise and reshuffle the streamable datasets at the beginning of each epoch
+    # Only required for streaming: Trainer automatically shuffles non-streaming datasets
+    class ShuffleCallback(TrainerCallback):
+        def on_epoch_begin(self, args, state, control, train_dataloader, **kwargs):
+            if isinstance(train_dataloader.dataset, IterableDatasetShard):
+                pass  # set_epoch() is handled by the Trainer
+            elif isinstance(train_dataloader.dataset, IterableDataset):
+                train_dataloader.dataset.set_epoch(train_dataloader.dataset._epoch + 1)
+
+    progress_callback = WandbProgressResultsCallback(trainer, samples_dataset)
+
+    # Initialize Trainer
+    trainer = Seq2SeqTrainer(
+        model=model,
+        args=training_args,
+        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,
+        data_collator=data_collator,
+        compute_metrics=compute_metrics
+        if training_args.predict_with_generate
+        else None,
+        callbacks=[ShuffleCallback()],
+    )
+
+    trainer.add_callback(progress_callback)
+    
+    # 12. 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()  # Saves the feature extractor too for easy upload
+
+        metrics = train_result.metrics
+        if data_args.max_train_samples:
+            metrics["train_samples"] = data_args.max_train_samples
+        trainer.log_metrics("train", metrics)
+        trainer.save_metrics("train", metrics)
+        trainer.save_state()
+
+    # 13. Evaluation
+    results = {}
+    if training_args.do_eval:
+        logger.info("*** Evaluate ***")
+        metrics = trainer.evaluate(
+            metric_key_prefix="eval",
+            max_length=training_args.generation_max_length,
+            num_beams=training_args.generation_num_beams,
+        )
+        if data_args.max_eval_samples:
+            metrics["eval_samples"] = data_args.max_eval_samples
+
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    # 14. Write Training Stats
+    kwargs = {
+        "finetuned_from": model_args.model_name_or_path,
+        "tasks": "automatic-speech-recognition",
+        "tags": "whisper-event",
+    }
+    if data_args.dataset_name is not None:
+        kwargs["dataset_tags"] = data_args.dataset_name
+        if data_args.dataset_config_name is not None:
+            kwargs[
+                "dataset"
+            ] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
+        else:
+            kwargs["dataset"] = data_args.dataset_name
+        if "common_voice" in data_args.dataset_name:
+            kwargs["language"] = data_args.dataset_config_name[:2]
+        if model_args.model_index_name is not None:
+            kwargs["model_name"] = model_args.model_index_name
+
+    if training_args.push_to_hub:
+        trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
+
+    return results
+
+
+if __name__ == "__main__":
+    main()

runs/Dec14_09-02-25_129-146-107-47/events.out.tfevents.1671008564.129-146-107-47.118226.0

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