Ranjit
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Whisper_Small_Odia_10k_steps

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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
-# 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__)
-@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 data_args.streaming else DatasetDict()
-    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,
-            use_auth_token=True if model_args.use_auth_token else None,
-            streaming=data_args.streaming,
-        )
-    if training_args.do_eval:
-        raw_datasets["eval"] = load_maybe_streaming_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,
-            streaming=data_args.streaming,
-        )
-    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")
-    if model_args.freeze_feature_encoder:
-        model.freeze_feature_encoder()
-    if model_args.freeze_encoder:
-        model.freeze_encoder()
-    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.streaming
-            else raw_datasets["train"].select(range(data_args.max_train_samples))
-        )
-    if data_args.max_eval_samples is not None:
-        raw_datasets["eval"] = (
-            raw_datasets["eval"].take(data_args.max_eval_samples)
-            if data_args.streaming
-            else 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, max_length=448, truncation=True).input_ids
-        #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 and data_args.streaming:
-            # manually shuffle if streaming (done by the trainer for non-streaming)
-            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
-    if training_args.do_train:
-        vectorized_datasets["train"] = vectorized_datasets["train"].filter(
-            is_audio_in_length_range,
-            input_columns=["input_length"],
-        )
-    # 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)
-    # 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()] if data_args.streaming else None,
-    )
-    # 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.split('-')[0]
-        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()