eval model on test sv

.gitignore

@@ -0,0 +1,6 @@
+*venv/
+runs/
+creds.txt
+wandb/
+dlc/
+tests/

requirements.txt

@@ -0,0 +1,10 @@
+torch>=1.7
+torchaudio==0.13.1
+git+https://github.com/huggingface/transformers
+git+https://github.com/huggingface/datasets
+librosa
+jiwer
+evaluate>=0.3.0
+more-itertools
+tensorboard
+wandb

requirements_mac.txt

@@ -0,0 +1,10 @@
+torch>=1.7
+torchaudio==0.11.0
+git+https://github.com/huggingface/transformers
+git+https://github.com/huggingface/datasets
+librosa
+jiwer
+evaluate>=0.3.0
+more-itertools
+tensorboard
+wandb

run.sh

@@ -0,0 +1,40 @@
+python $1run_speech_recognition_seq2seq_streaming.py \
+	--model_name_or_path="openai/whisper-medium" \
+	--dataset_train_name="mozilla-foundation/common_voice_11_0,mozilla-foundation/common_voice_11_0,mozilla-foundation/common_voice_11_0,babelbox/babelbox_voice,NbAiLab/NST,NbAiLab/NPSC,google/fleurs,google/fleurs,google/fleurs" \
+	--dataset_train_config_name="sv-SE,da,nn-NO,nst,no-distant,16K_mp3_nynorsk,sv_se,da_dk,nb_no" \
+	--language_train="sv,da,no,sv,no,no,sv,da,no" \
+	--train_split_name="train+validation,train+validation,train+validation,train,train+test,train+validation,train+validation,train+validation,train+validation" \
+	--dataset_eval_name="mozilla-foundation/common_voice_11_0" \
+	--dataset_eval_config_name="sv-SE" \
+	--language_eval="sv" \
+	--eval_split_name="test" \
+	--model_index_name="Whisper Medium Nordic" \
+	--num_train_epochs="1" \
+	--output_dir="./" \
+	--per_device_train_batch_size="32" \
+	--per_device_eval_batch_size="16" \
+	--logging_steps="25" \
+	--learning_rate="1e-5" \
+	--warmup_steps="500" \
+	--evaluation_strategy="steps" \
+	--eval_steps="1000" \
+	--save_strategy="steps" \
+	--save_steps="1000" \
+	--generation_max_length="225" \
+	--length_column_name="input_length" \
+	--max_duration_in_seconds="30" \
+	--text_column_name="sentence,text,raw_transcription" \
+	--freeze_feature_encoder="False" \
+	--report_to="wandb" \
+	--save_total_limit="3" \
+	--metric_for_best_model="wer" \
+	--greater_is_better="False" \
+	--load_best_model_at_end \
+	--gradient_checkpointing \
+	--overwrite_output_dir \
+	--do_eval \
+	--fp16 \
+	--predict_with_generate \
+	--do_normalize_eval \
+	--streaming \
+	--use_auth_token

run_speech_recognition_seq2seq_streaming.py

@@ -0,0 +1,933 @@
+#!/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 json
+import logging
+import os
+import subprocess
+import sys
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional, Union
+
+import datasets
+import torch
+import wandb
+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.models.whisper.tokenization_whisper import TO_LANGUAGE_CODE, LANGUAGES
+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__)
+
+SENDING_NOTIFICATION = "*** Sending notification to email ***"
+RECIPIENT_ADDRESS = "marinone94@gmail.com"
+
+wandb_token = os.environ.get("WANDB_TOKEN", "None")
+hf_token = os.environ.get("HF_TOKEN", None)
+if (hf_token is None or wandb_token == "None") and os.path.exists("./creds.txt"):
+    with open("./creds.txt", "r") as f:
+        lines = f.readlines()
+    for line in lines:
+        key, value = line.split("=")
+        if key == "HF_TOKEN":
+            hf_token = value.strip()
+        if key == "WANDB_TOKEN":
+            wandb_token = value.strip()
+        if key == "EMAIL_ADDRESS":
+            os.environ["EMAIL_ADDRESS"] = value.strip()
+        if key == "EMAIL_PASSWORD":
+            os.environ["EMAIL_PASSWORD"] = value.strip()
+
+if hf_token is not None:
+    try:
+        os.makedirs("/root/.huggingface", exist_ok=True)
+        with open("/root/.huggingface/token", "w") as f:
+            f.write(hf_token)
+        logger.info("Huggingface API key set")
+    except (PermissionError, OSError):
+        logger.warning("Huggingface API key not set, relying on ~/.huggingface/token")
+else:
+    logger.warning("Huggingface API key not set, relying on ~/.huggingface/token")
+
+wandb.login(key=wandb_token, relogin=True, timeout=5)
+wandb.init(project="whisper", entity="pn-aa")
+
+logger.info("Wandb API key set, logging to wandb")
+
+
+@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_train_name: str = field(
+        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
+    )
+    dataset_train_config_name: Optional[str] = field(
+        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
+    dataset_eval_name: str = field(
+        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
+    )
+    dataset_eval_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_train: 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`."
+            )
+        },
+    )
+    language_eval: 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
+    task_id: int
+    # TODO: remove - infer language from dataset
+    language_id: int = -100
+
+    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]
+        # lang_features = [f"<|{TO_LANGUAGE_CODE[feature['language']]}|>" 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
+        
+        # lang_token_ids = self.processor.tokenizer(lang_features).input_ids
+        # # Replace language and task if they are in the beginning, otherwise add them
+        # if (labels[:, 1] == self.task_id).all().cpu().item():
+        #     labels[:, 0] = lang_token_ids
+        #     labels[:, 1] = torch.full_like(labels[:, 1], self.task_id)
+        # else:
+        #     # convert task id to tensor of labels dim to concatenate
+        #     task_id = torch.full_like(labels[:, 0], self.task_id)
+        #     labels = torch.cat((lang_token_ids, task_id, labels), dim=1)
+
+        # Set language to pad token
+        if (labels[:, 0] == self.decoder_start_token_id).all().cpu().item():
+            labels[:, 1] = torch.full_like(labels[:, 1], -100)
+        # labels[:, 0] = torch.full_like(labels[:, 0], -100)
+        # labels[:, 1] = torch.full_like(labels[:, 1], -100)
+
+        # remove start of sentence token from labels
+        # if (labels[:, 0] == self.decoder_start_token_id).all().cpu().item():
+        #     labels = labels[:, 1:]
+
+        # # add start of sentence token to labels + language + task
+        # labels = torch.cat((torch.full_like(labels[:, 0], self.task_id).unsqueeze(0).T, labels), dim=-1)
+        # labels = torch.cat((torch.full_like(labels[:, 0], self.language_id).unsqueeze(0).T, labels), dim=-1)
+        # labels = torch.cat((torch.full_like(labels[:, 0], self.decoder_start_token_id).unsqueeze(0).T, labels), dim=-1)
+
+        batch["labels"] = labels
+
+        return batch
+
+
+def notify_me(recipient, message=None):
+    """
+    Send an email to the specified address with the specified message
+    """
+    sender = os.environ.get("EMAIL_ADDRESS", None)
+    password = os.environ.get("EMAIL_PASSWORD", None)
+    if sender is None:
+        logging.warning("No email address specified, not sending notification")
+    if password is None:
+        logging.warning("No email password specified, not sending notification")
+    if message is None:
+        message = "Training is finished!"
+
+    if sender is not None:
+        import smtplib
+        from email.mime.text import MIMEText
+
+        msg = MIMEText(message)
+        msg["Subject"] = "Training updates..."
+        msg["From"] = "marinone.auto@gmail.com"
+        msg["To"] = recipient
+
+        # send the email
+        smtp_obj = smtplib.SMTP("smtp.gmail.com", 587)
+        smtp_obj.starttls()
+        smtp_obj.login(sender, password)
+        smtp_obj.sendmail(sender, recipient, msg.as_string())
+        smtp_obj.quit()
+
+
+def rename_col_and_resample(dataset, dataset_name, text_column_names, text_col_name_ref, audio_column_name, sampling_rate):
+    raw_datasets_features = list(dataset.features.keys())
+    logger.info(f"Dataset {dataset_name} - Features: {raw_datasets_features}")
+                
+    if text_col_name_ref not in raw_datasets_features:
+        if len(text_column_names) == 1:
+            raise ValueError("None of the text column names provided found in dataset."
+                                f"Text columns: {text_column_names}"
+                                f"Dataset columns: {raw_datasets_features}")
+        flag = False
+        for text_column_name in text_column_names:
+            if text_column_name in raw_datasets_features:
+                logger.info(f"Renaming text column {text_column_name} to {text_col_name_ref}")
+                dataset = dataset.rename_column(text_column_name, text_col_name_ref)
+                flag = True
+                break
+        if flag is False:
+            raise ValueError("None of the text column names provided found in dataset."
+                                f"Text columns: {text_column_names}"
+                                f"Dataset columns: {raw_datasets_features}")
+    if audio_column_name is not None and sampling_rate is not None:
+        ds_sr = int(dataset.features[audio_column_name].sampling_rate)
+        if ds_sr != sampling_rate:
+            dataset = dataset.cast_column(
+                audio_column_name, datasets.features.Audio(sampling_rate=sampling_rate)
+            )
+    
+    raw_datasets_features = list(dataset.features.keys())
+    raw_datasets_features.remove(audio_column_name)
+    raw_datasets_features.remove(text_col_name_ref)
+    # Keep only audio and sentence
+    dataset = dataset.remove_columns(column_names=raw_datasets_features)
+    return dataset
+
+
+def load_maybe_streaming_dataset(
+    dataset_names,
+    dataset_config_names,
+    split="train",
+    streaming=True,
+    audio_column_name=None,
+    sampling_rate=None,
+    **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).
+    """
+    text_column_names = None
+    if "text_column_name" in kwargs:
+        text_column_names = kwargs.pop("text_column_name").split(",")
+        text_col_name_ref = text_column_names[0]
+
+    if "," in dataset_names or "+" in split:
+        # load multiple splits separated by the `+` symbol with streaming mode
+        dataset_splits = []
+        for dataset_name, dataset_config_name, split_names in zip(
+            dataset_names.split(","), dataset_config_names.split(","), split.split(",")
+        ):
+            for split_name in split_names.split("+"):
+                if dataset_config_name:
+                    dataset = load_dataset(dataset_name, dataset_config_name, split=split_name, streaming=streaming, **kwargs)
+                else:
+                    dataset = load_dataset(dataset_name, split=split_name, streaming=streaming, **kwargs)              
+                
+                dataset = rename_col_and_resample(
+                    dataset,
+                    dataset_name,
+                    text_column_names,
+                    text_col_name_ref,
+                    audio_column_name,
+                    sampling_rate
+                )
+
+                dataset_splits.append(dataset)
+
+        # interleave multiple splits to form one dataset
+        interleaved_dataset = interleave_datasets(dataset_splits, stopping_strategy="all_exhausted")
+        return interleaved_dataset
+    else:
+        # load a single split *with* streaming mode
+        
+        dataset = load_dataset(dataset_names, dataset_config_names, split=split, streaming=streaming, **kwargs)
+        dataset = rename_col_and_resample(
+            dataset,
+            dataset_names,
+            text_column_names,
+            text_col_name_ref,
+            audio_column_name,
+            sampling_rate
+        )
+        return dataset
+
+
+def print_data_samples(dataset, tokenizer, max_samples=5):
+    shown_samples = 0
+    for batch in dataset:
+        print("Target: ", tokenizer.decode(batch["labels"]))
+        shown_samples += len(batch)
+        if shown_samples >= max_samples:
+            break
+
+
+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.
+    logger.info("*** Parse args ***")
+    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
+    logger.info("*** 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)
+
+    # Load feature extractor
+    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=hf_token if model_args.use_auth_token else None,
+    )
+
+    # 4. Load dataset
+    logger.info("*** Load dataset ***")
+    raw_datasets = IterableDatasetDict() if data_args.streaming else DatasetDict()
+
+    if len(data_args.language_eval.split(",")) > 1:
+        raise ValueError("Implementation does not support multiple language evaluation.")
+
+    if training_args.do_train:
+        raw_datasets["train"] = load_maybe_streaming_dataset(
+            data_args.dataset_train_name,
+            data_args.dataset_train_config_name,
+            split=data_args.train_split_name,
+            use_auth_token=hf_token if model_args.use_auth_token else None,
+            streaming=data_args.streaming,
+            text_column_name=data_args.text_column_name,
+            audio_column_name=data_args.audio_column_name,
+            sampling_rate=int(feature_extractor.sampling_rate),
+            # language=data_args.language_train
+        )
+
+    if training_args.do_eval:
+        raw_datasets["eval"] = load_maybe_streaming_dataset(
+            data_args.dataset_eval_name,
+            data_args.dataset_eval_config_name,
+            split=data_args.eval_split_name,
+            use_auth_token=hf_token if model_args.use_auth_token else None,
+            streaming=data_args.streaming,
+            text_column_name=data_args.text_column_name,
+            audio_column_name=data_args.audio_column_name,
+            sampling_rate=int(feature_extractor.sampling_rate),
+            # language=data_args.language_eval
+        )
+
+    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. "
+            "Make sure to set `--audio_column_name` to the correct audio column - one of "
+            f"{', '.join(raw_datasets_features)}."
+        )
+
+    data_args.text_column_name = data_args.text_column_name.split(",")[0]
+    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. "
+            "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
+    logger.info("*** 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=hf_token if model_args.use_auth_token else None
+    )
+
+    # Forced decoder ids will be overwritten before evaluation 
+    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})
+
+    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=hf_token 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=hf_token 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()
+    
+    tokenizer.set_prefix_tokens(language="swedish", task=data_args.task)
+
+    # if data_args.language_train is not None and len(data_args.language_train.split(",")) == 1:
+    #     # We only need to set the task id when the language is specified (i.e. in a multilingual setting)
+    #     # If more than a langugae is specified, it will be specified in the data collator
+    #     tokenizer.set_prefix_tokens(language=data_args.language_train, task=data_args.task)
+    # elif data_args.language_train is not None and len(data_args.language_train.split(",")) > 1:
+    #     # make sure language and task are not stored in the model config
+    #     model.config.forced_decoder_ids = None
+
+    # 6. Resample speech dataset if necessary
+    # logger.info("*** Resample dataset ***")
+    # dataset_sampling_rate = next(iter(raw_datasets.values())).features[data_args.audio_column_name].sampling_rate
+    # if dataset_sampling_rate != feature_extractor.sampling_rate:
+    
+
+    # 7. Preprocessing the datasets.
+    # We need to read the audio files as arrays and tokenize the targets.
+    logger.info("*** Preprocess dataset ***")
+    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).input_ids
+        return batch
+
+    with training_args.main_process_first(desc="dataset map pre-processing"):
+        # raw_datasets_features.remove("language")
+        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
+    logger.info("*** 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
+    logger.info("*** Init 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
+    task_token = data_args.task
+    if not task_token.startswith('<|'):
+        task_token = f'<{task_token}>'
+    task_id = tokenizer(task_token).input_ids[0]
+    data_collator = DataCollatorSpeechSeq2SeqWithPadding(
+        processor=processor,
+        decoder_start_token_id=model.config.decoder_start_token_id,
+        task_id=task_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
+    logger.info("*** Set shuffle callback ***")
+    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
+    logger.info("*** Init 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,
+    )
+    logger.info("*** Trainer initialized ***")
+
+    orig_push_to_hub = trainer.args.push_to_hub 
+    trainer.args.push_to_hub = False
+
+    # 12. Training
+    if training_args.do_train:
+        logger.info("*** Train ***")
+        print_data_samples(vectorized_datasets["train"], tokenizer)
+        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)
+        logger.info("*** Training completed ***")
+        logger.info("*** Saving model ***")
+        # We don't want to push the model to the hub now
+        # so we temporarily set to false the push_to_hub attribute
+        # and then reset it to the original value
+        trainer.save_model()  # Saves the feature extractor too for easy upload
+        logger.info("*** Model saved ***")
+        metrics = train_result.metrics
+        if data_args.max_train_samples:
+            metrics["train_samples"] = data_args.max_train_samples
+        logger.info("*** Logging metrics ***")
+        trainer.log_metrics("train", metrics)
+        logger.info("*** Metrics logged ***")
+        logger.info("*** Saving metrics ***")
+        trainer.save_metrics("train", metrics)
+        logger.info("*** Metrics saved ***")
+        logger.info("*** Saving state ***")
+        trainer.save_state()
+        logger.info("*** State saved ***")
+
+    # Run a test prediction to check outputs
+    predictions = trainer.predict(
+        test_dataset=vectorized_datasets["eval"].shuffle(seed=training_args.seed).take(5),
+        metric_key_prefix="test",
+        max_length=training_args.generation_max_length,
+        num_beams=training_args.generation_num_beams,
+    )
+    logger.info("*** Test prediction done ***")
+    preds = tokenizer.batch_decode(predictions.predictions)
+    labels = tokenizer.batch_decode(predictions.label_ids)
+    pred_labels = [f"Prediction: {pred}\nLabel: {label}\n" for pred, label in zip(preds, labels)]
+    logger.info("Before setting language and task")
+    logger.info(f"{pred_labels}")
+    language_name = LANGUAGES[data_args.language_eval]
+    trainer.model.config.forced_decoder_ids = \
+        tokenizer.get_decoder_prompt_ids(language=language_name, task=data_args.task, no_timestamps=True)
+    preds = tokenizer.batch_decode(predictions.predictions)
+    labels = tokenizer.batch_decode(predictions.label_ids)
+    pred_labels = [f"Prediction: {pred}\nLabel: {label}\n" for pred, label in zip(preds, labels)]
+    logger.info("After setting language and task")
+    logger.info(f"{pred_labels}")
+
+    # 13. Evaluation
+    results = {}
+    if training_args.do_eval:
+        logger.info("*** Evaluate ***")
+        print_data_samples(vectorized_datasets["eval"], tokenizer)
+        metrics = trainer.evaluate(
+            metric_key_prefix="eval",
+            max_length=training_args.generation_max_length,
+            num_beams=training_args.generation_num_beams,
+        )
+        logger.info("*** Evaluation done ***")
+        if data_args.max_eval_samples:
+            metrics["eval_samples"] = data_args.max_eval_samples
+        logger.info("*** Logging metrics ***")
+        trainer.log_metrics("eval", metrics)
+        logger.info("*** Metrics logged ***")
+        logger.info("*** Saving metrics ***")
+        trainer.save_metrics("eval", metrics)
+        logger.info("*** Metrics saved ***")
+
+    # 14. Write Training Stats
+    logger.info("*** Writing training stats ***")
+    kwargs = {
+        "finetuned_from": model_args.model_name_or_path,
+        "tasks": "automatic-speech-recognition",
+        "tags": "whisper-event",
+    }
+    if data_args.dataset_train_name is not None:
+        dataset_names = list(data_args.dataset_train_name.split(","))
+        kwargs["dataset_tags"] = dataset_names
+        # if data_args.dataset_train_config_name is not None:
+        #     dataset_config_names = list(data_args.dataset_train_config_name.split(","))
+        #     dataset_config_names_list = [f"{ds_name} {ds_cfg_name}" for ds_name, ds_cfg_name in zip(dataset_names, dataset_config_names)]
+        # else:
+        #     dataset_config_names_list = dataset_names
+        # kwargs["dataset"] = "\n".join(dataset_config_names_list)
+        # if "common_voice" in data_args.dataset_name:
+        #     kwargs["language"] = data_args.dataset_config_name[:2]
+        if data_args.language_train is not None:
+            languages = list(set(data_args.language_train.split(",")))
+            kwargs["language"] = languages
+        if model_args.model_index_name is not None:
+            kwargs["model_name"] = model_args.model_index_name
+
+    logger.info("*** Training stats written ***")
+    logger.info(json.dumps(kwargs, indent=4))
+
+    # Training complete notification
+    logger.info("*** Training and eval complete ***")
+    logger.info(SENDING_NOTIFICATION)
+    with open(os.path.join(training_args.output_dir, "train_results.json"), "r") as f:
+            train_results = json.load(f)
+    with open(os.path.join(training_args.output_dir, "eval_results.json"), "r") as f:
+            eval_results = json.load(f)
+    notify_me(recipient=RECIPIENT_ADDRESS,
+              message=f"Training complete! {train_results = } {eval_results = }")
+    
+    trainer.args.push_to_hub = orig_push_to_hub
+    if training_args.push_to_hub:
+        logger.info("*** Pushing to hub ***")
+        trainer.push_to_hub(**kwargs)
+        logger.info("*** Pushed to hub ***")
+        logger.info(SENDING_NOTIFICATION)
+    else:
+        logger.info("*** Creating model card ***")
+        trainer.create_model_card(**kwargs)
+        logger.info("*** Model card created ***")
+        logger.info(SENDING_NOTIFICATION)
+
+    with open(os.path.join(training_args.output_dir, "README.md"), "r") as f:
+        readme = f.read()
+    notify_me(recipient=RECIPIENT_ADDRESS,
+              message=f"Model pushed to hub! {readme = }")
+
+    return results
+
+
+if __name__ == "__main__":
+    main()