Add training scripts and weights

.gitattributes

@@ -30,3 +30,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.whisper filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,47 @@
+---
+language:
+- en
+tags:
+- esc
+datasets:
+- switchboard
+---
+To reproduce this run, execute: 
+```python 
+#!/usr/bin/env bash
+CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_whisper.py \
+	--model_name_or_path="medium.en" \
+  --dataset_name="esc-benchmark/esc-datasets" \
+  --dataset_config_name="switchboard" \
+	--max_steps="5000" \
+	--output_dir="./" \
+	--run_name="whisper-switchboard" \
+	--max_steps="5000" \
+	--output_dir="./" \
+	--run_name="whisper-switchboard" \
+	--wandb_project="whisper" \
+	--per_device_train_batch_size="64" \
+	--per_device_eval_batch_size="16" \
+	--logging_steps="25" \
+	--learning_rate="1e-4" \
+	--warmup_steps="500" \
+	--report_to="wandb" \
+	--preprocessing_num_workers="16" \
+	--evaluation_strategy="steps" \
+	--eval_steps="1000" \
+	--save_strategy="steps" \
+	--save_steps="1000" \
+	--generation_max_length="224" \
+	--length_column_name="input_lengths" \
+	--gradient_checkpointing \
+	--group_by_length \
+	--freeze_encoder \
+	--fp16 \
+	--overwrite_output_dir \
+	--do_train \
+	--do_eval \
+	--do_predict \
+	--predict_with_generate \
+	--use_auth_token
+
+```

medium.en.whisper

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4a667fb55dfa9d15928c4169d324d45f59e371fafcd41661c8e54da370c8a415
+size 3055771163

run_speech_recognition_whisper.py

@@ -0,0 +1,747 @@
+#!/usr/bin/env python
+# coding=utf-8
+#
+# 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 OpenAI Whisper models for speech recognition.
+"""
+# You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.
+# flake8: noqa: E501
+import logging
+import os
+
+import whisper
+import sys
+from dataclasses import dataclass, field
+import tempfile
+
+from typing import Optional, Dict, Union, List
+
+import numpy as np
+import torch
+
+import datasets
+from datasets import DatasetDict, load_dataset
+import transformers
+from torch import nn
+from transformers import (
+    HfArgumentParser,
+    Seq2SeqTrainingArguments,
+    set_seed,
+    Seq2SeqTrainer,
+)
+from transformers.trainer_utils import get_last_checkpoint, is_main_process
+from transformers.utils import check_min_version
+from transformers.utils.versions import require_version
+
+import wandb
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.17.0.dev0")
+
+require_version("datasets>=1.18.0", "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt")
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/tokenizer we are going to fine-tune from.
+    """
+    model_name_or_path: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to pretrained model or model identifier from OpenAI Whisper NGC."}
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Where to store the pretrained models downloaded from huggingface.co or OpenAI Whisper NGC."},
+    )
+    use_auth_token: bool = field(
+        default=False,
+        metadata={
+            "help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
+                    "with private models)."
+        },
+    )
+    manifest_path: str = field(
+        default="data",
+        metadata={
+            "help": "Manifest path."
+        },
+    )
+    tokenizer_path: str = field(
+        default="tokenizers",
+        metadata={
+            "help": "Tokenizer path."
+        },
+    )
+    freeze_encoder: bool = field(
+        default=False,
+        metadata={"help": "Freeze the acoustic encoder of the model. Recommend when fine-tuning on small datasets."}
+    )
+    num_beams: int = field(
+        default=1,
+        metadata={"help": "Number of beams for evaluation."},
+    )
+    length_penalty: float = field(
+        default=1.0,
+        metadata={"help": "Length penalty for evaluation."},
+    )
+    use_adam8bit: bool = field(
+        default=False,
+        metadata={"help": "Whether to use bitsandbytes 8bit AdamW optimiser."}
+    )
+    dropout_rate: float = field(
+        default=0.0,
+        metadata={"help": "The dropout ratio for all dropout layers (default=0)."}
+    )
+
+
+@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)."},
+    )
+    dataset_cache_dir: Optional[str] = field(
+        default=None, metadata={"help": "Path to cache directory for saving and loading datasets"}
+    )
+    overwrite_cache: bool = field(
+        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of processes to use for the preprocessing."},
+    )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of training examples to this "
+                    "value if set."
+        },
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
+                    "value if set."
+        },
+    )
+    max_predict_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of test 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 training 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"}
+    )
+    max_eval_duration_in_seconds: float = field(
+        default=None,
+        metadata={
+            "help": "Truncate eval/test audio files that are longer than `max_duration_in_seconds` seconds to 'max_duration_in_seconds`"
+        },
+    )
+    max_target_length: Optional[int] = field(
+        default=128,
+        metadata={
+            "help": "The maximum total sequence length for target text after tokenization. Sequences longer "
+                    "than this will be truncated, sequences shorter will be padded."
+        },
+    )
+    min_target_length: Optional[int] = field(
+        default=0,
+        metadata={
+            "help": "The minimum total sequence length for target text after tokenization. Sequences shorter "
+                    "than this will be filtered."
+        },
+    )
+    preprocessing_only: bool = field(
+        default=False,
+        metadata={
+            "help": "Whether to only do data preprocessing and skip training. "
+                    "This is especially useful when data preprocessing errors out in distributed training due to timeout. "
+                    "In this case, one should run the preprocessing in a non-distributed setup with `preprocessing_only=True` "
+                    "so that the cached datasets can consequently be loaded in distributed training"
+        },
+    )
+    train_split_name: str = field(
+        default="train",
+        metadata={
+            "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
+        },
+    )
+    eval_split_name: str = field(
+        default="validation",
+        metadata={
+            "help": "The name of the evaluation data set split to use (via the datasets library). Defaults to 'validation'"
+        },
+    )
+    test_split_name: str = field(
+        default="test",
+        metadata={"help": "The name of the test data set split to use (via the datasets library). Defaults to 'test'"},
+    )
+    wandb_project: str = field(
+        default="speech-recognition-whisper",
+        metadata={"help": "The name of the wandb project."},
+    )
+
+
+def write_wandb_pred(pred_str, label_str, prefix="eval"):
+    # convert str data to a wandb compatible format
+    str_data = [[label_str[i], pred_str[i]] for i in range(len(pred_str))]
+    # we'll log all predictions for the last epoch
+    wandb.log(
+        {
+            f"{prefix}/predictions": wandb.Table(
+                columns=["label_str", "pred_str"], data=str_data
+            )
+        },
+    )
+
+
+def to_pad_to_mel(array):
+    """Static function which:
+        1. Pads/trims a list of audio arrays to a max length of 30s
+        2. Computes log-mel filter coefficients from padded/trimmed audio sequences
+        Inputs:
+            array: list of audio arrays
+        Returns:
+            input_ids: torch.tensor of log-mel filter bank coefficients
+    """
+    padded_input = whisper.pad_or_trim(np.asarray(array, dtype=np.float32))
+    input_ids = whisper.log_mel_spectrogram(padded_input)
+    return input_ids
+
+
+def to_mel_to_pad(array):
+    """Static function which:
+        1. Computes log-mel filter coefficients from padded/trimmed audio sequences
+        2. Pads/trims a list of audio arrays to a max length of 30s
+        Inputs:
+            array: list of audio arrays
+        Returns:
+            input_ids: torch.tensor of log-mel filter bank coefficients
+    """
+    mels = whisper.log_mel_spectrogram(np.asarray(array, dtype=np.float32))
+    input_ids = whisper.pad_or_trim(mels, 3000)
+    return input_ids
+
+
+@dataclass
+class WhisperDataCollatorWithPadding:
+    """
+    Data collator that dynamically pads the audio inputs received. An EOS token is appended to the labels sequences.
+    They are then dynamically padded to max length.
+    Args:
+        eos_token_id (`int`)
+            The end-of-sentence token for the Whisper tokenizer. Ensure to set for sequences to terminate before
+            generation max length.
+    """
+
+    eos_token_id: int
+
+    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
+        """
+        Since Whisper models don't have a HF processor defined (feature extractor + tokenizer), we'll pad by hand...
+        """
+        # split inputs and labels since they have to be of different lengths
+        # and need different padding methods
+        input_ids = [feature["input_ids"] for feature in features]
+        labels = [feature["labels"] for feature in features]
+
+        # first, pad the audio inputs to max_len
+        input_ids = torch.concat([to_pad_to_mel(input_val)[None, :] for input_val in input_ids])
+
+        # next, append the eos token to our sequence of labels
+        labels = [lab + [self.eos_token_id] for lab in labels]
+        # finally, pad the target labels to max_len
+        label_lengths = [len(lab) for lab in labels]
+        max_label_len = max(label_lengths)
+        labels = [np.pad(lab, (0, max_label_len - lab_len), 'constant', constant_values=-100) for lab, lab_len in zip(labels, label_lengths)]
+
+        batch = {"labels": labels}
+        batch = {k: torch.tensor(np.array(v), requires_grad=False) for k, v in batch.items()}
+
+        batch["input_ids"] = input_ids
+
+        return batch
+
+
+def main():
+    # See all possible arguments in src/transformers/training_args.py
+    # or by passing the --help flag to this script.
+    # We now keep distinct sets of args, for a cleaner separation of concerns.
+
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    # Set wandb project ID before instantiating the Trainer
+    os.environ["WANDB_PROJECT"] = data_args.wandb_project
+    report_to_wandb = "wandb" in training_args.report_to
+
+    sample_rate = 16_000
+
+    # Detecting last checkpoint.
+    last_checkpoint = None
+    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
+        last_checkpoint = get_last_checkpoint(training_args.output_dir)
+        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
+            raise ValueError(
+                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
+                "Use --overwrite_output_dir to overcome."
+            )
+        elif last_checkpoint is not None:
+            logger.info(
+                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
+                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
+            )
+
+    # Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+    logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)
+
+    # Log on each process the small summary:
+    logger.warning(
+        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+        f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
+    )
+    # Set the verbosity to info of the Transformers logger (on main process only):
+    if is_main_process(training_args.local_rank):
+        transformers.utils.logging.set_verbosity_info()
+    logger.info("Training/evaluation parameters %s", training_args)
+
+    # Set seed before initializing model.
+    set_seed(training_args.seed)
+
+    # load the model 
+    if os.path.isfile(model_args.model_name_or_path):
+        checkpoint = torch.load(model_args.model_name_or_path)
+        need_to_rewrite_checkpoint = any(k.startswith("decoder.blocks") and ".mlp.3" in k for k in checkpoint.keys())
+        if need_to_rewrite_checkpoint:
+            new_checkpoint = {}
+            for k, v in checkpoint.items():
+                if k.startswith("decoder.blocks") and "mlp" in k.split("."):
+                    if int(k.split(".mlp.")[-1].split(".")[0]) in [2, 4]:
+                        continue
+                    elif int(k.split(".mlp.")[-1].split(".")[0]) == 3:
+                        k = k.replace(".mlp.3", ".mlp.2")
+
+                new_checkpoint[k] = v
+
+            with tempfile.TemporaryDirectory() as tmp:
+                file = os.path.join(tmp, "model.pt")
+                torch.save(new_checkpoint, file)
+                model = whisper.Whisper.load_trained(file)
+        else:
+            model = whisper.Whisper.load_trained(model_args.model_name_or_path)
+        del checkpoint
+    else:
+        model = whisper.load_model(model_args.model_name_or_path, dropout_rate=model_args.dropout_rate)
+
+    if training_args.do_train:
+        # set the dropout for the MLP layers -> we do this here as the MLP layers are written as a 'sequential'
+        # so changing the modelling code gives mis-matches in the state-dict
+        if not model_args.freeze_encoder:
+            # only apply dropout when training the encoder
+            for block_idx in range(len(model.encoder.blocks)):
+                mlp_layer = model.encoder.blocks[block_idx].mlp
+                # going very verbose to explain what we're doing here!
+                fc1 = mlp_layer[0]
+                act_fn = mlp_layer[1]
+                dropout = nn.Dropout(p=model_args.dropout_rate)
+                fc2 = mlp_layer[2]
+                model.encoder.blocks[block_idx].mlp = nn.Sequential(fc1, act_fn, dropout, fc2, dropout)
+
+            for block_idx in range(len(model.decoder.blocks)):
+                mlp_layer = model.decoder.blocks[block_idx].mlp
+                fc1 = mlp_layer[0]
+                act_fn = mlp_layer[1]
+                dropout_1 = nn.Dropout(p=model_args.dropout_rate)
+                fc2 = mlp_layer[2]
+                dropout_2 = nn.Dropout(p=model_args.dropout_rate)
+                model.decoder.blocks[block_idx].mlp = nn.Sequential(fc1, act_fn, dropout_1, fc2, dropout_2)
+        for block_idx in range(len(model.decoder.blocks)):
+            mlp_layer = model.decoder.blocks[block_idx].mlp
+            fc1 = mlp_layer[0]
+            act_fn = mlp_layer[1]
+            dropout1 = nn.Dropout(p=model_args.dropout_rate)
+            fc2 = mlp_layer[2]
+            dropout2 = nn.Dropout(p=model_args.dropout_rate)
+            model.decoder.blocks[block_idx].mlp = nn.Sequential(fc1, act_fn, dropout1, fc2, dropout2)
+
+    # load the tokenizer
+    whisper_tok = whisper.tokenizer.get_tokenizer(False, task="transcribe", language="en")
+    tokenizer = whisper_tok.tokenizer
+    tokenizer.pad_token = tokenizer.eos_token
+
+    # 4. Load dataset
+    raw_datasets = DatasetDict()
+
+    if training_args.do_train:
+        raw_datasets["train"] = load_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            split=data_args.train_split_name,
+            cache_dir=data_args.dataset_cache_dir,
+            use_auth_token=True if model_args.use_auth_token else None,
+        )
+
+    if training_args.do_eval:
+        raw_datasets["eval"] = load_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            split=data_args.eval_split_name,
+            cache_dir=data_args.dataset_cache_dir,
+            use_auth_token=True if model_args.use_auth_token else None,
+        )
+
+    if training_args.do_predict:
+        test_split = data_args.test_split_name.split("+")
+        for split in test_split:
+            raw_datasets[split] = load_dataset(
+                data_args.dataset_name,
+                data_args.dataset_config_name,
+                split=split,
+                cache_dir=data_args.dataset_cache_dir,
+                use_auth_token=True if model_args.use_auth_token else None,
+            )
+
+    if not training_args.do_train and not training_args.do_eval and not training_args.do_predict:
+        raise ValueError(
+            "Cannot not train, not do evaluation and not do prediction. At least one of "
+            "training, evaluation or prediction has to be done."
+        )
+
+    # if not training, there is no need to run multiple epochs
+    if not training_args.do_train:
+        training_args.num_train_epochs = 1
+
+    if data_args.audio_column_name not in next(iter(raw_datasets.values())).column_names:
+        raise ValueError(
+            f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'. "
+            "Make sure to set `--audio_column_name` to the correct audio column - one of "
+            f"{', '.join(next(iter(raw_datasets.values())).column_names)}."
+        )
+
+    if data_args.text_column_name not in next(iter(raw_datasets.values())).column_names:
+        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(next(iter(raw_datasets.values())).column_names)}."
+        )
+
+    # 6. Resample speech dataset ALWAYS
+    raw_datasets = raw_datasets.cast_column(
+            data_args.audio_column_name, datasets.features.Audio(sampling_rate=sample_rate)
+        )
+
+    # 7. Preprocessing the datasets.
+    # We need to read the audio files as arrays and tokenize the targets.
+    max_input_length = int(data_args.max_duration_in_seconds * sample_rate)
+    min_input_length = min(int(data_args.min_duration_in_seconds * sample_rate), 1)
+    max_eval_input_length = int(data_args.max_eval_duration_in_seconds * sample_rate) if data_args.max_eval_duration_in_seconds else None
+    max_target_length = data_args.max_target_length
+    min_target_length = data_args.min_target_length
+    audio_column_name = data_args.audio_column_name
+    num_workers = data_args.preprocessing_num_workers
+    text_column_name = data_args.text_column_name
+
+    if training_args.do_train and data_args.max_train_samples is not None:
+        raw_datasets["train"] = raw_datasets["train"].select(range(data_args.max_train_samples))
+
+    if training_args.do_eval and data_args.max_eval_samples is not None:
+        raw_datasets["eval"] = raw_datasets["eval"].select(range(data_args.max_eval_samples))
+
+    if training_args.do_predict and data_args.max_predict_samples is not None:
+        for split in test_split:
+            raw_datasets[split] = raw_datasets[split].select(range(data_args.max_predict_samples))
+
+
+    def prepare_dataset(batch):
+        # pre-process audio
+        sample = batch[audio_column_name]
+
+        # For training Whisper we perform the audio preprocessing in the WhisperDataCollator
+        # => we only need to supply it with the raw audio values
+        batch["input_ids"] = sample["array"]
+        batch["input_lengths"] = len(batch["input_ids"])
+
+        input_str = batch[text_column_name]
+        batch["labels"] = tokenizer(input_str).input_ids
+        return batch
+
+    vectorized_datasets = raw_datasets.map(
+        prepare_dataset,
+        remove_columns=next(iter(raw_datasets.values())).column_names,
+        num_proc=num_workers,
+        desc="preprocess train dataset",
+    )
+
+    # filter training data with inputs longer than max_input_length
+    def is_audio_in_length_range(input_length):
+        return min_input_length < input_length < max_input_length
+
+    if training_args.do_train:
+        vectorized_datasets["train"] = vectorized_datasets["train"].filter(
+            is_audio_in_length_range,
+            num_proc=num_workers,
+            input_columns=["input_lengths"],
+        )
+
+    if max_eval_input_length is not None:
+        # filter training data with inputs longer than max_input_length
+        def is_eval_audio_in_length_range(input_length):
+            return min_input_length < input_length < max_eval_input_length
+
+        if training_args.do_eval:
+            vectorized_datasets["eval"] = vectorized_datasets["eval"].filter(
+                is_eval_audio_in_length_range,
+                num_proc=num_workers,
+                input_columns=["input_lengths"],
+            )
+
+        if training_args.do_predict:
+            for split in test_split:
+                vectorized_datasets[split] = vectorized_datasets[split].filter(
+                    is_eval_audio_in_length_range,
+                    num_proc=num_workers,
+                    input_columns=["input_lengths"],
+                )
+
+    # filter training data with targets shorter than min_target_length or longer than max_target_length
+    def is_labels_in_length_range(labels):
+        return min_target_length < len(labels) < max_target_length
+
+    if training_args.do_train:
+        vectorized_datasets["train"] = vectorized_datasets["train"].filter(
+            is_labels_in_length_range,
+            num_proc=num_workers,
+            input_columns=["labels"],
+        )
+
+    # for large datasets it is advised to run the preprocessing on a
+    # single machine first with `args.preprocessing_only` since there will mostly likely
+    # be a timeout when running the script in distributed mode.
+    # In a second step `args.preprocessing_only` can then be set to `False` to load the
+    # cached dataset
+    if data_args.preprocessing_only:
+        cache = {k: v.cache_files for k, v in vectorized_datasets.items()}
+        logger.info(f"Data preprocessing finished. Files cached at {cache}.")
+        return
+
+    if model_args.freeze_encoder:
+        model.freeze_encoder()
+        logging.info("Model encoder has been frozen")
+
+    # 8. Load Metric
+    metric_wer = datasets.load_metric("wer")
+    metric_cer = datasets.load_metric("cer")
+
+    def compute_metrics(pred):
+        pred_ids = pred.predictions
+        pred.label_ids[pred.label_ids == -100] = tokenizer.eos_token_id
+
+        pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
+        pred_str = [x.lstrip().strip() for x in pred_str]
+
+        # we do not want to group tokens when computing the metrics
+        label_str = tokenizer.batch_decode(pred.label_ids, skip_special_tokens=True)
+
+        wer = metric_wer.compute(predictions=pred_str, references=label_str)
+        cer = metric_cer.compute(predictions=pred_str, references=label_str)
+
+        return {"wer": wer, "cer": cer}
+
+    def compute_metrics_and_predictions(pred):
+        pred_ids = pred.predictions
+        pred.label_ids[pred.label_ids == -100] = tokenizer.eos_token_id
+
+        pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
+        pred_str = [x.lstrip().strip() for x in pred_str]
+
+        # we do not want to group tokens when computing the metrics
+        label_str = tokenizer.batch_decode(pred.label_ids, skip_special_tokens=True)
+
+        wer = metric_wer.compute(predictions=pred_str, references=label_str)
+        cer = metric_cer.compute(predictions=pred_str, references=label_str)
+
+        return {"wer": wer, "cer": cer, "pred_str": pred_str, "label_str": label_str}
+
+    class WhisperTrainer(Seq2SeqTrainer):
+        def _save(self, output_dir: Optional[str] = None, state_dict=None):
+            # If we are executing this function, we are the process zero, so we don't check for that.
+            output_dir = output_dir if output_dir is not None else self.args.output_dir
+            os.makedirs(output_dir, exist_ok=True)
+            logger.info(f"Saving model checkpoint to {output_dir}")
+            # Save a trained model and configuration using `save_pretrained()`.
+            # They can then be reloaded using `from_pretrained()`
+            self.model.save_to(save_path=os.path.join(output_dir, model_args.model_name_or_path + ".whisper"))
+            # Good practice: save your training arguments together with the trained model
+            torch.save(self.args, os.path.join(output_dir, "training_args.bin"))
+
+    # Define data collator
+    whisper_data_collator = WhisperDataCollatorWithPadding(eos_token_id=tokenizer.eos_token_id)
+
+    # make sure model uses 50257 as BOS
+    bos = tokenizer("<|startoftranscript|>").input_ids[0]
+    model.config.decoder_start_token_id = bos
+
+    # Initialize Trainer
+    trainer = WhisperTrainer(
+        model=model,
+        args=training_args,
+        compute_metrics=compute_metrics,
+        train_dataset=vectorized_datasets['train'] if training_args.do_train else None,
+        eval_dataset=vectorized_datasets['eval'] if training_args.do_eval else None,
+        data_collator=whisper_data_collator,
+    )
+
+    # 8. Finally, we can start training
+
+    # Training
+    if training_args.do_train:
+
+        # use last checkpoint if exist
+        if last_checkpoint is not None:
+            checkpoint = last_checkpoint
+        elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path):
+            checkpoint = model_args.model_name_or_path
+        else:
+            checkpoint = None
+
+        train_result = trainer.train(resume_from_checkpoint=checkpoint)
+        trainer.save_model()
+
+        metrics = train_result.metrics
+        max_train_samples = (
+            data_args.max_train_samples
+            if data_args.max_train_samples is not None
+            else len(vectorized_datasets["train"])
+        )
+        metrics["train_samples"] = min(max_train_samples, len(vectorized_datasets["train"]))
+
+        trainer.log_metrics("train", metrics)
+        trainer.save_metrics("train", metrics)
+        trainer.save_state()
+
+    # Change decoding strategy for final eval/predict
+#    if training_args.do_eval or training_args.do_predict:
+#        trainer.model.num_beams = 2
+
+    trainer.compute_metrics = compute_metrics_and_predictions
+
+    results = {}
+    if training_args.do_eval:
+        if not training_args.do_train and report_to_wandb:
+            # manually init wandb
+            wandb.init(project=data_args.wandb_project, name=training_args.run_name)
+        # Have to run this as a predict step, otherwise trainer will try to log the pred/label strings to wandb
+        eval_results = trainer.predict(vectorized_datasets["eval"], metric_key_prefix="eval", num_beams=model_args.num_beams, length_penalty=model_args.length_penalty)
+        metrics = eval_results.metrics
+        max_eval_samples = (
+            data_args.max_eval_samples if data_args.max_eval_samples is not None else len(vectorized_datasets["eval"])
+        )
+        metrics["eval_samples"] = min(max_eval_samples, len(vectorized_datasets["eval"]))
+        pred_str = metrics.pop("eval_pred_str", None)
+        label_str = metrics.pop("eval_label_str", None)
+
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+        if report_to_wandb:
+            metrics = {os.path.join("eval", k[len("eval") + 1:]): v for k, v in metrics.items()}
+            wandb.log(metrics)
+            write_wandb_pred(pred_str, label_str, prefix="eval")
+
+    if training_args.do_predict:
+        if not training_args.do_train and not training_args.do_eval and report_to_wandb:
+            # manually init wandb
+            wandb.init(project=data_args.wandb_project, name=training_args.run_name)
+        for split in test_split:
+            predict_results = trainer.predict(
+                vectorized_datasets[split], metric_key_prefix=split, num_beams=model_args.num_beams, length_penalty=model_args.length_penalty)
+            metrics = predict_results.metrics
+            max_predict_samples = (
+                data_args.max_predict_samples if data_args.max_predict_samples is not None else len(vectorized_datasets[split])
+            )
+            metrics[f"{split}_samples"] = min(max_predict_samples, len(vectorized_datasets[split]))
+            pred_str = metrics.pop(f"{split}_pred_str", None)
+            label_str = metrics.pop(f"{split}_label_str", None)
+
+            trainer.log_metrics(split, metrics)
+            trainer.save_metrics(split, metrics)
+
+            if report_to_wandb:
+                metrics = {os.path.join(split, k[len(split)+1:]): v for k, v in metrics.items()}
+                wandb.log(metrics)
+                write_wandb_pred(pred_str, label_str, prefix=split)
+
+    # Write model card and (optionally) push to hub
+    config_name = data_args.dataset_config_name if data_args.dataset_config_name is not None else "na"
+    kwargs = {
+        "finetuned_from": model_args.model_name_or_path,
+        "tasks": "speech-recognition",
+        "tags": ["automatic-speech-recognition", data_args.dataset_name],
+        "dataset_args": (
+            f"Config: {config_name}, Training split: {data_args.train_split_name}, Eval split:"
+            f" {data_args.eval_split_name}"
+        ),
+        "dataset": f"{data_args.dataset_name.upper()} - {config_name.upper()}",
+    }
+    if "common_voice" in data_args.dataset_name:
+        kwargs["language"] = config_name
+
+    if training_args.push_to_hub:
+        trainer.push_to_hub(**kwargs)
+
+    return results
+
+
+if __name__ == "__main__":
+    main()

run_switchboard.sh

@@ -0,0 +1,35 @@
+#!/usr/bin/env bash
+CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_whisper.py \
+	--model_name_or_path="medium.en" \
+  --dataset_name="esc-benchmark/esc-datasets" \
+  --dataset_config_name="switchboard" \
+	--max_steps="5000" \
+	--output_dir="./" \
+	--run_name="whisper-switchboard" \
+	--max_steps="5000" \
+	--output_dir="./" \
+	--run_name="whisper-switchboard" \
+	--wandb_project="whisper" \
+	--per_device_train_batch_size="64" \
+	--per_device_eval_batch_size="16" \
+	--logging_steps="25" \
+	--learning_rate="1e-4" \
+	--warmup_steps="500" \
+	--report_to="wandb" \
+	--preprocessing_num_workers="16" \
+	--evaluation_strategy="steps" \
+	--eval_steps="1000" \
+	--save_strategy="steps" \
+	--save_steps="1000" \
+	--generation_max_length="224" \
+	--length_column_name="input_lengths" \
+	--gradient_checkpointing \
+	--group_by_length \
+	--freeze_encoder \
+	--fp16 \
+	--overwrite_output_dir \
+	--do_train \
+	--do_eval \
+	--do_predict \
+	--predict_with_generate \
+	--use_auth_token