finetune.py

import json
import random
import re
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import numpy as np
import pandas as pd
import torch
import torchaudio
import transformers
import datasets
from datasets import ClassLabel, load_dataset, load_metric
from transformers import (Trainer, TrainingArguments, Wav2Vec2CTCTokenizer,
                          Wav2Vec2FeatureExtractor, Wav2Vec2ForCTC,
                          Wav2Vec2Processor)

import argparse
parser = argparse.ArgumentParser() 
parser.add_argument('--model', type=str, default="facebook/wav2vec2-xls-r-300m")
parser.add_argument('--unfreeze', action='store_true')
parser.add_argument('--lr', type=float, default=3e-4)
parser.add_argument('--warmup', type=float, default=500)
args = parser.parse_args()


print(f"args: {args}")

common_voice_train = datasets.load_dataset("mozilla-foundation/common_voice_8_0", "zh-HK", split="train+validation", use_auth_token=True)
common_voice_test = datasets.load_dataset("mozilla-foundation/common_voice_8_0", "zh-HK", split="test[:10%]", use_auth_token=True)

# common_voice_train = datasets.load_dataset("common_voice", "zh-HK", split="train+validation", use_auth_token=True)
# common_voice_test = datasets.load_dataset("common_voice", "zh-HK", split="test[:10%]", use_auth_token=True)

unused_cols = ["accent", "age", "client_id", "down_votes", "gender", "locale", "segment", "up_votes"]
common_voice_train = common_voice_train.remove_columns(unused_cols)
common_voice_test = common_voice_test.remove_columns(unused_cols)

chars_to_ignore_regex = '[\丶\,\?\.\!\-\;\:"\“\%\‘\”\�\．\⋯\！\－\：\–\。\》\,\）\,\？\；\～\~\…\︰\，\（\」\‧\《\﹔\、\—\／\,\「\﹖\·\']'

import string
def remove_special_characters(batch):
    sen = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
    # convert 'D' and 'd' to '啲' if there a 'D' in sentence
    # hacky stuff, wont work on 'D', 'd' co-occure with normal english words
    # wont work on multiple 'D'
    if "d" in sen:
        if len([c for c in sen if c in string.ascii_lowercase]) == 1:
            sen = sen.replace("d", "啲")
    batch["sentence"] = sen
    return batch

common_voice_train = common_voice_train.map(remove_special_characters)
common_voice_test = common_voice_test.map(remove_special_characters)

def extract_all_chars(batch):
    all_text = " ".join(batch["sentence"])
    vocab = list(set(all_text))
    return {"vocab": [vocab], "all_text": [all_text]}

vocab_train = common_voice_train.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_train.column_names,)
vocab_test = common_voice_test.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_test.column_names,)
vocab_list = list(set(vocab_train["vocab"][0]) | set(vocab_test["vocab"][0]))
vocab_list = [char for char in vocab_list if not char.isascii()] # remove english char from vocab_list, so tokenizer will replace english with [UNK]
vocab_list.append(" ") # previous will remove " " from vocab_list

vocab_dict = {v: k for k, v in enumerate(vocab_list)}
vocab_dict["|"] = vocab_dict[" "]
del vocab_dict[" "]

vocab_dict["[UNK]"] = len(vocab_dict)
vocab_dict["[PAD]"] = len(vocab_dict)

with open("vocab.json", "w") as vocab_file:
    json.dump(vocab_dict, vocab_file)

tokenizer = Wav2Vec2CTCTokenizer("./vocab.json", unk_token="[UNK]", pad_token="[PAD]", word_delimiter_token="|")

feature_extractor = Wav2Vec2FeatureExtractor(feature_size=1, sampling_rate=16000, padding_value=0.0, do_normalize=True, return_attention_mask=True,)

processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)
processor.save_pretrained("./finetuned-wav2vec2-xls-r-300m-cantonese")

# resamplers = {
#     48000: torchaudio.transforms.Resample(48000, 16000),
#     44100: torchaudio.transforms.Resample(44100, 16000),
# }

# def load_and_resample(batch):
#     speech_array, sampling_rate = torchaudio.load(batch["path"])
#     batch["array"] = resamplers[sampling_rate](speech_array).squeeze().numpy()
#     batch["sampling_rate"] = 16_000
#     batch["target_text"] = batch["sentence"]
#     return batch

# common_voice_train = common_voice_train.map(load_and_resample, remove_columns=common_voice_train.column_names,)
# common_voice_test = common_voice_test.map(load_and_resample, remove_columns=common_voice_test.column_names,)


common_voice_train = common_voice_train.cast_column('audio', datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate))
common_voice_test = common_voice_test.cast_column('audio', datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate))


def prepare_dataset(batch):
    batch["input_values"] = processor(batch["array"], sampling_rate=batch["sampling_rate"][0]).input_values
    with processor.as_target_processor():
        batch["labels"] = processor(batch["target_text"]).input_ids
    return batch

print(common_voice_train[0]['audio'])

common_voice_train = common_voice_train.map(prepare_dataset, remove_columns=common_voice_train.column_names, batched=True,)
common_voice_test = common_voice_test.map(prepare_dataset, remove_columns=common_voice_test.column_names,  batched=True,)


@dataclass
class DataCollatorCTCWithPadding:
    """
    Data collator that will dynamically pad the inputs received.
    Args:
        processor (:class:`~transformers.Wav2Vec2Processor`)
            The processor used for proccessing the data.
        padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
            Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
            among:
            * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
              sequence if provided).
            * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
              maximum acceptable input length for the model if that argument is not provided.
            * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
              different lengths).
        max_length (:obj:`int`, `optional`):
            Maximum length of the ``input_values`` of the returned list and optionally padding length (see above).
        max_length_labels (:obj:`int`, `optional`):
            Maximum length of the ``labels`` returned list and optionally padding length (see above).
        pad_to_multiple_of (:obj:`int`, `optional`):
            If set will pad the sequence to a multiple of the provided value.
            This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
            7.5 (Volta).
    """

    processor: Wav2Vec2Processor
    padding: Union[bool, str] = True
    max_length: Optional[int] = None
    max_length_labels: Optional[int] = None
    pad_to_multiple_of: Optional[int] = None
    pad_to_multiple_of_labels: Optional[int] = None

    def __call__(
        self, features: List[Dict[str, Union[List[int], torch.Tensor]]]
    ) -> Dict[str, torch.Tensor]:
        # split inputs and labels since they have to be of different lenghts and need
        # different padding methods
        input_features = [
            {"input_values": feature["input_values"]} for feature in features
        ]
        label_features = [{"input_ids": feature["labels"]} for feature in features]

        batch = self.processor.pad(
            input_features,
            padding=self.padding,
            max_length=self.max_length,
            pad_to_multiple_of=self.pad_to_multiple_of,
            return_tensors="pt",
        )
        with self.processor.as_target_processor():
            labels_batch = self.processor.pad(
                label_features,
                padding=self.padding,
                max_length=self.max_length_labels,
                pad_to_multiple_of=self.pad_to_multiple_of_labels,
                return_tensors="pt",
            )

        # replace padding with -100 to ignore loss correctly
        labels = labels_batch["input_ids"].masked_fill(
            labels_batch.attention_mask.ne(1), -100
        )

        batch["labels"] = labels

        return batch


data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)
# cer_metric = load_metric("./cer")

# def compute_metrics(pred):
#     pred_logits = pred.predictions
#     pred_ids = np.argmax(pred_logits, axis=-1)

#     pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id

#     pred_str = processor.batch_decode(pred_ids)
#     # we do not want to group tokens when computing the metrics
#     label_str = processor.batch_decode(pred.label_ids, group_tokens=False)

#     cer = cer_metric.compute(predictions=pred_str, references=label_str)

#     return {"cer": cer}

def compute_metrics(pred):
        pred_logits = pred.predictions
        pred_ids = np.argmax(pred_logits, axis=-1)

        pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id

        pred_str = tokenizer.batch_decode(pred_ids)
        # we do not want to group tokens when computing the metrics
        label_str = tokenizer.batch_decode(pred.label_ids, group_tokens=False)

        metrics = {k: v.compute(predictions=pred_str, references=label_str) for k, v in eval_metrics.items()}

        return metrics

model = Wav2Vec2ForCTC.from_pretrained(
    args.model,
    attention_dropout=0.1,
    hidden_dropout=0.1,
    feat_proj_dropout=0.0,
    mask_time_prob=0.05,
    layerdrop=0.1,
    gradient_checkpointing=True,
    ctc_loss_reduction="mean",
    pad_token_id=processor.tokenizer.pad_token_id,
    vocab_size=len(processor.tokenizer),
)

if not args.unfreeze:
    model.freeze_feature_extractor()

training_args = TrainingArguments(
    output_dir="./finetuned-wav2vec2-xls-r-300m-cantonese/wav2vec2-xls-r-300m-cantonese",
    group_by_length=True,
    per_device_train_batch_size=8,
    gradient_accumulation_steps=2,
    #evaluation_strategy="no",
    evaluation_strategy="steps",
    #evaluation_strategy="epoch",
    eval_steps=400,
    #eval_accumulation_steps=60,
    num_train_epochs=1,
    fp16=True,
    fp16_backend="amp",
    logging_strategy="steps",
    logging_steps=400,
    #logging_strategy="epoch",
    learning_rate=args.lr,
    warmup_steps=100,
    save_steps=2376, # every 3 epoch with batch_size 8
    #save_strategy="epoch",
    save_total_limit=3,
    ###################
    # fp16_full_eval=True,
    dataloader_num_workers=20,
)

trainer = Trainer(
    model=model,
    data_collator=data_collator,
    args=training_args,
    compute_metrics=compute_metrics,
    train_dataset=common_voice_train,
    eval_dataset=common_voice_test,
    tokenizer=processor.feature_extractor,
)
trainer.train()