whisper-fine-tuning-event/fine-tune-whisper-non-streaming-adyga.py

#!/home/haroon/python_virtual_envs/whisper_fine_tuning/bin/python

from datasets import load_dataset, DatasetDict, Audio
from transformers import (WhisperTokenizer, WhisperFeatureExtractor,
                          WhisperProcessor, WhisperForConditionalGeneration,
                          Seq2SeqTrainingArguments, Seq2SeqTrainer)
from transformers.models.whisper.english_normalizer import BasicTextNormalizer
import torch
from dataclasses import dataclass
from typing import Any, Dict, List, Union
import evaluate



from datasets import Dataset
import pandas as pd
import numpy as np
import soundfile as sf
from scipy.signal import resample


def convert_mp3_to_numpy(mp3_path: str) -> np.array:
    # Converts an MP3 file to a NumPy array with 16000 Hz mono and float64 data type.
    #  Returns a NumPy array containing the audio data.
    # Raises ValueError: If the audio is not mono or the sampling rate is not supported.
    # Read the audio data using soundfile
    audio, sample_rate = sf.read(mp3_path)
    # Check if audio is mono
    if audio.ndim != 1:
        raise ValueError("Audio must be mono channel.")
    # Resample audio to 16000 Hz using scipy.signal.resample
    if sample_rate != 16000:
        audio = resample(audio, int(audio.shape[0] * (16000 / sample_rate)))
    # Convert to NumPy array with float64 data type
    audio = np.array(audio, dtype=np.float64)
    return audio


def load_local_dataset(csv_file: str, audio_dir: str) -> DatasetDict:
    # data = pd.read_csv(csv_file, sep='|', names=['path', 'sentence'], header=None)
    # data = pd.read_csv(filepath_or_buffer=csv_file, sep='|', header=None, index_col=None)
    df = pd.read_csv(filepath_or_buffer=csv_file, sep='|', header=None, names=['path', 'sentence'])
    df['path'] = audio_dir + df['path'] + '.mp3'

    # df['path']
    # df['sentence']
    # print(df)

    # Create a Dataset from the data
    path_list = df['path'].tolist()
    # num_rows = df.shape[0]
    full_dataset = Dataset.from_dict({
        'path': path_list,
        'sentence': df['sentence'].tolist(),
        'audio': [{
            'path': path,
            'array': convert_mp3_to_numpy(path),
            'sampling_rate': 16000} for path in path_list]
    })
    # 'path', 'array', 'sampling_rate'

    # Split the dataset into train and test sets

    # dataset_dict = DatasetDict()
    # train_dataset = full_dataset.train_test_split(test_size=0.2, seed=42)['train']
    # test_dataset = full_dataset.train_test_split(test_size=0.2, seed=42)['test']
    #
    # dataset_dict['train'] = train_dataset
    # dataset_dict['test'] = test_dataset
    #
    # OR:
    return full_dataset.train_test_split(test_size=0.2, seed=42)


# ## Load Dataset

# Load data from the CSV file
# cat ../../IMS-Toucan_May_2023/Data/Fiftylangmale/metadata_base.csv | cut -d'|' -f1,2 > Data/Fiftylangmale/metadata_base.csv
# head -4 Data/Fiftylangmale/metadata_base.csv > Data/Fiftylangmale/metadata_small.csv

# /home/haroon/git_repos/whisper_related/community-events/Data/Fiftylangmale/mp3/
base_data_dir = '/home/haroon/git_repos/whisper_related/community-events/Data'
audio_dir = f'{base_data_dir}/Fiftylangmale/mp3/'
# csv_file = f'{base_data_dir}/Fiftylangmale/metadata_small.csv'
csv_file = f'{base_data_dir}/Fiftylangmale/metadata_base.csv'

dataset_dict = load_local_dataset(csv_file=csv_file, audio_dir=audio_dir)
common_voice = dataset_dict


# Hugging Face Hub:
# [mozilla-foundation/common_voice_11_0]
# (https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0).
# common_voice = DatasetDict()
# common_voice["train"] = load_dataset("mozilla-foundation/common_voice_11_0",
#                                      "hi",
#                                      split="train+validation",
#                                      token=True)
# common_voice["test"] = load_dataset("mozilla-foundation/common_voice_11_0",
#                                     "hi",
#                                     split="test",
#                                     token=True)
# print(f'YYY1a {common_voice=}')
# common_voice = common_voice.remove_columns([
#     "accent", "age", "client_id", "down_votes", "gender", "locale", "path", "segment", "up_votes"])
# print(f'YYY1b {common_voice=}')
# print(f'YYY2 {type(common_voice)=}')

# ## Prepare Feature Extractor, Tokenizer and Data
# The ASR pipeline can be de-composed into three stages:
# 1) A feature extractor which pre-processes the raw audio-inputs
# 2) The model which performs the sequence-to-sequence mapping 
# 3) A tokenizer which post-processes the model outputs to text format
# 
# In 🤗 Transformers, the Whisper model has an associated feature extractor and tokenizer, called
# [WhisperFeatureExtractor]
# (https://huggingface.co/docs/transformers/main/model_doc/whisper#transformers.WhisperFeatureExtractor)
# and [WhisperTokenizer]
# (https://huggingface.co/docs/transformers/main/model_doc/whisper#transformers.WhisperTokenizer)
# respectively.

# ### Load WhisperFeatureExtractor
# The Whisper feature extractor performs two operations:
# 1. Pads / truncates the audio inputs to 30s: any audio inputs shorter than 30s are padded to 30s
# with silence (zeros), and those longer that 30s are truncated to 30s.
# 2. Converts the audio inputs to log-Mel spectrogram input features, a visual representation of the
# audio and the form of the input expected by the Whisper model.

# We'll load the feature extractor from the pre-trained checkpoint with the default values:
feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-small")

# ### Load WhisperTokenizer
# The Whisper model outputs a sequence of token ids.
# The tokenizer maps each of these token ids to their corresponding text string.
# For Hindi, we can load the pre-trained tokenizer and use it for fine-tuning without any
# further modifications.
# We simply have to specify the target language and the task.
# These arguments inform the tokenizer to prefix the language and task tokens to the start of encoded
# label sequences:
# tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-small",
#                                              language="Hindi", task="transcribe")
tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-small",
                                             language="Turkish", task="transcribe")

# ### Combine To Create A WhisperProcessor
# To simplify using the feature extractor and tokenizer, we can wrap both into a single
# `WhisperProcessor` class. This processor object inherits from the `WhisperFeatureExtractor`
# and `WhisperProcessor`, and can be used on the audio inputs and model predictions as required.
# In doing so, we only need to keep track of two objects during training: 
# the `processor` and the `model`:
# processor = WhisperProcessor.from_pretrained("openai/whisper-small",
#                                              language="Hindi", task="transcribe")
processor = WhisperProcessor.from_pretrained("openai/whisper-small",
                                             language="Turkish", task="transcribe")

# ### Prepare Data
# Let's print the first example of the Common Voice dataset to see what form the data is in:
# print(common_voice["train"][0])
'''
print(common_voice["train"][0].keys())
common_voice["train"][0] --> keys: 'audio', 'sentence'
common_voice["train"][0]['audio'] -> keys: 'path': str, 'array': list(float), 'sampling_rate': int
common_voice["train"][0]['sentence'] -> text
'''

# Since our input audio is sampled at 48kHz, we need to downsample it to 16kHz prior to passing
# it to the Whisper feature extractor, 16kHz being the sampling rate expected by the Whisper model.
# We'll set the audio inputs to the correct sampling rate using dataset's
# [`cast_column`]
# (https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=cast_column#datasets.DatasetDict.cast_column)
# method.
# This operation does not change the audio in-place, but rather signals to `datasets` to resample
# audio samples on the fly the first time that they are loaded:
# common_voice = common_voice.cast_column("audio", Audio(sampling_rate=16000))

# Re-loading the first audio sample in the Common Voice dataset will resample it to the
# desired sampling rate:
# print(common_voice["train"][0])

# We'll define our pre-processing strategy. We advise that you **do not** lower-case the transcriptions
# or remove punctuation unless mixing different datasets.
# This will enable you to fine-tune Whisper models that can predict punctuation and casing.
# Later, you will see how we can evaluate the predictions without punctuation or casing, so that
# the models benefit from the WER improvement obtained by normalising the transcriptions while
# still predicting fully formatted transcriptions.
do_lower_case = False
do_remove_punctuation = False
normalizer = BasicTextNormalizer()

# Now we can write a function to prepare our data ready for the model:
# 1. We load and resample the audio data by calling `batch["audio"]`.
# As explained above, 🤗 Datasets performs any necessary resampling operations on the fly.
# 2. We use the feature extractor to compute the log-Mel spectrogram input features from our
# 1-dimensional audio array.
# 3. We perform any optional pre-processing (lower-case or remove punctuation).
# 4. We encode the transcriptions to label ids through the use of the tokenizer.


def prepare_dataset(batch):
    # load and (possibly) resample audio data to 16kHz
    audio = batch["audio"]

    # compute log-Mel input features from input audio array 
    batch["input_features"] = processor.feature_extractor(
        audio["array"], sampling_rate=audio["sampling_rate"]).input_features[0]
    # compute input length of audio sample in seconds
    batch["input_length"] = len(audio["array"]) / audio["sampling_rate"]
    
    # optional pre-processing steps
    transcription = batch["sentence"]
    if do_lower_case:
        transcription = transcription.lower()
    if do_remove_punctuation:
        transcription = normalizer(transcription).strip()

    # encode target text to label ids
    batch["labels"] = processor.tokenizer(transcription).input_ids
    return batch


# We can apply the data preparation function to all of our training examples using dataset's
# `.map` method.
# The argument `num_proc` specifies how many CPU cores to use. Setting `num_proc` > 1 will
# enable multiprocessing. If the `.map` method hangs with multiprocessing, set `num_proc=1`
# and process the dataset sequentially.
common_voice = common_voice.map(prepare_dataset,
                                remove_columns=common_voice.column_names["train"],
                                num_proc=2)

# Finally, we filter any training data with audio samples longer than 30s.
# These samples would otherwise be truncated by the Whisper feature-extractor which could affect
# the stability of training.
# We define a function that returns `True` for samples that are less than 30s, and `False` for
# those that are longer:
max_input_length = 30.0


def is_audio_in_length_range(length):
    return length < max_input_length


# We apply our filter function to all samples of our training dataset through 🤗 Datasets'
# `.filter` method:
common_voice["train"] = common_voice["train"].filter(
    is_audio_in_length_range,
    input_columns=["input_length"],
)

# ## Training and Evaluation
# Now that we've prepared our data, we're ready to dive into the training pipeline.
# The [🤗 Trainer]
# (https://huggingface.co/transformers/master/main_classes/trainer.html?highlight=trainer)
# will do much of the heavy lifting for us. All we have to do is:
# - Define a data collator: the data collator takes our pre-processed data and prepares PyTorch
# tensors ready for the model.
# - Evaluation metrics: during evaluation, we want to evaluate the model using the
# [word error rate (WER)] (https://huggingface.co/metrics/wer) metric.
# We need to define a `compute_metrics` function that handles this computation.
# - Load a pre-trained checkpoint: we need to load a pre-trained checkpoint and configure it correctly
# for training.
# - Define the training configuration: this will be used by the 🤗 Trainer to define the training
# schedule.
# Once we've fine-tuned the model, we will evaluate it on the test data to verify that we have
# correctly trained it to transcribe speech in Hindi.

# ### Define a Data Collator
# The data collator for a sequence-to-sequence speech model is unique in the sense that it treats
# the `input_features` and `labels` independently: the `input_features` must be handled by the
# feature extractor and the `labels` by the tokenizer.
# The `input_features` are already padded to 30s and converted to a log-Mel spectrogram of fixed
# dimension by action of the feature extractor, so all we have to do is convert the `input_features`
# to batched PyTorch tensors.
# We do this using the feature extractor's `.pad` method with `return_tensors=pt`.
# The `labels` on the other hand are un-padded. We first pad the sequences to the maximum length
# in the batch using the tokenizer's `.pad` method. The padding tokens are then replaced by `-100`
# so that these tokens are **not** taken into account when computing the loss.
# We then cut the BOS token from the start of the label sequence as we append it later during training.
# We can leverage the `WhisperProcessor` we defined earlier to perform both the feature extractor
# and the tokenizer operations:


@dataclass
class DataCollatorSpeechSeq2SeqWithPadding:
    processor: Any

    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.
        # First treat the audio inputs by simply returning torch tensors.
        input_features = [{"input_features": feature["input_features"]} for feature in features]
        batch = self.processor.feature_extractor.pad(input_features, return_tensors="pt")

        # get the tokenized label sequences
        label_features = [{"input_ids": feature["labels"]} for feature in features]
        # pad the labels to max length
        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
        # gets appended later.
        if (labels[:, 0] == self.processor.tokenizer.bos_token_id).all().cpu().item():
            labels = labels[:, 1:]
        batch["labels"] = labels
        return batch


# Let's initialise the data collator we've just defined:
data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)

# ### Evaluation Metrics
# We'll use the word error rate (WER) metric, the 'de-facto' metric for assessing ASR systems.
# For more information, refer to the WER
# [docs] (https://huggingface.co/metrics/wer).
# We'll load the WER metric from 🤗 Evaluate:
metric = evaluate.load("wer")

# We then simply have to define a function that takes our model predictions and returns the WER metric.
# This function, called `compute_metrics`, first replaces `-100` with the `pad_token_id` in the
# `label_ids` (undoing the step we applied in the data collator to ignore padded tokens correctly in
# the loss).
# It then decodes the predicted and label ids to strings. Finally, it computes the WER between the
# predictions and reference labels.
# Here, we have the option of evaluating with the 'normalised' transcriptions and predictions.
# We recommend you set this to `True` to benefit from the WER improvement obtained by normalising
# the transcriptions.

# Evaluate with the 'normalised' WER
do_normalize_eval = True


def compute_metrics(pred):
    pred_ids = pred.predictions
    label_ids = pred.label_ids

    # replace -100 with the pad_token_id
    label_ids[label_ids == -100] = processor.tokenizer.pad_token_id

    # we do not want to group tokens when computing the metrics
    pred_str = processor.tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
    label_str = processor.tokenizer.batch_decode(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]
    wer = 100 * metric.compute(predictions=pred_str, references=label_str)
    return {"wer": wer}


# ### Load a Pre-Trained Checkpoint
# Now let's load the pre-trained Whisper `small` checkpoint. Again, this is trivial through
# use of 🤗 Transformers!
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
# define your language of choice here
# model.generation_config.language = "hi"
model.generation_config.language = "tr"

# Override generation arguments - no tokens are forced as decoder outputs
# (see [`forced_decoder_ids`]
# (https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate.forced_decoder_ids)),
# no tokens are suppressed during generation
# (see [`suppress_tokens`]
# (https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate.suppress_tokens)).
# Set `use_cache` to False since we're using gradient checkpointing, and the two are incompatible:
model.config.forced_decoder_ids = None
model.config.suppress_tokens = []
model.config.use_cache = False

# ### Define the Training Configuration
# In the final step, we define all the parameters related to training.
# For more detail on the training arguments, refer to the Seq2SeqTrainingArguments
# [docs]
# (https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Seq2SeqTrainingArguments).
training_args = Seq2SeqTrainingArguments(
    output_dir="./",
    per_device_train_batch_size=8,
    gradient_accumulation_steps=8,  # increase by 2x for every 2x decrease in batch size
    learning_rate=1e-5,
    warmup_steps=500,
    max_steps=5000,
    gradient_checkpointing=True,
    fp16=True,
    evaluation_strategy="steps",
    per_device_eval_batch_size=4,
    predict_with_generate=True,
    generation_max_length=225,
    save_steps=1000,
    eval_steps=1000,
    logging_steps=25,
    report_to=["tensorboard"],
    load_best_model_at_end=True,
    metric_for_best_model="wer",
    greater_is_better=False,
    push_to_hub=True,
)

# **Note**: if one does not want to upload the model checkpoints to the Hub, set `push_to_hub=False`.

# We can forward the training arguments to the 🤗 Trainer along with our model, dataset, data collator
# and `compute_metrics` function:

trainer = Seq2SeqTrainer(
    args=training_args,
    model=model,
    train_dataset=common_voice["train"],
    eval_dataset=common_voice["test"],
    data_collator=data_collator,
    compute_metrics=compute_metrics,
    tokenizer=processor.feature_extractor,
)

# We'll save the processor object once before starting training. Since the processor is not trainable,
# it won't change over the course of training:
processor.save_pretrained(training_args.output_dir)

# ### Training
# Training will take approximately 5-10 hours depending on your GPU. The peak GPU memory for the
# given training configuration is approximately 36GB.
# Depending on your GPU, it is possible that you will encounter a CUDA `"out-of-memory"` error when
# you launch training. In this case, you can reduce the `per_device_train_batch_size` incrementally
# by factors of 2 and employ [`gradient_accumulation_steps`]
# (https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Seq2SeqTrainingArguments.gradient_accumulation_steps)
# to compensate.

# To launch training, simply execute:
trainer.train()

# We can label our checkpoint with the `whisper-event` tag on push by setting the appropriate
# keyword arguments (kwargs):
kwargs = {
    "dataset_tags": "mozilla-foundation/common_voice_11_0",
    "dataset": "Common Voice 11.0",  # a 'pretty' name for the training dataset
    #"language": "hi",
    "language": "tr",
    "model_name": "Whisper Small Hi - Sanchit Gandhi",  # a 'pretty' name for your model
    "finetuned_from": "openai/whisper-small",
    "tasks": "automatic-speech-recognition",
    "tags": "whisper-event",
}

# The training results can now be uploaded to the Hub. To do so, execute the `push_to_hub`
# command and save the preprocessor object we created:
trainer.push_to_hub(**kwargs)

# ## Closing Remarks
# If you're interested in fine-tuning other Transformers models, both for English and multilingual ASR,
# be sure to check out the examples scripts at
# [examples/pytorch/speech-recognition]
# (https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition).