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[Перевод] Тонкая настройка Whisper для многоязычного ASR с помощью Hugging Face Transformers

Предлагается пошаговое руководство по дообучению Whisper для любого многоязычного набора данных ASR с использованием Hugging Face 🤗 Transformers. Эта заметка содержит подробные объяснения модели Whisper, набора данных Common Voice и теории дообучения, а также код для выполнения шагов по подготовке данных и дообучению. Для более упрощенной версии с меньшим количеством объяснений, но со всем кодом, см. соответствующий Google Colab.

Содержание

  1. Введение

  2. Дообучение Whisper в Google Colab

    1. Подготовка среды

    2. Загрузка набора данных

    3. Подготовка извлекателя признаков, токенизатора и данных

    4. Обучение и оценка

    5. Создание демо

  3. Заключительные замечания

Введение

Whisper - это предварительно обученная модель для автоматического распознавания речи (ASR), опубликованная в сентябре 2022 года авторами Alec Radford и др. из OpenAI. В отличие от многих своих предшественников, таких как Wav2Vec 2.0, которые предварительно обучаются на неразмеченных аудиоданных, Whisper предварительно обучается на огромном количестве размеченных аудиоданных с транскрипцией, 680 000 часов, если быть точным. Это на порядок больше данных, чем неразмеченные аудиоданные, используемые для обучения Wav2Vec 2.0 (60 000 часов). Более того, 117 000 часов этих данных для предварительного обучения - это многоязычные данные ASR. Это приводит к тому, что контрольные точки могут быть применены к более чем 96 языкам, многие из которых считаются малоресурсными.

Это количество размеченных данных позволяет Whisper предварительно обучаться непосредственно на задаче распознавания речи, изучая отображение речи в текст из размеченных аудиоданных с транскрипцией. Название Whisper происходит от аббревиатуры "WSPSR", что означает "Web-scale Supervised Pre-training for Speech Recognition" (Масштабное веб-обучение с учителем для распознавания речи). В результате Whisper требует мало дополнительного дообучения, чтобы дать производительную модель ASR. Это в отличие от Wav2Vec 2.0, который предварительно обучается на задаче без учителя - маскированном предсказании. Здесь модель обучается изучать промежуточное отображение от речи к скрытым состояниям из неразмеченных аудиоданных. Хотя предварительное обучение без учителя дает высококачественные представления речи, оно не изучает отображение речи в текст. Это отображение изучается только во время дообучения, поэтому требуется больше дообучения, чтобы добиться конкурентоспособной производительности.

При масштабировании до 680 000 часов размеченных данных для предварительного обучения модели Whisper демонстрируют сильную способность обобщать на многие наборы данных и домены. Предварительно обученные контрольные точки достигают конкурентоспособных результатов по сравнению с передовыми системами ASR, с почти 3% показателем ошибок слов (WER) на подмножестве test-clean LibriSpeech ASR и новым рекордом на TED-LIUM с 4,7% WER (см. таблицу 8 статьи Whisper). Обширные многоязычные знания ASR, полученные Whisper во время предварительного обучения, могут быть использованы для других малоресурсных языков; благодаря тонкой настройке предварительно обученные контрольные точки могут быть адаптированы для конкретных наборов данных и языков, чтобы еще больше улучшить эти результаты.

Whisper - это модель на основе трансформера с encoder-decoder, также называемая моделью sequence-to-sequence. Она отображает последовательность признаков спектрограммы речи на последовательность текстовых токенов. Сначала исходные аудиовходы преобразуются в логарифмическую спектрограмму Мела с помощью извлекателя признаков. Затем трансформерный кодировщик кодирует спектрограмму, формируя последовательность скрытых состояний кодировщика. Наконец, декодер авторегрессивно предсказывает текстовые токены, условно как на предыдущих токенах, так и на скрытых состояниях кодировщика. Рисунок 1 иллюстрирует модель Whisper.

fcdfeab4a8ef2a9705fb553b43202e3d.png

Figure 1: Модель Whisper. Архитектура следует стандартной модели encoder-decoder на основе трансформера. На вход кодировщика подается лог-Мел спектрограмма. Последние скрытые состояния кодировщика передаются на вход декодера через механизмы внимания. Декодер авторегрессивно предсказывает текстовые токены, совместно зависящие от скрытых состояний кодировщика и предыдущих предсказанных токенов. Источник рисунка: OpenAI Whisper Blog.

В модели sequence-to-sequence кодировщик преобразует аудиовходы в набор представлений скрытых состояний, извлекая важные признаки из произнесенной речи. Декодер играет роль языковой модели, обрабатывая представления скрытых состояний и генерируя соответствующие текстовые транскрипции. Включение языковой модели внутри системной архитектуры называется глубоким слиянием. Это противопоставляется поверхностному слиянию, при котором языковая модель комбинируется внешне с кодировщиком, например, с CTC + n-граммами (см. Оценка внутренней языковой модели). При глубоком слиянии вся система может быть обучена end-to-end с одними и теми же данными и функцией потерь, что дает большую гибкость и, как правило, лучшую производительность (см. ESB Benchmark).

Whisper предобучается и дообучается с использованием функции потерь перекрестной энтропии, стандартной функции потерь для обучения систем sequence-to-sequence на задачах классификации. Здесь система обучается правильно классифицировать целевой текстовый токен из предопределенного словаря текстовых токенов.

Для модели Whisper существует пять контрольных точек различных размеров. Четыре самых маленьких обучены либо только на английском, либо на многоязычных данных. Самая большая контрольная точка является многоязычной. Все девять предобученных контрольных точек доступны на Hugging Face Hub. Контрольные точки суммированы в следующей таблице:

4fccdfd27554d809a795c4f5ea156f9b.png

В качестве демонстрации мы будем дообучать многоязычную версию маленькой контрольной точки с 244M параметрами (~= 1GB). Что касается наших данных, мы будем обучать и оценивать нашу систему на языке с низкими ресурсами, взятом из набора данных Common Voice. Мы покажем, что с всего лишь 8 часами данных для дообучения, мы можем достичь высокой производительности на этом языке.

Дообучение Whisper в Google Colab

Подготовка среды

Мы будем использовать несколько популярных пакетов Python для дообучения модели Whisper. Мы будем использовать datasets для загрузки и подготовки наших обучающих данных, а также transformers и accelerate для загрузки и обучения нашей модели Whisper. Нам также потребуется пакет soundfile для предварительной обработки аудиофайлов, evaluate и jiwer для оценки производительности нашей модели, и tensorboard для записи наших метрик. Наконец, мы будем использовать gradio для создания яркой демонстрации нашей дообученной модели.

$ pip install --upgrade pip
$ pip install --upgrade datasets transformers accelerate soundfile librosa evaluate jiwer tensorboard gradio

Мы настоятельно рекомендуем вам загружать контрольные точки модели прямо на Hugging Face Hub во время обучения. Hub предоставляет:

  • Интегрированный контроль версий: вы можете быть уверены, что ни одна контрольная точка модели не будет потеряна во время обучения.

  • Логи Tensorboard: отслеживайте важные метрики в течение обучения.

  • Карты моделей: документируйте, что делает модель и ее предполагаемые варианты использования.

  • Сообщество: простой способ поделиться и сотрудничать с сообществом!

Связывание блокнота с Hub очень просто - достаточно ввести ваш токен аутентификации Hub при запросе. Найдите свой токен аутентификации Hub здесь:

from huggingface_hub import notebook_login

notebook_login()

Ответ:

Login successful
Your token has been saved to /root/.huggingface/token

Загрузка набора данных

Common Voice - это серия наборов данных, созданных с помощью краудсорсинга, где дикторы записывают текст из Википедии на разных языках. Мы будем использовать последнюю версию набора данных Common Voice (версия 11). Что касается нашего языка, мы будем дообучать нашу модель на хинди, индоарийском языке, который говорят на севере, в центре, на востоке и западе Индии. Common Voice 11.0 содержит примерно 12 часов размеченных данных на хинди, 4 из которых являются тестовыми данными.

Давайте перейдем к Hub и посмотрим страницу набора данных Common Voice: mozilla-foundation/common_voice_11_0.

В первый раз, когда мы просматриваем эту страницу, нас попросят принять условия использования. После этого нам будет предоставлен полный доступ к набору данных.

После того, как мы предоставили аутентификацию для использования набора данных, нам будет представлен предварительный просмотр набора данных. Предварительный просмотр набора данных показывает нам первые 100 образцов набора данных. Более того, он загружен аудиообразцами, которые мы можем слушать в реальном времени. Мы можем выбрать подмножество Common Voice на хинди, установив подмножество на hi с помощью выпадающего меню (hi - это код идентификатора языка для хинди):

2ae2c1908b865e34b6f3da2c1fa19022.jpeg

Если мы нажмем кнопку воспроизведения на первом образце, мы сможем послушать аудио и увидеть соответствующий текст. Просмотрите образцы для обучающих и тестовых наборов, чтобы лучше понять аудио- и текстовые данные, с которыми мы работаем. Вы можете сказать по интонации и стилю, что записи взяты из озвученной речи. Вы также, вероятно, заметите большое разнообразие дикторов и качества записи, общую черту данных, собранных с помощью краудсорсинга.

Используя 🤗 Datasets, загрузка и подготовка данных чрезвычайно проста. Мы можем загрузить и подготовить разделы Common Voice всего в одну строку кода. Поскольку хинди является языком с очень низкими ресурсами, мы объединим обучающие (train) и проверочные (validation) разделы, чтобы получить примерно 8 часов обучающих данных. Мы будем использовать 4 часа тестовых (test) данных в качестве отложенного тестового набора:

from datasets import load_dataset, DatasetDict

common_voice = DatasetDict()

common_voice["train"] = load_dataset("mozilla-foundation/common_voice_11_0", "hi", split="train+validation", use_auth_token=True)
common_voice["test"] = load_dataset("mozilla-foundation/common_voice_11_0", "hi", split="test", use_auth_token=True)

print(common_voice)

Ответ:

DatasetDict({
    train: Dataset({
        features: ['client_id', 'path', 'audio', 'sentence', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment'],
        num_rows: 6540
    })
    test: Dataset({
        features: ['client_id', 'path', 'audio', 'sentence', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment'],
        num_rows: 2894
    })
})

Большинство наборов данных ASR предоставляют только входные аудиообразцы (audio) и соответствующий транскрибированный текст (sentence). Common Voice содержит дополнительную информацию метаданных, такую как акцент (accent) и местоположение (locale), которую мы можем игнорировать для ASR. Стараясь сделать блокнот максимально универсальным, мы рассматриваем только входное аудио и транскрибированный текст для дообучения, отбрасывая дополнительную информацию метаданных:

common_voice = common_voice.remove_columns(["accent", "age", "client_id", "down_votes", "gender", "locale", "path", "segment", "up_votes"])

Common Voice - это только один из многоязычных наборов данных ASR, которые мы можем загрузить с Hub - у нас есть гораздо больше доступных наборов данных! Чтобы просмотреть диапазон доступных наборов данных для распознавания речи, перейдите по ссылке: ASR Datasets on the Hub.

Подготовка извлекателя признаков, токенизатора и данных

Конвейер ASR можно разложить на три компонента:

  1. Извлекатель признаков, который предварительно обрабатывает входные аудиоданные

  2. Модель, которая выполняет отображение sequence-to-sequence

  3. Токенизатор, который обрабатывает выходные данные модели в текстовый формат

В 🤗 Transformers, модель Whisper имеет связанный с ней извлекатель признаков и токенизатор, называемые соответственно WhisperFeatureExtractor и WhisperTokenizer.

Мы подробно рассмотрим извлекатель признаков и токенизатор!

Загрузка WhisperFeatureExtractor

Речь представляет собой одномерный массив, который меняется со временем. Значение массива в любой данной точке времени - это амплитуда сигнала в этой точке. Исходя только из информации об амплитуде, мы можем восстановить спектр частот аудио и восстановить все акустические признаки.

Поскольку речь непрерывна, она содержит бесконечное количество значений амплитуды. Это вызывает проблемы для компьютерных устройств, которые ожидают конечные массивы. Таким образом, мы дискретизируем наш речевой сигнал, выбирая значения из нашего сигнала с фиксированными временными шагами. Интервал, с которым мы выбираем наше аудио, известен как частота дискретизации и обычно измеряется в выборках/сек или Герцах (Гц). Выборка с более высокой частотой дискретизации приводит к лучшему приближению непрерывного речевого сигнала, но также требует хранения большего количества значений в секунду.

Крайне важно, чтобы мы согласовали частоту дискретизации наших аудиовходов с ожидаемой частотой дискретизации нашей модели, поскольку аудиосигналы с разными частотами дискретизации имеют очень разные распределения. Аудиообразцы должны обрабатываться только с правильной частотой дискретизации. В противном случае это может привести к неожиданным результатам! Например, если взять аудиообразец с частотой дискретизации 16 кГц и прослушать его с частотой дискретизации 8 кГц, звук будет воспроизводиться как будто на половинной скорости. Точно так же передача аудио с неправильной частотой дискретизации может сбить с толку модель ASR, которая ожидает одну частоту дискретизации и получает другую. Извлекатель признаков Whisper ожидает аудиовходы с частотой дискретизации 16 кГц, поэтому нам нужно согласовать наши входы с этим значением. Мы не хотим случайно обучить систему ASR на замедленной речи!

Извлекатель признаков Whisper выполняет две операции. Сначала он дополняет/обрезает пакет аудиообразцов так, чтобы все образцы имели длину входа 30 секунд. Образцы, короче 30 секунд, дополняются до 30 секунд путем добавления нулей в конец последовательности (нули в аудиосигнале соответствуют отсутствию сигнала или тишине). Образцы, длиннее 30 секунд, обрезаются до 30 секунд. Поскольку все элементы в пакете дополняются/обрезаются до максимальной длины во входном пространстве, нам не требуется маска внимания при передаче аудиовходов в модель Whisper. Whisper уникален в этом отношении - с большинством аудиомоделей вы можете ожидать предоставления маски внимания, которая детализирует, где последовательности были дополнены, и, следовательно, где они должны быть проигнорированы в механизме самовнимания. Whisper обучен работать без маски внимания и напрямую выводить из речевых сигналов, где игнорировать входы.

Вторая операция, которую выполняет извлекатель признаков Whisper, - это преобразование дополненных аудиомассивов в логарифмические спектрограммы Mel. Эти спектрограммы - визуальное представление частот сигнала, похожее на преобразование Фурье. Пример спектрограммы показан на рисунке 2. Вдоль оси y находятся каналы Mel, которые соответствуют определенным частотным корзинам. Вдоль оси x - время. Цвет каждого пикселя соответствует логарифмической интенсивности этой частотной корзины в данное время. Логарифмическая спектрограмма Mel - это форма ввода, ожидаемая моделью Whisper.

Каналы Mel (частотные корзины) являются стандартными в обработке речи и выбраны для приближения к человеческому слуховому диапазону. Все, что нам нужно знать для дообучения Whisper, - это то, что спектрограмма - это визуальное представление частот в речевом сигнале. Для получения более подробной информации о каналах Mel обратитесь к Mel-frequency cepstrum.

c66da862c4e77e9a14d76a7812a05629.jpeg

Рисунок 2: Преобразование выборочного аудиомассива в логарифмическую спектрограмму Mel. Слева: выборочный одномерный аудиосигнал. Справа: соответствующая логарифмическая спектрограмма Mel. Источник рисунка: Блог Google SpecAugment.

К счастью для нас, извлекатель признаков Whisper от 🤗 Transformers выполняет как дополнение, так и преобразование спектрограммы всего в одну строку кода! Давайте загрузим извлекатель признаков из предварительно обученной контрольной точки, чтобы подготовить его для наших аудиоданных:

from transformers import WhisperFeatureExtractor

feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-small")

Загрузка WhisperTokenizer

Теперь давайте посмотрим, как загрузить токенизатор Whisper. Модель Whisper выводит текстовые токены, которые указывают индекс предсказанного текста среди словаря элементов словаря. Токенизатор отображает последовательность текстовых токенов на фактическую текстовую строку (например, [1169, 3797, 3332] -> "the cat sat").

Традиционно, когда мы используем модели только с кодировщиком для ASR, мы декодируем с использованием классификации по временной связности (CTC). Здесь нам требуется обучить токенизатор CTC для каждого набора данных, который мы используем. Одно из преимуществ использования архитектуры encoder-decoder состоит в том, что мы можем напрямую использовать токенизатор из предварительно обученной модели.

Токенизатор Whisper предварительно обучен на транскрипциях для 96 предварительно обученных языков. Следовательно, он имеет обширный byte-pair, который подходит для почти всех многоязычных приложений ASR. Для хинди мы можем загрузить токенизатор и использовать его для дообучения без каких-либо дальнейших модификаций. Нам просто нужно указать целевой язык и задачу. Эти аргументы информируют токенизатор о том, что нужно добавить токены языка и задачи в начало закодированных последовательностей меток:

from transformers import WhisperTokenizer

tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-small", language="Hindi", task="transcribe")

Совет: блог-пост можно адаптировать для перевода речи, установив задачу на "translate" и язык на язык целевого текста в предыдущей строке. Это добавит соответствующие токены задачи и языка для перевода речи при предварительной обработке набора данных.

Мы можем проверить, что токенизатор правильно кодирует символы хинди, закодировав и декодировав первый образец набора данных Common Voice. При кодировании транскрипций токенизатор добавляет "специальные токены" в начало и конец последовательности, включая токены начала/конца транскрипта, токен языка и токены задач (как указано в аргументах на предыдущем шаге). При декодировании идентификаторов меток у нас есть возможность "пропустить" эти специальные токены, что позволяет нам вернуть строку в исходной форме ввода.

input_str = common_voice["train"][0]["sentence"]
labels = tokenizer(input_str).input_ids
decoded_with_special = tokenizer.decode(labels, skip_special_tokens=False)
decoded_str = tokenizer.decode(labels, skip_special_tokens=True)

print(f"Input:                 {input_str}")
print(f"Decoded w/ special:    {decoded_with_special}")
print(f"Decoded w/out special: {decoded_str}")
print(f"Are equal:             {input_str == decoded_str}")

Ответ:

Input:                 खीर की मिठास पर गरमाई बिहार की सियासत, कुशवाहा ने दी सफाई
Decoded w/ special:    <|startoftranscript|><|hi|><|transcribe|><|notimestamps|>खीर की मिठास पर गरमाई बिहार की सियासत, कुशवाहा ने दी सफाई<|endoftext|>
Decoded w/out special: खीर की मिठास पर गरमाई बिहार की सियासत, कुशवाहा ने दी सफाई
Are equal:             True

Объединение для создания WhisperProcessor

Для упрощения использования извлекателя признаков и токенизатора мы можем объединить оба в один класс WhisperProcessor. Этот объект процессора наследуется от WhisperFeatureExtractor и WhisperProcessor и может использоваться на аудиовходах и прогнозах модели по мере необходимости. Таким образом, нам нужно отслеживать только два объекта во время обучения: processor и model:

from transformers import WhisperProcessor

processor = WhisperProcessor.from_pretrained("openai/whisper-small", language="Hindi", task="transcribe")

Подготовка данных

Давайте распечатаем первый пример набора данных Common Voice, чтобы увидеть, в какой форме находятся данные:

print(common_voice["train"][0])

Ответ:

{'audio': {'path': '/home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/607848c7e74a89a3b5225c0fa5ffb9470e39b7f11112db614962076a847f3abf/cv-corpus-11.0-2022-09-21/hi/clips/common_voice_hi_25998259.mp3',
           'array': array([0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 9.6724887e-07,
       1.5334779e-06, 1.0415988e-06], dtype=float32),
           'sampling_rate': 48000},
 'sentence': 'खीर की मिठास पर गरमाई बिहार की सियासत, कुशवाहा ने दी सफाई'}

Мы видим, что у нас есть одномерный входной аудиомассив и соответствующая целевая транскрипция. Мы много говорили о важности частоты дискретизации и о том, что мы должны согласовать частоту дискретизации нашего аудио с частотой дискретизации модели Whisper (16 кГц). Поскольку наше входное аудио дискретизируется с частотой 48 кГц, нам нужно уменьшить ее до 16 кГц перед передачей в извлекатель признаков Whisper.

Мы установим аудиовходы на правильную частоту дискретизации с помощью метода cast_column набора данных. Эта операция не изменяет аудио на месте, а скорее сигнализирует наборам данных (datasets) о ресемплировании аудиообразцов на лету при первой загрузке:

from datasets import Audio

common_voice = common_voice.cast_column("audio", Audio(sampling_rate=16000))

Перезагрузка первого аудиообразца в наборе данных Common Voice приведет его к желаемой частоте дискретизации:

print(common_voice["train"][0])

Ответ:

{'audio': {'path': '/home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/607848c7e74a89a3b5225c0fa5ffb9470e39b7f11112db614962076a847f3abf/cv-corpus-11.0-2022-09-21/hi/clips/common_voice_hi_25998259.mp3',
           'array': array([ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,
       -3.4206650e-07,  3.2979898e-07,  1.0042874e-06], dtype=float32),
           'sampling_rate': 16000},
 'sentence': 'खीर की मिठास पर गरमाई बिहार की सियासत, कुशवाहा ने दी सफाई'}

Отлично! Мы видим, что частота дискретизации была уменьшена до 16 кГц. Значения массива также изменились, так как теперь у нас примерно одно значение амплитуды на каждые три, которые были раньше.

Теперь мы можем написать функцию для подготовки наших данных для модели:

  1. Мы загружаем и ресемплируем аудиоданные, вызывая batch["audio"]. Как объяснялось выше, 🤗 Datasets выполняет все необходимые операции ресемплирования на лету.

  2. Мы используем извлекатель признаков для вычисления входных признаков логарифмической спектрограммы Mel из нашего одномерного аудиомассива.

  3. Мы кодируем транскрипции в идентификаторы меток с помощью токенизатора.

def prepare_dataset(batch):
    # load and resample audio data from 48 to 16kHz
    audio = batch["audio"]

    # compute log-Mel input features from input audio array
    batch["input_features"] = feature_extractor(audio["array"], sampling_rate=audio["sampling_rate"]).input_features[0]

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

Мы можем применить функцию подготовки данных ко всем нашим обучающим примерам, используя метод .map набора данных:

common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names["train"], num_proc=4)

Отлично! Теперь у нас есть полностью подготовленные данные для обучения! Давайте продолжим и посмотрим, как мы можем использовать эти данные для дообучения Whisper.

Примечание: В настоящее время наборы данных (datasets) используют как torchaudio, так и librosa для загрузки аудио и ресемплирования. Если вы хотите реализовать свою собственную настройку загрузки/выборки данных, вы можете использовать столбец "path" для получения пути к аудиофайлу и игнорировать столбец "audio".

Обучение и оценка

Теперь, когда мы подготовили наши данные, мы готовы погрузиться в процесс обучения. 🤗 Trainer выполнит большую часть работы за нас. Все, что нам нужно сделать, это:

  • Определить сборщик данных: сборщик данных берет наши предварительно обработанные данные и подготавливает тензоры PyTorch, готовые для модели.

  • Метрики оценки: во время оценки мы хотим оценить модель с использованием метрики ошибки на словах - word error rate (WER). Нам нужно определить функцию compute_metrics, которая обрабатывает этот расчет.

  • Загрузить предварительно обученную контрольную точку: нам нужно загрузить предварительно обученную контрольную точку и правильно настроить ее для обучения.

  • Определить аргументы обучения: они будут использоваться 🤗 Trainer при построении графика обучения.

После того, как мы дообучили модель, мы оценим ее на тестовых данных, чтобы убедиться, что мы правильно обучили ее транскрибировать речь на хинди.

Определить сборщик данных

Сборщик данных для модели речи sequence-to-sequence уникален тем, что он обрабатывает input_features и labels независимо: input_features должны обрабатываться извлекателем признаков, а labels - токенизатором.

Следует заметить, что input_features уже дополнены до 30 секунд и преобразованы в логарифмическую спектрограмму Mel фиксированного размера, поэтому все, что нам нужно сделать, это преобразовать их в пакетные тензоры PyTorch. Мы делаем это с помощью метода .pad извлекателя признаков с return_tensors=pt. Обратите внимание, что здесь не применяется дополнительное дополнение, поскольку входы имеют фиксированный размер, input_features просто преобразуются в тензоры PyTorch.

С другой стороны, labels не дополнены. Сначала мы дополняем последовательности до максимальной длины в пакете с помощью метода .pad токенизатора. Затем токены дополнения заменяются на -100, чтобы эти токены не учитывались при вычислении потерь. Затем мы вырезаем токен начала транскрипта из начала последовательности меток, так как мы добавляем его позже во время обучения.

Мы можем использовать WhisperProcessor, который мы определили ранее, для выполнения операций как извлекателя признаков, так и токенизатора:

import torch

from dataclasses import dataclass
from typing import Any, Dict, List, Union

@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's append later anyways
        if (labels[:, 0] == self.processor.tokenizer.bos_token_id).all().cpu().item():
            labels = labels[:, 1:]

        batch["labels"] = labels

        return batch

Давайте инициализируем коллатор данных, который мы только что определили:

data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)

Метрики оценки

Далее мы определяем метрику оценки, которую будем использовать на нашем наборе данных для оценки. Мы будем использовать метрику ошибки на словах (WER), "де-факто" метрику для оценки систем ASR. Для получения дополнительной информации обратитесь к документации WER. Мы загрузим метрику WER из 🤗 Evaluate:

import evaluate

metric = evaluate.load("wer")

Затем нам просто нужно определить функцию, которая принимает наши прогнозы модели и возвращает метрику WER. Эта функция, называемая compute_metrics, сначала заменяет -100 на pad_token_id в label_ids (отменяя шаг, который мы применили в сборщике данных, чтобы правильно игнорировать дополненные токены при потерях). Затем он декодирует предсказанные идентификаторы меток в строки. Наконец, он вычисляет WER между прогнозами и эталонными метками:

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] = tokenizer.pad_token_id

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

    wer = 100 * metric.compute(predictions=pred_str, references=label_str)

    return {"wer": wer}

Загрузка предварительно обученной контрольной точки

Теперь давайте загрузим предварительно обученную контрольную точку Whisper small. Опять же, это тривиально с использованием 🤗 Transformers!

from transformers import WhisperForConditionalGeneration

model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")

У модели Whisper есть идентификаторы токенов, которые принудительно выводятся как выходы модели перед началом авторегрессивного генерирования (forced_decoder_ids). Эти идентификаторы токенов контролируют язык транскрипции и задачу для ASR с нулевым выстрелом. Для дообучения мы установим эти идентификаторы в None, так как мы будем обучать модель предсказывать правильный язык (хинди) и задачу (транскрипцию). Есть также токены, которые полностью подавляются во время генерации (suppress_tokens). Эти токены имеют свои логарифмические вероятности, установленные в -inf, так что они никогда не выбираются. Мы переопределим эти токены в пустой список, что означает, что никакие токены не подавляются:

model.config.forced_decoder_ids = None
model.config.suppress_tokens = []

Определение аргументов обучения

На последнем шаге мы определяем все параметры, связанные с обучением. Ниже объясняется подмножество параметров:

  • output_dir: локальный каталог, в котором сохраняются веса модели. Это также будет название репозитория на Hugging Face Hub.

  • generation_max_length: максимальное количество токенов для авторегрессивного генерирования во время оценки.

  • save_steps: во время обучения промежуточные контрольные точки будут сохраняться и асинхронно загружаться на Hub каждые save_steps шагов обучения.

  • eval_steps: во время обучения оценка промежуточных контрольных точек будет выполняться каждые eval_steps шагов обучения.

  • report_to: где сохранять журналы обучения. Поддерживаются платформы "azure_ml", "comet_ml", "mlflow", "neptune", "tensorboard" и "wandb". Выберите свою любимую или оставьте "tensorboard", чтобы вести журнал на Hub.

Для получения более подробной информации о других аргументах обучения обратитесь к документации Seq2SeqTrainingArguments.

from transformers import Seq2SeqTrainingArguments

training_args = Seq2SeqTrainingArguments(
    output_dir="./whisper-small-hi",  # change to a repo name of your choice
    per_device_train_batch_size=16,
    gradient_accumulation_steps=1,  # increase by 2x for every 2x decrease in batch size
    learning_rate=1e-5,
    warmup_steps=500,
    max_steps=4000,
    gradient_checkpointing=True,
    fp16=True,
    evaluation_strategy="steps",
    per_device_eval_batch_size=8,
    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,
)

Примечание: если вы не хотите загружать контрольные точки модели в Hub, установите push_to_hub=False.

Мы можем передать аргументы обучения в 🤗 Trainer вместе с нашей моделью, набором данных, коллатором данных и функцией compute_metrics:

from transformers import Seq2SeqTrainer

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,
)

И с этим мы готовы приступить к тренировкам!

Обучение

Чтобы начать обучение, просто выполните команду:

trainer.train()

Обучение займет примерно 5-10 часов в зависимости от вашего GPU или того, который выделен в Google Colab. В зависимости от вашего GPU, возможно, вы столкнетесь с ошибкой "out-of-memory" CUDA при начале обучения. В этом случае вы можете уменьшить per_device_train_batch_size инкрементно на множители 2 и использовать gradient_accumulation_steps для компенсации.

e5d50444b9328a56e8a46c53d6728a87.png

Наш лучший WER составляет 32,0% - неплохо для 8 часов обучающих данных! Большой вопрос в том, как это сравнивается с другими системами ASR. Для этого мы можем просмотреть hf-speech-bench, таблицу лидеров, которая классифицирует модели по языку и набору данных, а затем ранжирует их в соответствии с их WER.

af0dbe22cac56bc4378e891c309c0e91.jpeg

Наша модель, прошедшая тонкую настройку, значительно превосходит по производительности "нулевой выстрел" small контрольной точки Whisper, что подчеркивает сильные возможности передачи обучения Whisper.

Мы можем автоматически отправить нашу контрольную точку на доску лидеров, когда мы отправляем результаты обучения на Hub - нам просто нужно установить соответствующие ключевые аргументы (kwargs). Вы можете изменить эти значения в соответствии с вашим набором данных, языком и названием модели.

kwargs = {
    "dataset_tags": "mozilla-foundation/common_voice_11_0",
    "dataset": "Common Voice 11.0",  # a 'pretty' name for the training dataset
    "dataset_args": "config: hi, split: test",
    "language": "hi",
    "model_name": "Whisper Small Hi - Sanchit Gandhi",  # a 'pretty' name for your model
    "finetuned_from": "openai/whisper-small",
    "tasks": "automatic-speech-recognition",
    "tags": "hf-asr-leaderboard",
}

Результаты обучения теперь можно загрузить на Hub. Для этого выполните команду push_to_hub.

trainer.push_to_hub(**kwargs)

Теперь вы можете поделиться этой моделью с кем угодно, используя ссылку на Hub. Они также могут загрузить ее с идентификатором "ваше-имя-пользователя/выбранное-вами-имя", например:

from transformers import WhisperForConditionalGeneration, WhisperProcessor

model = WhisperForConditionalGeneration.from_pretrained("sanchit-gandhi/whisper-small-hi")
processor = WhisperProcessor.from_pretrained("sanchit-gandhi/whisper-small-hi")

Хотя дообученная модель дает удовлетворительные результаты на тестовых данных Common Voice на хинди, она отнюдь не оптимальна. Цель этого блокнота - продемонстрировать, как предварительно обученные контрольные точки Whisper могут быть дообучены на любом многоязычном наборе данных ASR. Результаты, вероятно, могут быть улучшены путем оптимизации гиперпараметров обучения, таких как скорость обучения и исключение, а также использования более крупной предварительно обученной контрольной точки (medium или large).

Создание демо

Теперь, когда мы дообучили нашу модель, мы можем создать демо, чтобы продемонстрировать ее возможности в области ASR! Мы будем использовать конвейер (pipeline) 🤗 Transformers, который заботится обо всем конвейере ASR, начиная от предварительной обработки аудиовходов и заканчивая декодированием прогнозов модели. Мы создадим наше интерактивное демо с помощью Gradio. Gradio, возможно, самый простой способ создания демонстраций машинного обучения; с Gradio мы можем создать демо всего за несколько минут!

Запуск приведенного ниже примера сгенерирует демо Gradio, где мы сможем записывать речь через микрофон нашего компьютера и вводить ее в нашу дообученную модель Whisper для транскрибации соответствующего текста:

from transformers import pipeline
import gradio as gr

pipe = pipeline(model="sanchit-gandhi/whisper-small-hi")  # change to "your-username/the-name-you-picked"

def transcribe(audio):
    text = pipe(audio)["text"]
    return text

iface = gr.Interface(
    fn=transcribe,
    inputs=gr.Audio(source="microphone", type="filepath"),
    outputs="text",
    title="Whisper Small Hindi",
    description="Realtime demo for Hindi speech recognition using a fine-tuned Whisper small model.",
)

iface.launch()

Заключительные замечания

Мы рассмотрели пошаговое руководство по дообучению Whisper для многоязычного ASR с использованием 🤗 Datasets, Transformers и Hugging Face Hub. Обратитесь к Google Colab, если вы хотите попробовать дообучение самостоятельно. Если вас интересует дообучение других моделей Transformers, как для английского, так и для многоязычного ASR, обязательно ознакомьтесь с примерами скриптов в examples/pytorch/speech-recognition.

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  • 07.09.23 16:24 CherryTeam

    Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/

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  • 27.12.24 00:21 swiftdream

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  • 04.01.25 04:56 florencestella

    THE BEST CERTIFIED CRYPTOCURRENCY RECOVERY EXPERT DUNAMIS CYBER SOLUTION

  • 04.01.25 04:57 florencestella

    THE BEST CERTIFIED CRYPTOCURRENCY RECOVERY EXPERT DUNAMIS CYBER SOLUTION It sounds like you went through a very frustrating experience with Cointrack, where your access to your own funds was unjustly restricted for months without clear communication or a solution. The extended periods of account freezes, lack of transparency, and vague customer support responses would make anyone anxious. It’s understandable that you suspected the issue could be related to your login activity, but it’s surprising that something as minor as using the same Wi-Fi network could trigger such severe restrictions. I’m glad to hear that DUNAMIS CYBER SOLUTION Recovery was able to help you get your account unlocked and resolve the issue. It’s unfortunate that you had to seek third-party assistance, but it’s a relief that the situation was eventually addressed. If you plan on using any platforms like this again, you might want to be extra cautious, especially when dealing with sensitive financial matters. And if you ever need to share your experience to help others avoid similar issues, feel free to reach out. It might be helpful for others to know about both the pitfalls and the eventual resolution through services like DUNAMIS CYBER SOLUTION Recovery. [email protected] +13433030545 [email protected]

  • 06.01.25 19:09 michaeljordan15

    We now live in a world where most business transactions are conducted through Bitcoin and cryptocurrency. With the rapid growth of digital currencies, everyone seems eager to get involved in Bitcoin and cryptocurrency investments. This surge in interest has unfortunately led to the rise of many fraudulent platforms designed to exploit unsuspecting individuals. People are often promised massive profits, only to lose huge sums of money when they realize the platform they invested in was a scam. contact with WhatsApp: +1 (443) 859 - 2886 Email @ digitaltechguard.com Telegram: digitaltechguardrecovery.com website link:: https://digitaltechguard.com This was exactly what happened to me five months ago. I was excited about the opportunity to invest in Bitcoin, hoping to earn a steady return of 20%. I found a platform that seemed legitimate and made my investment, eagerly anticipating the day when I would be able to withdraw my earnings. When the withdrawal day arrived, however, I encountered an issue. My bank account was not credited, despite seeing my balance and the supposed profits in my account on the platform. At first, I assumed it was just a technical glitch. I thought, "Maybe it’s a delay in the system, and everything will be sorted out soon." However, when I tried to contact customer support, the line was either disconnected or completely unresponsive. My doubts started to grow, but I wanted to give them the benefit of the doubt and waited throughout the day to see if the situation would resolve itself. But by the end of the day, I realized something was terribly wrong. I had been swindled, and my hard-earned money was gone. The realization hit me hard. I had fallen victim to one of the many fraudulent Bitcoin platforms that promise high returns and disappear once they have your money. I knew I had to act quickly to try and recover what I had lost. I started searching online for any possible solutions, reading reviews and recommendations from others who had faced similar situations. That’s when I came across many positive reviews about Digital Tech Guard Recovery. After reading about their success stories, I decided to reach out and use their services. I can honestly say that Digital Tech Guard Recovery exceeded all my expectations. Their team was professional, efficient, and transparent throughout the process. Within a short time, they helped me recover a significant portion of my lost funds, which I thought was impossible. I am incredibly grateful to Digital Tech Guard Recovery for their dedication and expertise in helping me get my money back. If you’ve been scammed like I was, don’t lose hope. There are solutions, and Digital Tech Guard Recovery is truly one of the best. Thank you, Digital Tech Guard Recovery! You guys are the best. Good luck to everyone trying to navigate this challenging space. Stay safe.

  • 18.01.25 12:41 michaeldavenport218

    I was recently scammed out of $53,000 by a fraudulent Bitcoin investment scheme, which added significant stress to my already difficult health issues, as I was also facing cancer surgery expenses. Desperate to recover my funds, I spent hours researching and consulting other victims, which led me to discover the excellent reputation of Capital Crypto Recover, I came across a Google post It was only after spending many hours researching and asking other victims for advice that I discovered Capital Crypto Recovery’s stellar reputation. I decided to contact them because of their successful recovery record and encouraging client testimonials. I had no idea that this would be the pivotal moment in my fight against cryptocurrency theft. Thanks to their expert team, I was able to recover my lost cryptocurrency back. The process was intricate, but Capital Crypto Recovery's commitment to utilizing the latest technology ensured a successful outcome. I highly recommend their services to anyone who has fallen victim to cryptocurrency fraud. For assistance, contact [email protected] Capital Crypto Recover on Telegram OR Call Number +1 (336)390-6684 via email: [email protected]

  • 18.01.25 12:41 michaeldavenport218

    I was recently scammed out of $53,000 by a fraudulent Bitcoin investment scheme, which added significant stress to my already difficult health issues, as I was also facing cancer surgery expenses. Desperate to recover my funds, I spent hours researching and consulting other victims, which led me to discover the excellent reputation of Capital Crypto Recover, I came across a Google post It was only after spending many hours researching and asking other victims for advice that I discovered Capital Crypto Recovery’s stellar reputation. I decided to contact them because of their successful recovery record and encouraging client testimonials. I had no idea that this would be the pivotal moment in my fight against cryptocurrency theft. Thanks to their expert team, I was able to recover my lost cryptocurrency back. The process was intricate, but Capital Crypto Recovery's commitment to utilizing the latest technology ensured a successful outcome. I highly recommend their services to anyone who has fallen victim to cryptocurrency fraud. For assistance, contact [email protected] Capital Crypto Recover on Telegram OR Call Number +1 (336)390-6684 via email: [email protected]

  • 20.01.25 15:39 patricialovick86

    How To Recover Your Bitcoin Without Falling Victim To Scams: A  Testimony Experience With Capital Crypto Recover Services, Contact Telegram: @Capitalcryptorecover Dear Everyone, I would like to take a moment to share my positive experience with Capital Crypto Recover Services. Initially, I was unsure if it would be possible to recover my stolen bitcoins. However, with their expertise and professionalism, I was able to fully recover my funds. Unfortunately, many individuals fall victim to scams in the cryptocurrency space, especially those involving fraudulent investment platforms. However, I advise caution, as not all recovery services are legitimate. I personally lost $273,000 worth of Bitcoin from my Binance account due to a deceptive platform. If you have suffered a similar loss, you may be considering crypto recovery, The Capital Crypto Recover is the most knowledgeable and effective Capital Crypto Recovery Services assisted me in recovering my stolen funds within 24 hours, after getting access to my wallet. Their service was not only prompt but also highly professional and effective, and many recovery services may not be trustworthy. Therefore, I highly recommend Capital Crypto Recover to you. i do always research and see reviews about their service, For assistance finding your misplaced cryptocurrency, get in touch with them, They do their jobs quickly and excellently, Stay safe and vigilant in the crypto world. You can reach them via email at [email protected] OR Call/Text Number +1 (336)390-6684 his contact: [email protected]

  • 22.01.25 21:43 DoraJaimes23

    Recovery expert. I lost my bitcoin to fake blockchain impostors on Facebook, they contacted me as blockchain official support and i fell stupidly for their mischievous act, this made them gain access into my blockchain wallet whereby 7.0938 btc was stolen from my wallet in total .I was almost in a comma and dumbfounded because this was all my savings i relied on . Then I made a research online and found a recovery expert , with the contact address- { RECOVERYHACKER101 (@) GMAIL . COM }... I wrote directly to the specialist explaining my loss. Hence, he helped me recover a significant part of my investment just after 2 days he helped me launch the recovery program , and the culprits were identified as well , all thanks to his expertise . I hope I have been able to help someone as well . Reach out to the recovery specialist to recover you lost funds from any form of online scam Thanks

  • 23.01.25 02:36 [email protected]

    After falling victim to a fraudulent Bitcoin mining scam, I found myself in a desperate situation. I had invested $50,000 into a cloud mining website called Miningpool, which turned out to be a complete scam. For months, I tried reaching out to the company, but I was unable to access my funds, and I quickly realized I had been taken for a ride. In my search for help, I came across TrustGeeks Hack Expert, a service that claimed to help people recover lost funds from crypto scams. Though skeptical at first, I decided to give them a try. Here’s my experience with their service.When I initially contacted TrustGeeks Hack Expert Email.. Trustgeekshackexpert{At}fastservice{Dot}com , I was understandably hesitant. Like many others, I had been tricked into believing my Bitcoin investments were legitimate, only to discover they were locked in a non-spendable wallet with no way of accessing them. However, after sharing my story and details about the scam, the team assured me they had handled similar cases and had the expertise to help. They requested basic information about my investment and began their investigation immediately. The recovery process was nothing short of professional. Unlike many other services that promise quick fixes but fail to deliver, TrustGeeks Hack Expert kept me informed at every stage. They regularly updated me on their progress and were completely transparent about the challenges they faced. There were moments when I wondered if the process would work, but the team’s professionalism and reassurance gave me hope. They were honest about the time it would take and did not make any unrealistic promises, which I truly appreciated. After several weeks of work, TrustGeeks Hack Expert successfully recovered not just my $50,000 investment, but also the so-called profits that had been locked away in the scam's non-spendable wallet. This was a huge relief, as I had resigned myself to the idea that I had lost everything. The entire recovery process was discreet and handled with the utmost care, ensuring that the scam company remained unaware of the recovery efforts, which helped prevent further complications. TeleGram iD. Trustgeekshackexpert & What's A p p +1 7 1 9 4 9 2 2 6 9 3

  • 23.01.25 02:37 [email protected]

    After falling victim to a fraudulent Bitcoin mining scam, I found myself in a desperate situation. I had invested $50,000 into a cloud mining website called Miningpool, which turned out to be a complete scam. For months, I tried reaching out to the company, but I was unable to access my funds, and I quickly realized I had been taken for a ride. In my search for help, I came across TrustGeeks Hack Expert, a service that claimed to help people recover lost funds from crypto scams. Though skeptical at first, I decided to give them a try. Here’s my experience with their service.When I initially contacted TrustGeeks Hack Expert Email.. Trustgeekshackexpert{At}fastservice{Dot}com , I was understandably hesitant. Like many others, I had been tricked into believing my Bitcoin investments were legitimate, only to discover they were locked in a non-spendable wallet with no way of accessing them. However, after sharing my story and details about the scam, the team assured me they had handled similar cases and had the expertise to help. They requested basic information about my investment and began their investigation immediately. The recovery process was nothing short of professional. Unlike many other services that promise quick fixes but fail to deliver, TrustGeeks Hack Expert kept me informed at every stage. They regularly updated me on their progress and were completely transparent about the challenges they faced. There were moments when I wondered if the process would work, but the team’s professionalism and reassurance gave me hope. They were honest about the time it would take and did not make any unrealistic promises, which I truly appreciated. After several weeks of work, TrustGeeks Hack Expert successfully recovered not just my $50,000 investment, but also the so-called profits that had been locked away in the scam's non-spendable wallet. This was a huge relief, as I had resigned myself to the idea that I had lost everything. The entire recovery process was discreet and handled with the utmost care, ensuring that the scam company remained unaware of the recovery efforts, which helped prevent further complications. TeleGram iD. Trustgeekshackexpert & What's A p p +1 7 1 9 4 9 2 2 6 9 3

  • 23.01.25 14:20 nellymargaret

    DUNAM CYBER SOLUTION BTC-ETH RECOVERY EXPERT

  • 23.01.25 14:20 nellymargaret

    DUNAM CYBER SOLUTION BTC-ETH RECOVERY EXPERT I had tried to secure my Bitcoin wallet, maybe a bit too aggressively, enabling every security feature imaginable: two-factor authentication, biometric verification, intricate passwords-the whole shebang. I wanted to make it impossible for anybody to get to my money. I tried to make this impregnable fortress of security and ended up locking myself out of my wallet with $700,000 in Bitcoin. It wasn't until I tried to access my wallet that I realized the trap I had set for myself. I was greeted with an endless series of security checks-passwords, codes, facial recognition, and more. I could remember parts of my multi-layered security setup but not enough to actually get in. In fact, my money was behind this digital fortress, and the more I tried to fix it, the worse it seemed to get. I kept tripping over my own layers of protection, unable to find a way back in. Panic quickly set in when I realized I had made it almost impossible for myself to access my own money. That is when I called DUNAMIS CYBER SOLUTION From that very first call, they reassured me that I wasn't the first person to make this kind of mistake and certainly wouldn't be the last. They listened attentively to my explanation and got to work straight away. Their team methodically began to untangle my overly complicated setup. Patience and expertise managed to crack each layer of security step by step until they had restored access to my wallet. [email protected] +13433030545 [email protected]

  • 26.01.25 03:54 [email protected]

    Losing access to my crypto wallet account was one of the most stressful experiences ever. After spending countless hours building up my portfolio, I suddenly found myself locked out of my account without access. To make matters worse, the email address I had linked to my wallet was no longer active. When I tried reaching out, I received an error message stating that the domain was no longer in use, leaving me in complete confusion and panic. It was as though everything I had worked so hard for was gone, and I had no idea how to get it back. The hardest part wasn’t just the loss of access it was the feeling of helplessness. Crypto transactions are often irreversible, and since my wallet held significant investments, the thought that my hard-earned money could be lost forever was incredibly disheartening. I spent hours scouring forums and searching for ways to recover my funds, but most of the advice seemed either too vague or too complicated to be of any real help. With no support from the wallet provider and my email account out of reach, I was left feeling like I had no way to fix the situation.That’s when I found out about Trust Geeks Hack Expert . I was hesitant at first, but after reading about their expertise in recovering lost crypto wallets, I decided to give them a try. I reached out to their team, and from the very beginning, they were professional, understanding, and empathetic to my situation. They quickly assured me that there was a way to recover my wallet, and they got to work immediately.Thanks to Trust Geeks Hack Expert , my wallet and funds were recovered, and I couldn’t be more grateful. The process wasn’t easy, but their team guided me through each step with precision and care. The sense of relief I felt when I regained access to my crypto wallet and saw my funds safely back in place was indescribable. If you find yourself in a similar situation, I highly recommend reaching out to Trust Geeks Hack Expert. contact Them through EMAIL: [email protected] + WEBSITE. HTTPS://TRUSTGEEKSHACKEXPERT.COM + TELE GRAM: TRUSTGEEKSHACKEXPERT

  • 28.01.25 21:48 [email protected]

    It’s unfortunate that many people have become victims of scams, and some are facing challenges accessing their Bitcoin wallets. However, there's excellent news! With Chris Wang, you can count on top-notch service that guarantees results in hacking. We have successfully helped both individuals and organizations recover lost files, passwords, funds, and more. If you need assistance, don’t hesitate—check out recoverypro247 on Google Mail! What specific methods does Chris Wang use to recover lost funds and passwords? Are there any guarantees regarding the success rate of the recovery services offered? What are the initial steps to begin the recovery process with recoverypro247? this things i tend to ask

  • 02.02.25 20:53 Michael9090

    I lost over $155,000 in an investment trading company last year; I was down because the company refused to let me make withdrawals and kept asking for more money…. My friend in the military introduced me to a recovery agent Crypto Assets Recovery with the email address [email protected] and he’s been really helpful, he made a successful recovery of 95% of my investment in less than 24 hours, I’m so grateful to him. If you are a victim of a binary scam and need to get your money back, please don’t hesitate to contact Crypto Assets Recovery in any of the information below. EMAIL: [email protected] WHATSAPP NUMBER : +18125892766

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