Wav2vec2 Large Xlsr 53 Greek

🚀 Wav2Vec2-Large-XLSR-53-希臘語

本項目基於 Common Voice 和 CSS10 希臘語：單說話人語音數據集，對 facebook/wav2vec2-large-xlsr-53 模型進行了希臘語微調。使用該模型時，請確保語音輸入採樣率為 16kHz。

🚀 快速開始

在使用此模型前，請確保你的語音輸入採樣率為 16kHz。

✨ 主要特性

• 多數據集微調：基於 Common Voice 和 CSS10 希臘語單說話人語音數據集進行微調。
• 特定語言優化：專門針對希臘語進行優化，提升語音識別效果。

📦 安裝指南

文檔未提及安裝步驟，故跳過該章節。

💻 使用示例

基礎用法

該模型可以直接使用（無需語言模型），示例代碼如下：

import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

test_dataset = load_dataset("common_voice", "el", split="test[:2%]") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.

processor = Wav2Vec2Processor.from_pretrained("vasilis/wav2vec2-large-xlsr-53-greek") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
model = Wav2Vec2ForCTC.from_pretrained("vasilis/wav2vec2-large-xlsr-53-greek") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`

resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])

📚 詳細文檔

評估

可以使用以下代碼在 Common Voice 的希臘語測試數據上對模型進行評估：

import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re

test_dataset = load_dataset("common_voice", "el", split="test") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("vasilis/wav2vec2-large-xlsr-53-greek") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
model = Wav2Vec2ForCTC.from_pretrained("vasilis/wav2vec2-large-xlsr-53-greek") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
model.to("cuda")

chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]' # TODO: adapt this list to include all special characters you removed from the data

normalize_greek_letters = {"ς": "σ"}
# normalize_greek_letters = {"ά": "α", "έ": "ε", "ί": "ι", 'ϊ': "ι", "ύ": "υ", "ς": "σ", "ΐ": "ι", 'ϋ': "υ", "ή": "η", "ώ": "ω", 'ό': "ο"}
remove_chars_greek = {"a": "", "h": "", "n": "", "g": "", "o": "", "v": "", "e": "", "r": "", "t": "", "«": "", "»": "", "m": "", '́': '', "·": "", "’": "", '´': ""}
replacements = {**normalize_greek_letters, **remove_chars_greek}

resampler = {
    48_000: torchaudio.transforms.Resample(48_000, 16_000),
    44100: torchaudio.transforms.Resample(44100, 16_000),
    32000: torchaudio.transforms.Resample(32000, 16_000)
}


# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
    for key, value in replacements.items():
        batch["sentence"] = batch["sentence"].replace(key, value)
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler[sampling_rate](speech_array).squeeze().numpy()
    return batch


test_dataset = test_dataset.map(speech_file_to_array_fn)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

    with torch.no_grad():
        logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits

    pred_ids = torch.argmax(logits, dim=-1)
    batch["pred_strings"] = processor.batch_decode(pred_ids)
    return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
print("CER: {:2f}".format(100 * wer.compute(predictions=[" ".join(list(entry)) for entry in result["pred_strings"]], references=[" ".join(list(entry)) for entry in result["sentence"]])))

測試結果：18.996669 %

訓練

訓練使用了 Common Voice 訓練數據集，同時使用了 CSS10 希臘語 的所有歸一化轉錄數據。在文本預處理過程中，字母 ς 被歸一化為 σ，原因是這兩個字母發音相同，且 ς 僅用作單詞的結尾字符，因此這種更改可以輕鬆映射到正確的聽寫。嘗試去除字母上的所有重音符號也顯著提高了 WER。該模型在未收斂的情況下，WER 輕鬆達到了 17%。然而，後續修復轉錄所需的文本預處理會更加複雜。不過，語言模型應該可以輕鬆解決這些問題。另一個可以嘗試的方法是將所有 ι、η 等字母更改為單個字符，因為它們發音相同，對於 o 和 ω 也是如此，這將顯著有助於聲學模型部分，因為所有這些字符都映射到相同的聲音，但需要進一步的文本歸一化。

🔧 技術細節

模型信息

模型結果

📄 許可證

本項目使用 Apache-2.0 許可證。