Wav2vec2 Large Xlsr Dutch

🚀 Wav2Vec2-Large-XLSR-53-荷蘭語

本項目基於 Common Voice 數據集，對荷蘭語進行微調 facebook/wav2vec2-large-xlsr-53 模型。

使用此模型時，請確保語音輸入的採樣率為 16kHz。

🚀 快速開始

本模型在荷蘭語語音識別任務上進行了微調，可用於將荷蘭語語音轉換為文本。使用時需注意輸入語音的採樣率要求。

✨ 主要特性

• 微調模型：基於 facebook/wav2vec2-large-xlsr-53 在荷蘭語數據集上進行微調。
• 特定採樣率要求：語音輸入需採樣率為 16kHz。

📦 安裝指南

文檔未提供安裝步驟，暫不展示。

💻 使用示例

基礎用法

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

test_dataset = load_dataset("common_voice", "nl", split="test[:2%]")

processor = Wav2Vec2Processor.from_pretrained("simonsr/wav2vec2-large-xlsr-dutch")
model = Wav2Vec2ForCTC.from_pretrained("simonsr/wav2vec2-large-xlsr-dutch")

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

# Preprocessing the datasets.
# We need to read the audio 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])

高級用法

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

test_dataset = load_dataset("common_voice", "nl", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("{model_id}") #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("{model_id}") #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 = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\(\)\=\´\–\&\…\—\’]'
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):
    batch["sentence"] = unidecode.unidecode(batch["sentence"])
    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
    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)

# 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"])))

📚 詳細文檔

評估

本模型可在 Common Voice 的荷蘭語測試數據上進行如下評估：

上述高級用法中的代碼即為評估代碼，測試結果顯示字錯率（WER）為 38.74%。

訓練

訓練使用了 Common Voice 的 train、validation 等數據集。訓練腳本可在 此處 找到（需補充訓練腳本鏈接）。

🔧 技術細節

文檔未提供足夠技術細節，暫不展示。

📄 許可證

本模型採用 apache-2.0 許可證。

模型信息表格